title: Clinical correlates of graph theory findings in temporal lobe epilepsy.
abstract: OBJECTIVE
  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.
authors.affiliation: Department of Neurology, Baylor College of Medicine, Houston, TX, USA; Neurology Care Line, VA Medical Center, Houston, TX, USA. Electronic address: zulfi.haneef@bcm.edu.
authors.forename: Zulfi
authors.initials: Z
authors.lastname: Haneef
authors.affiliation: Department of Statistics, Rice University, Houston, TX, USA.
authors.forename: Sharon
authors.initials: S
authors.lastname: Chiang
mesh_terms: D001921:Brain / Q000473:pathology* / Q000503:physiopathology; D001931:Brain Mapping; D003952:Diagnostic Imaging; D004833:Epilepsy, Temporal Lobe / Q000175:diagnosis / Q000473:pathology* / Q000503:physiopathology; D009415:Nerve Net / Q000473:pathology* / Q000503:physiopathology; D012640:Seizures / Q000175:diagnosis / Q000473:pathology* / Q000503:physiopathology
keywords: Diffusion tensor imaging; Functional connectivity; Graph theory; Seizures; Small-world networks; Temporal lobe epilepsy