Exploring the electrophysiological correlates of the default-mode network with intracerebral EEG.

Abstract

While functional imaging studies allow for a precise spatial characterization of resting state networks, their neural correlates and thereby their fine-scale temporal dynamics remain elusive. A full understanding of the mechanisms at play requires input from electrophysiological studies. Here, we discuss human and non-human primate electrophysiological data that explore the neural correlates of the default-mode network. Beyond the promising findings obtained with non-invasive approaches, emerging evidence suggests that invasive recordings in humans will be crucial in order to elucidate the neural correlates of the brain's default-mode function. In particular, we contend that stereotactic-electroencephalography, which consists of implanting multiple depth electrodes for pre-surgical evaluation in drug-resistant epilepsy, is particularly suited for this endeavor. We support this view by providing rare data from depth recordings in human posterior cingulate cortex and medial prefrontal cortex that show transient neural deactivation during task-engagement.

Full text

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Frontiers in Systems Neuroscience www.frontiersin.org June 2010 | Volume 4 | Article 27 | 1
SYSTEMS NEUROSCIENCE
PersPective Article
published: 28 June 2010
doi: 10.3389/fnsys.2010.00027
quest to fully elucidate the function of intrinsic brain networks also
requires a solid understanding of the link between neuroimaging
findings and their electrophysiological underpinnings.
In this paper we provide perspectives on the necessity, feasibil-
ity, and limitations of tackling the electrophysiological properties
of DMN dynamics. We will discuss the utility and limitations of
non-invasive electrophysiological techniques such as electroen-
cephalography (EEG) and magnetoencephalography (MEG) in
this endeavor. Most importantly, we will focus on the potential of
direct electrophysiological recordings in humans to unravel the
spectral and temporal properties of task-related changes of popula-
tion activity in DMN structures.
A parallel stream of research in humans has revealed that blood-
oxygenation level-dependent (BOLD) signal increases are tightly
coupled with task-related power increases in the high-frequency
range (broad-band gamma, 50–150 Hz) of the intracranial EEG sig-
nal (Mukamel et al., 2005; Lachaux et al., 2007a; Nir et al., 2007). It is
therefore tempting to ask whether task-related BOLD deactivations,
typical for DMN areas, are in turn associated with suppressions of
high gamma power. While human studies of gamma power increases
are abundant (e.g., Lachaux et al., 2005; Crone et al., 2006; Jensen
et al., 2007; Jerbi et al., 2009a), little is known about task-related
gamma power suppression. Intracerebral studies from our group
were the first to provide direct evidence in humans for task-related
decreases of broad-band gamma (>50 Hz) power during perform-
ance of attention-demanding cognitive tasks (Lachaux et al., 2005,
2008). More recent studies (Mainy et al., 2008; Miller et al., 2009;
Jung et al., 2010) provide further evidence for the co-occurrence of
IntroductIon
The fact that parts of our brain are active even when we are not
overtly engaged with the external world may not appear to be that
much of a surprise per se. The fact that thoughts and inner mental
processes are ongoing, and that they are certainly more promi-
nent when we are not processing stimuli from the outside world,
makes the concept of ongoing brain activity not only plausible but
crucial. By contrast, what is definitely striking is the significant
discrepancy between how much we have learned about the spatial
characteristics of the so-called “default-mode” of brain function
(Raichle et al., 2001) and how little we know about the precise
neural mechanisms underlying its modulations and the fine-scale
temporal dynamics thereof.
Over recent years, the default-mode network (DMN) (Gusnard
and Raichle, 2001; Raichle et al., 2001) has been examined in the
light of its putative relationship to self-cognition (Gusnard et al.,
2001) and mind wandering (Mason et al., 2007). Deactivation of
the DMN has been implicated in attention and task-engagement
(Corbetta and Shulman, 2002) and its dysfunction has been linked
to various mental disorders (Greicius, 2008; Broyd et al., 2009). A
steady flow of seminal findings advancing our understanding of
intrinsic brain activity continues to emerge from neuroimaging
studies. Current important topics include the use of functional
magnetic resonance imaging (fMRI) to investigate intrinsic net-
work dynamics and connectivity patterns (Greicius et al., 2003; Fox
et al., 2005; Uddin et al., 2009) and the putative relationship between
DMN deactivations and behavioral performance (Weissman et al.,
2006; Shulman et al., 2007; Anticevic et al., 2010). Nevertheless, the
Exploring the electrophysiological correlates of the
default‑mode network with intracerebral EEG
Karim Jerbi1,2*†, Juan R. Vidal1,2†, Tomas Ossandon1,2, Sarang S. Dalal1,2, Julien Jung1,2, Dominique Hoffmann3,
Lorella Minotti3, Olivier Bertrand1,2, Philippe Kahane3 and Jean-Philippe Lachaux1,2
1 Institut National de la Santé et de la Recherche Médicale, U821, Brain Dynamics and Cognition, Lyon, France
2 Université Claude Bernard, Lyon 1, Lyon, France
3 Neurology Department, Grenoble Hospital, Grenoble, France
While functional imaging studies allow for a precise spatial characterization of resting state
networks, their neural correlates and thereby their fine-scale temporal dynamics remain elusive.
A full understanding of the mechanisms at play requires input from electrophysiological studies.
Here, we discuss human and non-human primate electrophysiological data that explore the
neural correlates of the default-mode network. Beyond the promising findings obtained with
non-invasive approaches, emerging evidence suggests that invasive recordings in humans will
be crucial in order to elucidate the neural correlates of the brain’s default-mode function. In
particular, we contend that stereotactic-electroencephalography, which consists of implanting
multiple depth electrodes for pre-surgical evaluation in drug-resistant epilepsy, is particularly
suited for this endeavor. We support this view by providing rare data from depth recordings
in human posterior cingulate cortex and medial prefrontal cortex that show transient neural
deactivation during task-engagement.
Keywords: default-mode network, electrophysiology, gamma-band activity, stereotactic-electroencephalography,
intracranial EEG
Edited by:
Lucina Q. Uddin, Stanford University,
USA
Reviewed by:
Biyu J. He, Washington University
School of Medicine, USA
Helmut Laufs, Johann Wolfgang
Goethe-University, Germany
*Correspondence:
Karim Jerbi, Institut National de la
Santé et de la Recherche Médicale,
U821, Brain Dynamics and Cognition,
Centre Hospitalier Le Vinatier, Bâtiment
452, 95 Boulevard Pinel, 69500 Lyon,
France.
e-mail: karim.jerbi@inserm.fr
†Karim Jerbi and Juan R. Vidal have
contributed equally to this work.

