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Rapport de Stage
CogMaster – Master en Sciences Cognitives
2ère année - Spéc. Neurosciences
Electrophysiological markers of Rapid Eye Movements
in wake and REM sleep:
insights from intracranial recordings in Humans
Thomas Andrillon
sous la direction de Giulio Tononi, MD PhD
en collaboration avec Yuval Nir, PhD
au sein du
Center for Sleep and Consciousness
Department of Psychiatry, University of Wisconsin-Madison,
6001 Research Park Blvd, Madison, WI, 53719, USA
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“We are such stuff.
As dreams are made on; and our little life
Is rounded with a sleep.”
(Shakespeare, The Tempest)
Abstract
When awake, rapid eye movements (REMs) direct our fixation to specific parts
of the visual scene and constantly guide our visual perception and behavior. Since
the discovery of Rapid Eye Movement (REM) sleep, it became clear that REMs
are also prevalent during certain periods of sleep, but the neuronal activity
underlying such events and their relation to visual dream experiences remains
unclear. Here we used a unique opportunity to examine and compare neuronal
activity associated with REMs in wakefulness and sleep by examining
simultaneously recorded electro-oculogram (EOG) along with intracerebral EEG,
and single-unit firing in multiple brain regions of neurosurgical epilepsy patients.
We find that REMs share similar properties across wakefulness and REM sleep.
Neuronal activity was robustly modulated with ocular movements in both
wakefulness and sleep, advocating for a functional continuity of REMs across
vigilance states. Many neurons showed a bi-phasic pattern of activity including a
decrease just before REMs onsets, possibly related to behavioral saccadic
suppression, and a large increase immediately after REMs onsets. During
wakefulness, the activity increase mainly followed a feedforward propagation with
a progression from entorhinal cortex to hippocampus. By contrast, REMs in sleep
were associated with a reversal of signal propagation suggesting a top-down
propagation mode. We conclude that REMs and underlying neuronal activities
share many similarities across wakefulness and sleep but the directionality of signal
propagation may differ.
Keywords: REM sleep, rapid eye movements, wakefulness, Humans, intracranial recordings,
information directionality
Words count1: 10,168 words, 51 pages
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Without the cover page, table of contents, references and acknowledgements (Total: 12,544).
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TABLE OF CONTENTS
INTRODUCTION ..................................................................................................4
Rapid eye movements (REMs), a key feature shaping our perception .................................................................... 4
REMs also occur during certain stages of sleep ..................................................................................................... 5
Are REMs functionally equivalent during sleep and wakefulness? ....................................................................... 7
Are REMs in REM sleep a bottom-up or top-down phenomenon? ...................................................................... 8
A unique dataset to explore REM sleep and dreams ........................................................................................... 9
METHODS ............................................................................................................ 10
Subjects .............................................................................................................................................................. 10
Polysomnographic sleep studies............................................................................................................................ 10
Visual recognition studies................................................................................................................................... 12
Data Acquisition............................................................................................................................................... 12
Detection of Rapid-Eye Movements (REMs) ..................................................................................................... 13
Evoked Potentials (EPs) in depth EEG ........................................................................................................... 14
Spiking activities during REMs......................................................................................................................... 15
Statistical Analysis ............................................................................................................................................ 17
RESULTS ............................................................................................................... 19
Data overview and analysis of sleep parameters................................................................................................... 19
Eye movement properties are similar in wakefulness and REM sleep ................................................................. 20
Modulation of neuronal activity during eye movements ........................................................................................ 23
Directionality of signal propagation following eye movements changes between wakefulness and sleep .................... 28
DISCUSSION ......................................................................................................... 31
The persistence of saccadic suppression and the nature of REMs in REM sleep.................................................. 31
Ponto-geniculo-occipital (PGO) waves and REMs ............................................................................................. 32
Signal propagation during wakefulness and REM sleep ..................................................................................... 33
Study limitations ................................................................................................................................................ 35
CONCLUSION: ..................................................................................................... 36
ACKNOWLEDGEMENTS ................................................................................... 37
REFERENCES: ..................................................................................................... 38
APPENDICES:....................................................................................................... 45
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Introduction
Rapid eye movements (REMs), a key feature shaping our
perception
Rapid eye movements can be defined as fast rotatory ocular movements. This
definition embraces a great variety of eye movements including saccades
performed by awake individuals, a phenomenon extensively studied and one of
the hallmarks of cognitive sciences. Saccades permit individuals to rapidly move
the fovea, acquiring high quality visual information from a limited portion of the
visual scene (Henderson 2003). Therefore REMs enable to scan in detail the
environment and orientate attention to a limited portion of the visual space
(Findlay 2004). As a consequence, saccades are omnipresent in primates' life. For
example, humans produce about 3 saccades per second (Schiller and Tehovnik
2005). By determining what you see and when you see it, ocular movements
actively shape our perception of the world (Ballard, Hayhoe et al. 1997).
Despite their apparent simplicity, fast REMs are precise and highly complex
movements. Numerous studies have dissected the neural circuits implementing
the execution and control of REMs (Tehovnik, Sommer et al. 2000; PierrotDeseilligny, Milea et al. 2004) . Saccades involve various neocortical and limbic
areas and reflect various cognitive processes including memory, decision-making,
and attention allocation (Henderson 2003). Saccades are classically separated into
two groups entailing different generators: (i) reflexive saccades (eye movements
triggered externally by the sudden apparition of a new feature in the visual scene),
(ii) voluntary saccades (ocular movements triggered internally after a cognitive
decision process) (Mort, Perry et al. 2003; Schraa-Tam, van Broekhoven et al.
2009). In real-world perception, both types of saccades occur.
REMs affect not only what we see but also how we see. Indeed, each saccade is
accompanied by a so-called saccadic suppression: visual information is processed
fully only during periods of gaze stability and smeared pictures caused by fast
ocular movements are wiped out (Henderson 2003; Burr and Morrone 2005).
Thus, although we have the phenomenological impression of continuity, our
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visual perception actually consists of a sequence of snapshots disrupted by REMs
(VanRullen and Koch 2003). REMs represent therefore an interesting index of
ongoing visual processing. Do such ‘snapshots’, such resets in visual flow also
occur in REM sleep? And are they accompanied by saccadic suppression?
REMs also occur during certain stages of sleep
Ocular movements are not only a predominant features of wakefulness, they are
also observed in sleep (Aserinsky and Kleitman 1953) and are indeed used to
define and categorize sleep stages: Rapid Eye Movements (REM) and Non-Rapid
Eye Movement (NREM) sleep (see Box 1)2. Michel Jouvet’s work highlighted the
importance of this distinction, REM and NREM sleep referring to different
cognitive states and brain activity (Jouvet 1992). While NREM is classically
defined as a state during which the brain has a broadly synchronized and
stereotypical activity, REM sleep is characterized by EEG activity similar to
wakefulness coupled to a generalized postural muscle atonia (Iber, Ancoli-Israel et
al. 2007; Kryger, Roth et al. 2011). This paradox of a sleep stage for which brain
and ocular activity are similar to wake led Michel Jouvet to designate REM sleep
as paradoxical sleep (Jouvet 1992). REM and NREM sleep are also characterized by
differences in consciousness. Reports of conscious experiences (dreams) are more
frequent and vivid following awakening from REM sleep compared with NREM
sleep (Tononi and Massimini 2008; Nir and Tononi 2010).
Despite being one of the prominent features of REM sleep, REMs are not
present continuously throughout REM sleep. In fact, REM sleep can be divided
into phasic REM sleep during which REMs are observed (14-27% of REM sleep)
and tonic REM sleep (73-86% of REM sleep) characterized by an absence of
REMs. REMs during REM sleep typically occur in bursts of 5-6 consecutive
ocular movements, which is not usual in wakefulness (Arnulf 2011). REMs
density in sleep is highly variable (5-35 REMs/min) and shows great interindividual variability (Takahashi and Atsumi 1997), whereas density during
However, REMs are not present in REM sleep in all species. For example, an absence of REMs was
demonstrated in owls during REM sleep (Susic, V. T. and R. M. Kovacevic (1973). "Sleep patterns in the
owl Strix aluco." Physiol Behav 11(3): 313-317.).
