Assessment of Physical Exercise Benefits on Brain Health for

Long-Duration Spaceflight Sleep and Health

Final Report Authors: Vladyslav V. Vyazovskiy1, Simon P. Fisher1 , Jessica Gemignani2, Leopold Summerer2 Affiliation: 1University of Oxford, Department of Physiology, Anatomy and Genetics, Sherrington Building, Parks Road, Oxford, OX1 3PT, UK, 2University, 2ESA ACT Date: 10 April 2017 Contacts: Vladyslav V. Vyazovskiy Tel: +44 01865 285396 Fax: +44 01865 272420 e-mail: vladyslav.vyazovskiy@dpag.ox.ac.uk

Leopold Summerer (Technical Officer) Tel: +31(0)715654192 Fax: +31(0)715658018 e-mail: act@esa.int

Available on the ACT website http://www.esa.int/act

Ariadna ID: 15/6301 Ariadna study type: Standard

Contract Number: 4000114025/15/NL/LF/as

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Table of Content

Summary ................................................................................................................. 3  Introduction ............................................................................................................. 5  Objectives ............................................................................................................. 11  Specific Experiments ........................................................................................... 12  Methods ................................................................................................................. 12  Results ................................................................................................................... 19  

Experiment 1 .................................................................................................................... 19  Experiment 2 .................................................................................................................... 24  Experiment 3 .................................................................................................................... 27  Experiment 4 .................................................................................................................... 28  

Discussion ............................................................................................................ 29  Conclusions and future perspectives ................................................................ 32  References ............................................................................................................ 37  

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Summary Poor sleep on the ISS remains one of the major factors, which affect negatively the

performance and psychological resilience of astronauts. Several studies have demonstrated

that sleep is severely disrupted during space flights, where factors such as microgravity,

workload, noise, light and other environmental influences play a role. Therefore, the

development of novel effective countermeasures for sleep loss, performance decrements

and psychological deficits associated with space missions is needed. An intriguing

possibility, which has not been tested previously, is that the benefits of exercise for the

brain can complement cognitive and psychological benefits provided by sleep.

Exercise has numerous benefits for health and is used widely as a prevention or

treatment strategy for a broad range of diseases. The prevailing view is that regular exercise

enhances overall physical fitness and primarily targets muscular strength and the cardio-

vascular system. However, evidence suggests that exercise also has numerous benefits for

mental and psychological health. The proposed mechanisms underlying the positive

influence of physical exercise on brain health are likely manifold and the role of improved

cerebral oxygen supply, increased neurotransmitter release, neuroproliferation and synaptic

plasticity have been studied in this context extensively.

Surprisingly, while long-term effects of exercise have been investigated in great

detail, few studies have addressed the activity of the brain directly during exercise, and the

immediate effects of exercise on brain functioning and sleep remain largely unknown.

Recently we obtained evidence that brain activity during wheel running in the mouse shows

similarities with certain activity patterns present during sleep. Although traditionally sleep

is considered a state markedly different from active behaviours, it appears to share with

exercise some of its beneficial effects for brain function, including cognitive enhancement,

improved mood and memory.

In this study we set out to investigate brain activity during voluntary exercise in

mice. In the first experiment, we performed detailed analysis of electroencephalogram

(EEG) and neuronal activity in two cortical regions of freely behaving mice during wheel

running behaviour. Unexpectedly, we found an overall suppression of cortical firing rates

during intense running at a constant speed, which raises an interesting possibility that

stereotypic waking behaviours may represent an economical ‘default’ mode of brain

functioning. Consistently, we found that running wheel activity correlated with wake

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duration, which led to the hypothesis that the build-up of ‘sleep need’ occurs at a slower

rate during running, thus enabling longer wakefulness.

Next we investigated the relationship between sleep deprivation (SD) and wheel

running. The animals were kept awake for 6 hours, when they had a choice to run or

explore novel objects. Interestingly, the animals would often prefer to run, and overall

running intensity correlated with decreased intrusions of sleep during the 6-h period of SD.

Furthermore, the proportion of waking during subsequent sleep opportunity interval was

reduced as a function of running intensity during the SD. We interpret this result as a

counteraction of sleep pressure provided by stereotypic running. Interestingly, this study

also revealed a positive association between the amount of running during waking and

faster EEG frequencies during subsequent sleep. Intrusion of faster frequencies into sleep

states typically indicates more superficial sleep state, which supports our hypothesis that

stereotypic running may reduce physiological sleep need. Furthermore, this result is

consistent with our observation that individual neurons, which are activated during running

may be also re-activated during sleep spindles: a type of cortical oscillation, which occurs

at a faster (typically 10-17 Hz) frequency, and has been linked to learning and cognitive

functions.

Next we hypothesized that running wheel availability directly affects subsequent

sleep, and to address this hypothesis, we performed an experiment where 3-h SD was

followed by 3-h waking period either with free access to the running wheel or with novel

objects to explore. Interestingly, when the animals were left undisturbed, those animals,

which had an opportunity to run, slept significantly less during the following 3-h interval,

as compared to animals, which instead explored novel objects. In a subset of animals, we

also performed a detailed EEG analysis, and found that running wheel activity was

associated with increased faster EEG delta activity (approximately 3-4 Hz) in the occipital

derivation, which suggests that wheel running has an influence on local network activity.

Finally, we used complex wheels, to investigate whether stereotypic locomotion has

differential effects as compared to learning a novel motor skill. We found distinct effects of

complex wheel running on wake EEG, manifested in faster and stronger theta (8-10 Hz)

EEG power during complex wheel running. Furthermore, 5 out of 8 animals showed

shorter sleep latency after a period of running on a complex wheel, and overall sleep was

increased by almost one hour. While this result suggests strong interindividual variability in

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the effects of voluntary exercise on sleep, it supports our hypothesis that voluntary

stereotypic running is associated with decreased sleep need, as compared with waking

dominated by demanding cognitive activities.

Altogether, our data suggest that stereotypic waking behaviours may either

counteract the build-up of sleep pressure, or even provide some of restorative functions,

typically provided by sleep. While further studies are undoubtedly necessary, this new

perspective opens novel opportunities for renormalisation of brain function in conditions

and settings where it is impossible to achieve long consolidated sleep periods, such as

throughout long-duration space missions, where confined environment, noise, irregular

light-dark exposure, microgravity and intense workload prevent sleep. Understanding the

neurophysiological mechanisms underlying the overlap in brain activity patterns between

exercise and sleep will allow the development of specific training schedules, which have

the potential to improve cognitive functioning and provide psychological benefits to

astronauts during long-duration space flights.

Introduction Physical exercise is known to have numerous health benefits and has been shown to be

effective in preventing or treating cardiovascular disorders, diabetes and cancer. Recently

however, the focus is shifting more towards investigating the benefits of physical exercise

on the human brain. There is evidence for beneficial effects of physical exercise in

neurodegenerative disorders, such as Alzheimer and Parkinson’s disease (Goodwin et al.,

2008; Rolland et al., 2008) and it has been used successfully as a non-pharmacological

therapy in mood disorders, such as depression (Strohle, 2009). Even in healthy individuals,

exercise can affect cognitive functions substantially. For example, it has been shown that

non-strenuous activities, like walking, can boost creativity by about 60% (Oppezzo and

Schwartz, 2014). Exercise also has potent psychological effects, which may be associated

with changes in brain plasticity and morphology. Specifically, increased grey matter

volume in the frontal, temporal, and parietal cortices has been reported in physically fit

people (Erickson et al., 2014; Gomez-Pinilla and Hillman, 2013). These parts of the brain

are important for decision-making, creativity, problem-solving, executive functions,

emotional regulation, memory, confidence, learning languages and processing sensory (e.g.

auditory) information. Important insights have been provided from studies in animal

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models. For example, it has been shown in mice that running leads to increased

neurogenesis in the hippocampus (van Praag et al., 1999) – the brain area crucially

implicated in memory and emotions (Buzsáki, 2006). Another recent study found that a

muscle secretory factor, cathepsin B (CTSB) protein, is important for the cognitive and

neurogenic benefits of running (Moon et al., 2016). Moreover, it has been reported in mice

that during locomotion the signal-to-noise ratio in visual stimulus detection increases

(Bennett et al., 2013; Niell and Stryker, 2010).

The mechanisms underlying the benefits of exercise for the brain are unknown but

may be related to specific changes in brain activity. Specifically, it is likely that the mode

of brain activity during exercise is fundamentally different from activity patterns during

other behaviours, because many types of exercise lack complexity and consist of repetitive

stereotypic or rhythmic movements, which are performed automatically without direct

volitional control and may involve central pattern generators (Marder and Bucher, 2001).

Surprisingly, while long-term effects of exercise have been investigated in great detail, few

Waking NREM sleep REM sleep

1 sec

EEG

MUA

Spikes

EMG

Figure 1. EEG and neuronal activity in three basic vigilance states. From top to bottom: surfaceelectroencephalogram (EEG) traces recorded from the somatosensory cortex during waking, NREMsleep and REM sleep. Multiunit activity (MUA) recorded simultaneously from a microwire array.Note high frequency tonic firing in waking and REM sleep, and the periods of population neuronalactivity and silence in NREM sleep. Raster plots of spike activity for the same 6 channels (eachvertical line is a spike). Note the close temporal relationship between the silent periods and thenegative phase of EEG slow waves. Bottom: Corresponding electromyogram (EMG).

