Hindawi Publishing Corporation
Epilepsy Research and Treatment
Volume 2012, Article ID 385626, 10 pages
doi:10.1155/2012/385626
Review Article
Spontaneous EEG-Functional MRI in Mesial Temporal Lobe
Epilepsy: Implications for the Neural Correlates of Consciousness
Zheng Wang,1 Loretta Norton,2 R. Matthew Hutchison,3
John R. Ives,4 and Seyed M. Mirsattari2, 4, 5
1 Department
of Psychology, Vanderbilt University, Nashville, 37211 TN, USA
of Psychology, University of Western Ontario, London, ON, Canada N6A 5A5
3 Department of Physiology and Pharmacology, University of Western Ontario, London, ON, Canada N6A 5A5
4 Department of Clinical Neurological Sciences, University of Western Ontario, London, ON, Canada N6A 5A5
5 Department of Medical Biophysics, University of Western Ontario, London, ON, Canada N6A 5A5
2 Department
Correspondence should be addressed to Seyed M. Mirsattari, smirsat2@uwo.ca
Received 20 September 2011; Revised 21 November 2011; Accepted 19 December 2011
Academic Editor: Warren T. Blume
Copyright © 2012 Zheng Wang et al. This is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
The combination of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) has been shown to
have great potential for providing a greater understanding of normal and diseased states in both human and animal studies.
Simultaneous EEG-fMRI is particularly well suited for the study of epilepsy in that it may reveal the neurobiology of ictal and
interictal epileptiform discharges and noninvasively localize epileptogenic foci. Spontaneous, coherent fluctuations of neuronal
activity and the coupled hemodynamic responses have also been shown to provide diagnostic markers of disease, extending
our understanding of intrinsically structured ongoing brain activity. Following a short summary of the hardware and software
development of simultaneous EEG-fMRI, this paper reviews a unified framework of integrating neuronal and hemodynamic
processes during epileptic seizures and discusses the role and impact of spontaneous activity in the mesial temporal lobe epilepsies
with particular emphasis on the neural and physiological correlates of consciousness.
1. Introduction
Temporal lobe epilepsy (TLE) manifests with partial seizures
whose semiology can reflect a dynamic interplay among
several anatomical divisions of the temporal lobe, in addition
to their sites of origin [1]. Approximately 20%–30% of TLE
patients are intractable to treatment with antiepileptic drugs
though they may still benefit from surgical resection of the
epileptogenic focus (EF) [2]. It is, therefore, essential to
localize the EF in patients with focal epilepsy and to discern
the large-scale cortical and subcortical networks involved in
seizure generation. This is best achieved with the use of simultaneous, multimodal techniques that are able to elucidate
complex functional relationships through converging or parallel operations. The development of electroencephalogram
(EEG) recording of epileptic patients inside a magnetic
resonance imaging (MRI) scanner was largely driven by the
necessity of localizing and delineating mesial or deeply originating EF electrically and metabolically during presurgical
evaluation [3–7]. The concurrent measures allow for the
monitoring of abnormal hypersynchronous events in the EF,
as well as the full-brain coverage of variations in blood flow
and oxygenation in response to epileptic discharges conducive to a wide range of research applications within epileptology. Simultaneous EEG-fMRI has the potential to become
a routine diagnostic test, complementing the diagnostic accuracy of clinical acumen and EEG to greatly improve medical and surgical managements of patients with intractable
epilepsy, a goal that was set since its first introduction [4].
Investigation of spontaneous, low-frequency, and bloodoxygenation-level-dependent (BOLD) fluctuations measured by fMRI has revealed spatially organized and distributed brain networks [8–16]. The coherent patterns of
hemodynamic oscillations occur in the absence of any overt
2
task and as such are referred to as resting-state networks
(RSNs). RSNs are believed to be the epiphenomenon of
endogenous neural activity [17] and shaped by structural
connections [16, 18–21] though their functional significance
and neuronal correlate remain an active area of research
[22, 23]. Alterations of RSNs have recently been reported in
several conditions, including psychiatric and developmental
disorders [24], Alzheimer’s disease [25], schizophrenia [26],
coma [14], and epilepsy [27–30], revealing changes in connectivity between network nodes. By contrast, spontaneous
neuronal rhythm measured using EEG evince endogenous
patterns of connectivity and synchronicity underlying states
of attention, perception, and consciousness [31]. A comprehensive understanding of the functional network dynamics
related to the electrical activity recorded from the EF during
ictal and interictal epileptiform discharges (IEDs) remains
elusive [32–35]. However, empirical evidence does indicate
disruption of the normal distributed networks; temporal
lobe IEDs of patients with complex partial seizures secondary
to TLE have been reported to affect the activity in regions
comprising the default-mode RSN (discussed below) [36].
