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Mouse Medial-Prefrontal Cortex Involvement in Trace Fear Memory during Wakefulness and Sleep
by
Hendrik William Steenland
A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy
Department of Physiology University of Toronto
© Copyright by Hendrik William Steenland 2010
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Mouse Medial-Prefrontal Cortex Involvement in Trace Fear
Memory during Wakefulness and Sleep
Hendrik William Steenland
Doctor of Philosophy
Department of Physiology University of Toronto
2010
Abstract
This thesis represents a culmination of work which seeks to examine the prelimbic and anterior
cingulate cortex (ACC) during trace fear memory across sleep and wakefulness states. In order to
accomplish this task, a technical platform needed to be developed. Accordingly, the first chapter
demonstrates that fear behavior can recorded utilizing neck electromyography (EMG). The
second chapter examines the role of the ACC in trace fear memory, discovering that many
neurons have premotor activity related to freezing behavior. Additionally, auditory-evoked
potentials in the ACC demonstrate learning curves which match learning curves of fear. We
suggest that the ACC is involved in affective-motor integration. The third chapter examines how
genetic enhancement of trace fear learning, with calcium/calmodulin-dependent protein kinase
IV (CaMKIV) over-expressed mice, can influence electro-cortical potentials during wakefulness,
learning and sleep. We found that CaMKIV potentiates electro-cortical brain waves during
learning and sleep. In particular 4-7.5Hz rhythms were potentiated in CaMKIV over-expressed
mice during learning, and are likely to be localized to regions of the prelimbic cortex. Taken
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together the results of this thesis demonstrate that the trace fear memory paradigm engages the
ACC and prelimbic regions, as evidenced at the single cell and cortical field potential level, for
sensory-affective and premotor functions related to anticipating painful stimulation. CaMKIV
appears to be a protein which modulates learning and electro-cortical potentials and may be a
potential target for sleep-dependent memory consolidation in the prefrontal cortex.
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Acknowledgments
Foremost, I wish to express my gratitude to my supervisor Dr. Min Zhuo for sharing his
expertise. It has been an exciting experience being mentored by Dr. Zhuo.
I would like to thank my committee member, Dr. Paul Frankland for his helpful
discussions, support and guidance. I would also like to recognize the contribution of student
fellowships: National Sciences and Engineering Research Council (NSERC) of Canada, Ontario
Graduate Scholarships (OGS).
I would like to thank my friends James Wilson, Dr. Eduard Bercovici and Kohei Koga
for most excellent discussions ranging from electronics to epilepsy. They are truly the best
quality collaborators I have encountered.
I owe more personal thanks to my mother Mary Steenland for her encouragement and
continuous support throughout my graduate studies.
One of the most important things I learned from my Ph.D. experience is that a scientist
should defer to the data and not to academic authority no matter how many bells and whistles to
the name.
Finally, I would like to recognize my wife Ketsarin for everything she is and everything
we are becoming together.
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Table of Contents
Abstract ii
Acknowledgements iv
Table of Contents v
List of Tables xiii
List of Figures xiv
List of Abbreviations
xvi
CHAPTER 1 BASIC SYSTEMS OF FEAR BEHAVIOR 1
1.1 Overview 2
1.2 Fear behavior 2
1.2.1 The defensive behavior of fear 2
1.2.2 Fear conditioning 3
1.3 Basic fear circuitry 5
1.3.1 Fear expression circuits (central amygdala) 5
1.3.2 Reception circuits (lateral amygdala) 7
1.3.3 Electrophysiological evidence of plasticity in the lateral amygdala 8
1.3.4 Contextual Integration (basolateral nucleus of the amygdala) 8
1.4 The unconditioned stimulus and basic pain transduction circuitry 9
1.4.1 Nociception 9
1.4.2 Spinothalamilic projections 10
1.4.3 Thalamocortical projection system 11
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1.5 The conditioning stimulus and the auditory transduction circuitry 12
1.5.1 Auditory cortex and cued fear conditioning 12
1.5.2 Auditory transduction circuits in the brain 13
1.6 Motor output 13
1.7 Comments 14
1.8 EXPERIMENT SET 1: EMG is an effective measure of fear behavior 15
1.8.1 Abstract 15
1.8.2 Rationale 17
1.8.3 Materials and methods 17
1.8.3A Animals 17
1.8.3B Surgical preparation 18
1.8.3C Electrophysiological recordings 18
1.8.3D Trace-fear conditioning 19
1.8.3E Analyses and statistics 20
1.8.4 Results 20
1.8.5 Discussion 25
CHAPTER 2 SYSTEMS CONTROLLING TRACE FEAR BEHAVIOUR 27
2.1 Elaborating the basic fear circuit 28
2.2 Hippocampus and trace fear 28
2.3 Amygdala and trace fear 29
2.3.1 Structure of hippocampus 29
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2.3.2 Hippocampus and trace fear conditioning 30
2.3.3 Hippocampus and trace fear memory 33
2.3.4 Gene expression in the hippocampus following trace fear conditioning 34
2.3.5 Conclusions 34
2.4 Prefrontal cortex and trace fear 35
2.4.1 Basic structure of the prelimbic cortex 36
2.4.2 Prelimbic cortex and trace fear conditioning 37
2.4.3 Prelimbic cortex and trace fear memory 39
2.4.4 Basic structure of the anterior cingulate cortex 39
2.4.5 Pain responsive neurons in the anterior cingulate cortex 40
2.4.6 Anterior cingulate cortex and trace fear conditioning 42
2.4.7 Anterior cingulate cortex and trace fear memory 43
2.5 Close examination of anterior cingulate cortex 43
2.5.1 Cued and contextual fear conditioning 43
2.5.2 Avoidance conditioning 44
2.5.3 Trace-eye blink conditioning 45
2.5.4 Anterior cingulate cortex and recent Vs remote memory 46
2.5.5 Anterior cingulate cortex and reward conditioning 47
2.5.6 Anterior cingulate cortex and motor control 47
2.6 Comments 49
2.7 EXPERIMENT SET2: Affective-motor integration of the mouse anterior cingulate cortex during trace fear conditioning
49
2.7.1 Abstract 49
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2.7.2 Rationale 50
2.7.3 Materials and methods 52
2.7.3A Animals 52
2.7.3B Preparation of electrodes for spike recording 53
2.7.3C Freely behaving spike recording preparation 53
2.7.3D Freely behaving field potential preparation 54
2.7.3E Anesthetized mouse preparation 55
2.7.3F In-vitro recording 56
2.7.3G Microdialysis 57
2.7.3H Trace fear conditioning 57
2.7.3I Histology 58
2.7.3J Recording 58
2.7.3H Analyses 60
2.7.4 Results 61
2.7.4A Characterization of spontaneous field potentials in the ACC during trace fear conditioning
61
2.7.4B Characterization of tone-evoked and motor-triggered potentials in the ACC during trace fear conditioning
64
2.7.4C Learning-related spontaneous and evoked field potentials in the ACC during trace fear conditioning
66
2.7.4D Identification of neuron types and properties 69
2.7.4E Identification of putative pyramidal and non-pyramidal neurons 69
2.7.4F Identification of bursting and regular spiking cells 72
2.7.4G Unit recordings in the ACC during trace fear conditioning 74
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2.7.4H Motor learning in the ACC during trace fear conditioning 80
2.7.4I Functional characterization of mouse ACC neurons to direct peripheral stimulation in freely behaving animals
82
2.7.4J Functional characterization of mouse ACC neurons to direct peripheral stimulation under anesthesia
84
2.7.4K Direct stimulation of the ACC 87
2.7.4L Histological verification of electrode sites 90
2.7.5 Discussion 92
2.7.5A Characterization of field potentials in the ACC during trace fear conditioning
92
2.7.5B Unit recordings in the ACC during trace fear conditioning 94
2.7.5C Functional characterization of mouse ACC neurons to direct peripheral stimulation
96
2.7.5D Neuron identification 97
CHAPTER 3 SLEEP, SYNAPTIC PLASTICITY AND FEAR 99
3.1 Overview 100
3.2 Description of sleep 100
3.3 Circuits 101
3.3.1 Wakefulness promoting systems 102
3.3.2. Sleep promoting systems 106
3.3.3 Thalamocortical system 107
3.3.4 Integrating the thalamocortical system with the arousal system 108
3.3.5 Slow corticothalamic oscillations 109
3.4 Plasticity 110
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3.4.1 Hebb narrows the focus to the neuron 110
3.4.2 Long-term potentiation induction 111
3.4.3 Long-term potentiation expression 112
3.4.4 CaMKIV molecular biology 113
3.4.5 CaMKIV and plasticity 115
3.5 SLEEP and SYNAPTIC PLASTICITY 117
3.5.1 Possible function of sleep in learning and memory 117
3.5.2 mRNA and protein expression during sleep 118
3.5.3 Slow wave activity, plasticity and learning 119
3.5.4 Neural replay during sleep 120
3.5.5 Trace fear memory and NREM sleep 122
3.6 Comments 123
3.7 EXPERIMENT SET3: CaMKIV over-expression boosts cortical 4-7Hz oscillations during learning and 1-4Hz delta oscillations during sleep
124
3.7.1 Abstract 124
3.7.2 Rationale 124
3.7.3 Materials and Methods 126
3.7.3A Animals 126
3.73.B Frontal-parietal EEG 127
3.7.3C Cortical field recording 127
3.7.3D Sleep recording 128
3.7.3E Trace fear memory recordings 129
3.7.3F Histology 130
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3.7.3F Analyses 130
3.7.3G Statistics 132
3.7.4 Results 133
3.7.4A CaMKIV over-expression impacts natural sleep 133
3.7.4B CaMKIV over-expression enhances learning and memory 136
3.7.4C CaMKIV over-expression and EEG responses to tone, trace interval, and shock
137
3.7.4C-I EEG responses to tone 137
3.7.4C-II EEG responses during the trace interval 140
3.7.4C-III EEG responses to shock and expected shock 141
3.7.4D Four-7.5Hz oscillation enhancements parallel learning and can be localized to the prelimbic cortex
142
3.7.4E CaMKIV over-expression impacts slow delta oscillations 146
3.7.4F slow delta oscillation enhancements are localized to the anterior cingulate cortex
150
3.7.4G Learning and theta rhythms but not slow delta oscillations are correlated with trace fear memory formation.
154
3.7.5 Discussion 156
3.7.5A CaMKIV and natural sleep 156
3.7.5B CaMKIV and trace fear conditioning 157
3.7.5C CaMKIV over-expression enhances sleep after learning 159
3.7.5D Variations in learning and 4-7.5Hz oscillation power are related to memory formation
160
CHAPTER 4 DISCUSSION AND FUTURE DIRECTIONS 163
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4.1 Integrated discussion 164
4.2 Future directions 168
4.2.1 Theme 1: Memory associations promoting wakefulness compete with sleep
168
4.2.2 Theme2: The role of the cholinergic system in memory consolidation during sleep
170
4.2.3 Theme 3: The role of protein synthesis in transfer of memory to the cortex
172
4.2.4 Theme 4: Can a cortical down states be reversed to an upstate 174
REFERENCES 176
xiii
List of Tables
List of Tables
Chapter 3
Table 3.1 CaMKIV over-expressed mice have normal sleep duration and bouts 135
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List of Figures
Chapter 1
Figure 1.1 Cued delay and trace fear conditioning 4
Figure 1.2 Basic fear memory circuits 6
Figure 1.3 Surgical preparation 20
Figure 1.4 Neck EMG recordings can provide measurements of fear behavior 21
Figure 1.5 Falsely scoring sleep as fear behavior 23
Figure 1.6 Discriminating freezing behavior from sleep-wake states 24
Chapter 2
Figure 2.1 Hippocampus and Prefrontal circuits 31
Figure 2.2 Elaborated Fear circuits 41
Figure 2.3 Trace fear conditioning and measurements of fear 62
Figure 2.4 Local field potential changes ACC accompany trace fear conditioning 63
Figure 2.5 Tone-evoked and motor-locked potentials in the ACC 65
Figure 2.6 Tone-evoked ACC potentials predict freezing behavior 68
Figure 2.7 In-vivo spike recording to investigate receptive functions of ACC 70
Figure 2.8 Identified pyramidal and interneurons clustering under in-vitro conditions
72
Figure 2.9 Analysis of ACC neuron during trace fear conditioning 76
Figure 2.10 ACC neurons respond to trace fear conditioning and demonstrate premotor activity
78
Figure 2.11 Pyramidal neurons are inhibited during the trace interval 79
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Figure 2.12 Motor plasticity in the ACC during trace fear conditioning. 81
Figure 2.13. Noxious and innocuous responding neurons under freely behaving conditions
83
Figure 2.14 Neurons responding to noxious and innocuous stimuli under anesthesia
86
Figure 2.15. Ictal Activity in the ACC produces circling behavior, vocalizing and synchronization of multiunit activity.
89
Figure 2.16. Verification of electrode positions 91
Chapter 3
Figure 3.1 Defining characteristics of sleep 101
Figure 3.2 Sleep circuitry 103
Figure 3.3 CaMKIV pathway 114
Figure 3.4 Experimental design 133
Figure 3.5 CaMKIV over-expression enhances delta sleep in the recording environment
134
Figure 3.6 CaMKIV over-expression enhances trace fear conditioning and subsequent memory
137
Figure 3.7 CaMKIV over-expression and EEG responses to tone, trace interval and pain
139
Figure 3.8 CaMKIV over-expression enhances 4-7.5Hz waves which can localize to the prelimbic cortex
144
Figure 3.9 CaMKIV over-expression enhances slow delta oscillation power during NREM sleep before and after trace fear training
147
Figure 3.10 Impact of trace fear conditioning on sleep duration and latency 150
Figure 3.11 Optimizing electrode implantation 152
Figure 3.12 Electrode positions for ACC motor cortex experiments
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Figure 3.13 Trace fear conditioning increases slow delta oscillations in the ACC in layer I-III
154
Figure 3.14 Variations in learning and 4-7Hz oscillation power are related to memory formation
155
List of Abbreviations
The majority of abbreviations are spelled out in the text; however, some of the common
abbreviations used in the neurosciences will be spelled out here to facilitate the readability of the
text.
ACC Anterior cingulate cortex
ACSF Artificial cerebrospinal fluid
ACTH Adrenocorticotropic hormone
AMPA (±)-α-amino-3-hydroxy-5-methylisoxazole-4-propionic acid hydrate
CaMKII Calcium/calmodulin-dependent protein kinase II
CaMKIV Calcium/calmodulin-dependent protein kinase IV
dnCaMKIV dominant negative form of CAMKIV
CEA Central nucleus of the amygdala
CNQX 6-cyano-7-nitroquinoxaline-2, 3-dione disodium salt
CREB Cre-response element binding protein
CS Conditioned stimulus
BDNF Brain derived neurotrophic factor
BLA Basolateral amygdala
DMN Dorsal motor nucleus
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D-APV D-2-amino-5-phosphonovaleric acid
EEG Electroencephalogram
EMG Electromyogram
ERK Extracellular-signal-regulated kinase
LA Lateral amygdala
LC Locus coeruleus
LDT Laterodorsal tegmentum
VPL Ventroposteriolateral
LTP Long-term potentiation
MAPK Mitogen activated kinase
MPOA Medial preoptic area
MDvc Mediodorsal thalamus (ventral caudal aspect)
MSK1 Mitogen- and stress-activated protein kinase
NMDA N-methyl-D-aspartate
NREM Non-rapid eye movement sleep
NR2A NMDA receptor subunit 2A
NR2B NMDA receptor subunit 2B
PP3 kinase Protein Phosphatase 3
PKA Protein kinase A
PKC Protein kinase C
PPT Pedunculopontine tegmentum
REM Rapid eye movement sleep
RT-PCR Reverse transcriptase-polymerase chain-reaction
SI Somatosensory cortex area 1
SII Somatosensory cortex area 2
xviii
SWA Slow wave activity (0.5-4Hz)
TE Temporal lobe (Auditory cortex)
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CHAPTER 1
BASIC SYSTEMS OF FEAR BEHAVIOUR
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1.1 Overview
This chapter provides an in-depth review of the basic fear system and includes an
examination of fear behaviour, fear circuits and a basic discussion of plasticity. This chapter
provides a schematic overview for which concepts will be elaborated in subsequent chapters.
Finally, this chapter forms the basis for the first set of experiments in this thesis, the objective of
which is to find a new method to observe fear behaviour.
1.2 Fear behaviour
1.2.1 The defensive behaviour of fear
Defensive behaviours represent a set of responses to threatening situations and stimuli
(Blanchard & Blanchard, 2008). These responses are highly conserved across mammalian
species and are particularly amenable to conditioning to situations and stimuli which are
associated with threats (Blanchard & Blanchard, 2008). Defensive behaviours include: flight,
avoidance, cessation of movement (i.e. freezing), threatening gestures, attacking the threat and
assessment of risk (Blanchard & Blanchard, 2008). These defensive behaviours are linked to the
temporal or physical distance from the threat. For example, a predator that is located far away is
more likely to elicit avoidance behaviour in the prey. By contrast, a predator who is close to the
prey, may elicit behavioural freezing or defensive attacking from the prey. This concept is know
as “defensive distance” (Blanchard & Blanchard, 2008). Thus, as the threat becomes imminent,
certain behaviours, such as freezing, will become more prominent. This model has been
expanded by others to include “defensive direction” (McNaughton & Corr, 2004). Defensive
direction refers to whether the behaviour of interest is directed toward or away from the
threatening stimuli. Behaviours directed away from the threat (e.g. freezing) are considered fear-
related defensive behaviours, while those directed toward the threat are considered anxiety-
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related behaviours (e.g. inspecting whether something is a threat) (McNaughton & Corr, 2004).
Freezing behaviour in rodents is defined as a cessation of movement with the exception of
breathing (Blanchard & Blanchard, 1969a; Bolles, 1970; Fanselow, 1984). Freezing behaviour
occurs in response to predators, when there is no option for escape from the situation (Blanchard
& Blanchard, 2008). Moreover, freezing appears to be a most effective behavioural strategy if
the prey has not already been spotted by the predator (Eilam, 2005). The most common way to
induce freezing behaviour, under experimental situations, is to present noxious painful stimuli
(typically to the feet). Models of fear conditioning in mice and rats have been instrumental in
uncovering the molecular biology and basic circuitry controlling fear behaviour.
1.2.2 Fear conditioning
Pavlovian fear conditioning has been used as a basic paradigm in the examination of fear
memory. In a typical fear-conditioning experiment, rodents are presented with an emotionally
neutral conditioning stimulus (CS), such as a tone, which is paired in time with an aversive foot
shock (US) (Fig. 1.1A). With repeated pairings of the CS and US, the presentation of the CS
alone begins to elicit defensive behaviours such as freezing (Blanchard & Blanchard, 1969b;
Fanselow & Bolles, 1979). This presentation of the tone continually updates the animal to the
impending danger. This type of conditioning is thought to rely predominantly on the amygdala
(LeDoux, 2000; LeDoux, 2003). By contrast, the trace fear conditioning paradigm involves the
association of the CS and US; however these stimuli are separated by a time interval (Fig. 1.1B).
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Figure 1.1 Cued delay and trace fear conditioning Conditioning and testing protocols for cued delay and trace fear memory. During conditioning, both a shock and tone are repeated for up to 10 trials, while fear behaviour (e.g. freezing) is monitored. The major difference between delay (A) and trace fear conditioning (B) is that; under delay conditioning the tone co-terminates with the shock stimuli, while under trace conditioning the tone terminates before the onset of the shock. Thus, under trace fear conditioning, the animal must make an association between tone and shock over a time (or trace) interval. Fear memory is tested similarly in both protocols, with the repeated presentation of the tone by itself. Provided the animal has made an association between the shock and the tone, the animal will demonstrate defensive behaviors such as freezing. The delayed fear paradigm is a typical method to examine the function of the amygdala fear circuit, while trace fear conditioning is thought to engage extra-amygdaloid structures necessary to bridge the trace interval (figure by H. Steenland, refs in text). Thus, trace fear conditioning generates a representation (a fear memory) which is maintained in
expectation of a shock in the absence of the CS. Despite the seemingly simple distinction
between these two paradigms, the trace fear paradigm must engage additional brain structures
which enable the bridging of the temporal gap between the US and CS, such as the anterior
cingulate cortex (ACC) (Han et al., 2003), prelimbic cortex (Baeg et al., 2001; Knight et al.,
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2004; Runyan & Dash, 2004; Runyan et al., 2004) and the hippocampus (McEchron et al., 1998;
McEchron & Disterhoft, 1999; McEchron et al., 2000; 2003; Gilmartin & McEchron, 2005a;
Burman et al., 2006). Moreover, the trace fear memory paradigm may be representative of
everyday events in which individuals anticipate negative future outcomes. Trace fear
conditioning will be extensively reviewed and diagrammed in chapter 2. However, for now, the
examination of basic fear circuits is most important.
1.3 Basic fear circuitry
This section examines the basic circuits of fear conditioning, from the initial point of
processing the tone and shock stimuli to the point of gernerating motor output (i.e. freezing
behaviour). Figure 1.2 is a model of the basic fear circuits and will serve as a network on which
to build more complicated circuits related to trace fear conditioning (Chapter 2).
1.3.1 Fear expression circuits (central amygdala)
The basic circuit of fear behaviour has been extensively studied and has centered on the
amygdala. The amygdala is a small almond shaped structure residing in the temporal lobe. The
amygdala can be subdivided into 3 nuclei which include the lateral nucleus, basolateral nucleus
and the central nucleus (LeDoux, 2000; LeDoux, 2003) (Figure 1.2). Neurons of the lateral
nucleus receive auditory (CS) and somatosensory inputs from the thalamic and cortical
processing regions (Turner & Herkenham, 1991; Mascagni et al., 1993; Romanski & LeDoux,
1993). The lateral amygdala connects with the central amygdala both directly and indirectly via a
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projection through the basolateral nucleus (Pitkanen et al., 1997; Pare & Smith, 1998).
Accordingly, the central amygdala is thought to be involved in controlling behaviours related to
fear expression (LeDoux, 2000; LeDoux, 2003). For example, the central nucleus of the
amygdala projects to the lateral hypothalamus to control blood pressure (LeDoux et al., 1988),
paraventricular nucleus of the hypothalamus to influence adrenocorticotropic hormone secretion
(Beaulieu et al., 1986; Gray et al., 1989), the periaqueductal gray to control conditioned freezing
(LeDoux et al., 1988; Bellgowan & Helmstetter, 1996; De Oca et al., 1998) and the nucleus
Figure 1.2 Basic fear circuits Representation of the fundamental systems involved in controlling fear behavior. The input stage represents the collection of information from pain and auditory modalities during fear conditioning. These systems transduce this information to be processed in the spinal cord, brainstem, thalamus and auditory cortex, and eventually converge in the lateral nucleus of the amygdala (LA). The central nucleus of the amygdala (CEA) then directs the main outputs controlling fear including: autonomic, behavioral and arousal systems. This thesis is focused on freezing behavior and so the projection from the periaqueductal gray (PAG) to the spinal cord is highlighted. From the spinal cord, the motor out-flow can control muscles of the body (e.g. neck) to express freezing. Note, higher order systems may play a role in fear conditioning, but will be reviewed in the context of trace fear conditioning and memory in chapter 2 (figure 2.2). TE, temporal, BLA basolateral nucleus of amygdala, CEA, central nucleus of amygdala, DMN, dorsal motor nucleus, VTA, ventral tegmental area, LC, locus coeruleus, LDT, laterodorsal tegmentum, ACTH, adrenocorticotropic hormone, VPL, ventroposterio-lateral, MDvc, mediodorsal vental caudal, MG, medial geniculate nucleus. (figure by H. Steenland, refs in text)
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reticularis pontis caudalis to control conditioned startle responses (Hitchcock & Davis, 1986;
Rosen & Davis, 1990; Rosen et al., 1991). The central nucleus of the amygdala is also thought to
control cortical arousal (LeDoux, 2000; LeDoux, 2003) through connections with the locus
coeruleus (Cedarbaum & Aghajanian, 1978) the cholinergic lateral dorsal tegmentum, (Semba &
Fibiger, 1992) and the dopaminergic ventral tegmental area (Kaufling et al., 2009).
1.3.2 Reception circuits (lateral amygdala)
Classical fear conditioning has two basic sensory components which generally include a
tone (CS) and shock (US) stimuli, occurring in close temporal proximity. For an organism to
associate these two modalities it could be speculated that there must be points of convergence in
the brain where these two systems overlap, and this appears to be the case. Tone-related
information is thought to reach the amygdala via the medial geniculate nucleus (in the thalamus)
terminating in the lateral amygdala (Clugnet & LeDoux, 1990). Neurons in the lateral amygdala
respond 12-25msec after the presentation of clicks or broadband white noise with frequency
preferences around 10-12kHz (Bordi & LeDoux, 1992; Bordi et al., 1993). It was also shown
that the dorsal aspect of the lateral amygdala receives converging input from both pain-related
information and tone-related information (Romanski et al., 1993). Unit recording studies have
shown that learning-related activity in the thalamic pathway occurs earlier than that in the
auditory cortex (Quirk et al., 1995; Quirk et al., 1997). Indeed it has been shown that complete
ablation of the auditory cortex has no effect on cued fear conditioning; however if the medial
geniculate nucleus of the thalamus is ablated, cued fear conditioning is impaired (Romanski &
LeDoux, 1992).
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1.3.3 Contextual integration (basolateral nucleus of the amygdala)
The basal nucleus of the amygdala receives large projections from the hippocampus
(Pikkarainen et al., 1999). In addition, perirhinal cortical stimulation produces long-term
potentiation (LTP) in the basolateral, but not in the lateral nucleus of the amygdala (Yaniv &
Richter-Levin, 2000). Long-term potentiation (LTP) is the main synaptic model of learning and
memory and will be addressed in chapter 3 (section 3.4). Unit recording from the basolateral
nucleus suggests that it is involved in learning multimodal sensory information (Toyomitsu et al.,
2002). Consistently, a recent study demonstrated that contextual conditioning, but not auditory
cued fear conditioning, is disrupted by pre-treatment of ibotenate-induced damage to the
basolateral amygdala (Onishi & Xavier, 2010). The results indicate that the basolateral amygdala
is not involved in fear expression, but rather in integrating information related to context (Onishi
& Xavier, 2010).
1.3.4 Electrophysiological evidence of plasticity in the lateral amygdala
As demonstrated above, the lateral amygdala represents a point of convergence of pain
and auditory information, and therefore may be expected to have changes associated with fear
learning and be modifiable though manipulations of molecular biology. Neuronal and field
potential recordings have shown that the tone-evoked neural responses in the lateral amygdala
are potentiated with fear conditioning to shock (Quirk et al., 1995; Quirk et al., 1997). An
alternative way to examine this pathway is to by-pass the painful peripheral input and directly
stimulate the thalamus in association with tone input. Tetanic stimulation of this thalamic
pathway enhanced evoked-potentials elicited from auditory stimulation (Clugnet & LeDoux,
1990; Rogan & LeDoux, 1995; Rogan et al., 1997).
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Understanding the network interactions responsible for retrieval of fear memory is a
challenging pursuit. Seidenbecher et al. (2003) examined how fear memory retrieval might be
possible between the amygdala and hippocampus. These investigators recorded local field
potentials from both the hippocampus and the lateral amygdala, and examined the spontaneous
activity which occurred during cued fear memory retrieval. Interestingly, during fear behaviours,
the amygdala and hippocampus exhibited 4-5Hz theta frequency brain waves (Seidenbecher et
al., 2003). Most importantly, these brain waves were synchronized, indicating that the two
regions were either communicating (Seidenbecher et al., 2003) or being driven by a common
oscillator. A follow-up study showed that this 4-5Hz activity occurred during contextual fear
memory retrieval as well, but it only occurred after 24hrs post-conditioning and not 2min, 30min,
2hrs or 30 days after learning (Narayanan et al., 2007). While this study reports that electrode
locations were in the lateral amygdala, it is likely that a substantial degree of the recorded
activity comes from the basolateral amygdala, since this is a terminal zone for the hippocampus
(Pikkarainen et al., 1999).
1.4 The unconditioned stimulus and basic pain transduction circuitry
1.4.1 Nociception
The skin contains a variety of receptors which can respond to damaging mechanical
stress, noxious heat and cold, and changes in local metabolism (e.g. acidity) (Craig, 2003). This
information is transduced at the periphery and terminates on lamina I neurons in the dorsal horn
of the spinal cord (Craig, 2003). Peripheral fibres which carry this information, consist of the Aδ-
and C-fibres (Craig, 2003). In particular, cutaneous Aδ-fibres can respond to pinch and rapidly to
heat >46ºC, or to pinch and slowly to heat >53ºC. Other cutaneous Aδ fibres respond only to heat
(Craig, 2003). C-nociceptors have a similar response profile, responding to heat, pinch or both.
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Other C-fibres respond to tissue damage and inflammation (Craig, 2003). Aδ-fibres activation is
associated with the early components of pain (e.g. sharp pain) while C-fibre tend to produce
sustained response to pain (1-2sec later), which may produce a burning sensation. Typically,
shock stimuli are used in fear conditioning. The skin is quite resistant to shock stimuli since its
resistance is about 100 kOhms (Fish & Geddes, 2009). The skin also acts as a capacitor, in that
rapidly changing voltage is the most effective means to stimulate the skin (Fish & Geddes,
2009). Shock stimuli would presumably activate c-fibers and Aδ-fibres, though there seems to be
a lack of information on this topic.
1.4.2 Spinothalamilic projections
Aδ- and C-fibres terminate in lamina I of the spinal cord in a somatotopic manner (Craig,
2003). Based on experiments of cutaneous stimulation, lamina I neurons which project through
the spinothalamic tract can be distinguished, based on their response to either sharp or burning
pain (Andrew & Craig, 2002; Craig & Andrew, 2002). Lamina V neurons receive input from
collateral Aδ-fibres. These neurons in this region are considered to respond to a wide variety of
modalities including noxious and innocuous stimulation (Craig, 2003). Collectively lamina I and
lamina V-VII both supply the spinothalamic tract. The lamina I spinothalamic tract projects
extensively to the posterior part of the ventromedial nucleus of the thalamus (Craig, 2004) where
neuron responses mirroring the activity lamina I of the spinal cord have been detected (Davis et
al., 1999; Craig, 2003), and to the ventral caudal part of the medial dorsal nucleus (MDvc)
(Craig, 2003). By contrast, parts of the spinothalamic tract which originate from layer V
terminate in the posterior part of the ventromedial nucleus (VMpo), ventral posterior inferior
nucleus (VPI) to supply the basal ganglia, motor and parietal areas (Craig, 2003). As discussed
above, the spinothalamic tract region of the thalamus is also connected to the lateral amygdala
11
(Ledoux et al., 1987; Turner & Herkenham, 1991). Indeed some of the cells in the lateral
amygdala respond to nociceptive stimulation (Romanski et al., 1993). Nociceptive information
from the spinal cord and the parabrachial nucleus may also be carried to the central nucleus of
the amygdala directly (Bernard & Besson, 1990; Cliffer et al., 1991).
1.4.3 Spinothalamo-cortical connections
Different parts of the thalamus project to distinct cortical regions to process distinct
aspects of pain. For example, the MDvc thalamus projects to the anterior cingulate cortex, where
the affective component of the pain is processed (Devinsky et al., 1995; Rainville et al., 1997;
Hofbauer et al., 2001). By contrast, the VPI and VMpo thalamic region projects to the
somatosensory SII and area 3a of SI (Bushnell et al., 1999; Willis et al., 2002; Craig, 2003;
Duncan & Albanese, 2003). However there is still debate over the organization of these thalamic
nuclei (Willis et al., 2002). The SI cortex is somatotopically organized and has been implicated
in both the localization of pain and the discrimination of pain (Bushnell et al., 1999; Willis et al.,
2002; Craig, 2003; Duncan & Albanese, 2003). It is also likely involved in top-down regulation
of pain. For example, direct glutamatergic activation of the SI region in rats activates descending
corticofugal inputs which potentiate noxious-evoked responses in the thalamus (Monconduit et
al., 2006). A direct comparison of the ACC with that of SI showed that the ACC neurons were
less responsive and had longer latencies than that of the SI to laser-heat stimuli (Kuo & Yen,
2005). The insula is thought to be innervated by projections from the thalamic VPI (Craig, 2003).
The insula cortex is a viscerosensory region which processes cardiovascular, cardiopulmonary,
gastrointestinal and gustatory information (Cechetto & Chen, 1990; Allen et al., 1991; Zhang &
Oppenheimer, 1997; Jasmin et al., 2004).
12
1.5 The conditioning stimulus and the auditory transduction circuitry
During fear conditioning, a tone is played in association with a shock. This information
must be transduced from the ear to relevant areas involved in fear memory for the association of
the shock and the tone to be made. As has been pointed out, the lateral amygdala receives
converging input from both pain and sound modalities from the thalamus, possibly to make fear
memory associations; however, the auditory cortex is also activated during fear conditioning.
Thus, this section serves as a basic review of the auditory system and how it may relate to fear
conditioning
1.5.1 Auditory cortex and cued-fear conditioning
It has been shown that lesions to the medial geniculate projection zone in the thalamus
block auditory fear conditioning; however, lesions to the auditory cortex for which the medial
geniculate projects, do not impair auditory fear conditioning (LeDoux et al., 1984). This finding
suggested that the medial geniculate has a variety of subcortical targets. Indeed, the medial
geniculate was found to connect with the amygdala and inferior colliculus (LeDoux et al., 1985).
The primary auditory cortex, TE1 (temporal lobe1), was not found to originally project to the
amygdala (LeDoux et al., 1991), but anterograde labelling from the ventral TE1, TE2 and caudal
TE3 regions of the auditory cortex do show a connection to the lateral amygdala (LeDoux et al.,
1991; Romanski & LeDoux, 1993). However, complete destruction of the auditory cortex does
not impair auditory fear conditioning (Romanski & LeDoux, 1992). Indeed, during conditioning
the neurons in the auditory cortex take more trials to learn, whereas the learning in the amygdala
happens rapidly (Quirk et al., 1997). Interestingly, a few auditory neurons were found to
anticipate cue stimuli; thus, the auditory system may be more involved in the prediction of future
events related to auditory stimuli (Quirk et al., 1997). To examine the role of the auditory cortical
13
responses in fear conditioning, Armony et al. (1998) removed the amygdala and showed that late
auditory-evoked responses, associated with conditioning in the auditory cortex, were attenuated.
Cued fear conditioning therefore appears to engage the auditory cortex but does not appear to use
the auditory cortex.
1.5.2 Auditory transduction circuits in the brain
The auditory system is not a fundamental topic of this thesis, though it deserves some
brief discussion since the conditioning stimulus used in this study is typically a white noise.
White noise is defined as a random signal with an equal power spectral density in each frequency
band (Wikipedia, 2010). In the inner ear, different positions along the basilar membrane of the
cochlea respond to different frequencies of sound (Kandel et al., 2000). Since white noise has a
range of spectral energies it would be expected that the cochlea would be active along its length.
This activity is mechanically transduced by hair cells and the auditory nerve (cranial nerve VIII)
which sends this information via the lateral lemniscus to the dorsal medulla inferior olive and
finally to the medial geniculate of the thalamus (Kandel et al., 2000).
1.6 Motor output
Freezing behaviour is quite obvious; however whole networks of motor neurons which
would normally activate movement must produce an absence of movement. Since the central
nucleus of the amygdala is essential for fear expression (section 1.3.1) the systems responsible
for behavioural arrest will be examined. Damage to the central amygdala abolishes freezing
behaviour related to conditioning (section 1.3.1) and this region projects prominently to the
periaqueductal central gray (LeDoux et al., 1988; Bellgowan & Helmstetter, 1996; De Oca et al.,
1998).
