MS-STFNN: A multi-scale spatio-temporal fusion neural network for fMRI-based depression diagnosis

Major depressive disorder (MDD) is a common mental disorder. Current clinical diagnosis primarily relies on subjective scales, underscoring an urgent need to develop objective neuroimaging diagnostic methods. Brain network analysis and deep learning techniques based on functional magnetic resonance imaging (fMRI) have been proven effective in uncovering abnormal spatial patterns associated with depression. However, the temporal dynamics of brain activity are also equally critical for understanding the neural mechanisms of MDD. Despite this importance, existing studies still suffer from insufficient characterization of spatio-temporal information. This study proposes a novel multi-scale spatio-temporal fusion neural network (MS-STFNN) that captures multi-granularity spatial features of the brain from local to global levels, while incorporating dynamic functional connectivity (DFC) and raw fMRI sequences to characterize time-varying properties at multiple resolution levels. Finally, effective classification of different depression subtypes is achieved through multi-scale feature fusion (MSFF). The results show that the proposed model outperforms baseline models across multiple subtype classification tasks. Ablation study further demonstrates the effectiveness of multi-granularity spatial feature extraction, multi-resolution temporal representation, and the spatio-temporal fusion strategy. This study provides a reliable framework for the objective diagnosis of depression, addressing the limitations of traditional subjective assessments, while also offering new insights into the efficient utilization of spatio-temporal features in brain network analysis.