A JOURNAL OF NEUROLOGY
Laminar analysis of slow wave activity in humans
Richa ´rd Csercsa,1Bala ´zs Dombova ´ri,1,2Da ´niel Fabo ´,1,3Lucia Wittner,1,3,4Lora ´nd Ero
La ´szlo ´ Entz,1,3Andra ´s So ´lyom,3Gyo ¨rgy Ra ´sonyi,3Anna Szu
Vera Juhos,5La ´szlo ´ Grand,1,2Andor Magony,1,6Pe ´ter Hala ´sz,2Tama ´s F. Freund,4
Zso ´fia Maglo ´czky,4Sydney S. Cash,7La ´szlo ´ Papp,8Gyo ¨rgy Karmos,1,2Eric Halgren9and
Istva ´n Ulbert1,2,3
00cs,3Anna Kelemen,3Rita Jakus,3
1 Institute for Psychology, Hungarian Academy of Sciences, Budapest, Hungary
2 Pa ´zma ´ny Pe ´ter Catholic University, Faculty of Information Technology, Budapest, Hungary
3 National Institute of Neuroscience, Budapest, Hungary
4 Institute of Experimental Medicine, Hungarian Academy of Sciences, Budapest, Hungary
5 Department of Neurology, Szent Istva ´n Hospital, Budapest, Hungary
6 School of Clinical and Experimental Medicine, University of Birmingham, Birmingham, UK
7 Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
8 Neuronelektro ´d Kft, Budapest, Hungary
9 Departments of Radiology, Neuroscience and Psychiatry, University of California, San Diego, La Jolla, CA, USA
Correspondence to: Istva ´n Ulbert,
Szondi u. 83-85,
Budapest, 1068, Hungary
Brain electrical activity is largely composed of oscillations at characteristic frequencies. These rhythms are hierarchically orga-
nized and are thought to perform important pathological and physiological functions. The slow wave is a fundamental cortical
rhythm that emerges in deep non-rapid eye movement sleep. In animals, the slow wave modulates delta, theta, spindle, alpha,
beta, gamma and ripple oscillations, thus orchestrating brain electrical rhythms in sleep. While slow wave activity can enhance
epileptic manifestations, it is also thought to underlie essential restorative processes and facilitate the consolidation of declara-
tive memories. Animal studies show that slow wave activity is composed of rhythmically recurring phases of widespread,
increased cortical cellular and synaptic activity, referred to as active- or up-state, followed by cellular and synaptic inactivation,
referred to as silent- or down-state. However, its neural mechanisms in humans are poorly understood, since the traditional
intracellular techniques used in animals are inappropriate for investigating the cellular and synaptic/transmembrane events in
humans. To elucidate the intracortical neuronal mechanisms of slow wave activity in humans, novel, laminar multichannel
microelectrodes were chronically implanted into the cortex of patients with drug-resistant focal epilepsy undergoing cortical
mapping for seizure focus localization. Intracortical laminar local field potential gradient, multiple-unit and single-unit activities
were recorded during slow wave sleep, related to simultaneous electrocorticography, and analysed with current source density
and spectral methods. We found that slow wave activity in humans reflects a rhythmic oscillation between widespread cortical
activation and silence. Cortical activation was demonstrated as increased wideband (0.3–200Hz) spectral power including
virtually all bands of cortical oscillations, increased multiple- and single-unit activity and powerful inward transmembrane
currents, mainly localized to the supragranular layers. Neuronal firing in the up-state was sparse and the average discharge
rate of single cells was less than expected from animal studies. Action potentials at up-state onset were synchronized with-
in?10ms across all cortical layers, suggesting that any layer could initiate firing at up-state onset. These findings provide
strong direct experimental evidence that slow wave activity in humans is characterized by hyperpolarizing currents associated
doi:10.1093/brain/awq169 Brain 2010: 133; 2814–2829 |
Received January 25, 2010. Revised and Accepted May 13, 2010. . Advance Access publication July 22, 2010
? The Author (2010). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved.
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with suppressed cell firing, alternating with high levels of oscillatory synaptic/transmembrane activity associated with increased
cell firing. Our results emphasize the major involvement of supragranular layers in the genesis of slow wave activity.
Keywords: current source density; unit activity; laminar recording; slow wave activity; sleep
Abbreviations: CSD = current source density; ECoG = electrocorticogram; LFP = local field potential; REM = rapid eye movement
Brain rhythms, a prominent characteristic of EEG discovered in its
initial recordings by Berger (1929), are thought to organize cortical
activity (Buzsaki and Draguhn, 2004). Especially prominent in sleep
(Loomis et al., 1937), microphysiological studies of their neural
basis have until now relied on animal models (Steriade, 2006).
Presurgical diagnostic procedures in epilepsy may allow the experi-
menter to open an invasive window on the brain and record local
field and action potentials in order to investigate the fine scale
generators of electrical brain oscillations (Worrell et al., 2004,
2008; Jirsch et al., 2006; Clemens et al., 2007; Urrestarazu
et al., 2007; Axmacher et al., 2008; Fabo et al., 2008; Cash
et al., 2009; Jacobs et al., 2009; Schevon et al., 2009; Crepon
et al., 2010). Traditionally, cortical oscillations have been divided
into distinct bands, with more or less distinct roles in, for example,
vigilance states, various cognitive functions and pathology. Most
importantly, the slow (delta) and especially the very fast rhythms
(ripple and fast ripple) have been found with fine scale intracranial
observations to influence pathological excitability effectively, and
may serve as a basic substrate underlying paroxysmal activity
(Bragin et al., 2002; Worrell et al., 2004, 2008; Jirsch et al.,
2006; Urrestarazu et al., 2007; Fabo et al., 2008; Jacobs et al.,
2009; Schevon et al., 2009; Crepon et al., 2010). More generally,
during normal cortical function, oscillations are hierarchically orga-
nized and this oscillatory hierarchy can effectively control neuronal
excitability and stimulus processing (Lakatos et al., 2005; Steriade,
2006). Low-frequency oscillations seem to play an important role
in cognitive functions even in the awake state (Lakatos et al.,
2008; Schroeder and Lakatos, 2009), despite the fact that under
other circumstances, slow rhythms are usually good signatures of
compromised cerebral functions (Ebersole and Pedley, 2003) and
sleep (Achermann and Borbely, 1997). In this work, we attempt
to link the slow- and higher-frequency cortical oscillations to gain
a better insight into the intricate mechanisms of human cortical
electrical activity, and show the organizing principles that may
govern the structure of human cortical electrical rhythms in sleep.
A fundamental mode of cortical activity in mammals is the pre-
dominance of slow (51Hz) oscillations during the deepest stage of
non-rapid eye movement (REM) sleep (Achermann and Borbely,
1997; Steriade et al., 2001; Timofeev et al., 2001; Luczak et al.,
2007). In humans, this stage (the third and deepest stage of
non-REM sleep; N3, also called slow wave sleep) is reached
when 20% or more of an epoch consists of slow wave activity
(0.5–2Hz) in the frontal EEG, having peak-to-peak amplitudes
larger than 75mV, and accompanied by the behavioural signs of
sleep (Iber et al., 2007). Intracellular recordings in cats during
natural slow wave sleep have revealed that slow oscillations are
composed of rhythmically recurring phases of increased cellular
and synaptic activity (up-states) followed by hyperpolarization
and cellular silence (down-states) (Steriade and Timofeev, 2003).
In human slow wave sleep, the surface positive slow wave activity
half-wave (up-state) contains increased alpha and beta power
compared with the surface negative slow wave activity half-wave
(down-state), suggesting that their basic neurophysiology may be
similar to animal findings (Molle et al., 2002; Massimini et al.,
2004). While the slow oscillation in animals is limited to below
1Hz (Steriade et al., 1993b), the recent American Academy of
Sleep Medicine guidelines suggest the 0.5–2Hz range for slow
wave activity in humans (Iber et al., 2007).
