Functional and structural brain networks in epilepsy: what have we learned?

Chronic antiepileptic drug use and functional network efficiency: A functional magnetic resonance imaging study

AIM

To increase our insight in the neuronal mechanisms underlying cognitive side-effects of antiepileptic drug (AED) treatment.

METHODS

The relation between functional magnetic resonance-acquired brain network measures, AED use, and cognitive function was investigated. Three groups of patients with epilepsy with a different risk profile for developing cognitive side effects were included: A “low risk” category (lamotrigine or levetiracetam, n = 16), an “intermediate risk” category (carbamazepine, oxcarbazepine, phenytoin, or valproate, n = 34) and a “high risk” category (topiramate, n = 5). Brain connectivity was assessed using resting state functional magnetic resonance imaging and graph theoretical network analysis. The Computerized Visual Searching Task was used to measure central information processing speed, a common cognitive side effect of AED treatment.

RESULTS

Central information processing speed was lower in patients taking AEDs from the intermediate and high risk categories, compared with patients from the low risk category. The effect of risk category on global efficiency was significant (P < 0.05, ANCOVA), with a significantly higher global efficiency for patient from the low category compared with the high risk category (P < 0.05, post-hoc test). Risk category had no significant effect on the clustering coefficient (ANCOVA, P > 0.2). Also no significant associations between information processing speed and global efficiency or the clustering coefficient (linear regression analysis, P > 0.15) were observed.

CONCLUSION

Only the four patients taking topiramate show aberrant network measures, suggesting that alterations in functional brain network organization may be only subtle and measureable in patients with more severe cognitive side effects.

New Insights on Temporal Lobe Epilepsy Based on Plasticity-Related Network Changes and High-Order Statistics

Epilepsy is a disorder of the brain characterized by the predisposition to generate recurrent unprovoked seizures, which involves reshaping of neuronal circuitries based on intense neuronal activity. In this review, we first detailed the regulation of plasticity-associated genes, such as ARC, GAP-43, PSD-95, synapsin, and synaptophysin. Indeed, reshaping of neuronal connectivity after the primary, acute epileptogenesis event increases the excitability of the temporal lobe. Herein, we also discussed the heterogeneity of neuronal populations regarding the number of synaptic connections, which in the theoretical field is commonly referred as degree. Employing integrate-and-fire neuronal model, we determined that in addition to increased synaptic strength, degree correlations might play essential and unsuspected roles in the control of network activity. Indeed, assortativity, which can be described as a condition where high-degree correlations are observed, increases the excitability of neural networks. In this review, we summarized recent topics in the field, and data were discussed according to newly developed or unusual tools, as provided by mathematical graph analysis and high-order statistics. With this, we were able to present new foundations for the pathological activity observed in temporal lobe epilepsy.

Predictive modeling of EEG time series for evaluating surgery targets in epilepsy patients

During the last 20 years, predictive modeling in epilepsy research has largely been concerned with the prediction of seizure events, whereas the inference of effective brain targets for resective surgery has received surprisingly little attention. In this exploratory pilot study, we describe a distributional clustering framework for the modeling of multivariate time series and use it to predict the effects of brain surgery in epilepsy patients. By analyzing the intracranial EEG, we demonstrate how patients who became seizure free after surgery are clearly distinguished from those who did not. More specifically, for 5 out of 7 patients who obtained seizure freedom (= Engel class I) our method predicts the specific collection of brain areas that got actually resected during surgery to yield a markedly lower posterior probability for the seizure related clusters, when compared to the resection of random or empty collections. Conversely, for 4 out of 5 Engel class III/IV patients who still suffer from postsurgical seizures, performance of the actually resected collection is not significantly better than performances displayed by random or empty collections. As the number of possible collections ranges into billions and more, this is a substantial contribution to a problem that today is still solved by visual EEG inspection. Apart from epilepsy research, our clustering methodology is also of general interest for the analysis of multivariate time series and as a generative model for temporally evolving functional networks in the neurosciences and beyond. Hum Brain Mapp 38:2509-2531, 2017. © 2017 Wiley Periodicals, Inc.

Evolving Network Model That Almost Regenerates Epileptic Data

In many realistic networks, the edges representing the interactions between nodes are time varying. Evidence is growing that the complex network that models the dynamics of the human brain has time-varying interconnections, that is, the network is evolving. Based on this evidence, we construct a patient- and data-specific evolving network model (comprising discrete-time dynamical systems) in which epileptic seizures or their terminations in the brain are also determined by the nature of the time-varying interconnections between the nodes. A novel and unique feature of our methodology is that the evolving network model remembers the data from which it was conceived from, in the sense that it evolves to almost regenerate the patient data even on presenting an arbitrary initial condition to it. We illustrate a potential utility of our methodology by constructing an evolving network from clinical data that aids in identifying an approximate seizure focus; nodes in such a theoretically determined seizure focus are outgoing hubs that apparently act as spreaders of seizures. We also point out the efficacy of removal of such spreaders in limiting seizures.

Temporally correlated fluctuations drive epileptiform dynamics

Macroscopic models of brain networks typically incorporate assumptions regarding the characteristics of afferent noise, which is used to represent input from distal brain regions or ongoing fluctuations in non-modelled parts of the brain. Such inputs are often modelled by Gaussian white noise which has a flat power spectrum. In contrast, macroscopic fluctuations in the brain typically follow a 1/fb spectrum. It is therefore important to understand the effect on brain dynamics of deviations from the assumption of white noise. In particular, we wish to understand the role that noise might play in eliciting aberrant rhythms in the epileptic brain. To address this question we study the response of a neural mass model to driving by stochastic, temporally correlated input. We characterise the model in terms of whether it generates "healthy" or "epileptiform" dynamics and observe which of these dynamics predominate under different choices of temporal correlation and amplitude of an Ornstein-Uhlenbeck process. We find that certain temporal correlations are prone to eliciting epileptiform dynamics, and that these correlations produce noise with maximal power in the δ and θ bands. Crucially, these are rhythms that are found to be enhanced prior to seizures in humans and animal models of epilepsy. In order to understand why these rhythms can generate epileptiform dynamics, we analyse the response of the model to sinusoidal driving and explain how the bifurcation structure of the model gives rise to these findings. Our results provide insight into how ongoing fluctuations in brain dynamics can facilitate the onset and propagation of epileptiform rhythms in brain networks. Furthermore, we highlight the need to combine large-scale models with noise of a variety of different types in order to understand brain (dys-)function.

