Brain network and dynamics of epilepsy

Kalamangalam G, Chelaru IM, Babajani-Feremi A.

PLoS One. 2025 Mar 31;20(3)

doi: 10.1371/journal.pone.0320648. PMID: 40163484; PMCID: PMC11957301.

Abstract

Spatial variation in the morphology of the electroencephalogram (EEG) over the head is classically described. Ultimately, location-dependent variation in EEG must arise from the cytoarchitectural and network structure of the portion of cortex sensed. In previous work, we demonstrated that over the lateral frontal lobe, sample entropy (SE) of intracerebral EEG (iEEG) over a subdural recording contact was predictive of that contact’s connectivity to other contacts. In this work, we used a publicly available repository (the Montreal Neurological Institute Atlas; MNIA) of whole-brain normative iEEG to calculate SE over the entire cortical surface. SE was averaged region-wise and classified by the state of arousal (awake, N2, N3 and REM). SE averages were transformed to a linear scale between zero and unity, mapped to continuous color scale and overlaid on segmented cortical surface models, one for each sleep-wake state. Wake SE followed a rostro-caudal gradient (RCG), with high values anteriorly and a global minimum in the posterior cortex. Superimposed on the RCG were other gradients radiating away from primary somatic sensorimotor, visual and auditory regions to their association areas. All gradients were attenuated in deep (N3) sleep. In REM, the majority of the cortex exhibited wake-like SE, with the prominent exception of primary cortical sensory and motor areas. Normative human intracerebral EEG exhibits rich spatial structure – cortical gradients – in the distribution of SE. SE in the wake state tracks temporal processing hierarchies in cerebral cortex, concordant to the distribution of several other cortical attributes of structure (e.g., cortical thickness, myelin content). Sleep disrupts these gradients, with REM sleep bringing out unusual discordances between primary sensory and their association areas. Our results deepen the interpretation of EEG from conventional descriptors such as Berger bands to a spatial perspective related to cortical biology.

Maria Jose Bruzzone, Naoum P. Issa, Shasha Wu, Sandra Rose, Yasar Taylan Esengul, Vernon L. Towle, Douglas Nordli, Peter C. Warnke, James X. Tao,

Epilepsy Research, Volume 182,
2022,
106914
ISSN 0920-1211
https://doi.org/10.1016/j.eplepsyres.2022.106914.
(https://www.sciencedirect.com/science/article/pii/S0920121122000651)

Abstract

Objective: To identify scalp EEG correlates of hippocampal spikes in patients with mesial temporal lobe epilepsy (mTLE).

Methods: We recorded scalp and intracranial EEG simultaneously in 20 consecutive surgical candidates with mTLE. Hippocampal spikes were identified from depth electrodes during the first hour of sleep on the first night of recording in the epilepsy monitoring unit, and their scalp EEG correlates were identified.

Results: Hippocampal spiking rates varied widely from 101 to 2187 (556 ± 672, mean ± SD) spikes per hour among the subjects. Of the 16,398 hippocampal spikes observed in this study, 492 (3.0%) of hippocampal spikes with extensive involvement of lateral temporal cortex were associated with scalp interictal epileptiform discharges (IEDs) including spikes and sharp waves; 198 (1.2%) of hippocampal spikes with limited involvement of lateral temporal cortex were associated with sharp transients or sharp slow waves, and 78 (0.05%)of hippocampal spikes with no lateral temporal involvement were associated with small sharp spikes (SSS). SSS were not correlated with independent temporal neocortical spikes.

Conclusions: There are morphologically heterogeneous scalp EEG correlates of hippocampal spikes including SSS, sharp transients, sharp slow waves, spikes, and sharp waves. SSS correlate with hippocampal spikes and are likely an EEG marker for mTLE. These findings have important clinical implications for the diagnosis and localization of mTLE, and provide new perspectives on criteria for defining scalp IEDs.