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Jerbi et al. Intracerebral EEG in default-mode networks
task-related increases and decreases of broad-band gamma activity
in distinct brain areas during goal-directed behavior. As a matter of
fact, because direct recordings from the cortex are not affected by
physiological noise (e.g., breathing or cardiac changes), such studies
are critical to refute claims that DMN observations constitute an
epiphenomenon not of neuronal origin (Birn et al., 2008). Besides,
we argue that depth recordings in humans will be key to probing
the temporal properties of DMN deactivation and to unraveling
the role of gamma activity therein. To further support this claim,
we present rare human intracerebral stereotactic-EEG (SEEG) data
recorded directly from two prominent DMN areas, namely the
posterior cingulate cortex (PCC) and the medial prefrontal cortex
(MPFC). Using time-frequency analysis we computed temporal and
spectral profiles of population-level activity depicting task-related
gamma-band deactivation in these areas during performance of
attention-demanding tasks. Finally, we discuss some implications
of our findings and we outline directions for future research in this
challenging and rapidly growing field.
InvestIgatIng dMn wIth anIMal electrophysIology
Unfortunately, our knowledge of the neural correlates of DMN
remains elusive. This is in part due to the fact that investigating
the electrophysiological correlates of the BOLD signal is a tech-
nically challenging endeavor and acquiring electrophysiological
signals from human DMN structures faces multiple challenges.
So what have electrophysiological approaches taught us about the
neural correlates of DMN and what are their current limitations?
Let us address this question first of all from the perspective of
animal studies. A highly interesting study by Hayden et al. (2009)
has reported significant task-related suppression of neuronal fir-
ing rate in macaque PCC a region considered to be a prominent
component of DMN. As in previous reports of task-related BOLD
deactivation, the reduction in neuronal firing in macaque PCC
occurred during task performance and was followed by a return to
higher baseline levels between trials. Most importantly, the firing-
rate suppression reported by Hayden et al. (2009) was predictive
of performance (errors and reaction times). Despite the fact that
the BOLD signal was not recorded in this study, the authors argue
that the relationship to fMRI findings is strengthened by the fact
that the activity of lateral intraparietal (LIP) neurons was enhanced
during the task, i.e., LIP showed the inverse effect observed in PCC.
Such non-human primate studies hold the potential to advance our
understanding of the neural correlates of the DMN. The degree to
which animal data can be generalized to humans may be restricted
by the limits of anatomo-functional cross-species comparison.
However, a more serious limitation to the study of DMN function
with animal recordings arises if we want to test specific hypoth-
eses about its putative role in mediating internally oriented mental
processes (e.g., self-cognition, episodic and prospective memory,
covert speech, etc.). Nevertheless, by contrast to electrophysiology,
imaging studies in anesthetized animal can provide insights into
the large-scale functional architecture of the DMN. As a matter of
fact, the detection of spontaneous BOLD correlations (typical of
resting state networks) in anesthetized monkeys (Vincent et al.,
2007) has direct implications on the ongoing debate on the cor-
relations between DMN connectivity and levels of consciousness
(Greicius et al., 2008).
non-InvasIve InvestIgatIon of dMn wIth eeg
Non-invasive electrophysiological techniques such as EEG or MEG
provide whole-head coverage at a high temporal (millisecond-range)
resolution and thus carry the potential to unravel the fine-temporal
dynamics of the brain’s intrinsic activity. Several EEG studies suggest
various relationships between resting state networks and multiple
spatial and spectral properties of the EEG. In particular, combin-
ing EEG and fMRI recordings provides a powerful framework for
the comparison between various electrophysiological components
and the BOLD responses during resting states (Laufs et al., 2003;
Debener et al., 2005; Mantini et al., 2007; Laufs, 2008; Scheeringa
et al., 2008; Jann et al., 2009). A first step toward assessing the EEG
correlates of DMN is to decipher the way non-invasive surface
measurements relate to the BOLD response. This question has
been addressed by correlating BOLD with EEG power in various
frequency bands. For instance, the BOLD signal has been shown to
correlate negatively with EEG power in the alpha band (Goldman
et al., 2002; Moosmann et al., 2003) and a recent study found positive
correlations between BOLD and MEG high gamma power (Zumer
et al., 2010). Numerous studies found correlations between DMN
activity patterns and the power in traditional EEG frequency bands
including theta (4–7 Hz), alpha (8–12 Hz), beta (13–30Hz), and
low-gamma (30–50 Hz) bands (Laufs et al., 2003; Mantini et al.,
2007; Chen et al., 2008; Scheeringa et al., 2008; Jann et al., 2009). In
contrast, putative links between BOLD responses and components
in the lower end of the EEG frequency spectrum, namely delta oscil-
lations (1–4 Hz), slow cortical potentials (SCPs), and infra-slow
fluctuations (0.01–0.1 Hz) have proven harder to establish (Khader
et al., 2008). Infra-slow EEG fluctuations (e.g., Monto et al., 2008)
and SCPs have been proposed to reflect slow fluctuations in fMRI
spontaneous activity (He and Raichle, 2009).
More generally, attempts to use non-invasive electrophysiological
methods such as EEG or MEG to elucidate the neural mechanisms
of intrinsic brain networks are challenged by two main limitations:
the poor spatial resolution of MEG/EEG and the relatively lim-
ited signal-to-noise ratio of surface measurements especially with
regards to detecting higher frequency components of the signal,
namely the high gamma-band (∼60–200 Hz). Advanced MEG/EEG
source reconstruction techniques yield cortical activation maps
that are physiologically easier to interpret than sensor-level topog-
raphies (e.g., Baillet et al., 2001; Dalal et al., 2008). Nevertheless,
the estimation of deeper sources in MEG/EEG is less reliable than
the localization of activity from sources close to the sensors. This
could be a severe limitation when it comes to detecting activity
from deep regions of the default-mode such as the PCC. Moreover,
the fact that high-frequency activity in the gamma-range is less
easily detected with surface recordings (Pfurtscheller and Cooper,
1975; Jerbi et al., 2009a) might also be considered a further obsta-
cle in this endeavor. As mentioned earlier, high gamma activity is
an important target signal for DMN investigations because of its
putative coupling with the BOLD signal (Logothetis et al., 2001;
Niessing et al., 2005; Nir et al., 2007; Lachaux et al., 2008). Recently
a number of studies have shown that MEG and EEG can, under
certain circumstances, be used to detect task-related activity above
60 Hz (e.g., Ball et al., 2008; Cheyne et al., 2008; Dalal et al., 2008,
2009; Tecchio et al., 2008; Waldert et al., 2008; Van Der Werf et al.,
2010; Zumer et al., 2010).