Studies into the neural mechanisms of slow waves have been
motivated by reports that they underlie restorative sleep functions
and serve memory consolidation (Huber et al., 2004; Marshall
et al., 2006; Vyazovskiy et al., 2008) via ensemble reactivation
(Born et al., 2006; Euston et al., 2007) and synaptic strength
normalization (Vyazovskiy et al., 2008). Slow oscillation can be
induced artificially by various anaesthetics in vivo (Steriade
et al., 1993b; Volgushev et al., 2006; Luczak et al., 2007) and
ionic environments in vitro (Sanchez-Vives and McCormick, 2000;
Shu et al., 2003; Haider et al., 2006). Slow oscillations are
generated in cortico-cortical networks, since they survive thala-
mectomy (Steriade et al., 1993a), but not the disruption of
However, recent data suggest a complex thalamocortical interplay
in slow oscillation generation (Crunelli and Hughes, 2010). Fine
scale laminar analysis of neuronal firing activity revealed that arti-
ficial slow oscillations in slice preparations are the earliest and most
prominent in the infragranular layers, where they are initiated, and
spread toward the superficial layers with a long ?100ms
inter-laminar delay (Sanchez-Vives
Subthreshold membrane potential fluctuations giving rise to local
field potentials (LFPs) clearly precede neuronal firing at up-state
onset; thus, firing may be the consequence rather than the cause
of up-state initiation (Chauvette et al., 2010). Current source
density (CSD) analysis of the low-frequency (51Hz) components
of the artificial, anaesthesia-induced slow oscillations (Steriade and
Amzica, 1996) localized the most prominent up-state-related sinks
to the middle and deepest cortical layers (most probably layers III
and VI). In contrast, the fast (30–40Hz) components were more
distributed, composed of ‘alternating microsinks and microsources’
along the whole cortical depth (Steriade and Amzica, 1996).
In another publication the same authors reported a massive
up-state-related sink in layers II–III, besides weaker ones in the
deeper layers during spontaneous and evoked K-complexes
(Amzica and Steriade, 1998). In still another cat study, the max-
imal up-state-related sink in natural sleep was located in the
Neural basis of slow waves in humansBrain 2010: 133; 2814–2829 |
middle and deep layers (Chauvette et al., 2010). The laminar dis-
tribution of the major up-state-related sink in the rat primary audi-
tory cortex was variable (Sakata and Harris, 2009). On average
across animals, the maximal up-state-related sink was located in
middle and deep layers (most probably layers III–V) in natural
sleep, whereas it was located in superficial layers (most probably
layers II and III) under urethane anaesthesia (Sakata and Harris,
2009). In intact animals the up-state onset-related initial firing,
intracellular membrane potential and LFP changes could be
detected in any layer in a probabilistic manner, with a short
inter-laminar delay (?10ms); however, on average, the earliest
activity was found in the infragranular layers (Sakata and Harris,
2009; Chauvette et al., 2010). Although the cellular and synaptic/
transmembrane mechanisms of slow waves during natural sleep
are thus under intense investigation in animals, these mechanisms
have not previously been studied in humans.
Here we show the basic correspondence between the surface
positive phase of the slow wave in humans and the up-state
described in animals. Besides spindle and beta band oscillations
(Molle et al., 2002), we found that the up-state in humans strong-
ly groups action potentials, alpha, gamma (30–150Hz) and ripple
oscillations (100–200Hz), which have been implicated in attention,
memory and epilepsy (Grenier et al., 2003; Molle et al., 2004;
Jensen et al., 2007). Despite these basic similarities, we found that
the neural mechanisms of natural slow wave activity in humans
show several differences compared with previous studies in ani-
mals. Specifically, the cortical synaptic/transmembrane generators
of the slow wave activity slow (52Hz) components, as well as the
associated high-frequency (10–200Hz)
strongly and consistently localized in the supragranular layers, in
partial contrast to previous proposals based on studies in animals
(Steriade and Amzica, 1996; Sakata and Harris, 2009; Chauvette
et al., 2010). In addition, we found that, as measured by cellular
discharges, the onset of the up-state was rather synchronous
across cortical layers, as in intact animals (Sakata and Harris,
2009; Chauvette et al., 2010) but unlike in slices from ferrets,
where long inter-laminar firing delays at up-state onset were
found (Sanchez-Vives and McCormick, 2000). Furthermore, the
average firing rate of human cortical neurons in the up-state
was a fraction of what has generally been observed in animal
studies (Steriade et al., 2001; Isomura et al., 2006; Luczak
et al., 2007). We consider the experimental, cytoarchitectonic,
pathological and phylogenetic aspects that may have contributed
to these important differences between the slow waves in humans
versus lower mammals.
oscillations, were all
Materials and methods
Patients and electrodes
Five patients with intractable epilepsy underwent chronic clinical sub-
dural grid and strip electrode implantation (Fig. 1) as a standard pro-
cedure for localization of the seizure focus and eloquent areas. Fully
informed consent was obtained from each subject under the auspices
of the Hungarian Medical Scientific Council and local ethical commit-
tee, National Institute of Neuroscience, Budapest, Hungary according
to the World Medical Association Declaration of Helsinki. Conventional
clinical subdural electrocorticography (ECoG) electrode strips and grids
were implanted to cover the frontal, temporal and parietal gyri. In
addition to the surface electrodes, a 350mm diameter, 24-contact ex-
perimental laminar multichannel microelectrode array was implanted
perpendicular to the cortical surface, underneath the clinical grids
(Ulbert et al., 2001a, b, 2004a; Cash et al., 2009; Keller et al.,
2009). The 40mm diameter Platinum/Iridium contacts were spaced
evenly at 150mm providing LFP recordings from a vertical, 3.5mm
long cortical track, spanning from layer I to layer VI. A silicone sheet
attached to the top of the microelectrode array shank prevented the
first contact from sliding more than 100mm below the pial surface
(Ulbert et al., 2001a). In each case, the explanted microelectrode
array was visually inspected under a microscope for structural
damage, and we did not find any alteration, indicating intact structure
throughout the recordings. The location and duration of the clinical
electrode implantation were determined entirely by clinical consider-
ations, and the microelectrode array was placed in cortex that was
likely to be removed at the definitive surgery.
The positions of the electrodes were confirmed by intraoperative navi-
gation, co-localization of intraoperative photographs, pre- and post-
operative magnetic resonance scans and 3D magnetic resonance
reconstructions (Fig. 1). Photographs were also taken during the
Figure 1 Grid, strip and multichannel microelectrode array (ME)
locations in all patients. Locations are superimposed on 3D
reconstructions of MRIs taken with the electrodes in place, aided
by intraoperative navigation and photographs. Grid and strip
electrode contacts are depicted in red and blue colours; the first
grid contact is marked with G1. Microelectrode array locations
of Patient (Pt) 1 (blue), left Brodmann area (BA) 9; Patient 2
(red), right BA 2; Patient 3 (green), left BA 46; Patient 4 (black),
right BA 9; Patient 5 (orange), right BA 8 are marked with circles.