Kalamangalam, Giridhar P., Chelaru, Mircea I.
Brain Connectivity December 2021 11(10):850
https://www.liebertpub.com/doi/full/10.1089/brain.2020.0816

Abstract

Motivation: Mechanisms underlying the variation in the appearance of electroencephalogram (EEG) over human head are not well characterized. We hypothesized that spatial variation of the EEG, being ultimately linked to variations in cortical neurobiology, was dependent on cortical connectivity patterns. Specifically, we explored the relationship of resting-state functional connectivity derived from intracranial EEG (iEEG) data in seven (N = 7) human epilepsy patients with the intrinsic dynamic variability of the local iEEG. We asked whether primary and association brain areas over the lateral frontal lobe—due to their sharply different connectivity patterns—were thus dissociable in “EEG space.”

Methods: Functional connectivity between pairs of subdural grid electrodes was averaged to yield an electrode connectivity (EC) whose time-average yielded mean electrode connectivity (mEC), compared with that electrode’s time-averaged sample entropy (SE; mean electrode sample entropy, mESE).

Results: We found that mEC and mESE were generally in inverse proportion to each other. Extreme values of mEC and mESE occurred over the Rolandic region and were part of a more general rostrocaudal gradient observed in all patients, with larger (smaller) values of mEC (mESE) occurring anteriorly.

Conclusions: Brain networks influence brain dynamics. Over the lateral frontal lobe, mEC and mESE demonstrate a rostrocaudal topography, consistent with current notions regarding the structural and functional parcellation of the human frontal lobe. Our findings distinguish the frontal association cortex from primary sensorimotor cortex, effectively “diagnosing” Rolandic iEEG independent of the classical mu rhythm associated with the latter brain region.

Giridhar P. Kalamangalam, Sarah Long, Mircea I. Chelaru
Clinical Neurophysiology,
Volume 132, Issue 7,
2021,
Pages 1550-1563,
ISSN 1388-2457,
https://doi.org/10.1016/j.clinph.2021.03.014.
(https://www.sciencedirect.com/science/article/pii/S1388245721004879)

Abstract

Objective: We recently proposed a spectrum-based model of the awake intracranial electroencephalogram (iEEG) (Kalamangalam et al., 2020), based on a publicly available normative database (Frauscher et al., 2018). The latter has been expanded to include data from non-rapid eye movement (NREM) and rapid eye movement (REM) sleep (von Ellenrieder et al., 2020), and the present work extends our methods to those data.

Methods: Normalized amplitude spectra on semi-logarithmic axes from all four arousal states (wake, N2, N3 and REM) were averaged region-wise and fitted to a multi-component Gaussian distribution. A reduced model comprising five key parameters per brain region was color-coded on to cortical surface models.

Results: The lognormal Gaussian mixture model described the iEEG accurately from all brain regions, in all sleep-wake states. There was smooth variation in model parameters as sleep and wake states yielded to each other. Specific observations unrelated to the model were that the primary cortical areas of vision, motor function and audition, in addition to the hippocampus, did not participate in the ‘awakening’ of the cortex during REM sleep.

Conclusions: Despite the significant differences in the appearance of the time-domain EEG in wakefulness and sleep, the iEEG in all arousal states was successfully described by a parametric spectral model of low dimension.

Significance: Spectral variation in the iEEG is continuous in space (across different cortical regions) and time (stage of circadian cycle), arguing for a ‘continuum’ hypothesis in the generative processes of sleep and wakefulness in human brain.

Giridhar P. Kalamangalam, Sarah Long, Mircea I. Chelaru
Clinical Neurophysiology,
Volume 131, Issue 3,
2020,
Pages 665-675,
ISSN 1388-2457,
https://doi.org/10.1016/j.clinph.2019.11.061.
(https://www.sciencedirect.com/science/article/pii/S1388245719313707)

Abstract

Objective: A library of intracranial electroencephalography (iEEG) from the normal human brain has recently been made publicly available (Frauscher et al., 2018). The library – which we term the Montreal Neurological Institute Atlas (MNIA) – comprises 30 hours of iEEG from over a hundred epilepsy patients. We present a Fourier spectrum-based model of low dimension that summarizes all of MNIA into a neurophysiological ‘brain map’.

