Universitätsklinik, Radiologische Klinik, Medizin Physik an der Albert-Ludwigs-Universität Freiburg im Breisgau

Motion Processing in MT+ and Structural Connectivity in

SPEM and MT+

INAUGURAL-DISSERTATION zur

Erlangung des Medizinischen Doktorgrades der Medizinischen Fakultät

der Albert-Ludwigs-Universität Freiburg i.Br.

Vorgelegt 2010 von Qing Mao

geboren in Henan, China

Dekan Prof. Dr. Dr. Hubert Erich Blum

1. Gutachter Prof. Dr. Jürgen Hennig

2. Gutachter Prof. Dr. Irina Mader

Jahr der Promotion 2010

Acknowledgements

This thesis would not have been possible without the assistance of many

people whose contributions I gratefully acknowledge.

My deepest gratitude is to my advisor, Prof. Dr. Jürgen Hennig for the

encouragement, guidance and support of this thesis. I am also very grateful to

I am especially grateful to Dr. Sabine Ohlendorf (Freiburg/Münich) for making

this thesis possible and for her generous guidance and support, I learned very

much from her!

I am grateful to my colleagues Dr. Pierre Leven, Dr.Kuanjin Lee, Dr. Thomas

Lange, Dr. Hsulei Lee, Benjamin Zahneisen Thimo Grotz and Dr. Daniel

Gallichan,for correcting this dissertation, and Dr. Marco Reisert and Susanne

Schnell for their suggestions and guidance in the fiber tracking study. I also

grateful to Constantin Anastasopoulos who gave me lots of suggestion in fiber

tracking, and I owe a special note of gratitude to my former colleague Rainer

Bögle (Freiburg/Munich) who supported me in many issues such as the

smooth pursuit experiment as well as dissertation corrections, and Kun Zhou

(Freiburg/Beijing) who invested considerable effort in the stimulus program.

Furthermore my special thanks go to Ms Laurence Haller, Prof. Dr. Kurt

Fritzsche in Psychosomatic Medicine und Psychotherapy Department of

Freiburg University Hospital and his wife, Mrs. Geneviève Plasson. They gave

me a lot of care in the past one and half years.

I would like to express my gratitude to all colleagues of the Medical Physics

Department who made it possible to work in a very open and friendly

atmosphere.

I owe my deepest gratitude to my wife and my parents, and the other family

members, this thesis would not have been possible without their persistent

support and encouragement.

And last but not least, I appreciate all subjects in my study, thereby offering

the opportunity to carry out this study.

Abbreviation

BA Brodmann Area

BOLD Blood Oxygen Level Dependency

CBF Cortical Blood Flow

CBV Cortical Blood Volum

EEG Electroencephanlography

EPI Echoplanar Imaging

EOG Electrooculography

FEF Frontal Eye Fields

FST Fundus area of Superior Temporal

FS Fiber Tracking

fMRI functional Magnetic Resonance Imaging

FEW Family Wise Error

GLM General Linear Model

GM Grey Matter

HRF Haemodynamic Response Function

IPL Inferior Parietal Lobule

IPS Inferior Parietal Sulcus

LGN Lateral Geniculate Nucleus

LIP Lateral Intraparietal area

LOC Lateral Occipital Complex

MNI Montreal Neurological Institute

MR Magnetic Resonance

MST Middle Superior Temporal

MT Middle Temporal

MT+ Middle Temporal Complex

pCG posterior Cingulate Gyrus

PCU Precuneus

PET Positron Emission Tomography

PIP Posterior Intraparietal area

PMd dorsal Premotor Cortex

PPC Posterior Parietal Cortex

RF Radio Frequency

ROI Region Of Interest

SC Superior Colliculus

SEF Supplementary Eye Fields

SMA supplementary Motor Area

SnPM Statistical non Parametric Mapping

SPEM Smooth Pursuit Eye Movements

SPL Superior Parietal Lobule

SPM Statistical Parametric Mapping

STS Superior Temooral Sulus

V1 Primary visual area

V2 Visual area 2

V3 Visual area 3

V4 Visual area 4

VIP Ventral Intraparietal area

WM White Matter

1

Chapter 1. General Introduction..................................................................................... 1

1.1 Basic features of MT+ and its subregions ................................................................ 2

1.1.1 MT+ ................................................................................................................... 2

1.1.1.1 MT+ location .............................................................................................. 2

1.1.1.2 MT+ properties ........................................................................................... 2

1.1.2 MT...................................................................................................................... 3

1.1.2.1 MT localization........................................................................................... 3

1.1.2.2 MT properties.............................................................................................. 3

1.1.3 MST ................................................................................................................... 4

1.1.3.1 MST localization......................................................................................... 4

1.1.3.2 MST properties ........................................................................................... 5

1.2 Smooth pursuit eye movements ................................................................................ 6

1.2.1 SPEM introduction............................................................................................. 7

1.2.2 Pursuit-related activity recorded other parts of the cortex................................. 7

1.2.3.1 Frontal eye fields (FEF) .............................................................................. 8

1.2.3.2 Supplementary eye fields (SEF) ................................................................. 9

1.2.3.3 Posterior parietal cortex (PPC) ................................................................. 10

1.3 Optic flow ............................................................................................................... 11

1.3.1 Optic flow introduction.................................................................................... 11

1.3.2 Optic flow processing in the cortex ................................................................. 11

1.3.3 Optic flow processing in humans..................................................................... 12

1.4 fMRI........................................................................................................................ 13

1.4.1 BOLD introduction .......................................................................................... 14

1.4.2 Approaches to fMRI data analysis................................................................... 15

1.5 Structural connectivity determination by means of DTI based fiber tracking........ 15

1.5.1 Fiber tracking................................................................................................... 16

1.5.2 Combining fiber tracking with fMRI............................................................... 17

1.6 Aim of this study..................................................................................................... 17

Chapter 2: Bold activation response of MT+ subregions to optic flow stimulations at

different locations of the visual field ............................................................................. 18

2. 1 Materials and methods ........................................................................................... 18

2

2.1.1 Subjects ............................................................................................................ 18

2.1.2 Eye movement measurements.......................................................................... 18

2.1.3 MR Imaging ..................................................................................................... 19

2.1.4 Visual stimulation ............................................................................................ 20

2.1.4.1Visual stimulation patterns ........................................................................ 20

2.1.5 fMRI methods .................................................................................................. 22

2.1.5.1 fMRI data analysis .................................................................................... 22

2.1.5.2 CARET analysis........................................................................................ 24

2.2 Results..................................................................................................................... 25

2.2.1 Eye Movement results...................................................................................... 25

2.2.2 fMRI results ..................................................................................................... 25

2.2.2.1 BOLD activation response to optic flow at different locations of the visual

field ....................................................................................................................... 25

2.2.2.2 Activations resulting from optic flow stimulation at different locations.. 26

2.2.2.3 Spatial relationship of cortical area........................................................... 28

2.3 Discussion ............................................................................................................... 31

2.3.1 Upper and lower visual fields stimulation ....................................................... 33

2.3.2 Vertical and horizontal visual fields stimulation ............................................. 34

2.3.3 Peripheral and central visual field stimulation................................................. 34

Chapter 3: Regions of cortical response to SPEM and motion, and the possible

structural connectivity in SPEM and motion sensitive cortex.................................... 36

3.1 Materials and methods ............................................................................................ 36

3.1.1 Subjects ............................................................................................................ 36

3.1.2 Eye movement measurements.......................................................................... 36

3.1.3 MR Imaging ..................................................................................................... 37

3.1.3.1 MR Imaging of SPEM .............................................................................. 38

3.1.3.2 MR Imaging of visual motion processing in MT+ ................................... 38

3.1.3.3 MR Diffusion Tensor Imaging.................................................................. 39

3.1.4 Visual Stimulation ........................................................................................... 39

3.1.4.1 Visual stimulation of SPEM ..................................................................... 40

3.1.4.2 Visual motion stimulation of MT+ ........................................................... 40

3

3.1.5 fMRI data analysis ........................................................................................... 41

3.1.6 Possible structural connectivity revealed by fiber tracking ............................. 42

3.1.6.1 Definition of ROIs seed ............................................................................ 42

3.1.6.2 Analysis of possible structural connectivity by fiber tracking.................. 43

3.2 Results..................................................................................................................... 43

3.2.1 Eye movement results ...................................................................................... 43

3.2.1.1 Results of SPEM....................................................................................... 43

3.2.1.2 Result of stimulation of motion sensitive MT+ ........................................ 44

3.2.2 fMRI results ..................................................................................................... 44

3.2.2.1 Cortical activation of SPEM ..................................................................... 44

3.2.2.2 Cortical activation of visual motion stimulation....................................... 47

3.2.3 Possible structural connectivity between SPEM areas and MT+ complex...... 50

3.3 Discussion ............................................................................................................... 52

3.3.1 SPEM and motion sensitive cortex .................................................................. 52

3.3.1.1 SPEM ........................................................................................................ 52

3.3.1.2 Functional role of MT+ in SPEM ............................................................. 53

3.3.1.3 FEF and SEF function in SPEM ............................................................... 53

3.3.1.4 PPC function in SPEM.............................................................................. 54

3.3.2 Motion processing in the area MT+................................................................. 54

3.3.3 Structural connectivity in SPEM and motion sensitive cortices...................... 54

3.3.3.1 Fiber tracking in MT+............................................................................... 54

3.3.3.2 Fiber tracking in PPC................................................................................ 55

3.3.3.3 Fiber tracking in FEF and SEF ................................................................. 55

3.3.3.4 Fiber tracking in the functional context of MT+ ...................................... 55

Chapter 4: Abstract ........................................................................................................ 57

Chapter 5: References .................................................................................................... 59

1

Chapter 1. General Introduction

A seemingly simple task like walking in an empty corridor without hitting the wall

becomes very difficult when asked to do so blindfold (Berg 2000). Toddlers, who

have just learned to walk, tip over when the walls of a movable room are set into

motion (Stoffregen et al. 1987). It is clear that visual motion plays an important

role in those activities. As for visual activation, optic flow is the pattern of apparent

motion of objects, surfaces and edges in a visual scene caused by the relative

motion of an observer (an eye or a camera) with respect to the scene. Motion

estimation and video compression have developed into a major aspect of optic

flow research (Lee 1980).

Motion-sensitive regions, especially in the primate (e.g. human or monkey) brain,

have been described in the posterior part of the medial temporal gyrus adjacent to

the superior temporal sulcus MT+ (human homologue of the monkey middle

temporal and medial superior temporal parietal area MT/MST). The MT+ area is

characterized by its high proportion of direction-selective and motion-sensitive

neurons, direct input from primary visual cortex, retinotopic representation of the

entire contralateral hemifield, and heavy myelination (Dubner and Zeki 1971; Zeki

1974; Ungerleider and Desimone 1986a; Ungerleider and Desimone 1986b;

Maunsell and Van Essen 1987). Areas MT and MST contain many neurons

sensitive to the direction of motion of the stimulus either in the frontal plane, in

depth, or in both (Dubner and Zeki 1971; Maunsell and van Essen 1983; Tanaka

et al. 1986; Ungerleider and Desimone 1986a; Ungerleider and Desimone 1986b).

Neurons in some of these areas are also known to be sensitive to binocular

disparity (Zeki 1974; Maunsell and van Essen 1983a; Maunsell and Van Essen

1983b; Maunsell and Van Essen 1987; Newsome et al. 1988), and neurons in

different portions of MST play a role in visuomotor processing (Newsome et al.

1988; Komatsu and Wurtz 1988a; Komatsu and Wurtz 1988c; Kaas and Morel

1993; Cusick et al. 1995; Stepniewska and Kaas 1996; Felleman et al. 1997). MT

2

and MST are reciprocally connected to more peripheral portions of V2 and V4,

enabling the extraction of motion information (Ungerleider 2005).

In many studies retinotopic activation of primary visual areas has been

investigated (Vanni et al. 2006). The processing of the location of visual motion in

MT+ remains largely unclear since retinotopic mapping of MT+ in human single

subjects was only partially successful (Huk et al. 2002). Therefore, a general

conclusion of human retinotopic mapping of MT+ and its subregions is still difficult

to get. Functional responses in the MT+ complex and its subregions to visual

motion stimulations at different locations of the visual field, and the relationship of

motion sensitive cortical areas to other cortical areas in terms of structural

connectivity are still unclear.

Based on previous studies (Ohlendorf et al. 2007; Ohlendorf et al. 2008), we

investigate the BOLD activation response of the motion sensitive MT+ complex

and its subregions to optic flow stimulations at different locations of the visual field

and the possible structural connectivity between cortical smooth pursuit eye

movement areas and the motion sensitive areas, by means of fMRI and DTI

based fiber tracking.

1.1 Basic features of MT+ and its subregions

1.1.1 MT+

1.1.1.1 MT+ location The area MT+ is situated in posterior superior temporal sulcus (STS) (Allman and

Kaas 1971; Kaas and Morel 1993; Huk et al. 2002). In human brain, MT and MST

comprise the dominant part of MT+ (V5 in human brain) (See Fig. 1.1).

1.1.1.2 MT+ properties There is a controversy about whether the motion sensitivity observed in the area

MT+ is established by local circuits in this area or whether it is determined earlier

in visual processing (Hatakeyama et al. 2010). A peculiarity of primates is that

directionally selective ganglion neurons are absent in the primate retina (Hawken

3

et al. 1988). Previous studies found that in the primary cortex, directionally

selective neurons are recorded from V1 at the first stage of cortical processing of

visual information (Hubel and Wiesel 1968; Hawken et al. 1988). There is

experimental evidence that these directionally selective neurons in V1 exclusively

provide the direct input to MT+ (Movshon and Newsome 1996). There is indirect

input from V1 to MT+ via V2 (Ponce et al. 2008). This indirect route seems to be

especially important for the selectivity of MT neurons for the processing of

disparity, i.e., the differences in left and right visual hemifields. Finally, there are

also descriptions of a direct input to MT+ from the LGN (Sincich et al. 2004; Nassi

et al. 2006), of which the function is not entirely clear at present.

1.1.2 MT

1.1.2.1 MT localization The superior temporal sulcus (STS) of the monkey contains a series of visual

areas that are involved in visual motion processing (Komatsu and Wurtz 1988a;

Ilg 2008). The middle temporal area (MT) in the posterior bank of STS was first

described less than 40 years ago (Allman and Kaas 1971; Dubner and Zeki 1971).

Researchers (Dubner and Zeki 1971; Zeki 1974) first determined that a large

fraction of the neurons in this region show a directionally selective response to

moving spots of light. MT in turn projects to other areas on the floor and anterior

bank of the STS (Maunsell and van Essen 1983a; Maunsell and Van Essen

1983b).

1.1.2.2 MT properties MT can be characterized by two key features: first, this area is anatomically

defined by the intensive staining of myelin. Second, almost all neurons respond to

visual motion in a direction-selective manner. If a visual stimulus, usually a group

of coherent moving dots within a stationary aperture adjusted to the location of the

receptive field, moves in the preferred direction, the firing rate of the neurons

increases above spontaneous activity. In contrast, if the stimulus moves in the

opposite direction, the firing rate is decreased below spontaneous activity. If more

4

directions of motion are tested, the directional tuning of the neurons can be

determined. Besides the sensitivity to direction, MT neurons are also sensitive to

the speed of the visual stimulation. Interestingly, the inhibition caused by stimulus

movement in the non-preferred direction is constant and not speed-dependent.