14
The periaqueductal gray is located in the midbrain. Lesion studies show that removal of
the caudal aspect of the periaqueductal gray blocks conditioned freezing behaviour (Liebman et
al., 1970; LeDoux et al., 1988), while having no impact on arterial pressure related to
conditioning (LeDoux et al., 1988). To understand what motor regions the periaqueductal gray
may innervate, retrograde and anterograde labeling studies have been conducted (Mouton &
Holstege, 1994). It was found that the periaqueductal gray robustly projects to the upper cervical
spinal cord in the lateral parts of lamina V, VII and the dorsal part of lamina VIII. Importantly,
this part of the cervical spinal cord is involved in the control of neck muscle activity; thus, this
pathway may mediate head movements related to freezing behaviour (Mouton & Holstege,
1994). It should be mentioned that the dorsolateral periaqueductal central gray is innervated by
both dorsal and ventral aspects of the ACC, while the prelimbic and infralimbic cortices
innervate both dorso- and ventrolateral divisions of the periaqueductal central gray (Floyd et al.,
2000; Gabbott et al., 2005). The results suggest that higher order control of freezing may be
possible through direct innervation of the periaqueductal gray.
1.7 Comments
This concludes our examination of the basic circuitry involved in auditory fear
conditioning. A few themes appear important with this evaluation. Firstly, fear behaviour
circuitry appears to have parallel systems involving the spinothalamic tract, projecting to the
amygdala, ACC, SI, SII and insula brain regions. Secondly, the amygdala, ACC, infralimbic, and
prelimbic cortex all innervate the periaqueductal central gray, each possibly exerting control over
freezing behaviour. However, it should be mentioned that simple forms of fear conditioning may
prime systems for action without their actual necessity for the task or paradigm at hand. For
15
example, the insula may be primed for activity in parallel with the amygdala during cued delay
fear conditioning, but might not be required for the actual conditioning itself.
1.8 EXPERIMENT SET 1: EMG is an effective measure of fear behaviour
1.8.1 Abstract
Conventional methodology for examining conditioned fear has involved visual-based
technologies such as cameras and direct observation. These methods are excellent screening tools
of fear behaviour (i.e. freezing). However, they do not provide a physiological output, which
could be useful to examine neurophysiological correlates of fear. We found that dorsal neck
electromyography can be used as a method to score fear behaviour and is as effective as visual-
based scoring. Since the electromyogram is a physiological measure it can be used in either the
light or dark to score freezing behaviour, without use of any camera or visual observation. We
also show that electromyogram-based scoring methodologies, in conjunction with an
electroencephalogram, are useful to discriminate fear from sleep.
1.8.2 Rationale
Genetic mouse models are a fundamental tool to examine the molecular basis of
behaviour (Tang et al., 1999; Mogil et al., 2000; Wei et al., 2002; Fanselow & Poulos, 2005; Ko
et al., 2005; Zhao et al., 2005; Steenland et al., 2008a). Conditioned fear is one of the most
widely used methodologies to examine emotional memory in rodents (LeDoux, 2000). Fear
behaviour can be identified as an absence of movement and is known as freezing (Bolles, 1970;
Blanchard & Blanchard, 1972; Fanselow, 1984; LeDoux et al., 1986). However a similar
definition holds for the behavioural description of sleep, which is also seen as a state of
immobility (Siegel, 2005). Thus it is essential that freezing, which consists of a defensive
16
behaviour (LeDoux, 2000; McNaughton & Corr, 2004; Kim & Jung, 2006) be dissociated from
sleeping, which ultimately disengages an animal from the external environment. Confusion over
identifying these two behaviours could become an issue when examining fear memory in genetic
mice which also demonstrate alterations in sleep (Espinosa et al., 2004; Steenland et al., 2008a).
The most common method to examine fear behaviour is through direct visual observation
or through use of motion sensitive cameras (Bolles, 1970; Blanchard & Blanchard, 1972;
Fanselow, 1984; Anagnostaras et al., 2000; Marchand et al., 2003; Espinosa et al., 2004;
Takahashi, 2004; Kopec et al., 2007). Direct visual observation can produce inaccuracies in
measurement due to varying reaction times and attention. Camera systems do not provide a
physiological correlate of behaviour, but infrared cameras are available for recordings in the dark
(Takahashi, 2004). Thus, it would be useful for fear measurement methods to provide both a
physiological correlate and for freezing behaviour to be recorded regardless of the time of day.
Trace-fear conditioning in rodents, involves the association of a neutral conditioned
stimulus (CS; generally a tone) with an aversive unconditioned stimulus (US; shock) over a time
interval (referred to as the trace interval). The acquisition and retention of trace-fear memory
requires the amygdala, hippocampus and prefrontal cortex (McEchron et al., 1998; Buchel et al.,
1999; McEchron & Disterhoft, 1999; McEchron et al., 2000; Crestani et al., 2002; Han et al.,
2003; Knight et al., 2004; Runyan et al., 2004; Chowdhury et al., 2005; Misane et al., 2005;
Bangasser et al., 2006; Carter et al., 2006; Knight et al., 2006; Burman & Gewirtz, 2007).
Methodologies which might permit precise timing of a freezing behaviour, such as
electromyography (EMG), may be helpful for examining the neural correlates which dictate fear.
Additionally, some evidence suggests that contextual information may influence trace-fear
conditioning (McEchron et al., 1998; Quinn et al., 2002). Accordingly, conducting trace-fear
conditioning in the dark would eliminate confounding information of the environmental context.
17
The current study validated a simple, inexpensive, physiological method to examine trace-fear
conditioning in the dark or light, utilizing EMG of the neck.
1.8.3 Materials and Methods
1.8.3A. Animals
Surgery was performed on 6 C57BL6 mice for in vivo electrophysiology studies (8-14
weeks old). Mice were maintained on a 12:12-h light-dark cycle (lights on at 8:00 A.M.) and had
access to food and water ad libitum. Procedures conformed to the recommendations of the
Canadian Council on Animal Care and the University of Toronto Animal Care Committee
approved the protocols.
1.8.3B Surgical preparation
Mice were anesthetized with 1-2% isoflurane which was mixed with oxygen (30%
balanced with nitrogen) and delivered to the mice via nose cone throughout the surgery. All
electrodes were pre-attached to a miniature connector. The abdomen and scalp of mice were
shaved and then cleaned with iodine (Triadine) and alcohol. The skull of the mouse was fixed
into a stereotaxic adapter (502063, WPI, Sarasota, Fl, USA) mounted on a stereotaxic frame
(Kopf Model 962, Tujunga, CA, USA). Three small holes (1.19 mm diameter) were drilled into
the skull for differential frontal-parietal recordings. Electrodes, consisting of a wire attached to a
jeweler’s screw (with contact end ground flat), were fixed into the holes to record EEG
(electroencephalogram) at the following coordinates relative to bregma: frontal cortex (AP 2.2,
ML 1.0), parietal cortex (AP -2.2, ML -2.5) and ground (AP -3.0, ML 3.0). For dorsal neck
muscle recording, left and right nuchal muscles were exposed and Teflon-coated stainless steel
electrodes (part# AS632, Cooner Wire) were sutured (4.0 silk) to each muscle to record neck
18
EMG. All wires and connectors were secured to the skull with dental cement and cyanoacrylate
glue (Krazy glue). Mice were injected, intraoperatively with buprenorphine (0.05 mg/kg, SC) as
an analgesic, and 1.0 ml sterile saline (IP) for hydration. Mice were placed on a warm heating
pad until they showed signs of ambulation, and were permitted to recover ~14 days prior to
recording.
1.8.3C Electrophysiological recordings
On the day of the experiment, a light-weight cable was connected to the assembly on the
mouse head. The signals were routed through a commutator (Crist Instruments, Hagerstown,
MD, USA). The mice were placed in a shock chamber (Med Associates, St. Albans, VT) to
either examine fear conditioning or to record EEG. The shock chamber was situated in a sound
attenuated cubicle (ENV-017M, Med Associates, St. Albans, VT, USA). Electrophysiological
signals were amplified and filtered (Super-Z headstage amplifiers and BMA-400 amplifiers and
filters, CWE Inc., Ardmore, PA, USA) as follows: cortical EEG 1000X at 1-100Hz and neck
EMG 2000X at 100-1000Hz.
1.8.3D Trace-fear conditioning
Trace-fear conditioning was performed in an isolated shock chamber. The CS was an
80dB white noise, delivered for 15s, and the US was a 0.75mA-scrambled foot-shock for 0.5s.
The mouse was acclimated for 60s, and presented with ten CS–trace–US–ITI trials (trace of 30s,
inter-trial interval (ITI) of 210s). For the camera-based method, data were video recorded using
FreezeFrame Video-Based Conditioned Fear System and analyzed by Actimetrics Software
(Coulbourn Instruments, Wilmette, IL). In addition, whenever freezing behaviour was observed
directly, the experimenter delivered a 3V pulse to the analog to digital converter, via depression
19
of a foot pedal. The pulse was recorded with Spike2 software alongside the EMG record.
Freezing was defined as the absence of movement (of at least 1s) with the exception of breathing
for visual and camera-based methods. Since neck EMG is well known to correlate with an
animal’s movements, we tentatively defined EMG-based freezing as an absence of muscle twitch
activity (of at least 1s). All three scoring methodologies were conducted simultaneously to
examine whether or not EMG freezing measures would correlate with visual-based freezing
measures.
1.8.3E Analyses and statistics
For all comparisons, differences were considered significant if the null hypothesis was
rejected at P < 0.05 using a two-tailed test. Repeated-measures ANOVA (RM-ANOVA) was
performed and followed by post-hoc comparisons with the Bonferroni-corrected P to infer
statistical significance for EMG magnitude comparisons. Freezing behaviour was analyzed using
Pearson-product moment correlation to examine the validity of using EMG as a measure for
conditioned freezing behaviour. Analyses were performed using Sigma-Stat (SPSS Inc.,
Chicago, IL, USA). Data were plotted with SigmaPlot (Systat Software, San Jose, CA, USA).
For the sleep study the mice were scored to be in Non-rapid eye movement sleep
(NREM) if the EMG was of low muscle tone and the EEG was characterized by high voltage
slow waves (1-4 Hz range). Wakefulness was characterized by low voltage, high frequency EEG,
and muscle tone associated with movement. EEG was analyzed in 5s epochs for periods of
wakefulness and NREM sleep using Spike2 software. Scripts for EEG analysis (Sudsa-version
2.2) were obtained from Cambridge Electronic Design (CED). Fast-Fourier transform was used
to convert EEG waveforms into total power (μV2), which was binned every 5s for the following
frequency bands: δ1 (0.5-2Hz), δ2 (2-4Hz), θ (4-7.5Hz), β1 (7.5-13Hz), β2 (13-20Hz) and α (20-
20
30Hz). All neck EMG recordings were smoothed (25ms time constant) and rectified (Spike2
software, 1401 interface, CED Ltd., Cambridge, UK) to calculate the area under the curve. For
EMGs during active wakefulness and NREM sleep, the area under the curve was calculated
every 5s. For EMG-based freezing behaviour the magnitude of the EMG was determined for the
length of the freezing bout and normalized to a 5s period for comparison to other behavioural
states.
1.8.4. Results
Figure 1.3A shows the placement of the neck-EMG recording electrodes to examine
freezing behaviour and sleep. Figure 1.3B shows an example of a baseline EMG recording and
how the signal was processed for quantification.
Figure. 1.3 Surgical preparation (A) Left and right neck muscles (arrows). The muscle overlying the neck muscles has been gently pulled back for better visualization. Two loops of exposed wire were made at the end of a Teflon coated stainless steel wire. The loops were then sewn to left and right muscles. (B) Representative neck EMG traces. The upper trace is and EMG recorded between left (L) and right (R) neck muscles. The second trace is the same signal which has been rectified (Rt). The third signal is the same signal rectified and smoothed (Rt/Sm). Upward arrows denote an increase in movement. Arb., is arbitrary units.
Animals were conditioned in the trace-fear paradigm while their fear was simultaneously
recorded via visual observation, freeze frame camera and with neck EMG electrodes. Figure
1.4A shows simultaneous visual scoring with camera and neck recordings during trace-fear
21
Figure. 1.4 Neck EMG recordings can provide measurement of fear behavior (A) Simultaneous recordings from a visual observer, freeze-frame camera, and neck EMG during the traceconditioning paradigm (during 10th training trial). The conditioning protocol is depicted at the top, showing that awas played followed by an interval (trace interval) then by a shock. It was readily observed that the rectifiedsmoothed EMG signal paralleled the camera-based movement detection, with periods of quiescent EMG activittwitches) correlating with periods of inactivity. Under each measurement, the freezing behavior duration is indicsimilar to the visual-based scoring. Visual-based scoring paralleled both the EMG and the camera-based scoring methowever it was obvious that the latencies of the detection were longer (based on observer reaction times). (B) Scatterand regression lines (+/-95% confidence intervals) for the percentage freezing during tone and trace intervals for eachof the ten trace-fear training trials. Comparisons are made for each of the fear conditioning scoring methods. All scmethods were highly correlated with one another. Abbreviations as for figure 1.3. conditioning. The animal was found to develop visually observable freezing behaviour during the
conditioning. The automated camera system also detected this freezing behaviour. Interestingly,
the neck muscle EMG was found to shift from twitch-like muscle activity during visually
observed movements to a non-twitch activity, with a stable level of muscle tone. All methods of
collecting freezing behaviour data appeared to correlate with one another. The percentage of
22
freezing during the tone and during the trace interval was quantified for 10 trials of conditioning.
Figure 1.4B, C, and D, show correlations plotted for the percentages of freezing for all three data
collection methods. Figure 1.4B shows that there was a strong statistically significant positive
correlation between the visual scoring and the camera scoring (r2= 0.82, p<0.001). Figure 1.4C
shows that there was a strong statistically significant positive correlation between the camera
scoring and the neck EMG scoring (r2= 0.88, p<0.0001). Figure 1.4D shows that there was a
strong statistically significant positive correlation between the EMG scoring and the visual
scoring (r2= 0.83, p<0.0001). Based on these findings we conclude that EMG scoring is as
accurate as both the visual method and the camera method. Thus the EMG-based method would
be sufficient to yield measures of fear behaviour during any point in a 24 hour day, whether
lights are on or off.
Fear behaviour is not always easy to discriminate from sleep when visually observing
rodents. Importantly, fear memory is often studied when rodents would naturally be sleeping (i.e.
lights on phase). We therefore examined whether the automated camera system and simple EMG
recording could mistake sleep for fear memory in an animal which was not fear conditioned.
Figure 1.5A shows an example of the automated camera system detecting fear behaviour in an
untrained animal. Consistently, an examination of the EMG suggests an absence of movement.
However, in this experiment, simultaneous EEG was conducted to examine brain wave
signatures associated with sleep. Figure 1.5B shows that the addition of and EEG recording
identifies this state as NREM sleep rather than fear. The EEG is characterized by high voltage
slow waves compared to the wakefulness that follows the sleep. Figure 1.5C and D show
quantified EMG activity and EEG power confirming the behavioural states of the mouse.
23
Figure 1.5 Falsely scoring sleep as fear behaviour (A) Shows a simultaneous recording with a freeze-frame camera while EMG activity was recorded during the light phase, when mice normally sleep. There is a long period of behavioural quiescence, as detected by the automated camera system and also viewed with neck muscle EMG recording. The animal then appears to become active toward the end of the measurement period. Freezing behaviour is scored below each trace. (B) When the EEG activity was observed during this period (shaded boxes in A and B) it was revealed that the mouse was actually sleeping (NREM) as indicated by high voltage slow waves in the EEG (panel I). The animal can be then seen to arouse from sleep (panel II) as revealed by high frequency low amplitude EEG and the increase in EMG activity. Thus, camera and even EMG methodologies may be recording sleep behaviour when they should be recording fear behaviour. (C) Reduced neck EMG is observed during NREM sleep in comparison to arousal from sleep. (D) Quantification of EEG frequency bands during NREM sleep, demonstrating an increase in power of high voltage slow frequencies relative to wakefulness. Abbreviations as for figure 1.3. We next examined whether the magnitude of the EMG would be useful for discriminating
freezing behaviour from both NREM sleep and wakefulness. EMG values were quantified for 5
chronically instrumented animals during EMG-based freezing behaviour, wakefulness and
24
NREM sleep. RM-ANOVA revealed that there was a significant effect of the recorded state on
neck muscle EMG. (F(2,10)=20.17, P=0.001). Figure 1.6B shows that there was a significant
decrease in EMG from wakefulness to freezing behaviour (t=2.90, P=0.032), and from freezing
behaviour to NREM sleep (t=3.04, P=0.013). The finding suggests that the magnitude of the
EMG is sufficient to discriminate each of these states.
Fig. 1.6 Discriminating freezing behaviour from sleep-wake states Grouped data (n=5) showing that each state could be quantitatively discriminated based on muscle tone measurements. Abbreviations as for figure 1.3, * is p<0.05. We therefore suggest that EMG-based freezing behaviour is characterized by neck
muscle activity that is greater in magnitude than NREM sleep but less than that of wakefulness.
In addition, freezing behaviour occurs in the absence of neck muscle twitches and must last for at
least 1 second to be scored.
25
1.8.5 Discussion
The current investigation sought to examine an inexpensive alternative approach for fear
behaviour scoring, to examine mice across the natural light-dark cycle. We have shown that
freezing behaviour can be scored with use of a simple EMG record. We also show that the way
in which freezing is behaviourally defined overlaps with the way that sleep is defined. That is;
immobility with the exception of breathing. Thus EEG recordings in conjunction with EMG
recording can be helpful to discriminate fear behaviour from sleep.
A major advantage of recording neck EMG for freezing behaviour is that it provides
precise timing of when the freezing occurs which is crucial for the investigation of the neural
correlates of trace-fear memory. Moreover, the EMG activity can be collected using the same
analog-digital converter as that for recording EEG or brain cells.
The major drawbacks to the EMG method described here are that it requires surgery and
there is currently no automated method to score the EMG-based freezing activity. Thus, rapid
screening would be problematic. However, this does not preclude using a camera system and
automated scoring method to do initial screening. The examination of the neurophysiology could
then be followed up with the EMG-based methodology to score freezing.
It has been suggested that resting or sleeping, as scored via visual observation, only
occurs after extinction training of at least 20 minutes (Marchand et al., 2003). Preliminary
observations from our laboratory also suggest that this is the case. Accordingly, studying animals
that have altered sleep habits might cause these animals to fall asleep during memory testing or
extinction which would be detected as freezing behaviour. We have shown that visual-based
measures of freezing and EMG measures can mistake sleep for freezing behaviour. However, we
also show that the magnitude of the EMG can help clarify if an animal is sleeping or in a state of
26
fear. Thus it is recommended that EMG and EEG be used to verify the true behavioural state of
an animal during memory testing of fear conditioning paradigms.
27
CHAPTER 2
SYSTEMS CONTROLLING TRACE FEAR BEHAVIOR
28
2.1 Elaborating the fear circuits with trace fear
In contrast to delay or cued fear conditioning, trace fear conditioning involves the
presentation of the CS and US separated by a time interval (Figure 1.1, Chapter 1). As mentioned
in Chapter 1, this may engage more circuits that would be necessary to make an association
between CS and US across the trace fear interval. Chapter 1 demonstrated that neck EMG can be
a useful means to measure freezing behaviour in fear paradigms. Moreover, this technique
permits the investigator to know precisely when the mouse begins or terminates freezing
behaviour. Thus, for the first time electrophysiological brain cell recordings can be conducted in
parallel with a precise measure of the onset of freezing behaviour. The ensuing Chapter will
elaborate upon the circuitry presented in Chapter 1, to include systems involved in trace fear
memory (Figure 2.1). This chapter will conclude with detailed experiments investigating the role
of the ACC in trace fear conditioning.
2.2 Amygdala and trace fear
Since the function of the amygdala has already been reviewed in the previous chapter,
this section will focus on the potential role of the amygdala in trace fear conditioning. Pare and
Collins (2000) conducted a study in which warning signals would predict a shock. In their
paradigm several tones were played which preceded shock stimulus. Field potential and unit
recordings were obtained from the lateral amygdala during conditioning. This paradigm is
somewhat analogous to the trace fear paradigm in that tones predicted a shock over a time
interval. In this study neurons were found to become locked with the tone signals, coincident
with augmented tone-evoked potentials and in a few cases neuron activities began to ramp,
appearing to be related to the expectation of the shock. Cell activity also became more
synchronized, appearing to be correlated with the theta band field potentials (4-7Hz).
29
Collectively, the results indicate that either the lateral amygdala itself or other structures are
capable of maintaining the activation of the central amygdala, even in the absence of tone
stimuli. In a more recent study, conducted in humans using fMRI, it was found that the central
nucleus of the amygdala became active when threatening stimuli (shock) were in close proximity
(Mobbs et al., 2007). However, when the threat was relatively far away, the basolateral amygdala
tended to be more active, suggesting that it may be involved in the assessment of the threat and
possibly escaping it. Thus the lateral and basolateral amygdala appear to be important in the
anticipation of threats, while the central amygdala appears important for imminent threat. Thus,
the inputs that modulate or drive these parts of the amygdala may help elaborate the networks
involved in trace fear conditioning.
2.3 Hippocampus and trace fear
2.3.1 Basic structure of the hippocampus
The medial temporal lobe system has been long implicated in the formation of declarative
memory (Squire & Zola, 1996). Declarative memory is defined by memory of facts and events
(Squire & Zola, 1996). The medial temporal lobe consists of the hippocampal formation and
extra-hippocampal structures, such as the parahippocampal and perirhinal cortices. (Squire &
Zola, 1996; Amaral & Lavenex, 2006). The hippocampal formation is organized into seven
divisions including Cornu-Ammonis regions 1-4 (CA1-4), the dentate gyrus, subiculum and
entorhinal cortex (Squire & Zola, 1996), and has been well implicated in trace fear memory
(McEchron et al., 1998; Quinn et al., 2002; McEchron et al., 2003; Weitemier & Ryabinin,
2004; Misane et al., 2005; Quinn et al., 2008). The entorhinal cortex is the major input to the
hippocampus and also serves as a major interface between the brain and the hippocampus. The
basic circuit involves information flow through the entorhinal cortex to the dentate gyrus, then to
30
CA3, then CA1, and finally through the subiculum where it travels back out through the
entorhinal cortex (Amaral & Lavenex, 2006). A summary of the major connections of the
hippocampal formation is depicted in Figure 2.1 (pg 31). Damage to hippocampus in amnesia
patients produces declaritive memory problems and impairs trace eye-blink conditioning (Clark
& Squire, 1998). Thus, it was proposed that awareness is required for trace eye-blink
conditioning (Clark & Squire, 1998). Consistently, it was found that fear (as measured by skin
response) was experienced during trace fear acquisition only when the subject was cognitively
aware of the CS which predicted the aversive stimulus (Knight et al., 2006).
2.3.2 Hippocampus and trace fear conditioning
Pharmacological inhibition of the dorsal and ventral hippocampus prior to training impairs trace
fear memory when tested 24 hours after conditioning (Esclassan et al., 2009). Consistently, it
was found that pre-training lesions restricted to the dorsal hippocampus of the rabbit impairs
trace fear cardiac responses (McEchron et al., 2000). By contrast, it was observed that
excitotoxic lesions to the ventral, but not dorsal, hippocampus impair trace fear acquisition
(Yoon & Otto, 2007); however the dorsal and ventral hippocampus were found to be important
for retrieval of trace fear memory 24 hours after conditioning (Yoon & Otto, 2007). The ventral
hippocampus (CA1 and subiculum) is known to project to the basolateral amygdala (Canteras et
al., 1992), and damage to this region disrupts contextual fear conditioning (Maren & Fanselow,
1995). Thus, while the involvement of the hippocampus in trace fear conditioning is
unequivocal, the hippocampal region which is most important cannot be agreed upon.
31
Figure 2.1 Hippocampus and prefrontal circuits Top middle: Areas of interest, including hippocampus, amygdala and prefrontal cortex. Left side: Cross-section through the hippocampus showing the Cornu Ammonis regions (CA1-3), dentate gyrus (DG), and entorhinal cortex (EC). Schematic organization of hippocampal connections is depicted below. Top right: divisions of the prefrontal cortex (each color represents a division). This thesis focuses on regions within the medial division including prelimbic (PrL) and anterior cingulate (labelled as CG1 in the mouse). Bottom right: is a coronal cross-section showing layer by layer efferent and afferent connections to and from relevant structures involved in fear conditioning (center circle). Connections which are common to both the ACC and the prelimbic cortex are colored in black. Connections which belong solely to the prelimbic cortex are in red. Notice that the prelimbic cortex but not the ACC receives input from the CA1 region of the hippocampus. In addition the ACC is only connected with the dorsal aspect for the striatum but the prelimbic cortex is connected with both dorsal and ventral striatum. Also, note that the hippocampus, prelimbic cortex and the ACC all share connections with the basolateral amygdala (BLA). Ifl, infralimbic, AID, agranular insula dorsal, IV, agranular insula ventral, M1, motor cortex,, MD, mediodorsal thalamus, PAG, periaqueductal gray, PrC, precentral, Lat-hypothal, lateral hypothalamus, LO, lateral orbito, VLO, ventral lateral orbito ,VO, ventral orbito. (figure by H. Steenland, refs in text)
The trace fear interval is typically between 15 and 30 seconds, and manipulations of this
time may be useful to determine the minimum interval necessary to engage the hippocampus or
32
other structures. Injection of NMDA receptor antagonist D-APV into the dorsal hippocampus (in
and around CA1) prior to conditioning, blocks trace fear acquisition for time intervals of 15 to 30
seconds, but not intervals less than 15 seconds (Misane et al., 2005). These results suggest that
the dorsal hippocampus plays an important role in bridging the time interval between tone and
shock.
It was hypothesized that the hippocampus maintains temporal contiguity for the tone and
shock stimuli across the trace fear interval (Bangasser et al., 2006). To examine this issue,
Bangasser et al. (2006) first confirmed that lesioning the rat dorsal and ventral hippocampus
impaired trace fear memory 24 hours later. However, if rats are trained with additional tone
stimuli (2sec) coincident with the shock, the lesions did not impair trace fear memory. Thus, the
hippocampus appears to be important for associating two discontiguous events (i.e. events that
occur at different times) (Bangasser et al., 2006). How exactly the hippocampus is involved in
this contiguity is still left up to debate.
It has been shown that CA1 neurons are activated during early conditioning to the CS and
this effect decreases during later conditioning trials (Gilmartin & McEchron, 2005a).
Consistently, the activity of tone-evoked neuron responses in CA1 correlated with the degree of
freezing behaviour during learning in rats (Chen et al., 2009). Functional imaging studies in
humans also demonstrated that the magnitude of activity in the hippocampus is greatest during
early trial of trace fear training, and its activity is predictive over the behavioural association of
the shock and tone (Knight et al., 2004). However, the dentate gyrus has also demonstrated
increased firing for both the US and CS progressively across training trials (Gilmartin &
McEchron, 2005a). Thus, converging evidence indicates that the hippocampus is related to the
US and CS association and the early processing during trace fear acquisition.
33
Knockout of NMDA receptors in CA1 pyramidal neurons is sufficient to slow the
acquisition and impair the retention of trace fear memory (Huerta et al., 2000). It has been shown
that dorsal intra-hippocampal injections of NR2A antagonist (NVP-AAM077), prior to training,
impairs contextual and trace fear acquisition and decreases c-Fos expression (Gao et al., 2009).
By contrast, the NR2B antagonist (RO-25-6981) blocked trace fear conditioning without any
impact on contextual fear (Gao et al., 2009). Cultured primary hippocampal neuron studies
revealed that the NR2A antagonism likely affects the ERK1/2 pathway to impact c-Fos through
MSK1 (Gao et al., 2009). MSK1 is known to be a down-stream effecter of the ERK pathway
(Sindreu et al., 2007). However, activation of the NR2B subunit was not found to influence the
ERK pathway (Gao et al., 2009). Interestingly, a study conducted much earlier (Runyan et al.,
2004) had shown that infusion of an ERK inhibitor into the hippocampus, prior to conditioning,
reduced both contextual fear and trace fear memory 48 hours after trace fear acquisition.
Collectively, the ERK pathways appears important for contextual and trace fear memory,
possibly mediated through activation of NR2A containing NMDA receptors.
2.3.3 Hippocampus and trace fear memory retrieval
Examination of CA1 neurons in rabbits and rats, during trace fear memory recall,
revealed that these neurons fire when animals are anticipating the shock stimuli (McEchron et
al., 2003; Chen et al., 2009). In addition, the activity of these neurons appeared to be related to
the strength of the CS-US association (McEchron et al., 2003). Consistently, neurotoxic lesions
to the dorsal hippocampus demonstrate that it is involved in the storage of recent but not remote
memory (Quinn et al., 2008). Chowdhury et al. (2005) discovered that lesions of the dorsal
hippocampus (mostly in CA1), 1-2 days after conditioning, impaired trace fear memory recall.
34
2.3.4 Gene expression in the hippocampus following trace fear conditioning
One way to explore how trace fear memory differs from cued or delay fear memory is to
look at the expression of genes or proteins following induction of trace fear memory or after
retrieval of this memory. Weitemier and Ryabinin (2004) conducted a study in which they used
immunohistochemistry to examine protein expression in the mouse hippocampus. Interestingly,
they found that c-fos was differentially enhanced in the CA3 and dentate gyrus as a result of
trace fear conditioning. Following memory recall (tested 24 hours after conditioning) zif268 was
elevated in the trace fear condition above that of the delay fear condition in CA3 and the DG. It
is interesting that neither acquisition nor later recall increased expression of Zif268 in the CA1
region of the hippocampus, which has been strongly implicated in trace fear memory (see
above). Putting technical issues aside, this may be explained, by asserting that the integrity of the
hippocampal circuitry is what is important for fear memory acquisition rather than any one
specific region (for example CA1 alone). Also, equally interesting from the perspective of motor
systems, is that during trace-fear acquisition there tended to be more immobility compared to that
of delay fear conditioned animals (Weitemier & Ryabinin, 2004). Thus, the results may simply
be related to differences in motor inactivation, a possibility which appears to be rarely taken into
account in studies of c-fos and Zif268.
A recent study has examined a large variety of genes using RNA microarray analysis of
the mouse hippocampus (unspecified locations in dorsal aspect) following trace-fear
conditioning (Sirri et al., 2009). Thirty minutes after trace fear conditioning there was a rise in:
jun-b (transcription factor), Arc (actin binding), Nr4a1 (transcription factor), zif268 (transcription
factor), c-fos (transcription factor), and Egr2 (transcription factor). However all of these RNAs
35
were also up-regulated by the US alone, indicating a lack of specificity to the paradigm. This
study also identified many RNAs which were up-regulated selectively by trace fear conditioning
and not contextual conditioning. These RNAs included: Dusp1(phosphoprotein phosphatise),
Tiparp (ADP-ribosylation), Btg2 (transcription factor) and Sgk1 (serine/threonine kinase) thirty
minutes after conditioning and Hspb1(protein folding), Zmym1(unknown), Pdki1l
(serine/threonine kinase), Hspa5 (Protein folding), Serpinh1(Protein folding) 2 hours after
conditioning.
2.3.5 Conclusions
Collectively the results suggest that the hippocampus is involved in trace fear memory to
bridge the temporal gap between shock and tone. It appears to be involved in early fear memory
acquisition and recent rather than remote memory storage. NMDA receptors in the hippocampus
are involved in memory acquisition and specifically involve NR2A or NR2B subunits which may
activate down stream products such ERK, Zif268 and c-fos.
2.4 Prefrontal cortex and trace fear
The prefrontal cortex of the rodent is a frontal midline structure with heterogeneous
functions (Dalley et al., 2004). The prefrontal cortex is defined in most species by reciprocal
connections to the mediodorsal thalamus (Uylings & van Eden, 1990). The rodent prefrontal
cortex is diagrammed in Figure 2.1 (pg 31). This region can be broken down into 3 major regions
which include the medial, ventral and lateral divisions and is extensively reviewed by Dalley et
al. (2004). The medial division can be further divided into dorsal regions, which include the
precentral cortex and ACC, and the ventral portion, which includes the prelimbic, infralimbic
and medial orbito cortices. The ventral division includes the ventro- and ventrolateral orbital
36
cortex. Finally the lateral division includes the lateral orbital cortex, ventral agranular insular
cortex and dorsal agranular insular cortex. Generally, learning and memory studies have
identified the dorsal-medial division of the prefrontal cortex as being involved in memory for
motor responses, including the temporal processing of information and response selection
(Dalley et al., 2004). By contrast, the ventral-medial divisions have been ascribed to attention,
and behaviour flexibility (Dalley et al., 2004). The prelimbic cortex and the ACC have also been
implicated in learning and memory in the trace fear paradigm, so the following section examines
what function these structures contribute to trace fear conditioning and memory.
2.4.1 Basic structure of the prelimbic cortex
The prelimbic cortex is a midline structure located below the ACC and above the
infralimbic cortex, at the rostral end of the brain. A summary of the input and output layers to the
prelimbic cortex is depicted in Figure 2.1. The mediodorsal thalamus projects to layer III of the
prelimbic cortex (Pirot et al., 1994) and layer VI projects back extensively to the mediodorsal
thalamus (Gabbott et al., 2005). Layer V of the prelimbic cortex is know to project prominently
to the dorsal and ventral striatum, the lateral hypothalamus, periaqueductal gray and the
basolateral amygdala (Floyd et al., 2000; Gabbott et al., 2005). Almost all of these regions were
discussed in chapter 1 and are known to play a role in cued fear conditioning. It has also been
shown that the basolateral amygdala projects to the medial prefrontal cortex with excitatory
synapses onto presumably inhibitory neurons (parvalbumin containing) in layers II-VI (Gabbott
et al., 2006). The output from the prelimbic cortex to the basolateral amygdala is from layers II
and V and is excitatory (Gabbott et al., 2005; Likhtik et al., 2005) suggesting a degree of
reciprocal laminar distribution. Considerable literature also suggests that the prelimbic and
infralimbic cortices receive monosynaptic input (~15msec lag) from the subiculum/CA1 region
37
of the hippocampus (Swanson, 1981; Ferino et al., 1987; Thierry et al., 2000). However this
direct input appears most prominent from the ventral hippocampus (Swanson, 1981; Ferino et al.,
1987; Thierry et al., 2000). Interestingly, there are reports of phase locking between the
prelimbic neuron activity and the hippocampus theta rhythm during spatial memory tasks (Jones
& Wilson, 2005; Siapas et al., 2005). This phase locking is offset by about 50msec (Jones &
Wilson, 2005; Siapas et al., 2005). There is also evidence that monosynaptic connections to the
prelimbic cortex activate inhibitory interneurons followed by activation of pyramidal neurons
(Gabbott et al., 2002; Tierney et al., 2004), which may be necessary for the generation of theta
(4-7.5Hz) waves (Siapas et al., 2005).
It has been found that there is convergent input from the basolateral amygdala and the
hippocampus in the prelimbic cortex (Ishikawa & Nakamura, 2003). If both inputs are stimulated
at the same time the response in the prelimbic cortex is enhanced; however, if either input is
stimulated before the other, a depressed response to the latter stimulation will occur (Ishikawa &
Nakamura, 2003). The results suggest that synchronizing inputs (for example, local theta field
potentials) from both hippocampus and the amygdala may have an augmenting effect on the
prelimbic cortex.