The epileptic amygdala: Toward the development of a neural prosthesis by temporally coded electrical stimulation

Many patients with epilepsy do not obtain proper control of their seizures through conventional treatment. We review aspects of the pathophysiology underlying epileptic phenomena, with a special interest in the role of the amygdala, stressing the importance of hypersynchronism in both ictogenesis and epileptogenesis. We then review experimental studies on electrical stimulation of mesiotemporal epileptogenic areas, the amygdala included, as a means to treat medically refractory epilepsy. Regular high-frequency stimulation (HFS) commonly has anticonvulsant effects and sparse antiepileptogenic properties. On the other hand, HFS is related to acute and long-term increases in excitability related to direct neuronal activation, long-term potentiation, and kindling, raising concerns regarding its safety and jeopardizing in-depth understanding of its mechanisms. In turn, the safer regular low-frequency stimulation (LFS) has a robust antiepileptogenic effect, but its pro- or anticonvulsant effect seems to vary at random among studies. As an alternative, studies by our group on the development and investigation of temporally unstructured electrical stimulation applied to the amygdala have shown that nonperiodic stimulation (NPS), which is a nonstandard form of LFS, is capable of suppressing both acute and chronic spontaneous seizures. We hypothesize two noncompetitive mechanisms for the therapeutic role of amygdala in NPS, 1) a direct desynchronization of epileptic circuitry in the forebrain and brainstem and 2) an indirect desynchronization/inhibition through nucleus accumbens activation. We conclude by reintroducing the idea that hypersynchronism, rather than hyperexcitability, may be the key for epileptic phenomena and epilepsy treatment.

Predicting neurosurgical outcomes in focal epilepsy patients using computational modelling

See Eissa and Schevon (doi:10.1093/aww332) for a scientific commentary on this article.

Surgery can be a last resort for patients with intractable, medically refractory epilepsy. For many of these patients, however, there is substantial risk that the surgery will be ineffective. The prediction of who is likely to benefit from a surgical approach is crucial for being able to inform patients better, conduct principled prospective clinical trials, and ultimately tailor therapeutic approaches to these patients more effectively. Dynamical computational models, informed with patient data, can be used to make predictions and give mechanistic insight. In this study, we develop patient-specific dynamical network models of epileptogenic cortex. We infer the network connectivity matrix from non-seizure electrographic recordings of patients and use these connectivity matrices as the network structure in our model. The model simulates the dynamics of a bi-stable switch at every node in this network, meaning that every node starts in a background state, but has the ability to transit to a co-existing seizure state. Whether a transition happens in a node is partly determined by the stochastic nature of the input to the node, but also by the input the node receives from other connected nodes in the network. By conducting simulations with such a model, we can detect the average transition time for nodes in a given network, and therefore define nodes with a short transition time as highly epileptogenic. In a retrospective study, we found that in some patients the regions with high epileptogenicity in the model overlap with those identified clinically as the seizure onset zone. Moreover, it was found that the resection of these regions in the model reduces the overall likelihood of a seizure. Following removal of these regions in the model, we predicted surgical outcomes and compared these to actual patient outcomes. Our predictions were found to be 81.3% accurate on a dataset of 16 patients with intractable epilepsy. Intriguingly, in patients with unsuccessful outcomes, the proposed computational approach is able to suggest alternative resection sites. The model presented here gives mechanistic insight as to why surgery may be unsuccessful in some patients. This may aid clinicians in presurgical evaluation by providing a tool to explore various surgical options, offering complementary information to existing clinical techniques.

Large-scale cortico-subcortical functional networks in focal epilepsies: The role of the basal ganglia

Objectives

The aim was to describe the contribution of basal ganglia (BG) thalamo-cortical circuitry to the whole-brain functional connectivity in focal epilepsies.

Methods

Interictal resting-state fMRI recordings were acquired in 46 persons with focal epilepsies. Of these 46, 22 had temporal lobe epilepsy: 9 left temporal (LTLE), 13 right temporal (RTLE); 15 had frontal lobe epilepsy (FLE); and 9 had parietal/occipital lobe epilepsy (POLE). There were 20 healthy controls. The complete weighted network was analyzed based on correlation matrices of 90 and 194 regions. The network topology was quantified on a global and regional level by measures based on graph theory, and connection-level changes were analyzed by the partial least square method.

Results

In all patient groups except RTLE, the shift of the functional network topology away from random was observed (normalized clustering coefficient and characteristic path length were higher in patient groups than in controls). Links contributing to this change were found in the cortico-subcortical connections. Weak connections (low correlations) consistently contributed to this modification of the network. The importance of regions changed: decreases in the subcortical areas and both decreases and increases in the cortical areas were observed in node strength, clustering coefficient and eigenvector centrality in patient groups when compared to controls. Node strength decreases of the basal ganglia, i.e. the putamen, caudate, and pallidum, were displayed in LTLE, FLE, and POLE. The connectivity within the basal ganglia–thalamus circuitry was not disturbed; the disturbance concerned the connectivity between the circuitry and the cortex.

Significance

Focal epilepsies affect large-scale brain networks beyond the epileptogenic zones. Cortico-subcortical functional connectivity disturbance was displayed in LTLE, FLE, and POLE. Significant changes in the resting-state functional connectivity between cortical and subcortical structures suggest an important role of the BG and thalamus in focal epilepsies.

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Focal epilepsies affect large-scale brain networks beyond the epileptogenic zones.

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The functional network topology shifted away from random in focal epilepsies.

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Subcortico-cortical connectivity decreased in epilepsy due to changes in weak links.

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Basal ganglia–thalamus circuitry connectivity was not disturbed in focal epilepsy.

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The connectivity between basal ganglia-thalamus circuitry and cortex was affected.

Whole-brain functional connectivity during acquisition of novel grammar: Distinct functional networks depend on language learning abilities

In an effort to advance the understanding of brain function and organisation accompanying second language learning, we investigate the neural substrates of novel grammar learning in a group of healthy adults, consisting of participants with high and average language analytical abilities (LAA). By means of an Independent Components Analysis, a data-driven approach to functional connectivity of the brain, the fMRI data collected during a grammar-learning task were decomposed into maps representing separate cognitive processes. These included the default mode, task-positive, working memory, visual, cerebellar and emotional networks. We further tested for differences within the components, representing individual differences between the High and Average LAA learners. We found high analytical abilities to be coupled with stronger contributions to the task-positive network from areas adjacent to bilateral Broca's region, stronger connectivity within the working memory network and within the emotional network. Average LAA participants displayed stronger engagement within the task-positive network from areas adjacent to the right-hemisphere homologue of Broca's region and typical to lower level processing (visual word recognition), and increased connectivity within the default mode network. The significance of each of the identified networks for the grammar learning process is presented next to a discussion on the established markers of inter-individual learners' differences. We conclude that in terms of functional connectivity, the engagement of brain's networks during grammar acquisition is coupled with one's language learning abilities.

Metrics of brain network architecture capture the impact of disease in children with epilepsy

BACKGROUND AND OBJECTIVE

Epilepsy is associated with alterations in the structural framework of the cerebral network. The aim of this study was to measure the potential of global metrics of network architecture derived from resting state functional MRI to capture the impact of epilepsy on the developing brain.