Therefore, spontaneous neural activity and its associated
hemodynamic manifestation (BOLD contrast) allow not
only for the noninvasive exploration of temporal and spatial
patterns of the brain but can also be envisaged as early
diagnostic or prognostic markers of disease states [14, 24, 37–
43]. This review places an emphasis on the fundamentals of
spontaneous EEG-fMRI and extends the unified framework
we previously proposed to a new avenue of exploring the
neuronal and physiological basis underlying intrinsic brain
activity. Only studies relevant to specific aspects of simultaneous EEG-fMRI in mesial TLE (mTLE) studies in the context of resting state including seizure-induced alterations of
the conscious state are discussed in here.
2. Emerging Issue in Simultaneous
EEG-fMRI Acquisition
The prevalent strategy is to continuously sample the interictal and ictal events while measuring the BOLD signal for
spontaneous EEG-fMRI study. Unfortunately, continuousrecording EEG on epileptic patients inside the magnet suffers
various kinds of noise problems such as muscle contraction
motions, eye movements, perspiration, 50/60 Hz power interference, and ballistocardiogram [44]. Motion-related artifacts of patients such as involuntary gross head movements,
swallowing, and coughing can also impose adverse consequence on both EEG signals and MRI images. As such, these
confounds make data acquisition in the high-field environment more difficult through deteriorating the shimming performance and causing unexpected susceptibility issues arising from magnetic field inhomogeneities at air-tissue interfaces. There have been a number of postprocessing methods developed to handle the epileptic electrophysiological
data by focusing on the focal spike density [45], phase coherence [32–35, 46], and noise separation [47]. But, many
of these artifacts including signal loss, image distortion, and
poor BOLD contrast-to-noise ratio can occur both focally
and globally in an unpredictable manner. They may also vary
Epilepsy Research and Treatment
slowly with physiological vitals, extending beyond straightforward image misregistration and invoking more advanced
mathematical techniques to mitigate these confounds. Independent component analysis (ICA) has emerged as a popular
data-driven method that has been used successfully to remove ballistocardiogram and other MRI-related artifacts
during the past decade [47–51]. For the purposes of reliably
detecting clean, discrete interictal and ictal spike events, noise
components either from statistically independent decomposition or adequately sampled recording in a specific reference
channel can be simply subtracted out from an unsaturated
raw EEG trace [6], or in more elaborate techniques such as
adaptive noise cancellation [44]. Nevertheless, ICA has been
successfully utilized to reveal intrinsic functional connectivity patterns of RSNs in human and animal fMRI data sets
[8, 11, 12, 52].
Besides the difficulties summarized above, the relation
between the detectable anatomical abnormalities, epileptic
neuronal activity (i.e., various types of epileptic seizures),
and functional MRI signal remains largely unclear [53, 54].
This elusive relationship also generates a strong motivation
for those seeking to improve the recording EEG inside the
MRI scanner. One of many emerging issues is to detect the
high-frequency oscillations with clinical instruments.
Previously BOLD changes have been reported to occur
before the happening of IEDs in patients with focal epilepsies, suggesting the alteration of synchronized neuronal activity in the spike field before the generation of EEG spike
[55]. Similar findings have been reported before the onset
of epileptic seizures [56, 57]. In the study by Jacobs and colleagues [55], the early focal BOLD changes were localized in
the mesial temporal lobe where neuronal activity might not
be detectable by the scalp EEG. Scalp potentials arise from
the spatial summation of synchronous dipole moments over
the neocortical volume with a weighting that depends upon
the anisotropic conductivity of the cranium [58, 59]. Areas
that are too deep or lack a sufficient amount of synchrony are
unable to produce a measurable potential difference on the
scalp. The “blurring” and low-pass filtering effects of the
skull and scalp can be overcome with invasive depth recordings, as only a fraction of spikes recorded intracranially in
mTLE patients are detectable by the EEG [59].