For example, pursuit eye movements must use motion information to match

movement of the eyes to motion of a target, thereby reducing the slip velocity of

the target image on the retina (Dursteler and Wurtz 1988; Heide et al. 1996).

Priebe (Priebe et al. 2001) hypothesized that, with an optical patch, it is possible

to separately control the local motion by changing the velocity of the dots and the

total rate of displacement by translating the patch; it has shown that the initial

pursuit eye acceleration and MT activation are determined primarily by the local

motion and not by displacement (Priebe et al. 2001; Krauzlis 2004). When the

local motions can be perceptually grouped as a single moving object, some MT

neurons immediately respond to the local motion of the stimulus components, but

they also begin to respond very fast to the global motion of the object very fast as

a whole (Pack and Born 2001; Krauzlis 2004).

1.1.3 MST

1.1.3.1 MST localization The medial superior temporal area (MST) in the STS which is adjacent to MT was

described by Desimone and Ungerleider, as well as Tanaka (Desimone and

Ungerleider 1986; Tanaka et al. 1986). The MST area is located in the upper bank

of the posterior STS and contains neurons sensitive to the direction of motion

either in the frontal plane, in depth, or in both (Dubner and Zeki 1971; Maunsell

and van Essen 1983a; Saito et al. 1986; Tanaka et al. 1986). It also contains

neurons that appear to play a role in visuomotor control (Newsome et al. 1988;

Komatsu and Wurtz 1988a; Komatsu and Wurtz 1988c).

5

1.1.3.2 MST properties MST mainly possesses neurons (bigger size) which demonstrate direction

selectivity but these differ from MT neurons in the size (smaller size) of their

receptive fields and frequently in the size and type of their preferred stimulus

(Newsome et al. 1988; Komatsu and Wurtz 1988a; Komatsu and Wurtz 1988c).

MST and MT are reciprocally connected to V4, thus providing the necessary

information to extract form information from motion (Ilg 2008). Some researchers

have determined the interaction of the smooth pursuit eye movements response

of the neurons with the response due to motion of the visual background (Duffy

and Wurtz 1991a; Duffy and Wurtz 1991b; Bradley et al. 1996). They localized

pursuit neurons in subregions of MT and MST: in fovea1 MT (MTf), in a

dorsal-medial area of MST (MSTd), and a lateral-anterior area of MST near MT

fovea (MSTl). Furthermore, they found two visual properties common to nearly all

pursuit neurons in these regions: direction selectivity and inclusion of the fovea in

the visual receptive field of the neurons, and two differences in the visual

properties between the regions. MTf neurons differ from MSTd neurons in

responding to small spots rather than full-field stimuli and in having small rather

than large receptive fields whereas MSTl neurons are a mixture of these neuron

types. Similarly, chemical lesions of the fovea1 representation of MT, which

probably influences MST or lesions of MST itself, produce a directional deficit

superimposed on the retinotopic pursuit deficit (Dursteler and Wurtz 1988).

6

Fig. 1.1 Spatial relationship of MT, MST and primary visual cortex in human (Huk

et al. 2002).

Fig. 1.1 MST and MT are generally references MT+. V1 is not visible because it lying on the medial surface. Secondary visual area such V2, V3, V3a are indicated.

1.2 Smooth pursuit eye movements

Why do we move our eyes at all? As with most animals with frontally directed

eyes, our retinas contain a specialized central area with an especially high density

of photoreceptors. To see things clearly, we continuously regulate the orientation

of our eyes so that the images of interesting objects are projected on or near this

part of the retina. We and other primates accomplish this using two types of eye

movements: saccades and smooth pursuit. Saccades are discrete movements

that quickly change the orientation of the eyes, thereby translating the image of

the object of interest from an eccentric retinal location to the fovea. Smooth

pursuit is a continuous movement that slowly rotates the eyes to compensate for

motion of the visual object, minimizing blur that would otherwise compromise

visual acuity (Krauzlis 2004).

7

1.2.1 SPEM introduction

Smooth pursuit eye movements (SPEM) are a special type of eye movements

that evolved in primates which enables the continual maintenance of sharp vision

on a moving target by enabling the eyes to follow the moving target closely. In

order to track a moving object, the pursuit system needs to analyze the velocity of

the object, which requires visual information processing on the signal input side.

Motion information then has to be transformed into motor command signals. If the

eyes do not move, the retinal coordinate frame is equivalent to the head-space

frame. Whenever the eyes move, the resultant eye movement information needs

to be integrated into the whole visual map (Ohlendorf et al. 2007; Kimmig et al.

2008).

For pursuit eye tracking, MT+ complex as well as dorsal cortical eye fields are

involved. Single-neuron activity during smooth pursuit has been found in the STS

(Sakata et al. 1985). The neurons in STS show continuous activity during the

pursuit eye movements, even when the eye movement is made in the dark room

where the only moving target is visible. However, the localization of these neurons

with respect to MT and MST and the nature of their inputs has not been

determined so far (Petit and Haxby 1999).

1.2.2 Pursuit-related activity recorded other parts of the

cortex

Qualitatively, pursuit-related cortex activations have been demonstrated in MT

and MST, frontal eye fields (FEF), supplementary eye fields (SEF), posterior

parietal cortex (PPC) or lateral intraparietal area (LIP), substantia nigra pars

reticulate (SNr), caudate nucleus (CN) and superior colliculus (SC), and even the

pontine precerebellar nucleus is in the smooth pursuit pathway (See Fig. 1.2).

8

Fig. 1.2 Functional network of SPEM in monkey (Krauzlis 2004)

Fig. 1.2 Connections of MT/MST are indicated to FEE, SEF and PPC. These are the region of interest being investigated in this M.D thesis.

1.2.3.1 Frontal eye fields (FEF) The human homologue of FEF lies in the dorsal premotor area at the function of

the superior precentral sulcus and the superior frontal sulcus. As the name infers,

the FEF are concerned with eye movements, and appears to program the

corresponding primary areas to guide gaze shifts (MacAvoy et al. 1991; Braun et

al. 1996).

The FEF receives projections from the primary and associated visual cortices in

the occipital lobe (BA 17, 18, 19), the auditory association (BA 22) and multimodal

visual association areas (BA 20) in the temporal lobe (Jones and Powell 1970;

Jones and Santi 1978; Barbas and Dubrovsky 1981), and the somatosensory

association area (Crowne 1983). They also shares interconnections with the

caudate, superior colliculus, and oculomotor nucleus (Astruc 1971; Kunzle and

Akert 1977; Segraves et al. 1987). Hence, the FEF receive information

concerning the auditory, tactual, and visual environment and are multimodally

responsive. The FEF coordinate and maintain eye and head movements, gaze

shifts, and thus orienting attentional reactions in response to predominantly visual,

(PPC)(PPC)

9

but also tactile and auditory stimuli (Wagman et al. 1961; Denny-Brown 1966;

Latto and Cowey 1971; Barbas and Mesulam 1981; Pragay et al. 1987; Segraves

et al. 1987; MacAvoy et al. 1991; Gottlieb et al. 1994; Braun et al. 1996). They are

also involved in focusing attention on certain regions within the visual field,

particularly the fovea (Segraves et al. 1987) as well as in making smooth pursuit

movements (Wagman et al. 1961; Denny-Brown 1966; Latto and Cowey 1971;

Barbas and Mesulam 1981; Pragay et al. 1987; Segraves et al. 1987; MacAvoy et

al. 1991; Gottlieb et al. 1994; Braun et al. 1996) and perhaps guiding eye

movements while reading and writing (Ritaccio et al. 1992). Impairment in the

FEF has dramatic effects on pursuit. Unilateral lesions result in difficulty in

generating ipsilateral pursuit, as with MST lesions, but recovery appears to be

much slower than in MST(Lynch 1987; Krauzlis 2004). Inactivation of the FEF

reduces both initial pursuit acceleration and steady-state velocity to about 25% of

its normal value, but produces only minor effects on pursuit latency (Shi et al.

1998). The resulting velocity traces look like scaled-down versions of normal

pursuit, suggesting that what has been reduced by inactivation is the overall eye

velocity command for pursuit. Impairments in FEF also abolish the predictive

pursuit eye movements that are normally elicited by stimuli with periodic

trajectories (Keating 1991; MacAvoy et al. 1991; Keating 1993).

1.2.3.2 Supplementary eye fields (SEF) The human momologue of the SEF lies in the medial precentral sulcus.

Anatomically, the FEF and SEF share much of their input and project to similar

areas. Apart from their mutual interconnections, afferent fibers come to them from

all over the cortex, but predominantly from associational visual areas, including

both the dorsal ‘where’ and ventral ‘what’ streams, though the SEF receive more

of the former.

SEF neurons show some preference for the direction of pursuit and also maintain

their discharge in the absence of a visual target, indicating that their responses

are not wholly visual in origin (Heinen 1995). Heinen and Liu came to the

conclusion that the activity of those neurons tends to be highest when target

motion changes, especially when the timing of these changes is predictable

10

(Heinen and Liu 1997). These findings suggest that the SEF might participate in

the planning of pursuit eye movements, similar to the role that the supplementary

motor area appears to play for other types of movements (Tanji and Shima 1996;

Krauzlis 2004). With its strong mutual connections with the cingulate cortex, long

regarded as an outpost of the limbic system and concerned with motivation and

attention, the SEF form part of a sort of oculomotor system, concerned not so

much with the moment-to-moment direction of activity but with “setting the tone for

behavior” (Hasegawa et al. 2000). The SEF are thought to be important in the

intent to perform rather than the execution of eye movement (Grosbras et al.

1999). Furthermore, there is flexibility in that the effects of electrical stimulation of

the SEF depend on what a monkey has been trained to do previously, suggesting

they play a role in adaptation and learning as well (Gaymard et al. 1999).

1.2.3.3 Posterior parietal cortex (PPC) The PPC is composed of multiple subregions with differing inputs and outputs,

thought to be involved in different functions. The PPC receives many and various

inputs including visual, auditory, somatosensory, limbic and motor output signals.

Evidence suggests strong links with the frontal cortex, the cingulate gyrus, and

the motor system (especially the cerebellum and the basal ganglia (Krauzlis

2004)). The parietal cortex is switched between vision and 'before' motor control

in the cortical information processing hierarchy. It corresponds to BA 5 and BA 7

in the monkey and 5, 7, 39 and 40 in the human. It has been suggested the PPC

may have something to do with spatial processing (Grefkes and Fink 2005). Other

ideas include motor command generation, oculomotor transformation, multimodal

integration, attention, and consciousness.

The PPC appears to be important for spatial processing and the control of eye

movements, and may also have a central role in visual attention. PPC damage

due to stroke often leads to the clinical syndrome of neglect, in which patients

seem unable to attend to events in the contralesional hemifield (Heide et al. 1996).

The PPC has been shown to be more active in spatial tasks, and it has been

suggested that it plays a role in the processing of attention shifts and subject

movement (Anderson et al. 1994; Heide et al. 1996).

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1.3 Optic flow

1.3.1 Optic flow introduction

Goal-directed spatial behavior relies on vision. Vision provides essential

information about the environment and the consequences of actions. Opti flow

has been commonly defined as the apparent motion of image brightness patterns

in an image sequence (Negahdaripour September 1998.). Optic flow provides

visual input to monitor self-motion, navigate and guide future movement, and

avoid obstacles along the path. Within the past years, neurophysiology has begun

to take the question of the neuronal mechanisms of optic flow processing

seriously. Physiological evidence for the use of optic flow has been accumulated

from a wide variety of animals (Hadani et al. 1980; Bingham 1993; Crowell and

Banks 1996; Gaymard et al. 1999; Muri et al. 1999; Vandenberghe et al. 2001;

Horio and Horiguchi 2004; Tsutsumi et al. 2007).

1.3.2 Optic flow processing in the cortex

In primate cortex, the processing of visual motion is attributed to a series of areas

in the so-called dorsal stream pathway, which is believed to be specialized in the

analysis of motion and spatial actions. Motion information proceeds from the

primary visual cortex (V1), passes through the V2 and V3 onto MT+ (MT and

MST), and several higher areas in the parietal cortex. The motion information

from the dorsal pathway passes to cortical regions in the parietal cortex as part of

an analysis of spatial relationships between objects in the environment and the

viewer (Andersen and Atchley 1997). Together these areas form a network of

information flow that transforms retinal motion information into high-level spatial

parameters that are used to direct and control spatial behavior. The existence of

several different motion processing areas for different visual motion tasks has

also been demonstrated in patients who show impaired performance in some

12

visual motion tasks but are normal in other visual tasks (Geesaman and Andersen

1996; Ptito et al. 2001; Martinez-Trujillo et al. 2005).

Using functional magnetic resonance imaging (fMRI), Tootell (Tootell et al. 1995b)

overlaid the BOLD responses in striated and extrastriate visual areas to visual

motion stimuli (expanding-contracting radial gratings). They found that the MT+

responds well to low stimulus contrast levels and saturates already at 4% contrast.

Prior adaptation to unidirectional motion prolongs the decay of the BOLD

response in MT+, which has been related to the perceptual motion aftereffect

(Tootell et al. 1995b). In a further study, the human homologue of V3A was found

by the same group (Tootell et al. 1997) to respond well to both high and low

contrast motion stimuli. It is obvious from these initial studies that several areas

(eg. MT+) in visual extrastriate and associational cortex respond selectively to

visual motion.

1.3.3 Optic flow processing in humans

A substantial body of knowledge about how humans analyze optic flow has been

accumulated in psychophysical studies. It is well accepted that human beings can

in principle use optic flow to determine their direction of heading and that the

visual system combines optic flow analysis with a multitude of other sensory

signals to infer self-motion. Gibson already noted that a forward translating

observer experiences visual motion in the “optic array surrounding the observer”

that contains a “singularity” or “singular point”, an idealized point in the flow field at

which visual motion is zero. For an observer moving in a straight line, the

destination of travel is such a point, because all visual motion seems to expand

radically from this point (Geesaman and Andersen 1996; Ptito et al. 2001) (See

Fig 1.3).

13

Fig. 1.3 Schematic drawing of optic flow (www-psych.stanford.edu)

Yet the issue is not that simple, because any natural self-motion might be

composed of eye, head, or body movements that have different effects on the

retinal image. Although the expanding optic flow is useful for guidance of

self-motion, it also raises issues of visual stability and the generation of eye

movements. Eye movement systems such as the ocular following reflexes have

evolved to stabilize the retinal image during self-motion. Research has shown that

optic flow also elicits tracking eye movements. Depending on the structure of the

visual scene and on the type of eye movement, the retinal flow field can differ

greatly from the simple radial structure of the optic flow, making a direct search of

the focus of expansion impossible (Bauer and Dow 1989; Inoue et al. 2000).

1.4 fMRI

fMRI is a non-invasive imaging method which can be used to reveal active

structures of the brain with a high spatial resolution. fMRI has come to dominate

the brain mapping field due to its relatively low invasiveness, absence of radiation

exposure, and relatively wide availability.

