Explainable depression classification: a machine learning approach based on brain network size and functional connectivity

Motivation: Major depressive disorder (MDD) affects ~6% of adults annually worldwide, but a lack of understanding of the pathology and heterogeneity may underlie its low treatment effectiveness. Goal(s): This study aimed to identify explainable functional MRI biomarkers of MDD on an individual level. Approach: Classification models were run to predict MDD for three functional measures. Results: In two datasets, >70% MDD accuracy was achieved for each measure. Highest performance was obtained with region-based functional connectivity but spatial extent provided novel perspectives on abnormal brain functioning, such as decreased cerebellum involvement in the frontoparietal network, potentially reflecting decreased emotion regulation or control during cognitive processes. Impact: This MRI research contributes to the identification of robust depression biomarkers, enhancing our understanding of the abnormal brain functioning. The explainability of the spatial extent feature provides additional insights into its pathology which may be utilized for diagnostic tools.