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studies have addressed the activity of the brain directly during exercise, and the immediate

effects of exercise on brain functioning remain largely unknown.

Notably, another behaviour that benefits mental health is sleep. A fundamental

difference between wakefulness and sleep is the extent to which the brain is engaged in the

acquisition and processing of information. In all species carefully studied so far, waking

and sleep alternate on a regular basis and continuous wakefulness rarely lasts

spontaneously for more than several hours or a few days (Tobler, 2005). This suggests that

sleep is necessary and serves a vital role for both the brain and the body as a whole. The

maintenance of waking and sleep states is regulated by the activity arising from several

subcortical structures in the brainstem, hypothalamus and basal forebrain, which provide a

neuromodulatory, such as monoaminergic, orexinergic, glutamatergic, GABAergic and

cholinergic, action on the neocortex (Brown et al., 2012; Fort et al., 2009). Importantly, the

same neuromodulatory systems are crucially involved in locomotion and other active

behaviours (Constantinople and Bruno, 2011; Mileykovskiy et al., 2005; Polack et al.,

2013; Wu et al., 1999). During sleep the electroencephalogram (EEG) is dominated by

oscillations occurring in a slow (0.5-4 Hz) frequency range, which are associated with an

overall decline of cortical neuronal activity (Figure 1). The decrease of spiking and

synaptic activity has been implicated in restorative functions of sleep (Vyazovskiy and

Harris, 2013).

It is obvious that both exercise and sleep are important for a healthy mind and are

thus vital for astronauts that have to withstand huge stresses during manned space missions,

like in the International Space Station. At the moment physical exercise is only obligatory

to counter the effects of osteoporosis during weightlessness in space. However, using

exercise in space may also lead to marked improvement of cognitive functions and

psychological state, which are crucial during long-term flights. Among the potentially

important factors affecting brain health negatively during space mission is a lack of sleep.

Several studies have demonstrated that sleep is severely disrupted during space flights,

where factors such as microgravity, workload, noise, light and other environmental

influences play a role (Barger et al., 2014; Mallis and DeRoshia, 2005). Notably, sleep

disturbances are also typical during preparation for space flights on Earth, as was observed

during a 520-day high-fidelity ground simulation of a Mars mission where a decline in

sleep quality or vigilance deficits were found in the majority of astronauts (Basner et al.,

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2013). “Strategic napping” has been recommended as an effective strategy for maintaining

performance in commercial aviation pilots, although the negative effects of so-called sleep

inertia represents an important confound (Hartzler, 2014). Therefore, the development of

novel effective countermeasures for sleep loss, performance decrements and psychological

deficits associated with space missions is needed.

Important potential confounds for the interpretation of the effects of exercise on

sleep and brain function in general, are physical fatigue, which can be incurred during

exercise, and the factor of motivation. These aspects are important since the compensatory

rebound of sleep or sleep intensity after sleep deprivation may depend on specific types of

behaviour, cognitive activities or merely locomotor activity, which is often elevated when

the animals or humans are sleep-deprived.

The role of sleep in recovery has been studied extensively in relation to exercise,

both in animals and humans (Horne, 2013). Early studies suggested that sleep facilitates

recovery of the fatigue acquired during exercise. In one of the first experiments, cats that

were subjected to treadmill running were found to have compensatory sleep changes which

manifested as a decrease in sleep latency and increase in synchronised EEG activity

(Hobson, 1968). These data were supported by the observation that rats showed an

increased daily sleep time after sleep deprivation by forced treadmill activity (Levitt,

1966). The hypothesis that sleep is associated with physical fatigue was also tested in a

study where several different regimens of exercise were studied in two human subjects

(Shapiro et al., 1975). This study found a whole-night increase in SWS which was related

to the amount of physical fatigue, while the amount of REM sleep was reduced. These

results have since been confirmed in more recent studies (Shapiro et al., 1981), while

another study found a consistent increase in SWA and stage 2 sleep, accompanied by a

reduction in REM sleep in fit subjects across four conditions varying by the amount of

exercise (running) (Torsvall et al., 1984). However, a follow up study from a different

laboratory, in which eight subjects underwent either a.m. or p.m. exercise identified

contradicting results (Horne and Porter, 1976). Specifically, while p.m. exercise resulted in

an increase in stage 3 during subsequent sleep, a.m. training did not lead to any changes in

sleep, which led the authors to conclude that recovery from muscle fatigue does not require

sleep, and can instead be fulfilled during waking.

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In rats, sleep deprivation by forced locomotion on a “water wheel” resulted in an

increased amplitude of the EEG recordings and intrusion of microsleeps during waking,

while the subsequent recovery sleep showed an increased level of high amplitude slow

waves during NREM sleep (Friedman et al., 1979). It is important to mention that it is

unclear whether locomotor activity or waking duration per se account for this effect. In a

control group, which had been awake for the same duration but walked much less, the

rebound of sleep was similar, leading the authors to suggest that exercise per se has little

influence on sleep. This notion was supported by a study showing that the effects of 12-h

sleep deprivation by forced locomotion in rats, was similar irrespective of wheel turning

speed (Borbely and Neuhaus, 1979). It should be noted, however, that forced exercise may

have different effects from voluntary exercise (Hanagasioglu and Borbely, 1982). In cats, it

was found that more physical exercise was associated with a larger increase of SWS during

recovery sleep (Susic and Kovacevic-Ristanovic, 1980). It cannot be excluded that

differences may be due to alternative factors such as the cognitive and motor effort needed

to keep up with the rotation of the treadmill or even attempts to counteract the treadmill

and accompanying stress associated with this. A similar conclusion has been reached in a

human study where six men marched for 34 km each day with 5 hours rest allowed

between the end of exercise and the subsequent sleep opportunity onset (Buguet et al.,

1980). The effects on subsequent sleep were variable between individuals however seemed

to correlate with the levels of urinary 17-hydroxycorticosteroids. In another study, cycling

for 3 h also did not affect subsequent sleep (Youngstedt et al., 1999). An interaction

between exercise and sleep deprivation was recently documented in a study where the

exercise-related increase in GH was elevated in sleep-deprived individuals (Ritsche et al.,

2014).

A significant limitation is that obvious technical difficulties prevent EEG

recordings in humans during running per se. It was suggested that exercise is closely linked

to cognition and does not entail merely physical effort, and this may underlie beneficial

effects of exercise on cognition in ageing (Horne, 2013). Interestingly, in overweight

adults, exercise led to a reduction in default mode network (DMN) activity in the precuneus

(McFadden et al., 2013). Another recent study found that functional connectivity in the

DMN was increased following 6-12 months of training, which led the authors to suggest

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that it reflects experience-dependent plasticity, as it was associated with an improvement in

executive function (Voss et al., 2010).

Notably, exercise appears to affect specific aspects of the compensatory sleep

response. Specifically, in a rat study it was found that both EEG wave incidence and

amplitude are responsive to prior wakefulness, but only incidence appears to be responsive

to prior exercise (Mistlberger et al., 1987). This result is interesting given the different

changes in network activity associated with specific parameters of EEG slow waves.

Increased slow wave incidence may suggest higher excitability or bistability of cortical

networks, rather than increased network synchrony, which could be expected to lead to

higher slow-wave amplitude as a result of longer and larger population silence

(Vyazovskiy, 2013; Vyazovskiy et al., 2009).

Rodent studies have shown that access to running wheels can substantially affect

the distribution, amount and architecture of vigilance states and activity across 24 h (Edgar

et al., 1991; Gu et al., 2015), as well as EEG SWA (Vyazovskiy et al., 2006). Notably,

frontal predominance of SWA was enhanced after a period of waking without running

wheel activity leading the authors to hypothesize that more diverse waking behaviours and

activity led to a higher need for local or global “recovery” (Vyazovskiy et al., 2006). An

intriguing observation was that when access to the wheel was not prevented, the duration of

the first spontaneous waking period after dark onset was substantially extended compared

to the wheel block condition (Vyazovskiy et al., 2006). While this may suggest that in the

absence of engaging activity the mice withdraw from active behaviours and therefore fall

asleep earlier, it may also suggest that exploratory or diverse behaviours lead to a faster

accumulation of sleep need or that voluntary exercise can in part be a substitute for sleep,

by providing “rest” to those brain areas which are not involved in stereotypical running

behaviour.

In summary, it is not surprising that variable and discrepant results have been

obtained in studies addressing the effects of exercise on sleep, or assessing the “recovery”

role of sleep after physical fatigue. It is likely that a simple relationship cannot be expected,

as the effects of exercise may be strongly determined by the kind of exercise, the type of

movement/locomotion in general, and the level of motivation.

