Interpretable deep learning for functional MRI-based auxiliary diagnosis of major depressive disorder with suicidal ideation.

Introduction: Suicidal ideation (SI) in patients with major depressive disorder (MDD) is frequently underrecognized in early clinical assessment, owing to the complexity of its underlying neurobiological mechanisms and the lack of complementary objective biomarkers. To address this issue, this study proposes an interpretable deep learning framework designed to assist in the diagnosis of patients with MDD with suicidal ideation (MDDSI) and to elucidate its underlying neural mechanisms. Methods: A modified BrainNet convolutional neural network architecture was employed, utilizing a whole-brain functional connectivity (FC) matrix derived from resting-state functional magnetic resonance imaging as input features. A gradient-weighted class activation mapping algorithm was subsequently applied to visualize key brain regions. The study included 356 patients with MDDSI and 107 patients with MDD without suicidal ideation (MDDNSI) as the control group. Results: Five-fold cross-validation indicated that the model achieved an accuracy of 88%, sensitivity of 95%, specificity of 85%, and an area under the curve of 0.93 on the test set. Feature visualization results revealed that the model's classification decisions primarily relied on abnormal FC patterns in regions such as the motor cortex, anterior cingulate cortex, occipital lobe, temporal lobe, parietal lobe, and cerebellum. Discussion: This work provides a valuable reference for both auxiliary diagnosis and the mechanistic investigation of MDDSI.