Cross-Domain Identification of Multisite Major Depressive Disorder Using End-to-End Brain Dynamic Attention Network

Abstract

Objectives: Establishing objective and quantitative imaging markers at individual level can assist in accurate diagnosis of Major Depressive Disorder (MDD). However, the clinical heterogeneity of MDD and the shift to multisite data decreased identification accuracy. To address these issues, the Brain Dynamic Attention Network (BDANet) is innovatively proposed, and analyzed bimodal scans from 2055 participants of the Rest-meta-MDD consortium. Methods: The end-to-end BDANet contains two crucial components. The Dynamic BrainGraph Generator dynamically focuses and represents topological relationships between Regions of Interest, overcoming limitations of static methods. The Ensemble Classifier is constructed to obfuscate domain sources to achieve inter-domain alignment. Finally, BDANet dynamically generates sample-specific brain graphs by downstream recognition tasks. Results: The proposed BDANet achieved an accuracy of 81.6%. The regions with high attribution for classification were mainly located in the insula, cingulate cortex and auditory cortex. The level of brain connectivity in p24 region was negatively correlated (p<0.05) with the severity of MDD. Additionally, sex differences in connectivity strength were observed in specific brain regions and functional subnetworks (p<0.05 or p<0.01). Conclusions and Significance: These findings based on a large multisite dataset support the conclusion that BDANet can better solve the problem of the clinical heterogeneity of MDD and the shift of multisite data. It also illustrates the potential utility of BDANet for personalized accurate identification, treatment and intervention of MDD.

Influence of depression severity on interhemispheric functional integration: An analysis from the REST-meta-MDD Database

“…Major depressive disorder (MDD) is a severe mental disorder that characterized by persistent and pervasive sense of sadness, a lack of interest, and various emotional and physical problems (Jia et al, 2010) that signi cantly impairs the social functioning and quality of life of patients (Yuan et al, 2024), leading to a substantial socioeconomical burden (Sliz and Hayley, 2012). Despite considerable research efforts, the neurophysiological mechanisms underlying MDD remain elusive.…”