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Jerbi et al. Intracerebral EEG in default-mode networks
Figure 1A). This represents a major advantage when it comes to
the investigation of DMN structures such as PCC, MPFC that are
rarely probed by other electrophysiological techniques. Nevertheless,
ECoG does occasionally involve placement of electrode strips on
the surface of the medial wall and could in these cases be used for
DMN investigations. Previous ECoG findings point toward SCP
and gamma-range power as two types of electrical signals that dis-
play correlation patterns that mirror those observed in spontaneous
fMRI BOLD signals (He et al., 2008; He and Raichle, 2009).
detectIon of task-related neural deactIvatIon
wIth seeg
Given that a defining property of DMN is its task-related deactiva-
tion (i.e., negative BOLD response) during exteroceptive goal-di-
rected behavior, the natural question that comes to mind is whether
the DMN also displays task-related deactivations detectable in the
electrophysiological signal. Robust SEEG deactivations in such
regions may represent a putative neural correlate of task-related
BOLD deactivations.
In the following, we further make the case for SEEG recordings
as a particularly promising approach to study DMN deactivation,
by providing samples of direct recordings from two regions of the
Intracerebral recordIngs: clInIcal settIng and
technIcal features
Fortunately, access to high resolution spatial and temporal signals
through direct recordings from the human brain is sometimes pos-
sible in some clinical settings. Various types of invasive recordings
from cortical and subcortical structures are used in conjunction with
several clinical conditions (Engel et al., 2005). The surgical treatment
of drug-resistant epilepsy requires intracranial recordings in multi-
ple brain areas in order to localize the epileptic tissue (Kahane et al.,
2004, 2006). During this pre-surgical evaluation period, electrical
cortical stimulation and task-related functional mapping (Crone
et al., 2006; Jerbi et al., 2009a) are used to map out healthy and
eloquent cortex that should be spared during surgery. The two main
invasive recording techniques used in the field of epilepsy consist
of grid electrode placement over the cortex, a procedure known as
Electrocorticography (ECoG) and of multi-lead depth electrode
implantation known as SEEG (reviewed in Jerbi et al., 2009a). From
the point of view of functional mapping, a major advantage of the
multi-lead depth electrode implantation used in SEEG is the fact
that the recordings are not limited to the cortical surface. An SEEG
electrode consists of upto 15 contacts that probe multiple sites from
lateral structures all the way through to medial wall regions (see
FiGurE 1 | intracerebral stereotactic-EEG (SEEG) setting and cognitive
paradigms. (A) SEEG recording procedure showing a typical implantation
sketch based on a post-implantation X-ray scan (left panel) and a typical SEEG
depth electrode array (right panel). See Materials and Methods in
Supplementary Material for a detailed description of SEEG data acquisition.
(B) Reading task (left panel): subjects were presented with stimuli that
consisted of words and pseudowords. To ensure both categories were read, the
subjects had to indicate whether the word represented a living or non-living item
and, in the case of a pseudoword, whether it was made up of two syllables or
not. Navon task (right panel): subjects were presented with a large letter (“global
letter”), which was itself composed of repeated smaller letters (“local letter”).
The global and local letters could be either the letter “H” or the letter “S,” leading
to four types of global-to-local stimulus configurations. At the beginning of a trial
the subjects were cued to focus either on the global or the local level of the
upcoming stimulus. The task consisted in identifying, as fast as possible, the
stimulus letter at the level indicated by the cue. In both experiments the stimuli
were presented for a maximum of 3 s and disappeared as soon as the
subject responded.

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Jerbi et al. Intracerebral EEG in default-mode networks
participated in routine localizer experiments including a series of
attention-demanding tasks such as a classical “global versus local”
attention task (Navon, 1977) and a “word versus pseudoword” read-
ing task (Figure 1B). Subsequent data analysis was strictly restricted
to recording sites that showed no pathological activity. Figures 2
and 3 show results obtained with data recorded directly in PCC
and MPFC respectively. Using time-frequency analysis of bipolar
human DMN: the posterior cingulate cortex (PCC) and the MPFC.
We then report, for the sake of comparison, electrode data acquired
in the same subjects but from sites not assumed to be part of the
DMN. The data presented here were acquired from subjects with
SEEG depth electrodes implanted at multiple locations of the brain
as part of their pre-surgical evaluation period (see supplementary
material for details of the experimental procedures). The subjects
FiGurE 2 | Task-related gamma-band power suppressions in posterior
cingulate cortex (PCC). (A) Anatomical location of the SEEG recording site in
PCC of subject 1 (Talairach coordinates: x = 10, y = −38, z = 35). (B) Time-
frequency representations of PCC activity for the reading (left panel) and Navon
(right panel) tasks. Values represent task-related power modulations across time
and frequency, compared with average baseline activity during fixation
(Wilcoxon test). In both tasks strong decreases in PCC gamma power were
found (indicated by negative Z values). (C) Time profile of percent power
decreases (below baseline levels) at this electrode site for the conditions of each
task (Left: Reading, Right: Navon). All conditions show significant gamma
suppression in this region of DMN. The red/blue horizontal lines indicate
statistical significance (p < 0.05) based on a Wilcoxon signed rank test and
confidence intervals represent ± s.e.m. (See Materials and Methods in
Supplementary Material for more details).

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Jerbi et al. Intracerebral EEG in default-mode networks
ditions in both tasks. In addition to this task non-specificity, the
fact that these intracranially recorded gamma suppressions occur
in two regions known to be part of the DMN is in agreement with a
putative link between SEEG gamma power deactivation and BOLD
deactivation reported in the human fMRI DMN literature (Raichle
et al., 2001). Importantly, this view is further supported by the fact
that task-related gamma suppressions were not ubiquitous across
recording sites. Applying the same spectral analysis to the data
acquired in the same subjects but at other recording sites which
recordings in these areas we derive task-related maps that depict
modulations of power across time and frequency, as compared to
baseline levels (methods as in Jerbi et al., 2009a). Strikingly, com-
pared to pre-stimulus baseline levels (during which subject simply
fixate a cross), the Reading and the Navon tasks were associated with
strong suppressions of power in the high gamma (∼50–150 Hz)
observed in both PCC and MPFC sites (Figures 2B and 3B). Most
importantly, as shown in Figures 2C and 3C, the gamma-band
deactivations were systematically present for all experimental con-
FiGurE 3 | Task-related gamma-band power suppressions in Medial
Prefrontal Cortex (MPFC). (A) Anatomical location of the SEEG recording site
in MPFC of subject 2 (Talairach coordinates: x = −4, y = −46, z = −3).