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wakefulness is more consistent. Despite their weaker prevalence during REM
sleep compared to wake, REMs share kinematic parameters with eyes closed
voluntary saccades (Sprenger, Lappe-Osthege et al. 2010).
Box 1: Sleep in a few words
Polysomnography: multi-parametric recordings used to assess sleep and classically
including scalp EEG (electroencephalogram), EOG (electrooculogram) and EMG
(electromyogram).
Sleep scoring: Sleep is usually divided in 4 stages (REM, NREM1, NREM2,
NREM3) defined by precise electrophysiological and behavioral criteria (Iber,
Ancoli-Israel et al. 2007)
Rapid Eye Movement (REM) sleep: sleep stage characterized by an electric
activity resembling wakefulness (low amplitude EEG, theta activity (4-7Hz)) coupled
with postural muscle atonia and rapid eye movements. Dreams usually occur during
REM sleep (Nir and Tononi 2010), which is predominant late at night. REM sleep is
also called paradoxical, desynchronized or active sleep. REM sleep represents about
20% of sleep time in adults.
Non Rapid Eye Movement (NREM) sleep: sleep phase divided in 3 stages
(NREM1-3). NREM1 is a stage of transition between wakefulness and sleep
characterized by a reduction in alpha (8-10 Hz) rhythm in parietal regions and a loss
of responsiveness. Slow eye movements are typical of the transition into sleep.
NREM2 and NREM3 are deeper sleep stages characterized by the presence of wellidentified sleep rhythms (slow-waves (0.5-4 Hz) and sleep spindles (11-16Hz), see
(Andrillon, Nir et al. 2011; Nir, Staba et al. 2011) for a description in our data). Slowwaves sleep (SWS) refers to NREM3 and is defined by the predominance of slowwaves synchronized throughout the cortex.
Sleep cycles: Classically, NREM and REM sleep alternate across the night, each
night comprising ~3-5 NREM-REM cycles. The ratio of REM to NREM sleep
within a cycle gradually increases throughout the night.
Hypnogram: diagram describing the occurrence of sleep stages and wakefulness
throughout the night (see Fig. 1D and App. 1C for examples).
As for their genesis, REMs were observed in close relation with PontoGeniculo-Occipital (PGO) waves described by Mircea Steriade as “the internal
activation of the brain during REM sleep” (Steriade 2003) suggesting that REMs
could be under the control of the brainstem. Many studies eventually observed
PGO waves and REMs as occurring simultaneously along visual and limbic areas
with a common generator in the Ventro-Medial part of the Caudal Pontine
Tegmentum (Pace-Schott and Hobson 2002; Arnulf 2011) as demonstrated by
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Carbachol microinjections in this structure (Vanni-Mercier and Debilly 1998). In
addition, an absence of readiness-potential was shown during sleep REMs
compared to voluntary saccades in wake (Klostermann, Kompf et al. 1994; Abe,
Ogawa et al. 2004) advocating for a cortical disengagement during REM sleep
REMs. Another study exploring the time course of blood oxygenation leveldependent responses locked to REMs in REM sleep (Miyauchi, Misaki et al. 2009)
supported the role of the brainstem in triggering REMs. However, due to the
spatial and temporal limitations of non-invasive electrophysiological or imaging
studies, it is hard to be conclusive. In addition, other imaging or neurosurgical
studies challenged the idea that only brainstem was involved in REMs generation
(Doricchi, Iaria et al. 2007; Hong, Harris et al. 2009).
The question of the function of REMs in REM sleep (if any) is a long-lasting
debate. REMs have been linked to brain maturation since they appear early in
development and increase during the first semester of life (Roffwarg, Muzio et al.
1966) in parallel with visual ontogenesis. REMs have also been thought to play a
role in brain plasticity (Datta 1999) and sensorimotor gating (Bowker and
Morrison 1976). The fact that REMs and dreams occur during the same phase of
sleep gave rise to the hypothesis that ocular movements during sleep are related to
dream content (scanning hypothesis, see (Arnulf 2011) for a review). According to
this hypothesis, the occurrence and direction of REMs would be correlated to the
dreamer’s actions or visual experience (Dement and Kleitman 1957). Another
influential view regards REMs as the mere reflection of brainstem activity,
occurring independently of dreams just as penile erections happen independently
of sexual dreams (see (Arnulf 2011) for a review).
Are REMs functionally equivalent during sleep and wakefulness?
Previous studies have explored the properties of REMs in REM sleep
compared to wakefulness to unravel whether they can be seen as similar events.
The absence of REMs in NREM sleep when reports of conscious experience are
less frequent and simpler (Hobson 1990; Massimini, Ferrarelli et al. 2005)
supports the notion that in REM sleep they may relate to visual experiences and
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exploration of visual scenes (Dement and Kleitman 1957; Arnulf 2011).
Moreover, the activation of primary visual cortex during REMs in REM sleep
contrary to self-paced saccades in complete darkness (Miyauchi, Takino et al.
1987; Peigneux, Laureys et al. 2001) may outline the relation of REMs with a
visual experience in REM sleep as in wake.
There is behavioral evidence of a relation between REMs and dream content: (i)
dream reports are usually more vivid during bursts of REMs (Berger and Oswald
1962); (ii) suppression of postural muscle atonia in cats (lesions in the
Dorsolateral Pontine Tegmentum (DT)) results in the expression, during REM
sleep, of a repertoire of actions similar to those
performed during waking.
Notably, limb movements coincide with REMs (Sastre and Jouvet 1979); (iii) in
DT-lesioned cats visual stimulation during REM sleep does not affect the
direction of REMs; (iv) more recently, a correspondence, albeit not perfect, has
been reported between REMs direction and limb movements acted out during
REM sleep in REM Behavior Disorder (RBD) patients, a pathology characterized
by the enactment of dreams due to an absence of muscle atonia (LeclairVisonneau, Oudiette et al. 2010).
Are REMs in REM sleep a bottom-up or top-down phenomenon?
One influential theory on dreams consisted in seeing dreams as generated
through bottom-up processes (Hobson and McCarley 1977) close to visual
perception during wakefulness. In this view, dream scenery would start from
activity in low-level sensory areas and would then be interpreted in higher-order
visual areas (Nir and Tononi 2010). The brainstem, sending simultaneously
corollary discharges to the whole cortex (i.e. visual, occulomotor and motor
cortices) would insure the timely coordination between visual images, eye
movements and body movements within the dream. In other words, the cortex
would be the spectator of a 4-dimensions movie (Arnulf 2011) driven by
brainstem activation: “the stuff dreams are made off” as described by Mircea
Steriade (Steriade 2003). On the contrary, in line with the Freudian tradition,
dreams could originate from memories and psychic motives. Thus, the dream
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scenery would be formed following a top-down fashion as in imagination, abstract
thoughts being represented as a scene within the dream.
Understanding how the information flow is directed (top-down vs. bottom-up)
during REM sleep and wakefulness could shed some light on this debated
question of the nature of dreams (Nir and Tononi 2010). Our approach will
consist in using REMs as triggers to explore the directionality of information in
these different stages.
A unique dataset to explore REM sleep and dreams
We propose to analyze intracranial recordings during REMs in wakefulness and
sleep to investigate and contrast their neurophysiological correlates. This unique
dataset has been recorded in UCLA in epileptic patients resistant to
pharmacological treatment and implanted with electrodes for potential surgical
treatment (18 patients, 129 regions recorded and hundreds of units, see Methods).