2.4.2 Prelimbic cortex and trace fear conditioning
Only a few studies have examined the involvement of the prelimbic cortex in trace fear
conditioning. Baeg et al (2001) found that the majority of neurons changed their firing to
coincide with the presentation of the CS for both delay and trace fear (2sec trace interval)
paradigms, and these responses decreased during fear memory extinction. Interestingly, putative
inhibitory interneurons and putative pyramidal neurons showed different patterns of responses.
Inhibitory neurons tended to show transient responses and increased their firing rates following
38
CS presentation; however, pyramidal neurons were observed to show long lasting depressed
activity following the CS (Baeg et al., 2001). Baeg et al. (2001) also report that there was no
obvious relationship between neuron activity and the magnitude of freezing itself. However a
recent study examining delay fear conditions showed that the majority of neurons in the
prelimbic cortex responded in an excitatory way to the CS and that in many cases this activity
was increased coincident with freezing behaviour (Burgos-Robles et al., 2009). These authors
suggest that the prelimbic cortex drives the basolateral nucleus directly to impact the central
nucleus for fear behaviour expression (Burgos-Robles et al., 2009).
Gilmartin and McEchron (2005b) showed that prelimbic neurons have learning-related
increases in activity to the CS and US in a trace fear paradigm (20sec trace interval).
Interestingly, a subset of prelimbic neurons also exhibited increases in activity during the trace
interval. These results suggest that the prelimbic cortex may be important for bridging the time
gap of the trace fear interval, through maintaining the association between tone and shock stimuli
(Gilmartin & McEchron, 2005b). Converging evidence for this concept comes from frontal
imaging studies in humans, in which the frontal operculum and middle frontal gyri were
differentially activated during trace conditioning relative to delay conditioning (Knight et al.,
2004). Gilmartin and McEchron (2005b) also showed that the neuron response of the infralimbic
cortex tends to decrease during the CS, US presentation, possibly implicating an inhibitory
output from the prelimbic to this region.
In a delay fear conditioning paradigm conducted by Burgos-Robles et al. (2009), it was
also found that prelimbic neurons become more synchronous with conditioning and burst more
often. This finding is consistent with recent data showing that theta oscillations recorded from
the prelimbic and infralimbic cortex demonstrate enhanced theta power during a trace (1.5sec)
reward test (Paz et al., 2008). It was also found that neuron activities tend to fire near the
39
negative trough of the field potential. This result indicates that the hippocampus may be driving
prefrontal memory recall (Paz et al., 2008).
2.4.3 Prelimbic cortex and trace fear memory
Runyan et al. (2004) found that immediately after trace fear training, ERK is
phosphorylated. Interestingly, blocking ERK activity prior to conditioning did not influence trace
fear acquisition but did impact memory retention 24 and 72 hours after conditioning. A similar
result was produced when ERK was blocked immediately after trace fear conditioning,
suggesting that some process occurs after trace-fear conditioning to consolidate memory,
possibly in the prelimbic cortex. Runyan and Dash (2004), also blocked dopamine receptors in
the prelimbic cortex prior to trace fear conditioning. Antagonizing dopamine was found to block
the rise in ERK phosphorylation and reduce long-term memory retention (Runyan & Dash,
2004). Consistently, blocking the PI3Kinase pathway in the prefrontal cortex after trace fear
conditioning interferes with remote memory retention, examined 3d and 6d after trace fear
conditioning training (Sui et al., 2008). Finally, neurotoxic lesions to the prelimbic cortex made
after conditioning demonstrate that the prelimbic cortex is involved in the storage of remote
memory (Quinn et al., 2008).
2.4.4 Basic structure of the anterior cingulate cortex
The ACC is an agranular (lacking layer IV) midline cortical area located dorsal to the
prelimbic cortex. The ACC is defined and innervated in layer III by projections originating from
midline and intralaminar nuclei of the medial thalamus (Vogt et al., 1979; Devinsky et al., 1995;
Hsu & Shyu, 1997; Hsu et al., 2000; Shyu et al., 2004). Layer VI of the ACC projects back
extensively to the mediodorsal thalamus (Gabbott et al., 2005). Also, Layer V of the ACC
40
projects prominently to the dorsal but not ventral striatum and no report has ever demonstrated a
direct connection with the hippocampus (to my knowledge), which distinguishes it from the
prelimbic and infralimbic cortices (Figure 2.1). Thus, striatal output and innervation from CA1
may be a helpful method to differentiate functions of ACC from that of the prelimbic cortex. For
example, the ventral striatum is argued to be important in allocating attention resources to cues
which predict danger (Dalley et al., 2004; McNally & Westbrook, 2006) which is consistent with
the mnemonic function of the prelimbic region. By contrast, the dorsal striatum has been
implicated in aversive learning (Delgado et al., 2008), which is consistent with neural responses
of the ACC (see sections 2.5.1 - 2.5.4). Similar to the prelimbic cortex, the ACC projects to the
lateral hypothalamus, periaqueductal gray and the basolateral amygdala (Floyd et al., 2000;
Gabbott et al., 2005). The connection between the ACC and the basolateral amygdala has been
identified as glutamatergic in nature (Bissiere et al., 2008).
2.4.5 Pain responsive neurons in the anterior cingulate cortex
Shock stimuli are a major component of the trace fear paradigm. Not only is information
regarding nociception sent to the amygdala, it is also sent to the ACC (Fig. 2.2 pg 41, ACC is
labelled as CG1 or 2 in mouse). Neurons of the ACC respond predominantly to noxious pinch or
electrical stimulation to the skin; however responses can also be elicited with pressure and
tapping of the skin, and colonic distension (Davis et al., 1994; Ploghaus et al., 1999; Wei et al.,
1999; Wei & Zhuo, 2001; Kung et al., 2003; Yang et al., 2006). In addition, the receptive field of
ACC neurons can respond to stimulation from large portions of either side of the body. Most
ACC neurons in these studies responded with excitation to peripheral stimulation and were
located in layer V in rats and layer III in rabbits (Sikes & Vogt, 1992; Yamamura et al., 1996). In
addition to spike recording conducted in the ACC, extensive evoked-field potential recording has
41
Figure 2.2 Elaborated fear circuits This is an elaboration of the fear circuit presented in Figure1.2, however this circuit now includes hippocampal (top) and prefrontal circuits (left) and the appropriate connections with the thalamus. Notice the added connections between the CG1 (rodent ACC), Prelimbic, CA1 hippocampus and the BLA. In the context of trace fear memory, the prelimbic cortex and CA1 hippocampus may maintain the memory over the trace interval through activation of the BLA. The BLA would then activate of central amygdala to produce freezing behavior. The cingulate cortex may be involved in the affective component of the shock stimuli, or be involved in modulating fear behavior though connection with the periaqueductal gray (PAG). Abbreviations are the same as for Figure 1.2 and 2.1, IFL is infralimbic. (figure by H. Steenland, refs in text)
42
been done in anesthetized rat preparations (Wei & Zhuo, 2001; Sun et al., 2006; Yang et al.,
2006). In these studies it was shown that noxious peripheral stimulation elicited sink currents in
lamina II/III and V of the ACC, reflecting synchronous depolarization of neuron populations.
These currents are mediated through glutamatergic synaptic transmission and could be abolished
by medial thalamic lesions (Yang et al., 2006). While both the rat and the mouse have brains of
comparable scale and shape, it cannot be assumed that the regional boundaries and neuron
response profiles of the mouse brain are the same as that of the rat. Based on the afferent and
efferent connections with the ACC, it is likely be involved in processing incoming sensory input
in layer III and possibly be involved in a motor output via layer V. However a basic description
of ACC neuron responses to peripheral stimulation has never been conducted in the mouse.
2.4.6 Anterior cingulate cortex and trace fear conditioning
Much less work has focused on the role of the ACC in trace fear memory. A study by
Han et al. (2003) demonstrated that lesions to the ACC in mice impairs trace fear conditioning
without affecting delay fear conditioning. In addition, it was reported that c-fos expression was
elevated in the CG1 subregion of the ACC when measured 30 minutes after trace fear
conditioning. Interestingly, ACC lesioned mice demonstrated impairments in trace fear
acquisition as early as the first training trial. The authors attribute their finds to the importance of
the ACC in attention processes. Indeed, distracting stimuli presented during training, are
sufficient to produce deficits in trace fear memory similar to the effect of ACC lesioning (Han et
al., 2003). Consistently, mouse models of Fragile-X syndrome and chronic pain (Complete
Freund's Adjuvant) both demonstrate impaired trace fear conditioning, possibly because both
animal models are impaired in attentional processing (Zhao et al., 2005; Zhao et al., 2006).
43
However, fMRI studies in humans has not demonstrated any significant role for the ACC
in trace fear conditioning that it doesn’t already play in delay conditioning (Buchel et al., 1999;
Knight et al., 2004). Since there is nearly equal evidence for and against the possibility of the
ACC in trace fear memory, its involvement should be considered unresolved. Rather, more basic
recordings need to be done to ascertain the activity of neurons during trace fear conditioning. To
really get an understanding of what the ACC does, the following section will examine research
on the role of the ACC in various learning paradigms and behaviours relevant to trace fear
conditioning.
2.5 Close examination of anterior cingulate cortex
2.5.1 Cued and contextual fear conditioning
Blocking glutamatergic transmission in the ACC is sufficient to impair the acquisition of
conditioned-place aversion (Johansen & Fields, 2004). Consistently, pharmacological blockade
or lesion of the ACC, at a position where it projects to the basolateral amygdala, impairs
acquisition of auditory but not contextual fear conditioning (Tang et al., 2005; Bissiere et al.,
2008). Tracer studies and immunohistochemistry revealed that this connection from ACC to the
basolateral amygdala is most likely excitatory (Bissiere et al., 2008). Thus removal of ACC input
might be predicted to reduce overall central amygdala output.
If the ACC is involved in the affective component of pain (Rainville et al., 1997), direct
stimulation of the ACC may be sufficient to produce aversive conditioning. Indeed glutamatergic
stimulation of the ACC is sufficient to produce avoidance learning (Johansen & Fields, 2004),
while long (10sec) trains of electrical stimulation to the ACC associated with an auditory cue can
induce conditionined freezing behaviour (Tang et al., 2005). At odds with these findings is the
observation that lesion of the ACC has no impact on cued delay fear conditioning (Han et al.,
44
2003). Interestingly, cue-evoked activity has been observed in the ACC, using fMRI in humans,
to aversive stimuli (Buchel et al., 1999). This finding is consistent with studies in rabbits in
which neurons were able to discriminate two tones, based on one of the tones having being
paired with a shock (Gabriel & Saltwick, 1977). Based on these studies alone, it appears that the
anterior cingulate may be involved in learning, both cue and aversive based information.
2.5.2. Avoidance conditioning
Avoidance conditioning involves learning to avoid aversive situations or environments.
The ACC has been thoroughly examined in pain avoidance conditioning in rabbits and monkeys
(Kubota & Gabriel, 1995; Freeman et al., 1996; Kubota et al., 1996; Koyama et al., 2000;
Koyama et al., 2001). A variety of studies have indicated that neurons of the ACC discriminate
cues which predict aversive stimuli as compared to cues that do not predict aversive stimuli
(Kubota & Gabriel, 1995; Freeman et al., 1996; Kubota et al., 1996; Koyama et al., 2000;
Koyama et al., 2001). Interestingly this discriminative neuron activity also occurs in the medial
dorsal thalamic nucleus, the main thalamic input to the ACC (Kubota & Gabriel, 1995; Freeman
et al., 1996), and in the caudate nucleus (Koyama et al., 2000), one of the outputs of the ACC
(Gabbott et al., 2005). These paradigms have also been able to uncover pre-avoidance activity in
the ACC (Kubota et al., 1996; Koyama et al., 2001) and in some cases neuronal responses would
ramp-up prior to the avoidance behaviour (Kubota et al., 1996). Finally, other neural responses
were directly linked to motor movements (Koyama et al., 2001).
The avoidance paradigm shares some similarity with trace fear conditioning. Firstly, both
involve conditioning to aversive stimuli. Secondly, there is usually an interval of time which
must be waited. Finally, they both involve a stimulus cue to inform of impending danger. The
main difference is that avoidance is a behaviour in which the aversive event is escapable while
45
freezing behaviour is not necessarily linked to escape. In the trace fear paradigm, the option of
escape from the aversive stimuli is not provided (however presumably the animal must discover
this). The inescapability of the situation may lead to a depression of neural responses rather than
a potentiation. As such, freezing behaviour is prominent in trace fear conditioning rather than
escape behaviors. Escape-attempts appear to be most prominent early on in conditioning
(personal observation)
More recently, a functional imaging study conducted in humans showed that the ACC
and ventromedial prefrontal cortex become active for cue-related information in a shock-
predatory paradigm. In this paradigm, subjects moved a cursor on a computer screen to avoid
getting caught by a digital predator. If the subject were caught, he/she would receive a shock of
differing intensities (Mobbs et al., 2007). Interestingly, activation of the medial PFC, ACC and
lateral amygdala was highest during distal threats as opposed to proximal threats which engaged
the PAG and central nucleus of the amygdala.
2.5.3 Trace eye-blink conditioning
Trace eye-blink conditioning has also been used to investigate the ACC (Weible et al.,
2003; 2007; Oswald et al., 2009). This conditioning involves the association of a cue stimulus
(e.g. tone) with an aversive stimulus such as a puff of air to the eye. When an animal learns the
association between these stimuli, it will blink its eye in response to the cue stimuli alone. In this
type of conditioning, the trace interval is usually greater than 200msec and less than a second.
Thus, there is no fear behaviour recorded, just the reflexive behaviour of the eye. Cued stimuli
have been found to evoke excitatory and inhibitory responses in the ACC after conditioning
(Weible et al., 2003). Consistent ACC responses to the touch of the body (section 2.4.5), neurons
also demonstrate excitatory and inhibitory responses with presentation of the aversive puff
46
stimuli (Weible et al., 2003). Interestingly, many of the neurons which exhibit responses to cued
stimuli also exhibit responses to the aversive stimuli (Weible et al., 2003) and a similar finding
was reported for aversive fear conditioning (Kubota et al., 1996). Finally, studies of the ACC and
prelimbic cortex during trace eye-blink conditioning have demonstrated reactivation of these
neurons during sleep (Takehara-Nishiuchi & McNaughton, 2008), possibly being involved in
remote memory consolidation (see below ).
2.5.4 Anterior cingulate cortex and recent Vs remote memory
A series of studies have evaluated the role of the ACC in long-term memory storage,
weeks after a learning event. Frankland et al. (2004) found that zif268 was elevated in the ACC
following remote memory retrieval (36 days post-conditioning) for contextual fear conditioning
but not for recent memory retrieval (1 day post-conditioning). On the contrary, the hippocampus
appeared to be involved in the opposite direction with zif368 elevation after recent memory
retrieval but not for remote memory retrieval. Consistently, it was found that blocking neural
activity in the ACC with lidocaine impaired remote but not recent memory (Frankland et al.,
2004). Follow-up studies revealed that the ACC is also involved in the consolidation of remote
but not recent memory for both spatial and taste aversion (Teixeira et al., 2006; Ding et al.,
2008). It has been suggested that one of the fundamental functions of sleep is to convert recent
memory into remote memory via communication between the hippocampus and the neocortex
(chapter 3 section 3.5.4). Since the ACC appears to be involved in long-term memory storage,
some process during acquisition may be necessary to tag the ACC for the transfer of
information/memory to this region.
47
2.5.5 Anterior cingulate cortex and reward conditioning
It cannot be denied that activity of the ACC is related to pain information; however a
substantial amount of evidence indicates its involvement in reward stimuli (Niki & Watanabe,
1979; Takenouchi et al., 1999; Koyama et al., 2001; Shidara & Richmond, 2002; Tsujimoto et
al., 2006; Sallet et al., 2007; Wu et al., 2009). In reward conditioning experiments, a cue is
conditioned with reward delivery. Enhanced neural activity has been detected during cue,
anticipation of reward, and reward responding periods (Niki & Watanabe, 1979; Takenouchi et
al., 1999; Koyama et al., 2001; Shidara & Richmond, 2002). Indeed, neural activity has been
found to co-vary with the proximity of the reward (Shidara & Richmond, 2002). Interestingly,
the ACC also demonstrates 4-7Hz theta rhythms (Tsujimoto et al., 2006). These rhythms were
found to increase just before execution of motor movements. Moreover, these potentials were
enhanced when the movement was rewarded, as compared to unrewarded trials. Thus reward-
related activity is reflected in single unit activity and local field potentials. The finding that the
ACC is involved in noxious stimulation and reward is somewhat conflicting, especially since
these neurons types appear to be intermingled (Koyama et al., 2001), though not necessarily
multimodal. A parsimonious explanation is that the ACC is involved in motor responding to
emotional information in general.
2.5.6 Anterior cingulate cortex and motor control
Among the many properties of ACC neurons, two functions seem to emerge most
consistently. The first is that the ACC seems capable of learning about cues which predict
positive or negative outcomes. The second is that the ACC seems particularly involved in motor
48
behaviour or preparation for motor behaviours. The latter concept will be elaborated on in this
section.
Electrical stimulation to the ACC in the rat elicits head and forelimb movements
(Sinnamon & Galer, 1984). Indeed, retrograde labelling from the primary motor area in rats
(Wang et al., 2008) reveals a connection between orofacial and forelimb motor areas and that of
the ACC, but not the posterior cingulate cortex. Consistently, Shima et al (1991) found that 60%
of neurons recorded from the monkey anterior and posterior cingulate cortex had activity that
preceded the execution of distal flexor muscles. Interestingly, when an animal was allowed to
move its finger to get a reward at its own pace, premotor neuron activity in the ACC was often
seen to occur between 500msec to 1 sec before the actual movement. In another study, the
neuron activity in the ACC appeared to be involved in selecting or switching motor responses in
order to retrieve a reward (Shima & Tanji, 1998). In this study, muscimol infusion into the ACC
impaired the ability to switch between low reward and high reward responses.
It is interesting to note that electrical stimulation to the ACC, where presumed pain cells
were recorded, did not elicit a subjective experience of pain (Hutchison et al., 1999). Recently,
the notion was advanced that all the presumed pain regions of the ACC correspond to motor
regions, known as the cingulate motor areas (Dum et al., 2009). Taking advantage of a trans-
synaptic virus, Dum et al. (2009) infected the cervical spinal cord of monkeys to label the
spinothalamic tract and the secondary and tertiary neurons. Neurons were labelled in the
thalamus and in the cortex. Twenty-four percent of the labelling neurons were found in the ACC
and they corresponded to cingulate motor areas. Within these regions, neurons were found to
project back to the spinal cord, the primary motor cortex and the dorsal and ventral premotor
areas (Dum et al., 2009). While this may appear to be an isolated finding, Meta-analysis of 30
49
imaging papers, which examined either pain or motor behaviour, demonstrates that these two
regions strongly overlap in the human ACC (Dum et al., 2009).
2.6 Comments
Based on the research presented here, the ACC appears to be at an interface between affective
input and motor output. The affective input is likely to be involved in learning cue information
which predicts rewarding or aversive stimuli. Moreover, this information may be helpful for the
ACC to select an adaptive motor behaviour so that the aversive stimuli can be avoided or the
rewarding stimuli can be obtained.
2.7 EXPERIMENT SET 2: Affective-motor integration of the mouse anterior cingulate
cortex during trace fear conditioning
2.7.1 Abstract
The ACC has been identified as a predominant region in responding to painful stimuli.
However, a variety of studies have shown that the ACC is also involved in the generation of
motor output related to reward and punishment. More recently it has been demonstrated that
regions involved in the processing of pain, in the monkey and human brain, share overlap with
regions commonly thought to be the cingulate motor area (CMA). The ACC is also thought to be
involved in fear memory. Fear expression involves changing motor behaviours (e.g. freezing)
based on the affective valence of the situation (e.g. perceived threat). These observations suggest
that the ACC may be a site of integration between affective and motor processing. To investigate
this, we used an in-vivo freely behaving mouse model to record local field potentials or brain
cells in combination with the trace fear paradigm. The trace fear paradigm involves making the
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association of a conditioned tone with that of a shock over a time interval. This paradigm is
known to involve attention, pain, auditory sensation and movement. Indeed, we found that tone-
evoked field potentials, and motor-related potentials, could be detected in the ACC during trace
fear training, indicating an involvement in both sensory and motor function. Interestingly, the
tone-evoked potentials exhibited a learning curve, which paralleled that of freezing behaviour.
Enhancement in 4-7.5 Hz and 13-20Hz rhythms were also found in the ACC during the trace
interval. This effect did not mirror the learning curves, indicating that this rhythm, at least in the
mouse ACC, is not directly predictive of fear behaviour. We also performed extracellular unit
recordings from the ACC in freely-behaving mice, anesthetized mice, and whole cell patch-
clamp recordings in mouse ACC slices. Slice work was carried out by Dr. XiangYao Li a post-
doctorate in Dr. Zhuo’s laboratory We used identified neuron types in-vitro, and their spike
characteristics, to help confirm the identity of putative pyramidal and non-pyramidal neurons in-
vivo. Neural activity was found to increase during the presentation of shock and tone stimuli,
however the majority of cells also responded to the movement of the mouse, as revealed by neck
muscle recordings. Collectively, the field potentials and neuron recordings indicate that the ACC
is involved in learning conditioned cues, responding to pain and executing premotor commands.
To further evaluate the role of the ACC in responding to sensory modalities, mice were probed
with noxious and innocuous stimuli during both anesthesia and under freely behaving conditions.
The data indicate that the ACC can respond to sensory input without the necessity of movement.
Finally, we show that activation of the ACC, with the potassium channel antagonist 4AP,
produces ictal population spikes, which corresponded to audible vocalizations and motor
manifestations (e.g. circling behaviour). We provide strong evidence that the mouse ACC is
neither a strictly pain nor motor area; rather it is involved in the integration of emotionally salient
information for motor action.
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2.7.2 Rationale
The experience of pain motivates us to physically withdraw from situations that harm the
body. Thus, neural systems involved with the perception of pain may be tightly coupled with
neural systems involved in motor movements away from the source of pain. The anterior
cingulate cortex is a cortical-limbic structure involved in the affective component of pain
(Devinsky et al., 1995; Rainville et al., 1997; Kung et al., 2003; Zhuo, 2007; Wei & Zhuo, 2008;
Toyoda et al., 2009). Recently it has been shown that pain-processing regions of the ACC
overlap with regions commonly referred to as the cingulate motor areas (CMAs) (Dum et al.,
2009), in monkeys and humans. This suggests that the ACC pain network is tightly linked with
motor output, though electrophysiological evidence is pending.
The ACC is defined and innervated by projections originating from midline and
intralaminar nuclei of the medial thalamus (Vogt et al., 1979; Devinsky et al., 1995; Hsu &
Shyu, 1997; Hsu et al., 2000; Shyu et al., 2004) which relays information related to peripheral
stimuli. For example, neurons of the ACC respond predominantly to noxious pinch or electrical
stimulation to the skin; however responses can also be elicited with pressure and tapping of the
skin, and painful colonic distension (Davis et al., 1994; Wei et al., 1999; Wei & Zhuo, 2001;
Kung et al., 2003; Yang et al., 2006). Thus, the ACC is accepted to have considerable
involvement in pain processing (Devinsky et al., 1995; Zhuo, 2007).
Other studies have shown that cingulate theta rhythms and neuron activity are involved in
anticipation and motor movements to reward stimuli (Shima & Tanji, 1998; Tanji et al., 2002;
Tsujimoto et al., 2006). In addition, stimulation of the ACC has been known to induce
vocalizations (Jurgens & Pratt, 1979; Jurgens, 2009), fear behaviour (Johansen & Fields, 2004;
Tang et al., 2005) and motor movements (Penfield & Welch, 1951; Showers, 1959; Hughes &
52
Mazurowski, 1962; Bancaud et al., 1976; Sinnamon & Galer, 1984). Indeed, because of the
many proposed functions of the ACC, it has been suggested that it could be an integrator of
cognitive, emotional and motor systems (Paus, 2001).
Trace fear conditioning is an excellent tool to examine the role of the ACC, because this
paradigm targets a number of functions for which the ACC is likely to be involved. Trace fear
conditioning involves the association of a tone with a noxious shock stimulus over an interval of
time (trace interval). Accordingly, the subject learns that the sound of a particular tone will
predict the delivery of a shock, some time after the tone has ended. Studies in mice have shown
that c-fos is elevated in the ACC following trace-fear conditioning and that removal of the ACC
impairs trace (but not delay) fear conditioning (Han et al., 2003). However, pharmacological
blockade of ACC also impairs the acquisition of cued fear behaviour (Bissiere et al., 2008).
Consistently, a study in humans demonstrated that classical conditioning of visual stimulus with
an aversive tone can trigger ACC activity (Buchel et al., 1999), suggesting an involvement in
cue-aversive associations. Unit recording studies in humans and monkeys have also shown that a
subset of ACC neurons can respond in anticipation of shock stimuli (Hutchison et al., 1999;
Koyama et al., 2001). Thus, the ACC appears to be involved in fear memory acquisition,
expression, and attention; however whether the ACC performs a discrete functional process
remains unknown.
We hypothesize that the ACC neural activity will be related to fear cues, anticipation of
shock, pain, and motor movement. Thus, we recorded neurons and field potentials in the mouse
ACC during trace fear conditioning, and to peripheral noxious and innocuous probe stimulations,
to examine the cellular mechanisms for which the ACC might be integral.
2.7.3 Materials and Methods
53
2.7.3A Animals
Experiments were performed on 28 C57BL6 mice (10-18 weeks old). Mice were
maintained on a 12:12-h light-dark cycle (lights on 8:00 AM), and had access to food and water
ad libitum. Procedures conformed to the recommendations of the Canadian Council on Animal
Care and the University of Toronto Animal Care Committee approved the protocols. Seven mice
were used for trace fear and field potential experiments (9 ACC field recordings), 4 mice for
microdialysis, 4 mice for trace fear spike experiments, 4 mice for freely behaving and 9 mice for
anesthesia experiments.
2.7.3B Preparation of electrodes for spike recording
Multiunit spike recording was done with tungsten electrodes (WE30030.5A, Micro Probe
Inc.). Electrodes were preloaded into a miniature 4 channel microdrive (constructed in-house).
The microdrive was equipped with a printed circuit board, to route signals, and 4 independently
manually operated drives to advance electrodes a minimum of 20μm. To lower the electrodes
impedance, without impacting its profile, the tip of the electrode was first plated with a gold
solution (Krohn, 24K gold plating solution) at 100-500μA (Grass Instruments, model CCUI)
with the negative terminal directed to the electrode connector. This layer of gold plating provided
an interface for the platinum plating. The electrode was washed with water then plated with a
platinum solution (VWR, part VWR3905-0) 100-500µA. The electrode was continuously plated
with platinum until the impedance matched the reference electrode (300-500 kOhms). All
impedances were measured on an impedance tester (BAK electronics Inc., model IMP2, Mount
Ary, MD, USA). A ground electrode consisted of a silver wire which was wrapped around the
body of the microdrive.
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2.7.3C Freely behaving spike recording preparation
Mice were anesthetized with isoflurane, delivered via nose cone throughout the surgery.
Oxygen (30%) was mixed with the halothane to ensure a healthy preparation. The scalp of mice
were shaved and then cleaned with iodine (Triadine) and alcohol. A midline scalp incision was
made to expose the skull and neck muscles. The skull of the mouse was fixed into a stereotaxic
adapter (Harvard Apparatus, Model, 51625, Holliston, MA, USA) mounted on a stereotaxic
frame (Kopf Model 962, Tujunga, CA, USA). Three small holes (1.19 mm diameter) were drilled
into the skull for mounting screws. An additional hole was drilled in the skull overlying the ACC
(AP 0.6-1.0, ML 0.5-0.8). The silver ground wire of the microdrive was then wrapped around
three mounting/grounding screws. The dura matter overlying the ACC was reflected and mineral
oil was placed on the exposed brain. The microdrive with loaded electrodes was lowered slowly
0.5mm below the surface of the brain to avoid the motor cortex. A small amount of mineral oil
mixed with bone wax was placed around the electrode penetration zone. After connecting the
mounting screws to the silver ground wire of the microdrive, Krazy glue and dental cement were
used to secure the microdrive to the mouse skull. Two Teflon-coated stainless steel electrodes
(Cooner Wire, AS632) were sutured to the left and right nuchal neck muscles with 4.0 silk
threads. Mice were injected, intraoperatively (SC) with buprenorphine (0.1 mg/kg) as an
analgesic, and 1.0 ml sterile saline (IP) for hydration. Mice were placed on a warm heating pad
until they showed signs of ambulation, and were permitted to recover 5-8 days prior to recording.
2.7.3D Freely behaving field potential preparation
Intracortical EEG recording was performed with sterile bipolar Teflon-coated tungsten
electrodes (AM-systems, 796000, 76.2 m wire diameter) with a 0.5mm tip offset. The electrode
impedances were 100-300 kOhms and were matched within ± 50 kOhms to improve common
55
source noise rejection. All impedances were measured with an impedance tester (BAK
electronics Inc., model IMP2, Mount Airy, MD, USA). Surgery was performed with a similar
technique, as above. When the animal’s skull was exposed, a hole was drilled over the frontal
cortex at (AP 0.6, ML 1.75) for the bipolar electrode. Holes were drilled for a stainless steel
ground screw (AP -3.0, -ML 3.0) and a support screw (AP 1.0, ML -1.0) to help secure the
electrode assembly. Bipolar electrodes were then lowered at a 45º angle into the ACC (-1.8 DV).
The regions where the electrodes penetrated the brain were covered with a mixture of bone wax
and mineral oil. The electrode assembly was then fixed to the skull with dental cement and Krazy
glue. Two Teflon-coated stainless steel electrodes (Cooner Wire, AS632) were sutured to the left
and right nuchal neck muscles with 4.0 silk threads. In one case, 4 electrodes were inserted at a
45 degree angle into the ACC (0.3mm tip offset). Operative and postoperative care was the same
as above.
2.7.3E Anesthetized mouse preparation
Mice were anesthetized with isoflurane, delivered via nose cone throughout the surgery.
Oxygen (30%) was mixed with the halothane to ensure a healthy preparation. The abdomen and
scalp of each mouse were shaved and then cleaned with iodine (Triadine) and alcohol. A midline
abdominal incision was made to expose the underlying muscle. A 1.0 cm incision was made in
the abdominal muscle from the xyphoid process in the caudal direction. Two Teflon-coated
stainless steel electrodes (Cooner Wire, AS632) were sutured to the right costal diaphragm using
a 4.0 sterile silk suture. The abdominal muscle was sutured and the skull of the mouse was fixed
into a stereotaxic adapter (Harvard Apparatus, Model, 51625, Holliston, MA, USA) mounted on
a stereotaxic frame (Kopf Model 962, Tujunga, CA, USA). Two small holes (1.19 mm diameter)
were drilled into the skull for differential frontal-parietal recordings. Electrodes, consisting of a
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wire attached to a jewelers screw (with contact end ground flat), were fixed into the holes to
record EEG at the following coordinates relative to bregma; frontal cortex (AP 1.0, ML -1.0),
parietal cortex (AP -2.2, ML -2.5). The animal was grounded with a wire connected to the neck
muscle. A small hole was carefully drilled near the midline of the frontal cortex (AP 0.62, ML
0.62) to expose the dura. The dura was carefully reflected with a 30 gauge needle to make a
window (1-2mm diameter) overlying the area of interest. Warm ACSF was then placed on top of
the exposed region to prevent drying. Electrodes for single neuron recordings could then be
lowered into the brain with a micromanipulator to record brain cells and local field potentials.
2.7.3F In-vitro recording
Mice were decapitated and the brain was quickly removed and immersed in oxygenated
(95% O2-5% CO2), cooled (4~6°C), artificial cerebrospinal fluid (ACSF) for 2-3 minutes. ACSF
contained (in mM): 124 NaCl, 2.5 KCl, 2 CaCl2, 2 MgSO4, 25 NaHCO3, 1 NaH2PO4, 10 glucose,
pH 7.4, 300-310 mOsm. A block of brain tissue containing the ACC was dissected, glued to a
small stage (LOCTITE 404 cyanoacrylate glue) and covered with ACSF. Coronal slices 300
microns thick containing ACC were made with a vibratome (Series 1000) and preincubated in
oxygenated ACSF at room temperature (22~26°C) for at least 1 hour, then transferred to a
submerged chamber and superfused (2~3 ml/min) with oxygenated ACSF at room temperature.
After 1hr recovery, slices were placed in a recording chamber on the stage of an Olympus
BX51WI microscope (Tokyo, Japan) with infrared DIC optics for visualization of whole-cell,
patch-clamp recordings. Action potentials in the ACC could then be recorded from layer I-III
neurons with an Axon 200B amplifier (Molecular Devices, CA). Recording electrodes (2–5 MΩ)
contained a pipette solution composed of (in mM): K-gluconate, 120; NaCl, 5; MgCl2 1; EGTA,
0.5; Mg-ATP, 2; Na3GTP, 0.1; HEPES, 10; pH 7.2; 280–300 mOsm. Access resistance was 15–
57
30 MΩ and was monitored throughout the experiment. Data were discarded if access resistance
changed more than 15% during an experiment. The membrane potential was held at -70 mV
throughout the experiment. A range of currents (5-300pA) were injected into each cell to
examine their responsiveness and spike wave form characteristics.
2.7.3G Microdialysis
Microdialysis probes (CMA/7, 240µm diameter, 1mm membrane, CMA Microdialysis
Inc) were implanted in the mouse ACC for freely behaving and anesthesia drug delivery. In the
case of freely behaving mice, a guide cannula (with a dummy probe) was implanted previously
on the day of surgery (at least 1 week before the experiment). For these experiments a frontal
parietal EEG electrode was also placed on the skull in addition to EMG electrodes to record from
the neck (see above). For anesthesia experiments the microdialysis probe was lowered into the
ACC on a steep 70º angle (AP 0.6 , ML 0.5, DV 2.3). Careful attention was paid to avoid cortical
surface vasculature. After 30 minutes had elapsed, the reference electrode was lowered in place
near the shaft of the microdialysis probe. The recording electrode was then slowly lowered into
the brain in 5µm steps, parallel to the probe. Mineral oil was applied to the exposed surface of
the brain every hour. Mice were placed on a heating pad (37 ºC) throughout the experiment. The
dialysis probe was perfused (1-3µL/min with ACSF for ~1hr or more following implantation).
The composition of the ACSF was the same as that used for in-vitro recording.
2.7.3H Trace fear conditioning
Trace fear conditioning was performed in an isolated shock chamber (Med Associates, St.
Albans, VT). The conditioned stimulus (CS) was a 80dB white noise, delivered for 15s, and the
unconditioned stimulus (US) was a 0.75mA-scrambled foot-shock for 0.5s (Wu et al., 2008).
Mice were acclimated for 60s, and presented with ten CS–trace–US–ITI trials (trace of 15 sec,
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inter-trial interval (ITI) of 225s). One day after training, mice were acclimated for 60s and
subjected to ten CS–ITI trials (ITI of 225s) in a novel chamber to test for trace fear memory
(Huerta et al., 2000). Freezing is typically defined as the absence of movement with the
exception of breathing. Conventional trace fear paradigms have been performed with animals
during the day, when animals normally sleep. This was previously necessary because
behavioural scoring and motion detection required light to determine freezing behaviour. Thus,
most studies have been carried out when the animal would naturally be sleeping. To circumvent
this potential confound we utilized neck EMG recording to examine freezing behaviour in the
dark (0.2 lux) when mice are naturally awake (Steenland & Zhuo, 2009). The additional
advantage of recording in the dark with EMG is that it may remove contextual cues. ACC field
potentials and unit recordings were obtained from animals during the paradigm to examine the
role of the ACC in trace fear conditioning.