Brain 2010: 133; 2814–2829 R. Csercsa et al.
resective surgery to confirm that the surface electrodes did not shift
during monitoring. The brain tissue containing the electrode track in
Patients 4 and 5 was removed en bloc for further anatomical analysis
(Ulbert et al., 2004b; Fabo et al., 2008). It was cut into 2–5mm blocks
and immersed into a fixative containing 4% paraformaldehyde, 0.1%
glutaraldehyde and 0.2% picric acid in 0.1M phosphate buffer (pH
7.4). The fixative was changed every hour to a fresh solution during
constant agitation for 6h, and then the blocks were post-fixed in the
same fixative overnight. Vibratome sections (60mm thick) were cut
from the blocks and photographs were taken from the electrode
immersed in 30% sucrose for 1–2 days and then frozen three times
over liquid nitrogen. Endogenous peroxidase was blocked by 1% H2O2
in phosphate buffer for 10min. Sections containing the electrode track
were processed for immunostaining against the neuron marker NeuN
(Fig. 2A), calretinin (Fig. 2B, reconstructed from camera lucida),
SMI-32 (Fig. 2C) and glial fibrillar acidic protein (Fig. 2D) to stain
every neuron, a subset of interneurons, pyramidal cells and glia, re-
spectively. Phosphate buffer was used for all the washes (3?3–10min
between each step) and dilution of the antisera. Non-specific
immunostaining was blocked by 5% milk powder and 2% bovine
serum albumin. Monoclonal mouse antibodies against NeuN (1:3000,
Chemicon, Temecula, CA, USA), SMI-32 (1:3000, Covance, Princeton,
NJ, USA), glial fibrillar acidic protein (1:2000, Novocastra, Newcastle,
uponTyne, UK)and calretinin
Switzerland) were used for 2 days at 4?C. Specificity of the antibodies
has been thoroughly tested by the manufacturers. For visualization
of immunopositive elements, biotinylated anti-mouse immunoglobulin
G (1:300, Vector) was applied as secondary antiserum followed
by avidin-biotinylated horseradish peroxidase complex (ABC; 1:300,
3,30-diaminobenzidine tetrahydrochloride (DAB; Sigma), as a chromo-
gen. Sections were then osmicated (0.25% OsO4in phosphate buffer,
buffer, sections were
30min) and dehydrated in ethanol (1% uranyl acetate was added at
the 70% ethanol stage for 30min) and mounted in Durcupan (ACM,
Fluka). Layers of the neocortex were outlined using all of the above
stains and a shrinkage correction factor published earlier (Turner et al.,
1995; Wittner et al., 2006).
Cell counting was performed in Patients 4 and 5 using camera lucida
drawing (Fig. 2B) of calretinin immunopositive cells (two sections per
patient). The normalized (between 0 and 1) calretinin immunopositive
cell density laminar depth profile (number of cells over unit area of
cortex) was calculated in each consecutive 150mm wide and variable
length (1–3mm) horizontal cortical stripes to match the depth struc-
ture of the electrophysiology measurements.
After electrode placement, the patients were transferred to the inten-
sive monitoring unit for 5–7 days, where continuous 24h video-EEG
observation took place in order to localize the seizure focus. ECoG
from clinical strip and grid electrodes (32–92 channels, mastoid
reference) was recorded concurrently with patient video using the
standard hospital system (band-pass: 0.1–200Hz, acquisition rate:
400–5000Hz/16bit). Video-EEG data for the duration of monitoring
were stored on hard disks for later analysis. The spatial LFP gradient,
the voltage difference between consecutive laminar electrode contacts,
was provided by a special preamplifier placed inside the head bandage
of the patient (Ulbert et al., 2001a). For simplicity, throughout the text
the spatial potential gradient is expressed in microvolts rather than the
formally correct microvolt per inter-contact distance (150mm). This
reference-independent measurement method was proven to be effect-
ive in minimizing the motion-related and electro-magnetic artefacts
(Ulbert et al., 2001a). The LFP gradient was split into the EEG range
(0.1–300Hz) and single- and multiple-unit activity frequency range
(300–5000Hz) by analogue band-pass filtering at the level of a
Figure 2 Electrode track histology, representative examples from Patient 4. (A) The microelectrode array electrode track (black contour
line) and inferred contact locations (black dots) are shown relative to cortical layers (Roman numbers) revealed by the NeuN stain.
Laminarization appears to be intact. (B) Camera lucida reconstruction of calretinin immunopositive (CR+) cell bodies and processes
next to the electrode track. (C) Well preserved pyramidal (SMI-32 stain) and (D) glial cells, stained with glial fibrillar acidic protein (GFAP)
next to the electrode track.
Neural basis of slow waves in humansBrain 2010: 133; 2814–2829 |
custom-made main amplifier (Ulbert et al., 2001a). An EEG range
signal was sampled at 2kHz/16bit; the multiple-unit activity range
was sampled at 20kHz/12bit and stored on a hard drive.
Slow wave activity detection
We have analysed the LFP gradient, multiple-unit activity, single-unit
activity and ECoG data acquired from each patient during one to three
nocturnal recording sessions. Since the sleep of the patients was frag-
mented due to medical care and distress from the hospitalization and
head wound, we cannot provide standard hypnograms that are usually
obtained fromhealthy subjects
Craniotomies may also distort the scalp distribution of the EEG due
to the lack of bone and excessive fluid accumulation below the scalp;
furthermore, if scalp electrodes are placed close to the frontal crani-
otomy wounds, they may induce infection, and therefore we avoided
placing more than two frontal scalp EEG electrodes. Partial sleep sta-
ging was performed based on readings of the available scalp EEG
and ECoG electrodes by expert neurologists. In this study, we have
analysed electrophysiological data obtained only from the deepest
stage of non-REM sleep (N3, or slow wave sleep) (Iber et al.,
2007). Behavioural sleep was confirmed by the video recording,
while slow wave sleep was electrographically identified in accordance
with the recent American Academy of Sleep Medicine guidelines
(Iber et al., 2007). Slow wave sleep periods were identified when
20% or more of an epoch consisted of slow wave activity (waves
in the 0.5–2Hz frequency range with peak-to-peak amplitude larger
than 75mV, measured over the frontal regions) (Iber et al., 2007).
Data containing interictal spikes (within 1min) and seizures (within
In addition to spectral (Fig. 3A) and autocorrelation analyses
(Fig. 3B), slow wave activity cycle detection was based on phase
and amplitude information, extracted from the narrow-band filtered
(0.3–3Hz, 24dB/octave, zero phase shift) layer II LFP gradient
(Fig. 4A) and ECoG (Fig. 4B) data. Instantaneous phase of the filtered
signal was calculated by the Hilbert transformation. In our implemen-
tation, a single slow wave activity cycle was defined between –180?
and +180?phase. The –180?phase value corresponded to the trough
of the negative half-wave (down-state) preceding the 0?phase, which
corresponded to the peak of the positive half-wave (up-state) and
finally the +180?phase corresponded to the following negative
half-wave trough (down-state). At each +180?crossing, the phase
was wrapped –360?for better visualization. To avoid the detection
of higher-frequency (e.g. theta) oscillations, waves with shorter than
500ms cycle lengths (corresponding to higher than 2Hz frequency)
were excluded from the analysis. Waves with non-monotonic phase
runs were also excluded, since phase inversions may indicate higher
frequency contamination. In addition to phase constraints, valid slow
wave activity cycles had to fulfil the following amplitude criteria: the
up-state peak amplitude had to be more positive than +50mV, and the
preceding or following down-state trough amplitude had to be more
negative than –50mV. The slow wave activity detection algorithm par-
ameters were tuned and carefully validated by expert electroencepha-
lographers. Similar algorithmic parameters were used for all of the
patients. To facilitate comparison of our results with previous animal
studies, the threshold level was set to +50mV on the filtered (0.3–3Hz,
24dB/octave, zero phase shift) upper layer III LFP gradient, and
the wave triggered (up-state locked) averages were calculated on
the unfiltered LFP gradient and multiple-unit activity (Supplementary
the studyto avoid epileptic
To quantify and compare slow wave activity parameters with other
studies, the frequency of slow wave activity occurrence (detected valid
cycles per minute), the interdetection interval histogram (Fig. 3C) and
the cycle length histogram (Fig. 3D) were calculated (Massimini et al.,
2004). The single sweep (Fig. 4C and D) and averaged (Fig. 5A)
time-frequency content of the slow wave activity signal was also com-
puted using wavelet transforms (Delorme and Makeig, 2004). In add-
ition, we attempted to describe the laminar distribution of the slow
wave activity in more detail using the LFP gradient fast Fourier trans-
formation power spectrum depth profile (Fig. 5B), the pairwise linear
coherence between each LFP gradient trace in the slow wave activity
(0.3–3Hz) frequency range (Fig. 5C) and the depth profile of the LFP
gradient autocorrelation (Fig. 5D).
