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Temporal Lobe Epilepsy Surgical Outcomes Can Be Inferred Based on Structural Connectome Hubs: A Machine Learning Study
Author(s) -
Gleichgerrcht Ezequiel,
Keller Simon S.,
Drane Daniel L.,
Munsell Brent C.,
Davis Kathryn A.,
Kaestner Erik,
Weber Bernd,
Krantz Samantha,
Vandergrift William A.,
Edwards Jonathan C.,
McDonald Carrie R.,
Kuzniecky Ruben,
Bonilha Leonardo
Publication year - 2020
Publication title -
annals of neurology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 4.764
H-Index - 296
eISSN - 1531-8249
pISSN - 0364-5134
DOI - 10.1002/ana.25888
Subject(s) - temporal lobe , connectome , epilepsy , neuroscience , diffusion mri , betweenness centrality , connectomics , psychology , drug resistant epilepsy , magnetic resonance imaging , epilepsy surgery , electrocorticography , medicine , computer science , centrality , radiology , functional connectivity , mathematics , combinatorics
Objective Medial temporal lobe epilepsy (TLE) is the most common form of medication‐resistant focal epilepsy in adults. Despite removal of medial temporal structures, more than one‐third of patients continue to have disabling seizures postoperatively. Seizure refractoriness implies that extramedial regions are capable of influencing the brain network and generating seizures. We tested whether abnormalities of structural network integration could be associated with surgical outcomes. Methods Presurgical magnetic resonance images from 121 patients with drug‐resistant TLE across 3 independent epilepsy centers were used to train feed‐forward neural network models based on tissue volume or graph‐theory measures from whole‐brain diffusion tensor imaging structural connectomes. An independent dataset of 47 patients with TLE from 3 other epilepsy centers was used to assess the predictive values of each model and regional anatomical contributions toward surgical treatment results. Results The receiver operating characteristic area under the curve based on regional betweenness centrality was 0.88, significantly higher than a random model or models based on gray matter volumes, degree, strength, and clustering coefficient. Nodes most strongly contributing to the predictive models involved the bilateral parahippocampal gyri, as well as the superior temporal gyri. Interpretation Network integration in the medial and lateral temporal regions was related to surgical outcomes. Patients with abnormally integrated structural network nodes were less likely to achieve seizure freedom. These findings are in line with previous observations related to network abnormalities in TLE and expand on the notion of underlying aberrant plasticity. Our findings provide additional information on the mechanisms of surgical refractoriness. ANN NEUROL 2020;88:970–983