2.7.3I Histology
At the completion of freely behaving animal experiments, mice were euthanized with
isoflurane and a DC current (100-500uA) was passed between recording electrodes and ground,
to lesion the brain. The brain was then removed and fixed with 10% formalin solution. After at
least a week, the brains were transferred to 30% sucrose, and cut in 40µm coronal sections with a
cryostat (CM 1850; Leica). Sections were mounted and observed under microscope immediately
after sectioning. Some brains were also processed for Nissl staining. Lesion and microdialysis
sites were localized and co-registered with the stereotaxic atlas of the mouse brain (Paxinos &
Franklin, 2000).
2.7.3J Recording
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Electrophysiological signals were amplified and filtered (Super-Z headstage amplifiers
and BMA-400 amplifiers and filters, CWE Inc., Ardmore, PA, USA) as follows: EEG 1000X at
1-100Hz, EKG 1000-5000X at 50-100Hz, and EMG 2000X at 100-1000Hz. An electronic
blanker (Model, SP1 EKG blanker) was specially modified for use with mice (Modified by
Charles Ward, CWE Inc.) to remove the EKG artifact from diaphragm EMG recording. The
blanker was modified to detect and remove EKG artifacts of heart beats up to 1000 beats/s. The
blanker also provided a rectified diaphragm signal. Since diaphragm recordings included
prominent EKG artifact superimposed on the diaphragm signal, the signal was sent to two
amplifier channels to obtain separate recordings of EKG and diaphragm EMG. Signals were
digitized, smoothed (25ms time constant), rectified when necessary (Spike2 software, 1401
interface, CED Ltd., Cambridge, UK) and recorded on a computer.
Spike recording was done with a custom headstage operational amplifer, built from a
TL064 package in the unity gain follower configuration (Jeantet & Cho, 2003). This is a basic
headstage model that can be used for in-vivo freely behaving animals or for in-vivo anesthetized
work. The headstage has four channels and is powered by a +/-3 V DC split battery supply. The
headstage was interfaced with BNC connectors and sent to a differential amplifier (Brownlee
Precision Instruments, BP-440, San Jose, California, USA). At the level of the differential
amplifier the signals were spliced and sent to two different channels to separate the local field
potential (for anesthesia only) from spike recording. Spike recording was filtered between 300
Hz to 10 kHz with 1000X amplification. Local field potentials were filted between 1-100Hz and
amplified by 1000X. Spike signals were then digitized at 20kH, EEG, field potential and EKG
signals at 400Hz, and EMG signals at 4000Hz with an analog to digital converter (1401
interface, CED Ltd., Cambridge, UK).
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2.7.3H Analyses
Physiological signals were analyzed in 5s epochs for the periods of wakefulness and sleep
using Spike2 software. Scripts for EEG analysis (Sudsa-version 2.2) were obtained from CED.
Custom scripts were written in our laboratory (by H.W.S.) for quantifying heart rate, breathing,
neck EMGs and spike recording. Fast-Fourier transform was used to convert EEG waveforms
into total power (μV2), binned every 5s, for the following frequency bands: δ1 (0.5-2Hz), δ2 (2-
4Hz), θ (4-7.5Hz), β1 (7.5-13Hz), β2 (13-20Hz) and α (20-30Hz). Neck muscle recording was
quantified by integrating the area under the rectified and smoothed EMG waveform every 5s.
Spike sorting was done off-line using a template matching program provided by Spike2
software. The threshold for analyzing spikes was set at 2x the baseline noise level. The spike
template ranged from 0.5 ms before the spike peak, to 1.5 ms after the peak. After template
generation, principle component analysis was performed and the templates were then revised
based k-means clustering of the spikes. Peristimulation-time histograms were generated with a
bin size of 10ms. Single units from each ensemble were normalized as z scores (Tsai et al.,
2004). In brief, the 5-s period before stimulation was used as a baseline period. The mean firing
rate and standard deviation of the 500 bins in this baseline period were calculated. All bin values
were transformed to Z scores according to the mean and standard deviation of the baseline
period. A 99% confidence level (Z>2.33 or Z<-2.33) was used for identifying if a single unit
responded (Kuo & Yen, 2005). Chi (χ2) test was used to compare different neural populations.
Under all recording conditions several features of extracellular recorded spikes were
recorded, including: firing rate, half-width time and peak to peak time. Interneurons and
pyramidal neurons were discriminated based on two dimensional plots of half amplitude versus
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peak to peak time. Autocorrelations were performed for each neuron. Bursting cells were
classified as cells whose autocorrelations peaked at 3-6 ms followed by an exponential decay.
Cells were classified as bursting if the, maximum peak on the auto-correlogram between 3 and 6
ms was > or = 50% of the maximum bin value of the first 50ms (Bartho et al., 2004). The
criterion for regular spiking (non-bursting) neurons was that the mode of the interspike-interval
histogram was >35ms. Regular spiking neurons rarely discharged in bursts and the auto-
correlogram showed an exponential rise from time 0 to tens of milliseconds. Cells that don’t
match either criterion were labeled as indeterminate.
2.7.4 Results:
2.7.4A Characterization of spontaneous field potentials in the ACC during trace fear
conditioning
Trace fear conditioning (Figure 2.3A) involves the association of a tone and shock across
a time interval and is thought to engage the ACC (Han et al., 2003). Since the trace fear
conditioning paradigm involves a variety of stimuli (e.g. sound and shock) and motor responding
(freezing behaviour) along with learning, it is an ideal paradigm to examine the function of the
ACC in sensory-motor integration and plasticity. Neck EMG was used as previously described
(Steenland & Zhuo, 2009), to determine when the mouse was freezing (Fig. 2.3B). As expected,
a significant alteration in freezing behaviour during the trace interval was detected during
conditioning (10 training trials) and memory testing (24 hours later) (F(20, 130) = 5.10.67; p <0.001
, one-way ANOVA). Post-hoc analysis revealed that the mice both learned and remembered the
tone and shock association (all t>3.45, all P<0.015, Fig. 2.3C).
Trace fear conditioning was conducted in parallel with local ACC field potential
recording to examine whether changes in spontaneous oscillation state or alteration in tone-
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Figure 2.3 Trace fear conditioning and measurements of fear (A) In the standard trace fear paradigm, a tone and shock are presented, separated by a time interval (trace interval). The paradigm is repeated until the mouse learns that the tone predicts the shock (as evidenced by freezing during the trace interval). Memory can be tested 24 hours later, with presentation of the tone alone. If the mouse expects to be shocked, it will freeze during and after the presentation of the tone. Highlighted green segments indicate regions of analysis in B. (B) Example of EMG-based freezing in one mouse during the trace fear interval. Each panel during conditioning and memory represents one trace interval trial. Green underline indicates periods scored as freezing. (C) Grouped data showing an increase in EMG-based freezing during both learning and memory compared to baseline (BL). * indicates statistical significance P<0.05, Large green dots correspond to freezing behavior in B. Arb., arbitrary units, BL, baseline, ES, expected shock, ITI, inter-tribal interval, Rt/Sm, rectified and smoothed signal, S, shock. Error bars are +/- SEM. evoked potentials could be detected. It was previously shown that local field potentials, in the 4-
7.5Hz range, can be augmented in the ACC and the prelimbic cortex during reward anticipation
(Tsujimoto et al., 2006; Paz et al., 2008) and in the amygdala and hippocampus during fear
behaviour (Pare & Collins, 2000; Seidenbecher et al., 2003; Narayanan et al., 2007). Examples
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Figure 2.4 Local field potential changes ACC accompany trace fear conditioning (A) Example Fourier transform computed for one mouse on spontaneous ACC field potentials during each phase of the trace fear paradigm. Comparisons are made directly to baseline (50-60sec of time before presentation of first tone). Clear power enhancements can be seen between 2-4Hz for tone and 4-7.5Hz for trace interval (arrows). A consistent increase was also seen between 13-20Hz. Shock stimuli did not change field potential power. Below each power spectrum is a range of frequencies for which the power spectrum was binned for analysis purposes. (B-G) Shows binned averages of all trials for each power spectral bin. The 2-4Hz power was significantly increased for the tone interval, while the 4-7.5 and 13-20Hz bands were enhanced for the trace fear interval. * indicates statistical significance P<0.05 compared to baseline (BL). of the Fourier transform (prior to frequency binning), for spontaneous field potentials from one
animal during trace fear conditioning, are depicted in figure 2.4A. Data was then binned into
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frequency ranges for statistical analysis (Fig. 2.4B-G). A significant alteration in 2-4Hz
powerwas detected during trace fear conditioning (F(3,27) = 4.49; p =0.011 , one-way ANOVA).
Post-hoc analysis revealed that 2-4Hz power was increased during the tone condition above that
of baseline (t = 2.70; p =0.035, Fig. 2.4C). This fits with the possibility that relevant sensory
information may come to activate the ACC with conditioning. A significant alteration in 4-7.5Hz
power was also detected during trace fear conditioning (F(3,27) = 5.027; p =0.007, one-way
ANOVA). Post-hoc analysis revealed that 4-7.5Hz power was increased during the trace interval
above that of baseline (t = 2.65; p <0.04, Fig. 2.4D) consistent with findings from other brain
regions (Pare & Collins, 2000; Seidenbecher et al., 2003; Tsujimoto et al., 2006; Narayanan et
al., 2007; Paz et al., 2008). Thus, these oscillations may be related to memory recall of fear
behaviour or attention to impending danger. A significant alteration in 13-20Hz power was also
detected during the trace fear interval (F(3,27) = 6.67; p =0.002 , one-way ANOVA). Post-hoc
analysis revealed that 13-20Hz power was increased during the trace fear interval above that of
baseline (t = 4.08.; p = 0.001, Fig. 2.4F). Interestingly no changes in spontaneous EEG were
detected following the shock stimuli, compared to baseline. However our analysis was restricted
to frequencies below 30Hz and stimulus artifacts precluded observation at the exact point of
stimulation. Thus, shock analysis was carried on the 15 seconds of data which was collected
immediately following the termination of the shock. Moreover, evoked-pain potentials may be a
more relevant measure in this case.
2.7.4B Characterization of tone-evoked and motor-triggered potentials in the ACC during trace
fear conditioning
The prelimbic cortex and lateral amygdala demonstrate enhanced tone-evoked potentials
with conditioning (Pare & Collins, 2000; Mears et al., 2009). Figure 2.5A (top panel) shows an
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Figure 2.5 Tone-evoked and motor-locked potentials in the ACC (A) Demonstrates that tone-evoked potentials could be detected in the ACC during trace fear conditioning and do not necessarily require a robust motor activation. Upper trace is an example of an average of ten trials during conditioning from one mouse. Middle trace is the field potential average and standard error of the mean for all mice recorded. Notice the peak around 50msec and the trough around 150msec. Lower trace is the rectified and smoothed EMG potential average and standard error of the mean for all animals recorded. (B) Demonstrates EMG triggered potentials during trace fear conditioning which appear to precede motor activation. Upper trace is an example of averaged EMG-triggered field potentials from 100 freezing-movement events. Middle trace is the field potential average and standard error of the mean for all animals recorded. Notice the peak activation around 70msec. Lower trace is the rectified and smoothed EMG potential average and standard error of the mean for all animals recorded. Arb., arbitrary units. AVG, average. Grey rectangle represents Z-scores for 99% confidence. example of an average of 10 tone-evoked potentials from the ACC of one mouse for all tones
during trace fear conditioning. Grouped data is also depicted (Fig. 2.5A, middle panel) in register
with changes in neck EMG (Fig. 2.5A, bottom panel). In 10 of 10 mice, tone-evoked potentials
could be observed with a significant upward deflection between 30 and 50msec, consistent with
other brain regions (Pare & Collins, 2000; Mears et al., 2009). A significant, latent, downward
deflection was also detected in 5 of 10 tone-evoked potentials with an average trough occurring
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around 150msec. An increase in EMG activity was only detected in 3 of 8 animals in response to
tone stimuli (Fig. 2.5A, lower panel). Thus, these tone-evoked potentials appeared to be related
to a sensory function of the ACC rather than a motor function per-se.
Potentials in the motor cortex and in premotor regions of the brain are commonly
associated with motor action (Donchin et al., 2001; Rickert et al., 2005; Roux et al., 2006).
When animals are trained during trace fear conditioning, they go through many periods of
spontaneous freezing which are eventually followed by spontaneous movement. These periods
occur during inter-trial intervals and during the conditioning trials. The execution of movement
from a point of freezing behaviour may be an attempt to explore the safety of the environment or
even to escape from that environment. We examined local field potentials at a point where
freezing (lasting at least 2 seconds) evolves into movement. To do this, local field potentials
were back-triggered and averaged from the onset of EMG activation. Figure 2.5B (top panel)
shows an example and average back-triggered potential for one animal from the time of freezing
to the time of movement. Grouped data is also depicted (Fig. 2.5B, middle panel) in register with
changes in neck EMG (Fig. 2.5B, bottom panel). In 8 of 10 animals, field potentials could be
observed with a significant upward deflection which peaked between 50-70msec after the
movement. The waveform repolarized even while the EMG remained elevated. Interestingly,
based on the average waveforms of all data, the spontaneous EMG-related potential started to
become elevated ~25msec before motor movement, demonstrating that the ACC potentials may
have a premotor component.
2.7.4C Learning-related spontaneous and evoked-field potentials in the ACC during trace fear
conditioning
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The association between tone and shock was increased with conditioning as demonstrated
by freezing measurements (Fig. 2.3B, C). Thus, neural correlates of this memory may be
reflected by learning curves of spontaneous and tone-evoked field potentials. The results from
figure 2.4C, D and F demonstrated that 2-4, 4-7.5 and 13-20Hz rhythms change during trace fear
conditioning. However, trial by trial analysis revealed that no significant learning curve existed
for the 2-4Hz frequency for the tone stimulus (F(10, 88) = 1.12; p <0.358 , one-way ANOVA, data
not shown). By contrast, a significant effect for the trace fear interval on 4-7.5 Hz power was
detected (F(10, 88) = 2.10; p <0.032 , one-way ANOVA). Post-hoc analysis revealed that a
significant learning curve for 4-7.5 Hz power (t=3.31, p=0.013, 7th time point, Fig. 2.6A).
Correlation analysis also revealed that the 4-7.5Hz learning curve co-varied with the freezing
learning curve (r2= 0.25; P<0.001). In addition, a significant effect for the trace fear interval on
13-20Hz power was detected (F(10, 88) = 2.25; p <0.022, one-way ANOVA). Post-hoc analysis
revealed that a learning curve occurred for 13-20Hz power (t=3.76, P=0.003, 8th time point, Fig.
2.6B). However, correlation analysis revealed that the 13-20Hz curve did not co-vary with the
freezing learning curve (r2= 0.33; P<0.083). It should be noted that; after the 7-8th conditioning
trial, 4-7.5 and 13-20Hz spontaneous field potential power decreased to the preconditioning
baseline level, while fear behaviour was still elevated. Thus, these potentials only correlate with
freezing behaviour for part of the conditioning. Interestingly the 4-7.5 and 13-20Hz power
learning curves mirrored one another and analysis revealed a robust correlation between them
(r2= 0.62; P< 0.007, Fig. 2.6C). Based on these results, we expect the enhanced cortical 4-7.5 and
13-20Hz rhythms may impact ongoing neural activity patterns during the trace fear interval (for
example; reduction of activity, see Fig. 2.11). While the role of these spontaneous field potentials
is currently speculative, they are not likely to reflect memory recall in this case.
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Figure 2.6 Tone-evoked ACC potentials predict freezing behaviour (A) Four to 7.5 Hz power recorded during the trace interval shows a significant learning curve, though the effect is not robust and does not completely parallel freezing behaviour (grey line). (B) Thirteen to 20Hz power recorded during the trace interval shows a significant learning curve, though the effect is not robust and does not completely parallel freezing behaviour (grey line). (C) The changes in 4-7.5Hz power was strongly correlated with changes in 13-20Hz power and may be related with the decrease in activity of regular spiking neurons (Fig. 2.11). (D) Example temperature plots of tone evoked potentials during 10 trials of trace fear conditioning. Arrow depicts points of analysis at 50msec (P50) after commencement of tone presentation. (E) Tone-evoked potentials (P50 values) show a significant and robust learning curve throughout conditioning that parallel freezing behaviour (grey line). (F) P50 values and freezing quantities were significantly correlated, suggesting that tone-evoked potentials can predict freezing magnitude during the trace interval, 15sec after the tone-evoked potential occurred.
We previously showed that tone-evoked potentials were augmented by trace fear
conditioning (Fig. 2.5A). To examine whether these potentials demonstrate a learning curve, trial
by trial analysis was conducted. A significant alteration in tone-evoked potentials was detected
during trace fear conditioning (F(9, 80) = 4.27; p <0.001, one-way ANOVA). Post-hoc analysis
revealed that a robust learning curve could be detected for the tone-evoked potentials at the post
stimulus 50msec (P50) time point (all t>3.25, all P<0.015, Fig. 2.6D and E). Correlation analysis
of these learning curves revealed a significant co-variation between fear behaviour and P50
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values (r2= 0.79; P<0.001, Fig. 2.6F). In contrast to spontaneous field potential power, tone-
evoked potentials remained elevated until the end of the conditioning trials. Collectively, the
results suggest that the association between the shock and tone stimuli gradually produce robust
enhancements of cue-related processing in the ACC, with modest enhancement in 4-7Hz field
potentials power during the trace interval.
2.7.4D Identification of neuron types and properties
The results from the field potential studies implicate the ACC in motor movements, learning the
association between cues and pain, and possibly processing during the trace interval. To examine
the neural correlates of trace fear conditioning, in-vivo freely behaving extracellular spike
recording was conducted (Fig. 2.7A). In addition, to further characterize ACC activity in the
mouse, neuron responses were probed for sensitivity to noxious and innocuous stimuli. Finally,
to parse out sensory neuron responding from motor-related activity, neuron recordings were
conducted in an anesthetized model where motor movements are inhibited (Fig. 2.7B). However,
prior to the analysis of neuron activities in these models, neuron types need to be identified. The
following segment describes the criteria by which neurons were sorted according to pyramidal
Vs non-pyramidal and bursting Vs regular spiking cells.
2.7.4E Identification of putative pyramidal and non-pyramidal neurons
Neurons were sorted (Fig 2.7C.) and identified as putative pyramidal and non-pyramidal,
based on their individual extracellular spike waveforms. A previous report in rats demonstrated
that neurons which have peak to peak values less than 0.5msec, cluster together, and are
considered to be non-pyramidal neurons, while those above this threshold are considered to be
pyramidal neurons (Bartho et al., 2004). Consistent with this idea, peak to peak time provided
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Figure 2.7 In-vivo spike recording to investigate receptive functions of ACC (A) Multichannel spike recording from the mouse ACC. Manual microdrives can independently position 3 electrodes to improve isolation of single units. The first trace represents a well-isolated neuron. The remaining traces are from two other electrodes in which neurons had not yet been isolated. (B) Multichannel recording in a mouse anesthesia preparation. In this preparation, spike recordings were performed from the ACC simultaneously with the local field potential. Frontal-parietal (F-P) EEG in conjunction with diaphragm (DIA) and electrocardiogram (EKG) were used to determine the health and stability of the preparation. Animals could then be stimulated with peripheral noxious or innocuous stimuli. (C) Multiunit recording from a single electrode (upper trace). Principal component analysis (PCA) was used to sort the multiunit record into individual units (lower three traces). Once sorted, the waveforms can be averaged to yield values of half-width and the peak to peak time. (D) Scatter-plot of peak to peak time and half-width for each individual neuron recorded during wakefulness in freely behaving animals, from sorted multiunit records. Neurons appeared to form clusters above and below 0.5msec peak to peak time. (E) Scatter plot of peak to peak time and half-width for each individual neuron recorded under anesthesia conditions, from sorted multiunit records. Neurons appeared to form clear clusters above and below 0.5msec peak to peak time. However it is interesting to note that a third cluster threshold appears around 0.8msec. Rt/Sm is rectified and smoothed, ch is channel.
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the most obvious clustering while half-width did not show obvious clustering for both freely
behaving and anesthetized conditions (Fig. 2.7D and E respectively). The division of neuron
clusters appeared around 0.5msec peak to peak time.
To confirm that our “blind” clustering technique was appropriate, we examined whether
clustering could be observed under in-vitro patch-clamp conditions in which the experimenter
was not blinded. In this study, neurons were identified under microscope, based on their
morphology (Fig. 2.8). Neurons were injected with a range of currents to evoke action potentials.
Similar to others (Henze et al., 2000), we found that if the instantaneous slope of the intracellular
signal were plotted relative to time, the intracellular signal could be converted to a waveform that
matches the time course of the extracellular signal (Fig. 2.8A). Pyramidal neurons were clearly
separable from non-pyramidal neurons based on peak to peak times of the converted intracellular
signal (Fig 2.8B). However, the threshold which divided the pyramidal from non-pyramidal
clusters was ~1.6msec which is about 3 times higher than in-vivo conditions (0.5msec) and is
likely attributed to differences in recording conditions (for example 25ºC in-vitro bath
temperature). Since clustering occurred under in-vitro anesthesia and freely behaving conditions,
and is consistent with others (Henze et al., 2000), we adopted the 0.5msec peak to peak time as
an adequate threshold to discriminate pyramidal from non-pyramidal neurons under in-vivo
conditions in the ACC. After neurons had been identified, we found that the relative proportions
of putative pyramidal to non-pyramidal neurons recorded under anesthesia and freely behaving
conditions were similar (Figures 2.10, 2.13 and 2.14), with pyramidal neurons predominating at
78-80% and interneurons composing a mere 20-25%.
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2.7.4F Identification of bursting and regular spiking cells
Neurons can also be classified based on their degree of bursting behaviour (Connors et
al., 1982; McCormick et al., 1985; Steriade et al., 1993c; Harris et al., 2001; Bartho et al., 2004).
Figure 2.8 Identified pyramidal and interneurons clustering under in-vitro conditions (A) Simultaneous patch clamp and lose patch recording from the same neuron. The middle panel shows superimpositions of the two averaged spike waveforms. Note, the timing of the extracellular waveform peaks do not match that of the intracellular peaks. However, when the instantaneous slope of the intracellular waveform plotted as a function of time, the time course of the intracellular signal matches the loose patch extracellular signal (lower panel). Thus, measurements of half-width and peak to peak time can be calculated from the converted intracellular signals for comparison with in-vivo extracellular data. (B) Scatter plot of peak to peak time and half-width for each individual neuron recorded under in-vitro conditions from visually identified pyramidal and non-pyramidal neurons. All non-pyramidal neurons had peak to peak times that were less than 1.6msec showing that peak to peak time was a very reliable designation for discriminating neurons (data contributed by Dr. XiangYao Li of the Min Zhuo laboratory).
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Autocorrelation of spike times was used to determine whether neurons exhibit bursting
behaviour (Bartho et al., 2004). Since bursting behaviour may be related to the generation of
oscillations observed during anesthesia (Steriade et al., 1993c) and oscillations were found to
change during trace fear conditioning (Fig. 2.4 and 2.6), we examined whether propensities of
observed bursting and regular spiking neurons would differ across all three in-vivo recording
conditions (anesthesia, general freely-behaving, and trace fear). Examples and data regarding
autocorrelations for bursting, indeterminate, and regular spiking cells for all three recording
conditions are given in figure 2.9B, 2.13A and 2.14A (right hand side). If a neuron is highly
correlated with itself, the activity of the neuron will fall consistently within a time frame of each
spike. If this occurs within 3-6ms, the neuron is classified as a bursting cell. If the neuron
autocorrelation shows a mode greater than the 35ms time point it is classified as regular spiking
(non-bursting). Neurons in which the mode of the autocorrelation fell between 35 and 6ms were
considered indeterminate neurons.
Anesthesia conditions were the most frequent condition in which to record bursting
neurons, with 53.5% of pyramidal bursting neurons compared to general freely behaving
recordings (7.1%, χ2 = 15.36, P <0.001) and trace fear recordings (6.4%, χ2 = 25.68, P <0.001). It
is interesting to note, that while 4-7.5% and 13-30 Hz field potential activity was elevated during
trace fear conditioning, this did not seem to increase the propensity of recorded bursting cells.
It is typical to expect the behaviour of interneurons to be regular spiking; however there
are reports of multipolar bursting interneurons in the cortex (Blatow et al., 2003). Indeed, we
found that 10% putative non-pyramidal neurons recorded under anesthesia displayed bursting
behaviour (Fig. 2.14A). Alternatively, this result could be related to the threshold criteria of the
neuron classifications.
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Regular spiking pyramidal neurons were much less frequently recorded under anesthesia
conditions (2.8%) than under freely behaving conditions (42.9%, χ2 = 24.39, P <0.001) or during
trace fear experiments (21.3%, χ2 = 8.63, P <0.003). There was a near significant decrease in
regular spiking pyramidal neurons recorded under the trace fear conditions relative to the freely
behaving conditions (χ2 = 3.37, P <0.07). This may be related to an inhibitory action over regular
spiking neurons in the trace fear paradigm (Fig. 2.11, next section). To compare proportions of
non-pyramidal regular spiking neurons, data were pooled from freely behaving and trace fear
experiments (because of low sample size). We found a similar amount of regular spiking non-
pyramidal neurons during anesthesia (20%, 4/20 neurons) when compared to the pooled freely
behaving data (28.5%, 6/21 neurons, χ2 = 0.031, P <0.86).
The results suggest that anesthesia conditions, in which clear oscillations can be seen in
the electroencephalogram and local potentials (Fig. 2.7B), may favor the excitation of pyramidal
bursting cells at the expense of regular spiking pyramidal cells. This may manifest as a change in
the sampling of neuron bursting types without altering the actual ratio of pyramidal to non-
pyramidal neurons.
2.7.4G Unit recordings in the ACC during trace fear conditioning
Lesion of the ACC blocks trace fear conditioning, but not delay fear conditioning (Han et
al., 2003). The ACC has also been implicated in conditioned avoidance and escape responses
(Gabriel et al., 1991a; Gabriel et al., 1991b; Freeman et al., 1996; Kubota et al., 1996; Koyama
et al., 1998; Koyama et al., 2000; Koyama et al., 2001; Johansen & Fields, 2004). However, the
ACC has been demonstrated to have premotor activity with expectation of reward (Shima &
Tanji, 1998; Tsujimoto et al., 2006). Unit recordings of the ACC have never been conducted
during trace fear conditioning. Thus we examined the neural responses of the ACC to the various
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conditions of the trace fear paradigm and closely examined how neural activity was related to
freezing and movement behaviour. Every neuron was tested in response to 4 tones and 4 shocks,
with each animal being trained for up to 12 trials on day one. In between training trials, animals
were permitted to rest (~1-2 hours), while new cells were manually isolated with the
advancement of electrodes using a microdrive. In addition, 2 animals were trained for 12
additional trials on a second day. Throughout all experiments, neck EMG was monitored for
freezing behaviour.
Figure 2.9 shows an example of a putative pyramidal neuron (2.9A) with indeterminate
bursting properties (2.9B), which has been analyzed in a variety of ways. This particular neuron
increased its activity after shock stimulation (Fig 2.9C); however, the activation of this neuron
also tended to mirror changes in neck muscle movements (Fig. 2.9C, lower panel). Thus we
decided to examine the activity of this neuron more closely. Figure 2.9D shows a single period of
freezing followed by a period of movement in which the neuron’s activity increased,
corresponding with the neck muscle movement (2.9D, lower panel). Figure 2.9E shows grouped
data of 18 such EMG movements in which the spike activity was highly correlated. On average
the neuron ramped its activity, starting 3.2 seconds before the motor movement. The neuron
reached a peak firing rate ~400msec before the peak in EMG activity. A significant correlation
(r2 =0.31, P<0.001) was detected between the EMG amplitudes and the spike rate of this neuron.
Figure 2.10 shows grouped data from all the neurons (represented with colour plots) recorded for
trace fear experiments, analyzed in the same fashion as in figure 2.9. Figure 2.10 shows that the
rate of recording responses did not depend on whether pyramidal neurons or non-pyramidal
neurons were recorded, with 40 of 47 tests (85.1%) yielding responses for pyramidal neurons and
10 of 12 tests (83.3%) for non-pyramidal neurons (χ2 = 0.09, P =0.766). Neurons
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Figure 2.9 Analysis of ACC neuron during trace fear conditioning (A) Example of spike-sorted putative-pyramidal neuron (B) Auto-correlation demonstrating this neuron is of the indeterminate type.(C) Upper panel shows that this neuron became active prominently after the application of the shock stimuli. Lower panel shows reduction in EMG activity during tone and trace intervals. EMG activity dramatically raises after the shock stimuli, coincident with the neural activation. Thus it appears that this cell not only responds to pain, but may also be involved in motor behavior. (D) Example of single unit activity during freezing behavior and during spontaneous neck muscle activation. (E) Upper 2 panels show trial by trial activations (temperature plot yellow = 11 spikes/bin), and average of this neuron during freezing and spontaneous neck muscle activation. Lower 2 panels show trial by trial activations (temperature plot yellow = 0.39 arbitrary EMG unit) and average of neck EMG. Notice that the neuron develops premotor activation and its peek activity precedes the motor movement by 400msec. (F) Correlation analysis revealed a strong significant relationship between spontaneous motor movement and neuron firing frequency. Green colored bins represent significant responses above baseline (excitatory) based on a Z-score for 99% confidence (grey bar).Yellow colored bins represent significant responses below baseline (inhibitory) based on a Z-score for 99% confidence (grey bar).
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responded most prominently to: motor movements which terminated freezing behaviour (67.8%),
shock stimuli (62.7%), tone stimuli (47.5%), and finally the trace interval (42.4%). It is easy to
see that there is considerable overlap with neurons that are involved in motor movement and
neurons activated during other phases of the trace fear paradigm (Fig. 2.10, left and right panels).
Out of all 59 neurons studied, only 7 (11.9%) neurons changed their responses to some part of
the trace fear paradigm, without also being related to motor movements. On the converse, a
similar quantity of neurons (9 of 59 =15.3%, χ2 = 0.0723, P =0.79) appeared to be strictly related
to some aspect of trace fear conditioning (tone, trace or shock stimuli).
Examination of inhibitory and excitatory responses shows that regular spiking, pyramidal
neurons were significantly more (6 of 10 = 60%) inhibited during trace fear conditioning as
compared to indeterminate cells (3 of 34 = 8.8%, χ2 = 9.49, P =0.002). Close inspection of these
results shows that this inhibition could occur during the tone, trace or shock intervals. This
finding is consistent with the slowing of the ACC field potentials during the tone and trace
intervals, and the trend toward reduced propensity to record regular spiking pyramidal neurons
during conditioning. We next examined whether pyramidal neurons were generally excited or
generally inhibited compared to non-pyramidal neurons during trace fear conditioning. To
explore this further, the average of each neuron for each conditioning phase was computed
(baseline, tone, trace interval and shock (Figure 2.11). Bursting neurons were excluded from the
analysis to keep the comparison symmetrical. A significant main effect of neuron type (F(1,54) =
4.67; p <0.035 , 2-way repeated measures ANOVA) and conditioning phase was found (F(3,162) =
4.67; p <0.035, pyramidal neurons. The only other interesting observation was that pyramidal
neurons during the trace interval were significantly inhibited compared to baseline (t=2.89,
P=0.026, Bonferroni t-test).
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Figure 2.10 ACC neurons respond to trace fear conditioning and demonstrate premotor activity The left hand column shows all recorded neurons, their responses to particular phases of the trace fear paradigm, and motor-related activations. Excitatory responses are represented by green boxes while inhibitory responses by yellow. If both excitatory and inhibitory responses happened during a particular phase the box is a yellow-green intermediate. It is easy to see that the vast majority of recorded neurons demonstrate premotor activity. In addition, regular spiking neurons are often inhibited during trace conditioning. Figures to the right show example responses to the different phases of the trace fear paradigm and EMG triggered activations. Each different phase occurs at the time points between the dotted lines. BL is baseline.
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Taken together, it appears that there was a net depression of pyramidal neuron firing during the
trace fear interval with the majority of the effect likely being carried by fast regular spiking
neurons.
Figure 2.11 Pyramidal neurons are inhibited during the trace interval Putative pyramidal neurons are inhibited selectively during the trace fear interval and tend to have lower firing rates then that of putative non-pyramidal neurons. * indicates statistical significance P<0.05 compared to pyramidal neuron baseline (BL).
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The propensity of neurons to commence firing before or after motor movements was compared.
To identify motor-related cells, the magnitude of the EMG activity for each time point, was
correlated to the neurons firing rate (as in Fig 2.9E and F). It should be mentioned that not all
neurons which had motor activations as judged by Z-score analysis, had significant correlations
with motor movement. Significant correlations were considered motor-related neurons. Out of
these neurons, a greater amount had premotor behaviour (41of 43 = 95.3%) compared to neurons
which became active during the motor movement itself (2 of 43 = 4.7%, χ2 = 24.0, P <0.001).
The data strongly suggest that the ACC has a prominent premotor function in the mouse.
2.7.4H Motor learning in the ACC during trace fear conditioning
Since the largest proportion of neurons recorded corresponded with premotor activity, we
examined whether neurons gradually develop this activity (Fig 2.12A and B) and whether they
alter their tuning-curves with conditioning (Fig 2.12C and D). Figure 2.12B shows the
percentage of neurons recorded that did and did not develop premotor activity. The proportions
of learning to non-learning neurons were similar (χ2 = 2.67, P =0.263). Neurons may also change
their tuning to motor behaviour. Figure 2.12D shows the percentage of neurons recorded that did
and did not change their tuning to motor movements. The proportion of tuning to non-tuning
neurons were similar (χ2 = 0.016, P =0.992). Collectively the results suggest that a variety of
neuron types in the ACC are capable of developing premotor activity and changing their tuning
to motor behaviour throughout the course of conditioning.
Based on the results from field potential recordings and current observations, the ACC
appears to be strongly related to mediating and modifying premotor output, learning aversive
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Figure 2.12 Motor plasticity in the ACC during trace fear conditioning. (A) Heat plot (2.0 spikes/bin =red) for an example neuron which developed premotor- related activity across 4 conditioning trials (for the second block of conditioning, trials 4-8).Twenty-four sweeps of data were taken during periods of spontaneous freezing behavior, irrespective of training trial. (B) Percentage of all neurons which developed locking to EMG activity. (C) Heat plot (7.0 spikes/bin =red) for an example neuron which changed its tuning to EMG activity across 4 conditioning trials (for the first block of conditioning). Twenty-four sweeps of data were taken during periods of spontaneous freezing behavior, irrespective of training trial. (D) Percentage of all neurons which changed their tuning to EMG activity.
cue-related information, and responding to pain stimuli. Based on the enhancement of 4-7.5 Hz
rhythms, during the trace interval, and the propensity for reduced firing of regular spiking
neurons during the trace interval, it appears that the ACC is an unlikely candidate to maintain
memory across this interval. However, the current analysis did not permit us to clearly
discriminate neurons involved in pain from neurons involved in motor movement. If ACC
neurons are related to the affective memory of pain, they may drive motor behaviour even when
no pain stimulus is present. The remaining studies and analyses are aimed at teasing apart
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whether neurons in the ACC can respond to pain stimuli without necessarily having premotor
activity.