(B) Time-frequency representations of MPFC activity for the reading (left panel)
and Navon (right panel) tasks. Strong decreases in MPFC gamma power were
found in both tasks. (C) Time profile of percent power decreases (below
baseline levels) at this electrode site for the conditions of each task (Left:
Reading, Right: Navon). As for PCC (Figure 2), gamma activity in MPFC is
significantly suppressed for all conditions. Display conventions and methods
used are identical to those of Figure.2.

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Jerbi et al. Intracerebral EEG in default-mode networks
key primary visual area (V1) (Shmuel et al., 2006). Task-related
decreases in high gamma power have also been reported with
intracerebral recordings in human V1 during processing of complex
visual stimuli (Lachaux et al., 2005). More generally, it is tempting to
ask whether the so-called task-positive networks and task-negative
networks revealed by the fMRI literature (Fox et al., 2005), are
spatially coincident with task-related gamma power enhancement
networks and task-related gamma power suppression networks
respectively. This view implies that DMN areas would exhibit less
gamma power during execution of attention-demanding tasks than
during resting baseline periods. Support for this hypothesis has
been reported in monkey PCC (Hayden et al., 2009). However, so
far, equivalent findings in humans have been scarce. The frequency
range of the high gamma-band (∼40–160 Hz) falls beyond the reach
of most EEG studies that have been performed so far with the aim
to assess the neural correlates of the DMN (e.g., Laufs et al., 2003;
Mantini et al., 2007). This limitation, as well as source localization
uncertainty (i.e., limited spatial resolution), can be in part overcome
by the high signal-to-noise ratio and spatio-temporal resolution of
intracerebral recordings. Although there have been a few reports of
task-related gamma deactivations in some specific components of
are not part of DMN shows the inverse effect, i.e., task-related
increases in the gamma-range and in both experiments (Figure 4).
This was the case for recording sites in the fusiform gyrus (S1)
and in the insula (S2). Moreover, it is noteworthy that the time
course of gamma power suppression (Figures 2C and 3C) sug-
gests that significant gamma-band deactivation starts on average
around 250 ms in the PCC and then around 500 ms in MPFC.
The deactivations are sustained in time lasting beyond 1000 ms
post stimulus presentation. However, the data presented here are
based only on two subjects. Clearly, more subjects will be needed
to reliably estimate the temporal dynamics of gamma suppression
and its relationship to behavior.
dIscussIon and perspectIves
A number of studies have established a tight relationship between
BOLD activations and task-related increases in the gamma-range of
the LFP signal in the same areas (Logothetis et al., 2001; Mukamel
et al., 2005; Niessing et al., 2005; Lachaux et al., 2007a; Nir et al.,
2007). Such observations lead to the corollary prediction that nega-
tive BOLD activity may also be correlated with gamma-band power
suppressions. This has been shown to be indeed the case in mon-
FiGurE 4 | Task-related gamma-band power increases. (A) Subject 1:
time-frequency representations during Reading (condition: pseudoword) (left
panel) and Navon (condition: local) (central panel) tasks for an electrode located
in the fusiform gyrus (right panel, Tailarach coordinates: x = 46, y = −43, z = −13).
Strong increases in gamma power were found in both tasks, in contrast to the
decreases found for the same subject in PCC (see Figure 2). (B) Subject 2:
time-frequency representations during Reading (condition: pseudoword) (left
panel) and Navon (condition: local) (central panel) tasks for an electrode located
in the fusiform gyrus (right panel, Tailarach coordinates: x = −26, y = 16, z = 8).
The task-related enhancement of gamma power found here in both tasks is
concurrent with task-related suppression in MPFC in the same subject (see
Figure 3). Note that the full temporal profile of task-related gamma increases for
the two conditions of each task for both subjects is provided in Figure S1 in
Supplementary Material.

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Jerbi et al. Intracerebral EEG in default-mode networks
networks (Debener et al., 2005; Mantini et al., 2007; Scheeringa
et al., 2008). The advent of simultaneous fMRI and intracranial
EEG in the near future will move the multimodal investigations in
this field a major step forward (Carmichael et al., 2010). However,
the important impact that intracerebral recordings are expected
to have on the study of the electrophysiological correlates of the
DMN does not lessen the need for non-invasive electrophysiologi-
cal approaches for this endeavor. Indeed, the precision of EEG and
MEG will continue to improve thanks to the use of advanced source
localization techniques (e.g., Baillet et al., 2001; Gross et al., 2001;
Dalal et al., 2008) and signal decomposition tools such as independ-
ent component analysis (Mantini et al., 2007). Besides, given the
putatively prominent role of high-frequency activity, improving the
sensitivity of surface recordings to high gamma activity will be a
critical issue (Jerbi et al., 2009a). Results of our recent study using
simultaneously acquired MEG and intracerebral EEG data suggest
that source imaging can indeed enhance our ability to detect the
cortical generator of gamma activity with MEG (Dalal et al., 2009).
In addition, several studies have shown that EEG signals can be
contaminated by signals in the gamma-range that originate from
eye muscles rather than cortical tissue (Reva and Aftanas, 2004;
Trujillo et al., 2005; Yuval-Greenberg et al., 2008). Therefore, ruling
out the effect of such saccade-related artifacts is a prerequisite for
a reliable assessment of cortical gamma-band power using non-
invasive techniques and constitutes an important topic for future
research. As a matter of fact, we have recently shown that gamma-
range saccadic artifacts might, in some cases, even contaminate
intracranial EEG recordings (Jerbi et al., 2009b).
Investigating the connectivity properties of intrinsic brain net-
works is clearly a topic where the input from electrophysiological
recordings will be critical. Correlation and anti-correlation phe-
nomena appear to be fundamental concepts surrounding resting
state networks (Fox et al., 2005). Much still needs to be learned
about how connectivity properties revealed with fMRI relate to
brain-wide neural interactions revealed by MEG, EEG, and iEEG.