METHODS

Pediatric patients were retrospectively identified with: 1. Focal epilepsy; 2. Brain MRI at 3 Tesla, including resting state functional MRI; 3. Full scale IQ measured by a pediatric neuropsychologist. The cerebral cortex was parcellated into approximately 700 gray matter network nodes. The strength of a connection between two nodes was defined as the correlation between their resting BOLD signal time series. The following global network metrics were then calculated: clustering coefficient, transitivity, modularity, path length, and global efficiency. Epilepsy duration was used as an index for the cumulative impact of epilepsy on the brain.

RESULTS

45 patients met criteria (age: 4-19 years). After accounting for age of epilepsy onset, epilepsy duration was inversely related to IQ (p: 0.01). Epilepsy duration predicted by a machine learning algorithm on the basis of the five global network metrics was highly correlated with actual epilepsy duration (r: 0.95; p: 0.0001). Specifically, modularity and to a lesser extent path length and global efficiency were independently associated with epilepsy duration.

CONCLUSIONS

We observed that a machine learning algorithm accurately predicted epilepsy duration based on global metrics of network architecture derived from resting state fMRI. These findings suggest that network metrics have the potential to form the basis for statistical models that translate quantitative imaging data into patient-level markers of cognitive deterioration.

EEG functional network topology is associated with disability in patients with amyotrophic lateral sclerosis

Amyotrophic Lateral Sclerosis (ALS) is one of the most severe neurodegenerative diseases, which is known to affect upper and lower motor neurons. In contrast to the classical tenet that ALS represents the outcome of extensive and progressive impairment of a fixed set of motor connections, recent neuroimaging findings suggest that the disease spreads along vast non-motor connections. Here, we hypothesised that functional network topology is perturbed in ALS, and that this reorganization is associated with disability. We tested this hypothesis in 21 patients affected by ALS at several stages of impairment using resting-state electroencephalography (EEG) and compared the results to 16 age-matched healthy controls. We estimated functional connectivity using the Phase Lag Index (PLI), and characterized the network topology using the minimum spanning tree (MST). We found a significant difference between groups in terms of MST dissimilarity and MST leaf fraction in the beta band. Moreover, some MST parameters (leaf, hierarchy and kappa) significantly correlated with disability. These findings suggest that the topology of resting-state functional networks in ALS is affected by the disease in relation to disability. EEG network analysis may be of help in monitoring and evaluating the clinical status of ALS patients.

Identifying the epileptogenic zone in interictal resting-state MEG source-space networks

OBJECTIVE

In one third of patients, seizures remain after epilepsy surgery, meaning that improved preoperative evaluation methods are needed to identify the epileptogenic zone. A potential framework for such a method is network theory, as it can be applied to noninvasive recordings, even in the absence of epileptiform activity. Our aim was to identify the epileptogenic zone on the basis of hub status of local brain areas in interictal magnetoencephalography (MEG) networks.

METHODS

Preoperative eyes-closed resting-state MEG recordings were retrospectively analyzed in 22 patients with refractory epilepsy, of whom 14 were seizure-free 1 year after surgery. Beamformer-based time series were reconstructed for 90 cortical and subcortical automated anatomic labeling (AAL) regions of interest (ROIs). Broadband functional connectivity was estimated using the phase lag index in artifact-free epochs without interictal epileptiform abnormalities. A minimum spanning tree was generated to represent the network, and the hub status of each ROI was calculated using betweenness centrality, which indicates the centrality of a node in a network. The correspondence of resection cavity to hub values was evaluated on four levels: resection cavity, lobar, hemisphere, and temporal versus extratemporal areas.

RESULTS

Hubs were localized within the resection cavity in 8 of 14 seizure-free patients and in zero of 8 patients who were not seizure-free (57% sensitivity, 100% specificity, 73% accuracy). Hubs were localized in the lobe of resection in 9 of 14 seizure-free patients and in zero of 8 patients who were not seizure-free (64% sensitivity, 100% specificity, 77% accuracy). For the other two levels, the true negatives are unknown; hence, only sensitivity could be determined: hubs coincided with both the resection hemisphere and the resection location (temporal versus extratemporal) in 11 of 14 seizure-free patients (79% sensitivity).

SIGNIFICANCE

Identifying hubs noninvasively before surgery is a valuable approach with the potential of indicating the epileptogenic zone in patients without interictal abnormalities.

High frequency oscillations and high frequency functional network characteristics in the intraoperative electrocorticogram in epilepsy

OBJECTIVE

High frequency oscillations (HFOs; > 80 Hz), especially fast ripples (FRs, 250-500 Hz), are novel biomarkers for epileptogenic tissue. The pathophysiology suggests enhanced functional connectivity within FR generating tissue. Our aim was to determine the relation between brain areas showing FRs and 'baseline' functional connectivity within EEG networks, especially in the high frequency bands.

METHODS

We marked FRs, ripples (80-250 Hz) and spikes in the electrocorticogram of 14 patients with refractory temporal lobe epilepsy. We assessed 'baseline' functional connectivity in epochs free of epileptiform events within these recordings, using the phase lag index. We computed the Eigenvector Centrality (EC) per channel in the FR and gamma band network. We compared EC between channels that did or did not show events at other moments in time.

RESULTS

FR-band EC was higher in channels with than without spikes. Gamma-band EC was lower in channels with ripples and FRs.

CONCLUSIONS

We confirmed previous findings of functional isolation in the gamma-band and found a first proof of functional integration in the FR-band network of channels covering presumed epileptogenic tissue.

SIGNIFICANCE

'Baseline' high-frequency network parameters might help intra-operative recognition of epileptogenic tissue without the need for waiting for events. These findings can increase our understanding of the 'architecture' of epileptogenic networks and help unravel the pathophysiology of HFOs.

The new approach to classification of focal epilepsies: Epileptic discharge and disconnectivity in relation to cognition

The new classification of epilepsy stratifies the disease into an acute level, based on seizures, and an overarching chronic level of epileptic syndromes (Berg et al., 2010). In this new approach, seizures are considered either to originate and evolve in unilateral networks or to rapidly encompass both hemispheres. This concept extends the former vision of focal and generalized epilepsies to a genuine pathology of underlying networks. These key aspects of the new classification can be linked to the concept of cognitive curtailing in focal epilepsy. The present review will discuss the conceptual implications for acute and chronic cognitive deficits with special emphasis on transient and structural disconnectivity. Acute transient disruption of brain function is the hallmark of focal seizures. Beyond seizures, however, interictal epileptic discharges (IEDs) are increasingly recognized to interfere with physiological brain circuitry. Both concomitant EEG and high-precision neuropsychological testing are necessary to detect these subtle effects, which may concern task-specific or default-mode networks. More recent data suggest that longstanding IEDs may affect brain maturation and eventually be considered as a biomarker of pathological wiring. This brings us to the overarching level of chronic cognitive and behavioral comorbidity. We will discuss alterations in structural connectivity measured with diffusion-weighted imaging and tractography. Among focal epilepsies, much of our current insights are derived from temporal lobe epilepsy and its impact on neuropsychological and psychiatric functioning. Structural disconnectivity is maximal in the temporal lobe but also concerns widespread language circuitry. Eventually, pathological wiring may contribute to the clinical picture of cognitive dysfunction. We conclude with the extrapolation of these concepts to current research topics and to the necessity of establishing individual patient profiles of network pathology with EEG, high-precision neuropsychological testing, and state-of-the-art neuroimaging. This article is part of a Special Issue entitled "The new approach to classification: Rethinking cognition and behavior in epilepsy".

