title: Connectomics and graph theory analyses: Novel insights into network abnormalities in epilepsy.
abstract: The assessment of neural networks in epilepsy has become increasingly relevant in the context of translational research, given that localized forms of epilepsy are more likely to be related to abnormal function within specific brain networks, as opposed to isolated focal brain pathology. It is notable that variability in clinical outcomes from epilepsy treatment may be a reflection of individual patterns of network abnormalities. As such, network endophenotypes may be important biomarkers for the diagnosis and treatment of epilepsy. Despite its exceptional potential, measuring abnormal networks in translational research has been thus far constrained by methodologic limitations. Fortunately, recent advancements in neuroscience, particularly in the field of connectomics, permit a detailed assessment of network organization, dynamics, and function at an individual level. Data from the personal connectome can be assessed using principled forms of network analyses based on graph theory, which may disclose patterns of organization that are prone to abnormal dynamics and epileptogenesis. Although the field of connectomics is relatively new, there is already a rapidly growing body of evidence to suggest that it can elucidate several important and fundamental aspects of abnormal networks to epilepsy. In this article, we provide a review of the emerging evidence from connectomics research regarding neural network architecture, dynamics, and function related to epilepsy. We discuss how connectomics may bring together pathophysiologic hypotheses from conceptual and basic models of epilepsy and in vivo biomarkers for clinical translational research. By providing neural network information unique to each individual, the field of connectomics may help to elucidate variability in clinical outcomes and open opportunities for personalized medicine approaches to epilepsy. Connectomics involves complex and rich data from each subject, thus collaborative efforts to enable the systematic and rigorous evaluation of this form of "big data" are paramount to leverage the full potential of this new approach.
authors.affiliation: Department of Neurology, Medical University of South Carolina, Charleston, South Carolina, U.S.A.
authors.forename: Ezequiel
authors.initials: E
authors.lastname: Gleichgerrcht
authors.affiliation: Department of Neurology, Medical University of South Carolina, Charleston, South Carolina, U.S.A.
authors.forename: Madison
authors.initials: M
authors.lastname: Kocher
authors.affiliation: Department of Neurology, Medical University of South Carolina, Charleston, South Carolina, U.S.A.
authors.forename: Leonardo
authors.initials: L
authors.lastname: Bonilha
mesh_terms: D000818:Animals; D001921:Brain / Q000473:pathology*; D063132:Connectome / Q000379:methods / Q000639:trends*; D004827:Epilepsy / Q000175:diagnosis* / Q000235:genetics*; D006801:Humans; D009415:Nerve Net / Q000473:pathology*
keywords: Biomarkers; Connectome; Epilepsy; Graph theory; Neural networks