Temporal lobe epilepsy (TLE) is the most common form of adult epilepsy. Accumulating evidence has shown that TLE is a disorder of abnormal epileptogenic networks, rather than focal sources. Graph theory allows for a network-based representation of TLE brain networks, and has potential to illuminate characteristics of brain topology conducive to TLE pathophysiology, including seizure initiation and spread. We review basic concepts which we believe will prove helpful in interpreting results rapidly emerging from graph theory research in TLE. In addition, we summarize the current state of graph theory findings in TLE as they pertain its pathophysiology. Several common findings have emerged from the many modalities which have been used to study TLE using graph theory, including structural MRI, diffusion tensor imaging, surface EEG, intracranial EEG, magnetoencephalography, functional MRI, cell cultures, simulated models, and mouse models, involving increased regularity of the interictal network configuration, altered local segregation and global integration of the TLE network, and network reorganization of temporal lobe and limbic structures. As different modalities provide different views of the same phenomenon, future studies integrating data from multiple modalities are needed to clarify findings and contribute to the formation of a coherent theory on the pathophysiology of TLE.

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SummaryObjectiveTo analyze data from Seizure Tracker, a large electronic seizure diary, including comparison of seizure characteristics among different etiologies, temporal patterns in seizure fluctuations, and specific triggers.MethodsZero‐inflated negative binomial mixed‐effects models were used to evaluate temporal patterns of seizure events (during the day or week), as well as group differences in monthly seizure frequency between children and adults and between etiologies. The association of long seizures with seizure triggers was evaluated using a mixed‐effects logistic model with subject as the random effect. Incidence rate ratios (IRRs) and odds ratios were reported for analyses involving zero‐inflated negative binomial and logistic mixed‐effects models, respectively.ResultsA total of 1,037,909 seizures were logged by 10,186 subjects (56.7% children) from December 2007 to January 2016. Children had more frequent seizures than adults did (median monthly seizure frequency 3.5 vs. 2.7, IRR 1.26; p < 0.001). Seizures demonstrated a circadian pattern (higher frequency between 07:00 a.m. and 10:00 a.m. and lower overnight), and seizures were reported differentially across the week (seizure rates higher Monday through Friday than Saturday or Sunday). Longer seizures (>5 or >30 min) had a higher proportion of the following triggers when compared with shorter seizures: “Overtired or irregular sleep,” “Bright or flashing lights,” and “Emotional stress” (p < 0.004).SignificanceThis study explored a large cohort of patients with self‐reported seizures; strengths and limitations of large seizure diary databases are discussed. The findings in this study are consistent with those of prior work in smaller validated cohorts, suggesting that patient‐recorded databases are a valuable resource for epilepsy research, capable of both replication of results and generation of novel hypotheses.

Purpose Temporal lobe epilepsy (TLE) is considered a brain network disorder, additionally representing the most common form of pharmaco-resistant epilepsy in adults. There is increasing evidence that seizures in TLE arise from abnormal epileptogenic networks, which extend beyond the clinico-radiologically determined epileptogenic zone and may contribute to the failure rate of 30–50% following epilepsy surgery. Graph theory allows for a network-based representation of TLE brain networks using several neuroimaging and electrophysiologic modalities, and has potential to provide clinicians with clinically useful biomarkers for diagnostic and prognostic purposes. Methods We performed a review of the current state of graph theory findings in TLE as they pertain to localization of the epileptogenic zone, prediction of pre- and post-surgical seizure frequency and cognitive performance, and monitoring cognitive decline in TLE. Results Although different neuroimaging and electrophysiologic modalities have yielded occasionally conflicting results, several potential biomarkers have been characterized for identifying the epileptogenic zone, pre-/post-surgical seizure prediction, and assessing cognitive performance. For localization, graph theory measures of centrality have shown the most potential, including betweenness centrality, outdegree, and graph index complexity, whereas for prediction of seizure frequency, measures of synchronizability have shown the most potential. The utility of clustering coefficient and characteristic path length for assessing cognitive performance in TLE is also discussed. Conclusions Future studies integrating data from multiple modalities and testing predictive models are needed to clarify findings and develop graph theory for its clinical utility.