14

1.4.1 BOLD introduction

The Blood Oxygenation Level Dependent (BOLD) effect is the source of contrast

in fMRI images. When neurons are activated, the resulting increased need for

oxygen is overcompensated by a large increase in perfusion. As a result, the

venous oxyhemoglobin concentration increases and the deoxyhemoglobin

concentration decreases. As the latter has paramagnetic property, the intensity of

the activated areas increases in the fMRI images. When the conditions are

alternated, the signal of the activated voxels increases or decreases according to

the paradigm. The BOLD signal was discovered by Seiji Ogawa at AT&T Bell labs

in 1990 (Ogawa et al. 1990; Magistretti and Pellerin 1999; Logothetis et al. 2001).

BOLD signal changes are well correlated with blood flow. The blood supply is

tightly regulated in space and time to provide the nutrients for brain metabolism.

Recent research has shown that local field potentials form a marginally better

correlation with blood flow than the spiking action potentials, and it is most directly

associated with neural communication (Logothetis et al. 2001). Presumably,

BOLD reflects the complex nature of metabolic processes, which form a superset

with regards to electrical activity. Some recent results have suggested that the

increase in cerebral blood flow (CBF) following neural activity is not causally

related to the metabolic demands of the brain region, but rather is driven by the

presence of neurotransmitters such as glutamate, serotonin, nitric oxide,

acetylcholine, dopamine and noradrenalin (Yang 1996; Bonvento et al. 2000).

The BOLD contrast contains CBF contributions from larger arteries and veins,

smaller arterioles and venules, and capillaries. By using larger magnetic fields,

the BOLD signal can be weighted to the smaller vessels, and hence closer to the

active neurons. For example, while about 70% of the BOLD signal arises from

larger vessels in a 1.5 tesla scanner, about 70% arises from smaller vessels in a

7.0 tesla scanner. Furthermore, the size of the BOLD signal increases roughly as

the square of the magnetic field strength (Di Salle et al. 2003). Hence there has

been a push for scanners with larger field strength to both improve localization

and increase the signal.

15

1.4.2 Approaches to fMRI data analysis

The ultimate goal of fMRI data analysis is to detect correlations between brain

activation and the task the subject performs during the scan. Echo-planar imaging

(EPI) sequences allows for relatively rapid acquisition of many images. Software

in the scanner platform itself then performs the reconstruction of images from

Fourier space. Some types of artifacts, for example spike noise, become more

difficult to remove after image reconstruction, but if the MRI scanner is working

well these artifacts are thought to be relatively unimportant.

On this statistical models, e.g. by using statistical parametric mapping (SPM,

Wellcome department of Congnitive Neurology, London, UK. fil.ion.ucl.ac.uk/spm/)

is applied on the data. The different evaluations steps comprise movement

correction, coregistion, normalization, calculation of a general liner model by

using a canonical hemodynamic function response function. Primarily these steps

are performed on single subject level. Statistical t-maps are statistically evaluated

for group study.

1.5 Structural connectivity determination by means of DTI based fiber tracking

Diffusion tensor imaging (DTI) is unique in its ability to non-invasively visualize

white matter fiber tracts in the human brain in vivo (Basser et al. 1994). In the last

decade, the quantitative description of this anisotropy with DTI has become well

established in the research environment and its first applications in the clinic are

now being reported. In white matter fibers there is a pronounced directional

dependence on diffusion. With white matter fiber tracking or tractography,

projections among brain regions can be detected in the three dimensional

diffusion tensor dataset according to the directionality of the fibers (Staempfli et al.

2008).

16

1.5.1 Fiber tracking

Fiber tracking uses the directional anisotropy of the diffusion information of the

diffusion signal and allows an stimulation of the main diffusion direction which can

be seen for fiber tracking (Kreher et al. 2008). Fiber tracking enables clinicians to

observe the actual trajectories of fibers and the connectivity of the living human

brain (Mori and van Zijl 2002).

The method used for fiber tracking is shown schematically in Figure 1.4 (Kreher et

al. 2008). The connected area in yellow is the resulting connecting track between

the seed areas A (red area) and B (blue area). Starting from a seed A, a line in the

direction of maximum diffusion is followed until the edge of the current voxel is

encountered. The line then abruptly changes direction to follow the maximum

diffusion in the new voxel. The process is repeated until some criterion for the end

of the track is met. The criterion used in the studies presented here was that the

relative anisotropy falls below some threshold value, indicating that the track is no

longer in white matter and that the uncertainty in the direction of maximum

diffusion is large (Kreher et al. 2008).

The predominately algorithms was used in the FACT algorithms (fiber assignment

by continuous tracking) (Mori et al. 1999). This method uses the main diffusion

direction within the particular voxel. It is susceptible to fiber crossing, strictly

bending and low fractional anisotropy. Therefore, PICO (Parker et al. 2003) or

probability of maps (Kreher et al. 2008) have seen the established which counted

the visit of an algorithms in each voxel. Additional specificity can be observed by

combing the visiting maps (See Fig. 1.4).

17

Fig. 1.4 Schematic drawing of fiber tracking (Kreher et al. 2008)

1.5.2 Combining fiber tracking with fMRI

One of the exciting applications of fiber tracking is in its combination with

functional magnetic resonance imaging (fMRI). This allows in vivo mapping of the

functional areas of the cortex. Fiber tracking provides complementary information,

potentially showing the way in which these activated areas communicate with

each other. This powerful combination of techniques has a wide range of potential

applications. From a basic neuroscience perspective, it provides the unique ability

to determine in vivo both function and connectivity within the brain cortex.

1.6 Aim of this study

This M.D. dissertation had two primary objectives: in the first part, to investigate

whether optic flow at different locations of the visual field leads to spatially

different activations of MT+. In the second part, we study the possibility of direct

structural pathways between the motion sensitive MT+ complex and areas

participating in the cortical control of smooth pursuit eye movements.

18

Chapter 2: Bold activation response of MT+ subregions to optic flow stimulations at different locations of the visual field

2. 1 Materials and methods

In this chapter, we present a study of cortical activations of the MT+ area, using

optic flow visual stimulation in different spatial locations in the visual field.

2.1.1 Subjects

After giving their informed consent, 17 right-handed volunteers (11 males, six

females, age range 22-37 years, average age = 28±6.4) were included in this

study. The vision of all subjects was normal or corrected to normal by wearing

contact lenses during the MR measurement. All subjects were experienced

observers, well practiced at maintaining fixation (Ohlendorf et al. 2007). The study

was approved by the local the ethics committee.

2.1.2 Eye movement measurements

Based on previous studies (Ohlendorf et al. 2007; Kimmig et al. 2008; Ohlendorf

et al. 2008), visual stimulation in this study did not include any eye movements

tasks, but only required fixation of a stationary red dot in the visual center (Ø 0.3°

of visual angle). We used the Freiburg MR-Eyetracker system, a fiber-optic limbus

device for eye movement tracking during scanning. For eye movement recording,

a LabVIEW interface (National Instruments, Austin, Texas, USA) was used. The

19

eye movement signal was displayed in parallel to the eye movement

measurements. Eye movement measurements were used to control for the

subjects’ compliance and permanent fixation of the central dot and were

controlled visually during measurements by the investigator.

2.1.3 MR Imaging

Magnetic resonance imaging was performed with a 3.0 Tesla Magnetom TRIO

scanner (Siemens, Erlangen, Germany). The data were acquired with a

12-channel phased array headcoil. Functional imaging was performed with a

T2*-weighted echo-planar imaging (EPI) sequence, using parallel imaging

(Generalized Autocalibrating Partially Parallel Acquisition, GRAPPA) at an

acceleration factor of R=2. All images were distortion-corrected with a fully

automated distortion and motion correction method (Zaitsev et al. 2004).

High-resolution, sagittal T1-weighted images (for parameters of functional

measurements see Fig. 2.1) were acquired with the MP-RAGE (magnetization

prepared rapid acquisition gradient echo) sequence to obtain a 3D anatomical

reference of the head and brain. Shimming was performed for the entire brain

using an auto-shim routine supplied by Siemens for improving magnetic field

homogeneity. 192 echo planar volumes were acquired in each session (duration

480s). To minimize head motion, the subject's head was fixed in the MR headcoil

with foam pads. Gradient noises were reduced by sound-dampening

headphones.

Fig. 2.1 Location and parameters of functional EPI images

TE: 25 ms, TR: 2.5s

Excitation Flip Angle 75°

Field of view: 232×232×232 mm3

Matrix Size: 150×150×35

Voxel Size: 1.5×1.5×1.5mm3

192 echo planar volumes

20

2.1.4 Visual stimulation

Stimuli were created in Matlab (The Mathworks, Natick, USA) with Cogent

Graphics (developed by J.Romaya, LON at Wellcome Department of Imaging

Neuroscience, UK) and back-projected onto a translucent screen via an

LCD-projector (NEC MT 1050, Tokyo, Japan; 1024 ×768 spatial resolution at 60

Hz). The screen was placed in the gantry at a distance of 75cm to the subjects’

eyes. Subjects saw the visual stimulation in a mirror mounted on the MR-headcoil

(Fig. 2.2). Great care was taken to reduce light in the room. The brightness of the

screen was substantially reduced by a polarizing filter to dim the background area

so that mainly the visual stimulation dots were visible.

Fig. 2.2 Schematic drawing of Visual stimulation

2.1.4.1Visual stimulation patterns The stimulation paradigm was based on that described by Huk (Huk et al. 2002).

In contrast to their stimulation (Ø 15° diameter either located at the right or left

visual hemifield), the optic flow patches in our stimulation were relatively small (Ø

4.4° diameter) and could occur in four (up, low, right and left) peripheral (8.6°,

from the patch centre to the visual centre) and four (up, low, right and left) central

locations (3.3°, from the patch centre to the visual centre) of the visual field.

During the whole scan session (480 s each), subjects had to fixate a red dot

projected in the center of the screen (see Fig. 2.3). Stimuli consisted of alternating

periods of moving and stationary white dots randomly distributed in a circular

pattern with a radius of 2.2° and a density of 2.7 dots/degree2. The white dots

remained stationary for the first 15 s and then began to move radially from the

Eye movement trace

Eye tracker detector

Mirror

Translucent screen Projector

Stimulation PC

21

center of the circular area to the periphery with increasing speed from 0.3 to 1.3

°/s (mean velocity 0.8 °/s), changing direction once per second.

Fig. 2.3 Optic flow stimulation at different locations of the visual field

Fig. 2.3 Schematic drawing of optic flow patterns. A red fixation dot was projected in the center of the screen. A. An optic flow patch was presented above the centre of the visual field close to the central position (radius of circular patch 2.2°; distance of the center of the patch to the central fixation dot 3.3°). B. The patch of stationary dots was presented above the centre of the visual field close to the central position. C. One optic flow patch was presented above the centre of the visual field in a peripheral position. D. The patch of stationary dots was presented above the visual field in a peripheral position; the center of the peripheral pattern was at 8.6° of distance from the central fixation dot.

Visual stimulation included 8 motion stimulation conditions (4 central optic flow

conditions: The optic flow patch appeared in central up, low, right, left positions; 4

peripheral optic flow conditions: The optic flow patch appeared in peripheral up,

low, right, left positions;) and 8 rest conditions (4 rest conditions where the

A

15s

15s

2.2°

3.3°

VS

VS

15s

15s

B

C D 2.2°

8.6°

22

stationary dot patch appeared in central up, low, right, left positions ; 4 peripheral

rest conditions where the stationary dot patch appeared in peripheral up, low,

right, left positions).

During visual stimulation visual motion and rest conditions were presented

intermittently. The different conditions varied in a pseudo-randomized order.

2.1.5 fMRI methods

2.1.5.1 fMRI data analysis fMRI data (T2*-weighted image series) were analyzed using the software

package SPM5 (Wellcome Department of Cognitive Neurology, London, UK).

Residual head motion was corrected using SPM5 realignment. The EPI volumes

were normalized using white and gray matter segmentation parameters of the

anatomical T1 image. Spatial smoothing was performed with Gaussian spatial

kernels of 4mm (full width at half maximum). For statistical analysis a general

linear model was fit to the data to establish parameter estimates for each subject.

For group comparisons we used a random effects second level analysis. We

included the resulting main contrast images of the single subjects into 2nd level full

factorial design (FFD) (the one factor is optic flow position: peripheral/central, and

the other factor is the position of each optic flow appearance e.g up, low, right, left

positions). Clusters of adjacent voxels surpassing an individual threshold of

p<0.001 (cluster-level corrected; all activated clusters were larger than 30 clusters)

was considered as significant activation.

2.1.5.1.1 Spatial Stimulation combination

23

Fig. 2.4 Combination of Spatial Visual stimulation

Fig. 2.4 Schematic drawing of visual stimuli combinations. A. Upper visual stimulation (left), compares with the lower visual stimulation (right). B. Vertical visual stimulation (left) compares with the horizontal visual stimulation (right). C. Peripheral visual stimulation (left): compares with the central visual stimulation (right). In the eight optic flow motion conditions, the visual stimuli in different visual field

were selected and combined (Fig. 2.4). The different combination conditions were

A

VS

VS

B

VS

C

24

analyzed by different contrasts in the SPM model. Following contrasts were

created.

Optic flow located in the upper and lower half of the visual field

The optic flow located in upper half of the visual field included the upper central

and upper peripheral patterns, and the optic flow located in lower half of the visual

field included the lower central and lower peripheral patterns (Fig. 2.4.A).

Optic flow located in the vertical and horizontal visual field

The optic flow located in central upper, central lower, peripheral upper and

peripheral lower patterns were combined to form the vertical visual stimulation.

The optic flow of right central left central, right peripheral and left peripheral

patterns were combined to form the horizontal visual stimulation (Fig. 2.4.B).

Optic flow located in the peripheral and central visual field

The optic flow located in the upper, right, lower and left peripheral patterns were

combined to form the peripheral visual optic stimulation. The optic flows in the four

central visual fields were combined to form the central visual stimulation

(Fig.2.4.C).

2.1.5.2 CARET analysis For visualization purposes we projected the functional group results onto the

bilateral hemispheres of the human Colin surface-based atlas mapped to PALS

(‘Population-Average Landmark- and Surface-based’-atlas) (Van Essen 2005).

Data were mapped onto the flatmaps template and the three dimensional cortical

template of the atlas. This was done using the Computerized Anatomical

Reconstruction and Editing Toolkit (CARET) version 5.3 (http://brainvis.wustl.edu).

Statistical representations of the three group main conditions were mapped to

different colors in functional overlays. Co-activated regions were displayed by

weighted additive color while primary colors (red and blue) indicated regions

activated by only one of the tasks (intensity scale 0–255 referring to the maximum

activation of each contrast). Note that the flatmaps were only used for visualizing

of activation locations. They exclusively show grey matter activation surpassing a

minimum cluster threshold of T=3.0 (CARET threshold, clusters of activation area

25

passing the threshold tests are identified) without considering effect size

differences of stimulation tasks.

2.2 Results

2.2.1 Eye Movement results

As seen by visual inspection all subjects kept good fixation on the central red dot

and did not make any substantial saccades to the patches of moving or stationary

dots shown in different locations of the visual field.