Current source density analysis
CSD analysis identifies synaptic/transmembrane generators of LFP in
laminated neural structures (Freeman and Nicholson, 1975; Nicholson
and Freeman, 1975). The negative of the second spatial derivative of
the LFP closely approximates the macroscopic current density over a
unity cell membrane area. Since the LFP gradient (Fig. 6A) is the first
spatial derivative of LFP, one additional spatial derivation yielded the
CSD (Fig. 6C) for the EEG range (0.1–300Hz) data. Inhomogeneous
conductivity and electrode spacing were not taken into account
Figure 3 Spectral and temporal properties of slow wave activity
cycles. (A) Representative examples of the fast Fourier trans-
formation power spectrum and (B) autocorrelation of supra-
granular LFP gradient in Patients 3 and 5. For additional fast
Fourier transformation and autocorrelations see Supplementary
Fig. 5G and H. (C) Representative examples of interdetection
interval histograms (y-axis: counts, x-axis: time between
up-state detections, 166ms bin) and (D) cycle length histogram
(y-axis: counts, x-axis: valid cycle lengths, 33ms bin) in Patient
4. For additional data see Supplementary Figs 3 and 4.
Brain 2010: 133; 2814–2829R. Csercsa et al.
(both were substituted by the dimensionless number 1 in the calcula-
tions); high spatial frequency noise and boundary effects were reduced
by Hamming-window smoothing and interpolation (Ulbert et al.,
2001a), and thus CSD was expressed in microvolts. It was shown
previously (Ulbert et al., 2001a, b, 2004a, b; Wang et al., 2005;
Halgren et al., 2006; Knake et al., 2007; Fabo et al., 2008; Cash
et al., 2009; Steinvorth et al., 2009; Wittner et al., 2009) that our
recording and analysis techniques can reliably detect CSD activity in
each layer of the human cortex and hippocampus.
Statistical analysis of electrophysiology
ANOVA with Tukey’s honestly significant difference test were applied
to the normalized values (LFP gradient and CSD: between –1 and +1;
multiple-unit activity, gamma band LFP gradient and CSD: between
0 and 1). Normalized values were grouped by layers (I–VI), and the
grand average (across all patients) of LFP gradient, multiple-unit ac-
tivity, CSD, gamma band (30–150Hz) LFP gradient and gamma band
CSD power depth profile at the up-state peak were tested to deter-
mine statistically significant differences (P50.01) between electro-
physiological activations in different layers of the cortex (Fig. 6E–H).
Results are depicted on box-whisker plots [small box=mean; big
box=standard error (SE); whisker=standard deviation (SD)]. For de-
tailed statistical data, see Supplementary Fig. 9.
In Patients 4 and 5 (with available histology), the averaged, normal-
ized calretinin immunopositive cell density (Fig. 8A) and averaged,
normalized electrophysiology depth profiles (consecutive values at
each cortical depth) were constructed. Average depth profiles of the
LFP gradient (Fig. 8B) and CSD (Fig. 8C) at the peak of the up-state
were normalized between –1 and +1, while average depth profiles of
multiple-unit activity (Fig. 8D) and spectral measures (Fig. 8E and F)
(gamma band LFP gradient and CSD) at the peak of the up-state were
normalized between 0 and 1. The calretinin immunopositive cell dens-
ity depth profile was normalized between 0 and 1. SE is marked by
whisker in this case. The normalized cell density and electrophysiology
measures were compared using the Pearson r correlation method with
P50.01 significance level criterion.
Single- and multiple-unit activity
A continuous estimate of population neuronal firing rate was
calculated from the multiple-unit activity range (300–5000Hz) data.
The signal was further filtered (500–5000Hz, zero phase shift, 48dB/
octave), rectified and decimated at 2kHz, applying a 0.5ms sliding
average rectangular window, followed by a final, smoothing low-pass
filter (20Hz, 12dB/octave) (Fig. 6B). Putative single units (Fig. 10B,
Supplementary Fig. 7) were analysed by conventional threshold detec-
tion and clustering methods using Dataview and Klustawin (Heitler,
2006) and custom-made MATLAB software. Putative single units
from Patients 1, 4 and 5 were included in the analyses, which were re-
corded stably for at least 600–1000s in slow wave sleep. In Patient 2,
multiple-unit activity was not recorded for technical reasons, while
Patient 3 showed no discriminable single units. After threshold recog-
nition (mean?3–5 SD) (Csicsvari et al., 1998) at a given channel,
Figure 4 Similarity between LFP gradient recorded with a microelectrode array within the cortex and ECoG recorded from macrocontacts
subdurally. (A) Upper: ‘raw’ traces of single sweeps containing slow wave activity; broadband (0.1–300Hz) LFP gradient from layer II.
Middle: ‘filtered’ traces after band-pass (0.3–3Hz) filtering. Lower: ‘phase’ traces showing the instantaneous phase of the ‘filtered’ trace
above derived by the Hilbert transform. Grey rectangles indicate automatically detected up-states (surface positive half-waves). Patient 4.
(B) Same as (A), but recorded from neighbouring ECoG contacts. (C) Colour map of LFP gradient and (D) ECoG spectral power during the
slow wave activity shown in (A) and (B). x-axis: time, y-axis: frequency, z-axis: colour coded relative spectral power in dB, compared with
the mean of the entire interval in each frequency band (relative spectrogram). For more examples of single sweep traces, see
Supplementary Figs 1 and 2.
Neural basis of slow waves in humansBrain 2010: 133; 2814–2829 |
three representative amplitude values were assigned to each unclus-
tered spike waveform. These triplets were projected into 3D space and
a competitive expectation-maximization based algorithm (Harris et al.,
2000) was used for cluster cutting (Heitler, 2006). If the autocorrelo-
gram of the resulting clusters contained spikes within the 2ms refrac-
tory interval, it was reclustered. If reclustering did not yield a clean
refractory period, the cell was regarded as multiple units and omitted
from the single cell analysis. Given the gradient recording, spikes at
neighbouring traces appeared as mirror images, thus from adjacent
channels (150mm apart) only one channel (the one that yielded the
better signal to noise ratio) was included in the analysis. To reveal
double detection, the cross-correlogram was constructed (Staba
et al., 2002b) for next to adjacent (300mm apart) pairs of putative
single cells. No coincident interactions [99% confidence limit at 0ms
(Staba et al., 2002b)] were found. A spike train was determined as a
burst, if at least three consecutive spikes occurred within a maximum
20ms long interval, which was preceded and followed by at least
20ms long intervals with neuronal silence (Staba et al., 2002a).
Phase dependence of single cell firing rate (Fig. 10C) was computed
for 30?phase bins; the total number of firing in a given bin was
divided by the total time that the cortex spent in that phase bin
(thus producing a phase histogram). The Rayleigh test (P50.01) was
used to judge if the resulting circular distribution was significantly dif-
ferent from the uniform distribution.
We have shown previously (Ulbert et al., 2001a, b, 2004a, b;
Wang et al., 2005; Halgren et al., 2006; Fabo et al., 2008; Cash
et al., 2009; Wittner et al., 2009) that our single-unit activity,
multiple-unit activity recording and analysis techniques can reliably
detect task or epilepsy-related modulation of neuronal firing from
each layer of the human cortex and hippocampus.
Previous studies of slow wave activity in humans (Achermann and
Borbely, 1997; Massimini et al., 2004, 2005, 2007; Molle et al.,
2004; Marshall et al., 2006) have been limited to macroelectrode
recordings that superimpose activity from several squared centi-
metres of cortex. These recordings are ambiguous as to the circuits
involved, are not sensitive to neuronal firing and do not distinguish
between excitatory and inhibitory mechanisms. We used laminar
multichannel microelectrode array recordings (Ulbert et al., 2001a,
b, 2004a, b; Wang et al., 2005; Halgren et al., 2006; Knake et al.,
2007; Fabo et al., 2008; Cash et al., 2009; Steinvorth et al., 2009;
Wittner et al., 2009) to estimate neuronal firing and synaptic/
transmembrane currents in different cortical layers. Since cortical
neuronal populations and synaptic inputs are organized into dis-
tinct layers, these recordings allowed us to resolve the cortical
generators underlying slow wave activity in humans.