2.7.4I Functional characterization of mouse ACC neurons to direct peripheral stimulation in
freely behaving animals
We characterized the response of ACC neurons to both noxious and innocuous peripheral
stimuli. Figure 2.13 shows a summary of all the neuron types recorded under freely behaving
conditions (2.13A, left), with select examples for responsive neurons (2.13A, right). Responses
were detected for noxious pinching of the tail, pinching right and left ear and for innocuous
approaches of a probe, brushing and air puffs to the fur. Pinch stimuli were applied with
tweezers for up to 5 seconds. Fur was brushed on the back of the animal with a probe, in a
direction opposite to the hairs’ growth, for up to 5 seconds. Air puffs were applied via a
pressurized canister. Figure 2.13B shows raw recording traces from different neurons to various
peripheral stimuli. The propensity of recoding responses to peripheral stimulation did not depend
on whether pyramidal or non-pyramidal neurons were recorded, with 31 of 74 tests (41.8%)
yielding responses for pyramidal neurons and 11 of 22 tests (47.6%) for non-pyramidal neurons
(χ2 = 2.47, P <0.12). In addition, it is of interest to note that the propensity to record neurons with
excitatory responses was higher (38 of 95 tests, 40.0%) when compared to inhibitory responses
(3 of 95 tests, 3.2%, χ2 = 35.95, P <0.001). Analysis was conducted to examine whether the ACC
responded differentially to noxious pain Vs innocuous stimuli. The propensity to record
responses did not depend on whether noxious or innocuous stimulation were applied, with 9 of
33 tests (27.3%) yielding responses to noxious stimulation and 30 of 62 tests (48%) for
innocuous stimulation (χ2 = 3.143, P =0.076). Thus, the ACC of the mouse does not seem to have
a preference for responding to noxious nociceptive stimuli. Since neuron responses in freely
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Figure 2.13 Noxious and innocuous responding neurons under freely behaving conditions (A) The left hand column represents the number of neurons recorded and responding (either excitatory or inhibitory) to particular probe stimuli. The figures in the middle column show examples of neural responses to probe stimuli. Green coloured bins represent significant responses above baseline (excitatory) based on a Z-score for 99% confidence (grey rectangle).Yellow colored bins represent significant responses below baseline (inhibitory) based on a Z-score for 99% confidence. Application of stimulus occurs at the time points between the dotted lines (Stim). The right-most columns depict autocorrelations from each of the example neurons. The different colors in the graphs represent the different ranges to classify each type of neuron as bursting (white), indeterminate (yellow) and regular spiking (green). (B) The first trace shows a unit recording from the ACC in which the cell responded to the approach of probe stimuli. In this case the neuron responded after termination of the approach. The same neuron was found to respond to a pinch of the tail (trace two). In this case however, the response lasted the duration of the pinch extending well after the pinch. Trace three shows the response to a brush of the fur. In this case the neuron responded only during the brush stimuli. The fourth trace shows a neuron that was inhibited following a puff of air to the face of the animal. The neuron reduced its activity during the stimulation extending to after the puff had terminated. The final trace shows that when EMG activity was recorded it could be seen to correlate with spike activity. Appro, approach of probe, BL, baseline, LEP, left ear pinch, REP, right ear pinch, PT, pinch tail.
behaving conditions are complicated by motor movement, we next examined responses under
anesthesia.
2.7.4J Functional characterization of mouse ACC neurons to direct peripheral stimulation under
anesthesia
Since nearly every means of peripheral stimulation will result in some form of motor
movement, we also used an anesthesia model in which we could monitor responses to peripheral
stimulation while precluding motor output. A variety of anesthesia experiments have investigated
ACC neurons in rats and rabbits in a similar manner (Sikes & Vogt, 1992; Yamamura et al.,
1996; Hsu & Shyu, 1997; Hsu et al., 2000; Gao et al., 2006; Yang et al., 2006).
Figure 2.14 shows a summary of all the neuron types recorded in the anesthesia
experiments (2.14A, left) with select examples of responsive neurons (2.14B, right). Responses
were detected for pinching of the tail, right and left foot, right and left ear and to innocuous
brushing of the fur. Similar to freely behaving conditions the propensity of recording responses
to peripheral stimulation did not depend on whether pyramidal or non-pyramidal neurons were
recorded, with 52 of 167 tests (31.1%) yielding responses for pyramidal neurons to peripheral
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stimulation and 29 of 58 tests (50%) for non-pyramidal neurons (χ2 = 2.47, P =0.12). In addition,
it is of interest to note that the propensity to record responding neurons was higher for excitatory
responses (67 of 206 tests, 32.5%) as compared to inhibitory responses (14 of 206 tests, 6.8% χ2
= 26.13, P <0.001).
Analysis was next conducted to examine whether the ACC responded differentially to
noxious pain Vs innocuous brush stimuli. Similar to freely behaving conditions, the rate of
recording responses did not depend on whether noxious or innocuous stimulation were applied,
with 66 of 183 tests (36.1%) yielding responses to noxious pinch stimulation and 14 of 42 tests
(33.3%) for brush stimulation (χ2 = 0.024, P =0.88). These values are in a similar range to that
observed in freely behaving conditions for noxious and innocuous stimuli (27% and 48%
respectively). Finally, the propensity to record neural responses to peripheral stimulation under
anesthesia (81 of 225 tests = 36%) and that of freely behaving conditions (39 of 95 testes = 41%,
χ2 = 0.530, P <0.46) did not yield any difference. This result suggests that the anesthesia
conditions did not dramatically reduce the degree to which units responded to peripheral
stimulation, indicating that the ACC can process sensory input without the necessity of premotor
response. Collectively, these findings confirm that the ACC responds to noxious and innocuous
sensory input in the mouse, consistent with findings in other species (Sikes & Vogt, 1992;
Yamamura et al., 1996; Hsu & Shyu, 1997; Hsu et al., 2000; Gao et al., 2006; Yang et al., 2006).
Moreover, it suggests that the ACC is important as an integrator of sensory/affective input with
premotor output.
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Figure 2.14 Neurons responding to noxious and innocuous stimuli under anesthesia (A) The left hand column represents the number of neurons recorded and responding (either excitatory or inhibitory) to particular probe stimuli. The figures in the middle column show examples of neural responses to probe stimuli. Green colored bins represent significant responses above baseline (excitatory) based on a Z-score for 99% confidence. Yellow colored bins represent significant responses below baseline (inhibitory) based on a Z-
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score for 99% confidence. Application of stimulus occurs at the time points between the dotted lines. The right-most columns depict autocorrelations from each of the example neurons. The different colors in the graphs represent the different ranges to classify each type of neuron as bursting (white), indeterminate (yellow) and regular spiking (green). (B) Electrode trajectory and recordings from different depths from the cortex within the ACC. Anesthesia was used in these experiments to reduce motor movements. In these experiments, electrodes were lowered and neurons probed with peripheral stimulation to the body. In this example a pinch stimulus (horizontal bar above each trace) was applied to the tail. While probe stimuli seem to have minor effects for the second and third trace, a robust excitation can be seen in the 5th trace. BL, baseline, CG cingulate, M, motor, LEP is left ear pinch, REP is right ear pinch, Appro is approach of probe, PT is pinch tail.
2.7.4K Direct stimulation of the ACC
Stimulation of the ACC has been known to induce vocalizations (Jurgens & Pratt, 1979;
Jurgens, 2009), fear behaviour (Johansen & Fields, 2004; Tang et al., 2005), and motor
movements (Penfield & Welch, 1951; Showers, 1959; Hughes & Mazurowski, 1962; Bancaud et
al., 1976; Sinnamon & Galer, 1984). In addition, it has been reported that cingulate seizures in
humans are characterized by paroxysmal motor attacks during sleep, vocalizations, arousals from
sleep and nocturnal paroxysmal dystonia (Schindler et al., 2001; Vetrugno et al., 2005). Eliciting
ictal discharges in a brain region is a useful method to uncover the function of that region. For
example, blockade of GABA type-A receptors in the motor cortex has been found to elicit ictal
activity coinciding with electromyography activity, even under anesthesia (Castro-Alamancos &
Borrell, 1995). Recently it was shown that the potassium channel blocker, 4-amino pyridine (4-
AP), produces ictal activity in an ACC slice preparation (Panuccio et al., 2009). To examine
whether the activation of the ACC would induce emotion-related motor movements, we focally
applied 4-AP (via microdialysis) to the ACC while recording EEG to measure ictal activity, in
freely behaving animals.
In 2 animals microdialysis probes were implanted into the midline ACC. A frontal cortex
screw electrode was placed near the midline above the ACC. An additional reference electrode
was placed over the parietal cortex for differential recording. Thirty minutes after probe
implantation and ACSF perfusion (1-3ul/min), mice showed normal sleep and wakefulness EEG
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oscillations (Fig. 2.15A). The perfusate was washed for an additional 1-2 hours before
experimental manipulations. When the perfusate was switched to 1-5mM 4-AP, ictal oscillations
appeared in the EEG. Ictal EEG events corresponded closely with EMG movements of the neck
(Fig. 2.15A). Mice were seen to run in circles (Fig. 2.15A, middle panel), vocalize, jump and had
enhanced startle response to sound (clapping of the hands). Mice were also seen to exhibit
behaviour which resembled small, fast forward motions followed by a backward motion of the
same magnitude. Post-ictal behaviour resembled that of freezing, with a sustained absence of
movement. Interestingly, audible vocalizations were often accompanied by changes in high
frequency EEG (Fig. 2.15, right most panel).
To examine the evolution of the seizure activity from the ACC, local field potential and
unit recording were carried out in combination with frontal-parietal recordings under anesthesia
(n=2). Application of 1mM 4-AP to the ACC changed multiunit recordings and local field
potentials along a similar time frame (Fig. 2.15B). Ictal activity was seen in the local field
potentials, but could also invade the more filtered spike recordings (filtered between 300Hz and
20kH). It appeared that the ictal activity was a summation of multiunit activity, since it became
difficult to observe the multiunit activity during ictal spikes (Fig. 2.15B, left to right panel). Ictal
activity was seen to gradually spread from the ACC to the surface EEG recording during the
perfusion. An additional caveat of our findings is that the 4-AP was able to activate brain cells
even in the down state of the frontal parietal oscillation (Fig 2.15B, right-most panel). To our
knowledge there is only one other method to block the down states and that is with intracellular
injection of caesium to block potassium channels (Metherate & Ashe, 1993). Our anesthesia
experiments demonstrate that 4-AP facilitates population spikes. The output of these spikes
appear to produce emotional motor behaviours as seen by the effects of 4-AP in the ACC in
freely behaving animals.
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Figure 2.15. Ictal activity in the ACC produces circling behavior, vocalizing and synchronization of multiunit activity (A) Left panel: simultaneous recording of EMG and EEG during ACSF perfusion into the ACC. Lower trace is expanded view in which no correlation can be seen between EEG and motor movement. Middle panel: application of 4AP to the ACC generates rhythmic ictal events which are synchronized with movements of the neck. Right panel: mouse vocalizes and EEG changes to a higher frequency. (B) Left panel: simultaneous recording of ACC spikes, local field potentials and frontal parietal (F-P) EEG during ACSF perfusion into the ACC. Middle panel: application of 4AP to the ACC generates rhythmic ictal events in the local field potential which invades multiunit recording trace. Arrows indicate locations of individual spikes and arrowheads indicate areas where many spikes appear to be summated to produce a population spike. Right panel: After abatement of the ictal events neurons appear to fire during the down state of the surface EEG (arrows).
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2.7.4L Histological verification of electrode sites
Nearly all experiments involved histological verification of electrode cites (Fig. 2.16).
The only experiments in which electrode cites were not verified were the anesthesia experiments
in which the depth of the electrodes or microdialysis probes could be estimated accurately with
the micromanipulator. In other experiments (freely behaving spike, and trace fear, and local field
potential), DC current (100-500uA) was passed between the ground electrode and the recording
electrodes to make a lesion (Fig. 2.16C). Finally, in freely behaving microdialysis experiments, a
sizeable lesion site was made by the microdialysis probe and was used as an estimate of the
electrode and probe locations. Nissl staining was performed for microdialysis experiments, freely
behaving, and trace fear unit recording studies. The majority of electrodes were on target within
the ACC. However, one of the electrode sets in the trace fear experiments was slightly near the
border of M2 and CG1. Moreover, during surgery, electrodes were prepositioned ~500um below
the cortical surface to avoid hitting the motor cortices, so this particular electrode set was an
unlikely cause for concern.
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Figure 2.16. Verification of electrode positions (A) Locations of the field potential electrodes. Each line represents the deepest location of one tip of the bipolar electrode (tips had a 0.5mm offset). (B) Locations of spike electrodes used in trace fear experiments. Each line represents 2-4 electrodes used in each of four mice. (C) The first panel shows Nissl stain and the effect of passing current across 4 electrodes in the ACC (4 round lesions clustered together). The remaining two panels show the locations of the electrode tracts from 4 separate mice (each line represents 2-4 electrodes). (D) For the anesthesia preparation, the tract was estimated from the angle of brain penetration and the depth to which the electrode was moved. There were two major hot spots for recording, which included the middle and superficial layers. (E) Location of the region where neurons were visually selected for in-vitro slice recording. Most recorded neurons were situated in layer II-III while a select few were in layer I. (F) The first panel shows Nissl stain and the lesion site left by the microdialysis probe (arrows). To the right is the identified location of 2 microdialysis probes for freely behaving experiments.
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2.7.5 Discussion
The current study examined the input and output relationship of the ACC using the trace
fear conditioning paradigm, peripheral stimulation, pharmacology and an anesthesia model. We
show that tone-evoked potentials, rather than spontaneous field potentials in the ACC, are the
best predictor of freezing behaviour during the trace fear interval. This establishes the ACC as a
major player in making associations between tone and shock stimuli. The recordings of tone-
evoked neuron activity are consistent with this observation.
In contrast to what we expected, putative pyramidal neurons were largely inhibited during
the trace interval, suggesting that the ACC is not involved in bridging the temporal gap between
the shock and the tone. Interestingly, the majority of recorded neurons demonstrated premotor
activity that co-varied with the magnitude of the motor movement. Many of these neurons also
responded to shock stimuli. To tease apart whether the ACC can be active independent of motor
output, neuron proportions were compared between freely behaving conditions and those of
anesthesia. Similar proportions of neurons were found to respond to noxious and innocuous
stimuli under these conditions, even when the animal was not moving during anesthesia. The
results indicate that the ACC can receive input without necessarily being linked to motor output.
Finally, direct stimulation of the ACC, by blocking 4-AP sensitive potassium, produces an array
of emotionally-related behaviour. Based on these findings we suggest that the ACC is one of the
major affective-motor systems of the brain.
2.7.5A Characterization of field potentials in the ACC during trace fear conditioning
Changes in spontaneous field potentials in the 4-7.5 Hz range are know to occur in the
frontal cortex during the expectation of reward stimuli (Tsujimoto et al., 2006; Paz et al., 2008)
and avoidance conditioning (Stark et al., 2007; 2008). Our experiments show that local field
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potential recordings in the 4-7.5 and 13-20Hz range were increased during trace fear
conditioning and paralleled one another. However this activity did not completely parallel the
learning curve and was not robust. Indeed the slowing of this activity may actually be related to
the overall decrease in pyramidal neuron spike frequency during the trace interval (Fig. 2.11). It
is of interest that 4-7.5Hz in the amygdala and hippocampus has been linked with freezing
behaviour itself (Pare & Collins, 2000; Seidenbecher et al., 2003; Narayanan et al., 2007) and
therefore may represent communication between a numbers of structures involved in fear
behaviour. However, there is no evidence that the ACC receives direct activation from the
hippocampus, which might drive these 4.7.5Hz rhythms. By contrast the prelimbic cortex
receives a direct and converging input from CA1 hippocampus and amygdala (Thierry et al.,
2000; Gabbott et al., 2002; Ishikawa & Nakamura, 2003; Tierney et al., 2004). Indeed there is
evidence that the hippocampus and the prelimbic cortex are involved in bridging the temporal
gap of the trace interval (Pare & Collins, 2000; Baeg et al., 2001; Gilmartin & McEchron,
2005b). So it might be expected that the prelimbic cortex would demonstrate non-decrementing
4-7.5 rhythms which correlate closely with freezing behaviour.
Tone-evoked potentials have been reported to become enhanced with fear conditioning in
the prelimbic cortex (Mears et al., 2009). These results suggest that there is a reduction of
inhibitory gating to the prelimbic cortex. Similarly, visually-evoked signals are enhanced in
aversive conditioning in the human ACC (Buchel et al., 1998). Consistently we find that the
tone-evoked potentials are robustly enhanced with increased conditioning, and also parallel the
behavioural learning curve remarkably well. These potentials are not locked to motor behaviours
(Fig. 2.5A), suggesting that they are related largely to sensory or affective processing. Moreover,
it was commonly observed that neurons were activated with the presentation of the tone stimulus
(Neuron 2 and 10, Fig. 2.10). Thus the ACC seems to be important for the ability of an animal to
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remain receptive to biologically relevant information (e.g. tone) which signals the future
occurrence of an aversive stimulus (e.g. shock). Whether this functions to focus attention to
impending danger, or retrieve memory of previous aversive events, remains to be discovered.
2.7.5B Unit recordings in the ACC during trace fear conditioning
Lesions of the ACC blocks trace fear conditioning, but not delay fear conditioning (Han
et al., 2003). However, the ACC was shown to be equally active in delay fear conditioning as it
was in trace fear conditioning in humans, and deactivations of the ACC were seen with
conditioning (Knight et al., 2004). Our results show that the activity of ACC neurons is reduced
during the trace interval. These de-activations tended to occur in pyramidal cells (Fig. 2.11) with
the highest percentage occurring in regular spiking (Fig. 2.10) neurons. However, when motor
movements were observed during the tone or trace interval, they tended to be robust and
correlated with neuron firing frequency. Thus, it is possible that reducing the activity of ACC
neurons during periods of freezing may permit these neurons to rebound into a firing mode
associated with motor movements. This would account for why many neurons demonstrate trace
interval activations (usually with motor movements, Fig. 2.10) while the population average
(Fig. 2.11) shows a net decrease in pyramidal activity during these periods. This finding fits
nicely with previous observations that regular spiking neurons of the prelimbic cortex are largely
inhibited during trace fear conditioning (Baeg et al., 2001).
While we found a decrease in ACC neuron activity during the trace interval, other studies
show that c-fos is elevated selectively in the ACC by trace fear (Han et al., 2003). In addition, a
recent report showed that blocking or lesioning the ACC prevents cued-delay fear acquisition
(Bissiere et al., 2008). The major difference between those studies and the current one is that unit
and field potential recording have excellent temporal resolution; while lesions, pharmacology
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and immediate early gene studies do not. It should be emphasized that the ACC tone-evoked
potentials were augmented in a learning-dependent fashion. This suggests that the ACC is
permitting information flow though its network. Thus, previous studies may have blocked tone-
evoked potentials or measured increases in c-fos related to these potentials. Experiments need to
be conducted to selectively turn off neuron activity during tone-evoked potentials, to examine
whether these potentials have any bearing over behaviour, or rather, are epiphenomena of an
already sensitized network.
The seemingly multimodal behaviour of neurons, overlapping with motor movements and
pain, makes sense if an animal is to avoid pain in the future through motor behaviour, and fits
nicely with recent predictions for the overlap of pain and movement systems (Dum et al., 2009).
However, it is surprising that ACC neuron activity would correlate with motor behaviour even
when the animal is not experiencing pain. One explanation for this is that the ACC retains
information important for escape from painful situations. In the case of chronic pain and visceral
hypersensitivity, where the pain or discomfort is inescapable, the ACC is known to become
sensitized (Gao et al., 2006; Zhao et al., 2006; Toyoda et al., 2009) and spontaneous firing rates
of ACC are reported to have doubled (Gao et al., 2006). Consistently, we found that a significant
number of neurons developed premotor activity and changed their tuning throughout
conditioning, implicating their sensitivity to the conditioning paradigm. Moreover, if the ACC is
involved in an affective (Devinsky et al., 1995; Rainville et al., 1997) or a motivational
component to escape aversive stimuli (Gabriel et al., 1991a; Gabriel et al., 1991b; Freeman et
al., 1996; Kubota et al., 1996; Koyama et al., 1998; Takenouchi et al., 1999; Koyama et al.,
2000; Koyama et al., 2001; Johansen & Fields, 2004; Ding et al., 2008), premotor activation may
reflect these processes, rather than direct motor activations per-se.
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In the current study it was found that 4-AP induced vocalizations and other motor
behaviours, was consistent with studies of others (Penfield & Welch, 1951; Showers, 1959;
Hughes & Mazurowski, 1962; Bancaud et al., 1976; Sinnamon & Galer, 1984). Interestingly,
electrically-evoked vocalizations in the ACC can be blocked with glutamatergic antagonism in
the periaqueductal gray (Kyuhou & Gemba, 1999), which is also a site involved in the expression
of fear behaviour (De Oca et al., 1998; Mobbs et al., 2007). Thus, the ACC may be situated a
step or two back from the actual motor execution. In such a case the ACC could be involved in
integrating affect and motor execution and would have the capacity of plastic modification and
tuning.
2.7.5C Functional characterization of mouse ACC neurons to direct peripheral stimulation
The ACC is well characterized in response peripheral noxious and innocuous stimulation,
in humans, monkeys, rabbits, and rats (Sikes & Vogt, 1992; Yamamura et al., 1996; Hsu &
Shyu, 1997; Hutchison et al., 1999; Hsu et al., 2000; Gao et al., 2006; Yang et al., 2006).
However, a similar characterization has never been conducted in mice. We repeated earlier
studies of peripheral noxious and innocuous stimulation. We found that a significant proportion
of neurons were activated in the ACC in response to shock (62.7%), which is comparable to that
recorded with laser-heat stimulation (51%) (Kuo & Yen, 2005). Noxious pinch stimulation to the
body activated 27.3% of neurons in freely behaving animals and 36.1% of neurons under
anesthesia, which is comparable to the 39% of noxious responsive neurons in the ACC of an
anesthetized rat preparation reported previously (Yamamura et al., 1996). Since the number of
responsive neurons recorded under anesthesia is comparable to that of freely behaving
conditions, the premotor activity of the ACC is unlikely to be necessary for motor acts; rather
this activity may be involved in modulating motor output.
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2.7.5D Neuron identification
One of the major weaknesses of extracellular recording is the ability to identify
interneurons and pyramidal neurons. We tried to circumvent this problem by using a pre-
established grouping/clustering method (Bartho et al., 2004) and attempted to support our results
by using the same method in-vitro (Fig. 2.8). In this analysis it was found that peak to peak
thresholds were much larger in-vitro (1.6msec) as compared to in-vivo conditions (0.5msec).
However all interneurons showed lower peak to peak times as compared to pyramidal neurons
in-vitro. The discrepancy between in-vivo and in-vitro conditions may be related to the
conversion of the intracellular waveform to the extracellular wave-form, or to the lower
temperature of the in-vitro bathing solution. In all recordings conducted, for each set of
experiments, we achieved the same relative number or pyramidal Vs non-pyramidal neurons
suggesting that our sampling of each experiment had reasonable reproducibility. We used burst
analysis (Bartho et al., 2004) to further classify neurons. It is of interest to note that the
propensity to record bursting pyramidal neurons tended to be higher under anesthesia conditions
(53.5%). Indeed, only 2.8% of the recorded pyramidal neurons were regular spiking. This data is
reasonably consistent with a recent in-vitro study from our laboratory in which the lowest
recorded neuron number was from regular spiking neurons (24%) followed by bursting (31%)
and indeterminate (or intermediate) neurons (45%) (Cao et al., 2009). Moreover, McCormick et
al. (1985) demonstrated that bursting behaviour in the cingulate cortex neurons is likely to be
removed if neurons are depolarized sufficiently toward threshold prior to being activated. This
observation is the simplest explanation for the increased sampling of burst-related cells seen
under anesthesia conditions. This would also explain why many bursting cells respond to
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noxious stimulation under anesthesia, while many regular or indeterminate spiking cells respond
to noxious stimulation in freely behaving conditions.
The ACC is typically referred to either as a motor area (CMA) or as an affective area
(ACC). However, our data suggests that the ACC integrates affective information with premotor
commands in the mouse brain. Premotor commands may be related to vocalization, directing
escape, or safety behaviour. Moreover, the activity of the ACC seems to be tightly linked with
cue information indicating the emotional significance of potentially dangerous situations.
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CHAPTER 3
SLEEP, SYNAPTIC PLASTICITY AND TRACE FEAR
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3.1 Overview
The previous chapter focused on how trace fear conditioning influences brain waves and
unit activity in the ACC. The following chapter investigates how fear memory may be related to
genetics and sleep. As such, the mechanisms of sleep and the molecular biology of memory
formation are reviewed. The chapter finishes with a study of the influence of learning and
memory enhancement on sleep.
3.2 Description of sleep
Sleep is defined as a state of immobility with reduced responsiveness to the environment.
However, sleep can be reversed, which distinguishes it from anesthesia and coma (Siegel, 2005).
Another defining characteristic is that the removal of sleep results in the body attempting to
recover sleep (Siegel, 2005). The most common way to measure sleep is through use of cortical
electroencephalogram or electrocorticogram in conjunction with muscle recordings from the
body. Sleep is largely classified into two additional states. The first state is refered to as non-
rapid eye-movement (NREM) sleep or slow wave sleep (SWS), while the other state is referred
to as rapid eye-movement (REM) (Figure 3.1) (Aserinsky & Kleitman, 1953). During
wakefulness, the electrocorticogram, recorded from the surface of the cortex, is composed of
low-amplitude fast-frequency signals (30-60Hz) (Maloney et al., 1997). Electromyography of the
neck also shows that the muscles are generating tone and are involved in motor movements of
the head. By contrast, NREM sleep is characterized by high voltage and predominant slow wave
potentials (0.5-4 Hz), sleep spindles (7-13Hz) and behavioural quiescence, evidenced by a
reduction in postural muscle tone. Rapid eye-movement (REM) sleep is characterized by EEG
very much similar to that of wakefulness; however muscle tone is completely abolished with the
exception of occasional muscle twitches and rapid movements of the eyes. Considerable research
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literature exists on connecting REM sleep with learning and memory for which several reviews
exist (Smith, 1996; Hobson et al., 1998), however an emerging literature also suggests that slow
wave sleep, learning and memory are connected (Benington & Frank, 2003; Tononi & Cirelli,
2006); The review presented here will focus on systems involved in controlling slow wave sleep
and wakefulness and how these may connect to learning and memory.
Figure 3.1 Defining characteristics of sleep Upper trace: shows EMG activity across each sleep and wakefulness state. EMG gradually declines from one state to the next. Finally, during REM sleep postural muscles develop atonia, with the exception of the occasional muscle twitches. Lower trace: is the frontal-parietal EEG changes that occur across sleep and wakefulness states. The EEG activity during wakefulness is high frequency low voltage. During NREM sleep the voltage changes to high voltage low frequency. Finally, during REM sleep the EEG appears similar to wakefulness.
3.3 Circuits
The following section details the primary circuits involved in the regulation of
wakefulness and slow wave sleep behaviour states. These circuits include a vast array of
ascending neurotransmitter systems impinging on the thalamus and the cortex to maintain
wakefulness. The decrease in the activity of these circuits accompanies a progression into sleep.
It should be mentioned that a variety of descending mechanisms are also involved in the
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reduction in muscle tone during sleep and are reviewed elsewhere (Chase & Morales, 1990;
Rekling et al., 2000).
3.3.1. Wakefulness promoting systems
There is a variety of arousal systems which project to the thalamus and the cortex. Each
system distributes a specific neurotransmitter and their output varies as a function of the sleep
and wakefulness states. Systems involved in wakefulness will be discussed here, followed by a
description of systems involved in sleep promotion in the next section (3.2). Figure 3.2 is a
depiction of all the circuits, and some of the important neurotransmitters mechanisms discussed
in this and later sections.
A region involved in producing arousal was first discovered through a variety of
transaction studies (Moruzzi & Magoun, 1949; Moruzzi et al., 1956; Moruzzi, 1957). Neurons
responsible for generating arousal were concentrated in the oral pontine and mesencephalic brain
stem reticular formation. They produce arousal through a dorsal thalamic relay to the cortex and
a ventral relay coursing through the hypothalamus and basal forebrain (Siegel, 2004; Jones,
2005).
The dorsal and ventral pathways originate from two cholinergic synthesizing nuclei
known as the lateral dorsal tegmentum (LDT) and the pedunculopontine tegmentum (PPT)
(Jones & Beaudet, 1987). Neurons of the LDT and PPT discharge during waking, decrease their
discharge during NREM sleep and increase their discharge during REM sleep (el Mansari et al.,
1989; Steriade et al., 1990). Thus, the activity of the cholinergic system appears to parallel the
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Figure 3.2 Sleep circuitry Basic circuits involved in both the generation of wakefulness and NREM sleep. The bottom left corner shows key neurotransmitter systems composed of cells which turn on either during NREM sleep (Sleep ON) or wakefulness (Arousal). During wakefulness many of these regions project to the cortex (top left) releasing neurotransmitters which can depolarize or modulate neural activity. In the thalamus (middle circle) relay neurons are activated by these neurotransmitter systems, this has the effect of inactivating an Ih current and blocking a K-leak current. The net effect is that the thalamus is kept in a tonic mode; whereby information from the periphery can be relayed to the cortex. During NREM sleep, neurons of the inhibitory basal forebrain and ventrolateral preoptic area (VLPO) (bottom left) become active and inhibit a variety of wake-on circuits. This reduces neurotransmitter release to the cortex and the thalamus. Thus the thalamus is disfacilitated and begins to oscillate in a burst more, in parallel with the cortex. The interaction between Ih and IT channels produces burst oscillations in the thalamus (see text for more detail). In addition feed-back inhibition from the reticular nucleus of the thalamus is involved in these thalamic oscillations, producing sleep spindles and delta waves. (figure by H. Steenland, refs in text).
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changes in global EEG activity. The dorsal pathway activates midline and intralaminar nuclei of
the thalamus (Woolf & Butcher, 1986; Hallanger et al., 1987; Steriade et al., 1990) which in turn
projects diffusely to the cortex (Minciacchi et al., 1986). In the thalamus, acetylcholine activates
nicotinic and muscarinic receptors to enhance cortical activation (Curro Dossi et al., 1991;
McCormick, 1992b; Steriade et al., 1993a). Consistently, stimulation of the LDT or PPT
cholinergic system produces cortical arousal via activation of muscarinic and nicotinic receptors
in the thalamus (Curro Dossi et al., 1991; Steriade et al., 1993a).
The ventral pathway consists of cholinergic neurons which project to the cholinergic
basal forebrain and the histaminergic neurons of the hypothalamus, which in turn project to the
cortex, producing arousal (Siegel, 2004; Jones, 2005). The basal forebrain consists of the
substantia-innominata nucleus basalis complex, diagonal band of Broca and the medial septal
nuclei which are cholinergic containing. Neurons of the substantia-innominata nucleus basalis
complex project prominently to the cortex (Rye et al., 1984). Like the cholinergic neurons of the
brainstem, basal forebrain cholinergic neurons are active during wakefulness, decrease their
activity during NREM sleep and increase their activity during REM sleep (Lee et al., 2005a).
Interestingly, the discharge of these neurons is correlated with gamma (30-60Hz) and theta brain
frequencies (4-8 Hz), while correlating negatively with 1-4Hz frequencies (Lee et al., 2005a).
The locus coeruleus is a pontine structure which forms a diffuse projection system
throughout the forebrain and the thalamus (Lindvall et al., 1974; Jones et al., 1977; Jones &
Moore, 1977; Kromer & Moore, 1980). The neurons of this structure demonstrate state-
dependent activity, with the highest firing rates during wakefulness, with progressively lower
activity during NREM sleep and cessation of firing during REM sleep (Aston-Jones & Bloom,
1981). Pharmacological activation of the locus coeruleus in an anesthetized rat preparation
resulted in pronounced shifts from low frequency high voltage EEG to that of high frequency
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low voltage EEG, characteristic of wakefulness (Berridge & Foote, 1991). Similarly, electrical
stimulation of the locus coeruleus in cats also produces cortical arousal (Steriade et al., 1993a).
Dorsal and median Raphé nuclei project diffusely to the forebrain and to the thalamus
(Cropper et al., 1984; Vertes, 1991; Vertes et al., 1999). Serotonergic neurons of the dorsal
Raphé nucleus are most active during wakefulness, progressively decrease their discharge during
NREM sleep and further inactivate during REM sleep (Jacobs & Fornal, 1991). Interestingly,
stimulation of the Raphé nuclei induce EEG signs of drowsiness and lesion produces a state of
cortical arousal (Yamamoto et al., 1979); however, contrary findings have also been observed,
with Raphé output promoting arousal (Houdouin et al., 1991).
Blocking histamine receptors is known to produce enhancements in slow wave sleep,
while increasing histamine levels produces arousal (Lin et al., 1988). The tuberomammillary
nucleus of the posterior hypothalamus consists of histamine-containing neurons and is a major
source of diffuse projections to the cortex and the thalamus (Saper, 1985; Panula et al., 1989).
The activity of these neurons is highest during wakefulness, low during NREM sleep and abates
during REM sleep (Brown et al., 2001; Saper et al., 2001). However a more recent study, with
identified juxtacellular labelled histaminergic neurons demonstrated that these neurons are only
active in wakefulness (Takahashi et al., 2006).
The posterior hypothalamus is also the locus of orexinergic neurons (de Lecea et al.,
1998; Sakurai et al., 1998). Orexins stimulate behavioural arousal and cortical activation via
diffuse projections and excitatory influences on the cerebral cortex, lateral dorsal tegmentum,
substantia innominata, thalamocortical, lateral hypothalamic, dorsal and median Raphé, and
locus coeruleus (Peyron et al., 1998; Espana et al., 2005). Orexins are also known to have
excitatory effects on a variety of these arousal regions (Ivanov & Aston-Jones, 2000; Liu et al.,
2002; Kohlmeier et al., 2004; Wu et al., 2004; Xia et al., 2005). Neurons of the orexin system
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are most active during wakefulness, decrease during NREM sleep and REM sleep (Lee et al.,
2005b; Mileykovskiy et al., 2005).
All of these systems are involved in promoting wakefulness or maintaining cortical
arousal. Thus removal of activity in each of these systems may be responsible for the production
of NREM or REM sleep states. The following section addresses how this may come about.
3.3.2 Sleep promoting systems
Neurons of the non-cholinergic basal forebrain and the hypothalamic preoptic area
discharge at higher rates during NREM sleep (Szymusiak & McGinty, 1989; Szymusiak, 1995;
Szymusiak et al., 1998; Lee et al., 2005a). Interestingly, after lesioning the ventrolateral
preoptica area (VLPO), the degree of neuron loss correlates closely with the loss of NREM sleep
(Lu et al., 2000). In addition, c-fos immunoreactivity is elevated in these regions during recovery
from sleep deprivation, and co-localize with GABAergic neurons (Lu et al., 2000; Modirrousta et
al., 2004). GABAergic VLPO and basal forebrain neurons appear to innervate orexin neurons
(Gritti et al., 1994; Jones, 2005; Sakurai et al., 2005), histaminergic neurons (Jones & Cuello,
1989; Saper et al., 2001), LC neurons (Jones & Cuello, 1989; Sherin et al., 1998; Saper et al.,
2001; Steininger et al., 2001) and the dorsal and median Raphé (Sherin et al., 1998; Steininger et
al., 2001). The VLPO also terminates within the cholinergic basal forebrain, PPT and LDT
(Saper et al., 2001). Thus these GABAergic systems may function to inhibit arousal-related
systems. The interactions of these neural networks have led some to devise models for how
wakefulness may switch to sleep (Saper et al., 2001). However, these models appear to be rather
premature. At this point it is probably safe to postulate that sleep onset occurs through active
inhibitory processes from sleep-active inhibitory neurons onto wake-active neurons. The net
effect may be the deactivation of arousal-related input to the cortex and thalamus. The
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relationship between these arousal systems and the thalamus is discussed next within this
context.