Changes in corticocortical and corticohippocampal network during absence seizures in WAG/Rij rats revealed with time varying Granger causality

PURPOSE

Spike-and-wave discharges (SWDs) recorded in the cortical EEGs of WAG/Rij rats are the hallmark for absence epilepsy in this model. Although this type of epilepsy was long regarded as a form of primary generalized epilepsy, it is now recognized that there is an initiation zone - the perioral region of the somatosensory cortex. However, networks involved in spreading the seizure are not yet fully known. Previously, the dynamics of coupling between different layers of the perioral cortical region and between these zones and different thalamic nuclei was studied in time windows around the SWDs, using nonlinear Granger causality. The aim of the present study was to investigate, using the same method, the coupling dynamics between different regions of the cortex and between these regions and the hippocampus.

METHODS

Local field potentials were recorded in the frontal, parietal, and occipital cortices and in the hippocampus of 19 WAG/Rij rats. To detect changes in coupling reliably in a short time window, in order to provide a good temporal resolution, the innovative adapted time varying nonlinear Granger causality method was used. Mutual information function was calculated in addition to validate outcomes. Results of both approaches were tested for significance.

RESULTS

The SWD initiation process was revealed as an increase in intracortical interactions starting from 3.5s before the onset of electrographic seizure. The earliest preictal increase in coupling was directed from the frontal cortex to the parietal cortex. Then, the coupling became bidirectional, followed by the involvement of the occipital cortex (1.5s before SWD onset). There was no driving from any cortical region to hippocampus, but a slight increase in coupling from hippocampus to the frontoparietal cortex was observed just before SWD onset. After SWD onset, an abrupt drop in coupling in all studied pairs was observed. In most of the pairs, the decoupling rapidly disappeared, but driving force from hippocampus and occipital cortex to the frontoparietal cortex was reduced until the SWD termination.

CONCLUSION

Involvement of multiple cortical regions in SWD initiation shows the fundamental role of corticocortical feedback loops, forming coupling architecture and triggering the generalized seizure. The results add to the ultimate aim to construct a complete picture of brain interactions preceding and accompanying absence seizures in rats.

Disrupted functional connectivity affects resting state based language lateralization

Pre-operative assessment of language localization and lateralization is critical to preserving brain function after lesion or epileptogenic tissue resection. Task fMRI (t-fMRI) has been extensively and reliably used to this end, but resting state fMRI (rs-fMRI) is emerging as an alternative pre-operative brain mapping method that is independent of a patient's ability to comply with a task. We sought to evaluate if language lateralization obtained from rs-fMRI can replace standard assessment using t-fMRI. In a group of 43 patients scheduled for pre-operative fMRI brain mapping and 17 healthy controls, we found that existing methods of determining rs-fMRI lateralization by considering interhemispheric and intrahemispheric functional connectivity are inadequate compared to t-fMRI when applied to the language network. We determined that this was attributable to widespread but nuanced disturbances in the functional connectivity of the language network in patients. We found changes in interhemispheric and intrahemispheric functional connectivity that were dependent on lesion location, and particularly impacted patients with lesions in the left temporal lobe. We then tested whether a simpler measure of functional connectivity to the language network has a better relation to t-fMRI based language lateralization. Remarkably, we found that functional connectivity between the language network and the frontal pole, and superior frontal gyrus, as well as the supramarginal gyrus, significantly correlated to task based language lateralization indices in both patients and healthy controls. These findings are consistent with prior work with epilepsy patients, and provide a framework for evaluating language lateralization at rest.

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Existing methods of determining rs-fMRI lateralization are inadequate for language.

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Functional connectivity to language network correlates with task lateralization.

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Lesion location affects functional connectivity.

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Lesions exhibit some interhemispheric hyperconnectivity within language network.

EEG-Based Prediction of Epileptic Seizures Using Phase Synchronization Elicited from Noise-Assisted Multivariate Empirical Mode Decomposition

In this study, we examined the phase locking value (PLV) for seizure prediction, particularly, in the gamma frequency band. We prepared simulation data and 65 clinical cases of seizure. In addition, various filtering algorithms including bandpass filtering, empirical mode decomposition, multivariate empirical mode decomposition and noise-assisted multivariate empirical mode decomposition (NA-MEMD) were used to decompose spectral components from the data. Moreover, in the case of clinical data, the PLVs were used to classify between interictal and preictal stages using a support vector machine. The highest PLV was achieved with NA-MEMD with 0-dB white noise algorithm (0.9988), which exhibited statistically significant differences compared to other filtering algorithms. Moreover, the classification rate was the highest for the NA-MEMD with 0-dB algorithm (83.17%). In terms of frequency components, examining the gamma band resulted in the highest classification rates for all algorithms, compared to other frequency bands such as theta, alpha, and beta bands. We found that PLVs calculated with the NA-MEMD algorithm could be used as a potential biological marker for seizure prediction. Moreover, the gamma frequency band was useful for discriminating between interictal and preictal stages.

Epileptic brain reorganization dynamics on the basis of the probability of connections

Ictal and interictal epileptiform discharges affect brain functional dynamics, but the issue of how they occur is still under debate. The present study evaluated the brain electrical activity that underlies epileptic seizures by focusing analysis on four electroencephalographic time stages around seizure onset. The dynamics of the functional organization of the brain regions at rest, and then immediately before, during, and after, epileptic seizures in a group of five patients diagnosed with intractable temporal epilepsy was examined. The analysis is based on the probability of connections between different brain regions as determined by partial directed coherence. A probability-based graph is constructed for each stage and then the dynamics of reorganization is described using invariant measures on the basis of the graphs obtained. The functional reorganization of brain connectivity is illustrated for each time period, reflecting their temporal variations. The graph method applied proved to be useful in depicting temporal variations in functional brain connectivity because of ictal disruptions in temporal epilepsy, thus providing the possibility of further evaluation of these changes in individual cases to support medical decisions.