The dataset includes brain recordings at different scales (scalp EEG, depth EEG,
LFP and single-unit recordings), which gives us the possibility to bridge the gap
between human and animal studies and may provide a better comprehension of
the mechanisms involved. Compared to previous electrophysiological or imaging
studies in humans, we benefit here from a good spatial and temporal resolution.
In our analysis, we will focus on two main questions: (i) Are REMs equivalent
in wake and REM sleep? In particular, can we see a saccadic suppression
associated to REMs in REM sleep? (ii) Can we observe changes in the
information flow associated to REMs in REM sleep and wakefulness?
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Methods
Subjects
Eighteen patients with pharmacologically intractable epilepsy (ages 19-52)
underwent monitoring with depth electrodes for seizure foci identification and
potential surgical treatment (Fried, Wilson et al. 1999). Patients provided written
informed consent prior to participation in the research study, under the approval
of the Medical Institutional Review Board at the University of California, Los
Angeles, USA. Electrode location was based only on clinical criteria, and Itzhak
Fried performed all surgery. For each subject, localization of the seizure onset
zone was based on recordings during hospital monitoring, in combination with
prior functional and anatomical neuroimaging (see appendix table 1 for patient
information). The data were recorded at UCLA prior to the start of my internship.
Thirteen patients participated in a full overnight sleep study and nine patients
participated in a visual object recognition paradigm (four of these individuals
participated in both experiments).
Polysomnographic sleep studies
Sleep recordings were conducted at a minimal interval of 12 hours from
identifiable seizures, 48-72 hr after surgery, and lasted for about 7 hours between
23:00 and 06:00. Recordings were performed by Yuval Nir3. In addition to
continuous video monitoring4, a classical polysomnographic montage was used,
including two electrooculogram (EOG), two electromyogram (EMG), and four
scalp electrodes (positioned at C3, C4, Pz and Fz) as well as two earlobe
electrodes used as references. Sleep-wake stages (wakefulness, NREM sleep stages
N1-N3 and REM sleep) were scored according to established guidelines (Iber,
Ancoli-Israel et al. 2007) (see Box 1 for further details and App. 1A for examples).
I participated to one recording session during a previous internship in Dr Tononi’s research center.
Because of legal restrictions pertaining to patient confidentiality, I could not have access to the
continuous video monitoring stored at UCLA.
3
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Figure 1: Data overview, sleep parameters and detection of eye movements (REMs).
A. Illustration of flexible probes used for recording depth EEG (blue: platinum contact) and unit activity (green:
microwires). B. Medial view of 129 depth electrode locations (purple dots) spanning multiple brain regions in the 13
individuals having participated in the sleep study. SM, Supplementary motor; PC/P, posterior cingulate/parietal
cortex; PH, parahippocampal gyrus; HC, hippocampus; E, entorhinal cortex; Am, amygdala; LH, left hemisphere;
RH, right hemisphere. C. Power spectra of scalp EEG in one representative individual in sleep stages N2 (blue),
N3 (red), and REM sleep (green). These power spectra are typical signature of REM and NREM sleep. D.
Hypnogram in the same individual. W: Wake; R: REM sleep; N1–N3: NREM sleep stages 1–3. E. REMs
detection. 1st row: raw EOG showing 2 typical REMs in REM sleep. 2nd row: Band-pass filtered (0.1-3Hz) EOG
with mean+2SD associated threshold (black lines). 3rd row: Detection of epochs over threshold for one EOG
channel (blue curve, green dots). 4th row: Detection is confirmed for the second EOG channel (red curve, yellow dot). 5th
row: Detection of potential epileptic artifacts in high-pass filtered trace. 6th row: Visual confirmation and
confirmation of onset. The second REM displayed would be then similarly detected. F. Occurrence of REMs
across different vigilance states. Note the near-absence of REMs in NREM sleep. Error bars represent SEM
across subjects (REM sleep: 11 patients, Wakefulness: 12 patients, NREM: 13 patients).
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Visual recognition studies
Eight patients participated in a visual object recognition study. These data are
subject to a separate analysis but they were used here as a control to elucidate the
directionality of signal propagation along the visual hierarchy upon visual
processing in wakefulness in a well-controlled situation.
The experimental procedure was as follows. Before each session, 2 sets of 6
pictures each were chosen based on their effectiveness in eliciting responses in the
recorded neurons in a 'visual screening' experiment performed earlier that day, as
in (Quiroga, Reddy et al. 2005). Each session included two blocks, lasting 12
minutes each. During each block, subjects were presented with 4 face and 2 place
images (see App. 3 for examples) for 200ms on a laptop computer, and subjects
performed a face/place task by pressing two different buttons. Each picture was
presented 24 times in a pseudo-randomized order, and trials were separated by a
random interval between 2 and 8 seconds (uniform distribution). Behavioral
responses were recorded along with intracranial and scalp electrophysiological
data. Overall 22 such sessions were recorded in 8 individuals.
Data Acquisition
For each patient, 8 to 12 flexible polyurethane depth electrodes (1.25 mm
diameter, see Fig. 1A) were placed in the following regions: hippocampus,
amygdala, entorhinal cortex, parahippocampal gyrus, temporal gyrus, fusiform
gyrus, temporo-occipital junction; anterior, middle and posterior cingulate;
supplementary motor area, inferior frontal gyrus, orbitofrontal cortex, and parietal
cortex. Electrode location varied between patients based on their clinical profiles
(see Fig. 1B for an overview and App. Table 1). Subsequently, I confirmed the
position of each electrode using post-implant computed tomography (CT) coregistered with pre-implant magnetic resonance (MR) imaging (Brain Navigator,
Grass-Telefactor Corp., Philadelphia, PA) based on a Talairach atlas (Lancaster,
Rainey et al. 1997; Lancaster, Woldorff et al. 2000).
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Scalp and intracranial depth EEG data were continuously recorded sampled at
2 kHz, bandpass-filtered in hardware between 0.1Hz and 500Hz and rereferenced offline to the mean signal of the earlobes electrodes. Scalp EEG,
EOG, EMG and behavioral data were collected and preprocessed according to
established guidelines for sleep study polysomnography (Iber, Ancoli-Israel et al.
2007). Specifically, two EOG electrodes were pasted below the left and above the
right canthi, and referenced to contralateral reference electrodes. In addition, each
depth electrode terminated in a set of eight insulated 40-μm platinum-iridium
microwires (impedances 200 to 500 kΩ, see Figure 1A) (Fried, Wilson et al. 1999).
Microwire signals were simultaneously recorded continuously (Cheetah Recording
System; Neuralynx, Tucson, AZ for 10 patients; Neuroport Recording System;
Blackrock, Salt Lake City, UT for 3 patients), sampled at 28 kHz (10 patients) or
30 kHz (3 patients), band-pass filtered in hardware between 1Hz and 9kHz, and
referenced locally to a ninth non-insulated microwire.
Detection of Rapid-Eye Movements (REMs)
REMs were detected in a semi-automatic manner as illustrated in Fig. 1E. The
two EOG signals recorded during sleep studies (Fig. 1E, 1st row, blue and red
curves) were first band-pass filtered between 0.1 and 3 Hz using a zero-phase 2ndorder Butterworth filter, to attenuate fast background activity (Fig. 1E, 2nd row). A
detection threshold was then set at mean + 2 SD of the EOG signals across the
whole night (epochs with absolute amplitude over 1,000µV were discarded from
this computation so that such abnormal voltage would not influence our
estimation) (Fig. 1E, 2nd row). Events crossing this threshold were considered
REMs candidates (Fig. 1E, 3rd row) and further inspected so that (1) the segments
above threshold had a duration of less than 1.5s; (2) they corresponded to an
amplitude exceeding an equivalent threshold in the other EOG trace and of
opposite sign; (3) their maximal slope (temporal 1st derivative) was above a
threshold of 1µV/ms (Fig. 1E, 4th row). We discarded REMs too close from each
other (<0.5s) as well as REMs in close vicinity (within 0.5s) with detected
epileptiform interictal spikes. Epileptiform interictal spikes were identified in the
EOG by searching for abnormal increase (absolute amplitude superior to mean +
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10 SD) in the 100-150 Hz band (Fig. 1E, 5th row). We converged on these
parameters after careful visual examination of the detected REMs and rejected
candidates. We selected only the EOGs deflections with opposite phases since
they correspond, in our EOG montage, to horizontal eye movements. Signal
deflections that were in-phase across the two EOG traces were not analyzed since
these may reflect both vertical eye movements as well as cortical potentials (e.g.
slow-waves or K-complexes in NREM sleep) leaking to EOG measurements.