General features of human slow
Clinical subdural strip and grid electrodes and multichannel micro-
electrode arrays were implanted into the frontal and parietal cor-
tices of patients (n=5) with intractable epilepsy (Fig. 1) in order to
identify the seizure focus and eloquent cortex prior to surgical
therapy. The focus was eventually localized to the frontal lobe
in four patients and to the temporal lobe in one. Histology
of the microelectrode array penetration track was recovered in
two patients; it showed intact laminarization (Fig. 2A) and
well-preserved interneurons, pyramidal cells and glia (Fig. 2B–D),
indicating no discernable epilepsy or implantation-related damage
of the examined cortex. None of the patients had pre-operative
pathological MRI findings in the 1–2cm vicinity of the microelec-
trode array implantation site.
Figure 5 Spectro-temporal and spatial properties of slow wave
activity, representative data from Patient 3. (A) Increased
broadband spectral activity during up-states: up-state locked,
averaged, relative spectrogram of layer II LFP gradient (x-axis:
time, y-axis: frequency, z-axis: colour-coded averaged relative
spectral power in dB). For the neighbouring ECoG, the averaged
relative spectrogram is depicted in Supplementary Fig. 5A. Red
(LFP gradient) and green (ECoG) traces show the average
potentials. (B) Depth distribution profile of the LFP gradient fast
Fourier transformation power spectrum (EEG range: 0.1–300Hz
data, no additional digital filtering was used, x-axis: frequency,
y-axis: cortical depth, with corresponding layers, z-axis:
colour-coded fast Fourier transformation power). For more
power spectrum examples, see Supplementary Fig. 5B. (C)
Depth distribution profile of pairwise coherence of LFP gradient
channels in different cortical layers. x-axis: cortical depth, with
corresponding layers, y-axis: cortical depth, with corresponding
layers, z-axis: colour-coded pairwise coherence of the band-pass
(0.3–3Hz) LFP gradient. For more pairwise coherence examples,
see Supplementary Fig. 5C. (D) Depth distribution profile of LFP
gradient autocorrelation. x-axis: time, y-axis: cortical depth, with
corresponding layers, z-axis: colour-coded autocorrelation of the
LFP gradient. For more laminar autocorrelation examples,
see Supplementary Fig. 5D.
Brain 2010: 133; 2814–2829R. Csercsa et al.
Automatic slow wave activity cycle detection was based on
amplitude and phase information using an LFP gradient (Fig. 4A)
and ECoG (Fig. 4B) recorded during slow wave sleep (also see
Supplementary Figs 1 and 2). On average, 20 slow wave activity
cycles (mean=20 1/min, range=12–26 1/min, SD=7 1/min)
were detected per minute (Supplementary Figs 3 and 4).
Cycle lengthpeaked onaverage
range=0.6–1.4s, SD=0.3s) (Fig. 3D). Interdetection interval
(Fig. 3B) peaked on average at 1.1s (mean=1.1s, range=
(Massimini et al., 2004). LFP gradient (Fig. 3A and C) and
ECoG (not shown) fast Fourier transformation power spectrum
and autocorrelation (Fig. 3A and B, Supplementary Fig. 5G and
H) also corresponded well to previous human (Achermann and
Borbely, 1997) and animal (Isomura et al., 2006) findings, indicat-
ing correct slow wave activity cycle identification and relatively
normal slow wave activity production.
The LFP gradient recorded in layer II (Fig. 4A) closely resembled
the locally recorded ECoG (Fig. 4B), with Pearson r40.9 (P50.01)
in all patients (Supplementary Fig. 1). Time-locked averages to the
peak of the surface positive half-wave (up-state) showed similar
LFP gradient and ECoG waveforms regardless if time locking was
based on the LFP gradient or ECoG (Fig. 5A, red and green
traces). Both LFP gradient and ECoG (Figs 4C and D and 5A,
Supplementary Fig. 5A) showed broadband (10–200Hz) spectral
increases during up-states and decreases during down-states.
Laminar distribution of slow wave
To estimate the laminar contribution of various activities, micro-
electrode array channels were assigned into six putative layers
(I–VI) based on the histological findings (Fig. 2A) when available
and cortical depth when not. This analysis revealed a substantial
concentration of the 0.3–3Hz band LFP gradient fast Fourier
transformation power within layers I–III (Fig. 5B, Supplementary
Fig. 5B) in each patient, indicating strong supragranular synaptic/
transmembrane activity. The slow wave activity shape similarities
between electrode contacts were significantly greater in supra-
granular versus infragranular layers in each patient (0.634 versus
0.423, grand average pairwise coherence, Kruskal–Wallis ANOVA,
P50.01) (Fig. 5C, Supplementary Fig. 5C), while autocorrelation
profiles revealed a more precisely paced rhythm supragranularly
(Fig. 5D, Supplementary Fig. 5D) in each patient.
Several measurements, both in individual patients (Fig. 6A–D)
and in grand averages (Fig. 6E–H), reflecting different aspects of
Figure 6 Role of supragranular layers in slow wave activity generation. Representative depth profile map examples from Patient 4 (A–D)
and grand averages of all patients (E–H). (A) LFP gradient (LFPg), (B) multiple-unit activity and (C) CSD depth profile maps. x-axis: time,
y-axis: cortical depth, with corresponding laminarization, z-axis: colour-coded amplitude of LFP gradient, multiple-unit activity and CSD
units. Positive values are red, negative are blue, except for CSD, where sink is depicted in red and source in blue. (D) LFP gradient
spectrograms (SPC) from nine representative channels in layers I–VI. Axes are similar to Fig. 5A. Box-whisker plots of (E) LFP gradient,
(F) multiple-unit activity, (G) CSD, (H) LFP gradient (red) and CSD (blue) gamma power (30–150Hz); normalized grand average of all
patients at the peak of the up-state in each layer. Mean: small box, standard error (SE): large box, standard deviation (SD): whisker.
For detailed statistical analysis see Supplementary Fig. 9.
Neural basis of slow waves in humans Brain 2010: 133; 2814–2829 |
population synaptic/transmembrane and firing activity, were max-
imal in supragranular layers (for detailed statistical analysis, see
Supplementary Fig. 9) at the up-state peak. Normalized, grand
average depth profiles of LFP gradient (Figs 6E and 8B) were
marked by maximally positive deflections in layers I–III, inverting
in layers V and VI into a small negativity. Multiple-unit activity
was also maximal in layer III (Figs 6F and 8D). The CSD depth
profile at the peak of the slow wave activity up-state showed a
maximal source (outward current) in layer I and maximal sink
(inward current) in layers II and III, and only very small CSD de-
flections were observed infragranularly (Figs 6G and 8C). In con-
trast, the CSD depth profile of a population of interictal spikes
detected manually and locked to the surface positive LFP gradient
(similarly as in the case of the up-state) were markedly different,
exhibiting a large sink–source pair in the infragranular layers
(Supplementary Fig. 11). Significant increases [bootstrap analysis
(Delorme and Makeig, 2004), P50.01] in LFP gradient spectral
power were detected in all layers at 10–100Hz frequencies during
up-states (Fig. 6D). Gamma power of LFP gradient and CSD was
maximal in layer III (Figs 6H, 8E and F). Separate averages of
different slow wave activity cycle lengths corresponding to appro-
priate (0.6–0.8Hz, 0.8–1Hz, 1–1.3Hz and 1.3–2Hz) oscillation
frequencies also yielded qualitatively similar LFP gradient, spectral
LFP gradient, multiple-unit activity and CSD distribution (Fig. 7,
Supplementary Fig. 6). We have found no statistically significant
differences in any layers (ANOVA, Tukey’s honestly significant
difference post hoc test, P40.3) in the CSD or multiple-unit ac-
tivity at the peak of the up-state between any of the four fre-
quency bands indicating similar cortical generator mechanisms
above (up to 2Hz) and below 1Hz (down to 0.6Hz).
