Major depressive disorder (MDD) diagnosis, research, and treatment is especially challenging given that current diagnosis entirely depends on clinical symptoms, whereas the underlying brain pathology remains largely unclear. Applying a novel multilayer network analysis, considering communication within functional and structural networks as well as the interactions between them, we found aberrant centrality measures of various brain regions in MDD compared to controls that were not detected using single structural or functional connectomes. In addition, using multilayer network features as predictors in a machine learning algorithm resulted in higher predictive values compared to classification models based on single layer.
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