Methods: Normalized amplitude spectra of the MNIA data were modelled as log-normal distributions around individual canonical Berger frequencies. The latter were concatenated to yield the composite spectrum with high accuracy. Key model parameters were color-coded into a visual representation on cortical surface models.

Results: Each brain region has its own spectral characteristics that together yield a novel composite intracranial EEG brain map.

Conclusions: iEEG from normal brain regions can be accurately modelled with a small number of independent parameters. Our model is based in the canonical Berger bands and naturally suits clinical electroencephalography.

Significance: Due to its applicability to iEEG from all sampled regions, the model suggests a certain universality to brain rhythm generation that is independent of precise cortical location. More generally, our results are a novel abstraction of resting cortical dynamics that may help diagnostics in epileptology, in addition to informing structure-function relationships in the field of human brain mapping.

Giridhar P. Kalamangalam, Mircea Chelaru
F125. Brain connectivity related to sleep-wake state: An intracranial EEG study
Clinical Neurophysiology
Volume 129, Supplement 1
2018
Page e114
ISSN 1388-2457
https://doi.org/10.1016/j.clinph.2018.04.288.
(https://www.sciencedirect.com/science/article/pii/S1388245718305704)

Abstract

Introduction: Understanding brain connectivity in health and disease is a major challenge for basic and translational neuroscience. Direct EEG recordings from the brain surface (electrocorticography; ECoG) in epilepsy provide unique opportunity for studying human neuro-electric connectivity with reference to the wake, sleep and epileptic states. A major conundrum is reconciling the views of sleep being a disconnected state (Massimini et al., 2005) with the hypersynchronicity of sleep that favors seizure occurrence in the partial epilepsies, implying a heightened connectivity. Using spectral analysis and graph-theoretic measures (Bullmore and Sporns, 2009) applied to ECoG recordings in 6 patients undergoing continuous monitoring, we demonstrate how a reconciliation between these two scenarios is possible.

Methods: ECoG data in average reference format from six patients with refractory focal epilepsy undergoing prolonged pre-surgical video-EEG telemetry with subdural grid electrodes was analyzed. Segments of wakefulness and sleep (25–30 s long) were concatenated into contiguous epochs and normalized to zero mean and unit variance. Power spectra were computed by standard methods and the amplitude spectra curve-fitted empirically with a three-parameter function. The analysis was repeated over sliding windows of 30 s duration across the whole epoch. A fuzzy C-means method was used to cluster each parameter triplet into an activity score between [0–1], with 0 representing deep sleep, and 1 being alert wakefulness. The epoch time series were then filtered into the canonical Berger δ, θ, α, β and γ EEG bands. For each band, standard graph-theoretic measures were computed over sliding window segments across whole epochs to correspond with the activity score computations. Pearson correlations between the activity score and concurrently computed graph-theoretic connectivity metrics were calculated, and statistically significant correlations following Bonferroni correction (by a factor of 30) retained.

Results: We found that the coherence network modularity in the beta bands – relevant to high-frequency seizure-onset rhythms – correlated positively with wakefulness, while delta, theta and alpha modularity correlated negatively. An approximately reverse relationship was observed with respect to clustering coefficient.

Conclusion: Our results complement those obtained by resting state fMRI (Cox et al., 2014) and cortico-cortical evoked potentials (Usami et al., 2015) of sleep. We suggest that ECoG-based brain connectivity metrics are both state (sleep-wake) dependent and timescale (waveform frequency) dependent. It is possible for the cortex to be ‘disconnected’ with respect to frequencies ostensibly underlying conscious wakefulness in the sleep state, yet ‘hyperconnected’ on time scales relevant to the transmission of epileptic seizures.