2.2.2 fMRI results

2.2.2.1 BOLD activation response to optic flow at different locations of the visual field

Fig. 2.5 Activations results from optic flow stimulation

A Optic flow located along a vertical centre line of the visual field

Optic flow located along a horizontal centre line of the visual field

C

D

Optic flow located in the upper half of the visual field

Optic flow located in the lower half of the visual field

A

B

26

Fig. 2.5 Activation due to optic flow stimulation (calculated against rest state) shown in axial, sagittal and coronal planes of SPM glass brains. (A): Optic flow located in the upper half of the visual field; (B): Optic flow located in the lower half of the visual field; (C): optic flow located along a vertical centre line of the visual field; (D): optic flow located along a horizontal centre line of the visual field; (E): optic low located in the periphery of the visual field; (F): optic flow near the centre of the visual field. Data were analyzed by random effects analysis, FWE corrected, p<0.05, n=17.

All these paradigms results continuously in an activation of MT+.

2.2.2.2 Activations resulting from optic flow stimulation at different locations

Table 2.1 Locations of activity induced by optic flow stimulation located in the

upper part and the lower part of the visual field

Optic flow located in the upper half of the visual field Optic flow located in the lower half of the visual field

Voxel coord. Voxel coord Anatomical

Area

X Y Z

BA/fR Cluster T value

X Y Z

BA/fR Cluster T value

R Middle Temporal

Gyrus 52 -66 4 BA37 638 8.99 52 -66 4 BA37 382 7.27

44 -63 4 IPC 8.03

57 -63 10 IPC 5.73

L Middle Temporal

Gyrus -45 -70 4 BA37 482 8.24

L Middle Occipital

Gyrus -46 -72 6 BA19 263 8.94

-32 -84 3 BA19 5.63

L Cuneus -15 -100 3 BA17 97 6.95

-8 -102 6 BA18 6.19

Table 2.1 Voxel coordinates show the local maximum of an active voxel cluster (one sample t-test, FWE corrected, p<0.05); BA = Brodmann Area; fR = functional region; IPC= inferior parietal cortex; T = t-value at voxel level.

A

F

Optic flow located in the periphery of the visual field

Optic flow located near the centre of the visual field

E

27

Cortical activation of upper visual field stimuli was found in bilateral BA37/MT+ in

the middle temporal gryus and reached into the inferior parietal cortex (IPC) (See

Table 2.1). The lower visual field stimulation also activated MT+ in the right middle

temporal gryus, and in addition left middle occipital gyurs and left BA17/V1 and

BA18 in the left cuneus. In both conditions the activated cluster in the right

BA37/MT+ area was larger than in the left one, and the activated cluster resulting

from upper visual field stimulation (right cluster=638; left cluster=482) was larger

than the one resulting from lower visual field stimulation (right cluster=382; left

cluster=263). As for the response of lower visual field stimulation, in addition to

the activation in the right BA37/MT+ and left BA19/MT+, we found activation in the

left cuneus BA17/V1 and small activations located in the left BA19 and BA18.

Table 2.2 Locations of activity induced by optic flow located along a vertical and

Horizontal centre line of the visual field Optic flow located along a vertical centre line of the visual

field Optic flow locaed along a horizontal centre line of the

visual field

Voxel coord. Voxel coord Anatomical Area

X Y Z BA/fR Cluster T

value X Y Z

BA/fR Cluster T value

R Middle Temporal

Gyrus 52 -66 4 BA37 959 10 52 -66 4 BA37 978 9.93

46 -90 6 IPC 5.8 44 -64 3 IPC 9.5

36 -78 4 IPC 5.7 50 -62 3 IPC 8.98

L Middle Temporal

Gyrus -45 -70 4 BA37 697 10.34 -45 -70 6 BA37 530 10.06

L Cuneus -51 -66 8 6.77 -45 -63 -2 BA17 5.7

-50 -75 -3 6.42

Table 2.2 Voxel coordinates show the local maximum of an active voxel cluster with SPM second level group results (one sample t-test, FWE corrected, FWE corrected, p<0.05); BA = Brodmann Area; fR = functional region; IPC= inferior parietal cortex; T = t-value at voxel level.

Cortical activation resulting from optic flow stimulation along a vertical centre line

and resulting from optic flow stimulation along a horizontal centre line was found

in BA37/MT+ in both hemispheres. In both conditions, percentage of activated

voxels in the right BA37/MT+ was higher than left. The activated cluster in the left

BA37/MT+ by vertical stimulation was larger than the one activated by the

horizontal stimulation (See Table 2.2).

28

Table 2.3 Location of activation resulting from optic flow stimulation located in the

periphery of the visual field or close to the centre of the visual field

Optic flow located in the periphery of the visual field Optic flow located near the centre of the visual field

Voxel coord. Voxel coord Anatomical Area

X Y Z

BA/fR Cluster T value

X Y Z

BA/fR Cluster T value

R Middle Temporal

Gyrus 52 -66 6 BA37 257 7.1 52 -66 4 BA37 788 9.33

45 -63 3 IPC 6.08 44 -66 3 IPC 9.06

50 -62 10 IPC 8.14

L Middle Temporal

Gyrus -45 -70 6 BA37 178 7.8 -45 -70 6 BA37 543 9.13

L Cuneus -44 -86 6 BA19 6.71

Table 2.3 Voxel coordinates show the local maximum of an active voxel cluster with SPM second level group results (one sample t-test, FWE corrected, p<0.05); BA = Brodmann Area; fR = functional region; IPC= inferior parietal cortex; T = t-value at voxel level.

Optic flow stimulation located in the periphery of the visual field and near to the

centre of the visual field lead to cortical activation in bilateral BA37/MT+ (right T

value/cluster=7.1/257; left T value/cluster=7.8/178). Activation resulting from

peripheral stimulation response was less extended than activation resulting from

central stimulation (right T value/cluster= 9.33/788; left T value/cluster=9.13/543).

In addition, we found activation in the left cuneus (BA19) resulting from central

visual stimulation (see Table 2.3).

2.2.2.3 Spatial relationship of cortical area Fig. 2.6 Flatmaps of activity induced by optic flow stimulation located in upper and

lower visual field

29

Fig. 2.6 Activation overlay on flatmaps and slightly or more inflated cortical surface hemispheres of the 3D PALS brain template generated by CARET5.3. Activation maps are RGB color-coded, intensity was scaled to arbitrary values between 0-255. Red represents the response to the optic flow located in the upper half of the visual field, green represents the response to the optic flow located in the lower half of the visual field. The upper part of the figure shows flatmaps of the right and left hemisphere with overlaid activation, the second level shows the inflated 3D PALS templates brain with overlaid activation, the third level shows the posterior part of a more inflated 3D PALS brain, the lowest level shows schematic drawings of the optic flow located in the upper half of the visual field (red) and the optic flow located in the lower half of the visual field (green) cortical responses p<0.05 T=3.0.

Fig. 2.7 Flatmaps of activity induced by optic flow stimulation located in vertical and horizontal visual field

Stimulation of vertical visual central

Stimulation of horizontal visual central

Stimulation of upper visual field

Stimulation of lower visual field

30

Fig. 2.7 The schematic layout shows the bilateral activation in MT+, red was the response to the activation of optic flow stimulation along a central vertical line of the visual field and green was the response of the optic flow stimulation along a central horizontal line of the visual field. The lowest level in the figure shows the schematic drawings of the optic flow located in the periphery of the visual field (red) and the optic flow located near the centre of the visual field (green) cortical responses.

Fig. 2.8 Flatmaps of activity induced by optic flow stimulation located peripheral and central visual field

Fig. 2.8 The schematic layout shows bilateral activation in MT+, the red response shows the activation from the stimulation in the peripheral visual field and green shows the activation response from the central visual stimulation. The bottom of the figures shows the schematic drawings of peripheral (red) and central (green) cortical responses.

Optic flow at all spatial stimulation positions activated MT+ in both hemispheres.

Optic flow located in the upper half of the visual field activated a more anterior and

dorsal portion of MT+ than optic flow in the lower half of the visual field. Optic flow

stimulation along a vertical central line of the visual field led to a more widespread

activation in anterior dorsal-ventral direction of MT+ than optic flow along a

horizontal centre line of the visual field. Optic flow located in the peripheral visual

field activated a smaller and dorsal anterior portion of MT+; visual stimulation of

the four central optic flow patches activated a larger and more ventral portion of

MT+.

Stimulation of peripheral visual field

Stimulation of central visual field

31

2.3 Discussion

Up to now fMRI studies investigating retinotopic mapping of the human MT+

complex and its subregions mostly show single subject results at voxel resolutions

of 2-3 mm3 (Huk et al. 2002; Yan and Wu 2010). In those studies the results of the

optic flow stimulation corresponding to activation locations in MT+ areas and

retinotopy of MT+ seem to be interindividually very different or not consistent. The

present study does not directly investigate the retinotopy of motion processing of

the human MT+ complex e.g. via rotating wedges including moving dots but

compares the processing of motion stimulation at different locations of the visual

field in a more generalized way.

We can show changes of activation localization in the MT+ complex depending on

the location of motion stimulation in the visual field. To our knowledge, this is the

first study showing different BOLD activation locations in MT+ complex resulting

from different spatial positions of optic flow stimulation in a larger group of

subjects and this is also the first study using high resolution fMRI to get a better

insight into this very small visual area.

Fixation performance in our experiments was almost perfect. Group MT+

activations due to optic flow stimulation at different positions were different in

quantity and location depending on the position of the stimulation. These results

basically confirm the single subject results of Huk (Huk et al. 2002) concerning the

motion processing in MT+ of stimuli located in the upper and lower visual field and

the processing of central and peripheral optic flow stimulation (See Fig.2.9).

In the present study, we show the optic flow located in the upper half of the visual

field activated a more anterior and dorsal portion of MT+ than the lower half of the

visual field.

32

Fig. 2.9 Flat-map schematic of MT and MST, and the different BOLD activation locations in MT resulting from different spatial positions (Huk et al. 2002)

Fig. 2.9 Schematize the retinotopic organization of MT, with more posterior/ventral portions representing the lower visual field (LVM) and more anterior/dorsal transition representing the upper visual field (UVM). Representation of visual field eccentricity is indicated as central (Cen) or peripheral (Per) (Huk et al. 2002).

Unfortunately the difference of optic flow stimulation localized along a horizontal

centre line of the visual field against optic flow stimulation localized along a

vertical centre line of the visual field was not that clear. However, we saw a more

widespread activation in anterior dorsal-ventral direction of MT+ due to vertical

optic flow stimulation, than due to the horizontal optic flow (See Fig. 2.10). This

was similar but not as prominent as in the comparison of stimulation in the upper

half of the visual field with motion stimulation in the lower half of the visual field.

This is perhaps due to the effect that motion stimulation in the upper part of the

visual field lead in general to stronger activations than motion stimulation of the

lower half of the visual field. Since we did not explicitly differentiate MT and MST

in each single subject in this experiment the activation differences seem to

comprise the whole MT+ region instead of only parts of it.

Fig. 2.10 Flat-map schematic of optic flow stimulation localized along a vertical

centre line of the visual field

Horizontal centre line of visual field

Vertical centre line of visual field

33

Fig. 2.10 Schematize the optic flow stimulation localized along a ventical centre line of the visual field. Dot line shows the horizontal and vertical centre line of visual. A more widespread activation is in anterior dorsal-ventral direction (solid arrows) of MT+ due to vertical optic flow stimulation.

2.3.1 Upper and lower visual fields stimulation

Studies of macaques extrastriate visual cortex have found differences in the

organization of connections associated with the upper and lower visual field, both

in the interhemisphereic projections (Van Essen et al. 1982) and in the

intrahemiphereic projection from striate cortex (Van Essen et al. 1986). The

electrophysiological measurements in the monkey have revealed that the motion

sensitive cortex is not orderly as in early visual cortex like V1. Manusell and Van

Essen (Maunsell and Van Essen 1987) studied the brains of three macaca

fascicularis and observed that the lower visual field was often over-represented in

comparison with the upper visual field in MT. In Huk’s human study (Huk et al.

2002), the more posterior/ventral portions represented the lower visual field and

more anterior/dorsal transition represented the upper visual field. But all

responding portions were restricted in the MT area which was refined manually. In

our group result, the optic flow is located in the upper half of the visual field

activated a more anterior and dorsal portion of MT+ than the lower half of the

visual field. These results confirmed the single result of Huk (Huk et al. 2002),

however in our data the response of optic flow stimulation in the upper and the

lower half of the visual field seems to be present not only in MT but also in MT+

areas in our study including a larger group of subjects.

Studies of physiology seem also to explain the asymmetric responses between

upper and lower stimuli. Kraft (Kraft et al. 2010) investigated the physiological

correlates of upper and lower field preferences within the parietal attention

network (FPN), and Rubin (Rubin et al. 1996) showed that the perception of

illusory contours is significantly better in the lower visual field. In our study, the

same optic flow stimulation was performed in both upper and lower visual fields.

In contrast to the named results our results showed that the results showed the

upper visual stimulation activated a larger cortical area than lower stimulation.

34

2.3.2 Vertical and horizontal visual fields stimulation

The difference of optic flow stimulation localized along a horizontal centre line of

the visual field against optic flow stimulation localized along a vertical centre line

of the visual field was not that clear. Macaluso and Patria (Macaluso and Patria

2007) performed the classical spatial cueing task (Posner 1980) to a group

subjects and studied the spatial attention along vertical and horizontal axes. Their

results revealed the activation of a ventral network composed of the posterior part

of the temporal lobule and the inferior frontal gyrus. Another macaque monkey’s

study (Maunsell and Van Essen 1987) released that in the topographic

organization of MT, the larger and more ventral responses area by the vertical

visual stimulation (5°-15° to the visual centre horizontal line) and the smaller and

more dorsal response area by the horizontal visual stimulation (10°-20° to the

visual centre vertical line). Our results show the optic flow stimulation along a

central line of the central vertical line of the visual field active to a more

widespread activation in anterior dorsal-ventral direction of MT+ than the

horizontal optic flow stimulation (See Fig. 2.10), the bilateral MT+ got stronger

respond by the vertical optical flow than horizontal optic flow stimulation. Our

results extend the parts of abovementioned results in human and animal studies

(Maunsell and Van Essen 1987; Macaluso and Patria 2007), however, further

fMRI, and electrophysiology studies investigating the MT+ area will be necessary

to better explain the result of different responses by the optic flow stimulation of

vertical and horizontal visual stimulations.

2.3.3 Peripheral and central visual field stimulation

Huk (Huk et al. 2002) and Yan (Yan and Wu 2010) used the similar optic flow

stimulation to study the retiontopy and functional subregions of human MT+, as

same as the monkey’s result (Maunsell and Van Essen 1987), the more

lateral/ventral neurons exhibiting central receptive fields and more anterior/dorsal

neurons exhibiting more peripheral receptive fields. The central topic of our

35

experiment is to provide additional evidence (e.g. peripheral vs. central visual field

stimulation in this study) that visual information may be processed differently

based on the location of the stimulus in the visual field rather than on stimulus

characteristics alone. But we would assess that the activated cortices by both

peripheral and central optic flow stimulations are located at not only MT but also

the other parts of MT+ areas which extended the similar results with the single

subject fMRI study by Yan and Wu (Yan and Wu 2010).