REM onsets were defined automatically as the first crossing of the 2 raw EOGs
traces before detected REMs (Fig. 1E, 6th row). Finally, to ensure the quality of
this automatic detection and since our study needed a high precision in
determining the exact onset of REMs, I visually reviewed all the automatically
detected REMs and rejected candidates, as well as their onset times. It was then
decided to reject or include the REMs and fine-tune their time of onset. REMs
onsets were used as reference time points throughout our analysis after a
comparison with other REMs parameters (REMs peak amplitude or maximum
slope).
In addition to this detection, random epochs were selected to be used as a
control data set. For each detected REMs, we detected within the same 10s
segments, 2s epochs during which there was no significant increase in either EOG
channel and no traces of epileptiform interictal spikes. To this end, we detected
epochs presenting an increase within the [100, 150] Hz band (zero-phase 2nd-order
Butterworth filter) superior to a threshold set as 10 SD. These “random” events
matched REMs detection in terms of density, sleep stages and occurrence within
the ultradian cycle (i.e. time within sleep) for each patient.
Evoked Potentials (EPs) in depth EEG
EPs for depth EEG channels were computed as follows. Depth EEG channels
were segmented in 10s segments, notch-filtered (2nd-order IIR notch filter at 60
Hz and its harmonic) to eliminate electrical noise and corrected for baseline
activity by subtracting the mean amplitude across 10s segments. Epochs around
REMs onsets were selected ([-1000, 800] ms). Epileptic-spikes free epochs were
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then averaged by EEG channel and vigilance stage. Figure 5 show the average
EPs at times of REMs occurrence for depth EEG across subjects. The exact same
procedure was repeated for random epochs to assess the amplitude level of
random fluctuations with this extent of data. Statistical deviance between REMs
and random EPs was assessed at each sample across subjects for each depth
electrode and stage using a paired Mann-Whitney U-test (alpha=0.05, n=11
patients). Simes’ method was used to correct for multiple comparisons (Rodland
2006). Only the most medial recording sites (blue contact in Fig. 1A) were
analyzed, although each electrode included other EEG contacts along the shaft of
the electrode.
Spiking activities during REMs
Unit identification and spike sorting. Units were identified using the 'wave_clus'
software package (Quiroga, Nadasdy et al. 2004) as follows: (i) extracellular
microwire recordings were highpass filtered above 300Hz, (ii) a 5 SD threshold
above the median noise level was computed, (iii) detected events were clustered
using superparamagnetic clustering and (iv) classed as single-unit, multi-unit
clusters or noise based on the reliability of action potential waveforms and by the
presence of a refractory period for single units (see App. 2A-B and (Nir, Mukamel
et al. 2008)). The enduring nature of unit recordings throughout hours of
continuous recordings (sleep studies: ~7h) was assessed by ensuring that action
potential waveforms, and inter-spike-interval distributions were conserved in 1hour intervals and that clusters remained separable throughout the recording
session. Multiunit activity (MUA) traces, used in Figure 3 (green curves) were
extracted from each microwire by filtering the recorded signal offline between
300Hz and 3000Hz. Overall, for sleep recordings, 600 units were identified across
129 regions and 1,334 units for the visual recognition sessions (see App. 2C).
Analysis of neuronal discharges around REMs. A triggered-averaging analysis was
performed for each unit separately as follows. Spike data were time-locked to
REMs onsets and averaged for each unit across a [-600, 800] ms window around
REMs onsets. The post-event-time histogram was smoothed using a Gaussian
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kernel (σ = 60ms) and normalized by its minimum and maximum values,
facilitating the comparisons between low- and high-firing rate neurons. The same
procedure was repeated for random epochs (see above) and statistical deviance
between triggered-averages for REMs and random events was assessed using a
paired Mann-Whitney U-test (α=0.05). Simes’ method was used to correct for
multiple comparisons. After carefully inspecting the results for each neuron
separately, two patterns were identified in line with previous studies (Schall 1991;
Schall 1991): (i) Group A: units showing a decrease in activity just before REMs
onset and a larger increase shortly after REMs onsets, (ii) Group B: units with
increased firing rates preceding REMs. In order to quantitatively distinguish
between these profiles and categorize units based on these groups, we analyzed
the relation between spike times and the phase of the EOG signal taking
inspiration from my prior work on this very dataset (Andrillon, Nir et al. 2011).
Thus, EOG channels were band-pass filtered (0.1-3Hz, a range used and
optimized for REMs detection) and the instantaneous phase of the filtered EOG
was computed via the Hilbert transform. Then, for each unit, we extracted the
distribution of EOG phases corresponding to spikes occurrence within a [-600,
800] ms interval around REMs onsets. The nonuniformity of phase distributions
was assessed using Rayleigh’s test for nonuniformity (circ_mean function, Circular
Statistics Toolbox for Matlab). Since EOG skewness can introduce nonuniformity
above and beyond units’ preference for a given phase (Siapas, Lubenov et al.
2005), we compared the p-value obtained with the Rayleigh’s test to surrogate
data. For each unit separately, we shuffled spikes occurrence for the different
REMs across the same [-600, 800] ms window, extracted EOG phases for each
spikes and computed a new p-value using Rayleigh’s test. This operation was
repeated 500 times. We then extracted the position of the original p-value within
the distribution of bootstrapped p-values. This process was repeated for both
EOG channels and results were averaged for each unit. Finally we split the data in
2 groups by gathering units whose phase original p-value was superior to the
median of bootstrapped p-values (units with phase preference) and units whose
original p-value was inferior to this median value (units without phase preference).
On the whole, units showing a tendency to fire spikes at specific phases of the
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REMs waveform (as in Figure 4A) showed a firing pattern of group A, while units
without a particular phase preference typically showed a firing pattern of group B.
Finally, neuronal activity around REMs was averaged by group (Fig. 4C) and
across regions (see Figure 4B) for each group separately (Fig. 4D).
Latencies of neuronal activity following REMs. We computed the latency of such
activity increases as follows. For each unit in group A (see above), we extracted
spikes occurring within 1s after REMs onsets (for wake and REM sleep
separately). Then, spikes were binned across fifteen 100ms windows (50ms
overlap). For each window and across REMs, we compared the firing rate to the
baseline activity (computed on a [-500 0] ms window before REMs onsets) with a
Mann-Whitney U-test. The timing of the first window showing a significant
increase in firing rate compared to baseline (α=0.05, Simes correction) was
defined as the latency of increased activity following REMs for this particular unit
and vigilance state. We focused on group A units since they show an increase in
their firing rate after REMs onsets. Therefore, for this group of units, REMs
trigger an increase in activity, allowing us to analyze its propagation between brain
regions. The same procedure was repeated for the object recognition paradigm,
using the time of image presentation as time zero (instead of REMs onsets). Only
units showing responses to pictures (96 out of 1334 neurons, 18% of MTL
neurons) were selected for this analysis as for the sleep study (see App. 3 for
examples). To this end, standard post-stimulus-time-histograms (PSTH) were
computed with 100ms bins across 144 trials and the firing rate in the time interval
300-500ms after image presentation was compared via a one-tail Student t-test to
the baseline firing rate (α=0.001).