More recently with small clinical contacts and clinical
amplifiers, high-frequency oscillations (HFOs, referred to as
ripples 80–250 Hz and fast ripples 250–500 Hz) have been
recorded in animal and human subjects [60–62], suggesting
a possible relation with ictogenesis [61, 63, 64] and epileptogenesis [65]. Moreover, Châtillon and colleagues demonstrated that the contact size did not significantly affect HFO
detection in intracerebral EEG recordings in a rat epilepsy
model [60]. They have shown that the optimal size of a recording electrode should be dependent on the potential generation, distribution, amplitude, and frequency of the targeted signal, even though it was conventionally deemed that
the contact size may influence HFO recording ability by
affecting impedance and sampling volume. Specifically, they
suggested there should be no difference in HFO detection
in human recordings using contact from 0.036 mm2 to
1.698 mm2 . The generation of the HFO varies in anatomical
Epilepsy Research and Treatment
structures and pathological conditions that could lead to the
difference in its detectability reported by different groups.
Further research with regard to synchrony in large neuronal
circuitry is needed for detailed and complete understanding
of the underlying mechanism from a global network perspective. Note that analysis of changes in phase synchronization,
frequency bands, and rhythmicity was not conducted by
Jacobs and colleagues and presents an exciting avenue for
future study.
3
3. Correspondence between Spontaneous
EEG and fMRI
focal epilepsy [71]. With regard to the framework of EEGfMRI, the integration of superior spatial information provided by MR images and temporal information of EEG can
be improved by linking them with hemodynamic transfer
functions as we previously proposed. In our framework, the
variation of the HRF can be estimated rather than simply
assuming a fixed canonical response. Another advantage is
that intermodal, interregional, and interindividual variability can be explicitly taken into account in the estimation.
Thus, it becomes more straightforward to fit epileptic discharge events into continuous fMRI recordings under a
spontaneous condition since this type of data does not contain sparse/blocked stimulus inputs [41].
3.1. Methodological Consideration. The challenges of concurrent EEG-fMRI are not solely limited to noise removal,
but also arise when developing the framework of analysis.
This represents a fundamental issue which can influence the
seeking of the correspondence between the measured spontaneous EEG and resting-state fMRI signals (see Figure 1 for
a unified framework). Since little is known about the BOLD
response to epileptic discharges prior to the analysis, two
fundamental assumptions must be made to establish the link
between neural activity and hemodynamic modulations. The
first is that the shape and time course of the hemodynamic
response function (HRF) derived from the normal human or
animal subjects is also appropriate for epileptic brains [3, 27,
66]. The second is that the nature of neural hemodynamic
coupling derived from an epileptic subject is homogenous
across different brain regions regardless of the location inside
or outside the epileptogenic focus. Judicious selection of an
HRF and experimentally based assumptions about the nature
of the cortical electrical activity are important first steps in
the general linear model analysis of fMRI data sets [27, 49,
50]. Many of the published epilepsy studies using fMRI have
been bound by these two underlying assumptions.
A growing body of evidence is challenging the validity of
these two working hypotheses and suggest that the HRF and
its uniformity of epilepsy patients deviate from the normal
population [55, 66, 68–70]. The result has been a greater
preference for the utilization of data-driven or exploratory
methods such as ICA and temporal clustering analysis [71,
72]. A major advantage of data-driven analyses is the flexibility to circumvent a priori hypothesized HRF while simultaneously deducing the activated fMRI response [73].
However, it suffers an inherent deficit: the lack of statistical
measure to assess the intended hypothesis. To be considered
clinically useful, the epileptogenic zones defined by hemodynamic and metabolic modulation through ICA analysis must
be spatially consistent and statistically robust across individuals and scanning sessions. Consequently, one has to rely on
comparison of the results with the linear model method for
validation [49, 50]. McKeown has proposed a hybrid method
which can gracefully navigate from a fully data-driven approach to a fully model-driven approach [74]. Building on
this early work, we further improved it by replacing a vital
priori hypothesis (presumed neuronal response) used in
McKeown’s work with the actual EEG signal and obtained
statistically robust BOLD activation in an animal model of
3.2. Neuronal and Physiological Consideration. Paradoxical
imaging changes can occur in the nonepileptogenic and
epileptogenic zones that do not always accurately reflect the
underlying electrical activity [57, 66]. During absence seizures, it still remains largely unknown if a decrease in fMRI
signal during seizure activity has been found in those regions
such as spike-wave discharges, relative silence, or some other
electrophysiological phenomenon occurring [27, 49, 75–79].
From a large-scale network perspective, it is uncertain if in
the complex partial seizures of temporal lobe origin that
either reduced excitation or increased inhibition (or both)
results in decreased activity in the frontoparietal cortex [80–
82].