To summarize, in this experiment using high resolution fMRI we can show in a

group of subjects that localization dependant motion processing in the MT+

complex exists. However, location differences of processing do not seem to be

clearly retinotopic but of a more abstract organization. In our experiment the

source of motion varied with the localization of the stimulation patch and motion

was present in all directions. A further step to investigate this more abstract

organization could be to vary the source of motion and motion direction at

different locations of the visual field to find out more about characteristics of

motion processing in MT+

36

Chapter 3: Regions of cortical response to SPEM and motion, and the possible structural connectivity in SPEM and motion sensitive cortex.

In this chapter, we presented a study of the cortical activation associated with

SPEM and motion sensitivity by visual stimulation, and the possible structural

connectivity in SPEM and motion sensitive cortices by means of DTI based fiber

tracking.

3.1 Materials and methods

3.1.1 Subjects

After giving their informed consent, 5 right-handed volunteers (3 males, 2 females,

age range 23-31 years, average age = 25±5.1) took part in the smooth pursuit eye

movement study, one additional male joined the motion sensitive study (right

handed, 4 males, 2 females, range 21-31 years, average age = 24±4.9), and the 5

volunteers who had taken part in SPEM study were invited for the fiber tracking

(FT) study. The vision of all subjects was normal or corrected to normal. All

volunteers were experienced observers, well practiced at maintaining fixation.

The study was approved by the local ethics committee.

3.1.2 Eye movement measurements

Eye movement and visual motion stimulation reproduced stimulations similar to

previous studies previous studies (Ohlendorf et al. 2007; Kimmig et al. 2008;

37

Ohlendorf et al. 2008). For eye movement recording, a LabVIEW interface

(National Instruments, Austin, Texas, USA) was used. The eye movement signal

was displayed in parallel to the eye movement measurements. Eye movement

measurements were used to control for the subjects’ compliance and permanent

fixation of the dot and were controlled visually during measurements by the

investigator.

In the SPEM stimulation, the stimulus position was recorded and displayed in

parallel to the eye movement data. The MR scanner provided a TTL-pulse at the

beginning of each volume acquisition. This pulse was used to trigger our

stimulation and to provide an exact time marker for the eye movement acquisition

programs.

In the visual motion study, stimulating the motion sensitive MT+ complex, eye

movement data were mainly used to control for the subjects’ compliance and

permanent fixation of the central dot since visual stimulation in this experiment did

not include any tasks of eye movements, but required exclusively fixation of a

stationary red dot in the visual center, eye movement data were mainly used to

control for the subjects’ vigilance and permanent fixation of the central dot.

In the fiber tracking study, no visual stimulation was required, therefore the eye

movement measurement was unnecessary.

3.1.3 MR Imaging

Magnetic resonance imaging was performed with a Magnetom Trio 3T system

(Siemens Healthcare, Germany). The data were acquired with a 12-channel

phased array head coil. Functional imaging was performed with T2*-weighted

echo-planar imaging (EPI) sequence, using parallel imaging (Generalized

Autocalibrating Partially Parallel Acquisition, GRAPPA) at an acceleration factor R

= 2. Moreover, all images were distortion-corrected with a fully automated

distortion correction method based on point spread function mapping (Zaitsev et

al. 2004). High-resolution, sagittal T1-weighted images were acquired with a

magnetization prepared rapid acquisition gradient echo (MP-RAGE) sequence to

38

obtain a 3D anatomical reference of the head and brain. Shimming was

performed for the entire brain using an auto-shim routine supplied by Siemens for

improving magnetic field homogeneity.

3.1.3.1 MR Imaging of SPEM The parameters for the functional measurements were showed in Fig. 3.1. 200

echo planar volumes were acquired in each session (total scan duration = 500 s).

The stimulation protocol for each stimulation sequence consisted of twenty

intervals of 25 s, including 10 periods of stimulation state and 10 periods of rest

state.

Fig. 3.1 Location and parameters of SPEM functional EPI image

3.1.3.2 MR Imaging of visual motion processing in MT+ The parameters for the functional measurements were showed in Fig. 3.2. The

stimulation protocol for each stimulation sequence consisted of thirty intervals of

15 s, including 15 periods of stimulation state and 15 periods of rest state. This

protocol produced 180 echo planar volumes in each session (total scan duration =

450 s).

Fig. 3.2 Location and parameters of MT+ motion sensitive functional EPI imag

TE: 25 ms, TR: 2.5s

Excitation Flip Angle 75°

Field of view: 232×232×232 mm3

Matrix Size: 150×150×35

Voxel Size: 1.5×1.5×1.5mm3

TE: 30 ms, TR: 2.5s

Excitation Flip Angle 90°

Field of view: 192×192×109.4 mm3

Matrix Size: 64×64×36

Voxel Size: 3×3×3mm3

39

3.1.3.3 MR Diffusion Tensor Imaging A total of 51 slices were acquired using a diffusion sensitive spin-echo EPI

sequence with CSF suppression with 61 diffusion-encoding gradient directions, b

factor =1000 s/mm, the parameters for the functional measurements are showed

in Fig. 3.3. The acquisition volume contained most parts of the

temporal-occipital-frontal lobe (excluding the cerebellum). During image

reconstruction, scans were corrected for motion and distortion artifacts based on

a reference measurement.

Fig. 3.3 Location and parameters of fiber tracking EPI image

3.1.4 Visual Stimulation

To define the activated locations of SPEM and motion sensitive areas, we

repeated experiments similar to Ohlendorf et al. (Ohlendorf et al. 2007; Ohlendorf

et al. 2008).

Stimuli were created by Matlab (The Mathworks, Natick, USA) in combination with

Cogent Graphics (developed by J.Romaya, at the LON at the Wellcome

Department of Imaging Neuroscience, UK) and back-projected onto a translucent

screen via a LCD-projector (NEC MT 1050, Tokyo, Japan; 1024 ×768 spatial

resolution at 60 Hz). The screen was placed in the gantry at a distance of 75cm to

the subjects’ eyes. Subjects saw the visual stimulation via a mirror which was

mounted on the MR headcoil. Great care was taken to reduce light in the room

(See Fig. 2.2). The light of the projector was substantially reduced by a polarizing

filter and by darkening the screen. It has to be clarified that in the SPEM study, the

screen was masked additionally by a piece of black paper, whose center was cut

TE: 96 ms, TR: 7.6 s

Excitation Flip Angle 75°

Field of view: 224×224×224 mm3

Matrix Size: 112×112×51

Voxel Size: 2×2×2 mm3

b factor =1000 s/mm

40

out leaving only the track of the stimulus (See 3.1.4.1). Another filter was used to

suppress all colors except red such that subjects saw nothing except the target

dot.

3.1.4.1 Visual stimulation of SPEM A red dot (0.5° of visual angle) was projected to the central screen position.

During the fMRI measurement period (25 s stimulation alternating with 25 s rest),

the subjects had to fixate the red dot when it was stationary and they had to

pursuit the red target when it moved. In the stimulation, the dot moved

sinusoidally in the horizontal plane, with amplitude of 10° or 2° at mean

frequencies of 0.19Hz. The stimulation started in the rest state and the following

eye movement trials occurred in pseudo randomized order. Each experimental

condition was repeated twice and each subject performed two sessions.

Fig. 3.4 Visual stimulation of SPEM

Fig. 3.4 Schematic drawing of SPEM. The red to be fixated dot (0.5° of visual angle) was projected to the central screen position. The stimulation started in the rest state and moved to a random condition.

3.1.4.2 Visual motion stimulation of MT+ 3.1.4.2.1 MT+ localizer stimulus

To localize MT+ a red fixation dot (Ø 0.3° of visual angle) was projected in the

center of the screen (Ø 0.3° of visual angle). Subjects had to fixate this central red

dot during the whole session. White dots (Ø 0.3°) were randomly distributed in a

circular area (radius 8.0°). In the rest condition, the white dots remained stationary

(during 15 s). In the stimulation condition (during 15 s), the dots moved radially

from appearance at the center of the flow field to disappearance at the pattern

periphery with increasing speed of 0.5 to 4.2°/s (mean velocity 2.5°/s), and

41

change of direction once per second. The dot pattern was spread around the

fixation dot (Fig. 3.5A).

3.1.4.2.2 Ipsilateral stimulation

To separate MT and MST (according to their different response to contra- and

ipsilateral field stimulation) the circular dot pattern was located either in the right

or left visual hemifield (offset of center of the circular pattern from the mid-line 8°;

maximum radius of the dot pattern 6°; shortest distance from the dot patch to the

fixation dot 2°; number of white dots = 113; Fig. 3.5 B and C). In the stimulation

condition, we used the same velocity profiles as in the localizer task. The rest

condition corresponded to the right and left hemifield stimulations, but the dots

remained stationary. These pairs of stimuli occurred in a pseudo randomized

order. Each stimulation type and its rest condition (Localizer stimulus, ipsilateral

stimulus +8°, ipsilateral stimulus -8°) was repeated five times in the fMRI session

(during 500s).

Fig. 3.5 Visual stimulation of motion sensitive area MT+

Fig. 3.5 Schematic drawing of MT+ stimulation. A red fixation dot was projected to the center of the screen (Ø 0.3° of visual angle). MT+ localizer stimulus (A): White dots (n=150; Ø 0.3°) were randomly distributed in a circular area (radius 8.0°). The dot pattern was distributed radially around the fixation dot; stimulation started in the moving condition (during 15s) and then remained stationary (15s). Ipsilateral stimulation (B and C): the circular dot pattern was located either in the right (B) or left (C) visual hemifield, the same velocity profiles as in the localizer task were used and the stimuli occurred in a pseudo randomized order.

3.1.5 fMRI data analysis

fMRI data (T2*-weighted image series) were analyzed using SPM8 (Wellcome

Department of Cognitive Neurology, London, UK). Residual head motion was

8°

r= 6°

r= 8°

8°

r = 6°

A B C

42

corrected using the realignment tool in SPM8. We normalized the EPI volumes

using white and gray matter segmentation parameters of the anatomical T1 image.

Spatial smoothing was performed with Gaussian spatial kernels of 6 mm in SPEM

and 4 mm in motion sensitive study (full width at half maximum). For statistical

analysis, a general linear model was fit to the data to establish parameter

estimates for each subject. For group comparisons we used a random effects first

level analysis. We used the resulting main contrast images of the single subjects

for one sample t-tests. We corrected for multiple comparisons at the cluster level.

Clusters of adjacent voxels surpassing an individual threshold of p<0.05 and

cluster level correction (greater than 30 clusters) were considered as significant

activations.

3.1.6 Possible structural connectivity revealed by fiber

tracking

3.1.6.1 Definition of ROIs seed To refine the coordinate of the activated cortices by SPEM and motion sensitive

stimulation, we measured the totally same subjects in this fiber tracking

experiments. The seed regions for the FT were extracted from the t-maps of the

SPEM and MT+ fMRI random effects analyses of both experiments. We define

the ROIs by using wfupickatlas (Toolbox of Matlab

www.nitrc.org/projects/wfu_pickatlas/); these coordinates of ROIs were set at the

gray matter which was very close to the white matter. Within the major activation

clusters the peak voxels were identified, resliced to the native space of each

subject’s data, and enlarged to a sphere with a radius of 2 mm. each containing

10 seed coordinates which included hypothetical MT, MST, PPC, FEF and SEF.

Based on the previous studies in human (Dukelow et al. 2001; Huk et al. 2002;

Ohlendorf et al. 2008), the hypothetical seeds of MT were localized more

posterior and ventral with respect to the MST, but still in the area of MT+.

43

3.1.6.2 Analysis of possible structural connectivity by fiber tracking. DTI data were analyzed using a recently developed method of pathway extraction,

which is implemented in the Matlab-based DTI/HARDI data and Fiber Toolbox

(Freiburg, fibertools, German). First, the diffusion tensor was computed from the

movement and distortion-corrected diffusion-weighted imaging dataset. Second,

we used the Monte Carlo simulation of random walks (MCRW) similar to the

probabilistic index of connectivity (PICo) method to calculate the probabilistic

maps separately for each seed region (Kreher et al. 2008). The number of random

walks was set to 105 and maximum fiber length to 150 voxels. The tracking area

was restricted to a white-matter mask to avoid tracking across anatomical borders.

To ensure contact of the cortical seed regions with white matter, a rim of gray

matter was included in the mask. Third, region-to-region anatomical connectivity

between two seed regions (A and B) was computed using a specification of

probability maps. By combining particular maps, a certain specificity of connecting

fibers can be observed. The found probability maps do not reflect the degree of

connectivity, but are influenced, e.g., by the distance of the seed points and by the

thickness of the fiber bundles. The probability maps were overlaid on T1 anatomy

images using MRIcron and Fancy Rander (Version 4.4.4).

3.2 Results

3.2.1 Eye movement results

3.2.1.1 Results of SPEM In the whole SPEM experiment, the subjects almost perfectly tracked the target

synchronously during the target movement.

44

3.2.1.2 Result of stimulation of motion sensitive MT+ In the motion sensitive experiment, all subjects kept good fixation on the center

red dot and did not make substantial saccades to the stimuli shown in the visual

hemifields.

3.2.2 fMRI results

3.2.2.1 Cortical activation of SPEM Fig. 3.6 Activation results of SPEM stimulation

Fig. 3.6 Activation due to SPEM shown in axial, sagittal and coronal planes of SPM glass brains. A: Schematic drawing of SPEM; B: Functional area in the glass brain in the three planes. Data were analyzed by random effects analysis, FWE corrected, p<0.05, n=5.

In the SPEM experiments, cortical activation (all stimulations combined) was

found in MT+, SEF, FEF, PPC, V1, the cingulate gyrus, parts of the basal ganglia,

and parts of cerebellum. The stimulation activated mainly occipito-parietal regions

of MT+ and middle temporal parietal regions of MT+. The largest activation

clusters were located at bilateral middle temporal parietal regions of MT+, in the

right FEF and the left SEF.

MT+ 5°SPEM visual stimulation

PPC

FEF

SEF

45

Fig. 3.7 Activation results of SPEM overlaid on template T1 brain

Fig. 3.7 SPEM functional data overlaid on template T1 images by MRIcron, A: axial planes; B: coronal planes; C: sagittal planes. Data were analyzed by random effects analysis, FWE corrected. P<0.05, n=5, cluster level corrected with cluster threshold of 30. Co-activated regions are displayed by weighted additive color by MRIcorn (intensity scale 0–255 referring to the maximum activation of each contrast). The color bar range is based on the activation intensity. Yellow regions represent significant cortical activation.

Figure 3.7 shows the activations of SPEM overlaid onto the template T1 brain in

three planes. Contrast was compared with corresponding rest conditions.

Activation was found in MT+, SEF, FEF, precuneus, PPC, primary visual cortex

and cingulate gyrus. The color bar indicates the activation intensity value.