Statistical Analysis
Error bars in figures denote standard error of the mean (SEM = SD/√(n-1),
where n is the number of data points). Student T-tests were performed after
confirming normal distributions via Kolmogorov-Smirnov tests or the MannWhitney U-test (non-parametric) when the normality was not confirmed. When
17
necessary, corrections for multiple comparisons were performed using Simes’
correction5 (Rodland 2006).
5
Simes correction is an optimized version of Bonferroni correction.
18
Results
Data overview and analysis of sleep parameters
Thirteen
neurosurgical
patients
with
intractable
epilepsy
underwent
polysomnographic sleep studies. Recordings were conducted overnight (421 ± 20
min (mean ± SEM)) and included scalp EEG, EOG, EMG and continuous video
monitoring. Sleep–wake stages were scored following established guidelines (Iber,
Ancoli-Israel et al. 2007) as wakefulness, NREM sleep (3 stages) and REM sleep
(see Box 1 for a brief description of sleep scoring and App. 1A for examples).
One patient did not show any REM sleep before the sleep recording was
terminated. Overall, sleep parameters including sleep efficiency and the time spent
in different sleep stages (App. 1B) were in general agreement with typical findings
in healthy young adults (Riedner, Vyazovskiy et al. 2007). The reduction in the
amount of REM sleep (13% compared to 20% usually in adults) probably reflects
the fact that sleep recordings were usually terminated around 6AM and REM
sleep is predominant in the morning hours (McCarley 2007). Hypnograms (timecourse of sleep-wake stages, see Box 1) showed typical sleep architecture and
NREM-REM sleep cycles (Fig. 1D and App. 1C). Power spectra of scalp EEG
(Fig. 1C for an example and App. 1C) resembled those found during sleep of
healthy individuals, including robust slow-wave activity (0.5-4 Hz) and spindle (9–
16 Hz) power that was specific to NREM sleep stages. These results indicate that
sleep measures could be assimilated to those of natural sleep in individuals
without epilepsy, which makes us confident that it is reasonable to generalize our
results to the healthy population.
During sleep studies, we recorded brain signals from 129 medial brain regions
bilaterally within the medial temporal lobe, frontal and parietal cortices (Fig. 1B).
Electrodes were implanted based on clinical criteria and spanned mostly medial
brain areas. It should be noted that some regions that are implicated in controlling
and generating eye movements (REMs), such as the Frontal Eye Field and the
occipital lobe (Pierrot-Deseilligny, Milea et al. 2004), were not monitored in these
neurosurgical patients. We simultaneously recorded scalp EEG, depth EEG, and
19
spiking activity from a total of 600 units (355 putative single units, 245 multi-unit
clusters (see Methods and App. S2C)).
Eye movement properties are similar in wakefulness and REM
sleep
We
detected
REMs
in
EOG channels in a semiautomatic
fashion
(see
Methods and Fig. 1E) across
all vigilance states (Fig. 1F).
Epochs of wakefulness and
REM sleep with at least 50
detected
eye
movements
(n=12 and 11 individuals,
respectively) were used for
further analysis (Fig. 2). On
average, 2.6 ± 0.5 REMs per
minute (mean ± SEM) were
detected
in
wakefulness
(range: 0.6-5.1 REMs/min).
During REM sleep, REMs
occurred with an average
density of 4.4 ± 1.6 REMs
per minute (range: 1.0-8.7).
This rate is at the very low
range of that reported in
previous
studies
(Arnulf
Figure 2: Eye movement properties are similar in
wakefulness and REM sleep.
A. Average traces of REMs for each subject in
wakefulness (black, n=12) and REM sleep (red, n=11).
B. Comparison of eye movement characteristics in
wakefulness and REM sleep including their density,
amplitude, slope, duration and inter-eye-movementinterval (IMI). P-values denote statistical significance
(paired Mann-Whitney test, n=11 pairs). Note that
apart from REMs amplitude being significantly higher
in wakefulness, all other REMs parameters are
comparable across wake and REM sleep.
2011), and likely reflects the fact that with our recording montage, only horizontal
REMs could be observed, thus leading to less detected events. Contrary to
previous studies (Schiller and Tehovnik 2005; Arnulf 2011), in our data REMs
were significantly more numerous in REM sleep than wake (p<0.05, paired MannWhitney U-test, n=11 pairs of patients). This is probably a sign of the conditions
20
in which our recordings were obtained during wakefulness – typically, subjects
while awake in bed were not performing a visual task, or may have eyes closed.
Importantly, the rate of REMs detection in NREM sleep was not significantly
different from zero (Fig. 1F; p=0.17, Mann-Whitney U-test, n=13 patients),
attesting to a low rate of false detections of our algorithm.
Except for a larger amplitude of REMs in wakefulness compared to REM sleep
(p<0.001, paired Mann-Whitney U-test, n=11 pairs), other parameters (initial
slope, duration and time between REMs (IMI)) were not significantly different
between the two stages (Fig. 2B). Moreover, the shape of REMs as recorded by
EOGs signals was highly similar between REM sleep and wakefulness (Fig. 2A).
Overall, these results support the notion that properties of eye movement are
similar in wakefulness and REM sleep (Sprenger, Lappe-Osthege et al. 2010).
21
Figure 3: Example of neuronal activity during eye movements
Example of neuronal activities across multiple medial temporal lobe regions during 15sec of
wakefulness in one individual. Rows (top to bottom) depict EOG recordings (red), and unit activity in
right Amygdala (rAm), right Hippocampus (rHC), right parahippocampal gyrus (rPHG), left Amygdala
(lAm), left Hippocampus (lHC) and left parahippocampal gyrus. Black lines: single-unit spikes; blue
curves: multi-unit activity; green lines: onset of detected REMs. Note that bursts of activity often follow
REMs in different MTL regions.
22
Modulation of neuronal activity during eye movements
Neurons often exhibited clear bursts of activity around REMs occurrence.
Figure 3 provides an example of single- and multi-unit activities across multiple
medial temporal lobe (MTL) regions during REMs in wakefulness. To quantify
the modulations in neuronal firing across the entire dataset (n=437 wake and 349
in sleep in 12 individuals), we performed a triggered-averaging analysis of spiking
activity around REMs onsets. Inspection of the results for each neuron separately
revealed complex firing rate modulations, in line with previous reports (Schall
1991; Schall 1991; Timofeev, Grenier et al. 2001). Overall, modulations could be
readily divided in two categories based on the tendency of neurons to fire at a
specific instantaneous phase of the EOG signal (Fig. 4A). In wakefulness, neurons
in group A (63% of units) showed a bi-phasic pattern of activity including a
decrease just before REMs onsets (down to 29% of maximal firing rate at -105ms,
p<10-10 uncorrected Mann-Whitney U-test) and a large increase immediately after
REMs onsets (66% of maximal firing rate at +315ms, p<0.05 corrected MannWhitney U-test). In REM sleep, a similar pattern was observed, although the
proportion of units in group A was slightly reduced (52% of units) and so was the
amplitude of their average firing rate modulations (down to 37% of maximum
firing rate at -224ms and up to 57% at +348 ms).
The robust decrease immediately prior to REMs in the activity of group A
neurons is reminiscent of effects often associated with saccadic suppression (see
Discussion). By contrast, neurons in group B (37% and 48% of units in
wakefulness and REM sleep, respectively) showed a slight increase in firing rate
before REMs (54% at -176ms in wake, p<0.05 corrected Mann-Whitney U-test)
followed by a decrease after REMs onsets (38% at +366ms in wake, p<0.05
corrected). In REM sleep the proportion of units in group B as well as their
increase in firing rate before REMs was slightly larger (57% at -187ms, p<0.05
corrected). The two patterns of responses resemble previous results obtained with
intracellular recordings in cats (Timofeev, Grenier et al. 2001), regular-spiking
neurons showing a decrease of activity after REMs and fast-spiking neurons an
increase. In our study, a significant difference was found between group A and B
23
based on units’ average firing rate in wakefulness (p=0.03, two-tailed Student ttest across units in group A and B) but not in REM sleep (p=0.14). A better
characterization of our recorded units’ cell types would be needed here, rather
than the mere average firing rate.