The characterization of the neuronal correlate of spontaneous BOLD signal still remains an open issue. Typical observations of spontaneous EEG contain a complex spectral composition including the alpha rhythm (8–12 Hz oscillations),
sleep spindles (∼12–14 Hz oscillations), and individual IEDs
[83]. BOLD fMRI contrast should be sensitive to these and
any changes in neuronal function that result in alteration
(either increase or decrease) of brain metabolism [84–86]. It
should be noted that failure to consider the background EEG
oscillations when seeking the electrical basis of the BOLD
response may cause poor correlation between these two measures [69] and confusion when attempting to explain their
direct correspondence [55, 87]. For example, evidence from
simultaneous fMRI and depth recordings in monkeys and
individuals undergoing invasive clinical monitoring has suggested that spiking, multiunit activity, and band-limited
power changes in the gamma frequency range were the primary correlate of resting state fMRI activity and connectivity
[17, 88]. This is in conflict with other work in humans suggesting a direct linkage of low-frequency BOLD fluctuations
and the posterior alpha rhythm [89]. Further, He and Raichle
proposed that the low-frequency end of field potentials
(<4 Hz, also termed as “slow cortical potential”, SCP) is also
correlated with BOLD signal in its raw spontaneous fluctuations in the light of its temporal scale overlapping with that of
fMRI signal [15, 90]. Correlation at or below delta band LFPs
is also in agreement with a rat investigation [91]. De Munck
and colleagues have further reported that power fluctuations
of different EEG bands are significantly correlated and are
similar to the alpha harmonics [89]. In striking contrast,
other studies yield different results as to the role what other
4
Epilepsy Research and Treatment
ICA analysis
EEG data
Data-derived HRF
EEG components
Deconvolution
Model-driven (GLM)
Effects of interest
X =G∗β
Hybrid route
fMRI data
G = W −1
Confounds
Data-driven (ICA)
X = W −1 ∗ C
Figure 1: Unified framework of simultaneous EEG-fMRI analysis.
F2
F1
(1) Default mode network
0
(μV2 /cm2 )
F3
(2) Right dorsal pathway
F4
F5
(3) Sensory motor system
F6
F7
(4) Auditory system
0.6
Figure 2: Statistical factor analysis on the microstates inferred by EEG and fMRI. The top row shows the sLORETA CSD maps (µV2/cm2 )
of the specific electrophysiological landscape or microstate, which is used to inform the fMRI analysis in four selected single subject at rest
(the bottom row). In each column of the bottom row, on the left, a CSD map projected onto a 3D Talairach model of the brain while on the
right axial, sagittal and coronal views of a microstate CSD map is shown. Reproduced and modified with permission from Musso et al. [67].
frequency bands play [92, 93], some suggesting unique frequency profiles across multiple bands representing characteristic EEG microstates [67, 94] (see Figure 2 for the EEG
microstates informed fMRI analysis). It has yet to be adequately clarified how changes in distinct bands of electrical
signal are related to the hemodynamic response under different states of brain activity.
Therefore, two important caveats should be noted when
one attempts to resort to a consistent interpretation of these
controversial observations. One is that intrinsic synchrony of
brain activity is network specific and exists at multiple spatial
levels [24]. Another is that spontaneous activity not only
reflects the functional architecture of the brain by forming
structured spatiotemporal profiles but can also encode traces
of previous behavior history (memory retrieval) and predict
future decisions in view of its “ongoing” internal representations [15]. In other words, the role of the brain oscillations
within an interested frequency range could be highly contextual. Taken together, further study of humans and animal
models with the use of simultaneous, spontaneous EEGfMRI recording is necessary to elucidate the complex, dynamic, behavior of intrinsic hemodynamic oscillations, and
their neuronal correlate. Revealing the underlying mechanisms of brain oscillations, coupling, and functional significance will provide a valuable measure for the assessment
of both normal and pathological brain functions, especially
epilepsy.