Recent studies suggest that running wheel activity resembles some forms of

naturalistic behaviours (Meijer and Robbers, 2014), and may have an intrinsic

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reinforcement value (Belke and Pierce, 2014; Belke and Wagner, 2005). In rats, this was

demonstrated using conditioned place preference, where prolonged access to the wheel was

associated with plasticity in the mesolimbic reward circuitry, including the nucleus

accumbens and the ventral tegmental area (Greenwood et al., 2011). Furthermore, it has

been reported that wheel running interacts with the rewarding properties or effects of drugs

of abuse (Lacy et al., 2014; Zlebnik et al., 2014), suggesting an overlap in their underlying

neurophysiological mechanisms. Consistent with this, it has recently been shown that mice

have a preference towards wheel running when given a choice between engaging in this

behaviour and eating a highly palatable food, and this effect was related to dopaminergic

transmission (Correa et al., 2015).

Objectives The overarching aim of this project is to begin addressing the hypothesis that the benefits

of exercise for the brain can complement or even partially replace cognitive and

psychological benefits provided by sleep. Specifically, we posited that specific types of

exercise could compensate for the lack of consolidated sleep during long-duration space

missions enabling the preservation of optimal brain functioning. To address this hypothesis,

as a first step it is necessary to characterise in detail the patterns of brain activity during

waking with and without exercise, and to test whether exercise affects subsequent sleep.

Finding similarities in brain activity between exercise and sleep, and understanding their

underlying neurophysiological substrate will allow the development of new methodologies

for substantially improving the brain function of astronauts.

This study aims to investigate the fundamental neurophysiological basis of the

beneficial effects of physical exercise on the brain. The first objective is to investigate

differences in the patterns of brain activity between periods of restful waking, exercise and

sleep in laboratory animals. The second objective is to identify and investigate similarities

between the patterns of brain activity during exercise and sleep. The third objective is to

develop specific research-based exercise regimens, which would maximize their beneficial

effects on specific brain functions. The ultimate goal of this research is to inform future

human studies and provide recommendations for astronauts with respect to specific types,

duration and intensity of exercise to improve their overall health, cognitive functioning and

psychological well-being.

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Specific Experiments In this project the following experiments have been conducted:

1. In the first experiment, we performed detailed analysis of electroencephalogram (EEG)

and neuronal activity in two cortical regions of freely behaving mice during wheel running

behaviour, during waking without running and during sleep. We performed EEG spectral

analyses, and analysed the amount and pattern of extracellularly recorded neuronal activity

in the primary motor and primary somatosensory cortex.

2. In the second experiment, we performed sleep deprivation (SD), where the animals were

kept awake for 6 hours, when they had a choice to run or explore novel objects. We

investigated the relationship between wheel running behaviour and several measures of

sleep and sleep EEG.

3. In the third experiments we asked whether running wheel availability directly affects

subsequent sleep, and to address this hypothesis, we performed an experiment where 3-h

SD was followed by 3-h waking period either with free access to the running wheel or with

providing the animals with novel objects to explore. We then investigated subsequent sleep

and sleep EEG.

4. In the final experimental series we used complex wheels, to investigate whether

stereotypic locomotion has differential effects from learning a novel motor skill. This

experiment aimed at investigating whether the pattern of activity during running has an

influence on brain activity and subsequent sleep.

Methods Experimental animals. Adult male mice, C57BL/6J strain, were used in this study (age

range between 5 months and 1 year). All mice were individually housed in custom-made

clear plexiglass cages (20.3 x 32 x 35cm) with free access to a running wheel (RW, see

below, Figure 2). Cages were housed in ventilated, sound-attenuated Faraday chambers

(Campden Instruments, Loughborough, UK, two cages per chamber) under a standard

12:12 h light-dark cycle (lights on 0900, ZT0, light levels ~120-180 lux). Food and water

were available ad libitum. Room temperature and relative humidity were maintained at 22

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± 1°C and 50 ± 20%, respectively. Mice were habituated to both the cage and recording

cables for a minimum of four days prior to recording. All procedures conformed to the

Animal (Scientific Procedures) Act 1986 and were performed under a UK Home Office

Project Licence in accordance with institutional guidelines.

Surgical procedures and electrode configuration. Surgical procedures were carried out

using aseptic techniques under isoflurane anaesthesia (3-5% induction, 1-2% maintenance).

During surgery animals were head fixed using a stereotaxic frame (David Kopf

Instruments, CA, USA) and liquid gel (Viscotears, Alcon Laboratories Ltd, UK) was

applied to protect the eyes. One day prior to surgery animals received dexamethasone (0.2

mg/kg, i.p) to suppress the local immunological response. Metacam (1-2 mg/kg, s.c.,

Boehringer Ingelheim Ltd, UK) and dexamethasone (0.2 mg/kg, s.c.) were administered

preoperatively (and for at least three days after surgery). Post operatively animals were

Figure 2. Experimental set up and electrode positions. (a) Animals were individually housed incustom-made home cages, providing continuous free access to a running wheel, inside ventilated,sound-attenuated chambers provided with autonomous light-dark (12:12) control. (b) A photograph ofa sample 16-ch microwire array, which were used in this study for local field potentials (LFP) andmultiunit activity (MUA) recordings in all animals, and a representative stainless screw used to recordEEG signals and as a reference and ground. Both devices are shown alongside the mouse brain toillustrate relative size proportions. (c) Schematic depiction of the positions of the 16-ch microwirearrays used to record MUA and LFP in the primary motor cortex (M1) and somatosensory cortex(SCx), the frontal and the occipital EEG screws and the position of reference and ground screws placedabove the cerebellum.

a

Occipital  EEGReferenceGround

b

Frontal  EEG/M1  arraycSCx  array

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administered saline (0.1ml/20g body weight, s.c.) and provided thermal support throughout

and following surgery. A minimum two week recovery period was permitted prior to

cabling the animals.

For this study it was essential to maintain long-term stable recordings and

unrestricted movement during spontaneous wheel running, so we avoided using large

electrodes (e.g. with a high channel count or laminar probes) or multiple arrays and probes.

All mice were implanted with a single polyimide-insulated tungsten microwire array

(Tucker-Davis Technologies Inc (TDT), Alachua, FL, USA). The arrays consisted of 16-

channels (2 rows each of 8 wires) with properties as follows: wire diameter 33µm,

electrode spacing 250 µm, row separation L-R: 375µm, tip angle 45 degrees. We used

customized arrays where the lateral row of the wires was longer by 250 µm. A 1x2 mm

craniotomy was made using a high-speed drill (carbon burr drill bits, 0.7 mm, InterFocus

Ltd, Cambridge, UK) in the region of interest, with the midpoint of the craniotomy relative

to bregma as follows: M1: anteroposterior (AP) +1.5-2mm, mediolateral (ML) ~2 mm;

SCx: AP -1mm, ML 3.25mm. The dura was dissected using a 25 gauge needle and saline

was applied to the cranial opening to keep the exposed brain moist. In most cases, removal

of the dura did not cause bleeding (when bleeding occurred, it was stopped with gelfoam

soaked in sterile saline). The electrode array was advanced into the brain until the longer

row of microwires was at the level of cortical layer 5 (M1: ~0.7-0.8 mm below the pial

surface, SCx: ~ 0.5-0.6 mm). A two-component silicon gel (KwikSil; World Precision

Instruments, FL, USA) was used to seal the craniotomy and protect the surface of the brain

from dental acrylic. After 5-10 min, required for the gel to polymerise, dental acrylic was

applied to fix the array to the skull. In all animals, EEG screws were placed in the frontal

(motor area, AP +2mm, ML 2mm) and occipital (visual area, V1, AP -3.5-4mm, ML

2.5mm) cortical regions contralateral to the arrays using procedures previously described

(Cui et al., 2014). A reference and ground screw electrodes were placed above the

cerebellum and an additional anchor screw was placed behind or opposite to the array to

ensure implant stability. EEG screws were soldered (prior to implantation) to custom-made

headmounts (Pinnacle Technology Inc. Lawrence, USA) and all screws and wires were

secured to the skull using dental acrylic. Two single stranded, stainless steel wires were

inserted either side of the nuchal muscle to record electromyography (EMG). A schematic

diagram of implantation locations is shown in Figure 2.

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Signal processing and analysis. Data acquisition was performed using the Multichannel

Neurophysiology Recording System (TDT, Alachua FL, USA). Extracellular neuronal

spike data were collected continuously (25 kHz, 300 Hz - 5kHz) concomitantly with local

field potentials (LFPs) from the same electrodes, and cortical EEG was recorded from

frontal and occipital derivations. EEG/EMG data were filtered between 0.1-100 Hz,

amplified (PZ5 NeuroDigitizer pre-amplifier, TDT Alachua FL, USA) and stored on a local

computer at a sampling rate of 256.9 Hz. LFP/EEG/EMG data were resampled offline at a

sampling rate of 256 Hz. Signal conversion was performed using custom-written Matlab

(The MathWorks Inc, Natick, Massachusetts, USA) scripts and was then transformed into

European Data Format (EDF) using open source Neurotraces software

(www.neurotraces.com). For each recording, EEG and LFP power spectra were computed

by a Fast Fourier Transform (FFT) routine for 4-s epochs (fast Fourier transform routine,

Hanning window), with a 0.25 Hz resolution (SleepSign Kissei Comtec Co, Nagano,

Japan).