To better understand the neuronal activity around REMs, we examined the
firing rate modulations in each brain region separately. Units were grouped into 9
regional clusters, as illustrated in Figure 4B, based on electrodes location (PHG:
parahippocampal gyrus; Am: amygdala; EC: entorhinal cortex; HC: hippocampus;
OFC: orbito-frontal cortex; AC: anterior cingulate; SMA: supplementary motor
area; Par: parietal cortex; Temp: temporal cortex). Regional analysis of firing rate
modulation (Fig. 4D) revealed that group A neurons in all regions showed
consistent increase in firing rate right after REMs onset, preceded by a decrease in
firing rate before REMs onsets, and this modulation was more robust in
wakefulness than in REM sleep. Neurons in group B showed similar modulations
across regions and vigilance states, albeit with more variability. Interestingly,
neurons in frontal regions including AC and SMA exhibited particularly robust
firing rate modulations well before REMs, in line with their possible role in motor
preparation (Shibasaki and Hallett 2006; Fried, Mukamel et al. 2011) and REMs
generation during wakefulness (Pierrot-Deseilligny, Milea et al. 2004). Finally,
biphasic changes in neuronal activity around REMs onsets in both wakefulness
and REM sleep were accompanied by evoked potentials in depth intracranial
EEG in the MTL (Fig. 5). REMs were associated with sharp positive potentials
highly synchronized across all MTL regions and electrodes often referred to as the
spike positivity in the literature (Berchicci, Stella et al. 2012) and possibly
associated with “reset” of activity at those times (see Discussion). Overall, REMs
onsets are accompanied by robust modulations in neuronal activity that is similar
across wakefulness and REM sleep.
24
25
Figure 4: Quantitative analysis of unit activity underlying REMs in wakefulness and REM sleep
A. Example of relation between the firing of one unit and the instantaneous phase of EOG during
wakefulness. Bar graph (black) shows number of spikes around REMs onsets with a 60ms moving average.
Red curve shows the average instantaneous phase for one EOG channel extracted via the Hilbert transform.
Note the preference of this unit to fire at a given phase of the EOG signal (~0 rad). B. Medial view of the
brain with regional clusters used in panel D and the position of the corresponding electrodes. (PHG,
parahippocampal gyrus; HC, hippocampus; Am, amygdala; EC, entorhinal cortex; OFC, orbitofrontal
cortex; AC, anterior cingulate; SMA, supplementary motor; Par, parietal cortex; Temp, temporal cortex) C.
Categorization of units based on their preferred phase. Left: wakefulness; Right: REM sleep. Bar graphs
(black) represent average normalized units’ activity around REMs onsets. Red curves, superimposed smoothed
activity patterns (60ms Gaussian kernel). Yellow, proportion of units in each category. Green horizontal bars,
time points where firing rate modulation is significantly different than chance (p<0.05, corrected MannWhitney U-test). D. Unit activities during REMs by vigilance state (left: wakefulness; right: REM sleep), by
category (top: group A; bottom: group B) and by region (colors as in panel B). Note that robust and similar
firing rate modulations can be seen in the activities of each region and state separately.
26
Figure 5: Intracranial evoked potentials during eye movements
Evoked potentials (EPs) in MTL depth intracranial EEG recordings triggered around REMs onsets during
wakefulness (panel A : PHG (n=11 electrodes), HC (n=19), Am (n=15), EC (n=8)) and REM sleep (panel B
PHG (n=9 electrodes), HC (n=12), Am (n=12), EC (n=7)). EPs were computed by averaging depth EEG in [1000, 800]ms windows around REMs onsets after subtraction of mean voltage. Gray shade highlights the
presence of a significant spike positivity (see Methods for the statistical analysis) across all MTL regions in
wakefulness and REM sleep.
27
Directionality of signal propagation following eye movements
changes between wakefulness and sleep
Next, we set out to explore the fine temporal dynamics of signal propagation
along different brain structures in the activity immediately following REMs in
wakefulness and REM sleep (neurons in group A). We hypothesized that in
wakefulness the latency of activity monotonically increases along the visualmnemonic pathway in the MTL, thereby reflecting feedforward processes. We
analyzed latencies of activity during spontaneously generated REMs during
wakefulness. In order to match these results to well-established results on visual
processing (Felleman, Burkhalter et al. 1997; Mormann, Kornblith et al. 2008), we
examined in parallel the latencies in response to visual stimulation in the morecontrolled paradigm of an object recognition task (see Methods). The results
indicate that during visual stimulation (Fig. 6A) unit activity showed a sequence of
activation matching the anatomical hierarchy of information processing (Felleman
and Van Essen 1991), consistent with a previous study using intracranial
recordings (Mormann, Kornblith et al. 2008). Latencies increase from the
fusiform gyrus (295 ± 25 ms, mean ± SEM, 31 units), to PHG (337 ± 35 ms, 32
units), EC (385 ± 25 ms, 19 units) and to HC (421 ± 49 ms, 10 units). A similar
direction of signal propagation was observed during spontaneous REMs in
wakefulness (Fig. 6B) with the following latencies (PHG: 286 ± 29 ms, 48 units;
Am: 305 ± 35 ms, 32 units; EC: 378 ± 60 ms, 13 units; HC: 432 ± 37 ms, 41
units). Interestingly, an anterior-posterior propagation was also noted in parietal
and frontal cortices (Parietal: 304 ± 63 ms, 12 units compared with OFC: 330 ±
66 ms, 9 units; AC: 362 ± 67 ms, 13 units; and SMA: 401 ± 69 ms, 13 units), in
line with previous findings (Felleman and Van Essen 1991). These results confirm
the prediction that in wakefulness, as observed during the visual object
recognition task, the latency of neuronal activity triggered by REMs primarily
follows a feedforward direction.
Next, we examined the latency of activity around REMs in REM sleep (Fig.
6C). A partial reversal of latencies was observed within the MTL where activity
was seen earlier in the HC (264 ± 38 ms, 25 units) compared with EC (287 ± 46
28
ms, 20 units). While the sequence of activity was not a perfect mirror-image of
that in wakefulness (e.g. PHG was active on average at 275 ± 39 ms following
REMs (24 units)), the reverse order between EC and HC is particularly relevant
since only unidirectional anatomical pathways link these two structures (Felleman,
Burkhalter et al. 1997). Although during REMs the differences in latencies
between regions and within each state reflected a trend rather than a statistically
significant effect (visual stimulation: p<0.05; wake: p=0.2; REM: p=0.7), a
comparison of latencies in EC and HC between wakefulness and sleep revealed a
significant difference (Mann-Whitney U-test, α = 0.05), supporting the notion that
the directionality of signal propagation in REM sleep may reflect a top-down
mode of signal propagation at least with respect to the hippocampal formation
(see also Discussion).
29
Figure 6: Directionality of signal
propagation following eye movements
in wakefulness and sleep
A. Left: Latencies of unit responses in the
MTL to images during an object
recognition task. Latencies reflect the
first time point following image
presentation that showed a significant
increase in firing rate (see Methods).
Numbers on the abscissa refer to the
number of units in each MTL region.
Right:
Post-stimulus-time-histogram
(PSTH) analysis of unit responses to
stimuli by region (smoothed with 60ms
Gaussian kernel). Note that both types of
analysis show that response latencies are
longer in the entorhinal cortex and
hippocampus, in line with the anatomical
hierarchical organization of the visualmnemonic systems. B. Latencies of
increases in unit activity following eye
movements in wakefulness (group A
only). Note that during spontaneous
REMs in wakefulness the directionality of
signal propagation resembles that found
during visual object processing, and that
posterior-anterior gradients are also
found in parietal and frontal regions. C.