3.3.3 Thalamocortical system
The thalamus, residing in the middle of the brain, is a key gateway to the cortex. It is
generally organized according to two main neuron types. The first neuron type is referred to as
the relay neuron, which passes information to the cortex (McCormick, 1992b; McCormick &
Bal, 1997). The second type of neuron is referred to as the reticular neuron, and these neurons
form a layer of GABAergic interneurons which inhibit the relay neurons (McCormick, 1992b).
In Chapter 2, regions of the thalamus which receive information from the spinothalamic tract and
subsequently project to the anterior cingulate cortex were reviewed. Here we cover more
extensively the architecture and cellular properties of the thalamus and its role in triggering and
generating electrocortical activity associated with sleep and wakefulness.
Thalamocortical neurons generate repetitive burst discharges during sleep that appear to
ride on a slower depolarizing potential (Hirsch et al., 1983; McCarley et al., 1983). By contrast,
wakefulness is characterized by tonic firing of these neurons (Hirsch et al., 1983; McCarley et
al., 1983). There is a general agreement that when the thalamus switches to this bursting mode
during NREM sleep, information flow through the thalamus is largely blocked (Livingstone &
Hubel, 1981). However, some evidence suggests that information may reach the cortex if
stimulation to the thalamus occurs during the down state of the oscillation, rather than during the
upstate (Watson et al., 2008).
Thalamocortical neurons are endowed with unique properties. These neurons demonstrate
rebound burst properties following intracellular hyperpolarization and tonic firing properties with
stimulation (Livingstone & Hubel, 1981). This rebound property is well accepted to be mediated
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by low threshold calcium currents IT (Jahnsen & Llinas, 1984a; b; McCormick & Bal, 1997) and
has been extensively reviewed by McCormick (1992b). Specifically, this channel is activated as
membrane potential rises toward -65 mV, in which a burst of action potentials will be produced
and the channel becomes inactivated. To de-inactivate this channel and promote bursting
behaviour, other channels must be present to reduce membrane potential into a target range of
the IT channel. Indeed, the in-activation of a hyperpolarization-activated cationic (Na+ and K+) Ih
current during each burst of spikes produces a hyperpolarizing overshoot. This overshoot then
activates both the IT and Ih , following the burst, thus de-inactivating the IT for another calcium
spike and burst of action potentials. The frequency of the bursting behaviour is in a range which
is consistent with delta waves (0.5-4Hz) (McCormick & Pape, 1990; Dossi et al., 1992;
McCormick & Huguenard, 1992).
The reticular nucleus of the thalamus is an inhibitory network that surrounds the relay
cells of the thalamus (McCormick & Bal, 1997). The reticular cells actively inhibit the relay cells
and neighbouring reticular cells of the thalamus; however the relay corticothalamic neurons
actively excite the reticular cells (McCormick & Bal, 1997). The interactions are thought to be
involved in the generation of sleep spindles during NREM sleep (McCormick & Bal, 1997).
3.3.4 Integrating the thalamocortical system with the arousal system
The ascending arousal system and the burst properties of the thalamus have already been
covered. It is well understood that neurotransmitters from the ascending arousal systems are
responsible for producing states of wakefulness, whilst their withdrawal produces sleep. As seen
in figure 3.2, cholinergic input from the LTD and PPT, norepinephrine from the locus coeruleus,
serotonin from the Raphé nuclei, and histamine from the tuberomammillary nucleus all impinge
upon the thalamus to influence whether it is in a bursting or tonic firing mode (McCormick,
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1992b). Specifically, the activation of muscarinic receptors by acetylcholine, H1 receptors by
histamine, α1-adrenoceptors by norepinephrine reduce potassium leak currents of relay neurons
and therefore produce a net depolarization (McCormick & Prince, 1987a; b; 1988; McCormick
& Williamson, 1991; McCormick, 1992a; McCormick & von Krosigk, 1992). Thus, removal of
this input then activates K-leak channels and hyperpolarizes relay neurons. The
hyperpolarization permits activation of the IT current, promoting a bursting mode and reinforcing
slow wave oscillations. By contrast, activation of histamine H2 receptors, serotonergic receptors,
and β-adrenergic receptors influence the Ih current in relay neurons (Pape & McCormick, 1989;
McCormick & Williamson, 1991).
3.3.5 Slow corticothalamic oscillations
Experimental data in sleeping animals suggests that delta oscillations and spindles are
organized according to a slow oscillation (<1Hz) originating in the cortex (Steriade et al.,
1993b). Indeed, the slow oscillation has been found to survive brainstem transaction and
thalamic lesions (Steriade et al., 1993c). The hyperpolarization phase of this slow oscillation is
associated with inactivity of both pyramidal and interneurons (Steriade et al., 1993d) while the
depolarization phase of this rhythm is associated with neuron activation, delta (1-4Hz) and
spindle activity (7-14Hz) (Steriade et al., 1993d; Amzica & Steriade, 1995; Contreras &
Steriade, 1995). These slow oscillations appear to require cortico-cortical connections since
synchronization is impaired with block of intracortical connections (Amzica & Steriade, 1995).
Moreover, slow and delta oscillations are thought to be initially generated in layer V of the
cortex (Sanchez-Vives & McCormick, 2000; Sirota et al., 2003). If the thalamocortical system
were to become potentiated during wakefulness (i.e. during fear conditioning), it may be
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expected that this potentiation may be observed during the large delta potentials triggered during
NREM sleep.
3.4 PLASTICITY
Since the object of this chapter is to focus on how learning is related to sleep, it is
important to first outline the key concepts and signalling molecules involved in synaptic
plasticity. This section will end with an explanation of the calcium/calmodulin-dependent protein
kinase IV (CaMKIV) signalling pathway and how it may relate to sleep.
3.4.1 Hebb narrows the focus to the neuron
Great strides in the structural composition of the brain were made with Golgi and Cajal,
while Lorente de Nó began to bridge the gap between electrophysiology and anatomy (Cooper,
2005). However a central question remained; how does the brain change or retain information?
Donald Hebb is typically credited for a simple hypothesis regarding how learning may occur
through changes in neurons. Hebb states his neurophysiological postulate as follows:
“When axon of cell A is near enough to excite a cell B and repeatedly or
persistently takes part in firing it, some growth process or metabolic change takes
place in one or both cells such that A’s efficiency, as one of the cells firing B, is
increased” (p.62) (Hebb, 1949).
This postulate includes three concepts, the first of which is that a memory trace is represented by
a reactivation of a set of neurons. The second concept suggests that with repeated activation a
change of structure or chemical process occurs. The third concept is that the resistance of the
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circuit is reduced, so that cell A can excite cell B easier (Cooper, 2005). This postulate appears to
have withstood the test of time and from here this discussion will diverge into contemporary
views of synaptic plasticity.
3.4.2 Long-term potentiation induction
Long-term potentiation (LTP) has been largely accepted as a paradigmatic example of
Hebbian plasticity. LTP was originally induced in the hippocampus of anesthetised rabbits, by
tetanic stimulation of the perforant path of the dentate gyrus, the resultant effect being a long-
lasting potentiation of evoked post-synaptic potentials (Bliss & Lomo, 1973). Later studies
identified the NMDA receptor as a key glutamate receptor involved in the induction of LTP
(Bliss & Collingridge, 1993; Malenka & Nicoll, 1999). The NMDA receptor is ionotropic,
conducting potassium, sodium and calcium cations, with calcium influx important for both
depolarization and intracellular signalling (Collingridge & Lester, 1989). The Ca2+ entry
through the NMDA receptor is thought to be a key messenger to trigger LTP (Lynch et al., 1979;
Baimbridge & Miller, 1981; Lynch et al., 1983). LTP can also be produced through closely
pairing presynaptic activation with postsynaptic activation, known as spike timing-dependent
plasticity (Bi & Poo, 1998).
Thus far, it appears that the most commonly accepted pathway for LTP induction
involves that of calcium/calmodulin-dependent protein kinase II (CaMKII) (Malenka & Nicoll,
1999; Lisman et al., 2002). A variety of other kinases have been implicated in the mediation or
modulation of LTP induction, including: protein kinase A (PKA) (Yasuda et al., 2003), protein
kinase C (PKC) (Malenka et al., 1986; Hu et al., 1987; Linden et al., 1987; Linden &
Routtenberg, 1989; Malinow et al., 1989; Bliss & Collingridge, 1993), mitogen activated kinase
(MAPK) cascade which activates extracellular-signal-regulated kinase (ERK) (English & Sweatt,
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1997; Sweatt, 2004), phosphotidylinositol 3-kinase (PI3-K) (Man et al., 2003), tyrosine kinase
Src (Yu et al., 1997; Salter & Kalia, 2004) and CaMKIV (Kang et al., 2001; Wei et al., 2002;
Wu et al., 2008). The involvement of CaMKIV will be examined in detail in subsequent sections
(section 3.4.4 and 3.4.5). Moreover, if sleep is involved in memory formation, these two
processes should show mechanistic overlaps with respect to molecular biology (section 3.5.2).
However, for now, examining the consequences of LTP induction is worthwhile.
3.4.3 Long-term potentiation expression
The major mechanisms for the expression of LTP involves increasing the
phosphorylation of AMPA receptors, thereby increasing the current thorough the receptor
(Benke et al., 1998; Derkach et al., 1999; Lee et al., 2003) or the insertion of new AMPA
receptors into the postsynaptic membrane (Malenka & Nicoll, 1999; Bredt & Nicoll, 2003).
Examination of LTP protocols readily show that LTP is short-lived (30-60 mins) (Malenka &
Bear, 2004) relative to the memories which can last an entire lifetime. However it has been well
established that the longer forms of LTP require gene transcription and protein synthesis
(Abraham, 2003; Pittenger & Kandel, 2003).
A host of signalling molecules are implicated in the induction of “late phase” LTP and
include CaMKIV, PKA, and MAPK, which are involved in the activation of a key transcription
factor known as Cre-response element binding protein (CREB) (Silva et al., 1998; Abraham,
2003; Pittenger & Kandel, 2003). While a vast literature exists on CaMKII, which is positioned
in the cytosol, much less literature has focussed on CaMKIV which is located in both the cytosol
and the nucleus of neurons (section 3.4.4). Thus, the CaMKIV may be in a unique position for
controlling protein expression linked with LTP (section 3.4.5). Such a protein may be more
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relevant to long-term changes in the brain. The following section will focus on what is known
about CaMKIV in synaptic plasticity, learning and memory.
3.4.4 CaMKIV molecular biology
CaMKIV is localized throughout the brain with strong expression is found in the
neocortex, hippocampus and cerebellum and slightly lesser expression in the ACC and amygdala
(Nakamura et al., 1995). Within brain cells, CaMKIV is localized within the nuclei of neurons
with lesser expression in the cytosol (Jensen et al., 1991; Nakamura et al., 1995; Bito et al., 1996;
Ibata et al., 2008). Importin-α is the protein which transports CaMKIV from the cytosol to the
nucleus (Kotera et al., 2005) and catalytic activation of CaMKIV is required for this to happen
(Lemrow et al., 2004).
Figure 3.3 summarizes the CaMKIV pathway based on data that is presented in this and
later sections. When calcium enters a neuron it can bind to a calcium sensor known as
calmodulin (Chin & Means, 2000). The activation of calmodulin produces a conformational
change that exposes its hydrophobic residues (Chin & Means, 2000). Exposure of these residues
permits activation of CaMKIV (Means et al., 1997). In addition, CaMKinaseKinase can
phosphorylate the activation loop of CaMKIV at residue Thr-196, increasing its
calcium/calmodulin dependent activity (Kasahara et al., 2001; Tokumitsu et al., 2004). However,
Protein phosphatase2A has been found to de-phosphorylate CaMKIV at Thr-196, to undo the
activity of CaMKinaseKinase (Tokumitsu et al., 1994). Interestingly, CaMKIV can also exhibit
activity in the absence of calcium (Chatila et al., 1996; Tokumitsu et al., 2004) and is capable of
auto-phosphorylation (Okuno et al., 1995; Chatila et al., 1996).
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Figure 3.3 CaMKIV pathway Top: Depiction of CaMKIV activation pathway leading to DNA transcription. Following DNA transcription, AMPA receptors may be inserted in the plasma membrane to amplify incoming glutamate signaling. Lower left: panel showing the DNA activation machinery with nuclear CaMKIV phosphorylating CREB and CREB binding protein (CBP). Lower right: schematic diagram of CaMKIV domain structure. ER, endoplasmic reticulum, AC1, adenylate cyclase 1,TrKB, tyrosine receptor kinaseB, IP3 inositol-triphosphate, imp importin, TBP, TATA binding protein. BDNF, brain derived neurotrophic factor. (figure by H. Steenland, refs in text).
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CaMKIV phosphorylates Ser-133 of CREB (Bito et al., 1996; Kasahara et al., 2001) and
CREB binding protein (CBP) (Chawla et al., 1998) and this stimulates gene transcription.
CaMKIV is also known for cross-talk between signalling pathways. For example, CaMKIV can
phosphorylate the calcium/calmodulin binding domain to prevent it from activating the AC1
pathway (Wayman et al., 1996) and can directly phosphorylate the small GTP-binding protein
Rap-1b (Sahyoun et al., 1991). This may lead to the activation of the MAP-kinase pathway,
which as mentioned is important in the hippocampus and prelimbic regions for trace fear
acquisition (Runyan & Dash, 2004; Runyan et al., 2004; Gao et al., 2009).
3.4.5 CaMKIV and plasticity
Induction of LTP in the CA1 hippocampus, with high frequency stimulation, results in
rapid activation of CaMKIV that lasts for up to 30 minutes (Kasahara et al., 2001). This results in
the phosphorylation of CREB, which then produces an increase in c-fos expression (Kasahara et
al., 2001). There is also evidence that CaMKIV transcription regulation impacts brain derived
neurotrophic factor expression (Shieh et al., 1998). Consistently, CaMKIV knockout mice have
dramatically reduced CREB phosphorylation, LTP induction in the hippocampus, and LTD
induction in the cerebellum (Ho et al., 2000). However, CaMKIV knockout mice show no
deficits in spatial memory or locomotor activity (Ho et al., 2000; Ribar et al., 2000). By contrast,
interfering with CaMKIV expression with a dominant negative form of CaMKIV impaired the
hidden platform version of the Morris spatial water maze task (Kang et al., 2001).
Studies involving fear memory have also been conducted with CaMKIV mice (Wei et al.,
2002; Ko et al., 2005; Fukushima et al., 2008; Wu et al., 2008). CaMKIV knockout mice show
contextual and auditory cue fear memory deficits at 1 and 7 days after conditioning, and reduced
elevations in pCREB following conditioning in the ACC, CA1 hippocampus and amygdala (Wei
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et al., 2002). It was also found that CaMKIV knockout mice have impaired LTP to high
frequency stimulation in ACC (Wei et al., 2002). Interestingly, this study did not reveal any
difference in responsiveness in these mice to inflammatory pain in the formalin test (Wei et al.,
2002), suggesting some selectivity to modalities which aren’t involved in pain.
Over-expression of the dominant negative form of CaMKIV, which inhibits calcium
stimulated CaMKIV only in the postnatal brain, has shown that CaMKIV is important for late-
phase but not early-phase LTP (Kang et al., 2001). Indeed, this manipulation had the same effect
as anisomycin on late phase LTP in the CA1 hippocampus (Kang et al., 2001). Consistently, this
manipulation impairs phosphorylation of CREB and c-fos expression (Kang et al., 2001). In
contrast to others (Wei et al., 2002), this study found that freezing behaviour was reduced for
contextual but not cued-fear when studied 7 days after conditioning.
Recently, it has been shown that CaMKIV over-expressed mice demonstrate
enhancement in trace fear conditioning and enhancement in an early-stage LTP in the
hippocampus and ACC (Fukushima et al., 2008; Wu et al., 2008). Moreover, these animals do
not show any alteration in acute pain, suggesting the enhanced memory was not due to increased
pain sensitivity (Wu et al., 2008). Consistently, these animals have elevations of pCREB in the
cortex and in the hippocampus (Fukushima et al., 2008; Wu et al., 2008) and enhanced
consolidation of contextual fear memory (Fukushima et al., 2008).
CaMKIV has also been implicated in synaptic scaling (Ibata et al., 2008). Ibata et al.
(2008) used a model in which blocking calcium spikes resulted in an up-regulation of AMPA
receptors as measured by GluR2-EYFP tags. Interestingly, transfection with neurons with the
dominant negative form of CaMKIV (dnCaMKIV) blocked up-regulation of AMPA receptors.
The results suggest that if there is a drop in post-synaptic firing, there is a paradoxical increase of
CaMKIV activity. Interestingly this was found to be dependent on gene transcription. The
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opposite may be the case for the induction of late-phase LTP which can be blocked by
dnCaMKIV or anisomycin (Kang et al., 2001). Thus under conditions of low calcium, somehow
CaMKIV is activated (Ibata et al., 2008) and under conditions of high calcium CaMKIV is
activated (Kang et al., 2001). It appears that CaMKIV may therefore be necessary to maintain a
balance, so that if a neuron is not receiving much input, it up-regulates AMPA receptors to
increase sensitivity to glutamate. By contrast, if a cortical neuron undergoes plasticity, CaMKIV
may stimulate the production of AMPA receptors. Such a mechanism may simply be related to
the amount of calcium in the cell.
Based on this summary of plasticity and CaMKIV, it appears that CaMKIV would be an
ideal candidate to examine the neurophysiological correlates of trace fear memory. In addition
since trace fear memory involves pain, and CaMKIV mice are largely normal with respect to
acute pain processing, learning processes can be potentiated without interference from different
processing streams. As the next section details, CaMKIV may also be related to sleep functions.
3.5 SLEEP and SYNAPTIC PLASTICITY
3.5.1 Possible function of sleep in learning and memory
Sleep occurs across a broad range of species, from mammals to fruit flies, yet its function
has not been firmly established and is highly debated (Benington & Frank, 2003; Sirota et al.,
2003; Siegel, 2005; Stickgold & Walker, 2005; Marshall & Born, 2007; Cirelli & Tononi, 2008).
Much of this debate likely stems from the fact that there are no behavioural correlates to relate
ongoing brain activity during sleep. Currently, much interest has surrounded the examination of
whether or not sleep is related to learning, memory and synaptic plasticity (Benington & Frank,
2003; Sirota et al., 2003; Stickgold & Walker, 2005; Marshall & Born, 2007; Cirelli & Tononi,
2008). For example, Tononi and Cirelli proposed a model for how the homeostasis of sleep
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might be related to synaptic homeostasis in the brain (2003). They make four central claims.
Firstly, being awake is associated with synaptic potentiation. Secondly, synaptic potentiation is
tied to the homeostatic regulation of slow brain waves (1-4Hz). Thirdly, slow brain waves are
associated with synaptic down scaling. Finally, synaptic downscaling is related to the beneficial
effects on sleep for neural functioning. There are varying degrees of proof for each of these
postulates which will be reviewed shortly; however the third postulate, which is probably the
most important one, is most likely to be disputed. For example Benington et al, (2003) argue that
synaptic depression or potentiation are equally plausible during NREM sleep and emphasize that
REM sleep, a period in which dreams often occur, may also play a part in the formation of
memory. Setting these hypotheses aside, the remaining discussion will focus on what evidence
there is for the role of NREM sleep in synaptic plasticity/modification and memory. Discussions
of the impact of sleep deprivation on learning and memory have been largely left out, as sleep
deprivation procedures may themselves produce confounding levels of stress.
3.5.2 mRNA and protein expression during sleep
An incredible amount of data has been collected using RT-PCR, microarray technology
and in-situ hybridization showing that many genes are unregulated during wakefulness, including
those related to synaptic plasticity (Cirelli & Tononi, 2000b). Some of these genes include: c-fos,
pCBP, Arc, PKC, BDNF, calmodulin, CREB, NMDA and AMPA receptors, GluR1, GluR1-P
and CaMKII-P (Cirelli & Tononi, 2000a; b; Cirelli, 2002; Cirelli et al., 2004). By contrast a
number of genes appear to be upregulated with sleep and include: CaMKII inhibitor-α, CaMKIV
and Protein phosphatise 3 (Cirelli et al., 2004). The finding that CaMKIV mRNA is upregulated
following bouts of sleep suggests that it may be related to plasticity associated with sleep and
wakefulness. Consistently, knockout mice, for α- and δ-isoforms of CREB, spent significantly
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less time awake then control animals (Graves et al., 2003) and is consistent with the reduced rest
rebound in drosophila with CREB knockout (Hendricks et al., 2001).
While changes in gene expression which occur across sleep and wakefulness states seems
a provocative topic, it should be acknowledged that similar changes were often seen in the
cerebellum (Cirelli et al., 2004), which is not known to demonstrate slow wave oscillations
(Hobson & McCarley, 1972). However, ablation of the locus coeruleus results in reductions of
pCBP, pCREB, c-Fos and Arc in hippocampus and cortex without affecting baseline EEG values
across sleep and wakefulness (Cirelli et al., 1996; Cirelli & Tononi, 2000a). It was later found
that when locus coeruleus lesioned animals were sleep deprived, they show reduced slow wave
sleep rebound (Cirelli et al., 2005), suggesting that there is some connection between gene
expression and slow wave activity.
One way to assess whether sleep is related to synaptic plasticity is to examine whether
sleep modulates proteins. In studies conducted in monkeys and rats, it was found that cortical
protein synthesis (as measured with radioactive leucine incorporation) was positively related to
slow wave sleep depth (Ramm & Smith, 1990; Nakanishi et al., 1997). It remains an intriguing
possibility that mRNA accumulation during wakefulness is converted into protein during sleep.
3.5.3 Slow wave activity, plasticity and learning
Studies have implicated that slow wave activity is related to learning and synaptic
plasticity (Vyazovskiy et al., 2008). Consistent with the idea that wakefulness potentiates
synapses, it has been found that cortically-evoked potentials increased as a function of
wakefulness and decreased after sleep (Vyazovskiy et al., 2008). The data indicate that, as
wakefulness progresses throughout the day, the degree of potentiation increases only to be reset
by sleep (Vyazovskiy et al., 2008). Consistent with the notion of wakefulness potentiation, local
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BDNF infusion into the cortex increases subsequent slow wave activity, while blocking the
TrKB receptor reduces local slow wave activity (Faraguna et al., 2008). These findings are
consistent with the increase in cortical BDNF and slow wave activity following exploratory
behaviour (Huber et al., 2007). Slow wave activity increases are also detected in humans. Indeed,
performance of a rotation-adaptation task results in increased slow waves, overlying the brain
region which is involved in the task, during subsequent sleep (Huber et al., 2004). In addition,
improvement was seen in this task after sleep and correlated significantly with the slow wave
activity increase (Huber et al., 2004).
By contrast, it could be argued that slow wave enhancements are a result of residual
extracellular glutamate accumulation throughout the day. For example, astrocytes glutamine
synthetase and glutamate/aspartate transporter (GLAST) are up-regulated following periods of
wakefulness (Cirelli et al., 2004). Moreover, in rest-deprived drosophila, an up-regulation
bruchpiliot was detected (Gilestro et al., 2009) and this protein is known to be involved in
glutamate release machinery (Wagh et al., 2006). Consistently, it has been shown that
extracellular levels of glutamate are increased throughout the day, and decline after periods of
sleep (Dash et al., 2009). This extracellular glutamate was found to be significantly related to
slow brain waves (Dash et al., 2009). Thus, it appears just as likely that either synaptic plasticity
or enhanced glutamate levels might account for enhanced slow wave activity following learning.
3.5.4 Neural replay during sleep
One method to examine learning and memory during sleep is to track neuron activity
during wakefulness, when it is locked to learning, and examine whether these units are
reactivated during sleep. Neurons in the hippocampus which correlated activity during
wakefulness have been found to be reactivated during NREM sleep, and this activity seems to
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correlate with sharp-wave field potential ripples (~120-200Hz) (Wilson & McNaughton, 1994;
Kudrimoti et al., 1999). In addition, this neural replay has also been detected in the prelimbic and
cingulate cortices following trace eye blink conditioning (Takehara-Nishiuchi & McNaughton,
2008). Interestingly, in the absence of training, neurons which responded to trace-eyeblink
conditioning could be detected in the prefrontal cortex several weeks after learning, consistent
with the concept that consolidation of memory may happen in the cortex (Takehara-Nishiuchi &
McNaughton, 2008). The replay of this neural activity is time-compressed, occurring up to 5-7X
the rate that was observed during learning (Euston et al., 2007; Takehara-Nishiuchi &
McNaughton, 2008).
Not only does the hippocampus and neocortex display neural replay during sleep, there is
also evidence that these two brain regions communicate during sleep (Sirota et al., 2003). In the
study by Sirota et al. (2003), it was found that during NREM sleep delta (1-4Hz) and spindle (12-
18Hz) activity in the somatosensory cortex become correlated with CA1 fast ripple oscillations
(120-200Hz). Additionally, neocortical somatosensory and visual unit activity has been found to
precede that of the hippocampus (50-100msec) (Sirota et al., 2003; Ji & Wilson, 2007). By
contrast, it was found that the prefrontal cortex unit activity lags that of the hippocampus by
~100ms (Wierzynski et al., 2009), suggesting that different brain areas may have different
directions of information flow during sleep.
Since hippocampus and cortex appear to communicate during sleep, the next question is
whether their communication might be related to learning or plasticity. It has been shown that
animals trained to run in figure-“8” formation in a maze demonstrate sequential reactivations of
neural activity in both the visual cortex and the hippocampus during NREM sleep (Ji & Wilson,
2007). Most interestingly, the replay in the hippocampus and cortex were simultaneously
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coordinated with one another, as if the networks responsible for the entire experience itself were
reactivated (Sirota et al., 2003).
The replay of information during sleep might be a very provocative mechanism for
memory consolidation, however there is substantial evidence that replay of information also
occurs during wakefulness (Foster & Wilson, 2006; Karlsson & Frank, 2009). Foster and Wilson
(2006) found that when animals run a track for food, previous place fields in the hippocampus
replayed during periods immediately after the spatial experience. Interestingly, this replay
occurred frequently in reverse order, and like the replay during NREM sleep, often occurred in
conjunction with ripple events. Similarly, Karlsson and Frank (2009) found that replay even
occured when the environmental context had been changed, suggesting that local cues in the
environment aren’t necessary to trigger replay. Importantly, a causal link has not been
established between neural memory replay and memory consolidation. Similarly, a causal link
has not been established between slow wave activity and synaptic plasticity. Accordingly,
manipulations of pharmacology and genetics may be necessary to move from correlative
observations to causal relationships.
3.5.5 Fear memory and NREM sleep
This section will review relevant work that has been conducted on trace fear memory and
sleep.
Ruskin and LaHoste (2008) sleep-restricted rats for three days prior to trace fear
conditioning animals. They found that rats that were sleep-restricted had the same amount of
freezing memory as did animals which received no shock at all. In addition, contextual fear
conditioning was found to be impaired. However, an earlier report demonstrated that cued fear
conditioning was not affected by sleep deprivation prior to training (Ruskin et al., 2004). The
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authors have taken this to suggest that the amygdala does not require prior sleep activity to
function properly (Ruskin & Lahoste, 2008).
On the assumption that associative memory would be replayed during early NREM sleep,
Walmsley and Antrobus (2009) trained 2 sets of participants in either a trace fear conditioning
paradigm or a cued delay fear paradigm. Participants were trained with either a tone which
predicted a shock or a tone that didn’t predict a shock. When the subjects fell asleep after
training, they would play the neutral or the conditioned tone back to the subjects. It was found
that cues which predicted a shock were the most likely to induce sub-awakening arousals
(changes in EEG). In addition, cues which predicted a shock for trace fear conditioning were the
most likely to evoke K-complex waves in the cortex. The experimenters eventually woke the
subjects up and asked questions about the dreaming mentation. Most interestingly, under
conditions where cues predicted shock, emotional valence of dreams was ranked highest for trace
fear but not delay fear (Wamsley & Antrobus, 2009). This effect was seen in the early night
where NREM sleep occurs most frequently. The result indicates that there may be some replay of
trace fear memory during NREM sleep.
3.6 Comments
This concludes the examination of the relationship between sleep, synaptic plasticity and
fear memory. It is apparent that there is a lack of information regarding sleep and fear memory.
In addition, there is no information on whether CaMKIV influences sleep, however considerable
information links CaMKIV to fear memory. The experiments to follow will attempt to relate all
of these processes.
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3.7 EXPERIMENT SET3: CaMKIV over-expression boosts cortical 4-7Hz oscillations
during learning and 1-4Hz delta oscillations during sleep
3.7.1 Abstract
Mounting evidence suggests that neural oscillations are related to the learning and
consolidation of newly formed memory in the mammalian brain. Four to seven Hertz (4-7Hz)
oscillations in the prefrontal cortex are also postulated to be involved in learning and attention
processes. Additionally, slow delta oscillations (1-4Hz) have been proposed for memory
consolidation or down scaling during sleep. The molecular mechanisms which link learning-
related oscillations during wakefulness to sleep-related oscillations remain unknown. We show
that increasing the expression of calcium/calmodulin dependent protein kinase IV (CaMKIV), a
key nucleic protein kinase, selectively enhances 4-7.5Hz oscillation power during trace fear
learning and slow delta oscillations during subsequent sleep. These oscillations were found to be
boosted in response to the trace fear paradigm and are likely to be localized to regions of the
prefrontal cortex. Correlation analyses demonstrate that a proportion of the variance in 4-7.5Hz
oscillations, during fear conditioning, could account for some degree of learning and subsequent
memory formation, while changes in slow delta power did not share this predictive strength. Our
data emphasize the role of CaMKIV in controlling learning and sleep related oscillations and
suggest that oscillatory activity during wakefulness may be a relevant predictor of subsequent
memory consolidation.
3.7.2 Rationale
There is evidence that prefrontal 4-7.5Hz rhythms, which exist in mice, rats, monkeys
and humans, are involved in attention and learning processes (Tsujimoto et al., 2003; Jones &
Wilson, 2005; Delorme et al., 2007; Sirota et al., 2008). In addition, human and rodent studies
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suggest that slow oscillations during sleep are related to memory (Huber et al., 2004; Huber et
al., 2006; Marshall et al., 2006; Vyazovskiy et al., 2008). Parallel studies examining single
neuron recording in the prefrontal and hippocampal cortex show that there is a network of
information replay during sleep, which reflects learning during the days experience (Wilson &
McNaughton, 1994; Sirota et al., 2003; Euston et al., 2007; Ji & Wilson, 2007).However, there is
also evidence for neural replay in the hippocampus during periods of wakefulness (Foster &
Wilson, 2006; Karlsson & Frank, 2009). While studies have largely focused on the possibility of
memory consolidation being related to neural oscillations during sleep, few studies have
attempted to relate the neural oscillations during learning with that of subsequent sleep.
Moreover, genetic manipulation may be helpful to connect neural oscillations which occur
during learning with those that occur during sleep
At the molecular level, gene transcription and new protein synthesis play critical roles in
memory consolidation. Inhibition of protein translation processes prevent or inhibit memory
consolidation (McGaugh, 2000; Wiltgen et al., 2004). The cAMP response element-binding
protein (CREB) is a major activity-dependent transcriptional factor involved in the formation of
long-term memory (Bourtchuladze et al., 1994; Kandel, 2001; Kida et al., 2002; Pittenger et al.,
2002; Deisseroth et al., 2003). Two major pathways responsible for CREB activation are the
cAMP signaling pathway and the CaMKIV pathway (Bourtchuladze et al., 1994; Kandel, 2001;
Mayr & Montminy, 2001; Wu et al., 2001; Kida et al., 2002; Pittenger et al., 2002; Wei et al.,
2002; Deisseroth et al., 2003). CaMKIV is a serine-threonine kinase and is activated by a
combination of elevated intracellular Ca2+ and CaMK kinase during neuronal activity (Wayman
et al., 2008) and phosphorylates CREB (Enslen et al., 1994; Bito et al., 1996). While the roles of
CREB-related pathways in synaptic potentiation and initial learning are becoming clear, their
possible contribution to memory consolidation and cortical slow delta oscillations during sleep
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has not been examined. CaMKIV is involved in synaptic plasticity (Wei et al., 2002), synaptic
homeostasis (Ibata et al., 2008), learning and memory (Kang et al., 2001; Wei et al., 2002; Ko et
al., 2005; Fukushima et al., 2008; Wu et al., 2008). In addition, CaMKIV influences the CREB
pathway (Enslen et al., 1994; Matthews et al., 1994), and its mRNA is up-regulated following
sleep (Cirelli et al., 2004). Thus, we hypothesized that CaMKIV over-expression will impact
EEG oscillations associated with learning, memory and sleep. To test these hypotheses we used
transgenic mice, over-expressing CaMKIV in the forebrain under the control of alpha CaMKII
promoter (Fukushima et al., 2008; Wu et al., 2008).
3.7.3 Materials and methods
3.7.3A Animals
Genetic mice were generated in the laboratory of Dr. Satoshi Kida (Department of
Bioscience, Faculty of Applied Bioscience, Tokyo University of Agriculture, Tokyo 156-8502,
Japan) using a transgene that contained a αCaMKII promoter, hybrid intron in the 5’ untranslated
leader, the coding region of the CaMKIV fused with the Flag sequence at the N-terminus and a
polyadenylation signal (Fukushima et al., 2008). Surgery was performed on 11 male CaMKIV
over-expressed mice (average age = 12.3 weeks) and 11 male wild type (WT) control (4 C57Bl6
from Charles River and 7 cage-mate control (average age 12.2 weeks, C57Bl6 background) for
frontal-parietal EEG recording). Surgery was performed on 12 C57Bl6 mice from Charles River
for intracortical field potential recording (average age 9.9 weeks). Mice were maintained on a
12:12-h light-dark cycle (lights on 8:00 A.M.), and had access to food and water ad libitum.
Procedures conformed to the recommendations of the Canadian Council on Animal Care and the
University of Toronto Animal Care Committee approved the protocols.
.
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3.7.3B Frontal-parietal EEG
Mice were anesthetized with 1-2% isoflurane which was mixed with oxygen (30%
balanced with nitrogen) and delivered to the mice via nose cone throughout the surgery. All
electrodes were pre-attached to a miniature connector. The abdomen and scalp of mice were
shaved and then cleaned with iodine (Triadine) and alcohol. The skull of the mouse was fixed
into a stereotaxic adapter (502063, WPI, Sarasota, Fl, USA) mounted on a stereotaxic frame
(Kopf Model 962, Tujunga, CA, USA). Three small holes (1.19 mm diameter) were drilled into
the skull for differential frontal-parietal recordings. Electrodes, consisting of a wire attached to a
jewelers screw (with contact end ground flat), were fixed into the holes to record EEG
(electroencephalogram) at the following coordinates relative to bregma: frontal cortex (AP 2.2,
ML 1.0), parietal cortex (AP -2.2, ML -2.5) and ground (AP -3.0, ML 3.0) (Paxinos & Franklin,
2000). Dorsal neck muscles were also exposed and Teflon-coated stainless steel electrodes
(Cooner Wire) were sutured (4.0 silk) to each muscle to record neck EMG (electromyogram).