Scoring and analysis of vigilance states. Vigilance states were scored offline through

manual visual inspection of consecutive 4-s epochs (SleepSign, Kissei Comtec Co,

Nagano, Japan). Two EEG channels (frontal and occipital), EMG and RW activity were

displayed simultaneously to aid vigilance state scoring. Vigilance states were classified as

waking (low voltage, high frequency EEG with a high level or phasic EMG activity),

NREM sleep (presence of EEG slow waves, a signal of a high amplitude and low

frequency) or REM sleep (low voltage, high frequency EEG with a low level of EMG

activity). Great care was taken to eliminate epochs contaminated by eating, drinking or

gross movements resulting in artifacts in at least one of the two EEG derivations or in any

of the multiunit activity (MUA) channels. Detailed analyses of neuronal activity (see

below) were based on selected time intervals to ensure stability of neuronal waveforms

within the time window specified.

Sleep deprivation. Sleep deprivation (SD) was performed during 6 hours starting either at

light onset, or starting 3 h before dark onset. During SD, the animals were awake on

average >95% of time, and their behavior and polysomnographic recordings were under

constant visual observation. In the 2nd experiment, SD was performed in the animal’s home

16

cage, where they had free access to running wheels throughout the procedure, which they

used intermittently. Throughout the SD procedure, the animals were also regularly

provided with novel objects to mimic naturalistic conditions of wakefulness an in

ethologically relevant manner (Palchykova et al., 2006; Tobler and Borbely, 1986;

Vyazovskiy et al., 2007). In the 3rd experiments, the animals did not have access to the

running wheel during the first 3 hours of SD, and during subsequent 3 hours, starting from

dark onset, were either given access to the wheel or kept awake by providing them

constantly with novel objects. All mice were well habituated to the experimenter and to the

exposure to the novel objects prior to the experiment. Novel objects included nesting and

bedding material from other cages, wooden blocks, small rubber balls, plastic, metallic,

wooden, or paper boxes and tubes of different shape and color.

Running-wheel activity. In this study animals had access to running wheels (Campden

Instruments, Loughborough, UK, wheel diameter 14 cm, bars spaced 1.11 cm apart

inclusive of bars) for at least two weeks prior to the experiment, and were therefore well

adapted to the wheels. The wheels were custom made for tethered animals, and did not

prevent the animals from running ad libitum. Each running wheel was fitted with a digital

counter (Campden Instruments) which uses an infra-red (IR) emitter/receiver to detect each

rung passing the IR beam as the wheel rotates. In our study running wheel activity was

recorded with a high temporal resolution (one full wheel revolution consists of 38

individually detected rung counts, thus 10 counts per second corresponds to 10.11

centimeters/sec) within the same system used to record electrophysiological signals. This

allowed us to achieve a precise synchrony between instantaneous changes in

electrophysiological signals/behaviour and RW activity/speed. The wheel counter output is

a 5V TTL pulse (0V with no output), that triggers an edge detector in the TDT acquisition

system, and in turn creates a time stamp that is stored for each wheel count. For most

analyses, RW-activity was analysed in 1-sec epochs that were grouped based on the

number of counts during the corresponding epochs. Since the number of 1-sec epochs with

progressively higher numbers of counts decreased progressively, we also used

progressively wider ranges for the bins (0; 1; 2-3; 4-7; 8-11; 12-32), to obtain a sufficiently

high yet reliable number of counts within each speed category.

17

To obtain an acceleration-deceleration index (ADI) we calculated mean second

derivative of the time series corresponding to the timings of consecutive counts for each 1-

sec epoch, and subsequently grouped epochs according to ADI from largest negative

(acceleration) to largest positive (deceleration) values into ten 10% deciles prior to

calculating averages between animals. As a result, the first 10% decile corresponds to

fastest acceleration, the tenth decile - to fastest deceleration, while the middle of the

distribution (deciles 5-6) correspond mostly to steady running with a close to zero net

speed change.

In the 4th experiment the animals were recorded for an undisturbed 24-h while

provided with regular running wheel. On the next day, the regular wheel was replaced with

a complex wheel, where instead of 38 rungs, only 22 rungs were available (Figure 3). The

animals were given an opportunity to run on the complex wheel during the 12-h dark

period.

Analysis of extracellular neuronal activity. Online spike sorting was performed to

eliminate artifactual waveforms caused by electrical or mechanical noise. This was

performed with OpenEx software (TDT), by manually applying an amplitude threshold for

online spike detection. Whenever the recorded voltage exceeded this predefined threshold

(at least -25 µV), a segment of 46 samples (0.48 ms before, 1.36 ms after the threshold

crossing) was extracted and stored for later use, together with the corresponding time

stamps. To investigate the relationship between running behaviour and individual putative

single units’ firing, we employed offline spike sorting. Firstly, an artifacts removal

procedure was implemented in order to eliminate remaining artifactual waveforms and to

Regular Wheel Complex Wheel

Figure 3. A photograph of a regular wheel (38 rungs) and complex wheel (22 rungs)

18

facilitate subsequent clustering. Typical spurious spike waveforms included waveforms

which were likely the result of a combination of several spikes from different neurons

occurring concurrently and preventing their reliable classification. After removal of the

artifactual spikes, principal components were computed using principal component analysis

(PCA) on a segment of the spike waveform between the 5th and the 35th time sample,

because this is the segment that contains the initial negative peak after threshold crossing

and subsequent afterhyperpolarisation, and is therefore more informative about the overall

spike waveform. In PCA an orthogonal linear transformation of the original data is

performed, through singular value decomposition of the data matrix (Jolliffe, 2002). After

performing PCA, each spike between samples 5 and 35 was thus described by 31 variables,

each being a linear combination of the original sampling values. Each PC corresponds to an

eigenvalue and an eigenvector from the singular value decomposition of the data matrix.

The first PC is associated with the eigenvector on the direction of maximal variance of the

dataset, and so on in decreasing order for the remaining PCs. The ratio between each

eigenvalue and the sum of all eigenvalues represents the ratio of the total variance

described by a single PC.

Clustering was performed based on the k-means algorithm (Hartigan, 1975). This is

a partitioning method that aims to divide n observations into k clusters in which each

observation belongs to the cluster with the nearest mean, serving as a prototype of the

cluster. There are several k-means algorithms available which are based on different ways

to proceed through the iterations. We used the k-means function in Matlab, which was

implemented according to Lloyd’s algorithm (Lloyd, 1982). In particular, it

chooses k initial cluster centers (centroid) through an initialisation step, and then computes

point-to-cluster-centroid Euclidean distances of all observations to each centroid. Based on

the distance, each observation is assigned to the cluster with the closest centroid. As a next

step, the algorithm computes the average of the observations in each cluster to obtain k new

centroid locations. Finally, it repeats previous steps until cluster assignments do not

change, or the maximum number of iterations is reached. This approach requires the user to

a priori select the number of clusters. Based on visual inspection, it was found unlikely that

more than 5 distinct waveforms were present in the same MUA channel, and so the

procedure has been performed with k=2, 3, 4 and 5. For each case, several features were

extracted within each cluster and were used to validate the quality of clustering and to

19

select the best outcome of the clustering procedure within each animal and recording

channel. These were the average spike waveform with standard deviation, interspike

interval distribution, the time course of peak-to-peak amplitude across the recording period,

the corresponding time course of average firing rates and the autocorellogram of the spike

trains. All selected clusters were screened and visually classified according to their signal

to noise ratio, waveshape of the action potential, stability of the amplitude across time and

interspike interval (ISI) distribution histogram, and spurious or unstable clusters were

excluded from the analysis. We should emphasize that based on our recording and sorting

technique it cannot be excluded that two or more neurons having identical spike waveform

shapes, amplitude and firing properties are present simultaneously on the same channel.

Statistical analysis. All statistical analyses were performed in Matlab (The MathWorks Inc,

Natick, Massachusetts, USA) and all values reported are mean ± SEM. ANOVAs for

repeated measures were used to detect statistically significant effects of running wheel

speed or acceleration on firing rates. Two-tailed paired t-tests were used for

comparisons. In addition, we also employed a normalisation procedure by expressing some

of the variables in relative terms (as % of the mean value across all artifact free epochs

during the recording period). We have done this in those cases where interindividual

variability of absolute values is not relevant for the effects observed, as it allowed us to

assess the relative magnitude of the effects. Normalisation of values in this manner is a

widely used approach and therefore facilitates comparison between studies, where relative

values are reported.

Results Experiment 1 RUN on and RUN off neurons in the neocortex during spontaneous wheel running. In the

first experiment, multiunit activity (MUA) in the motor cortex (M1) and EEG were

recorded in mice which had free access to a running wheel in their home cage (Figure 4). In

every animal, several prolonged waking periods dominated by running wheel (RW) activity

were observed during the dark period, interspersed by naps of various durations. As

typically observed (Vyazovskiy et al., 2006), during waking epochs with wheel running

(wRUN), EEG was dominated by theta-activity (6-9 Hz), which was substantially lower

20

during waking epochs when the animals did not run (nwRUN). We also observed that

during NREM sleep, the cortical LFP recorded from M1 was dominated by high-amplitude

positive slow waves (~0.5-4 Hz), associated with a brief reduction of MUA across the

majority or even all of the electrodes within the 16-channel array (Figure 4).