Latencies of increases in unit activity
following eye movements in REM sleep
(group A only). Note the tendency of
entorhinal cortex neurons to increase
their firing before those in the
hippocampus, suggesting a reversal of
signal
propagation
directionality
compared with wakefulness. D. Different
regions are color-coded based on their
latencies and projected on a medial view
of the right hemisphere. Top to bottom:
visual recognition, wakefulness and REM
sleep.
30
Discussion
The persistence of saccadic suppression and the nature of REMs in
REM sleep
Saccadic suppression is a behavioral phenomenon consisting of a transient
increase in visual perception thresholds during saccadic eye movements (Dodge
1905). Although this phenomenon can be readily observed (e.g. one cannot see
one’s own eyes moving in a mirror but can perfectly see somebody else’s eyes
moving), its neural underpinnings remain unclear. Initially, saccadic suppression
was regarded as a side effect of the eye movement itself (Volkmann 1962) but
later studies established that not all visual features are equally suppressed (Melcher
2005; Watson and Krekelberg 2009) and that behavioral suppression starts before
REMs onsets and proprioceptive feedback (Latour 1962; Matin, Clymer et al.
1972) thus pointing to a more complex neuronal process.
The most robust pattern of neuronal activity accompanying REMs observed in
visual regions of the monkey thalamus and cortex is a bi-phasic modulation of
firing rates - with decreased activity just before saccade onset followed by a larger
post-saccade burst (Reppas, Usrey et al. 2002), and such activity persists in the
absence of visual input (Kayama, Riso et al. 1979). Activity suppression is believed
to relate to a corollary discharge accompanying the production of saccades in the
brainstem (Wurtz 2008; Wurtz, Joiner et al. 2011) and affecting high-level visual
regions through the colliculus-pulvinar-frontal pathway (Kleiser, Seitz et al. 2004).
However, activity suppression is not seen in all brain regions (e.g. V1) (Wurtz
2008). The differential neuronal effects along the visual pathway could reflect the
fact that parallel mechanisms evolved to implement different information
processing tasks during saccades (Bremmer, Kubischik et al. 2009). Modulation of
firing rates at times of saccadic suppression may be further mediated by
attentional shifts involving parietal cortex (Duhamel, Colby et al. 1992; Hamker,
Zirnsak et al. 2011). Interestingly, reflexive and voluntary saccades may involve
exogenous and endogenous attentional processes respectively (Wurtz, Joiner et al.
2011). Overall, underlying neuronal activity patterns are highly variable across
31
units and regions and suggest a complex interplay between systems of motor
preparation, corollary discharge, visual perception, and attentional modulation
(Super, van der Togt et al. 2004; Wurtz 2008; Watson and Krekelberg 2009;
Cloherty, Mustari et al. 2010).
The present results confirm the presence in humans of a bi-phasic modulation
of unit activity, accompanied by evoked field potentials, across multiple regions of
the human brain during eye movements in wakefulness (Figures 4 and 5). A more
novel finding is that a similar profile of neuronal activity around REMs is also
found during REM sleep, in the absence of visual input. This underscores the
similarity between cortical activity in wakefulness and REM sleep (Nir and Tononi
2010), and supports the notion that saccadic suppression-related activity may be
linked to perceptual and attentional processes, above and beyond its possible
relation to retinal input. It has been proposed that cortical activity may follow a
succession of quasi-stable patterns of activity or "micro-states" (Lehmann 1971;
Jones, Fontanini et al. 2007). Conceivably, eye movements and underlying
neuronal activity could play a role in "resetting" such states of cortical activity.
More generally, given the prevalence of dreaming in REM sleep, it is tempting to
speculate that eye movements in REM sleep reflect transitions between perceptual
"snapshots" in dream content.
Ponto-geniculo-occipital (PGO) waves and REMs
Ponto-geniculo-occipital (PGO) waves are sharp field potentials occurring
concomitantly with eye movements in REM sleep (Morrison and Pompeiano
1966). They often appear during the transition from NREM to REM sleep and
alongside muscle atonia and are therefore considered a hallmark characteristic of
REM sleep (Jouvet 1992; Steriade 2003; Kryger, Roth et al. 2011). PGO waves
were first observed in the pons, lateral geniculate nucleus of the thalamus and in
the occipital cortex of cats (Jouvet and Michel 1959; Brooks and Bizzi 1963) and
later described in other species (Gucer and Viernstein 1979; Farber, Marks et al.
1980) and across multiple brain regions (Amzica and Steriade 1996). PGO waves
are generated in the brainstem (Sastre and Jouvet 1979) and induce broad
32
synchronized waves through thalamic relays (Amzica and Steriade 1996). Due to
their widespread effects on brain activity, PGO waves have been implicated in the
putative functions of REM sleep: brain plasticity (Datta 1999), sensory gating
(Bowker and Morrison 1976), sensorimotor processing (Callaway, Lydic et al.
1987) and dream generation (Hobson 1990). However, some evidence suggests
that PGO waves are akin to lambda responses triggered by visual stimulation in
wakefulness (Barlow and Ciganek 1969). According to this view, PGO waves
reflect the corollary discharges and saccadic suppression associated with REMs
and may not be exclusive to REM sleep (Brooks 1968; Brooks 1968).
There are a few reports documenting intracerebral PGO waves in humans
(Lim, Lozano et al. 2007; Fernandez-Mendoza, Lozano et al. 2009) as well as their
non-invasive correlates (Peigneux, Laureys et al. 2001). In our study, none of the
patients were implanted with electrodes in the pons, LGN or the occipital cortex,
so the current data cannot confirm or dismiss the existence of human PGO waves
unequivocally. However, we observed time-locked potentials associated with
REMs during REM sleep across multiple neocortical and MTL limbic regions,
which may support a candidate for PGO waves observed in animals. Moreover,
similar potentials associated with REMs were found in wake and REM sleep
(Mccarley, Winkelman et al. 1983), supporting the notion that such events reflect
similar processes across wakefulness and sleep (Brooks 1968; Brooks 1968).
Signal propagation during wakefulness and REM sleep
When we are awake with our eyes open, every saccade invariably leads to a new
image impinging on the retina. The new information is processed by the nervous
system, leading to a barrage of neuronal activity along visual pathways. Although
far from being well understood, the process of visual object recognition is
believed to start with a feedforward sequence of activations along hierarchical
processing nodes in the ventral occipitotemporal cortex (Hochstein and Ahissar
2002; Riesenhuber and Poggio 2002). Activity in initial stages of this process (e.g.
occipital cortex) is more closely linked to features of the visual image while later
processing better reflects abstract representations that transcend retinal location,
33
size, viewing angle etc (Liu, Agam et al. 2009). In the medial temporal lobe, both
the latency and selectivity of responses is higher in entorhinal cortex and
hippocampus than in the parahippocampal gyrus (Mormann, Kornblith et al.
2008) suggesting a ‘feed-forward’ mode of signal propagation during passive
viewing. Interestingly, a recent study in primates shows that feed-forward visual
propagation reverses to a ‘top-down’ mode during mnemonic processes
(Takeuchi, Hirabayashi et al. 2011).
In dreaming, our internally-generated visual experience resembles that of
wakefulness (Nir and Tononi 2010). However, it remains unclear whether dream
imagery originates in activity of low-level visual areas and it is later processed by
higher-level areas as in visual perception (Nir and Tononi 2010; Arnulf 2011) or
whether dreams originate as memories or abstract thoughts “objectified in the
dream, and represented as a scene” (Freud 1931) as during imagination. According
to one influential view, dream imagery is largely ‘bottom-up’: it is triggered by
PGO waves that excite the visual cortex and this activity is then interpreted and
synthesized by high-order areas (Hobson 1990; Nir and Tononi 2010). Indeed, the
absence of aminergic neuromodulation in REM sleep may facilitate feed-forward
processing (Hobson 1990; Hasselmo 1999). However, various lines of evidence
including lesion studies, cognitive and developmental studies, suggest that
dreaming may be more closely related to imagination than it is to perception (Nir
and Tononi 2010). Accordingly, dreams may originate from memories and psychic
motives as originally proposed by Freud (Freud 1931). In terms of signal
propagation, this may be associated with a reversal of the information flow where
memory representations supported by the hippocampus may give rise to visual
representations supported by the visual system, reflecting ‘top-down’ signal
propagation. Our data, collected during REMs in both wakefulness and REM
sleep and recorded simultaneously in multiple regions in the medial temporal lobe,
offer a rare opportunity to explore this question in humans.