5° A B

C

46

Table 3.1 Cortical activation of SPEM Smooth Pursuit Visual Stimulation

Voxel coord. Anatomical Area

X Y Z

BA/fR Cluster T value

R Superior Frontal Gyrus 12 -1 64 BA6/SEF 149 10.55

L Superior Frontal Gyrus -9 -4 64 BA6/SEF 101 8.43

R Medial Frontal Gyrus 39 -4 49 BA6/FEF 1334 13.89

42 -1 52 BA6/FEF 13.71

L Medial Frontal Gyrus -36 -7 46 BA6/FEF 1334 9.31

-27 -4 58 9.06

L Middle Frontal Gyrus -36 -7 46 BA6/FEF 13.32

R Inferior frontal Gyrus 33 29 -2 BA45 17 5.38

L Inferior frontal Gyrus -54 41 -5 BA45 25 6.03

L Precentral Gyrus -51 2 7 BA6 64 7.23

-57 11 1 BA46/FEF 6.01

R Parietal lobule 21 -64 58 BA7/PPC 254 13.89

L Parietal lobule -33 -49 52 BA7/PPC 130 12.1

R Inferior Parietal lobule 36 -40 52 BA40 11.21

48 -31 28 BA40 7.26

R Supramarginal Gyrus 45 -34 25 BA40 30 7.32

R Precuneus 27 -76 34 BA19 12.62

L Cuneus -24 -76 25 BA7 12.79

L Middle Temporal Gyrus -51 -64 7 BA37/ MT+ 1257 14.37

R Middle Occipital Gyrus 48 -67 4 BA37/MT+ 1657 14.43

27 -79 16 BA19 9.36

L Middle Occipital Gyrus -39 -73 1 BA19 11.18

L Lingual Gyrus -12 -76 -5 BA18 6.4

Table 3.1 Voxel coordinates show the local maximum of an active voxel cluster with SPM group results of SPEM (one sample t-test, FWE corrected, p<0.05); BA = Brodmann Area; fR = functional region; BA; T = t-value at voxel level.

Cortical activation of SPEM was found in BA37/MT+, BA6/SEF, BA6/FEF,

precuneus, posterior parietal cortex BA7/PPC, visual cortex BA19, BA18. The

bilateral BA37/MT+ were the most active regions of cortex (right T-value/cluster =

14.43/1657; left T-value/cluster = 14.37/1257), and BA6/FEF (right T-value/cluster

=13.89/1334), bilateral BA7/PPC (right t-value/cluster = 13.89/254, left

T-value/cluster = 12.1/130) and BA6/SEF (right T-value/cluster = 10.55/149, left

T-value/cluster = 8.43/101) were less activated.

47

3.2.2.2 Cortical activation of visual motion stimulation In the motion sensitive experiment, the localizer stimulus led to bilateral

activations in the medial occipital gyrus and middle temporal gyrus, corresponding

to the MT+ region on the group level. The activations were found in the left

contralateral lingual gyrus; the group analysis of the ipsilateral stimulus in the left

visual hemifield showed activation in the right contralateral MT+ and small

activation in the left ipsilateral MT+. Further significant activation was located in

the right lingual gyrus. The above stated activations in MT+ were all located within

the activated areas of the MT+ localizer stimulus. Furthermore, we found

activations in the left insula (BA18) and very small activation in the inferior parietal

lobule (Table 3.2). The group analysis of the ipsilateral stimulus in the right visual

hemifield showed activation in the left MT+ and a smaller activation in the right

MT+ (Fig. 3.8). In the right hemisphere, they were clearly separated in a more

anterior, ipsilateral activation, and an adjacent, more posterior, contralateral

activation in the MT+. In the center of activation the two subareas slightly

overlapped. In the left hemisphere ipsilateral activation was similarly located in the

frontal part of the MT+ region (Fig. 3.8). However, it was not separated from the

contralateral activation which spread out further to the anterior part of MT+. The

localizer stimulus did not lead to any activation in visual areas lower than the most

primary visual cortex. Furthermore, during stimulation of the ipsilateral visual

hemifield we did not find any activation outside MT+.

48

Fig. 3.8 Activation due to visual motion stimulation

Fig. 3.8 Functional data of motion sensitivity in the glass brain in the horizontal, sagittal and coronal planes. A: Schematic drawing of localizer visual stimulation and functional response in glass brain; B: right ipsilateral stimulation. C: left ipsilateral stimulation. Data were analyzed by random effects analysis, FWE corrected, p<0.05. n=6.

Localizer stimulation of MT+

Right ipsilateral stimulation

r = 8°

8°

r = 6°

8°

r = 6°

Left ipsilateral stimulation

A

B

C

MT+

MST

49

Table 3.2 Cortical activation of motion sensitivity

MT+ Localizer visual stimulation MT+ Right ipsilateral stimulation MT+ Left ipsilateral stimulation

Voxel coord. Voxel coord. Voxel coord. Anatomical Area

X Y Z

BA/fR Cluster T value

X Y Z

BA/fR Cluster T value

X Y Z

BA/fR Cluster T value

R Superior Parietal lobule 33 -80 30 BA19(V3A) 397 8.28

L Superior Parietal lobule -26 -79 30 BA19(V3A) 399 7.74

R Postcentral Gyrus 48 -64 4 63 -30 19 BA42 12 6.46

R Middle Temporal Gyrus 51 -68 4 BA37(MT+) 3303 18.89 44 -76 7 BA37(MST) 359 9.09 50 -64 4 BA37(MT+) 5316 16.63

51 -73 4 BA39(MT+) 10 5.65 8.37

R Middle Occipital Gyrus 45 -76 -14 BA18(V3A) 32 6.7

21 -88 -14 5 5.76

L Middle Occipital Gyrus -51 -73 2 BA37(MT+) 2416 16.51 -48 -64 4 BA19(MT+) 5763 14.1 -45 -72 7 BA39(MST) 243 5.59

R Superior Temporal Gyrus 68 -31 16 BA42 17 6.1

Table 3.2 Voxel coordinates show the local maximum of an active voxel cluster with SPM group results of motion sensitive stimulation (random effects analysis, FWE corrected, p<0.05, n = 6); BA = Brodmann Area; fR = functional region; Active Percentage = active percent of activated voxels in corresponding BA; T = t-value at voxel level. The localizer stimulation show that both BA37/MT+ in bilateral hemispheres are activated (right/left T value= 18.89/16.51), with larger activated clusters (right/left cluster = 3303/2416), parts of bilateral super parietal lobule cortex (BA19/V3A) are activated as well and some cortical activation is observed at left linsula (BA18/V2); Right ipsilateral stimulation activates left BA19/MT+ (t-value = 14.1, cluster = 5763) and right BA37/MST (t-value = 9.09, cluster = 359), activated clusters in MST are less than the contralateral MT+. Left ipsilateral stimulation activates right BA37/MT+ (t-value = 16.63, cluster = 5316) and left BA39/MST (t-value = 5.59, cluster = 243), the cluster in left MST is smaller than in right MT+.

50

3.2.3 Possible structural connectivity between SPEM

areas and MT+ complex

Fig.3.9 Fiber tracking interconnection between the ipsilateral SPEM and motion sensitive cortical

Fig. 3.9 Fiber tracking interconnection between the ipsilateral SPEM and motion sensitive cortical. Fiber tracking results overlaid on sagittal images. A. left MT+ to left PPC (red), B. right MT+ to right PPC (blue), C. left MT+ to left FEF (red), D. right MT+ to right FEF (blue), E. left MT+ to left SEF (red), F. right MT+ to right SEF (blue).

B

C C

B

D

D

E

D

E

A

C

B

⑦

⑧

①

②

D

E

E

F

D

51

Fig.3.10 Transcallosal connection between the contalateral SPEM and motion sensitive cortical

Fig.3.10 Transcallosal connection between the SPEM and motion sensitive cortical. Fiber tracking results overlaid on coronal images. A. left MT+ to right PPC through the splenium of the corpus callosum (red), B. left PPC to right PPC through the splenium of the corpus callosum (red), C. left FEF to right FEF (red) and left SEF to right SEF through the body of the corpus callosum (blue).

Fig.3.11 Fiber tracking between monkey and human

Fig.3.11 A: Schematic diagrams depicting the ILF interconnect MT and MST with PPC in the coronal section of rhesus monkey’s brain (Yeterian and Pandya 2010). B: the similar fiber connection between MT+ and PPC (blue) in the left hemisphere in our result. C: schematic diagrams depicting the pathway ILF-SLF-II from lateral and ventral preoccipital cortices to the frontal lobe (BA 8Av) in monkey (Yeterian and Pandya 2010); D: the similar fiber pathway (red) from MT+ to FEF in the right hemisphere in our result.

G CA B D

MT+

FEF

MT+

PPC

A

C

B

52

Fig.3.12 Interconnection in the SPEM and motion sensitive activation areas

Fig.3.12 A. Supra-lateral view of fiber tracking-obtained interconnections between the left ipsilateral cortical activations (blue: MT+ to PPC, purple: MT+ to SEF and FEF). B: Antero-lateral view of fiber pathways interconnecting the contralateral activations of the SPEM network (red: SEF to SEF, turquoise: FEF to FEF, green: PPC to PPC, orange: MT+ to MT+).

3.3 Discussion

3.3.1 SPEM and motion sensitive cortex

3.3.1.1 SPEM In this part, the methods were based on the previous study (Ohlendorf et al.

2007) who used two dots as the SPEM stimulation, we only used one dot in the

visual field as our visual pursuit target, but almost the same cortical areas were

activated by this stimulation. Eye pursuit of a small moving target with induced

widespread activation in MT+ (BA 19, 37, 39), PPC (BA 5, 7), FEF (BA 6, 8, 9,

46), SEF (BA 6) and additional activation in precuneus, cuneus and V1, the

cingulate gyrus. Thus, besides the parieto-occipital regions involved in sensory

input processing, the smooth eye pursuit movement task causes activation in

more frontal regions. This implies that SEF and FEF are involved in processing

and control of oculomotor output signals (Heide and Gotz 1996). In the primary

visual cortex, pursuit eye movements appeared to activate V1 slightly, since

the experiment was performed in complete darkness (even after adaptation to

darkness, subjects could see nothing else but the dot). Retinal slip during

pursuit might have added to the response in V1 and other parts of the primary

cortex.

A B

53

3.3.1.2 Functional role of MT+ in SPEM The visual stimulus activated a circumscribed subregion of MT+ in the dorsal

anterior part. This has not been shown so far, because traditionally large field

motion patterns were used to stimulate MT+ in previous studies (Tootell and

Taylor 1995; Tootell et al. 1995a; Tootell et al. 1995b; Dukelow et al. 2001;

Huk et al. 2002). In contrast, eye tracking of the single moving dot yielded

strong and widespread activation, extending into ventral and posterior parts.

Ohlendorf et al. (Ohlendorf et al. 2007; Ohlendorf et al. 2008) used two visual

stimulation dots and showed that MT+ was invoked by the four different visual

stimulations. In our study, we used the stimulation with only a single dot, but

the response of visual motion still extended across the MT+ and the

conjunctive areas. We assumed that the eye movement is dispersed across

the visual map of the MT+. This indicates a remapping of MT+ and the new

MT+ map would then encode the object trajectory.

In humans, the motion sensitive regions of MT+ were identified in early studies

(Zeki 1974; Tootell and Taylor 1995; Tootell et al. 1995a; Tootell et al. 1995b).

Evidence for a spatial separation in V5 and V5A (in analogy to MT and MST in

monkey) has been sparse to date. Huk (Huk et al. 2002) found some evidence

by testing the inter-subject variability. Dukelow (Dukelow et al. 2001) described

a subregion that was activated by non-visual pursuit located in the

anterolateral portion of MT+. Compared to the activation evoked by wide-field

optic flow during fixation, the visual pursuit led to extended activation of MT+.

The work of Dukelow (Dukelow et al. 2001) therefore indirectly supports our

assumption that pursuit of a single dot in darkness activates most MT+ areas.

3.3.1.3 FEF and SEF function in SPEM The overlapping region seen in our experiments in the FEF and SEF responds

to eye movements, and the FEF and SEF both respond significantly which is in

accordance with a previous study (Ohlendorf et al. 2008). Lesions in the FEF

impair performance on saccadic and pursuit tasks (Ott et al. 1987; Heide et al.

1996). Activation of the FEF has been observed in both saccadic and

smooth-pursuit eye tracking in humans (Ohlendorf et al. 2007). Activation

studies suggest that the FEF are involved in task execution, while prefrontal

54

association cortex and dorsolateral prefrontal cortex contribute to initiation and

monitoring of eye movements (Paus 1996).

The SEF are thought to be important in the intent to perform rather than the

execution of eye movement itself (Grosbras et al. 1999). There is flexibility in

that the effect of electrical stimulation of the SEF depends on what a monkey

has been trained to do previously, suggesting that it plays a crucial role in

adaptation and learning (Gaymard et al. 1999).

3.3.1.4 PPC function in SPEM A further region participating in the processing of eye movements in our study

is located in PPC. Neurons in area BA 7A of the parietal cortex encode the eye

position in space rather than the target position on the retina (Andersen et al.

1990). In further studies, the intraparietal sulcus has been shown to be an area

of convergence of multimodal stimuli (Andersen and Atchley 1997).

3.3.2 Motion processing in the area MT+

This study extended the previous study (Ohlendorf et al. 2008) using a smaller

voxel size for fMRI measurements. Comparing optic flow and static visual

stimulation in the two visual hemifields we replicated the shown activations of

two subregions within the human MT+, one subarea located more in the

anterior part of MT+ area, presumably MST, and one subarea located more in

the posterior part of MT+, presumably MT. The location of the subregions MT

and MST determined the group level data analysis is in line with previous

results showing single subject data (Dukelow et al. 2001; Huk et al. 2002;

Smith et al. 2006).

3.3.3 Structural connectivity in SPEM and motion

sensitive cortices

3.3.3.1 Fiber tracking in MT+ MT+ was interconnected with contralateral MT+ by transcallosal fibers through

the splenium of the corpus callosum. This finding is corroborated by previous

studies in monkeys (Tusa and Ungerleider 1988). On the ipsilateral

55

hemisphere MT+ was connected to PPC either via U-fibers as shown by Tusa

and Ungerleider in monkeys (Tusa and Ungerleider 1988) or via the inferior

longitudinal fascicle (ILF) as shown by Schmahmann et al (Schmahmann et al.

2007). As these findings originate from the monkey, it seems to be possible

that in humans, the ILF is the fiber connection from MT+ and PPC, because

the human parietal cortex is larger than that of the monkeys, and a long fiber

association might be more probable. Yeterian and Pandya (Yeterian and

Pandya 2010) suggested a connection from MT+ to FEF and SEF along the

ILF and superior longitudinal fascicle subcomponent II (SLF-II), but could not

give full proof of this pathway in the own data. They consider ILF/SLF-II for the

most probable pathway between MT+ and the frontal lobe.

3.3.3.2 Fiber tracking in PPC PPC was interconnected to the contralateral PPC via transcallosal fibers

through the splenium of the corpus callosum (Tusa and Ungerleider 1988).

Other connections were not taken into account as the main source of

investigation was the motion sensitive MT+ area.

3.3.3.3 Fiber tracking in FEF and SEF Transcallosal fibers from FEF to contralateral FEF were running through the

body of the corpus callosum which is in line with the study of Tusa and

Ungerleider in monkeys (Tusa and Ungerleider 1988). Similar fibers were

found for the SEF, but not proven in animal models so far. For the whole SMA,

however, transcallosal fibers could be established by electrical stimulation in

owl monkeys (Gould et al. 1986).