Identification of Interictal Epileptic Networks from Dense-EEG

Epilepsy is a network disease. The epileptic network usually involves spatially distributed brain regions. In this context, noninvasive M/EEG source connectivity is an emerging technique to identify functional brain networks at cortical level from noninvasive recordings. In this paper, we analyze the effect of the two key factors involved in EEG source connectivity processing: (i) the algorithm used in the solution of the EEG inverse problem and (ii) the method used in the estimation of the functional connectivity. We evaluate four inverse solutions algorithms (dSPM, wMNE, sLORETA and cMEM) and four connectivity measures (r ², h ², PLV, and MI) on data simulated from a combined biophysical/physiological model to generate realistic interictal epileptic spikes reflected in scalp EEG. We use a new network-based similarity index to compare between the network identified by each of the inverse/connectivity combination and the original network generated in the model. The method will be also applied on real data recorded from one epileptic patient who underwent a full presurgical evaluation for drug-resistant focal epilepsy. In simulated data, results revealed that the selection of the inverse/connectivity combination has a significant impact on the identified networks. Results suggested that nonlinear methods (nonlinear correlation coefficient, phase synchronization and mutual information) for measuring the connectivity are more efficient than the linear one (the cross correlation coefficient). The wMNE inverse solution showed higher performance than dSPM, cMEM and sLORETA. In real data, the combination (wMNE/PLV) led to a very good matching between the interictal epileptic network identified from noninvasive EEG recordings and the network obtained from connectivity analysis of intracerebral EEG recordings. These results suggest that source connectivity method, when appropriately configured, is able to extract highly relevant diagnostic information about networks involved in interictal epileptic spikes from non-invasive dense-EEG data.

How does vagal nerve stimulation (VNS) change EEG brain functional connectivity?

An effect of vagal nerve stimulation (VNS) on cortical synchronization has been postulated but remains to be verified. In this study we investigated the impact of VNS on functional connectivity (Fc) using direct intracerebral recordings (stereotactic EEG, SEEG). Five patients with epilepsy who underwent SEEG recordings during ongoing VNS therapy were investigated. Interdependencies between twenty-six selected bipolar SEEG channels were estimated by nonlinear regression analysis during ON and OF periods of stimulation. In comparison with OFF periods, the ON periods disclosed higher values for four patients (P1, P3, P4, P5) and lower values for one patient (P2). From thresholded graphs, we observed increased connections between several brain regions in P1 and P5 and decreased connections in P2. Finally, the only decreased Fc occurring during VNS corresponded to the responder patient, suggesting that therapeutic impact might be related to this mechanism.

Estimation of brain network ictogenicity predicts outcome from epilepsy surgery

Surgery is a valuable option for pharmacologically intractable epilepsy. However, significant post-operative improvements are not always attained. This is due in part to our incomplete understanding of the seizure generating (ictogenic) capabilities of brain networks. Here we introduce an in silico, model-based framework to study the effects of surgery within ictogenic brain networks. We find that factors conventionally determining the region of tissue to resect, such as the location of focal brain lesions or the presence of epileptiform rhythms, do not necessarily predict the best resection strategy. We validate our framework by analysing electrocorticogram (ECoG) recordings from patients who have undergone epilepsy surgery. We find that when post-operative outcome is good, model predictions for optimal strategies align better with the actual surgery undertaken than when post-operative outcome is poor. Crucially, this allows the prediction of optimal surgical strategies and the provision of quantitative prognoses for patients undergoing epilepsy surgery.

Detection of functional brain network reconfiguration during task-driven cognitive states

Network science offers computational tools to elucidate the complex patterns of interactions evident in neuroimaging data. Recently, these tools have been used to detect dynamic changes in network connectivity that may occur at short time scales. The dynamics of fMRI connectivity, and how they differ across time scales, are far from understood. A simple way to interrogate dynamics at different time scales is to alter the size of the time window used to extract sequential (or rolling) measures of functional connectivity. Here, in n=82 participants performing three distinct cognitive visual tasks in recognition memory and strategic attention, we subdivided regional BOLD time series into variable sized time windows and determined the impact of time window size on observed dynamics. Specifically, we applied a multilayer community detection algorithm to identify temporal communities and we calculated network flexibility to quantify changes in these communities over time. Within our frequency band of interest, large and small windows were associated with a narrow range of network flexibility values across the brain, while medium time windows were associated with a broad range of network flexibility values. Using medium time windows of size 75-100s, we uncovered brain regions with low flexibility (considered core regions, and observed in visual and attention areas) and brain regions with high flexibility (considered periphery regions, and observed in subcortical and temporal lobe regions) via comparison to appropriate dynamic network null models. Generally, this work demonstrates the impact of time window length on observed network dynamics during task performance, offering pragmatic considerations in the choice of time window in dynamic network analysis. More broadly, this work reveals organizational principles of brain functional connectivity that are not accessible with static network approaches.

Preoperative evaluation using magnetoencephalography: Experience in 382 epilepsy patients

OBJECTIVE

Identifying epilepsy patients for whom clinical MEG is likely to be beneficial avoids or optimizes burdensome ancillary investigations. We determined whether it could be predicted upfront if MEG would be able to generate a hypothesis about the location of the epileptogenic zone (EZ), and in which patients MEG fails to do so.

METHODS

MEG recordings of 382 epilepsy patients with inconclusive findings regarding EZ localization prior to MEG were acquired for preoperative evaluation. MEG reports were categorized for several demographic, clinical and MEG variables. First, demographic and clinical variables were associated with MEG localization ability for upfront prediction. Second, all variables were compared between patients with and without MEG location in order to characterize patients without MEG location.

RESULTS

Our patient group had often complex etiology and did not contain the (by other means) straightforward and well-localized cases, such as those with concordant tumor and EEG location. For our highly-selected patient group, MEG localization ability cannot be predicted upfront, although the odds of a recording with MEG location were significantly higher in the absence of a tumor and in the presence of widespread MRI abnormalities. Compared to the patients with MEG location, patients without MEG location more often had a tumor, widespread EEG abnormalities, non-lateralizing MEG abnormalities, non-concordant MEG/EEG abnormalities and less often widespread MRI abnormalities or epileptiform MEG activity. In a subgroup of 48 patients with known surgery outcome, more patients with concordant MEG and resection area were seizure-free than patients with discordant results.

CONCLUSIONS

MEG potentially adds information about the location of the EZ even in patients with a complex etiology, and the clinical advice is to not withhold MEG in epilepsy surgery candidates. Providing a hypothesis about the location of the EZ using MEG is difficult in patients with inconclusive EEG and MRI findings, and in the absence of specific epileptiform activity. More refined methods are needed for patients where MEG currently does not contribute to the hypothesis about the location of the EZ.