Calretinin immunopositive cell density depth profiles (Fig. 8A)
were calculated in two patients and correlated with the depth
profile at the up-state peak of the LFP gradient, CSD, multiple-unit
activity, LFP gradient and CSD gamma power (Fig. 8B–F).
Figure 7 Depth profiles at different slow wave activity frequencies. Up-state-locked averages of LFP gradient (LFPg), LFP gradient
spectrogram (SPC), multiple-unit activity and CSD in Patient 3 at four different slow wave activity frequencies. Frequencies 1.3–2Hz,
correspond to a cycle length of: 500–750ms; 1–1.3Hz to 750–1000ms; 0.8–1Hz to 1000–1250ms and 0.6–0.8Hz to 1250–1500ms.
Roman numerals mark putative cortical layers. Colour calibrations are on the bottom. CSD sink is depicted in red, source in blue. Each
spectrogram window shows the spectral content (z-axis, colour coded) versus time (x-axis) of a representative LFP gradient channel from a
given layer from 1 to 100Hz (y-axis), measures are expressed in dB relative to a distant baseline (–2500 to –1500ms).
Brain 2010: 133; 2814–2829R. Csercsa et al.
Calretinin immunopositive cell density between Patient 4 and 5
showed high similarity (r=0.95, P50.01). The highest positive
correlation was found between calretinin immunopositive cell
density and CSD gamma power (r=0.85, P50.01).
Multiple-unit activity timing at
The time courses of multiple-unit activity were examined to de-
termine if one layer may lead others. It was shown in ferret slices
(Sanchez-Vives and McCormick, 2000) that layer V’s multiple-unit
activity consistently led layers II and III by an average of over
100ms. In our study, the up-state-associated multiple-unit activity
peak-locked averages indicated no evident timing difference in
any of the patients, between layers III and V, regardless of
whether peak alignment was based on layer III or layer V activity
(Fig. 9A and B, Supplementary Fig. 5E and F).
To characterize the multiple-unit activity timing between differ-
ent layers further, it was cross-correlated (3 SD threshold, 10ms
bin size) between each pair of channels, within 200ms of every
up-state onset. In agreement with animal studies (Sakata and
Harris, 2009; Chauvette et al., 2010), delay maps and histograms
(Fig. 9C and D) indicated a short inter-laminar multiple-unit activ-
ity timing difference at up-state onset; most of the delays were
within the?10ms bin. We also calculated how often (in percent-
age of all sweeps) any given multiple-unit activity channel shows
the earliest firing at up-state onset. In all patients (where
multiple-unit activity was available), the initial firing was quite
uniformly distributed across cortical depths. Unlike in a ferret
in vitro study (Sanchez-Vives and McCormick, 2000), we found
no evidence for long (?100ms) lead or lag times between differ-
ent layers (Fig. 9E and F).
Figure 9 Timing of up-state-related multiple-unit activity in
different layers. (A and B) Simultaneity of multiple-unit activity
response in supra- and infragranular layers of Patient 4.
Multiple-unit activity from layers III (red trace) and V (blue) are
shown when aligned and averaged on the up-state-associated
multiple-unit activity peak detected in (A) layer III and (B) in
layer V. There is no visible multiple-unit activity delay between
layers III and V regardless of which layer is used for time locking.
(C) Multiple-unit activity cross-correlation peak latencies (x-axis
versus y-axis) between each pair of channels in Patient 1.
Positive latencies (red) indicate that x channel leads over
y channel, while negative latencies (blue) represent lagging.
(D) Histogram of leading and lagging values from (C). (E and F)
Percentage of a given multiple-unit activity channel showing
the earliest firing at up-state onset, representative data from
Patients 1 and 3.
Figure 8 Depth profiles of calretinin immunopositive (CR+) cell
density and slow wave activity. (A) Averaged normalized
calretinin immunopositive cell density profile of Patients 4 and 5,
whiskers represent standard errors. The number in the upper
right corner indicates the Pearson r correlation between the two
patients. (B) LFP gradient (LFPg), (C) CSD, (D) multiple-unit
activity, (E) LFP gradient gamma power and (F) CSD gamma
power of the averaged normalized depth profile of up-state in
Patients 4 and 5 with standard error (whisker). Number in the
upper right corner indicates the Pearson r correlation between
calretinin immunopositive density and (B–F).
Neural basis of slow waves in humansBrain 2010: 133; 2814–2829 |
Single-unit correlates of slow oscillation
Recordings from three patients yielded good quality (Supplemen-
tary Fig. 7) single-unit activity (Fig. 10). Epochs (?1000s) showing
the largest slow wave activity detection frequencies were selected
for analysis from the first sleep cycle. Overall 33 single units were
clustered (9, 12 and 12 from Patients 1, 4 and 5) with mean firing
rate of 0.66Hz (range=0.12–2.0Hz, SD=0.48) and mean burst
frequency of 3.1 1/min (range=0–14 1/min, SD=3.6). Both the
average firing rate and the spontaneous burst rate were well
below the reported epileptic threshold found in cortical and hip-
pocampal structures (Staba et al., 2002a).
Nearly all cells (31 of 33) showed significantly non-uniform
spiking (Fig. 10A and C, Supplementary Fig. 7D and E) over
the slow wave activity cycle (Rayleigh test, P50.01), with a
peak up-state firing rate mean of 1.63Hz (range=0.45–4.6Hz,
SD=0.96). We found no significant differences between patients
in mean firing rates (Kruskal–Wallis ANOVA, P40.2), indicating
homogeneous distribution. Although mean firing rates grouped by
supra- versus infragranular layers showed no significant differences
(P40.1), supragranular peak up-state firing rates were significant-
ly higher (2.2Hz versus 1.2Hz, Kruskal-Wallis ANOVA, P50.01)
than the same measure for infragranular layers. We found the
proportion of firing cells and the rate at which they fire in any
given up-state (Fig. 10D–F, Supplementary Fig. 8) remarkably low.
On average, only 27% of the clustered cells were active (firing at
least one spike) during any given up-state (20%, 25% and 36%
in each patient). Thus, an average neuron fired in every third to
fifth up-state. Moreover, on average, each neuron fired only 0.32
spikes per up-state (0.44, 0.2 and 0.32 in each patient). As an
example, out of the 12 clustered neurons in Patient 4, the
most probable number of active cells in a given up-state was 2
(Fig. 10F, see also Supplementary Fig. 8C and F), and the most
probable number of overall spikes the 12 cells fired within a
given up-state was also 2 (Fig. 10E, see also Supplementary
Fig. 8B and E).
Figure 10 Single-unit firing in slow wave activity. (A) Superimposed (40 consecutive sweeps) and (B) individual single sweeps of the
simultaneously recorded supragranular LFP gradient (LFPg) and multiple-unit activity/single-unit activity (SUA) (Patient 5). Solid and
dashed red lines represent LFP gradient mean and standard deviation. (C) Representative normalized (from 0 to 1) firing rate versus phase
histograms (from –180?to +180?, in 30?bins) of clustered cells from different layers and patients. Red line: positive half-wave (up-state),
green line: negative half-wave (down-state). (D) Columns represent individual slow wave activity cycles (1–252), rows represent clustered
neurons (cell 1–12 of Patient 4) and colour represents the firing of a given cell. Blue: no firing in the given up-state for the given cell, green:
one, yellow: two, red: three or more action potentials. (E) Histogram of the overall number of spikes for all the 12 clustered cells during
up-states. (F) Histogram of the number of active cells, firing at least one action potential during up-states. These data illustrate sparse firing
in up-states, only a small fraction of the clustered cells fire and these cells together generate only a few action potentials. For additional
data, see Supplementary Figs 7 and 8.
Brain 2010: 133; 2814–2829 R. Csercsa et al.
Our results establish a close similarity between human slow wave
activity and the animal slow oscillation at the level of field poten-
tial, cellular firing activity and spectral measurements (Steriade,
2006), but they also reveal a number of novel, unexpected find-
ings. Consistent with prior studies in animals, we have shown in
humans that the up-state was associated with increased firing and
elevated spindle, alpha, beta, gamma and ripple power during the
surface-positive LFP half-wave, while the down-state was charac-
terized by the widespread surface negative LFP half-wave with
decreased firing and oscillatory activity (Cash et al., 2009).
