Epilepsy patients (EP) endure harmful effects both on their health and quality of life. Early identification of these individuals would be incredibly helpful to gauge management and expectations. Implementing a novel multilayer network analysis, considering communication within functional and structural networks as well as the interactions between them, we tested whether this whole-brain comprehensive network hierarchy can be used as predictors of epilepsy. Using multilayer network features as predictors in a machine learning algorithm we were able to classify EP and controls with overall accuracy of 84%, demonstrating the applicability of multi-modal imaging for diagnostics of epilepsy.
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