Electroencephalography based functional networks in newly diagnosed childhood epilepsies

OBJECTIVE

It remains unclear to what extent brain networks are altered at an early stage of epilepsy, which may be important to improve our understanding on the course of network alterations and their association with recurrent seizures and cognitive deficits.

METHODS

89 Drug-naïve children with newly diagnosed focal or generalized epilepsies and 179 controls were included. Brain networks were based on interictal electroencephalography recordings obtained at first consultation. Conventional network metrics and minimum spanning tree (MST) metrics were computed to characterize topological network differences, such integration and segregation and a hub measures (betweenness centrality).

RESULTS

Network alterations between groups were only identified by MST metrics and most pronounced in the delta band, in which a loss of network integration and a significant lower betweenness centrality was found in children with focal epilepsies compared to healthy controls (p<0.01). A reversed group difference was found in the upper alpha band. The network topology in generalized epilepsies was relatively spared.

CONCLUSIONS

Interictal network alterations - only identifiable with the MST method - are already present at an early stage of focal epilepsy.

SIGNIFICANCE

We argue that these alterations are subtle at the early stage and aggravate later as a result of persisting seizures.

Computational analysis in epilepsy neuroimaging: A survey of features and methods

Epilepsy affects 65 million people worldwide, a third of whom have seizures that are resistant to anti-epileptic medications. Some of these patients may be amenable to surgical therapy or treatment with implantable devices, but this usually requires delineation of discrete structural or functional lesion(s), which is challenging in a large percentage of these patients.

Advances in neuroimaging and machine learning allow semi-automated detection of malformations of cortical development (MCDs), a common cause of drug resistant epilepsy. A frequently asked question in the field is what techniques currently exist to assist radiologists in identifying these lesions, especially subtle forms of MCDs such as focal cortical dysplasia (FCD) Type I and low grade glial tumors. Below we introduce some of the common lesions encountered in patients with epilepsy and the common imaging findings that radiologists look for in these patients. We then review and discuss the computational techniques introduced over the past 10 years for quantifying and automatically detecting these imaging findings. Due to large variations in the accuracy and implementation of these studies, specific techniques are traditionally used at individual centers, often guided by local expertise, as well as selection bias introduced by the varying prevalence of specific patient populations in different epilepsy centers. We discuss the need for a multi-institutional study that combines features from different imaging modalities as well as computational techniques to definitively assess the utility of specific automated approaches to epilepsy imaging. We conclude that sharing and comparing these different computational techniques through a common data platform provides an opportunity to rigorously test and compare the accuracy of these tools across different patient populations and geographical locations. We propose that these kinds of tools, quantitative imaging analysis methods and open data platforms for aggregating and sharing data and algorithms, can play a vital role in reducing the cost of care, the risks of invasive treatments, and improve overall outcomes for patients with epilepsy.

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We introduce common epileptogenic lesions encountered in patients with drug resistant epilepsy.

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We discuss state of the art computational techniques used to detect lesions.

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There is a need for multi-institutional studies that combine these techniques.

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Clinically validated pipelines alongside the advances in imaging and electrophysiology will improve outcomes.

Neurological Diseases from a Systems Medicine Point of View

The difficulty to understand, diagnose, and treat neurological disorders stems from the great complexity of the central nervous system on different levels of physiological granularity. The individual components, their interactions, and dynamics involved in brain development and function can be represented as molecular, cellular, or functional networks, where diseases are perturbations of networks. These networks can become a useful research tool in investigating neurological disorders if they are properly tailored to reflect corresponding mechanisms. Here, we review approaches to construct networks specific for neurological disorders describing disease-related pathology on different scales: the molecular, cellular, and brain level. We also briefly discuss cross-scale network analysis as a necessary integrator of these scales.

Prognostic and diagnostic value of EEG signal coupling measures in coma

OBJECTIVE

Our aim was to assess the diagnostic and predictive value of several quantitative EEG (qEEG) analysis methods in comatose patients.

METHODS

In 79 patients, coupling between EEG signals on the left-right (inter-hemispheric) axis and on the anterior-posterior (intra-hemispheric) axis was measured with four synchronization measures: relative delta power asymmetry, cross-correlation, symbolic mutual information and transfer entropy directionality. Results were compared with etiology of coma and clinical outcome. Using cross-validation, the predictive value of measure combinations was assessed with a Bayes classifier with mixture of Gaussians.

RESULTS

Five of eight measures showed a statistically significant difference between patients grouped according to outcome; one measure revealed differences in patients grouped according to the etiology. Interestingly, a high level of synchrony between the left and right hemisphere was associated with mortality on intensive care unit, whereas higher synchrony between anterior and posterior brain regions was associated with survival. The combination with the best predictive value reached an area-under the curve of 0.875 (for patients with post anoxic encephalopathy: 0.946).

CONCLUSIONS

EEG synchronization measures can contribute to clinical assessment, and provide new approaches for understanding the pathophysiology of coma.

SIGNIFICANCE

Prognostication in coma remains a challenging task. qEEG could improve current multi-modal approaches.

Disrupted Ipsilateral Network Connectivity in Temporal Lobe Epilepsy

OBJECTIVE

The current practice under which patients with refractory epilepsy are surgically treated is based mainly on the identification of specific cortical areas, mainly the epileptogenic zone, which is believed to be responsible for generation of seizures. A better understanding of the whole epileptic network and its components and properties is required before more effective and less invasive therapies can be developed. The aim of the present study was to partially characterize the evolution of the functional network during the preictal-ictal transition in partial seizures in patients with temporal lobe epilepsy (TLE).

METHODS

Scalp and foramen ovale (FOE) recordings from twenty-two TLE patients were analyzed under the complex network perspective. The density of links, average path length, average clustering coefficient, and modularity were calculated during the preictal and the ictal stages. Both linear-Pearson correlation-and non-linear-phase synchronization-measures were used as proxies of functional connectivity between the electrode locations areas. The transition from one stage to the other was evaluated in the whole network and in the mesial sub-networks. The results were compared with a voltage-dependent measure, namely, the spectral entropy.

RESULTS

Changes in the global functional network during the transition from the preictal to the ictal stage show, in the linear case, that in sixteen cases (72.7%) the density of the links increased during the seizure, with a decrease in the average path length in fifteen cases (68.1%). There was also a preictal and ictal imbalance in functional connectivity during both stages (77.2% to 86.3%). The SE dropped during the seizure in 95.4% of the cases, but did not show any tendency towards lateralization. When using the nonlinear measure of functional connectivity, the phase synchronization, similar results were obtained.

CONCLUSIONS

In TLE patients, the transition to the ictal stage is accompanied by increasing global synchronization and a more ordered spectral content of the signals, indicated by lower spectral entropy. The interictal connectivity imbalance (lower ipsilateral connectivity) is sustained during the seizure, irrespective of any appreciable imbalance in the spectral entropy of the mesial recordings.