4. Alterations of Spontaneous EEG-fMRI
in Mesial TLE
TLE is often considered the prototype of localization-related
epilepsy, even though evidence indicates a multiplicity of
sources involving cortical and subcortical structures outside
Epilepsy Research and Treatment
5
0.6
3
Z-score
Figure 3: The human default-mode network represents a critical junction in the study of temporal lobe epilepsy, consciousness, and spontaneous brain activity. The resting-state network is displayed on a 3D cortical representation of the human brain. Overlaid color maps
represent thresholded z scores derived using independent component analysis network of resting-state fMRI data (N = 13).
the epileptogenic temporal lobe [28]. For instance, mTLE is
the most common type of focal epilepsy in adult patients and
is usually caused by hippocampal sclerosis (HS) [29]. A large
number of concurrent EEG-fMRI studies have sought fMRI
activations during IEDs to localize the epileptogenic focus
in their presurgical evaluation of patients with medically
refractory partial epilepsies [54]. Surgical outcomes in mTLE
are good, but far from optimal, particularly in long-term
followup studies [41, 95]. One of potential reasons of poor
clinical outcome could be that hemodynamic activation and
deactivation patterns are not always of localizing merit since
current understanding of neurovascular regulation is limited
[53]. When studying functional connectivity of the mesial
temporal lobe, one must consider that the subregions within
temporal lobe structures are critical components of other
cortical networks [35]. TLE focal seizure onset can propagate
to subcortical structures impairing the ascending reticular
activating system (ARAS) which mediates arousal and in
turn deactivating the cortical hierarchy, particularly the large
frontal-parietal associative cortical areas [96]. This fits within
the “network inhibition hypothesis” suggesting that partial
complex seizures that lead to impaired consciousness affect
an entire network as opposed to only the local epileptogenic
focus [1, 96, 97].
Interestingly, these cortical areas are implicated in the
default-mode network (DMN), a RSN, which comprises the
posterior cingulate cortex/precuneus, lateral parietal cortex,
ventral anterior cingulate cortex/mesial prefrontal cortex,
angular gyrus, and inferior temporal cortex, in addition to
the mesial temporal lobes (see Figure 3). Mesial TLE patients
show significantly increased connectivity within the mesial
temporal lobes and decreased connectivity within/between
the frontal and parietal lobes implicated in the DMN [28].
In addition, cerebral blood flow (CBF) has been shown to
increase in the medial thalamus correlating with a reduction
in activity within the DMN regions [81]. Morgan et al. [72]
showed increased negative connectivity across thalamic,
brainstem, frontal, and parietal brain regions, in accordance
with the idea that there is inhibited function in subcortical
and cortical structures during ictal propagation. These findings suggest that both thalamocortical activation and suspension or disturbance of the default state contribute to the
abnormality in responsiveness of patients with medial temporal origin. They are in concordance with what Gotman and
coworkers found using concurrent EEG-fMRI. The brain
could temporarily suspend baseline processes and engender
deactivations in this higher-order associative network [27].
The overlap of cortical between the DMN and TLE may provide some context as to why altered consciousness occurs in
TLE (see Figure 4 for illustration of the network inhibition
hypothesis).
6
Epilepsy Research and Treatment
(a)
(b)
(c)
(d)
Figure 4: Network inhibition hypothesis for loss of consciousness during the onset and propagation of unilateral focal seizure in mesial
temporal lobe. Reproduced with permission from Blumenfeld and Taylor [1].
The role of the DMN is still largely unclear, however, two
hypotheses have been proposed relating to conscious awareness (for review see [98]). The DMN has been suggested to
be responsible for unconstrained conscious internal mental
processes such as mind wandering [99] or alternatively may
involve low-level monitoring of the external environment
[10, 100]. Regardless of the fact that one or both hypotheses
may be correct, both relate to awareness (either of self or
environment), and thus the contents of consciousness. The
potential role of the DMN governing conscious awareness
has been investigated through imaging in altered states of
consciousness where awareness is thought to be absent including such conditions as anesthesia [16, 101], sleep [102],
vegetative state [38, 42, 103], and coma [14, 37]. Under light
sedation in healthy subjects, Greicius et al. [101] found significantly reduced connectivity in the posterior cingulate cortex (PCC) within the DMN suggesting that focal reductions
of the PCC may reflect decreased levels of consciousness. In
deep sleep, a more natural occurrence of decreased consciousness, a decoupling of the medial prefrontal cortex to
the rest of the cortical regions in DMN, have been shown to
occur [102]. A disruption of functional connectivity in the
DMN in vegetative state (VS), a pathological disorder of consciousness whereby a patient lacks awareness while regaining
arousal, has also been reported. In three VS patients, it was
found that the degree of functional connectivity within the
network correlated with the severity of the neurological impairment [103]. An absence of DMN has also been reported
in a VS patient who was scanned 21 years after anoxic injury
[42]. Vanhaudenhuyse et al. [43] examined DMN connectivity in 14 patients with range of altered states of consciousness including locked-in syndrome, minimally conscious,
vegetative states, and comatose patients and found that the
DMN connectivity decreased in proportion to the degree of
consciousness impairment. When taken together, the integrity of the DMN may correlate with levels of consciousness,
and a fully intact DMN may be needed for normal levels of
consciousness to occur.
