Comparative Analysis of Interpretable Machine Learning Approaches for Major Depressive Disorder Discrimination using rsfMRI

Motivation: Resting-state functional magnetic resonance imaging (rsfMRI) holds significant promise as a predictive tool for assessing treatment response in individuals with major depressive disorder (MDD). Goal(s): We aim to assess the credibility of model predictions using various explainers and identify the most salient regions contributing to MDD discrimination. Approach: 3 representatives of explainable machine learning methods (CAM, LIME, SHAP) are employed in this study to explain model prediction in various views. Results: This study demonstrates the superiority of LIME and SHAP for model explanation in the task of MDD discrimination using rsfMRI. Impact: Our findings will provide effective guidance for the clinical diagnosis and treatment of MDD.