Common functional connectivity alterations in focal epilepsies identified by machine learning

Authors

Taha Gholipour, Department of Neurology, George Washington University, Washington, District of Columbia, USA.
Xiaozhen You, Center for Neuroscience, Children's National Hospital, Washington, District of Columbia, USA.
Steven M. Stufflebeam, Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA.
Murray Loew, Department of Biomedical Engineering, George Washington University, Washington, District of Columbia, USA.
Mohamad Z. Koubeissi, Department of Neurology, George Washington University, Washington, District of Columbia, USA.
Victoria L. Morgan, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
William D. Gaillard, Department of Neurology, George Washington University, Washington, District of Columbia, USA.

Document Type

Journal Article

Publication Date

3-1-2022

Journal

Epilepsia

Volume

63

Issue

3

DOI

10.1111/epi.17160

Keywords

functional MRI; lateralization; support vector machine; temporal lobe epilepsy

Abstract

OBJECTIVE: This study was undertaken to identify shared functional network characteristics among focal epilepsies of different etiologies, to distinguish epilepsy patients from controls, and to lateralize seizure focus using functional connectivity (FC) measures derived from resting state functional magnetic resonance imaging (MRI). METHODS: Data were taken from 103 adult and 65 pediatric focal epilepsy patients (with or without lesion on MRI) and 109 controls across four epilepsy centers. We used three whole-brain FC measures: parcelwise connectivity matrix, mean FC, and degree of FC. We trained support vector machine models with fivefold cross-validation (1) to distinguish patients from controls and (2) to lateralize the hemisphere of seizure onset in patients. We reported the regions and connections with the highest importance from each model as the common FC differences between the compared groups. RESULTS: FC measures related to the default mode and limbic networks had higher importance relative to other networks for distinguishing epilepsy patients from controls. In lateralization models, regions related to somatosensory, visual, default mode, and basal ganglia showed higher importance. The epilepsy versus control classification model trained using a 400-parcel connectivity matrix achieved a median testing accuracy of 75.6% (median area under the curve [AUC] = .83) in repeated independent testing. Lateralization accuracy using the 400-parcel connectivity matrix reached a median accuracy of 64.0% (median AUC = .69). SIGNIFICANCE: Machine learning models revealed common FC alterations in a heterogeneous group of patients with focal epilepsies. The distribution of the most altered regions supports the hypothesis that shared functional alteration exists beyond the seizure onset zone and its epileptic network. We showed that FC measures can distinguish patients from controls, and further lateralize focal epilepsies. Future studies are needed to confirm these findings by using larger numbers of epilepsy patients.

APA Citation

Gholipour, Taha; You, Xiaozhen; Stufflebeam, Steven M.; Loew, Murray; Koubeissi, Mohamad Z.; Morgan, Victoria L.; and Gaillard, William D., "Common functional connectivity alterations in focal epilepsies identified by machine learning" (2022). GW Authored Works. Paper 186.
https://hsrc.himmelfarb.gwu.edu/gwhpubs/186

Department

Neurology