Network analysis for a network disorder: The emerging role of graph theory in the study of epilepsy

Recent years have witnessed a paradigm shift in the study and conceptualization of epilepsy, which is increasingly understood as a network-level disorder. An emblematic case is temporal lobe epilepsy (TLE), the most common drug-resistant epilepsy that is electroclinically defined as a focal epilepsy and pathologically associated with hippocampal sclerosis. In this review, we will summarize histopathological, electrophysiological, and neuroimaging evidence supporting the concept that the substrate of TLE is not limited to the hippocampus alone, but rather is broadly distributed across multiple brain regions and interconnecting white matter pathways. We will introduce basic concepts of graph theory, a formalism to quantify topological properties of complex systems that has recently been widely applied to study networks derived from brain imaging and electrophysiology. We will discuss converging graph theoretical evidence indicating that networks in TLE show marked shifts in their overall topology, providing insight into the neurobiology of TLE as a network-level disorder. Our review will conclude by discussing methodological challenges and future clinical applications of this powerful analytical approach.

Time-dependent degree-degree correlations in epileptic brain networks: from assortative to dissortative mixing

We investigate the long-term evolution of degree-degree correlations (assortativity) in functional brain networks from epilepsy patients. Functional networks are derived from continuous multi-day, multi-channel electroencephalographic data, which capture a wide range of physiological and pathophysiological activities. In contrast to previous studies which all reported functional brain networks to be assortative on average, even in case of various neurological and neurodegenerative disorders, we observe large fluctuations in time-resolved degree-degree correlations ranging from assortative to dissortative mixing. Moreover, in some patients these fluctuations exhibit some periodic temporal structure which can be attributed, to a large extent, to daily rhythms. Relevant aspects of the epileptic process, particularly possible pre-seizure alterations, contribute marginally to the observed long-term fluctuations. Our findings suggest that physiological and pathophysiological activity may modify functional brain networks in a different and process-specific way. We evaluate factors that possibly influence the long-term evolution of degree-degree correlations.

Are Absence Epilepsy and Nocturnal Frontal Lobe Epilepsy System Epilepsies of the Sleep/Wake System?

System epilepsy is an emerging concept interpreting major nonlesional epilepsies as epileptic dysfunctions of physiological systems. I extend here the concept of reflex epilepsy to epilepsies linked to input dependent physiological systems. Experimental and clinical reseach data were collected to create a coherent explanation of underlying pathomechanism in AE and NFLE. We propose that AE should be interpreted as epilepsy linked to the corticothalamic burst-firing mode of NREM sleep, released by evoked vigilance level oscillations characterized by reactive slow wave response. In the genetic variation of NFLE the ascending cholinergic arousal system plays an essential role being in strong relationship with a gain mutation of the nicotinic acethylcholin receptors, rendering the arousal system hyperexcitable. I try to provide a more unitary interpretation for the variable seizure manifestation integrating them as different degree of pathological arosuals and alarm reactions. As a supporting hypothesis the similarity between arousal parasomnias and FNLE is shown, underpinned by overlaping pathomechanism and shared familiarity, but without epileptic features. Lastly we propose that both AE and NFLE are system epilepsies of the sleep-wake system representing epileptic disorders of the antagonistic sleep/arousal network. This interpretation may throw new light on the pathomechanism of AE and NFLE.

Assessing the equivalence between etomidate and seizure network dynamics in temporal lobe epilepsy

OBJECTIVE

Etomidate mimics some typical epileptic neurophysiological features, such as the appearance of spikes and high frequency oscillations, when it is administrated to epileptic patients. However, little is known about its influence on the underlying cortical network. An assessment of comparable cortical dynamics between seizures and etomidate would allow for a more detailed study of the network parameters underlying the ictal stage by using etomidate as a proxy. The objective of the present work is to show that temporal lobe seizures produce network changes comparable to the ones elicited by etomidate administration.

METHODS

Scalp and foramen ovale electrodes (FOE) recordings from nine temporal lobe epilepsy patients were analyzed under the complex network perspective. The clustering coefficients, average path lengths, density of links, modularity and spectral entropy were calculated during the pre-ictal and ictal stages and post-etomidate administration. Etomidate administration produced no seizure in any of the analyzed cases.

RESULTS

The density of lines (six of nine patients) and spectral entropy (eight of nine patients) displayed similar behavior to the preictal-ictal transition when etomidate effects altered the epileptic network (FOE+scalp). When considering only the mesial sub-network, changes induced by etomidate perfusion replicated the same type of imbalance observed during the ictal stage in the nine patient's sample and in eight out of nine regarding the preictal stage. Both statistical significance at a level of 1% and size effects, evaluated by using the standardized mean differences, show similar network changes during the preictal-ictal and preictal-etomidate transitions.

CONCLUSIONS

Etomidate perfusion in patients with temporal lobe epilepsy induces network changes comparable to the changes resulting from seizures.

SIGNIFICANCE

The finding reported here could improve the study of network dynamics during the ictal phase, not only with electrophysiological methods, but also in other cases, such as functional magnetic resonance imaging.

Chow-Liu trees are sufficient predictive models for reproducing key features of functional networks of periictal EEG time-series

Seizure freedom in patients suffering from pharmacoresistant epilepsies is still not achieved in 20-30% of all cases. Hence, current therapies need to be improved, based on a more complete understanding of ictogenesis. In this respect, the analysis of functional networks derived from intracranial electroencephalographic (iEEG) data has recently become a standard tool. Functional networks however are purely descriptive models and thus are conceptually unable to predict fundamental features of iEEG time-series, e.g., in the context of therapeutical brain stimulation. In this paper we present some first steps towards overcoming the limitations of functional network analysis, by showing that its results are implied by a simple predictive model of time-sliced iEEG time-series. More specifically, we learn distinct graphical models (so called Chow-Liu (CL) trees) as models for the spatial dependencies between iEEG signals. Bayesian inference is then applied to the CL trees, allowing for an analytic derivation/prediction of functional networks, based on thresholding of the absolute value Pearson correlation coefficient (CC) matrix. Using various measures, the thus obtained networks are then compared to those which were derived in the classical way from the empirical CC-matrix. In the high threshold limit we find (a) an excellent agreement between the two networks and (b) key features of periictal networks as they have previously been reported in the literature. Apart from functional networks, both matrices are also compared element-wise, showing that the CL approach leads to a sparse representation, by setting small correlations to values close to zero while preserving the larger ones. Overall, this paper shows the validity of CL-trees as simple, spatially predictive models for periictal iEEG data. Moreover, we suggest straightforward generalizations of the CL-approach for modeling also the temporal features of iEEG signals.