The DMN has also been proposed to have some other
intrinsic role in functional brain organization [16]. Preserved
DMN connectivity has been documented in one case of VS
[38], in coma [14, 37], and in a possible homologous network under isoflurane anesthesia in the monkey [16]. It has
been suggested that DMN may be needed for consciousness
to occur but cannot be exclusively responsible for conscious
awareness [16, 37]. Possibly, DMN connectivity may be an
indicator of the extent of cortical disruption and predict
reversible impairments in consciousness [14].
The role of DMN in mediating consciousness is supported by known metabolic impairments in VS patients
which occur in cortical areas known to be implicated in
the DMN such as the prefrontal, temporoparietal association
areas, and posterior cingulate cortex/precuneus [104]. In
addition, the emergence from reversible vegetative state coincides with recovery-related metabolic changes in areas such
as the precuneus and thalamus [40, 105]. The DMN is an
attractive candidate for the neural correlate of consciousness
as it has a great deal of functional and structural connectivity
Epilepsy Research and Treatment
across large associative cortical areas [98, 106] including
long-range corticothalamic connections, in addition to its
role in internal mental processes and/or involvement in gathering information about the external environment, as well as
disruptions in altered states of consciousness. The DMN fits
within the global workspace (GW) theory of consciousness
that suggests long-distance connectivity between multiple
cortical networks that usually operate separately work together in an organized fashion to enable consciousness to
occur [107]. Intracerebral EEG signals in TLE seizures resulting in alterations in consciousness have been shown to cause
oversynchronization in long-range corticocortical and corticothalamic connections [108]. Excessive synchrony of these
connections, reported to be important in the GW, is thought
to prevent the variation and complexity needed to allow
for a conscious state [109]. Intracerebral EEG recordings in
the thalamus and temporal lobe structures (hippocampus,
entorhinal cortex, and neocortex), have shown increased
neuronal synchrony with the occurrence of seizures and early
loss of consciousness [35].
The concept of consciousness has long been central to
epileptology since generalized seizures and complex partial
seizures both can induce a variable degree of impairment
in consciousness [31], as frequently observed in mTLE patients. There is a series of discussion on this theme most
recently [109–115]. Besides linking the SCP to the resting
BOLD fluctuations as discussed above, He and Raichle made
a remarkable stride forward in arguing that this slow frequency of cortical oscillation might contribute directly to the
emergence of consciousness [90]. Their work has triggered a
debate in the field of whether the SCP per se in widespread
cortical networks carries specific details sufficient to express
our vivid daily conscious experience, as opposed to the proposed role of gamma oscillation established in higher level
of cognitive experience like attention [116]. It has been well
known that the contents of conscious states as well as the
level of awareness are affected to varying degrees during different types of epileptic seizures [27, 31, 110, 111, 115].
Accordingly, epilepsy can be used to provide an easily accessible entry into the working mechanism of altered conscious states. In principle, carefully constructed experiments
that manipulate the onset/offset and subjective level of
consciousness loss could dissociate hardly defined awareness
from activity of either the SCP or gamma oscillation (or
both).
5. Conclusions
Over the past decade, simultaneous EEG-fMRI recording
has become technically feasible with applications in multiple
areas of basic and applied sciences. Converging evidence
from fMRI and depth EEG in humans and animals has
already revealed that generalized seizures do not affect all
brain areas indiscriminately, whereas complex partial seizures alter functional brain activity less focally than previously thought. Spontaneous EEG-fMRI investigations into
topics such as asymmetry of the background activity in the
homeostatic neural networks, paroxysmal focal delta or theta
7
oscillations, and other nonepileptic but abnormal phenomena may provide additional clinically meaningful information, particularly relevant to impairment of conscious experience in patients. It will greatly enhance our understanding
of the underlying mechanism in generation of generalized
seizures, provide unique insight into the brain regions involved in the generation and/or propagation of epileptiform
activity, determine the neural substrate of various forms of
dyscognitive seizures, and offer clinicians neuronally validated spatial guidance for surgical planning to benefit patients with medically intractable TLE.
Acknowledgment
This work was partly funded by The Physicians’ Services
Incorporated Foundation (SM).
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