Slow fluctuations in baseline activity observed with fMRI may
be indirectly linked to higher frequency amplitude modulations
via slow-to-fast cross-frequency interactions (Jensen and Colgin,
2007). Previous studies have shown that SCP can modulate higher
frequency EEG activity (Vanhatalo et al., 2004), but also behavio-
ral performance (Birbaumer et al., 1990; He et al., 2008; He and
Raichle, 2009). Interestingly, in a recent study, Monto et al. (2008)
used infra-slow EEG to provide evidence for very slow EEG fluctua-
tions (∼0.01 Hz) that were correlated with slow perceptual perform-
ance modulations. The authors reported phase-amplitude coupling
between these slow fluctuations and patterns of faster cortical oscil-
lations. The use of within and cross-frequency coupling measures
to assess local and long-range interactions in scalp-EEG, MEG, and
intracranial EEG data is a rapidly growing field of research, yet its
potential contribution to understanding the mechanisms of DMN
is still largely underexploited.
Real-time monitoring of the electrophysiological activity within
the DMN may open up the exciting perspective of performing
online monitoring of vigilance or attention. What’s more, real-
time monitoring of DMN neuronal populations may allow for
novel experimental designs with stimulation parameters that
adapt online to the subjects state. While several challenges still
human DMN using intracerebral recordings (Lachaux et al., 2008;
Miller et al., 2009; Jung et al., 2010), an exhaustive investigation of
all DMN structures and their fine-temporal dynamics using such
techniques is hard to achieve and is still lacking.
The SEEG data presented here provides evidence for suppres-
sion of high-frequency activity in the human PCC and MPFC dur-
ing task-engagement. This gamma-band deactivation (40–150 Hz)
was task-related and occurred systematically across all experimental
conditions. It is noteworthy that the Navon task we implemented
(local versus global visual processing) induced significant gamma
power suppressions in PCC, a region that has previously been
shown to display negative BOLD in responses to the same para-
digm performed with fMRI (Weissman et al., 2006). Remarkably,
the high gamma suppression, found in the DMN, co-occurred with
task-related enhancement outside the DMN (Figure 4). Elevated
gamma power in the fusiform gyrus and in the anterior insula may
reflect visual processing of the stimulus and intrinsic alertness activ-
ity respectively. Interestingly, increases in anterior insular gamma
activity could be related to its role as part of the putative core task-
set system (Dosenbach et al., 2006). Our observation of concurrent
positive and negative high gamma responses, outside and inside the
DMN respectively, is in line with the hypothesis that gamma modu-
lations represent an electrical correlate of BOLD signal modulations.
Critically, the population-level deactivation presented here extends
a number of electrophysiological studies of DMN deactivation (e.g.,
Hayden et al., 2009; Miller et al., 2009) and strongly argues against
the DMN being an epiphenomenon (Birn et al., 2008). Further stud-
ies across large populations of implanted patients are needed to
strengthen and fine-tune these physiological interpretations. The
illustrative data we report in PCC and MPFC highlight the potential
of SEEG recordings as a tool to investigate the neurophysiology of
DMN, and more generally speaking, of the resting state networks.
Our group is actively pursuing the detection of brain-wide spatial
distributions of gamma power decreases and increases in attention-
demanding tasks as well as the investigation of correlation patterns
within the involved networks (Ossandon et al., 2009).
More generally, if we assume that broad-band gamma power sup-
pressions observed in the default-mode areas reflect de facto neural
disengagement, then we should also expect a concurrent reduction
in local neuronal firing. An assessment of this hypothesis in the light
of the tight relationship between spiking activity and broad-band
gamma (Mukamel et al., 2005; Niessing et al., 2005; Manning et al.,
2009; Whittingstall and Logothetis, 2009), leads to the hypothesis
that default-mode areas may be characterized by task-related sup-
pression of neuronal firing during attentive states. Although there is
some recent evidence for this in monkey PCC (Hayden et al., 2009),
little is known about task-related modulations of spiking activity
specifically in default-mode structures of the human brain. This
is primarily due to the rarity of unit recordings in human cortex
and may change in the future if microelectrode recordings are used
more often in clinical settings to probe DMN structures. Until then,
various hypotheses about spike firing-rate modulations in human
DMN may be inferred indirectly from the analysis of the broad-band
gamma-range component of the EEG.
Furthermore, combining fMRI and EEG in simultaneous record-
ings will undoubtedly continue to provide unique insights into the
links between electrophysiological and BOLD signals in resting state

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Jerbi et al. Intracerebral EEG in default-mode networks
Cheyne, D., Bells, S., Ferrari, P., Gaetz,
W., and Bostan, A. C. (2008). Self-
paced movements induce high-
frequency gamma oscillations in
primary motor cortex. Neuroimage
42, 332–342.
Corbetta, M., and Shulman, G. L. (2002).
Control of goal-directed and stimulus-
driven attention in the brain. Nat. Rev.
Neurosci. 3, 201–215.
Crone, N. E., Sinai, A., and Korzeniewska,
A. (2006). High-frequency gamma
oscillations and human brain map-
ping with electrocorticography. Prog.
Brain Res. 159, 275–295.
Dalal, S. S., Baillet, S., Adam, C., Ducorps,
A., Schwartz, D., Jerbi, K., Bertrand,
O., Garnero, L., Martinerie, J., and
Lachaux, J.-P. (2009). Simultaneous
MEG and intracranial EEG recordings
during attentive reading. Neuroimage
45, 1289–1304.
Dalal, S. S., Guggisberg, A. G., Edwards, E.,
Sekihara, K., Findlay, A. M., Canolty, R.
T., Berger, M. S., Knight, R. T., Barbaro,
N. M., Kirsch, H. E., and Nagarajan, S.
S. (2008). Five-dimensional neuroim-
aging: localization of the time-fre-
quency dynamics of cortical activity.
Neuroimage 40, 1686–1700.
Debener, S., Ullsperger, M., Siegel, M.,
Fiehler, K., von Cramon, D. Y., and
Engel, A. K. (2005). Trial-by-trial
coupling of concurrent electroen-
cephalogram and functional mag-
netic resonance imaging identifies the
dynamics of performance monitoring.
J. Neurosci. 25, 11730–11737.
Dosenbach, N. U., Visscher, K. M., Palmer,
E. D., Miezin, F. M., Wenger, K. K.,
Kang, H. C., Burgund, E. D., Grimes,
A. L., Schlaggar, B. L., and Petersen,
S. E. (2006). A core system for the
implementation of task sets. Neuron
50, 799–812.
RefeRences
Anticevic, A., Repovs, G., Shulman, G.
L., and Barch, D. M. (2010). When
less is more: TPJ and default network
deactivation during encoding pre-
dicts working memory performance.
Neuroimage 49, 2638–2648.
Baillet, S., Mosher, J. C., and Leahy, R. M.