Differences from prior studies were found in the laminar distribu-
tion of the up-state, average firing rates during the up-state and
the consistency of generators for oscillations above versus below
1Hz. These contrasts could reflect cortical cytoarchitectonic differ-
ences or they could be due to the circumstances of the recordings,
including natural sleep versus different types of anaesthesia, or
in vivo versus in vitro preparations. They could also be due to
epileptic pathology or to phylogenetic differences.
Epilepsy and slow wave activity
Epilepsy is a multi-causal disease with diverse aetiology. Focal
epilepsies have circumscribed seizure initiating regions without
severe pathological alterations in other areas. Surgical candidates
are selected exclusively from this patient group during the careful
pre-operative evaluation, based on several diagnostic findings
(CT, MRI, functional MRI, PET, single-photon emission computed
tomography, video-EEG, magnetoencephalography,
neurophysiological tests, Wada-test and seizure semiology). In
the present study we included only patients with evidence for
The laminar LFP gradient, CSD, multiple-unit activity and spec-
tral profile of interictal activity in vivo and in vitro have already
been established by our group (Ulbert et al., 2004a, b; Fabo et al.,
2008; Wittner et al., 2009). We have shown that the initial phase
of the interictal discharges are large amplitude brief events char-
acterized by substantial action potential, LFP gradient, CSD,
multiple-unit activity and spectral surges, often emerging from
the granular and infragranular (Supplementary Fig. 11) layers of
the cortex (Ulbert et al., 2004a). These events are clearly distinct
from the background activity and exquisitely visible in single
sweeps. Based on our prior knowledge, we carefully excluded
any suspicious pathological events from the analysis presented in
this article, and we also carefully avoided analysing data derived
from electrodes in the proximity of the seizure focus.
Several other considerations suggest that the current findings on
the neuronal mechanisms underlying slow wave activity, although
recorded in epileptic patients, might also apply to healthy subjects.
Our slow wave activity morphology corresponded well to those
oscillations collected from standard scalp sleep EEG recordings
from healthy subjects. Similarities included not only the slow
Borbely, 1997), but the asymmetric shape, the briefer and sharper
deflection in the down-state (Massimini et al., 2004) and higher
beta power content in the up-state (Molle et al., 2002). Our
results of detection frequency, cycle length and interdetection
interval histograms are all comparable to previous findings from
healthy subjects (Massimini et al., 2004), despite both the record-
ing and analysis methodologies being different. Minor deviations
in the exact numbers are therefore natural and may reflect meth-
odological differences rather than disease-related alterations. In
addition, neither the firing rate nor the burst rate exceeded the
pathological criteria found for single neurons in slow wave sleep
(Staba et al., 2002a, b). Finally, the lack of any MRI abnormalities
and intact laminarization of the excised tissue strongly suggests
that we recorded from structurally intact regions, free of gross
Nevertheless, there are some observations in our study that may
be related to the patients’ pathology. Out of the 33 clustered units
in three patients, two single cells in one patient (Patient 1) showed
uniform firing during the slow wave activity cycle (layer III unit #3,
average firing rate=1.17Hz, layer V unit #6, average firing
rate=0.54Hz). While some firing during the down-states could
be expected due to biological variability of the slow wave activity
or due to inaccuracy of the state detection algorithm, a lack of
significant modulation by slow wave activity in these cells may
reflect a pathological resistance by a small subgroup of cortical
neurons to the network-wide deactivation occurring in the
Localization of cortical oscillations in
slow wave activity
A consistent CSD pattern of our study was the prominent sink–
source pair in the supragranular layers compared with the weak
infragranular activation. This localization was true for both the
low- (0.5–2Hz) and the high-frequency (10–200Hz) oscillations
in all patients, in frontal and parietal areas.
In our interpretation, the prominent layer II and III sink during
the up-state reflects the large active inward currents flowing
across the distal dendritic membrane compartments of layers V
and VI pyramidal cells and distal, proximal and basal dendritic
membrane compartments and perhaps on the somatic membrane
of layers II and III pyramidal cells. The corresponding passive,
return, source currents are flowing in layer I, across the most
superficial apical dendritic membrane compartments of the pyram-
idal cells. The spatial CSD pattern in the down-state is inverted,
exhibiting a large active current source in layers II and III due to
hyperpolarizing currents (likely outward potassium flows from the
pyramidal cells) and a passive return sink in layer I (Cash et al.,
Our CSD findings from the frontal and parietal areas in natural
sleep are in contrast with a study in the cat suprasylvian area,
albeit under ketamine/xylazine anaesthesia (Steriade and Amzica,
1996). At low frequencies (?1Hz), the maximal up-state-related
sink in the cat was located in the middle rather than in the super-
ficial layers, surrounded by not only a superficial but also a large
deep source. In addition, a substantial up-state-related sink in the
deepest layer was present in the cat, which was practically invisible
in our human recordings. The same authors also showed a massive
supragranular (layers II and III) up-state-related sink besides one or
Neural basis of slow waves in humansBrain 2010: 133; 2814–2829 |
two deeper and weaker sinks, during the spontaneous and evoked
K-complex (Amzica and Steriade, 1998). Moreover, at higher fre-
quencies (?35Hz) and during the K-complex, a series of ‘alter-
nating microsinks and microsources’ was found throughout the
depth of the cat suprasylvian area (Steriade and Amzica, 1996;
Amzica and Steriade, 1998). Such alternating patterns are best
explained by insufficient spatial resolution in the LFP sampling
(8 contact, 250mm spacing electrode array) and corresponding
spatial aliasing error, and not by neuronal sources (Tenke et al.,
A recent study in the cat suprasylvian area in natural sleep with
adequate spatial sampling (100mm) revealed alternation free
middle and deep layer sinks and a superficial source during the
up-state (Chauvette et al., 2010). Thus, besides cytoarchitectonic
differences between different types of cortices (e.g. frontal and
parietal areas in humans versus suprasylvian area in the cat),
and methodological errors, it is also plausible to assume that neur-
onal mechanisms of natural sleep and ketamine/xylazine anaes-
thesia may be different, further accounting for the divergent
findings. Another recent study, on the rat primary auditory
cortex using high spatial resolution (50mm) CSD mapping,
showed the maximal up-state-related sink to be in the presumed
supragranular layers under urethane anaesthesia, while in natural
sleep it is rather the presumed granular and probably infragranular
layers that exhibited the largest up-state-related sink (see average
data, Supplementary Figs 10 and 16 in Sakata and Harris, 2009).
Our CSD results in natural sleep are quite close to the results of
Sakata and Harris (2009) obtained under urethane anaesthesia,
except for the large source deep in the infragranular layers.
Discrepancies between cat, rat and human data thus most
probably stem from multiple sources, including but not limited
to the recording methodology (spatial density of sampling, elec-
trode types implanted), use of anaesthetics (ketamine/xylazine
versus urethane versus natural sleep), cytoarchitectonics of the
cortex (suprasylvian cortex in cat versus auditory cortex in rat
versus frontal and parietal areas in humans), as well as species
According to our observations, the slow wave activity depth
profile, represented by the up-state peaks, was similar between
the four investigated frequency ranges including the slow (51Hz)
and delta band (up to 2Hz). In our opinion, these frequency
bands are thus substantially overlapping, hence a less strict distinc-
tion should be applied between activities above versus below 1Hz.
We agree that the slow activity and thalamic delta have obviously
different neuronal mechanisms, but it seems that these waves
cannot be distinguished using exclusively a frequency band
We have found similar signs of elementary hierarchical organ-
ization of low- and high-frequency oscillations in humans as it was
shown in animal models (Lakatos et al., 2005; Steriade, 2006).