The wires and connector were secured to the skull with dental cement and cyanoacrylate glue
(Krazy glue). Mice were injected, intraoperatively (SC) with buprenorphine (0.1 mg/kg) as an
analgesic, and 1.0 ml sterile saline (IP) for hydration. Mice were placed on a warm heating pad
until they showed signs of ambulation, and were permitted to recover ~14 days prior to
recording.
3.7.3C Cortical field recording
Intracortical EEG recording was performed with sterile bipolar Teflon coated tungsten
electrodes (AM-systems, 796000, 76.2 m wire diameter) with a 0.5 mm tip offset. The
electrode impedances were 0.1-0.3 Mega-Ohms and matched within ± 50 kOhms of each other to
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improve common source noise rejection. All impedances were measured with an impedance
tester (BAK electronics Inc., model IMP2, Mount Airy, MD, USA). Surgery was performed with
similar technique as above. However, in these experiments, 2 bipolar electrodes were implanted
in to the cortex. When the animal’s skull was exposed, two holes were also drilled over frontal
(0.1 AP, 0.6 ML relative to bregma) and motor cortex (0.1 AP, 0.6 ML). Bipolar electrodes were
then lowered at a 45 degree angle into the anterior cingulate cortex (ACC) (-1.8 DV) and in the
motor cortex (-1.0 DV) from the skull. Holes were drilled for a stainless steel ground screw (AP -
3.0, -ML 3.0) and a support screw (AP 1.0, ML -1.0) to help secure the electrode assembly. The
regions where the electrodes penetrated the brain were covered with a mixture of bone wax and
mineral oil. The electrode assembly was then fixed to the skull with dental cement and Krazy
glue. Neck electrodes were then implanted and the animal was left to recover as above. For
localization of 4-7.5Hz rhythms in the frontal cortex 4 electrodes were inserted at a 45 degree
angle into the prelimbic region (AP 1.4). The electrodes tip separation was 0.3mm for these
recordings.
3.7.3D Sleep recording
The mice were placed in a clear plastic container situated in a cubicle (ENV-017M, Med
Associates, St. Albans, VT, USA) in the evening before any experimentation. On the day of the
experiment, between 7 and 8AM, a lightweight cable was connected to the assembly on the
animal’s head. The signals were routed through a commutator (Crist Instruments, Hagerstown,
MD, USA) and data collected continuously for 8-24 hours. Electrophysiological signals were
amplified and filtered (Super-Z headstage amplifiers and BMA-400 amplifiers and filters, CWE
Inc., Ardmore, PA, USA) as follows: cortical EEG 1000X at 1-100Hz and neck EMG 2000X at
100-1000Hz. Neck EMG recordings were smoothed (25 ms time constant) and rectified (Spike2
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software, 1401 interface, CED Ltd., Cambridge, UK). For intracortical field potential recording,
data was collected with a unity gain headstage (made in house) and the signals were amplified by
1000X and filtered from 1-100Hz (differential amplifier DP-304, Warner Instruments, Hamden,
CT, USA).
3.7.3E Trace fear memory recordings
Trace fear conditioning was performed in an isolated shock chamber (Med Associates, St.
Albans, VT). The conditioned stimulus (CS) is an 80db white noise, delivered for 15s, and the
unconditioned stimulus (US) is a 0.75mA-scrambled foot-shock for 0.5s (Wu et al., 2008). Mice
are acclimated for 60s, and presented with ten CS–trace–US–ITI trials (trace of 30 or 15 sec,
inter-trial interval (ITI) of 210 s). One day after training, mice are acclimated for 60s and
subjected to ten CS–ITI trials (ITI of 210 or 225s) in a novel chamber to test for trace fear
memory (Huerta et al., 2000). Freezing is typically defined as the absence of movement with the
exception of breathing. Conventional trace fear paradigms have been performed with animal
during the day, when animals normally sleep. This was previously necessary because behavioral
scoring and motion detection require light to determine freezing behavior. Thus, most studies
have been carried out when the animal would be naturally sleeping. To circumvent this potential
confound we utilized neck EMG recording to examine freezing behavior in the dark (0.2 lux)
when mice are naturally awake (Steenland & Zhuo, 2009). The additional advantage of recording
in dim light or the dark is that visual cues may be reduced. Frontal-parietal EEG or prelimbic
field potentials were obtained from animals during the paradigm to determine if there were any
global EEG or local field changes respectively.
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3.7.3F Histology
At the completion of each local field potential experiment, animals were sacrificed with
2% isoflurane. A DC current was applied between the recording electrodes and ground
electrodes to produce a lesion. The brain was then removed and fixed in 10% formalin. Three
days later the brain was cryoprotected in a 30% sucrose solution until the brain was dehydrated.
Relevant brain regions were sectioned in the coronal plane on a cryostat (CM 1850; Leica), at
40µM, for verification of electrode lesion sites. Coronal cross-sections were then registered with
the stereotaxic atlas of the mouse brain (Paxinos & Franklin, 2000) and the position of the
electrodes recorded.
3.7.3F Analyses
Periods of active wakefulness, NREM sleep, and rapid-eye-movement (REM) sleep were
scored visually off-line and the scorer was blinded to the experimental condition. Active
wakefulness was characterized by high frequency low voltage EEG and neck muscle movement
(twitches) lasting for at least 30s. Abatement of muscle movement for 10s or more constituted
the end of the active wakefulness bout. NREM sleep was characterized by high voltage slow
wave activity, with no muscle movement, low muscle tone and lasting a minimum of 30s.
Increases in EMG or change in EEG to wakefulness for 3s or more was considered arousal from
sleep and terminated the NREM sleep bout. REM sleep was scored with the record of neck
muscle atonia coinciding with high frequency low voltage EEG (mostly in the 4-13Hz range) and
lasting more than 30s. Arousal from REM sleep consisted of an increase in neck muscle EMG
coinciding with a reduction of the typical REM 4-13Hz power. Scripts for EEG analysis (Sudsa-
version 2.2) were obtained from CED. Fast-Fourier transform was used to convert EEG
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waveforms into total power (μV2), which was then binned every 5s for the following frequency
bands: δ1 (1-2Hz), δ2 (2-4Hz), θ1 (4-7.5Hz), θ2 (7.5-13Hz), β1 (13-20Hz), β2 (20-30Hz) and γ
(30-100Hz), similar to others (Hamrahi et al., 2001; Steenland et al., 2008b). While this analysis
results in a decrease in resolution, it will by definition produce a conservative estimate of
changes in brain wave frequencies. Statistical analyses were conducted on absolute power
spectra rather than relative (or normalized) power spectra for two reasons. Firstly, the number of
animals in each group was of sufficient size that the actual power values should be reported.
Secondly, EEG frequencies recorded in higher ranges (above 7.5Hz) were relatively similar
between WT and CaMKIV over-expressed mice suggesting that the observation were not a result
of differences in electrode positions or differing amounts of contact between the dura and
electrode. Thus normalization is only necessary if all the frequencies in the EEG were shifted in
an upward or downward direction, due to inconsistency in recording. Neck EMG was quantified
by integrating the area under the rectified and smoothed EMG waveform every 5s. For each
animal, every sleep and wakefulness period was scored and quantified. All data were copied to a
spreadsheet and sorted according to the sleep or wakefulness states. A grand average across each
sleep and wakefulness state was then computed for each animal.
To examine slow delta oscillations, the power from the two slow wave bands (1-2 and 2-
4) were summed for each file of data collected. A grand average of slow delta power was
collected for each hour beginning at the light period (8AM) for a total of 8 hours. Slow delta
oscillation power was calculated for baseline sleep and sleep following trace fear training. This
analysis was used for frontal-parietal EEG recordings. Normalization was used for intracortical
field potential recordings and was necessary since the magnitude of the voltage from the motor
cortex across all frequencies was much larger than the ACC across the entire night during
baseline. Thus, for the motor cortex to be used as an appropriate control, both ACC and motor
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cortex slow delta power were normalized. Normalization was calculated by dividing the slow
delta power of each hour (before or after fear conditioning) by the average slow delta power of
the baseline recording prior to conditioning.
3.7.3G Statistics
The analyses performed for each statistical test are included in the text where appropriate.
For all comparisons, differences were considered significant if the null hypothesis was rejected at
p < 0.05 using a two-tailed test. Mixed factor (1 between and 1 within subject variable) repeated
measures ANOVA was performed and followed by post-hoc comparisons with the Bonferroni
corrected P to infer statistical significance. Analyses were conducted with SigmaStat (SPSS Inc.,
Chicago, IL, USA) and data were plotted with SigmaPlot (Systat Software, San Jose, CA, USA).
In some cases, where statistical tests involved multiple post-hoc comparisons, only the ANOVA
is reported in text with significance indicated in the figures. Pearson-product moment correlation
was used to examine the relationship between brain waves and fear behavior. For this analysis,
changes in fear behavior and changes in EEG power were measured relative to baseline (EMG-
freezing before conditioning, 4-7.5Hz power before conditioning, slow wave power from the
baseline sleep period of the same hour) and expressed as a percentage of that baseline to yield
percentage change.
3.7.4 Results
3.7.4A CaMKIV over-expression impacts natural sleep
To examine whether CaMKIV over-expression alters sleep, learning and memory, we
performed electroencephalogram (EEG) recordings with frontal-parietal electrodes according to
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Figure 3.4 Experimental design Pre-conditioning sleep and wakefulness recordings were taken from mice on day one followed by trace fear conditioning. Mice were then returned to their home cage where their post-conditioning sleep was examined. Trace fear conditioning was conducted for 45 minutes in a shock chamber. This paradigm involved repeated trials of a presentation of a tone, followed by an interval (trace interval) and subsequently by a foot shock. Trace fear testing was conducted the following day in a similar fashion, only in this case the shock stimulus was omitted. Both WT and CaMKIV over-expressed mice were chronically instrumented with frontal parietal EEG recordings to examine EEG activity and freezing behavior for each part of the experimental design.
the experimental design outlined in Figure 3.4. Figure 3.5A-D shows representative traces and
grouped data of natural sleep and wakefulness states for CaMKIV over-expressed and WT mice.
A significant interaction was detected between genetics and behavioural state for delta (2-4Hz)
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Figure 3.5 CaMKIV over-expression enhances delta sleep in the recording environment (A) Example raw traces of sleep measurements in WT and CaMKIV over-expressed mice for wakefulness, NREM and REM sleep. To the left is a drawing, showing the location of recording configurations of the electrodes. CaMKIV over-expressed mice demonstrate relatively normal EEG during wakefulness and REM sleep but had elevated slow oscillations during NREM sleep (arrow). The raw neck and the smoothed and rectified (Sm/Rt) neck signals show that EMG activity in CaMIV over-expressed mice was relatively normal across sleep and wakefulness states. (B) CaMKIV over-expressed mice have normal wakefulness EEG power compared to WT mice. (C) CaMKIV over-expressed mice have enhanced delta EEG power in the 2-4Hz range compared to WT mice (D) CaMKIV over-expressed mice have normal REM sleep EEG power compared to WT mice. (E) CaMKIV over-expressed mice have normal neck EMG across all behavioral states compared to WT mice. * indicate significant differences compared to wild type (WT) mice with P < 0.05, Values are means + SEM.
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power (F(3,38) = 2.83; p = 0.05, two-way ANOVA). Post-hoc analysis revealed that there was a
significant increase in delta power selective to NREM sleep (t = 2.80; p = 0.008, Fig. 3.5C).
From figure 3.5D, it also appears that there was an enhancement in 7.5-13Hz power during REM
sleep, however the ANOVA yielded neither a main effect (F(1,20) = 2.49; p = 0.124, two-way
ANOVA) nor an interaction (F(3,38) = 1.46; p = 0.241, two-way ANOVA) for this frequency,
between CaMKIV and WT mice.
Neck electromyogram amplitudes were not different between CaMKIV over-expressed
mice and WT mice (F(1,20) = 2.83; p = 0.644, two-way ANOVA), confirming that the sleep
scoring was done consistently (Fig. 3.5E). It was also found that the time spent in each particular
sleep-wake state in the light (F(1,6) = 0.594, p = 0.470, two-way ANOVA) and dark (F(1,6) =
0.275, p = 0.630, two-way ANOVA) was not different between WT and CaMKIV over-
expressed mice (Table 3.1). Finally, the frequency of occurrence of each particular sleep-wake
state during the light (F(1,6) = 0.016, p = 0.904, two-way ANOVA) and dark period (F(1,6) = 0.001,
p = 0.975, two-way ANOVA) was not different between WT and CaMKIV over-expressed mice
(Table 3.1). Overall, these results show that CaMKIV over-expression augments delta-related
EEG oscillations in the sleep recording environment without impacting the time spent sleeping or
the frequency of sleep.
Table 3.1 CaMKIV over-expressed mice have normal sleep duration and bouts CaMKIV over-expressed mice demonstrate normal sleep bouts frequency and time spent sleeping. Values are means ± SEM.
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3.7.4B CaMKIV over-expression enhances learning and memory
CaMKIV over-expressed mice exhibit learning and memory enhancements for the trace
fear paradigm (Wu et al., 2008). In the present set of trace fear experiments, animals were
trained and tested in the dark while EMG and EEG were simultaneously recorded. An EMG-
based method, previously developed in our laboratory, was used to score freezing behavior
(Steenland & Zhuo, 2009). This EMG recording method permits the measure of freezing in the
dark phase of the cycle, when nocturnal rodents are normally active. By having mice trained at
this time, mice do not need to be disturbed during the light phase for training or testing, as is
usually done in learning experiments. Secondly, this method, in conjunction with EEG can help
discriminate sleep from fear behavior (Steenland & Zhuo, 2009), and help minimize contextual
visual information which could confound our study. Brain wave and freezing behavior measures
were compared to the first 60s baseline taken in the recording chamber during trace fear
conditioning. Figures 3.6A and 3.6B show summarized and raw EMG-based freezing data for
WT and CaMKIV over-expressed mice during conditioning and testing days. There was
significant effect of time interval (F(7,140) = 24.25; p < 0.001, two-way ANOVA), with both WT
and CaMKIV over-expressed mice showing learning and memory retention. There was a
significant effect of the genetic manipulation on freezing behaviour (F(1,20) = 24.58 p < 0.001,
two-way ANOVA), with CaMKIV over-expressed mice showing more freezing during both the
conditioning phase and the testing phase. Thus, CaMKIV over-expressed mice learn and
remember better then WT controls.
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Figure 3.6 CaMKIV over-expression enhances trace fear conditioning and subsequent memory (A) Trace fear conditioning (30 sec trace interval) learning curve for both WT and CaMKIV over-expressed mice (* is P<0.05 compared to baseline (BL)). However, CaMKIV over-expressed mice have enhanced fear behaviors during trace fear conditioning and testing above that of WT mice (# is P<0.05). (B) Example of EMG-based freezing and scoring (redlines under the trace) from WT and CaMKIV over-expressed mice. Raw EMG data show periods of quiescence during the conditioning and memory trials of the mice. During conditioning and memory testing, CaMKIV over-expressed mice demonstrate more EMG-based freezing behavior than WT mice. S/ES represents shock and expected shock, for the conditioning and test trials respectively.
3.7.4C CaMKIV over-expression and EEG responses to tone, trace interval, and shock
There are numerous reports of brain wave oscillations associated with learning, memory,
and attention in a variety of behavioural paradigms (Buzsaki, 2002; Seidenbecher et al., 2003;
Tsujimoto et al., 2006; Stark et al., 2008). We investigated the EEG responses for CaMKIV and
WT mice to tone and trace intervals, and following a shock to the foot during the trace fear
conditioning day (Fig. 3.7A-F). We also examined the EEG responses during the testing day.
However in this case, a shock was not delivered and so we examined the EEG of an expected (or
anticipated) shock.
3.7.4C-I EEG responses to Tone
It is possible that CaMKIV over-expressed mice demonstrate enhanced arousal or attention
to sound stimuli during conditioning. To investigate this, we analyzed EEG during the
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presentation of the tone. Mice were found to have normal EEG power, for most frequencies,
during the presentation of the tone (Fig. 3.7). However, there was a significant effect of the tone
on 4-7.5Hz power (F(2,39) = 20.89; p < 0.001, two-way ANOVA) and a significant interaction
between genetics and the tone presentation (F(2,39) = 7.64; p < 0.002, two-way ANOVA). Post-
hoc analysis revealed that the presentation of tone during conditioning enhanced 4-7.5Hz power
in CaMKIV over-expressed mice (t = 6.31; p < 0.001) and WT mice (t = 2.76; p < 0.026). When
the tone was played on the testing day, post-hoc analysis revealed that 4-7.5Hz power was
increased for CaMKIV mice (t =6.73; p < 0.001) but not WT mice (t = 0.331; p = 1.0) compared
to their respective baseline. Finally, CaMKIV mice were found to have greater 4-7.5Hz power
during the tone on the testing day when compared to WT mice (t = 6.04; p < 0.001).
There was a significant alteration of 7.5-13Hz power during the tone presentation (F(2,39)
= 24.32; p < 0.001, two-way ANOVA) and a significant interaction between genetics and tone
presentation (F(2,39) = 5.79 p < 0.006, two-way ANOVA). Post-hoc analysis revealed that, for
CaMKIV over-expressed mice, presentation of a tone decreased 7.5-13Hz power during both
conditioning (t = 6.06; p < 0.001) and testing days (t = 6.92; p < 0.001) compared to baseline
(Fig. 3.7D). Additionally CaMKIV over-expressed mice had elevated 7.5-13Hz power above that
of WT mice (t = 3.04; p < 0.005) during baseline. There was also a significant alteration of 13-
20Hz power during the tone presentation (F(2,39) = 4.38; p < 0.019, two-way ANOVA). Post-hoc
analysis revealed CaMKIV over-expressed mice had reduced power during tone presentation on
trace fear testing days (t = 3.64; p < 0.002) compared to baseline (Fig. 3.7E).
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Figure 3.7 CaMKIV over-expression and EEG responses to tone, trace interval and pain CaMKIV over-expressed mice demonstrate alterations in EEG rhythms during different phases of trace fear conditioning. EEG data was averaged across ever trial during trace fear conditioning (left side of graph) and again for every trial during memory testing (right side of graph) for WT and CaMKIV over-expressed mice. (A) None of the interventions had a significant effect on 1-2Hz frequency EEG power. (B) Trace fear training increased 2-4Hz power in both WT and CaMKIV over-expressed mice above baseline (* P <0.05) for the trace interval. (C) Trace fear conditioning significantly enhanced 4-7.5Hz (4-7.5Hz) EEG power in CaMKIV over-expressed mice for both the tone and the trace interval conditions compared to baseline. WT mice only demonstrated an enhancement in 4-7.5Hz EEG power for the tone interval (* is P<0.05). In addition, there was a significant enhancement in 4-7.5Hz EEG power of CaMKIV mice above that of WT mice for the trace interval during conditioning and the E-Shock and tone interval during memory (# is P <0.05 compared to WT). (D) The baseline 7.5-13Hz EEG power was increased in CaMKIV over-expressed mice with initial placement into the fear conditioning chamber (# is P<0.05 compared to WT) and reduced during the tone, trace and E-shock intervals (* is P<0.05 compared to baseline). (E) 13-20Hz EEG power was reduced during the tone presentation on the test day relative to baseline in CaMKIV over-expressed mice. (F) None of the interventions had a significant effect on 20-30Hz EEG power. E-shock is expected shock, BL is baseline, and values are means + SEM.
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3.7.4C-II EEG responses to the trace interval
For a fear response to be conditioned across the trace interval, information about the tone
must be retained until the shock is perceived. Since hippocampus, in conjunction with the
prefrontal cortex, is thought to maintain contiguity of fear memory during the trace interval
(Buchel et al., 1999; Bangasser et al., 2006) we examined whether or not there would be neural
oscillations which corresponded to the trace interval. There was a significant effect of the trace
interval on 2-4Hz delta power (F(2,39) = 18.89; p < 0.001, two-way ANOVA). Post-hoc analysis
revealed that there was a significant increase in 2-4Hz power for both WT (t = 2.84; p = 0.016)
and CaMKIV mice (t = 5.89; p < 0.001) relative to baseline on the conditioning day (Fig. 3.7B).
For the testing day, CaMKIV over-expressed mice had significantly elevated 2-4Hz power (t =
3.40; p < 0.005) compared to baseline. Overall, there was no effect of genetic manipulation on 2-
4 Hz power, so this frequency is unlikely to explain the learning and memory enhancement in
CaMKIV over-expressed mice.
There was a significant effect of trace interval on 4-7.5Hz power (F(2,39) = 21.45; p < 0.001,
two-way ANOVA) and a significant interaction between genetics and the trace interval (F(2,39) =
5.99; p = 0.005, two-way ANOVA-RM). Post-hoc analysis revealed that there was a significant
increase in 4-7.5Hz power for CaMKIV mice above baseline (t = 7.42; p < 0.001) for the
conditioning day and above the baseline for the testing day (t = 3.73; p < 0.002, Fig. 3.7C). In
addition CaMKIV over-expressed mice had a significant enhancement in 4-7.5Hz power over
WT mice during the conditioning day (t = 3.10; p = 0.004). These findings are consistent with
the enhanced learning and memory of the CaMKIV mice (Fig. 3.6) and may serve as a possible
mechanism of attention processing or memory encoding.
A significant alteration in 7.5-13Hz power was detected across the conditioning interval
(F(2,39) = 23.82; p < 0.001, two-way ANOVA). In addition, a significant interaction was detected
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between genetics and trace interval (F(2,39) = 4.46; p < 0.018, two-way ANOVA). Post-hoc
analysis revealed that 7.5-13Hz power was decreased in CaMKIV over-expressed mice
compared to baseline for the conditioning day (t = 5.78; p < 0.001) and the testing day (t = 6.58;
p <0.001, Fig. 3.7D).
3.7.4C-III EEG responses to shock and expected shock
Our previous studies have shown that CaMKIV over-expressed mice have normal
responses to acute pain (Wu et al., 2008). It is conceivable that CaMKIV mice have enhanced
cortical arousal in response to foot shock which could be detected with cortical EEG. This might
provide a possible explanation for enhanced learning and memory in these animals in the trace
fear paradigm. Thus, we analyzed whether or not EEG arousal responses to the foot shock
differed between animals (Figs. 3.7A to 3.7F). A significant alteration in 2-4Hz power was
detected for the shock/expected shock condition (F(2,39) = 15.69; p < 0.001, two-way ANOVA).
Post-hoc analysis revealed that 2-4Hz power was increased in CaMKIV over-expressed mice (t =
3.12; p <0. 0.010) during the expected shock test condition, compared to baseline (Fig. 3.7 B).
There was a significant alteration of 4-7.5Hz power following the shock/expected shock stimuli
(F(2,39) = 15.88; p < 0.001, two-way ANOVA) and a significant interaction with the genetic
manipulation (F(2,39) = 4.17; p < 0.023, two-way ANOVA). Post-hoc analysis revealed an
enhancement in 4-7.5Hz power (t = 4.16; p = 0.001) during the expected shock condition,
compared to baseline (Fig. 3.7C). In addition post-hoc analysis revealed that CaMKIV over-
expressed mice had significantly greater 4-7.5Hz power during the expected shock condition
(F(2,39) = 2.71; p < 0.009, two-way ANOVA). The increase of 4-7.5Hz power appeared to be a
result of the freezing behaviour which carried over into the expected shock interval. A significant
alteration in 7.5-13Hz power was detected during the shock/expected shock interval (F(2,39) =
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13.17; p < 0.001, two-way ANOVA). Post-hoc analysis revealed that 7.5-13Hz power was
decreased in CaMKIV over-expressed mice compared to baseline (t = 3.92; p = 0.001) during
shock, but was unaltered in WT mice (t = 0.22; p = 1.00, Fig. 3.7D). However, when WT mice
were compared directly to CaMKIV over-expressed mice, no significant difference was detected
(t = 0.55 p = 0.583), suggesting that their actual 7.5-13Hz EEG power responses to the shocks
are not likely to account for the enhanced learning and memory of CaMKIV mice. In conclusion,
based on our previous findings (Wu et al., 2008) and the current EEG responses, we suggest that
CaMKIV over-expressed and WT mice respond similarly to pain on the conditioning day but
differently to the expectation of pain on the test day
3.7.4D Four-7.5Hz oscillation enhancements parallel learning and can be localized to the
prelimbic cortex.
Since 4-7.5Hz oscillations are enhanced in CaMKIV mice during learning and memory,
above that of baseline and WT mice (Fig. 3.7), we expected that these oscillations might
represent neural correlates of attention or memory of the expected shock during the trace
interval. We therefore expanded our 4-7.5Hz power analysis to examine its time course as the
mice learned. Figure 3.8A and B depict the 4-7.5Hz activity across intervals of trace fear
conditioning and testing. There was a significant main effect of the conditioning interval on 4-
7.5Hz power (F(7,136)= 17.28; p < 0.001, two-way ANOVA). There was neither a main effect of
genetics (F(1,20)= 2.994; p = 0.099, two-way ANOVA) nor or interaction between genetics and
trace fear interval (F(7,136)= 1.97; p = 0.064, two-way ANOVA). Since these tests were nearly
significant and an interaction between genetics and trace condition was found in Figure 3.7C, we
ran post-hoc tests to see whether theta power learning curves were different between CaMKIV
and WT animals. Four-7.5Hz power was also significantly elevated in CaMKIV over-expressed
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mice above baseline during the testing phase (~24 hours later) (all p <0.05, as in Fig. 3.8A). The
increase in 4-7.5Hz power appeared to be related to a combination of increased voltage (Fig.
3.8B) and the number of occurrences during the trace interval. For WT mice, the 4-7.5Hz EEG
power increased during the conditioning but not the testing phase. This is the first evidence to
show that CaMKIV manipulation can enhance, in a parallel fashion, the expression of fear
behavior and 4-7.5Hz power.
To examine whether changes in 4-7.5Hz power were related to changes in freezing behavior
during the trace interval; the net change in 4-7.5Hz power from baseline, for every animal of
every trial, was quantified, plotted, and correlated against the change in freezing behavior from
baseline for the respective trace fear interval. A significant correlation was found between the
change in trace fear learning and changes in 4-7.5Hz power (r2 = 0.112, p < 0.001, Fig. 3.7C).
The results indicate that variation in freezing behavior (albeit a small amount) is related to
variation in 4-7.5Hz power.
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Figure 3.8 CaMKIV over-expression enhances 4-7.5Hz waves which can localize to the prelimbic cortex (A) Grouped data showing that WT (open circles) and CaMKIV over-expressed mice (closed circles) have significant enhancements in 4-7.5Hz rhythm (4-7.5Hz), during the trace fear interval, throughout the conditioning protocol (* is P<0.05 compared to baseline). This effect mirrored the learning-curve of these mice, which was significantly greater in CaMKIV over-expressed mice as compared to WT for both conditioning and testing (# is P<0.05). (B) Example EEG activity corresponding to panel A, which was band-pass filtered for visual inspection of 4-7.5Hz rhythms. High voltage 4-7.5Hz activity occurred during periods of freezing behavior and appeared to be more frequent and often larger in CaMKIV over-expressed mice. (C) Trial by trial changes in freezing during the trace interval were positively correlated to changes in 4-7.5Hz power during conditioning. (D) When WT mice are trained with a 15 sec trace fear interval, prominent 4-7.5Hz rhythms can be localized to the prelimbic cortex, during conditioning (* is P<0.05 compared to baseline) but not on the testing day. Values are means +/- SEM. (E) Trace fear conditioning (15 sec trace interval) learning curve for WT mice (* is P<0.05 compared to baseline). Notice the memory enhancement when compared to Fig 3. BL is baseline, and values are means + SEM.
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Since 4-7.5Hz frontal-parietal EEG power was increased during the trace fear interval,
we wanted to identify if this frequency band can to be localized to a particular brain structure.
Therefore, we chose to accentuate trace fear learning (analogous to the effect of CaMKIV over
expression) by reducing the duration of the trace fear interval in order to make the time between
shock and tone shorter. Since the prelimbic cortex is known to generate both theta rhythms
(Jones, 2005; Siapas et al., 2005; Paz et al., 2008) and be involved in trace fear conditioning
(Baeg et al., 2001; Gilmartin & McEchron, 2005a) we placed intracortical electrodes in this
region. Prelimbic field potential recordings were performed in WT mice under a 15 second trace
interval protocol to localize 4-7.5Hz rhythms. There was a significant effect of conditioning on
4-7.5Hz activity recorded in the prelimbic cortex (F(7,29) = 3.64; p < 0.006, one-way ANOVA).
Post-hoc analysis revealed significant enhancement of 4-7.5Hz rhythms during trace fear
conditioning (Fig. 3.8D). Consistently, there was a significant effect of conditioning on fear
behavior (F(7,35) = 13.61; p < 0.001, one-way ANOVA). Post-hoc analysis revealed significant
learning and memory above baseline (Fig. 3.8E). Thus when trace fear memory is accentuated in
WT mice by making the trace interval shorter, enhanced 4-7.5Hz rhythms can be localized to the
prelimbic cortex during conditioning. Thus, it is possible that the enhancements seen in CaMKIV
over-expressed mice with frontal-parietal EEG recordings would also localize to the prelimbic
cortex. Moreover, our results show that accentuation of trace fear learning and memory, whether
by CaMKIV over-expression or through manipulation of the trace fear paradigm, produces
prominent enhancement in 4-7.5Hz power. The finding that enhancement in prelimbic 4-7.5Hz
rhythms occurs during conditioning (15s protocol), but not testing contrasts with the finding that
CaMKIV show enhancements during both conditioning (30s protocol) and testing. The results
suggest that there may be additional structures, for example the hippocampus, which are
activated during trace conditioning and memory recall.
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3.7.4E CaMKIV over-expression impacts slow delta oscillations
Since sleep in the recording environment increased 2-4Hz EEG power, we decided to
examine the time course of these slow delta oscillations (1-4Hz) across the entire recording
session. Slow delta oscillations are thought to be related to sleep need (Borbely & Achermann,
2000) and are potentiated by learning paradigms (Huber et al., 2006). Slow delta oscillation
power was averaged for each hour starting when the lights were turned on, for pre- and post-
conditioning NREM sleep. For this analysis, separate statistical comparisons were made directly
between WT and CaMKIV over-expressed mice for each hour. There was a significant effect of
the genetic manipulation for the second (F(1,23) = 8.64; p = 0.007, two-way ANOVA), fifth (F(1,22)
= 9.52; p = 0.005, two-way ANOVA) and sixth hour (F(1,22) = 6.05; p = 0.021, two-way
ANOVA) (Figs. 3.9A and 3.9B). Further analyses revealed that CaMKIV over-expression
elevated slow delta oscillation power above WT mice during the pre-conditioning phase for the
second (t = 2.12; p = 0.034), fifth (t = 2.94; p = 0.007), sixth hour (t = 2.59; p = 0.015). This
effect may be due to the novelty to the recording environment or a selective enhancement in
brain waves that occurs during natural sleep.
We next examined the effect of trace fear conditioning on post-conditioning NREM
sleep. For this analysis, separate statistical comparisons were made directly between WT and
CaMKIV over-expressed mice for each hour. In addition we made separate comparisons between
pre and post-conditioning for each hour to examine interactions. While a similar trend for
elevated slow delta waves in CaMKIV over-expressed mice above that of WT was seen,
following trace fear conditioning, the trend did not achieve significance. However, a significant
interaction was detected for the first hour comparisons, showing that the level of slow delta
oscillation power depended, not only on whether the animal was trace fear trained but also on the
genetic manipulation (F(1,11) = 5.1; p = 0.045, two-way ANOVA).
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Figure 3.9 CaMKIV over-expression enhances slow delta oscillation power during NREM sleep before and after trace fear training (A) Example of EEG sleep recording in the first hour, after the lights had been turned on, in the recording chamber for both CaMKIV over-expressed mice and WT mice. However if the mouse had been trace fear conditioned, slow delta oscillation (1-4Hz) enhancements were detected in the recording for CaMKIV over-expressed mice but not WT mice. (B) Group data showing that CaMKIV over-expressed mice have elevated slow delta oscillation power above that of WT mice during NREM sleep in the recording chamber across an 8 hour span. A subset of the CaMKIV over-expressed and WT mice were then recorded after trace fear training during the same time points of the next day. CaMKIV over-expressed mice were found to have elevated slow delta oscillations above that of the first hour of their pre-conditioning. However, WT mice did not show a similar enhancement. * is P < 0.05 compared to WT for the corresponding hour. # is P <0.05 between pre and post-conditioning for the corresponding hour. (C) CaMKIV over-expressed and WT mice had similar 4-7.5Hz power during REM sleep prior to and following trace fear conditioning. 4-7.5HZ power was averaged across 4 hour time periods.
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Further analysis revealed that CaMKIV over-expressed mice have elevated slow delta oscillation
power when compared to their preceding pre-conditioning baseline for the first hour (t = 3.99; p
= 0.002). By contrast, WT mice did not show the same enhancement (t = 0.22; p = 0.827). This
result shows that genetic enhancement of learning and memory (Fig. 3.9A) also potentiates EEG
activity during subsequent sleep in CaMKIV over-expressed mice, linking genetics, sleep and
memory through the CaMKIV protein.
It was also examined whether REM sleep 4-7.5Hz power would be influenced following
trace fear conditioning. In contrast to the slow delta power analysis, in which 8 bins were used,
REM sleep was broken down into two bins (1-4 and 4-8 hours post-conditioning). This was done
because of the reliability of recording REM sleep for each individual hour was low. In contrast,
to the enhancements in slow delta oscillations during NREM sleep for CaMKIV over-expressed
mice, we found no effect of genetic manipulation on REM sleep 4-7.5Hz power before and after
training within the first four hours (F(1,20) = 1.07; p = 0.312, two-way ANOVA) and last four
hours (F(1,20) = 1.00; p = 0.330, two-way ANOVA) (Fig. 3.9C).
It is possible that the increase in delta power in CaMKIV over-expressed mice may be
secondary to an increase in the latency of falling asleep, following fear conditioning. There was
no significant effect of genetic manipulation on the latency to NREM sleep (F(1,14) = 0.775; p =
0.393, two-way ANOVA), and REM sleep (F(1,14) = 0.331; p = 0.574, two-way ANOVA)
following trace fear conditioning (Figs. 3.10A and 3.10B). Thus, the enhancement in slow delta
oscillation power after trace fear training cannot be accounted for by alterations in sleep latency.
It is also possible that CaMKIV over-expressed mice may have increased slow delta power
because their sleep is consolidated into a few bouts of very deep sleep, following trace
conditioning. Interestingly, we found a statistically significant interaction between genetics and
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NREM durations before and after trace fear training (F(1,13) = 6.86; p = 0.021, two-way
ANOVA). In contrast to our prediction, post-hoc analysis revealed that CaMKIV over-expressed
mice had more NREM sleep following trace fear conditioning (t = 2.23; p = 0.044). In WT mice,
NREM durations were not influenced by trace fear conditioning (t = 1.56; p = 0.142).
Additionally, REM sleep durations were not significantly different (F(1,14) = 0.244; p = 0.630,
two-way ANOVA) between WT and CaMKIV over-expressed mice (Figs. 3.10C and 3.10D).
The enhancement in delta power of CaMKIV over-expressed mice is further corroborated by the
enhancement in time spent sleeping following trace fear conditioning. Indeed the increase in
NREM sleep duration in CaMKIV over-expressed mice reinforces the concept that genetics,
sleep and memory may be linked through the CaMKIV protein. Thus, based on the findings we
expect that slow wave enhancements following trace fear training may be related to learning.