What graph theory actually tells us about resting state interictal MEG epileptic activity

Graph theory provides a useful framework to study functional brain networks from neuroimaging data. In epilepsy research, recent findings suggest that it offers unique insight into the fingerprints of this pathology on brain dynamics. Most studies hitherto have focused on seizure activity during focal epilepsy, but less is known about functional epileptic brain networks during interictal activity in frontal focal and generalized epilepsy. Besides, it is not clear yet which measures are most suitable to characterize these networks. To address these issues, we recorded magnetoencephalographic (MEG) data using two orthogonal planar gradiometers from 45 subjects from three groups (15 healthy controls (7 males, 24 ± 6 years), 15 frontal focal (8 male, 32 ± 16 years) and 15 generalized epileptic (6 male, 27 ± 7 years) patients) during interictal resting state with closed eyes. Then, we estimated the total and relative spectral power of the largest principal component of the gradiometers, and the degree of phase synchronization between each sensor site in the frequency range [0.5–40 Hz]. We further calculated a comprehensive battery of 15 graph-theoretic measures and used the affinity propagation clustering algorithm to elucidate the minimum set of them that fully describe these functional brain networks. The results show that differences in spectral power between the control and the other two groups have a distinctive pattern: generalized epilepsy presents higher total power for all frequencies except the alpha band over a widespread set of sensors; frontal focal epilepsy shows higher relative power in the beta band bilaterally in the fronto-central sensors. Moreover, all network indices can be clustered into three groups, whose exemplars are the global network efficiency, the eccentricity and the synchronizability. Again, the patterns of differences were clear: the brain network of the generalized epilepsy patients presented greater efficiency and lower eccentricity than the control subjects for the high frequency bands, without a clear topography. Besides, the frontal focal epileptic patients showed only reduced eccentricity for the theta band over fronto-temporal and central sensors. These outcomes indicate that functional epileptic brain networks are different to those of healthy subjects during interictal stage at rest, with a unique pattern of dissimilarities for each type of epilepsy. Further, when properly selected, three network indices suffice to provide a comprehensive description of these differences. Yet, since such uniqueness in the pattern of differences is also evident in the power spectrum, we conclude that the added value of the graph theory approach in this context should not be overestimated.

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We study MEG activity during interictal resting state with closed eyes.

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Generalized epilepsy presents higher total power over a widespread set of sensors.

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Frontal epilepsy shows higher relative power in beta band on fronto-central sensors.

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We also found altered functional brain networks in epilepsy using graph theory.

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The pattern of differences from control subjects is unique for each type of epilepsy.

Structural-functional coupling changes in temporal lobe epilepsy

Alterations in both structural connectivity (SC) and functional connectivity (FC) have been reported in temporal lobe epilepsy (TLE). However, the relationship between FC and SC remains less understood. This study used functional connectivity MRI and diffusion tensor imaging to examine coupling of FC and SC within the limbic network of TLE, as well as its relation to epilepsy duration, regional changes, and disease laterality in 14 patients with left TLE, 10 with right TLE, and 11 healthy controls. Structural and functional networks were separately constructed and the correlation estimated between structural and functional connectivity. This measure of SC-FC coupling was compared between left/right TLE and controls, and correlated with epilepsy duration. Elastic net regression was used to investigate regional structural and functional changes associated with SC-FC coupling. SC-FC coupling was decreased in left TLE compared to controls, and accompanied by reductions in FC for left and right TLE and in SC for left TLE. When examined in relation to disease duration, an increase in SC-FC coupling with longer epilepsy duration was observed, associated predominantly with structural loss of the fusiform and frontal inferior orbital gyrus in left TLE and functional hub redistribution in right TLE. These results suggest that decoupling between structural and functional networks in TLE is modulated by several factors, including epilepsy duration and regional changes in the fusiform, frontal inferior orbital gyrus, posterior cingulate, and hippocampus. SC-FC coupling may provide a more sensitive biomarker of disease burden in TLE than biomarkers based on single imaging modalities.

Methods and utility of EEG-fMRI in epilepsy

Brain activity data in general and more specifically in epilepsy can be represented as a matrix that includes measures of electrophysiology, anatomy and behaviour. Each of these sub-matrices has a complex interaction depending upon the brain state i.e., rest, cognition, seizures and interictal periods. This interaction presents significant challenges for interpretation but also potential for developing further insights into individual event types. Successful treatments in epilepsy hinge on unravelling these complexities, and also on the sensitivity and specificity of methods that characterize the nature and localization of underlying physiological and pathological networks. Limitations of pharmacological and surgical treatments call for refinement and elaboration of methods to improve our capability to localise the generators of seizure activity and our understanding of the neurobiology of epilepsy. Simultaneous electroencephalography and functional magnetic resonance imaging (EEG-fMRI), by potentially circumventing some of the limitations of EEG in terms of sensitivity, can allow the mapping of haemodynamic networks over the entire brain related to specific spontaneous and triggered epileptic events in humans, and thereby provide new localising information. In this work we review the published literature, and discuss the methods and utility of EEG-fMRI in localising the generators of epileptic activity. We draw on our experience and that of other groups, to summarise the spectrum of information provided by an increasing number of EEG-fMRI case-series, case studies and group studies in patients with epilepsy, for its potential role to elucidate epileptic generators and networks. We conclude that EEG-fMRI provides a multidimensional view that contributes valuable clinical information to localize the epileptic focus with potential important implications for the surgical treatment of some patients with drug-resistant epilepsy, and insights into the resting state and cognitive network dynamics.

Dissociated multimodal hubs and seizures in temporal lobe epilepsy

Objective

Brain connectivity at rest is altered in temporal lobe epilepsy (TLE), particularly in “hub” areas such as the posterior default mode network (DMN). Although both functional and anatomical connectivity are disturbed in TLE, the relationships between measures as well as to seizure frequency remain unclear. We aim to clarify these associations using connectivity measures specifically sensitive to hubs.

Methods

Connectivity between 1000 cortical surface parcels was determined in 49 TLE patients and 23 controls with diffusion and resting-state functional magnetic resonance imaging. Two types of hub connectivity were investigated across multiple brain modules (the DMN, motor system, etcetera): (1) within-module connectivity (a measure of local importance that assesses a parcel's communication level within its own subnetwork) and (2) between-module connectivity (a measure that assesses connections across multiple modules).

Results

In TLE patients, there was lower overall functional integrity of the DMN as well as an increase in posterior hub connections with other modules. Anatomical between-module connectivity was globally decreased. Higher DMN disintegration (DD) coincided with higher anatomical between-module connectivity, whereas both were associated with increased seizure frequency. DD related to seizure frequency through mediating effects of anatomical connectivity, but seizure frequency also correlated with anatomical connectivity through DD, indicating a complex interaction between multimodal networks and symptoms.

Interpretation

We provide evidence for dissociated anatomical and functional hub connectivity in TLE. Moreover, shifts in functional hub connections from within to outside the DMN, an overall loss of integrative anatomical communication, and the interaction between the two increase seizure frequency.