Firing Rates (wRUN / nwRUN)0 50 100 150 200

Num

ber o

f Neu

rons

0

10

20

30

40

nwRUN wRUN NREM

Firin

g R

ates

(%)

0

50

100

150

200p<0.01

RW counts / sec0 1 2-3 4-7 8-11 12-32

Firin

g ra

tes (

% o

f mea

n)60

80

100

120

140

160

SWA

(% o

f mea

n)

0

100

200

300

400

RW

cou

nts

/ sec

0102030

WNR

2 hours

a b

f

RW-activity

RUN off neuron

RUN on neuron

5 sec

100 µV0.5 ms

ed g

c

1 sec

RUN off RUN on

500 µVLFP

MUA

1 mm

Figure 4. Cortical neuronal activity in the primary motor cortex (M1) during voluntary wheel running in freely behaving mice. (a) Aphotograph of the custom-made cage providing continuous free access to a running wheel positioned on the rear. Scale bar: 5 cm. (b) Fromtop to bottom: 12-h profile of EEG slow-wave activity (SWA, EEG power between 0.5-4.0 Hz, represented as % of 12-h mean) recorded inthe frontal cortex, running-wheel (RW) activity (counts/sec) and the distribution of sleep-wake stages (W= wakefulness, N= NREM sleep, R=REM sleep) from a representative mouse. (c) Schematic representation of the position of the primary motor cortex (M1, blue area drawn withreference to Paxinos & Franklin, 2001) shown on the dorsal surface of the mouse head, and the position of the 16-ch microwire array aboveM1 (dots indicate the position of individual wires within the array). Traces on the right: top, representative local field potentials (LFP)recorded during NREM sleep from one row of microwires; bottom, raster plot of multiunit activity (MUA, each vertical line represents aspike) recorded from the same wires. Note the close temporal relationship between positive LFP waves and periods of generalised neuronalsilence. (d) Two MUA traces recorded from M1 in the same animal with corresponding RW-activity (bottom, each vertical bar represents asingle wheel rung count). Corresponding waveforms of the action potentials recorded extracellularly are shown on the right. (e) Thedistribution of all putative single units recorded in n=11 mice as a function of the ratio of their average firing rates (FR) during running(wRUN) and non-running waking (nwRUN). Note that a smaller proportion of neurons increase firing during wheel running (red, RUN onneurons), while the majority decrease FR during running (blue, RUN off neurons). (f) Average FR in M1 during nwRUN waking, wRUNwaking and NREM sleep. Mean values, SEM, n=11 (individual mice: grey symbols). Significant differences between vigilance states aredepicted above the bars (obtained by two-tailed paired t-test following significant one-way ANOVA). (g) FR in M1 shown as function ofrunning speed. Thick lines: mean values, SEM, n=9 mice. Values from individual animals are shown as thin line plots.

21

In contrast, during waking we observed a striking dissociation between the

activities of putative single units depending on the animal’s behaviour. Specifically, in

every mouse we observed clear cut cases when MUA slowed down substantially or ceased

altogether during intense running on the wheel (RUN off neurons, Figure 4). Overall,

cortical neuronal activity was on average 25.0± 9.3% lower during wRUN compared to

nwRUN waking (repeated measures ANOVA, factor ‘vigilance/behavioural state’,

F(2,18)=17,23, p=0.0001). Most neurons did not merely change firing when the animals

RW counts / sec0 1 2-3 4-7 8-11 12-32

Firin

g R

ates

(% o

f mea

n)

60

80

100

120

140

160

Firing Rates (wRUN / nwRUN)0 50 100 150 200

Num

ber o

f Neu

rons

0

5

10

15

20

25

a b

c1  sec

1  sec

d

RW-activity

MUA

100 µV

100 µV

1 mm

Figure 5. Cortical neuronal activity in the somatosensory cortex (SCx) during voluntarywheel running. (a) Schematic representation of the position of the 16-ch microwire arrayshown relative to the barrel cortex (drawn in reference to Paxinos & Franklin, 2001) shownon the dorsal surface of the mouse head (dots indicate the position of individual wires withinthe array). (b) Individual representative examples depicting multiunit activity (MUA) in SCxduring wheel running. Corresponding running wheel (RW)-activity is shown below each trace(each vertical bar represents a single wheel rung count). (c) The distribution of all putativesingle units recorded in SCx (in n=5 mice) as a function of the ratio of their average firingrates (FR) during running (wRUN) and non-running waking (nwRUN). Note that a smallerproportion of neurons increase FR during running (red), while the majority decrease spikingactivity (blue). (d) FR in SCx shown as a function of running speed (count/sec). Thick lines:mean values, SEM, n=5 mice. Values from individual animals are shown as thin line plots.

22

ran, but they often showed a distinct inverse relationship to running velocity. This resulted

in a significant negative relationship between the firing rates in M1 and running speed

(Figure 4, F(5,53)=8,61, p=1.3315e-005, ANOVA for repeated measures, factor ‘running

speed’). To investigate whether spontaneous wheel running affects firing rates in other

cortical areas, we also performed MUA recordings from the somatosensory cortex (SCx)

(Figure 5). Although visual inspection did not reveal individual neurons in SCx which

would stop firing altogether during running as observed in M1, the majority of neurons also

changed their firing activity during running. On average, firing rates in the SCx also

showed a decrease with increasing running speed (Figure 5, F(5,29)=19,42, p=4.6534e-

007, ANOVA for repeated measures, factor ‘running speed’).

0 1 2 3 4 5 6 7 8 9 10

Firin

g R

ates

(% o

f mea

n)

0

80

100

120

140

Acceleration-Deceleration Index (ADI)0 1 2 3 4 5 6 7 8 9 10

0

80

100

120

140

Figure 6. The relationship between cortical neuronal activity and changes in wheelrunning speed. (a, a’) Individual examples depicting cortical firing rates (FR) in the motorcortex (M1, blue) and in the somatosensory cortex (Scx, pink) modulated by running wheelbehaviour. From top to bottom: MUA recorded in one representative channel of the 16-channel array, and running-wheel (RW)-activity (each vertical bar represents a single wheelrung count). Note the irregular pattern of spiking associated with variability in running speed.(b, b’) Cortical FR in M1 (blue) and SCx (pink) shown as a function of the wheelacceleration-deceleration index. All 1-sec epochs were subdivided into ten 10% deciles, eachconsisting of the same number of epochs. These were sorted as a function of the change inwheel running speed within an epoch from fast acceleration to fast deceleration (schematicallyshown as arrows above), and corresponding average FR were calculated prior to averagingbetween animals. Mean values, SEM, M1: n=9 mice, SCx: n=5. Mean values are shown as barplots, the individual values from individual animals are shown as thin line plots.

b

a

1 sec 1 sec

50µV

RW-activity

MUA

b’

a’

Speed SpeedSpeed Speed SpeedSpeed

M1 SCx

23

The decrease in cortical firing rates occurs predominantly during stereotypic running.

Notably, spontaneous wheel running is not stable and uniform throughout running bouts.

Instead we frequently observed brief pauses in running, and fluctuations in running speed

were common, irrespective of the running bout length. On the other hand, moment-to-

moment changes in firing rates were frequently observed both within wRUN and nwRUN

waking. Most putative single units had a “preferred” frequency of firing, which was

affected substantially by wheel running behaviour. However, we noticed that the width of

the distribution of firing rates was substantially reduced when the mice engaged in running,

especially at a high speed, an effect which was present among the majority of individual

putative single units. This suggests that during running a more uniform or stereotyped

cortical state is

instated, which

contrasted

significantly with the

wider dynamic range

of firing during

nwRUN waking (M1:

F(4,44)=93.86,

p<0.0001, SCx:

F(4,24)=278.88,

p<0.0001, ANOVA

for repeated measures,

factor ‘RW-activity’).

We observed

striking changes in the

firing activity

depending on the

pattern and nature of

running behaviour,

such as during initial

acceleration, during

steady constant

Figure 7. Cortical activity during voluntary wheel running.Representative examples of ‘sleep-like’ OFF periods (arrow) in thesomatosensory cortex (SCx) occurring during high speed running.Top: EEG recorded in the occipital derivation. Note the presence ofstrong EEG theta-activity, typical for running. Traces belowrepresent local field potentials (LFP) recorded concomitantly in allsixteen channels of the 16-channel microwire array. Correspondingmultiunit activity (MUA) is shown below as a raster (each bar is anindividual spike). Bottom: corresponding running-wheel (RW)-activity (each bar represents a single wheel rung count). Note that apositive LFP wave is accompanied with reduced MUA across mostof the 16 channels.