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Figure 3.10 Impact of trace fear conditioning on sleep duration and latency (A) The latency to enter NREM sleep before (pre) and after (post) trace fear conditioning was unaltered in CaMKIV over-expressed and WT mice. (B) The latency to enter REM sleep before and after trace fear conditioning was unaltered in CaMKIV over-expressed and WT mice. (C) WT mice showed a trend for decreased NREM sleep following trace fear conditioning, while CaMKIV over-expressed mice showed a significant increase in NREM sleep duration. * is P < 0.05. (D) The duration of REM sleep before and after trace fear conditioning was unaltered in WT and CaMKIV over-expressed mice.
3.7.4F S low delta oscillation enhancements are localized to the anterior cingulate cortex
Frontal-parietal EEG recordings yield a selective enhancement in 1-4Hz slow delta power
following trace fear conditioning for CaMKIV over-expressed but not WT mice (Fig. 3.9). Thus
we needed to examine whether WT mice can also demonstrate enhanced delta power following
trace fear conditioning. We chose to accentuate trace fear learning (analogous to the effect of
CaMKIV over-expression) in WT mice by reducing the duration of the trace fear interval, in
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expectation that this would accentuate slow delta power during subsequent NREM sleep.
Provided WT actually demonstrate enhanced delta power, we wanted to know whether it would
localize to a particular brain region. C-fos is up-regulated in the anterior cingulate cortex
following trace fear conditioning without any effect in the motor cortex (Han et al., 2003). These
results suggest that the ACC is involved in learning and memory processes associated with trace
fear conditioning. Thus we examined whether slow delta oscillations would be potentiated in the
ACC following trace-fear conditioning, indicative of memory consolidation.
Simultaneous bipolar intracortical field recordings were performed in the ACC and the
motor cortex of WT mice. The motor cortex served as a control for the ACC to examine
regionally selective slow oscillation enhancements. The behavioral paradigm for this second set
of experiments is identical to that outlined in Figure 3.4. We first standardized the location of the
recordings from the ACC to carry out this examination (Fig. 3.11). This experiment revealed that
optimal placement of the bipolar electrodes in the ACC was that straddling layers I-III, consistent
with others (Tsujimoto et al., 2006). This recording configuration was set up so as to minimize
volume conducted theta rhythms from the hippocampus and 60Hz noise from the external
environment. Data was normalized for the purposes of comparison between motor cortex and the
ACC. This was necessary as the voltage of the motor cortex was often much larger than that
recorded from the ACC. During sleep, in the recording environment, the normalized slow delta
oscillation power of the motor cortex and ACC mirrored one another across the 8 hour recording
period (Figs. 3.12A and 3.12B). Following trace fear conditioning, there was a significant
difference between the ACC and motor cortex (F(1,6) = 6.37; p = 0.045, two-way ANOVA-RM).
Post-hoc analysis revealed that there was an enhancement in slow oscillation power in the ACC
above that of the motor cortex (Figs. 3.12A and 3.12B, t = 2.52; p = 0.045, Bonferroni’s paired t
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test). In two cases, where bipolar electrodes did not straddle layers I-III (Fig. 3.13B for electrode
positions), the ACC slow delta oscillation power did not mirror that of the motor cortex during
Figure 3.11 Optimizing electrode implantation Four electrodes were implanted into the ACC. Differential recordings were performed between adjacent electrodes. Field potential voltage is most robust when straddling layers I-III of the anterior cingulate cortex (ACC). The polarity of the recordings is opposite for the upper trace and lower trace since the electrode crosses the midline. The differences in polarity are most evident in NREM sleep. Bars indicate 200µV calibration.
pre-conditioning sleep (Fig. 3.12C), and so were not used to examine regional field potential
enhancements. Collectively, the result indicates that trace fear conditioning selectively activates
brain regions of the ACC in layers I-III during subsequent NREM sleep.
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Figure 3.12 Trace fear conditioning increases slow delta oscillations in the ACC in layer I-III (A) Group data showing that the motor cortex and the ACC (layer I-III electrode) slow wave oscillation power paralleled one another, when their data were normalized to their average slow delta oscillation power (across 8 hours) on the first day. However, after the animals were trace fear conditioned the slow delta oscillation power significantly increased in the ACC over that of the motor cortex (* is P < 0.05 compared to motor cortex post-conditioning). (B) Example of the slow delta oscillation during NREM sleep, recorded in the motor cortex (MTC) and the ACC before and after trace fear conditioning. When the mouse goes to sleep there is an increase in slow delta oscillation power in the EEG of the ACC with only a modest increase in the motor cortex. (C) Electrodes that did not straddle layers I-III did not show and increase in slow delta oscillation power following trace fear conditioning. Indeed the motor cortex (MTC) served as a poor control for the ACC in these cases. This can be seen during the pre-conditioning session, in which the normalized slow wave oscillation power of the ACC and MTC activity did not overlap.
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Figure 3.13. Position of electrodes Position of electrodes, in the motor cortex (MTC) and the ACC, from experiments in Figure 3.13. Data from Figure 3.13 was grouped according to whether the electrodes were located within layers I-III (A) of the ACC or outside this region (B). If electrodes were in layers I-III, the normalized baseline sleep correlated with that of the motor cortex. (Indicated as correlated or uncorrelated).
3.7.4G Learning and theta rhythms but not slow delta oscillations are correlated with trace fear
memory formation.
We first examined whether measures of fear conditioning were related to the formation of
trace fear memory. Data from CaMKIV over-expressed and WT animals were grouped together.
A statistically significant positive correlation was found between the total time spent freezing
during the trace interval and the total time spent freezing during the trace interval of the testing
day (r2 = 0.188, p<0.039, Fig. 3.14A). Interestingly, a significant positive correlation was also
found between the percent increase in 4-7.5Hz power during the trace interval and the total time
spent freezing during the trace interval of the testing day (r2 = 0.188, p<0.039, Fig. 3.14B).
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Figure 3.14 Variations in learning and 4-7Hz oscillation power are related to memory formation (A) The average change in freezing during the trace interval was positively correlated with the average change in fear memory on the testing day. (B) The average change in 4-7.5Hz power during the trace interval was positively correlated with the average change in fear memory on the testing day. (C) The average change in slow delta oscillation power during post-conditioning sleep was not positively correlated with the average change in fear memory on the testing day.
The result indicates that some degree of variability in 4-7.5Hz power during the trace interval
accounts for a degree of variability in memory encoding. However, in spite of the enhanced slow
wave activity in CaMKIV over-expressed mice during the first hour of post-conditioning sleep,
no correlation was detected between the percent increases in slow wave activity and the fear
memory the following day (r2 = 0.0215, p<0.538, Fig. 3.14C). The result suggests that for the
trace fear paradigm, the most important factors controlling the variability of the memory is the
degree of freezing and the change in theta oscillation power during conditioning.
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3.7.5 Discussion
Our findings provide the first direct genetic evidence that enhancing CaMKIV expression
in the forebrain enhances 4-7.5Hz oscillations during trace fear conditioning and boosts slow
delta oscillations during NREM sleep. We also show that accentuation of trace fear learning,
through reduction of the trace fear interval, produces increases in oscillation power during both
learning and subsequent sleep. This enhancement could be localized to the frontal cortex which
is also likely to be the origin of the enhancements seen in CaMKIV over-expressed mice.
Finally, correlation analyses implicate 4-7.5Hz oscillations in both the learning of the trace fear
paradigm and the memory of the conditioned response 24 hours later.
3.7.5A CaMKIV and natural sleep
CaMKIV mRNA is up-regulated following sleep (Cirelli et al., 2004) and CREB
knockout mice (Graves et al., 2003) have reduced levels of arousal, spending more time in
NREM sleep. Thus, it could be predicted that CaMKIV over-expression may also impact
behavioral states of arousal and sleep. However, unlike CREB knockout mice, CaMKIV over-
expression did not alter the amount of time spent in any behavioral state. When EEG power was
quantified in the recording environment, CaMKIV over-expressed mice showed an increase in
slow delta oscillation power values, which persisted across the first 8 hours of the light period.
There are three simple interpretations for this finding; firstly, the enhancement may be related to
processing in the novel recording environment, which then leads to and increase in slow delta
oscillation power during sleep. Indeed we did find an increase in 7.5-13Hz power during
wakefulness when animal was exposed to the novel fear conditioning chamber. Interestingly, this
is the same frequency that was decreased in CREB knockout during REM sleep (Graves et al.,
2003). Consistently, preliminary studies in our lab suggest that when a 4 day acclimatization
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period (in which animals are hooked to the recording device in the recording environment) is
given to CaMKIV mice; the slow wave enhancements during baseline are not present. Future
studies, using sample sizes comparable to those used in the current study, will need to confirm
this possibility. Alternatively, reduced slow wave sleep is thought to correlate with both ageing
and memory impairment (Landolt et al., 1996; Carrier et al., 1997; Landolt & Borbely, 2001;
Hornung et al., 2005). Since CaMKIV over-expression has been found to rescue memory
impairment in aged mice (Fukushima et al., 2008), the enhanced slow wave delta power in the
recording environment may reflect some degree of rescue of age-related decrements in slow
wave oscillations. Finally, CaMKIV over-expressed mice may have an alteration in their sleep
homeostat. However, if this were the case it may be expected that these mice would sleep longer
or more frequently during baseline pre-conditioning sleep recordings, which they did not (Table
3.1). Indeed, our preliminary studies (not presented here) suggest that there is no obvious
difference in slow wave sleep recovery as a consequence of 4 hours of sleep deprivation, induced
with novel objects and gentle handling in CaMKIV mice.
3.7.5B CaMKIV and trace fear conditioning
We have previously shown that CaMKIV over-expressed mice have enhancement in trace
fear learning and memory (Wu et al., 2008). We replicated this finding, utilizing a new EMG-
based scoring methodology (Steenland & Zhuo, 2009) which permitted evaluation of the mouse
behaviour in low light conditions. This permitted study of these animals during their natural
sleep and wakefulness cycle. In parallel with trace fear conditioning curves, we found an
enhancement in 4-7.5Hz power, which could be localized to the prelimbic cortex. There is
evidence that these prefrontal 4-7.5Hz rhythms, which exist in mice, rats, monkeys and humans,
are involved in attention and learning processes (Tsujimoto et al., 2003; Jones & Wilson, 2005;
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Delorme et al., 2007; Sirota et al., 2008). Indeed, trace fear conditioning requires prefrontal
cortical structures and sustained attention for learning to take place (Baeg et al., 2001; Han et al.,
2003; Knight et al., 2004; Runyan et al., 2004; Zhao et al., 2005). Thus we interpret this finding
as an increase in attention to, or recall of, the impending shock stimulus in the trace fear
paradigm. Indeed, this data is consistent with the enhanced LTP observed in the prefrontal region
of CaMKIV over-expressed mice (Wu et al., 2008).
While the hippocampus is known to be a fundamental structure exhibiting ~8Hz rhythms
(Buzsaki, 2002), we suspect that that the prefrontal 4-7.5Hz enhancement observed during the
trace interval does not necessarily correlate with the typical 8Hz CA1 rhythms. Moreover, we do
not think that the 4-7.5Hz signal recorded during the trace interval was not simply volume
conducted by the hippocampus because; the bipolar recording electrodes were placed close
together (0.3 to 0.5mm tip offset) so as to subtract common mode signals (Tsujimoto et al.,
2006). Since the hippocampal theta would be volume conducted from a distance, the signal
would be presumed to reach the bipolar electrodes. This common signal would then be
subtracted out by the inverting and non-inverting inputs of our amplifier. Thus we don’t expect a
meaningful level of volume conduction is a factor. Finally, cells demonstrating theta modulation
have been detected in the prelimbic region of the rat prefrontal cortex in response to spatial
learning tasks (Jones & Wilson, 2005; Siapas et al., 2005; Paz et al., 2008). Thus, our results
support the notions that the prefrontal 4-7.5Hz rhythms detected here originate in the prefrontal
cortex.
To examine the degree to which the 4-7.5Hz oscillations were related to learning,
correlation analyses were performed. A significant correlation was found between theta power
enhancements and learning, confirming that this brain wave frequency was indeed related to the
freezing behavior. Since, both freezing behavior and 4-7.5Hz power both demonstrate a learning
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curve; it is reasonable to suggest that this 4-7.5 Hz frequency reflects some degree of memory
recall. Indeed, this is supported by the observation that these oscillations were localized to the
trace fear interval and not the shock period (Fig 3.7C).
3.7.5C CaMKIV over-expression enhances sleep after learning
Various studies have suggested that slow oscillations during sleep are related to memory
(Huber et al., 2004; Huber et al., 2006; Marshall et al., 2006; Vyazovskiy et al., 2008).
Moreover, brain derived neurotrophic factor (BDNF) injection in the cortex was found to
enhance local slow wave EEG activity in rats (Faraguna et al., 2008). There is also evidence that
slow delta oscillations are a consequence of GluR1 phoshorylation (Vyazovskiy et al., 2008) or
enhanced extracellular glutamate (Dash et al., 2009), possibly as a consequence basic
experiences during the day. We show that trace fear conditioning, which is enhanced by
CaMKIV over-expression, results in concomitant slow delta oscillation enhancement in the
cortex. Thus, it appears that CaMKIV, a key upstream molecule which phosphorylates CREB
(Enslen et al., 1994), can modulate the expression of slow delta oscillations as a consequence of
learning. Interestingly, slow delta oscillation enhancements could be localized to the anterior
cingulate cortex, predominating in layer I-III. Consistently, removal of the anterior cingulate
cortex impairs trace fear, but not delay fear conditioning (Han et al., 2003). In addition,
inactivation of the ACC, days after fear training, blocks fear memory retrieval (Frankland et al.,
2004). Also, it has recently been reported that neurons of the prefrontal cortex consolidate trace
eye-blink memory days after conditioning (Takehara-Nishiuchi & McNaughton, 2008). Thus,
CaMKIV mediated phosphorylation of CREB may be a major mediator of this consolidation, in
part through enhancement of local slow delta oscillations during NREM sleep.
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It is possible that differences in post-conditioning sleep expression may be related to
differences in arousal during the training period, and not brain plasticity. However we did not
report any increase in CaMKIV mice above that of WT mice in high frequency EEG range
during the training trials (for frequencies greater than 13Hz). The most prominent change was an
increase in 4-7.5Hz EEG power and a decrease in 7.5-13Hz power (Fig. 3.7) relative to WT.
Indeed, all EEG responses to pain stimuli were identical between CaMKIV over-expressed and
WT mice. Moreover, we have previously shown that CaMKIV over-expressed mice have normal
responses to acute pain (Wu et al., 2008). Finally, we show that the slow wave enhancement
have some regional specificity to the ACC when compared to the motor cortex. This region is
thought to be involved in the gradual consolidation of memory during sleep (Takehara-Nishiuchi
& McNaughton, 2008).
3.7.5D Variations in learning and 4-7.5Hz oscillation power are related to memory formation
We found that CaMKIV over-expression enhances fear memory when studied ~24 hours
after training. While the elevations in 4-7.5Hz power during conditioning and slow delta power
(1-4Hz) during NREM sleep are seen in CaMKIV mice, suggestive of learning and memory
processes, the data remain merely suggestive. Thus, it was necessary to examine whether the
magnitude of changes in learning, 4-7.5Hz oscillations, and slow wave delta power actually
correlated with the magnitude of memory formation. To demonstrate that this was a valid test,
we first showed that the magnitude of learning and memory were correlated. We next
demonstrated that the change in 4-7.5Hz power during learning was significantly correlated with
subsequent memory. Interestingly learning and 4-7.5Hz r2 values were of identical magnitude but
were quite small. The result indicate that much of the freezing during the conditioning phase may
be related a general state of fear in that context. In future studies it may be useful to examine
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how well trace fear conditioning predicts contextual fear memory expression. Interestingly, we
did not find any relationship between trace fear memory and changes in slow wave power. Based
on the findings we suggest that the best predictors of memory formation, at least for the trace
fear paradigm, are the degree of freezing and the change in 4-7.5Hz power during wakefulness.
This should draw attention to the importance of encoding information during wakefulness.
Indeed, a few studies have suggested that neural replay of past events even occurs in wakefulness
(Foster & Wilson, 2006; Karlsson & Frank, 2009). The current study does not discriminate
whether the observed oscillations during the trace interval reflect a point of memory
consolidation, recall or attention. However experiments examining post-training sleep
oscillations suffer from the same confound, for which there is no direct behavioral correlate. Unit
recording of neurons which demonstrate learning, combined with local pharmacological
manipulations of relevant plasticity pathways, may help resolve this issue. Taken together,
studies which examine the interactions of sleep and memory may benefit from examining brain
waves changes occurring during wakefulness in addition to brain wave changes during sleep.
One difference between the current study and other studies of learning and NREM sleep;
is that our paradigm impacts the stress levels of an animal, which may counteract the depth of
sleep. Thus we may be measuring two processes, one of stress induced sleep prevention and the
other of sleep potentiation. Indeed, this is why using the motor cortex as a control electrode,
when recording from the anterior cingulate is ideal. In such a situation, the null hypothesis would
be that both regions of cortex will respond similarly to trace fear conditioning. Irrespectively, we
found that slow wave activity in the ACC was potentiated above that of the motor cortex. Based
on the observations, the slow delta oscillation power enhancements seen in this study may be
related to either learning or memory or even a build up of extracellular glutamate left in the
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synaptic cleft from the day’s events (Dash et al., 2009). Future experiments will have to clarify
its precise role.
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CHAPTER 4
DISCUSSION AND FUTURE DIRECTIONS
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4.1 Integrated discussion
Chapter 1 showed that a standard EMG recording was sufficient to score freezing
behaviour. In chapters 2 and 3 this method proved useful to examine trace fear memory. Its
utility lies in the fact that EMG-based freezing can be examined directly in parallel with brain
waves and brain cells. Moreover, this technique could be used in the dark (chapter 3) to examine
freezing behaviour during the natural wakefulness cycle of the animal. The main drawback of
using such a method is that it requires surgery and there were no automated analyses programs.
However, toward the end this thesis, I developed an automated fear scoring program, through
programming in the script language of the Spike2 software. A variety of other programs were
devised to analyze brain cell counts and EMG evoked potentials, all of which were based on
registering the recording of interest with that of the EMG.
Recording inside a shock chamber is more likely to produce 60Hz noise on neck
electrodes if the electrodes aren’t well impedance matched. The simplest way to fix this problem
was to ensure that both neck EMG electrodes were secured well to the neck muscles and that
their impedances are relatively similar before implantation. This increases the rejection of
common signals to both electrodes on the instrumentation amplifier (i.e. 60 Hz).
Another important point regarding technique is that bilateral neck recording should
theoretically be recording the same activity, and therefore produce a zero signal for the EMG.
This is one likely reason for the reduction in muscle tone during freezing behaviour. If both left
and right neck muscle contract similarly, and at the same time during freezing, the net effect of
the recording will be a flat signal. However, when the animal moves, one muscle is likely to
contract more than the other (as the head may turn left or right), in which case an increase in
muscle output is indicated. Regardless, this is the typical method to record EMG in rodents for
sleep studies and is sufficient for the experiments presented.
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Chapter 2 shows that tone-evoked potentials in the ACC were a good predictor of future
freezing behaviour during trace fear conditioning. It is important to note that these potentials
were not likely related to change in motor movement, as neck EMG was not much changed by
the presentation of the tone. Indeed, neuron activity also appears to be related to the presentation
of the cue in the ACC. However, when an animal was found to be spontaneously freezing, we
found that neuron activity would become active just before and during the termination of
freezing. Again, neck EMG was used as a trigger point for this analysis to determine precisely
when the animal stopped freezing. If neck EMG analysis were not used, one might interpret the
ACC activity as being completely decoupled from motor output, being somehow related to the
rather nebulous concept of “affect”. Even if the concept of affect were used, motor recordings
would be helpful to provide a direction or trajectory of the animals intention.
It was found that the ACC could be activated by peripheral stimulation during anesthesia
experiments. Thus, the ACC is expected to process information from the periphery, at least
eventually. It remains to be shown which layers of the ACC receive what input and produce what
output. The experiments conducted in chapter 2 could not precisely locate the layers that were
recorded from, since the mouse brain is very small and a layer can be passed quite quickly. Thus,
future studies will need to identify layers which are important for motor and non-motor
functions. It would also be interesting to see whether graded electrical stimulation to the foot
could produce graded activation in the ACC in awake animals. For example, if 100uA current
were applied to the foot, it may not produce pain, but may cause “discomfort” in which case it
may not be sufficient to elicit motor response. By contrast, a higher level of electrical shock (e.g.
750uA) would produce pronounced behavioural activation (jumping or orienting toward the leg
that was shocked). Neurons could then be recorded across different layers of the ACC to see at
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what intensity different networks are engaged. Moreover, in such a model, EMGs could be used
to quantify the degree of behavioural activation.
One of the major drawbacks to the experiments in chapter 2 is that this style of trace fear
paradigm does not permit repetitive training for one cell for more than 4 trials. The difficulty in
doing such experiments is that shocking the feet of the animal produces dramatic behavioural
activation, and may therefore result in loss of cell recording (for example if the animal bumps its
head). A change in design is likely needed. For example, it appears to be helpful to stimulate one
foot with a cuff electrode instead of stimulating all four feet. Reducing the shock intensity may
be helpful; however after surgery animals don’t appear to learn as well as do animals that have
never had surgery (unpublished observations) so reducing shock intensity may not be practical.
While chapter 1 and 2 highlight the periaqueductal gray as a possible site where the ACC
may exert control over the motor system, there is also evidence for the ACC connecting with the
striatum and motor cortices (chapter 2). Thus, connecting the ACC of the mouse with the
appropriate motor effectors may require careful examination using anterograde tracers in
combination with identified neuron hot-spots in the ACC.
That there was an overall decrease in pyramidal neuron activity during the trace fear
interval in the ACC comes as a surprise, especially because c-fos is elevated in the ACC during
trace fear conditioning (Han et al., 2003). However, we do show that the ACC neuronal activity
is increased at points where the animal is shocked and when the animal hears cue-related
information. It is possible that there are cell populations in the ACC which are activated during
the trace interval, but were not recorded. It is possible that these cells would be related to holding
memory online during the trace interval in expectation of shock. Based on the observation of the
electrode tracts, one may be more likely to encounter cells of this type near the midline of CG1.
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In chapter 3 we show that genetic enhancement of CaMKIV also boosts 4-7.5Hz rhythms
during wakefulness and 1-4Hz rhythms during sleep. This finding is interesting because it is the
first to link a genetically enhanced “smart” mouse with cortical oscillations, and learning and
memory. However, the majority of the neurons recorded in the ACC showed depressed activity
during the trace fear interval coincident with increased 4-7.5Hz rhythms. Thus, we suggest that
the major structure in the preferential cortex exhibiting 4-7.5Hz oscillations is the prelimbic
cortex which tended to maintain these oscillations in parallel with fear behaviour. Interestingly
the ACC was found to have increased tone-evoked potentials which may account for the
potentiation of slow wave power in the ACC during NREM sleep. However, similar to the ACC,
the prelimbic cortex has also been shown to have enhancements in tone-evoked potentials with
fear learning (Mears et al., 2009). It remains to be shown whether the prelimbic cortex will
demonstrate enhancements in 1-4Hz potentials during sleep following trace fear conditioning.
Since the ACC is a site of remote memory storage (Frankland et al., 2004; Teixeira et al.,
2006; Ding et al., 2008), it would be of interest to examine whether slow waves in this area are
associated with remote memory formation or consolidation. However, it should be noted that the
ACC does not receive direct connections from the hippocampus (chapter 2), so a mechanism
involving the hippocampus in memory transfer to the cortex may first require the prelimbic
cortex which is known to receive a direct connection from CA1 (chapter 2).
The experiments in chapter 1 and 2 did not use delay fear as a control, nor did they use a
neutral tone as a cue control. Accordingly, the changes observed in field potentials, EEG and
brain cells are not conclusively related to processes that occur in the trace fear paradigm alone.
However, analyses of fear were often restricted to the trace interval where neither the tone nor
the shock is present. Thus, activity which occurs during this interval, whether it be field potential
or brain cells or EMG- freezing should represent that of trace fear conditioning. In any case the
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addition of these controls to the experiments would be helpful to clarify what is specifically
related to trace fear and what could be related to simpler versions of fear conditioning.
4.2 Future Directions
Although the results of this thesis do not directly support the role of sleep and memory
processes, this field remains intriguing to me. This section will detail future directions I have
begun to take with this research.
4.2.1 Theme 1: Memory associations promoting wakefulness compete with sleep
Central question:
How can single memories keep us awake and does replay during wakefulness prevent
sleep?
Can we record individual neurons getting “sleepy”?
Rationale:
The ability to stay awake in anticipation of impending danger (e.g. pain ) or reward (e.g.
sex) has not been explored. Maintaining a wakefulness state in order to respond to danger (e.g.
trace fear memory), may be precipitated by memories which originate in the cortex.
The thalamocortical system is commonly thought to be involved in switching the cortex
to different states of wakefulness and sleep (Steriade, 2003); however, there is some suggestion
that cortiofugal projections outweigh that of corticothalamic projections (Deschenes et al., 1998).
Consistently, McCormick and von Krosigk (1992) demonstrated that stimulation of the
projection neurons from the cortex to the thalamus in-vitro, can change the thalamic bust mode
(reminiscent of sleep) to a transmission mode (reminiscent of wakefulness). In addition a recent
169
study has shown that antagonism of adenosine receptors in the prefrontal cortex can enhance the
time spent awake and reduce cortical synchronization (Van Dort et al., 2009). Thus, the top down
regulation of sleep has been demonstrated; however no model has examined whether memory
activation, related to the anticipation of a negative future event could maintain wakefulness in
spite of a competing drive to sleep. Since the trace fear memory paradigm involves making the
association of a shock and tone across a time span, it represents a unique opportunity to examine
competition between memory activation in anticipation of danger and the drive to sleep. Only
one study has shown that testing trace fear memory during NREM sleep in humans can reactivate
memory during sleep (Wamsley & Antrobus, 2009). However, a slight variation of this paradigm
might involve reactivation of the memory trace when an animal is sleepy (or falling asleep rather
than sleeping) to examine whether or not memory could compete with sleep drive. (I have done
some preliminary studies and it appears that reactivation of trace fear memory can help maintain
wakefulness when there is sleep pressure (compared to when shock and tone stimuli are
unpaired).
It has been postulated that long-range cortical-cortical connections are involved in the
maintenance of wakefulness (Massimini et al., 2005). Thus synaptic plasticity involving long
range connections may provide the strength to resist sleep or entrainment to slow oscillations. In
fear paradigms involving the pairing of a shock and tone, the auditory cortex (Quirk et al., 1997)
and prefrontal cortex will undergo plasticity (Baeg et al., 2001; Takehara-Nishiuchi &
McNaughton, 2008), as measured with brain cell recording. Indeed there is some evidence of
monosynaptic connections between these regions (Vaudano et al., 1991). If monosynaptic
cortico-cortical plasticity can oppose sleep, then auditory-frontal cortical connections in the trace
fear paradigm may be a suitable model to test this.
170
I hypothesize that cortical-cortical synaptic plasticity is likely to antagonize the drive to
sleep.
Experiments
1. Examine whether neurons involved in memory are resistant to gradual slow wave entrainment
during sleep onset as compared to other neurons.
2. Examine whether neuron pairs (prefrontal to auditory cortex or even hippocampus), which
show strong connections (cross-correlation) and are related to the memory of interest resist
entrainment to slow waves.
3. Attempt to boost memory pharmacologically or genetically and examine whether or not this
treatment impacts experiment 1 or 2.
4.2.2 Theme 2: The role of the cholinergic system in memory consolidation during sleep
Central question:
Is the cholinergic system permissive for memory consolidation during NREM sleep?
Rationale:
Neural activity is replayed in the hippocampus and prefrontal cortex during NREM sleep
(Wilson & McNaughton, 1994; Skaggs & McNaughton, 1996; Euston et al., 2007). In addition,
local manipulations, which induce synaptic potentiation in a specific cortical area, result in
higher slow wave activity in that area (Marshall et al., 2006; Hanlon et al., 2009), while
disruption of SWS impairs memory consolidation (JAeschbach et al., 2008). However, it is not
known whether local enhancements in slow waves are tied to alteration in neuron replay during
sleep. There is evidence that sharp wave ripple events in the hippocampus are related to neural
replay in the cortex (Wierzynski et al., 2009) and may be involved in the transition from the
171
down to the upstate in the neocortex (Battaglia et al., 2004). Since these findings are largely
correlative in nature, it may be of use to directly manipulate neural replay and slow waves to see
if they correspondingly change. Such manipulation may explain why the neural replay does not
persist for the whole night of sleep and is rather restricted to the first few hours.
Cholinergic neurons are crucial for synaptic plasticity, learning and sleep regulation and
their activity is high in waking and low during slow wave sleep (Jones, 1993; Vazquez &
Baghdoyan, 2001). In humans, enhancing cholinergic function during slow wave sleep via
systemic injection of physostigmine impairs declarative memory formation (Gais & Born, 2004).
In addition, PPT stimulation has been shown to produce cortical desynchronization (Steriade et
al., 1993a). It has more recently been shown that nicotine increases the threshold for spike-
timing dependent plasticity through an increase in GABAergic activity in the prefrontal cortex
(Couey et al., 2007).
I hypothesize that the low cholinergic tone in the cortex during NREM sleep is necessary
for consolidation of memory.
Experiments:
1. Use a combination of microdialysis, brain wave and spike recording to directly manipulate the
interaction between brain waves and brain cell activity. Use Acetylcholinesterase inhibitors to
increase endogenous levels of acetylcholine.
2. Can examine if this cholinergic stimulation during sleep disrupts memory.
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4.2.3 Theme 3: The role of protein synthesis in transfer of memory to the cortex
Central question:
Is new protein synthesis required for sleep-related memory consolidation?
Rationale:
Sleep has also been implicated in memory consolidation processes as evidenced through
examination of neural replay during sleep (Couey et al., 2007) and slow wave potentiation
(Marshall et al., 2006; Hanlon et al., 2009). However, no studies have attempted to unite the
requirement of new protein synthesis with memory consolidation during sleep.
In-vivo, the cortical up-states are likely initiated via thalamo-cortical triggering
mechanisms (Steriade, 2003), possibly permitting sharp wave communication from hippocampus
to cortex during down- to up-state transitions (Battaglia et al., 2004). Indeed the interplay
between hippocampus and cortex is thought to be involved in information transfer from a labile
hippocampus to a more fixed remote memory in the cortex. Some attempt has been made to
show that interacting prefrontal and hippocampal networks function on a time scale which could
permit spike-timing synaptic plasticity with a 100msec time window for coincident firing
(Wierzynski et al., 2009). Under In-vitro conditions spike-timing dependent plasticity depends
on the activation of the NMDA receptors (Bi & Poo, 1998), and for LTP or LTD to occur, the
timing of the spikes must be within 20 msec. While this is an attractive mechanism for synaptic
plasticity (Bi & Poo, 1998), the molecular candidates responsible for this plasticity in-vivo have
not been tested and verified.
Spike-timing dependent plasticity requires calcium influx to activate the CaMKII
pathway (Wang et al., 2005) which may lead to protein synthesis and postsynaptic GluR1
insertion (Pickard et al., 2001; Man et al., 2007). Consistently, CaMKII has been implicated in
173
the storage of remote fear memory in the ACC (Frankland et al., 2004). In addition, it is known
that NMDA receptors are required for the induction of remote memory for trace eye-blink
conditioning (Takehara-Nishiuchi et al., 2006). It has also been shown that application of protein
synthesis inhibitors immediately after trace fear training can affect the recall of fear memory 30
days later (Blum et al., 2006). Moreover, a recent study has shown that the blockade of protein
degradation in the CA1 region of the hippocampus prevents reconsolidation of memory (Lee et
al., 2008). Finally, it is known that sleep typically down regulates proteins, GluR1
phosphorylation and CaMKII (Vyazovskiy et al., 2008), suggesting an apparent paradox for
sleep and memory consolidation. Thus, the necessity of NMDA receptor activation, calcium
entry and CaMKII activity, protein synthesis Vs degradation, GluR1 insertion and
phosphorylation, during sleep-related neural replay in prefrontal and hippocampus has never
been explored.
I hypothesize that the molecular alterations during sleep are tied directly to memory
consolidation during sleep as evidenced by neural replay during sleep.
Experiments:
1. Examine the effect of NMDA antagonism on neural reactivation during sleep and on slow
wave potentiation. This requires the development of fluid-delivery mechanism which is rapid and
has no pressure artifacts.
2. Examine the effect of protein synthesis block on neural reactivation during sleep and on slow
wave potentiation.
3. Can protein levels be tested during sleep? Can brain tissue be removed in a sleeping animal?
174
4.2.4 Theme 4: Can a cortical down state be reversed to an upstate
Central question:
What do cortical up- and down-states confer to memory consolidation during sleep?
Rationale:
It has been proposed that cortical up-states during NREM sleep are fragments of
wakefulness (Destexhe et al., 2007). While these up-states are thought to be triggered by
thalamocortical circuitry (Steriade, 2003), their regulation is still becoming resolved. During
slow wave sleep there is a lack of sensory responsiveness, indicating that neural networks during
sleep are largely closed off from the external environment; however, the cortex still coordinates
with the hippocampus during sharp waves/ripples in NREM sleep (Battaglia et al., 2004). Indeed
these ripples predominate during the neocortical downstate (Battaglia et al., 2004). Thus, the
thalamocortical circuitry might only allow a window of opportunity for the hippocampus to
communicate with the cortex (for example, during the transition to the upstate). If this hypothesis
is indeed the case, reversing sleep oscillations locally may expand a time window whereby the
hippocampus could communicate during an expected down-state.
Caesium injection into pyramidal neurons, to block potassium channels, has been found
to reverse the hyperpolarization during the cortical down-state, implicating potassium channels
as a candidate mechanism for the termination of the cortical up-states (Metherate & Ashe, 1993).
Moreover, it was discovered that Kv1.2 potassium channel knockout mice have reduced NREM
sleep due to an increased incidence of wakefulness (Douglas et al., 2007), while Kv3.1 mutants
have an increase in gamma rhythms and a reduction in slow wave power (Joho et al., 1999).
Collectively these results implicate an important role of potassium channels in the maintenance
of the cortical down-state. Since gamma rhythms are at their nadir during NREM sleep, abolition
of the down-state would require a restoration of gamma rhythms. There is direct evidence that
175
parvalbumin-containing interneurons in connection with pyramidal neurons are involved in the
generation of gamma rhythms (Sohal et al., 2009). The parvalbumin cells are also found to be
labelled by Kv3.1b (Sekirnjak et al., 1997). Thus, potassium channels in both pyramidal and
interneurons may be a useful target.
Based on the literature, it appears that the Kv3.1 potassium channel opening may lead to
hyperpolarization of pyramidal neurons and may also impact parvalbumin-containing
interneurons controlling gamma rhythms.
I hypothesize that modulation of this potassium channel may permit a localized region of
the cortex to decouple from the basic slow wave entrainment similar to wakefulness, at
which point the impact on neural replay could be observed during the “expected’ cortical
down-state.
Experiments:
1. Find pharmacological methods to locally produce blockade of slow wave oscillations.
This would show that we can sleep-deprive one region of the brain in the intact animal and de-
fragment slow wave sleep into a continuous wakefulness.
2. Examine whether neural replay and its compressions will still occur when the cortical
oscillations are interfered with (and whether they occur during an expected down-state).
3. Example methods include: developing in-vivo intracellular recording and caesium injection.
176
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