3.3.3.4 Fiber tracking in the functional context of MT+ The found fiber tracts are in agreement with the concept of an orientation

selective and motion sensitive system. The main input to MT+ comes from

efferent fibers of V2, which receives itself afferent fibers from lamina IV B from

V1. From MT+ efferent fibers pass to the parietal cortex (PPC). The whole

pathway runs in a dorsally directed path (possibly ILF) and is called the dorsal

stream or the “where”-stream (Goodale and Milner 1992; Zilles and Gerd

1998). Connections from the preoccipital extrastriate cortex (MT+ or V5) to the

premotor areas FEF and SEF seem to implicate a role in ocular control,

56

reaching movements and grasping actions in rhesus monkeys (Goodale and

Milner 1992). A presence of a functional network for SPEM via a bilateral

neuronal network of FEF and possibly SEF via transcallosal fibers has not yet

been described. On the other hand in vertical saccades, one of the functions of

callosal connections of the FEF was thought to ensure a necessary

cooperation of both sides (Schlag et al. 1998). Even if direct evidence is not

given so far, a similar role for SPEM would be conceivable.

57

Chapter 4: Abstract

The aim of this work was to investigate whether optic flow at different locations

of the visual field leads to spatially different activations of MT+ and to

investigate possible structural connectivities between areas processing SPEM

and visual motion.

In the first study, we presented optic flow patches at different locations of the

visual field. Cortical activation results were overlaid on a flatmap template

brain to show local differences of activation in MT+ depending on the

localization of the stimulation. Optic flow located in the upper half of the visual

field activated a larger and more anterior and dorsal portion of MT+ than the

lower half of the visual field. Optic flow stimulation along a vertical centre line

of the visual field led to a more widespread activation in anterior dorso-ventral

directions of MT+ than optic flow stimulation along a horizontal centre line of

the visual field. Optic flow located in the periphery of the visual field activated a

smaller and more dorsal portion of MT+; visual stimulation near the centre of

the visual field flow patches activated a larger and ventral portion of MT+.

To summarize, in this experiment using high resolution fMRI we can show in a

group of subjects that localization dependant motion processing in the MT+

complex exists. However, location differences of processing do not seem to be

clearly retinotopic but of a more abstract organization.

In the second study, seed points from SPEM and motion sensitive stimulations

were used to perform fiber tracking. MT+ was found to be connected to

ipsilateral PPC via the ILF, and to FEF and SEF via SLF-II. Transcallosal

interconnections were found between MT+ and MT+, PPC and PPC, FEF and

FEF, and SEF and SEF. The found fiber connections were corrected by animal

studies in monkeys. This study showed the interconnections in human SPEM

cortices as being part of major association (ILF/SLF-II) and commissural

(corpus callosum) pathways.

58

Zusammungfassung

Das Ziel dieser Arbeit war es, zu untersuchen, ob die Präsentation von optischem

Fluss an verschiedenen Orten des visuellen Feldes Aktivierung zu lokal

unterschiedlichen Aktivierungen des MT+ Komplexes führt. Des weiteren wurden

mögliche strukturelle Konnektivitäten zwischen SPEM und Bewegung

verarbeitenden kortikalen Regionen untersucht, .

In der ersten Studie wurden Felder optischen Flusses an verschiedenen Orten

des visuellen Feldes präsentiert. Die aktivierten kortikalen Regionen wurden

einem Flatmap-Template überlagert, um darzustellen wie sich die Aktivierung in

MT+, abhängig von der Stimulus-Position im visuellen Feld, verändert. Die

Ergebnisse zeigen, dass optischer Fluss in der oberen Hälfte des visuellen Feldes

einen größeren und eher dorsalen/anterioren Teil von MT+ aktivieren, als

optischer Fluss in der unteren Hälfte des visuellen Feldes. Optischer Fluss

entlang einer zentralen vertikalen Linie des visuellen Feldes führt zu einer

ausgedehnteren Aktivierung in doso-ventraler Ausdehnung von MT+, als entlang

einer zentralen horizontalen Linie des visuellen Feldes. Optischer Fluss in der

Periphärie des visuellen Feldes aktiviert eine kleinere und eher anterior-dorsale

Region von MT+, während optischer Fluss in der Nähe des Zentrums des

visuellen Feldes einen größeren, mehr ventralen Teil von MT+ aktivierte.

Zusammenfassend konnten wir in dieser Studie, die eine höhere Auflösung der

fMRT-Messungen verwendete als vorherige Studien, auf Gruppenebene zeigen,

dass eine Abhängigkeit der lokalen Aktivierung von MT+ von der Lokalisation der

Bewegungsstimulation im visuellen Feld existiert. Die lokalen

Aktivierungsunterschiede scheinen jedoch nicht klar retinotopisch, sondern

abstrakter organisiert zu sein.

In der zweiten Studie, wurden Saatpunkte für das Fiber Tracking durch ein fMRI

experiment genommen, das SPEM sensitive Areale aktivierte. Dabei handelte es

sich um das Areal MT+, PPC, FEF, SEF. Alle vier Regionen waren jeweils zur

analogen Region der Gegenseite durch transkallosale Fasern verbundun.

Ipsilateral waren MT+ and PPC; sowie FEF und SEF über den ILF und SLF-II

miteinander verbunden.

Unsere Ergebnissen legen nahe, dass optischer Fluss in Abhängigkeit vom Ort im

visuellen Feld zu individueller MT+ Aktivierung führt und strukturelle Konnektivität

zwischen den SPEM-Arealen und den bewegungssensitiven Arealen existiert.

59

Chapter 5: References

Allman, J. M. and J. H. Kaas (1971). A representation of the visual field in the caudal third of the middle tempral gyrus of the owl monkey (Aotus trivirgatus). Brain Res 31(1): 85-105.

Andersen, G. J. and P. Atchley (1997). Smoothness of the velocity field and three-dimensional surface detection from optic flow. Percept Psychophys 59(3): 358-69.

Andersen, R. A., C. Asanuma, G. Essick and R. M. Siegel (1990). Corticocortical connections of anatomically and physiologically defined subdivisions within the inferior parietal lobule. J Comp Neurol 296(1): 65-113.

Anderson, T. J., I. H. Jenkins, D. J. Brooks, M. B. Hawken, R. S. Frackowiak and C. Kennard (1994). Cortical control of saccades and fixation in man. A PET study. Brain 117 ( Pt 5): 1073-84.

Astruc, J. (1971). Corticofugal connections of area 8 (frontal eye field) in Macaca mulatta. Brain Res 33(2): 241-56.

Barbas, H. and B. Dubrovsky (1981). Central and peripheral effects of tonic vibratory stimuli to dorsal neck and extraocular muscles in the cat. Exp Neurol 74(1): 67-85.

Barbas, H. and M. M. Mesulam (1981). Organization of afferent input to subdivisions of area 8 in the rhesus monkey. J Comp Neurol 200(3): 407-31.

Basser, P. J., J. Mattiello and D. LeBihan (1994). MR diffusion tensor spectroscopy and imaging. Biophys J 66(1): 259-67.

Bauer, R. and B. M. Dow (1989). Complementary global maps for orientation coding in upper and lower layers of the monkey's foveal striate cortex. Exp Brain Res 76(3): 503-9.

Berg, V. D. (2000). Human Ego-Motion Preception. Neuronal processing of optic flow. Dubai, Academic. 44: 3-25.

Bingham, G. P. (1993). Optical flow from eye movement with head immobilized: "ocular occlusion" beyond the nose. Vision Res 33(5-6): 777-89.

Bonvento, G., N. Cholet and J. Seylaz (2000). Sustained attenuation of the cerebrovascular response to a 10 min whisker stimulation following neuronal nitric oxide synthase inhibition. Neurosci Res 37(2): 163-6.

Bradley, D. V., A. Fernandes, M. Tigges and R. G. Boothe (1996). Diffuser contact lenses retard axial elongation in infant rhesus monkeys. Vision Res 36(4): 509-14.

Braun, D. I., D. K. Boman and J. R. Hotson (1996). Anticipatory smooth eye movements and predictive pursuit after unilateral lesions in human brain. Exp Brain Res 110(1): 111-6.

Crowell, J. A. and M. S. Banks (1996). Ideal observer for heading judgments. Vision Res 36(3): 471-90.

Crowne, D. P. (1983). The frontal eye field and attention. Psychol Bull 93(2): 232-60.

Cusick, C. G., B. Seltzer, M. Cola and E. Griggs (1995). Chemoarchitectonics and corticocortical terminations within the superior temporal sulcus of the rhesus monkey: evidence for subdivisions of superior temporal polysensory cortex. J Comp Neurol 360(3): 513-35.

Denny-Brown, D. (1966). Pathophysiology of focal cerebral ischemia. Res Publ Assoc Res Nerv Ment Dis 41: 196-204.

Desimone, R. and L. G. Ungerleider (1986). Multiple visual areas in the caudal superior temporal sulcus of the macaque. J Comp Neurol 248(2): 164-89.

Di Salle, F., F. Esposito, T. Scarabino, E. Formisano, E. Marciano, C. Saulino, S. Cirillo, R. Elefante, K. Scheffler and E. Seifritz (2003). fMRI of the auditory system:

60

understanding the neural basis of auditory gestalt. Magn Reson Imaging 21(10): 1213-24.

Dubner, R. and S. M. Zeki (1971). Response properties and receptive fields of cells in an anatomically defined region of the superior temporal sulcus in the monkey. Brain Res 35(2): 528-32.

Duffy, C. J. and R. H. Wurtz (1991a). Sensitivity of MST neurons to optic flow stimuli. I. A continuum of response selectivity to large-field stimuli. J Neurophysiol 65(6): 1329-45.

Duffy, C. J. and R. H. Wurtz (1991b). Sensitivity of MST neurons to optic flow stimuli. II. Mechanisms of response selectivity revealed by small-field stimuli. J Neurophysiol 65(6): 1346-59.

Dukelow, S. P., J. F. DeSouza, J. C. Culham, A. V. van den Berg, R. S. Menon and T. Vilis (2001). Distinguishing subregions of the human MT+ complex using visual fields and pursuit eye movements. J Neurophysiol 86(4): 1991-2000.

Dursteler, M. R. and R. H. Wurtz (1988). Pursuit and optokinetic deficits following chemical lesions of cortical areas MT and MST. J Neurophysiol 60(3): 940-65.

Felleman, D. J., Y. Xiao and E. McClendon (1997). Modular organization of occipito-temporal pathways: cortical connections between visual area 4 and visual area 2 and posterior inferotemporal ventral area in macaque monkeys. J Neurosci 17(9): 3185-200.

Gaymard, B., C. J. Ploner, S. Rivaud-Pechoux and C. Pierrot-Deseilligny (1999). The frontal eye field is involved in spatial short-term memory but not in reflexive saccade inhibition. Exp Brain Res 129(2): 288-301.

Geesaman, B. J. and R. A. Andersen (1996). The analysis of complex motion patterns by form/cue invariant MSTd neurons. J Neurosci 16(15): 4716-32.

Goodale, M. A. and A. D. Milner (1992). Separate visual pathways for perception and action. Trends Neurosci 15(1): 20-5.

Gottlieb, J. P., M. G. MacAvoy and C. J. Bruce (1994). Neural responses related to smooth-pursuit eye movements and their correspondence with electrically elicited smooth eye movements in the primate frontal eye field. J Neurophysiol 72(4): 1634-53.

Gould, H. J., 3rd, C. G. Cusick, T. P. Pons and J. H. Kaas (1986). The relationship of corpus callosum connections to electrical stimulation maps of motor, supplementary motor, and the frontal eye fields in owl monkeys. J Comp Neurol 247(3): 297-325.

Grefkes, C. and G. R. Fink (2005). The functional organization of the intraparietal sulcus in humans and monkeys. J Anat 207(1): 3-17.

Grosbras, M. H., E. Lobel, P. F. Van de Moortele, D. LeBihan and A. Berthoz (1999). An anatomical landmark for the supplementary eye fields in human revealed with functional magnetic resonance imaging. Cereb Cortex 9(7): 705-11.

Hadani, I., G. Ishai and M. Gur (1980). Visual stability and space perception in monocular vision: mathematical model. J Opt Soc Am 70(1): 60-5.

Hasegawa, R. P., A. M. Blitz, N. L. Geller and M. E. Goldberg (2000). Neurons in monkey prefrontal cortex that track past or predict future performance. Science 290(5497): 1786-9.

Hatakeyama, K., A. Horisaki, S. Niikura, Y. Narusaka, H. Abe, H. Yoshiaki, M. Ishida, H. Fukuoka and S. Matsumoto (2010). Mapping of quantitative trait loci for high level of self-incompatibility in Brassica rapa L. Genome 53(4): 257-65.

Hawken, M. J., A. J. Parker and J. S. Lund (1988). Laminar organization and contrast sensitivity of direction-selective cells in the striate cortex of the Old World monkey. J Neurosci 8(10): 3541-8.

Heide, G. and K. G. Gotz (1996). Optomotor control of course and altitude in Drosophila melanogaster is correlated with distinct activities of at least three pairs of flight steering muscles. J Exp Biol 199(Pt 8): 1711-26.

61

Heide, W., K. Kurzidim and D. Kompf (1996). Deficits of smooth pursuit eye movements after frontal and parietal lesions. Brain 119 ( Pt 6): 1951-69.

Heinen, S. J. (1995). Single neuron activity in the dorsomedial frontal cortex during smooth pursuit eye movements. Exp Brain Res 104(2): 357-61.

Heinen, S. J. and M. Liu (1997). Single-neuron activity in the dorsomedial frontal cortex during smooth-pursuit eye movements to predictable target motion. Vis Neurosci 14(5): 853-65.

Horio, N. and M. Horiguchi (2004). Retinal blood flow analysis using intraoperative video fluorescein angiography combined with optical fiber-free intravitreal surgery system. Am J Ophthalmol 138(6): 1082-3.

Hubel, D. H. and T. N. Wiesel (1968). Receptive fields and functional architecture of monkey striate cortex. J Physiol 195(1): 215-43.

Huk, A. C., R. F. Dougherty and D. J. Heeger (2002). Retinotopy and functional subdivision of human areas MT and MST. J Neurosci 22(16): 7195-205.

Ilg, U. J. (2008). The role of areas MT and MST in coding of visual motion underlying the execution of smooth pursuit. Vision Res 48(20): 2062-9.

Inoue, K., R. Kawashima, K. Satoh, S. Kinomura, M. Sugiura, R. Goto, M. Ito and H. Fukuda (2000). A PET study of visuomotor learning under optical rotation. Neuroimage 11(5 Pt 1): 505-16.

Jones, B. and A. Santi (1978). Lateral asymmetries in visual perception with and without eye movements. Cortex 14(2): 164-8.

Jones, E. G. and T. P. Powell (1970). An anatomical study of converging sensory pathways within the cerebral cortex of the monkey. Brain 93(4): 793-820.

Kaas, J. H. and A. Morel (1993). Connections of visual areas of the upper temporal lobe of owl monkeys: the MT crescent and dorsal and ventral subdivisions of FST. J Neurosci 13(2): 534-46.