1 sec

EEG

LFPs

MUA

RW-activity

200 µV

500 µV

24

running and during brief of wheel running and firing rates. To address this notion, we

calculated wheel acceleration / deceleration index (ADI) based upon the timing of

individual RW counts during 1-sec epochs. For most epochs the average ADI was close to

0 representing negligible net speed changes within an epoch as typical for stereotypic

steady speed running, although abrupt fluctuations in running speed, corresponding to

positive or negative ADI values during a consolidated running bout were also. Firing rates

were strongly related to the ADI in both M1 and SCx (M1: F(9,89)=6.31, p=1.5427e-006,

SCx: F(9,49)=13,9, p=2.5696e-009). Specifically, firing rates were on average 20-30%

lower during epochs with steady running, as compared to epochs with rapid acceleration or

deceleration (Figure 6). Calculating the relationship between firing rates and ADI

separately for RUN on and RUN off neurons revealed overall lower firing rates during

steady running irrespective of whether the neuron increased or decreased its firing rate

relative to nwRUN waking in both regions (not shown). In other words, even if a specific

neuron fired more strongly during wRUN waking as compared to nwRUN waking, it

tended to fire less frequently as running became more stereotypic.

It is well established that state-dependent changes in cortical activity are accounted

for by a variety of mechanisms, including intrinsic properties of individual neurons, local

and global neuromodulation, active inhibition and disfacilitation, as well as connectivity

within the network (Bacci et al., 2005; Buzsaki et al., 2007; Connors and Gutnick, 1990;

Lemieux et al., 2014; Okun et al., 2015; Rudolph et al., 2007; Timofeev et al., 2001; Zagha

and McCormick, 2014). Visual inspection of raw traces revealed the occurrence of isolated

brief (~100-200 ms) periods of neuronal silence accompanying positive LFP ‘slow’ waves,

even during intense wheel running. Such ‘OFF-periods’ were encountered both in M1 and

SCx, and usually encompassed a subset of recording channels, but in some cases were

visible across the entire 16-channel array (Figure 7). Such LFP/MUA OFF periods during

running were often characterised by a conspicuous brief surge of intense spiking activity

immediately preceding and/or following the period of prolonged silence.

Experiment 2 Running wheel activity during sleep deprivation. Since mice use wheels spontaneously

during the night, the question remains whether the relationship between cortical activity

and running behaviour is similar at different times of day, under different lighting

conditions, and when they are given a choice of other behaviours. To address this, fist we

25

analysed the data collected during the light period, when the animals were kept awake by

continuously providing them with novel objects in their home cages (Figure 8). As

expected, the animals spent time exploring, but they would also often run on the wheel,

running on average >200 meters during a 6 h sleep deprivation protocol. Running was

distributed evenly across the 6-h period of sleep deprivation (F(5,53)=1.24, p=0.31,

ANOVA for repeated measures, factor ‘1-h interval’), therefore suggesting that increased

sleep pressure did not prevent animals from running, even at a time of day when they

typically do not run. Notably, in all individual animals we again observed clear cut cases of

putative single units decreasing spiking substantially during running. In all animals, firing

rates also decreased with increasing running speed during sleep deprivation, similar to

spontaneous running during the dark phase (Figure 8).

To investigate whether access to running wheels affects sleep propensity, we next

quantified the occurrence of brief sleep episodes during the sleep deprivation period. As it

is typically observed, the animals tended to fall asleep briefly, especially towards the end of

the 6-h SD. The intrusion of sleep into awake state is considered a measure of sleep

pressure. Notably, the amount and intensity of wheel running during the 6-h interval

showed a negative relationship with the occurrence of sleep, which reached a statistical

significance (p<0.05) with the total number of running wheel counts, and a statistical

tendency (p<0.1) with the proportion of time spent running (Figure 8). Next, conversely,

we calculated the proportion between waking and sleep during subsequent sleep

opportunity. Typically after SD sleep pressure is high and the animals spend most of the

time asleep. However, if sleep pressure is lower, it is expected that the animals will spend

more time awake during this period. We found that running wheel activity during SD

correlated positively during the amount of subsequent waking (Figure 8). In other words,

the more intense was running during SD, the longer time animals spent awake after they

were left undisturbed, suggesting that sleep pressure was reduced.

26

As well known, physiological sleep pressure is reflected in the predominance of

slow EEG frequencies (<=4Hz) in the EEG signal. On the other hand, faster frequencies in

sleep EEG are considered a measure of cortical activation, and reflects less deep sleep. We

calculated a correlation between running wheel activity during 6-h SD and EEG spectra in

NREM sleep during subsequent sleep opportunity. Intriguingly, we found mostly positive

relationship between faster EEG frequencies in the frontal EEG derivation and the amount

of running prior to sleep (Figure 8). This suggested a higher level of ‘arousal’ during sleep

SWA

(% o

f mea

n)

0

200

400

600

WNR

Hours0 1 2 3 4 5 6 7

RW-activity

RW counts / sec0 1 2-3 4-7 8-32

Firin

g Ra

tes (

% o

f mea

n)70

80

90

100

110

120

F(4,44)=19.1p<0.001

RW revolutions / 6 h of SD0 200 400 600 800 1000

0.000.020.040.060.080.100.120.140.16

r=-0.51p=0.016

0 200 400 600 800 1000

Wak

e / S

leep

0.020.040.060.080.100.120.14

RW time (hours)0.0 0.4 0.8 1.2 1.6

Slee

p du

ring

SD (h

ours

)

0.000.020.040.060.080.100.120.140.16

r=-0.65p=0.06

0.0 0.4 0.8 1.2 1.6

Wak

e / S

leep

0.020.040.060.080.100.120.14

r=0.7p=0.03

r=-0.62p=0.07

0 5 10 15 20

r-val

ue

-0.20.00.20.40.60.81.0

Frequency (Hz)0 5 10 15 20

r-val

ue

-0.20.00.20.40.60.81.0

frontal EEG

occipital EEG

a b

c d

Figure 8. Cortical neuronal activity in M1 during wheel running during sleep deprivation. (a)Individual profile of EEG slow-wave activity (SWA, EEG power between 0.5-4.0 Hz) recorded in thefrontal cortex, running-wheel (RW) activity and the distribution of sleep-wake stages (W= wakefulness,N = NREM sleep, R= REM sleep) across 6-h of sleep deprivation (SD) and subsequent 1 hour of sleep inone representative mouse. (b) Average cortical firing rates in M1 shown as a function of running speed(x-axis). Thick lines: mean values, SEM, n=9 mice. Values from individual animals are shown as thinline plots. (c) The relationship between the total amount of running and time spent running during SD andthe amount of sleep during SD (left) or Wake/Sleep ratio during the 1st hour of recovery. (d) Therelationship between RW-activity and EEG power spectra during the first hour of sleep. Triangles abovethe curves denote frequency bins where Pearson’s correlation was significant (p<0.05).

27

NREM sleep

0 1 2 3

% o

f rec

ordi

ng ti

me

0

10

20

30

40

50

60

p<0.1p<0.05

REM sleep

Time [Hours]0 1 2 3

0

2

4

6

8

10noRUNRUN

Figure 9. Effects of wheel running on subsequent sleep. (a) Schematicdiagram of the wheel-block experiment. (b) Time course of NREM andREM sleep during the first 3 hours after the animals were leftundisturbed starting from Hour 15. Note that the amount of sleep isdecreased following 6-h waking period when the animal had access tothe wheel.

a

b

after running, which may reflect a lower level of sleep pressure. Notably, this effect was

‘local’, as the relationship was less pronounced in the occipital derivation, where the

positive correlation was mostly present in theta-frequency range (Figure 8). Altogether this

suggests that wheel running during SD reduced sleep pressure.

Experiment 3 Running wheel activity after sleep deprivation. Next, we performed 6-h SD, starting 3

hours before dark onset. All animals did not have access to running wheels during the first

3 hours of SD, but have been provided with novel objects continuously. Subsequently, the

animals were either allowed to run ad libitum on the wheel, or were kept awake by

providing them with novel

objects to explore for the next 3

hours. Subsequently in all

animals the wheel was blocked

and the animals were left

undisturbed (Figure 9). We

hypothesised that if running

discharges sleep need, sleep

time will be reduced during

subsequent sleep opportunity.

Consistent with this hypothesis,

we found that the overall

amount of sleep was reduced in

animals, which had free access

to the wheel during the second

3-h interval of SD (Figure 9).

In a subset of animals,

we also performed detailed EEG

analyses. Interestingly, sleep

after running was characterised

by a prominent increase of fast delta frequencies (3-4 Hz), which suggested that waking

experience has an influence on cortical network activity during subsequent sleep (not

shown). This effect was mostly pronounced in the occipital cortex, which is located closer

28

to the hippocampus. It remains to be determined whether the changes in the EEG we

observe arise from the cortico-hippocampal interactions, or merely represent a volume

conducted signals originating from limbic areas.

Experiment 4 Complex wheel running and subsequent sleep. As well known, hippocampus is strongly

implicated in movement and learning motor skills. On the other hand, evidence suggests

that motor skill learning, or more generally demanding cognitive activities during waking

necessitate sleep. Therefore, we hypothesized that the effects of running on sleep would

differ if the animals run voluntarily and stereotypically on a familiar regular wheel, as

compared to a complex wheel, when the animals have to learn a novel motor skill. To

address this hypothesis, a group of animals (n=8), well habituated to run on a regular wheel

was given a wheel where instead of 38 rungs, only 22 rungs were available (Figure 3).