(2001). Electromagnetic brain map-
ping. IEEE Signal Process. Mag. 18,
14–30.
Ball, T., Demandt, E., Mutschler, I., Neitzel,
E., Mehring, C., Vogt, K., Aertsen, A.,
and Schulze-Bonhage, A. (2008).
Movement related activity in the
high gamma range of the human EEG.
Neuroimage 41, 302–310.
Birbaumer, N., Elbert, T., Canavan, A.
G., and Rockstroh, B. (1990). Slow
potentials of the cerebral cortex and
behavior. Physiol. Rev. 70, 1–41.
Birn, R. M., Murphy, K., and Bandettini,
P. A. (2008). The effect of respiration
variations on independent component
analysis results of resting state func-
tional connectivity. Hum. Brain Mapp.
29, 740–750.
Broyd, S. J., Demanuele, C., Debener, S.,
Helps, S. K., James, C. J., and Sonuga-
Barke, E. J. (2009). Default-mode brain
dysfunction in mental disorders: a
systematic review. Neurosci. Biobehav.
Rev. 33, 279–296.
Carmichael, D. W., Thornton, J. S.,
Rodionov, R., Thornton, R., McEvoy,
A. W., Ordidge, R. J., Allen, P. J., and
Lemieux, L. (2010). Feasibility of
simultaneous intracranial EEG-fMRI
in humans: a safety study. Neuroimage
49, 379–390.
Chen, A. C., Feng, W., Zhao, H., Yin, Y., and
Wang, P. (2008). EEG default mode
network in the human brain: spectral
regional field powers. Neuroimage 41,
561–574.
Engel, A. K., Moll, C. K., Fried, I., and
Ojemann, G. A. (2005). Invasive
recordings from the human brain:
clinical insights and beyond. Nat. Rev.
Neurosci. 6, 35–47.
Fox, M. D., Snyder, A. Z., Vincent, J. L.,
Corbetta, M., Van Essen, D. C., and
Raichle, M. E. (2005). The human
brain is intrinsically organized into
dynamic, anticorrelated functional
networks. Proc. Natl. Acad. Sci. U.S.A.
102, 9673–9678.
Goldman, R. I., Stern, J. M., Engel, J. Jr.,
and Cohen, M. S. (2002). Simultaneous
EEG and fMRI of the alpha rhythm.
Neuroreport 13, 2487–2492.
Greicius, M. (2008). Resting-state func-
tional connectivity in neuropsychiat-
ric disorders. Curr. Opin. Neurol. 21,
424–430.
Greicius, M. D., Kiviniemi, V., Tervonen,
O., Vainionpaa, V., Alahuhta, S., Reiss,
A. L., and Menon, V. (2008). Persistent
default-mode network connectivity
during light sedation. Hum. Brain
Mapp. 29, 839–847.
Greicius, M. D., Krasnow, B., Reiss, A.
L., and Menon, V. (2003). Functional
connectivity in the resting brain: a
network analysis of the default mode
hypothesis. Proc. Natl. Acad. Sci. U.S.A.
100, 253–258.
Gross, J., Kujala, J., Hamalainen, M.,
Timmermann, L., Schnitzler, A., and
Salmelin, R. (2001). Dynamic imag-
ing of coherent sources: studying
neural interactions in the human
brain. Proc. Natl. Acad. Sci. U.S.A.
98, 694–699.
Gusnard, D. A., Akbudak, E., Shulman, G.
L., and Raichle, M. E. (2001). Medial
prefrontal cortex and self-referential
mental activity: relation to a default
mode of brain function. Proc. Natl.
Acad. Sci. U.S.A. 98, 4259–4264.
Gusnard, D. A., and Raichle, M. E. (2001).
Searching for a baseline: functional
imaging and the resting human brain.
Nat. Rev. Neurosci. 2, 685–694.
Hayden, B. Y., Smith, D. V., and Platt, M.
L. (2009). Electrophysiological cor-
relates of default-mode processing
in macaque posterior cingulate cor-
tex. Proc. Natl. Acad. Sci. U.S.A. 106,
5948–5953.
He, B. J., and Raichle, M. E. (2009). The
fMRI signal, slow cortical potential
and consciousness. Trends Cogn. Sci.
(Regul. Ed.) 13, 302–309.
He, B. J., Snyder, A. Z., Zempel, J. M.,
Smyth, M. D., and Raichle, M. E.
(2008). Electrophysiological correlates
of the brain’s intrinsic large-scale func-
tional architecture. Proc. Natl. Acad.
Sci. U.S.A. 105, 16039–16044.
Jann, K., Dierks, T., Boesch, C., Kottlow,
M., Strik, W., and Koenig, T. (2009).
BOLD correlates of EEG alpha phase-
locking and the fMRI default mode
network. Neuroimage 45, 903–916.
Jensen, O., and Colgin, L. L. (2007). Cross-
frequency coupling between neuronal
oscillations. Trends Cogn. Sci. (Regul.
Ed.) 11, 267–269.
Jensen, O., Kaiser, J., and Lachaux, J. P.
(2007). Human gamma-frequency
oscillations associated with attention
and memory. Trends Neurosci. 30,
317–324.
Jerbi, K., Ossandon, T., Hamame, C.
M., Senova, S., Dalal, S. S., Jung, J.,
Minotti, L., Bertrand, O., Berthoz, A.,
Kahane, P., and Lachaux, J. P. (2009a).
Task-related gamma-band dynamics
from an intracerebral perspective:
review and implications for surface
EEG and MEG. Hum. Brain Mapp.
30, 1758–1771.
Jerbi, K., Freyermuth, S., Dalal, S., Kahane,
P., Bertrand, O., Berthoz, A., and
need to be dealt with in order to achieve this with non-invasive
measurements, real-time monitoring of high gamma activity in
humans is readily achieved using depth SEEG recordings. We have
implemented an online system for the estimation and visualization
of power modulations in various frequency bands (including the
high gamma-band) in conjunction with depth recording in epilepsy
patients (Lachaux et al., 2007b). In addition to performing online
functional mapping, this interface (dubbed Brain TV) could be
seen as a window to the patient’s ongoing and spontaneous brain
activity. Therefore, with electrodes implanted in DMN areas, the
Brain TV set-up could be used to monitor real-time modulations
of power across the EEG spectrum during various states such as
mind wandering or focused attention. Besides, the online monitor-
ing of DMN activity could be beneficial to investigations into the
functional role of DMN. For example, it may be possible to define
online the timing of target or distractor stimulus presentation to
correspond to specific states of the DMN. Ultimately, performing
attention monitoring in real-time and non-invasively could have
numerous clinical applications. Overall, a better understanding
of the neural underpinnings and correlation dynamics within the
DMN and, more globally, within resting state networks could have
strong implications on the development of novel diagnostic and
rehabilitation solutions for numerous neurological impairments.