The current results show signs of possible reversal of signal propagation in the
medial temporal lobe immediately following REMs in REM sleep compared to
wakefulness (see Fig. 6). During wakefulness, REMs burst-activity is observed in
34
the parahippocampal gyrus before it is seen in entorhinal cortex and
hippocampus, suggesting that saccades recruit feed-forward visual processing of
successive retinal inputs. By contrast, during REM sleep, REMs burst-activity is
observed first in the hippocampus and later in the entorhinal cortex, as may be the
case during ‘top-down’ mental imagery. This result is at odd with a previous fMRI
study (Miyauchi, Misaki et al. 2009), however our recordings benefit from a largely
better temporal resolution. In addition, a larger proportion of units increase their
firing rates before saccades and notably within regions implicated in motor
preparation (e.g. SMA or AC), which would fit with an involvement of frontal
cortex in dream generation (see Fig. 4).
Study limitations
As mentioned before, our study bears some limitations notably due to the very
peculiar nature of our data. For example, electrode location being determined
exclusively by clinical criteria resulted in the absence of electrodes in critical
regions for identifying PGO waves (e.g. the occipital lobe). This constraint limits
our ability to unequivocally bridge a gap between PGO waves recorded in animals
and the neuronal markers of REMs observed in our data. Similarly, the EOG
montage used in our sleep studies did not permit us to record and analyze vertical
eye movements. Lastly, we did not explore the question of the lateralization of
brain responses to REMs in link with gaze direction (right vs. left eye
movements), a phenomenon observed in animals (Nelson, McCarley et al. 1983;
Steriade 2003). In the same line, it would have been interesting to better
characterize REMs produced in wakefulness by analyzing the videos recorded
during sleep studies. However, I could not access these sensitive recordings.
35
Conclusion:
“And the goddess stirred in him unwearying strength:
sleep never fell upon his eyes; but he kept sure watch always.”
(Hesiod, Aegimius)
We provide here an analysis of the neuronal activity underlying REMs in
different vigilance states, obtained in a unique dataset of intracranial recordings in
intractable epileptic patients. Our findings underscore many similarities in the
properties of rapid ocular movements in wakefulness and REM sleep. Not only
do REMs share similar mechanical properties, but they affect neuronal activity in
a similar way. More specifically, REMs seem to "reset" brain activity as evident by
robust evoked potentials across the brain and an inhibition of a large portion of
recorded neurons prior to REMs onsets, which could be related to the
phenomenon of saccadic suppression. The presence of this suppression in REM
sleep is consistent with the idea that REMs serve the same role of exploring the
visual scene in our dreams as when we are awake. Interestingly, we also observed
a reversal of the information flow along entorhinal-hippocampal pathways
between REM sleep and wakefulness, which could reflect differences in the
processes involved in dreams compared to visual perception. It is tempting indeed
to link our findings with the idea that our dreams do not reflect the mere
activation of the visual system by brainstem signals, but may originate from
memories in the sleeper’s mind that are then represented as a dream image.
Future work should be able to further quantify the neural dynamics following
REMs using approaches such as Granger causality.
36
Acknowledgements
I would like to express my deepest gratitude to Yuval Nir for his friendly
supervision and daily patience at every single step of this project as well as to
Giulio Tononi and Chiara Cirelli. I want to thank Aaron Nelson, Chadd Funk,
Michele Bellesi, Luisa de Vivo and all the members of the laboratory for their
help, comments and occasional empathy. My life stretching across the Atlantic, I
also thank Sid Kouider for his supervision during the first semester, the
administrative staff of the CogMaster for making our lives easier, Gecia Bravo
Hermsdorff for her never-ending enthusiasm and, last but not least, Chiara
Varazzani for everything and beyond. I am grateful to all the persons that showed
interest in my work and who gave me the needed feeling that this was, after all, a
decent occupation for an adult. Finally, my gratitude goes to all the anonymous
patients who accepted to participate in this study.
37
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Appendices:
Appendix Table 1: Patient information
Clinical information and sleep study details for all patients who participated in the sleep
studies. P415, P422, P423, P424 have also participated in the visual recognition paradigm.
Information not available for the 5 patients who participated in the visual paradigm only.
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46
Appendix 1: Sleep parameters are similar to natural sleep in healthy individuals
A. Representative examples of 30s epochs extracted from one patient depicting sleep stages used in our
scoring: wakefulness, NREM2, NREM3 and REM sleep. B. Sleep parameters computed across the
entire night and expressed as mean ± SEM (n=13 patients). Sleep efficiency corresponds to total sleep
time per time spent in bed. Sleep latency is calculated to stage NREM2. WASO refers to Waking After
Sleep Onset. C. Hypnograms and power spectra of scalp EEG in all derivations for 10 individuals (top to
bottom: W: wake; R: REM; NREM stages: N1, N2, and N3). Colors code in power spectra is: Blue,
NREM2; Red, NREM3; Green, REM sleep as in Fig. 1C.
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Appendix 2: Unit identification and stability of the recordings
A. Illustration of unit identification: LFP was high-pass filtered (top row) and a threshold was set at 5
SDs above the biological noise level (red line). Action potential waveforms were then clustered (middle
row, 3 representative examples). Inter-spike-intervals (ISIs) distribution was computed to help separate
clusters and to determine wetter clusters were single or multi-units (bottom row). Based on parameters,
clusters were categorized as: multi-unit clusters (example in blue), single-unit clusters (red) or noise
clusters (black). B. Waveforms and ISI distributions are conserved throughout sleep and remain
separable. Mean action potential waveforms for 5 distinct units recorded in the same patient. Each
color denotes a different unit and each trace denotes the mean waveform in separate 1h intervals
throughout sleep. Note the stability of our unit recordings throughout long (~7h) recordings. Inset
depicts ISI distributions for two units (red and green curves) separated by1h intervals. C. A full
description of the brain structures and number of units identified in our sleep recordings. Grey boxes
mark implanted brain regions and numbers show detected units. Total number of implanted regions
and units are displayed by patients. (LH, left hemisphere; RH, right hemisphere; H, hippocampus; A,
amygdala; EC, entorhinal cortex; PH, parahippocampal gyrus; TG, temporal gyrus; AC, anterior
cingulate; MC, middle cingulate; PC, posterior cingulate; OF, orbitofrontal and medial prefrontal
cortex; SM, supplementary motor area; P, parietal cortex; TO, temporo-occipital, PT, posterior
temporal cortex; FG, fusiform gyrus; IFG, inferior frontal gyrus)
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Appendix 3: Examples of units responsive to pictures
Two examples of units responsive to pictures in the object recognition task for a high-firing responsive unit
(panel A) and a low-firing unit (panel B) in one individual. In each panel, all stimuli (4 face, 2 place images)
used during the session are displayed. Below each picture, raster plots depicting units’ activity in all 24 trials
are shown. Post-event histograms are also displayed and were used to detect responsive units for each
visual stimulus. As described in the Methods, spikes were binned (100ms) and statistical deviance from
baseline was computed. Significant bins are colored in red (one-tail Student t-test, α=0.001). Responsive
units are highlighted with a green box. In our analysis only trials for which units were declared responsive are
analyzed. Note that units can have different pattern of responses to stimuli: the unit in panel A responds to
most pictures displayed while the unit in panel B is more selective.
51