The organizing substrate was the up-state of the slow wave
activity, which gave rise to a wide variety of higher-frequency
activity including spindle, alpha, beta, gamma and ripple oscilla-
tions. Each of these high-frequency oscillatory bursts was quite
different from sweep to sweep, showing for example occasional
spindle sequences or marked gamma or ripple band enhancements
at various peak frequencies. These observations suggest that each
slow wave activity cycle with unique oscillatory signature reflects
individual information content coded differently in the oscillatory
process. Given the variability of the high-frequency oscillatory ac-
tivity during up-states, it is plausible to assume that different
underlying neuronal populations might be responsible for the gen-
eration of each specific oscillatory pattern. This strategy might be
beneficial in the configuration of functional connectivity between
neurons to form stable ensembles that may promote the consoli-
dation of memory in sleep.
Paroxysmal activity is known to emerge more often from
non-REM sleep compared with REM (Steriade, 2003). Animal stu-
dies revealed that cortical hyperexcitability associated with ripple
oscillations often results in pathological synchronization leading to
epileptic seizures (Grenier et al., 2003). We have shown that
up-states are characterized by a large increase in cortical excitabil-
ity reflected in the increased power of gamma and ripple oscilla-
tions. Thus, we hypothesize that the active state of the slow
oscillation may play an important role in the generation of seizures
and other paroxysmal signs in the cortical epileptic network.
Laminar calretinin immunopositive
interneuron density and slow
The relatively high correlation in laminar location between LFP
gradient, CSD, multiple-unit activity and gamma power during
up-states and calretinin immunopositive cell density may provide
additional insights into the mechanism for the predominance of
oscillatory activity in supragranular layers. Calretinin immunoposi-
tive cells are relatively numerous for inhibitory cells, comprising
?8% of the total number of prefrontal neurons and ?14.2–
17.6% of layer II and III neurons, in human prefrontal cortex
(Gabbott et al., 1997). Calretinin immunopositive cell density in
the human, monkey, cat and rat cortex is highest in the supra-
granular layers (Fonseca and Soriano, 1995; Gabbott et al., 1997;
Schwark and Li, 2000). The layer II and III population of calretinin
immunopositive cells (78% of all calretinin immunopositive cells) is
significantly more numerous (+31%) in humans than in the rat
(Gabbott et al., 1997), and the supragranular calretinin immuno-
positive predominance in humans is not affected by epilepsy
(Barinka et al., 2010). The relatively high density and remarkable
vertically oriented dendritic alignment (Gabbott et al., 1997) of
layers II and III calretinin immunopositive cells (Fig. 2B) suggest
that this population on its own may contribute significantly to the
CSD. Unlike basket cells that target principal cells (Somogyi et al.,
1983) establishing local negative-feedback circuits, layers II and III
calretinin immunopositive interneurons preferentially target other
inhibitory cells locally in layers II and III and target pyramidal cells
in layer V (Meskenaite, 1997), forming local positive-feedback
circuits (Dantzker and Callaway, 2000) in the supragranular
layers and negative-feedback circuits between supra- and infra-
granular layers. The negative feedback imposed by the population
of calretinin immunopositive
disinhibition (Tamas et al., 1998) may amplify the supragranular
Brain 2010: 133; 2814–2829R. Csercsa et al.
synaptic/transmembrane oscillations in the gamma band and cel-
lular activity (Whittington et al., 1995).
In addition, GABAB receptors are more concentrated in the
upper layers of the cortex, at least in rodents (Lopez-Bendito
et al., 2002; Tamas et al., 2003), which might also contribute to
the potassium current that may play an important role in the
down-state generation (Timofeev et al., 2001).
Action potential activity in slow
In vitro slice studies in animals found that firing in infragranular
layers consistently lead supragranular layers by over 100ms at the
onset of the up-state (Sanchez-Vives and McCormick, 2000). In
contrast, we found that the onset of activity during up-states dif-
fers less than ?10ms between layers. Although the slice prepar-
ation is a powerful tool, it severs connections that are present in
the intact animal, removing background synaptic input and placing
the cell in an artificial medium.
In healthy humans, it is believed that each individual slow wave
cycle has a distinct origin and propagates uniquely across a number
ofbrain areas(Murphy etal.,2009).Similarpatterns werealsofound
in animal models (Ferezou et al., 2007; Mohajerani et al., 2010).
Thus, variable projections may be involved in its propagation, termi-
nating in variable cortical layers making the laminar distribution of
the initial unit firing also variable, as it was shown in our study.
The low average firing rate (0.6Hz) found in here is consistent
with a previous human report (Ravagnati et al., 1979). Animal
studies using extracellular silicon probes and intracellular sharp
electrodes report average firing rates in the 2–20Hz range from
the entire depth of the cortex (Steriade et al., 2001; Isomura
et al., 2006; Luczak et al., 2007), and cell-attached and whole-cell
patch-clamp studies from layers II and III neurons report average
firing rates in the 0.01–0.3Hz range (Margrie et al., 2002; Waters
and Helmchen, 2006; Hromadka et al., 2008). Some of these
differences may be related to the laminar location of the neurons
and to ‘collateral damage’ inherent to the different techniques.
Extracellular recordings might be biased toward higher average
firing rates, because of the use of a minimum spontaneous firing
rate (1–2Hz) constraint (Luczak et al., 2007). We did not use such
correction in our unit analysis, thus slower firing cells were also
included. Sharp electrode intracellular recordings (Steriade et al.,
2001; Isomura et al., 2006) disrupt the cell membrane and intro-
duce leakage current, which may also alter the firing rate. In
cell-attached recordings (Margrie et al., 2002; Hromadka et al.,
2008), the membrane is partially covered by the recording pipette
causing substantial mechanical stress, receptor, ion channel mask-
ing and membrane capacitance changes, while the whole-cell con-
figuration (Waters and Helmchen, 2006) disrupts the membrane
and causes cell dialysis. Indeed, when establishing the whole-cell
configuration, the spontaneous firing rate may double compared
with the cell-attached state (Margrie et al., 2002). Given these
apparently strong effects of recording methodology on cell
firing, it is hard to definitively relate the present findings to
animal experiments. However, it seems reasonable to expect
that any technique which physically contacts the cell would alter
the firing rate to a greater extent than techniques which do not.
To elucidate these differences further, unbiased extracellular action
potential techniques need to be implemented in different animal
In summary, the differences between our recordings of slow wave
activity and those previously described may be due to the
observed cortical areas, the experimental preparation, the type
of sleep induction and the neurological condition. We suggest
that the strong supragranular activity may characterize slow
wave activity, in contrast with certain types of epileptic discharges
and some specific components of sensory and cognitive-evoked
responses, which are mostly localized to the granular and infra-
granular layers (Ulbert et al., 2001b, 2004a; Wang et al., 2005;
Halgren et al., 2006). The strong supragranular oscillatory activity
in sleep may be beneficial for the local, higher-order processing of
sensory experience and perhaps memory consolidation, since these
layers are interconnected by dense cortico-cortical projections
forming fine-scale functional networks to perform integrative
functions (Yoshimura et al., 2005). The weaker infragranular ac-
tivity may reflect the relatively suppressed cortical executive,
output functions, which may prevent effective connectivity be-
tween distant cortical areas from developing in slow wave sleep
(Massimini et al., 2005).
Some differences between humans and cats or rats might also
be expected given that our last common ancestor was about 75
million years ago, and our prefrontal cortex is more than a hun-
dred times larger, with a striking increase in pyramidal cell den-
dritic complexity (Elston, 2003; Elston et al., 2006). Our finding
that the slow wave activity and corresponding high-frequency
rhythms including spindle, alpha, beta, gamma and ripple oscilla-
tions may involve supragranular layers is consistent with this mas-
sive cortical expansion of neuronal number (Herculano-Houzel
et al., 2007), inasmuch as increased dendritic complexity and
layers (Gonzalez-Burgos et al., 2000).
NeuroProbes IST-027017, Epicure LSH-CT-2006–037315, OTKA
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Neural basis of slow waves in humansBrain 2010: 133; 2814–2829 |