Acknowledgments
Support provided in part by the Fondation pour la Recherche
Médicale (FRM) to Karim Jerbi, BrainSync FP7 European Project
(Grant HEALTH-F2-2008-200728) to Juan R. Vidal, Marie Curie
Fellowship (FP7-221097) from the European Commission to Sarang
S. Dalal, and a doctoral fellowship from the Ministère de l’Education
Nationale et la Recherche (France) to Tomas Ossandon.
supplementARy mAteRiAl
The Supplementary Material for this article can be found online
at http://www.frontiersin.org/neuroscience/systemsneuroscience/
paper/10.3389/fnsys.2010.00027/

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Frontiers in Systems Neuroscience www.frontiersin.org June 2010 | Volume 4 | Article 27 | 9
Jerbi et al. Intracerebral EEG in default-mode networks
parietal cortex during reach planning.
J. Neurosci. 30, 1402–1412.
Vanhatalo, S., Palva,J. M., Holmes, M. D.,
Miller, J. W., Voipio, J., and Kaila, K.
(2004). Infraslow oscillations modu-
late excitability and interictal epileptic
activity in the human cortex during
sleep. Proc. Natl. Acad. Sci. U.S.A. 101,
5053–5057.
Vincent, J. L., Patel, G. H., Fox, M. D.,
Snyder, A. Z., Baker, J. T., Van Essen,
D. C., Zempel, J. M., Snyder, L. H.,
Corbetta, M., and Raichle, M. E.
(2007). Intrinsic functional archi-
tecture in the anaesthetized monkey
brain. Nature 447, 83–86.
Waldert, S., Preissl, H., Demandt, E.,
Braun, C., Birbaumer, N., Aertsen, A.,
and Mehring, C. (2008). Hand move-
ment direction decoded from MEG
and EEG. J. Neurosci. 28, 1000–1008.
Weissman, D. H., Roberts, K. C., Visscher,
K. M., and Woldorff, M. G. (2006). The
neural bases of momentary lapses in
attention. Nat. Neurosci. 9, 971–978.
Whittingstall, K., and Logothetis, N. K.
(2009). Frequency-band coupling in
surface EEG reflects spiking activity
in monkey visual cortex. Neuron 64,
281–289.
Yuval-Greenberg, S., Tomer, O., Keren,
A. S., Nelken, I., and Deouell, L. Y.
(2008). Transient induced gamma-
band response in EEG as a manifes-
tation of miniature saccades. Neuron
58, 429–441.
Zumer, J. M., Brookes, M. J., Stevenson,
C. M., Francis, S. T., and Morris, P.
G. (2010). Relating BOLD fMRI and
neural oscillations through convolu-
tion and optimal linear weighting.
Neuroimage 49, 1479–1489.
Conflict of Interest Statement: The
authors declare that the research was
conducted in the absence of any com-
mercial or financial relationships that
could be construed as a potential conflict
of interest.
Received: 09 February 2010; paper pending
published: 28 March 2010; accepted: 04 June
2010; published online: 28 June 2010.
Citation: Jerbi K, Vidal JR, Ossandon T,
Dalal SS, Jung J, Hoffmann D, Minotti L,
Bertrand O, Kahane P and Lachaux J-P
(2010) Exploring the electrophysiological
correlates of the default-mode network with
intracerebral EEG. Front. Syst. Neurosci.
4:27. doi: 10.3389/fnsys.2010.00027
Copyright © 2010 Jerbi, Vidal, Ossandon,
Dalal, Jung, Hoffmann, Minotti, Bertrand,
Kahane and Lachaux. This is an open-
access article subject to an exclusive license
agreement between the authors and the
Frontiers Research Foundation, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the
original authors and source are credited.
neuronal firing rate, gamma LFP, and
BOLD fMRI is related to interneu-
ronal correlations. Curr. Biol. 17,
1275–1285.
Ossandon, T., Jerbi, K., Bayle, D., Bertrand,
O., Kahane, P., and Lachaux, J.-P.
(2009). Task-related gamma band
suppressions: a plausible electro-
physiological correlate of the default-
mode network? Soc. Neurosci. Abstract
2009 (Program No 804.10) SfN 2009,
Chicago, IL.
Pfurtscheller, G., and Cooper, R. (1975).
Frequency dependence of the trans-
mission of the EEG from cortex
to scalp. Electroencephalogr. Clin.
Neurophysiol. 38, 93–96.
Raichle, M. E., MacLeod, A. M., Snyder, A.
Z., Powers, W. J., Gusnard, D. A., and
Shulman, G. L. (2001). A default mode
of brain function. Proc. Natl. Acad. Sci.
U.S.A. 98, 676–682.
Reva, N. V., and Aftanas, L. I. (2004). The
coincidence between late nonphase-
locked gamma synchronization
response and saccadic eye movements.
Int. J. Psychophysiol. 51, 215–222.
Scheeringa, R., Bastiaansen, M. C.,
Petersson, K. M., Oostenveld, R.,
Norris, D. G., and Hagoort, P. (2008).
Frontal theta EEG activity cor-
relates negatively with the default
mode network in resting state. Int. J.
Psychophysiol. 67, 242–251.
Shmuel, A., Augath, M., Oeltermann, A.,
and Logothetis, N. K. (2006). Negative
functional MRI response correlates
with decreases in neuronal activity in
monkey visual area V1. Nat. Neurosci.
9, 569–577.
Shulman, G. L., Astafiev, S. V., McAvoy,
M. P., d’Avossa, G., and Corbetta, M.
(2007). Right TPJ deactivation during
visual search: functional significance
and support for a filter hypothesis.
Cereb. Cortex 17, 2625–2633.
Tecchio, F., Zappasodi, F., Porcaro, C.,
Barbati, G., Assenza, G., Salustri,
C., and Rossini, P. M. (2008). High-
gamma band activity of primary
hand cortical areas: a sensorimotor
feedback efficiency index. Neuroimage
40, 256–264.
Trujillo, L. T., Peterson, M. A., Kaszniak, A.
W., and Allen, J. J. (2005). EEG phase
synchrony differences across visual
perception conditions may depend on
recording and analysis methods. Clin.
Neurophysiol. 116, 172–189.