This resulted in irregularly placed gaps between rungs, which required animals to learn to

cope with. Interestingly, all animals persisted running, which resulted in approximately the

same overall amount of running during the dark period. The wake EEG was affected

substantially by learning to run on a complex wheel. Specifically, EEG theta activity was

significantly higher during waking with complex wheels, as compared to regular wheels

Figure 10. The effects of running on a complex wheel on wake EEG and subsequent sleep. (a) EEGpower density during non-wheel running waking (nwRUN) and wheel running waking (wRUN) during thenight with complex wheels expressed as % of corresponding EEG power density during preceding controlnight, when mice had access to regular running wheels. Note an increase of fast theta activity during thenight with complex wheels. Mean values, n=8. (b) Distribution of individual animals as a function ofprolongation (n=3) or shortening (n=5) of the main waking period during the night with the complex wheelrelative to the previous night with a regular running wheel.

longer waking n=3

shorter waking n=5

nwRUN

Frequency (Hz)

0 5 10 15 20

EEG

pow

er d

ensit

y (%

regu

lar w

heel

)

80

100

120

140 FrontalOccipital

wRUN

0 5 10 15 20

80

100

120

140a b

29

(Figure 10), which likely reflects the process of learning or higher levels of arousal.

Interestingly, we noticed on several occasions that individual animals stopped running and

fell asleep substantially earlier when they were provided with complex wheels, as

compared to previous night with a regular wheel. Specifically, n=5 out of n=8 (62%)

showed longer sleep on the night with the complex wheel, and in total the animals slept

almost 1 hour longer (54.4 min) on the night with the complex wheel as compared to the

night with a familiar regular wheel. This suggests high interindividual variability in the

effects of waking exercise on subsequent sleep, but also indicates that stereotypic

locomotion is associated with a reduced sleep need as compared to waking dominated by

cognitively or physically demanding activities.

Discussion In this study we investigated changes in the firing rates of cortical neurons recorded from

motor (M1) and somatosensory (SCx) cortex in freely moving mice during voluntary

unrestricted running-wheel (RW) activity. Importantly, we found that wheel running

modulates a substantial proportion of cortical neurons, surprisingly leading to an overall

reduction in neuronal activity specifically during high speed and/or stereotypic running.

The mechanisms underlying the reduction in neuronal firing in M1 and SCx during

high speed and/or stereotypic running may be diverse. As is well known, movement and

behavioral state transitions are associated with widespread changes in the activity among

several neuromodulatory systems (Aston-Jones and Bloom, 1981; Day et al., 1991; Jacobs

and Fornal, 1997; Jones, 2005; Takahashi et al., 2010). Noradrenaline is essential for

maintaining a tonic depolarization of pyramidal neurons in the motor, somatosensory and

the visual cortex (Constantinople and Bruno, 2011; Polack et al., 2013; Schiemann et al.,

2015), and has been implicated in wake-to-sleep transitions (Carter et al., 2010). On the

other hand, while wake-promoting effects of acetylcholine are well established (Xu et al.,

2015), in vitro studies suggest diverse layer-specific effects of acetylcholine, manifested in

hyperpolarisation of layer 4 neurons and excitation of layer 2/3 and layer 5 pyramidal

neurons (Eggermann and Feldmeyer, 2009). In addition to “global” neuromodulatory

influences, extrinsic to the neocortex, local inhibition is also likely essential. Several

studies have highlighted diversity among local GABA-ergic neuronal populations which

are recruited in awake mice in a unique manner with respect to the ongoing behaviour,

30

movement or whisking (Gentet et al., 2010; Kvitsiani et al., 2013; Reimer et al., 2014).

Interestingly, pharmacogenetic inhibition of a subpopulation of vasoactive intestinal

peptide positive (VIP) GABA-ergic neurons in the visual cortex resulted in reduced cortical

activity irrespective of behavioural state (Jackson et al., 2016), while somatostatin-

expressing (SOM) GABAergic neurons of layer 2/3 of the barrel cortex were active during

spontaneous quiet wakefulness, but reduced activity and hyperpolarised in response to

whisking (Gentet et al., 2012). Moreover, again in the visual cortex VIP interneurons were

activated by locomotion, and in turn inhibited SOM interneurons, thus disinhibiting

excitatory cells (Stryker, 2014). In turn, stimulation of excitatory neurons may lead to a

strong activation of GABA-ergic neurons, which precipitates a net inhibitory effect in the

local microcircuit (Mateo et al., 2011). The observation of slow LFP waves during running,

which were associated with a surge of neuronal spiking and subsequent MUA suppression,

may reflect an arrival of extrinsic excitatory inputs recruiting local cortical inhibitory

networks (Logothetis et al., 2010; Vyazovskiy et al., 2013).

A strong determinant of the overall change in network activity was the pattern of

running, such as whether it was stereotypic or not. Intriguingly, a decrease in firing rates

during high speed running was observed not only among RUN off neurons, but even

among RUN on neurons, which on average fired at a higher rate when the animals ran, yet

they were relatively quiescent as the animal engaged in a stable speed running.

Notably, in our study the animals were habituated to spontaneous wheel running in

their home cages and had access to the wheels for many days prior to the experimental

night. It remains to be established whether shifts between high speed stereotypic running

and goal directed behaviours in freely behaving animals relate to changes in the levels of

arousal. Reduced firing rates in M1 and SCx when animals engaged in high speed running

may reflect increased arousal levels, while more stable and uniform firing activity may

correspond to an optimal state for sensory processing (Buran et al., 2014; McGinley et al.,

2015; Vinck et al., 2015). Our data suggest a distinct role of specific cortical circuits in

voluntary locomotion which is dependent on the precise nature or form of the behaviour,

for example stereotypic locomotion as compared to purposeful or exploratory behaviour.

It is possible that the effects would have been different, if the recordings had been

obtained while the animals were learning how to run, as in this case the running would

likely be less stereotypic, and active continuous involvement of the motor cortex would be

31

necessary (Isomura et al., 2009; Kawai et al., 2015). Consistent with this notion, when the

animals where provided with complex wheels, which required learning a novel skill, the

majority of animals fell asleep earlier, and the overall sleep was increased by on average

almost 1 hour. Thus, while our data seems to indicate that stereotypic behaviours may

translate into cortical states which are also more homogenous and lack complexity, the

possibility remains that more stereotyped waking behaviours also influence the dynamics

of the accumulation of sleep need. Strikingly, in our study we observed that mice readily

run during sleep deprivation (SD), which is surprising for at least two reasons. First, this

procedure was performed during the light period, when mice typically do not run. Second,

during SD the animals were constantly provided with novel objects, but rather than

spending time exploring, the animals often chose to engage in stereotypical running. While

the functional significance of this behaviour remains to be determined, our data suggest

that even when wheel running occurs outside of the usual time and setting, it has similar

effects on cortical activity as during spontaneous wheel running during the dark phase.

It has been previously shown that wheel availability substantially affects the amount

and distribution of vigilance states, such that animals would fall asleep later and have less

sleep altogether when they are given free access to the wheel (Vyazovskiy et al., 2006;

Vyazovskiy and Tobler, 2012). We confirm these observations in our current study.

Furthermore, we now show using several complementary approaches that sleep need may

indeed be reduced by wheel running.

First, we observed that the amount of intrusions of sleep into awake state during

sleep deprivation was negatively correlated with the amount of running wheel activity.

Second, we observed that the more animals ran during sleep deprivation, the more time

they spent awake during subsequent sleep opportunity. Furthermore, sleep EEG after SD

was characterised by a higher predominance of faster frequencies if the animals showed

more running during preceding waking. Finally, if SD during the dark period was

combined with running wheel activity, the amount of sleep in the following 3-h interval

was overall lower.

Overall, our data suggest that stereotypic waking behaviours, such as wheel running

may be associated with a “default” awake state, which in turn permits animals to stay

awake longer and/or decrease sleep need. Arguably, this possibility would confer a

significant advantage, as it would not only enable animals to remain effectively “online”

32

and therefore avoid dangers associated with a sensory disconnection during sleep, but it

may in some cases be necessary for specific behaviours such as during migration or a

mating season when prolongation of wakefulness is favoured ecologically (Lesku et al.,

2012; Siegel, 2008). We posit that an important feature of this default awake state is that it

may represent waking at a “lower cost”. Such a low cost default wake state is uniquely

suited to be effectively placed in the continuum between sleep and purposeful, goal-

directed behaviours, and may render substantial adaptive flexibility with respect to

changing environmental conditions, time of day and homeostatic needs.

