Clinical correlates of graph theory findings in temporal lobe epilepsy

Abstract

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.

Machine Learning of DTI Structural Brain Connectomes for Lateralization of Temporal Lobe Epilepsy

“…26 Comparisons of patients with TLE with normal controls revealed alterations of graph descriptors distributed predominantly in the ipsilateral temporoparietal lobe, including areas of the DMN. Our results are in accord with the reported network alteration in TLE demonstrated by fMRI, 27,28 supporting the validity of graph analyses using a DTI structural connectome. They are also in line with those of previous reports utilizing graph analyses to assess the DTI structural connectome.…”

“…[11][12][13][14][15] We did not observe the reported paradoxical increase in clustering coefficient or local efficiency, 12,13 and there has been some variation in the reported alteration of the clustering coefficient in patients with TLE. 28 One possible explanation for this discrepancy is that the clustering coefficient depends on the stage of disease; indeed, it has been reported to increase during most of the sclerotic process and decrease in the final stages of disease. 29 As for differences between left and right TLE, most studies have reported a stronger impact of left TLE on network function as assessed by DTI 7,15 and restingstate fMRI, 27 as in this study.…”

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

“…So far, only a few reports have also provided evidence for a relationship between topological disruptions, clinical markers, and cognitive outcomes [149]. Notwithstanding the group-level character of the majority of these findings, and the need for further replication, they suggest a diagnostic utility of network-level assessments.…”

The influence of construction methodology on structural brain network measures: A review

“…Recently, it has been proposed that the overload and failure of hubs and resultant disruption of the normal hierarchical architecture of brain networks might be the common pathway of neurological disorders (Stam, 2014;Crossley et al, 2014;Sporns, 2014;Fornito et al, 2015). Brain network analysis may challenge the traditional concept of brain diseases being either 'local' or 'global', and provide new sensitive and quantitative biomarkers for the diagnosis, prognosis and treatment evaluation of various brain diseases including epilepsy (Haneef and Chiang, 2014), dementia, schizophrenia, multiple sclerosis and others .…”