Uddin, L. Q., Kelly, A. M., Biswal, B. B.,
Xavier Castellanos, F., and Milham,
M. P. (2009). Functional connectiv-
ity of default mode network com-
ponents: correlation, anticorrelation,
and causality. Hum. Brain Mapp. 30,
625–637.
Van Der Werf, J., Jensen, O., Fries, P., and
Medendorp, W. P. (2010). Neuronal
synchronization in human posterior
of attentional and cognitive default
modes in spontaneous brain activity
fluctuations at rest. Proc. Natl. Acad.
Sci. U.S.A. 100, 11053–11058.
Logothetis, N. K., Pauls, J., Augath, M.,
Trinath, T., and Oeltermann, A. (2001).
Neurophysiological investigation of
the basis of the fMRI signal. Nature
412, 150–157.
Mainy, N., Jung, J., Baciu, M., Kahane,
P., Schoendorff, B., Minotti, L.,
Hoffmann, D., Bertrand, O., and
Lachaux, J. P. (2008). Cortical dynam-
ics of word recognition. Hum. Brain
Mapp. 29, 1215–1230.
Manning, J. R., Jacobs, J., Fried, I., and
Kahana, M. J. (2009). Broadband shifts
in local field potential power spectra
are correlated with single-neuron
spiking in humans. J. Neurosci. 29,
13613–13620.
Mantini, D., Perrucci, M. G., Del Gratta,
C., Romani, G. L., and Corbetta, M.
(2007). Electrophysiological signa-
tures of resting state networks in the
human brain. Proc. Natl. Acad. Sci.
U.S.A. 104, 13170–13175.
Mason, M. F., Norton, M. I., Van Horn, J.
D., Wegner, D. M., Grafton, S. T., and
Macrae, C. N. (2007). Wandering
minds: the default network and
stimulus-independent thought.
Science 315, 393–395.
Miller, K. J., Weaver, K. E., and Ojemann, J.
G. (2009). Direct electrophysiological
measurement of human default net-
work areas. Proc. Natl. Acad. Sci. U.S.A.
106, 12174–12177.
Monto, S., Palva, S., Voipio, J., and Palva,
J. M. (2008). Very slow EEG fluctua-
tions predict the dynamics of stimulus
detection and oscillation amplitudes in
humans. J. Neurosci. 28, 8268–8272.
Moosmann, M., Ritter, P., Krastel, I.,
Brink, A., Thees, S., Blankenburg, F.,
Taskin, B., Obrig, H., and Villringer, A.
(2003). Correlates of alpha rhythm in
functional magnetic resonance imag-
ing and near infrared spectroscopy.
Neuroimage 20, 145–158.
Mukamel, R., Gelbard, H., Arieli, A.,
Hasson, U., Fried, I., and Malach, R.
(2005). Coupling between neuronal
firing, field potentials, and FMRI in
human auditory cortex. Science 309,
951–954.
Navon, D. (1977). Forest before trees:
the precedence of global features in
visual perception. Cogn. Psychol. 9,
353–383.
Niessing, J., Ebisch, B., Schmidt, K. E.,
Niessing, M., Singer, W., and Galuske,
R. A. (2005). Hemodynamic signals
correlate tightly with synchronized
gamma oscillations. Science 309,
948–951.
Nir, Y., Fisch, L., Mukamel, R., Gelbard-
Sagiv, H., Arieli, A., Fried, I., and
Malach, R. (2007). Coupling between
Lachaux, J. P. (2009b). Saccade related
gamma-band activity in intracerebral
EEG: dissociating neural from ocu-
lar muscle activity. Brain Topogr. 22,
18–23.
Jung, J., Jerbi, K., Ossandon, T., Ryvlin,
P., Isnard, J., Bertrand, O., Guénot,
M., Mauguière, F., and Lachaux, J. P.
(2010). Brain responses to success and
failure: direct recordings from human
cerebral cortex. Hum. Brain Mapp. (in
press) doi: 10.1002/hbm.20930.
Kahane, P., Landre, E., Minotti, L.,
Francione, S., and Ryvlin, P. (2006).
The Bancaud and Talairach view on
the epileptogenic zone: a working
hypothesis. Epileptic Disord. 8(Suppl.
2), S16–S26.
Kahane, P., Minotti, L., Hoffmann, D.,
Lachaux, J., and Ryvlin, P. (2004).
“Invasive EEG in the definition of
the seizure onset zone: depth elec-
trodes,” in Handbook of Clinical
Neurophysiology. Pre-Surgical
Assessment of the Epilepsies with Clinical
Neurophysiology and Functional
Neuroimaging, eds F. Rosenow and
H. O. Lüders, (Amsterdam, Boston:
Elsevier Science), 109–133.
Khader, P., Schicke, T., Röder, B., and
Rösler, F. (2008). On the relationship
between slow cortical potentials, and
BOLD signal changes in humans. Int.
J. Psychophysiol. 67, 252–261.
Lachaux, J. P., Fonlupt, P., Kahane, P.,
Minotti, L., Hoffmann, D., Bertrand,
O., and Baciu, M. (2007a). Relationship
between task-related gamma oscillations
and BOLD signal: new insights from
combined fMRI and intracranial EEG.
Hum. Brain Mapp. 28, 1368–1375.
Lachaux, J. P., Jerbi, K., Bertrand, O.,
Minotti, L., Hoffmann, D., Schoendorff,
B., and Kahane, P. (2007b). A blueprint
for real-time functional mapping via
human intracranial recordings. PLoS
ONE 2, e1094. doi: 10.1371/journal.
pone.0001094.
Lachaux, J. P., George, N., Tallon-Baudry,
C., Martinerie, J., Hugueville, L.,
Minotti, L., Kahane, P., and Renault, B.
(2005). The many faces of the gamma
band response to complex visual stim-
uli. Neuroimage 25, 491–501.
Lachaux, J. P., Jung, J., Mainy, N., Dreher, J.
C., Bertrand, O., Baciu, M., Minotti, L.,
Hoffmann, D., and Kahane, P. (2008).
Silence is golden: transient neural
deactivation in the prefrontal cortex
during attentive reading. Cereb. Cortex
18, 443–450.
Laufs, H. (2008). Endogenous brain oscil-
lations and related networks detected
by surface EEG-combined fMRI.
Hum. Brain Mapp. 29, 762–769.
Laufs, H., Krakow, K., Sterzer, P., Eger,
E., Beyerle, A., Salek-Haddadi,
A., and Kleinschmidt, A. (2003).
Electroencephalographic signatures