Conclusions and future perspectives Overall, the results presented here provide initial evidence in support of our hypothesis that

waking dominated by simple, stereotypic behaviours may be associated with reduced sleep

need. Furthermore, our results suggest an intriguing possibility that such “default”

wakefulness may even contribute actively to the restorative sleep functions. Our work has

relevance for space travel, inasmuch as it suggests a potentially game-changing strategy to

mitigate cognitive deficits arising as a result of insufficient or disrupted sleep in space. As

well known, disrupted or insufficient sleep is a well-known health risk factor in astronauts

during spaceflight and pre-flight training (Pandi-Perumal and Gonfalone, 2016). Light

exposure and sleep pills as well as psychostimulants are used extensively to counteract

poor sleep and to improve waking performance (Barger et al., 2014; Basner et al., 2013;

Brainard et al., 2016). However, since various cognitive, psychological and motor

functions are impaired in space (Clement and Ngo-Anh, 2013; De la Torre, 2014; Pagel

and Chouker, 2016), the development of novel effective countermeasures for sleep loss,

performance decrements and psychological deficits associated with space missions is

necessary. One strategy is to invest efforts in the development of interventions or regimens

aimed at increasing the duration or quality of sleep. While this research direction is

certainly promising, it is important to bear in mind that we do not know yet which aspects

of sleep are crucial in this respect and should be targeted in the first place. For example,

sleep duration may be less important than sleep intensity, or the same amount of sleep may

confer greater benefits if taken during a specific phase of the endogenous circadian rhythm

(Borbely et al., 2016). Moreover, it may not be practically achievable to obtain the average

33

8 hours of consolidated sleep on a daily basis while on ISS, and the negative influence of

such factors as microgravity, noise or light on sleep quality may be difficult to eliminate.

We proposed an alternative strategy, which consists in targeting wakefulness in the

first instance. The central idea of our proposal is that it might be possible to develop

schedules of specific waking activities, which cumulatively decrease the need for sleep.

According to the traditional view, the need for sleep builds up progressively during

wakefulness and needs to be discharged during subsequent sleep (Borbely et al., 2016).

This sleep-wake dependent process is called Process S, and mathematical modelling studies

have allowed to formally describe its dynamics and to obtain better understanding of the

effects of sleep restriction on the amount and distribution of sleep as well as waking

performance (McCauley et al., 2009; Van Dongen et al., 2011). Notably, waking is

typically assumed to be a homogenous process, which is associated with a continuous

increase of sleep pressure largely irrespective of ongoing behaviour or activities. We

propose instead, that certain types of wakefulness have a lower ‘cost’ with respect to

accumulation of sleep need, and specific activities may, on the contrary, even reduce sleep

pressure and improve cognitive performance.

This possibility is supported by numerous studies demonstrating positive effects of

fitness on cognitive function, with largest exercise-induced benefits reported for executive-

control processes, such as such as working memory or planning (Colcombe and Kramer,

2003). Importantly, it was shown that the magnitude of beneficial effects of exercise on

cognitive functions was related to a specific type of exercise and the duration of training

sessions (Colcombe and Kramer, 2003). Moreover, exercise has been also proposed to hold

high promise to counteract or prevent mood changes (Schneider et al., 2010; Strohle,

2009), and there is evidence for beneficial effects of fitness in neurodegenerative disorders,

such as Alzheimer and Parkinson’s disease (Goodwin et al., 2008; Rolland et al., 2008) .

Notably, even non-strenuous activities, like walking, can boost creativity by about 60%

(Oppezzo and Schwartz, 2014). This suggests that it is not physical activity per se, but

possibly some other aspects of behaviour, which are relevant for the beneficial effects of

exercise.

One possibility is that exercise is associated with an occurrence of a unique brain

state, which contributes to the beneficial effects of fitness on cognition. Several studies

have shown that exercise is associated with enhancement of slow EEG frequencies, such as

34

in alpha and theta frequency ranges, specifically in the frontal cortex (reviewed in

(Dietrich, 2006)). Notably, circadian time and preceding sleep-wake history have also been

shown to modulate EEG activities in these frequency bands, both in humans (Cajochen et

al., 2002; Finelli et al., 2000; Landolt et al., 2004) and in laboratory animals (Leemburg et

al., 2010; Vyazovskiy and Tobler, 2005). Interestingly, the occurrence of slow frequencies

in wake EEG is considered a marker of intrusion of sleep-like patterns of activity into

awake state (Hung et al., 2013; Vyazovskiy et al., 2011). This indicates that brain systems,

which are relevant for sleep-wake control, may be also sensitive to exercise. In other

words, evidence suggests that exercise may elicit patterns of brain activity, which are

typically encountered during sleep only.

We hypothesized that the benefits of exercise, or more specifically, certain types of

waking activities, for the brain can, at least in theory, complement cognitive and

psychological benefits provided by sleep. It is obvious that both exercise and sleep are

important for a healthy mind and are thus vital for astronauts that have to withstand huge

stresses during manned space missions, like in the ISS. However, at the moment physical

exercise is only obligatory to counter the effects of osteoporosis during weightlessness in

space. Moreover, while hundreds of experiments performed on ISS had a neuroscience

component, which included testing psychological functions, neurocognitive performance,

circadian rhythms, spatial orientation, effect of microgravity upon kinematic and dynamic

characteristics of locomotion, vestibular reflexes, sensory functions, and psychomotor

vigilance (Clement and Ngo-Anh, 2013), it has never been studied whether specific types

of exercise can improve astronauts’ cognitive performance without the need of obtaining

longer sleep.

One study found increased performance following running sessions on an active

treadmill in a simulated space flight to Mars (Schneider et al., 2013). Notably, this study

also revealed a decreased activity of the neocortex in the prefrontal area, which is known to

be critical for executive brain functions, and also appears to be most vulnerable to sleep

loss (Anderson and Horne, 2003; Couyoumdjian et al., 2010; Curcio et al., 2006;

Drummond et al., 2000; Goel et al., 2009; Horne, 1993; Thomas et al., 2000). This

observation is consistent with the hypothesis that improvements in cognitive performance

after exercise is associated with “transient hypofrontality” (Dietrich, 2006). This theory

postulates a redistribution of cortical activity from the frontal cortex areas towards areas of

35

the brain implicated in motor control (i.e. motor cortex and associated areas). While this

idea is interesting, it remains unclear why hypofrontality should improve performance, and

our results provide important novel insights.

In this project we found that stereotypic wheel running is associated with reduced

neuronal activity in at least two cortical areas, and the amount of sleep was reduced

following an episode of running. Our results thus support our hypothesis and suggest that it

is possible to develop a regimen of waking activities, which would decrease the rate of

accumulation of sleep need. It is important to note that the subjects would remain

effectively awake during this time, rather than engaging in a non-functional ‘hybrid’ or

mixed state, which may be mal-functional.

Many fundamental questions relating to the link between physical exercise and

brain health remain to be addressed. The practical questions include the optimal content of

exercise which is most beneficial for the brain (type, duration, and intensity), and specific

regimens, which on the one hand should not interfere with the daily routine, but on the

other hand provide maximal effects. Next experiments should include human studies,

where EEG is recorded continuously during waking dominated by demanding cognitive

activities and stereotypic, but engaging tasks. We hypothesize that such experiment will

reveal less local signatures of ‘tiredness’ when subjects perform repetitive stereotypic

tasks. Ideally, such experiments need to be combined with performance measurements. An

intriguing possibility is that performance may improve if a cognitively demanding task is

combined with stereotypic movement or sensory stimulation, which we hypothesize could

decrease ‘signal-to-noise’ ratio in brain activity, thus allowing to re-allocate cognitive

resources to the task.

Further animal experiments could in turn provide further insights about specific

molecular and cellular mechanisms underlying the effects we observed. It is essential to

test other behaviours, which are less physically demanding than wheel running.

Furthermore, the factors of arousal and motivation need to be addressed. Next, it remains to

be determined whether stereotypic repetitive behaviours benefit brain function in general

and performance in cognitive tasks in particular. For example, the question remains

whether the animal or a human can learn a novel association while running stereotypically,

or whether tasks, which require sleep for memory consolidation (Rasch and Born, 2013)

can also benefit from exercise (Rhee et al., 2016; Weinberg et al., 2014). Finally, molecular

36

markers of cellular function and synaptic plasticity need to be investigated after a period of

stereotypic behaviour. It is well known that sleep and sleep deprivation are associated with

a marked change in gene expression (Cirelli et al., 2004). However, the possibility that

different kinds of waking lead to distinct changes in the transcriptome remain unexplored.

Redressing this omission will allow identifying targets for investigating the underlying

mechanisms and direct pharmacogenetic manipulations in future studies.

Notes

Parts of the introduction were modified from (Vyazovskiy, 2015), and results of the 1st

experiments have been reproduced with modifications from Fisher, Cui et al., Nature

Communications, in press (DOI: 10.1038/NCOMMS13138).

Acknowledgements In addition to ESA support the study was funded by: MRC NIRG (MR/L003635/1),

BBSRC Industrial CASE grant (BB/K011847/1), FP7-PEOPLE-CIG (PCIG11-GA-2012-

322050), John Fell OUP Research Fund Grant (131/032), Wellcome Trust Strategic Award

(098461/Z/12/Z). We would like to thank Prof. P. Somogyi, Drs. L. Katona, T. Viney, M.

Walton and T. Gheysens for discussions and advice on data analysis and interpretation, and

Drs F. Nodal and Z. Molnar for assistance with histology.

37

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