Keating, E. G. (1991). Frontal eye field lesions impair predictive and visually-guided pursuit eye movements. Exp Brain Res 86(2): 311-23.

Keating, E. G. (1993). Lesions of the frontal eye field impair pursuit eye movements, but preserve the predictions driving them. Behav Brain Res 53(1-2): 91-104.

Kimmig, H., S. Ohlendorf, O. Speck, A. Sprenger, R. M. Rutschmann, S. Haller and M. W. Greenlee (2008). fMRI evidence for sensorimotor transformations in human cortex during smooth pursuit eye movements. Neuropsychologia 46(8): 2203-13.

Komatsu, H. and R. H. Wurtz (1988a). Relation of cortical areas MT and MST to pursuit eye movements. I. Localization and visual properties of neurons. J Neurophysiol 60(2): 580-603.

Komatsu, H. and R. H. Wurtz (1988c). Relation of cortical areas MT and MST to pursuit eye movements. III. Interaction with full-field visual stimulation. J Neurophysiol 60(2): 621-44.

Kraft, A., W. H. Sommer, S. Schmidt and S. A. Brandt (2010). Dynamic upper and lower visual field preferences within the human dorsal frontoparietal attention network. Hum Brain Mapping Epub ahead of print.

Krauzlis, R. J. (2004). Recasting the smooth pursuit eye movement system. J Neurophysiol 91(2): 591-603.

Kreher, B. W., S. Schnell, I. Mader, K. A. Il'yasov, J. Hennig, V. G. Kiselev and D. Saur (2008). Connecting and merging fibres: pathway extraction by combining probability maps. Neuroimage 43(1): 81-9.

Kunzle, H. and K. Akert (1977). Efferent connections of cortical, area 8 (frontal eye field) in Macaca fascicularis. A reinvestigation using the autoradiographic technique. J Comp Neurol 173(1): 147-64.

62

Latto, R. and A. Cowey (1971). Visual field defects after frontal eye-field lesions in monkeys. Brain Res 30(1): 1-24.

Lee, D. N. (1980). The optic flow field: the foundation of vision. Philos Trans R Soc Lond B Biol Sci 290(1038): 169-79.

Logothetis, N. K., J. Pauls, M. Augath, T. Trinath and A. Oeltermann (2001). Neurophysiological investigation of the basis of the fMRI signal. Nature 412(6843): 150-7.

Lynch, J. C. (1987). Frontal eye field lesions in monkeys disrupt visual pursuit. Exp Brain Res 68(2): 437-41.

Macaluso, E. and F. Patria (2007). Spatial re-orienting of visual attention along the horizontal or the vertical axis. Exp Brain Res 180(1): 23-34.

MacAvoy, M. G., J. P. Gottlieb and C. J. Bruce (1991). Smooth-pursuit eye movement representation in the primate frontal eye field. Cereb Cortex 1(1): 95-102.

Magistretti, P. J. and L. Pellerin (1999). Cellular mechanisms of brain energy metabolism and their relevance to functional brain imaging. Philos Trans R Soc Lond B Biol Sci 354(1387): 1155-63.

Martinez-Trujillo, J. C., J. K. Tsotsos, E. Simine, M. Pomplun, R. Wildes, S. Treue, H. J. Heinze and J. M. Hopf (2005). Selectivity for speed gradients in human area MT/V5. Neuroreport 16(5): 435-8.

Maunsell, J. H. and D. C. van Essen (1983a). The connections of the middle temporal visual area (MT) and their relationship to a cortical hierarchy in the macaque monkey. J Neurosci 3(12): 2563-86.

Maunsell, J. H. and D. C. Van Essen (1983b). Functional properties of neurons in middle temporal visual area of the macaque monkey. II. Binocular interactions and sensitivity to binocular disparity. J Neurophysiol 49(5): 1148-67.

Maunsell, J. H. and D. C. Van Essen (1987). Topographic organization of the middle temporal visual area in the macaque monkey: representational biases and the relationship to callosal connections and myeloarchitectonic boundaries. J Comp Neurol 266(4): 535-55.

Mori, S., B. J. Crain, V. P. Chacko and P. C. van Zijl (1999). Three-dimensional tracking of axonal projections in the brain by magnetic resonance imaging. Ann Neurol 45(2): 265-9.

Mori, S. and P. C. van Zijl (2002). Fiber tracking: principles and strategies - a technical review. NMR Biomed 15(7-8): 468-80.

Movshon, J. A. and W. T. Newsome (1996). Visual response properties of striate cortical neurons projecting to area MT in macaque monkeys. J Neurosci 16(23): 7733-41.

Muri, R. M., S. Rivaud, B. Gaymard, C. J. Ploner, A. I. Vermersch, C. W. Hess and C. Pierrot-Deseilligny (1999). Role of the prefrontal cortex in the control of express saccades. A transcranial magnetic stimulation study. Neuropsychologia 37(2): 199-206.

Nassi, J. J., D. C. Lyon and E. M. Callaway (2006). The parvocellular LGN provides a robust disynaptic input to the visual motion area MT. Neuron 50(2): 319-27.

Negahdaripour, S. (September 1998.). Revised Definition of Optical Flow: Integration of Radiometric and Geometric Cues for Dynamic Scene Analysis. PAMI 20(9): 961–979.

Newsome, W. T., R. H. Wurtz and H. Komatsu (1988). Relation of cortical areas MT and MST to pursuit eye movements. II. Differentiation of retinal from extraretinal inputs. J Neurophysiol 60(2): 604-20.

Ogawa, S., T. M. Lee, A. R. Kay and D. W. Tank (1990). Brain magnetic resonance imaging with contrast dependent on blood oxygenation. Proc Natl Acad Sci U S A 87(24): 9868-72.

Ohlendorf, S., H. Kimmig, V. Glauche and S. Haller (2007). Gaze pursuit, 'attention pursuit' and their effects on cortical activations. Eur J Neurosci 26(7): 2096-108.

63

Ohlendorf, S., A. Sprenger, O. Speck, S. Haller and H. Kimmig (2008). Optic flow stimuli in and near the visual field centre: a group FMRI study of motion sensitive regions. PLoS One 3(12): e4043.

Ott, D., R. Eckmiller and O. Bock (1987). A head-mounted device for measurement of pointing to visual targets without seeing the pointing arm. Vision Res 27(2): 307-9.

Pack, C. C. and R. T. Born (2001). Temporal dynamics of a neural solution to the aperture problem in visual area MT of macaque brain. Nature 409(6823): 1040-2.

Parker, G. J., H. A. Haroon and C. A. Wheeler-Kingshott (2003). A framework for a streamline-based probabilistic index of connectivity (PICo) using a structural interpretation of MRI diffusion measurements. J Magn Reson Imaging 18(2): 242-54.

Paus, T. (1996). Location and function of the human frontal eye-field: a selective review. Neuropsychologia 34(6): 475-83.

Petit, L. and J. V. Haxby (1999). Functional anatomy of pursuit eye movements in humans as revealed by fMRI. J Neurophysiol 82(1): 463-71.

Ponce, C. R., S. G. Lomber and R. T. Born (2008). Integrating motion and depth via parallel pathways. Nat Neurosci 11(2): 216-23.

Posner, M. I. (1980). Orienting of attention. Q J Exp Psychol 32(1): 3-25.

Pragay, E. B., A. F. Mirsky and R. K. Nakamura (1987). Attention-related unit activity in the frontal association cortex during a go/no-go visual discrimination task. Exp Neurol 96(3): 481-500.

Priebe, N. J., M. M. Churchland and S. G. Lisberger (2001). Reconstruction of target speed for the guidance of pursuit eye movements. J Neurosci 21(9): 3196-206.

Ptito, M., R. Kupers, J. Faubert and A. Gjedde (2001). Cortical representation of inward and outward radial motion in man. Neuroimage 14(6): 1409-15.

Ritaccio, A. L., E. J. Hickling and V. Ramani (1992). The role of dominant premotor cortex and grapheme to phoneme transformation in reading epilepsy. A neuroanatomic, neurophysiologic, and neuropsychological study. Arch Neurol 49(9): 933-9.

Rubin, N., K. Nakayama and R. Shapley (1996). Enhanced perception of illusory contours in the lower versus upper visual hemifields. Science 271(5249): 651-3.

Saito, H., M. Yukie, K. Tanaka, K. Hikosaka, Y. Fukada and E. Iwai (1986). Integration of direction signals of image motion in the superior temporal sulcus of the macaque monkey. J Neurosci 6(1): 145-57.

Sakata, H., H. Shibutani, K. Kawano and T. L. Harrington (1985). Neural mechanisms of space vision in the parietal association cortex of the monkey. Vision Res 25(3): 453-63.

Schlag, J., P. Dassonville and M. Schlag-Rey (1998). Interaction of the two frontal eye fields before saccade onset. J Neurophysiol 79(1): 64-72.

Schmahmann, J. D., D. N. Pandya, R. Wang, G. Dai, H. E. D'Arceuil, A. J. de Crespigny and V. J. Wedeen (2007). Association fibre pathways of the brain: parallel observations from diffusion spectrum imaging and autoradiography. Brain 130(Pt 3): 630-53.

Segraves, M. A., M. E. Goldberg, S. Y. Deng, C. J. Bruce, L. G. Ungerleider and M. Mishkin (1987). The role of striate cortex in the guidance of eye movements in the monkey. J Neurosci 7(10): 3040-58.

Shi, D., H. R. Friedman and C. J. Bruce (1998). Deficits in smooth-pursuit eye movements after muscimol inactivation within the primate's frontal eye field. J Neurophysiol 80(1): 458-64.

Sincich, L. C., K. F. Park, M. J. Wohlgemuth and J. C. Horton (2004). Bypassing V1: a direct geniculate input to area MT. Nat Neurosci 7(10): 1123-8.

Smith, A. T., M. B. Wall, A. L. Williams and K. D. Singh (2006). Sensitivity to optic flow in human cortical areas MT and MST. Eur J Neurosci 23(2): 561-9.

64

Staempfli, P., C. Reischauer, T. Jaermann, A. Valavanis, S. Kollias and P. Boesiger (2008). Combining fMRI and DTI: a framework for exploring the limits of fMRI-guided DTI fiber tracking and for verifying DTI-based fiber tractography results. Neuroimage 39(1): 119-26.

Stepniewska, I. and J. H. Kaas (1996). Topographic patterns of V2 cortical connections in macaque monkeys. J Comp Neurol 371(1): 129-52.

Stoffregen, T. A., M. A. Schmuckler and E. J. Gibson (1987). Use of central and peripheral optical flow in stance and locomotion in young walkers. Perception 16(1): 113-9.

Tanaka, K., K. Hikosaka, H. Saito, M. Yukie, Y. Fukada and E. Iwai (1986). Analysis of local and wide-field movements in the superior temporal visual areas of the macaque monkey. J Neurosci 6(1): 134-44.

Tanji, J. and K. Shima (1996). Supplementary motor cortex in organization of movement. Eur Neurol 36 Suppl 1: 13-9.

Tootell, R. B., J. D. Mendola, N. K. Hadjikhani, P. J. Ledden, A. K. Liu, J. B. Reppas, M. I. Sereno and A. M. Dale (1997). Functional analysis of V3A and related areas in human visual cortex. J Neurosci 17(18): 7060-78.

Tootell, R. B., J. B. Reppas, A. M. Dale, R. B. Look, M. I. Sereno, R. Malach, T. J. Brady and B. R. Rosen (1995a). Visual motion aftereffect in human cortical area MT revealed by functional magnetic resonance imaging. Nature 375(6527): 139-41.

Tootell, R. B., J. B. Reppas, K. K. Kwong, R. Malach, R. T. Born, T. J. Brady, B. R. Rosen and J. W. Belliveau (1995b). Functional analysis of human MT and related visual cortical areas using magnetic resonance imaging. J Neurosci 15(4): 3215-30.

Tootell, R. B. and J. B. Taylor (1995). Anatomical evidence for MT and additional cortical visual areas in humans. Cereb Cortex 5(1): 39-55.

Tsutsumi, T., H. Inaoka, Y. Fukuoka, T. Masuda and K. Kitamura (2007). Cross-coupling in a body-translating reaction: interaural optokinetic stimulation reflects a gravitational cue. Acta Otolaryngol 127(3): 273-9.

Tusa, R. J. and L. G. Ungerleider (1988). Fiber pathways of cortical areas mediating smooth pursuit eye movements in monkeys. Ann Neurol 23(2): 174-83.

Ungerleider, L. G. and R. Desimone (1986a). Projections to the superior temporal sulcus from the central and peripheral field representations of V1 and V2. J Comp Neurol 248(2): 147-63.

Ungerleider, L. G. and R. Desimone (1986b). Cortical connections of visual area MT in the macaque. J Comp Neurol 248(2): 190-222.

Ungerleider, R. M. (2005). Optimizing response of the neonate and infant to cardiopulmonary bypass. Cardiol Young 15 Suppl 1: 142-8.

Van Essen, D. C. (2005). A Population-Average, Landmark- and Surface-based (PALS) atlas of human cerebral cortex. Neuroimage 28(3): 635-62.

Van Essen, D. C., W. T. Newsome and J. L. Bixby (1982). The pattern of interhemispheric connections and its relationship to extrastriate visual areas in the macaque monkey. J Neurosci 2(3): 265-83.

Van Essen, D. C., W. T. Newsome, J. H. Maunsell and J. L. Bixby (1986). The projections from striate cortex (V1) to areas V2 and V3 in the macaque monkey: asymmetries, areal boundaries, and patchy connections. J Comp Neurol 244(4): 451-80.

Vandenberghe, R., D. R. Gitelman, T. B. Parrish and M. M. Mesulam (2001). Functional specificity of superior parietal mediation of spatial shifting. Neuroimage 14(3): 661-73.

Vanni, S., L. Henriksson, M. Viikari and A. C. James (2006). Retinotopic distribution of chromatic responses in human primary visual cortex. Eur J Neurosci 24(6): 1821-31.

65

Wagman, I. H., H. P. Krieger, C. A. Papatheodorou and M. B. Bender (1961). Eye movements elicited by surface and depth stimulation of the frontal lobe of Macaque mulatta. J Comp Neurol 117: 179-88.

Yan, T. and J. Wu (2010). Wide-field retinotopy distinguish subregions of the human MT+ by functional magnetic resonance imaging. 2010 IEEE/ICME International Conference on Complex Medical Engineering (CME), (2010): 106-110.

Yang, S. T. (1996). Role of nitric oxide in the maintenance of resting cerebral blood flow during chronic hypertension. Life Sci 58(15): 1231-8.

Yeterian, E. H. and D. N. Pandya (2010). Fiber pathways and cortical connections of preoccipital areas in rhesus monkeys. J Comp Neurol 518(18): 3725-51.

Zaitsev, M., J. Hennig and O. Speck (2004). Point spread function mapping with parallel imaging techniques and high acceleration factors: fast, robust, and flexible method for echo-planar imaging distortion correction. Magn Reson Med 52(5): 1156-66.

Zeki, S. M. (1974). Cells responding to changing image size and disparity in the cortex of the rhesus monkey. J Physiol 242(3): 827-41.

Zilles, K. and R. Gerd (1998). Funktionelle Neruoanatomie: Lehrbuch und Altas Pages.