Multi-Connectivity Representation Learning Network for Major Depressive Disorder Diagnosis

计算机科学 功能磁共振成像 代表(政治) 人工智能 模态(人机交互) 特征学习 机器学习 模式识别(心理学) 理论计算机科学 神经科学 政治学 政治 法学 生物
作者
Youyong Kong,Wenhan Wang,Xiaoyun Liu,Shuwen Gao,Zhenghua Hou,Chunming Xie,Zhijun Zhang,Yonggui Yuan
出处
期刊:IEEE Transactions on Medical Imaging [Institute of Electrical and Electronics Engineers]
卷期号:42 (10): 3012-3024 被引量:5
标识
DOI:10.1109/tmi.2023.3274351
摘要

The pathophysiology of major depressive disorder (MDD) has been demonstrated to be highly associated with the dysfunctional integration of brain activity. Existing studies only fuse multi-connectivity information in a one-shot approach and ignore the temporal property of functional connectivity. A desired model should utilize the rich information in multiple connectivities to help improve the performance. In this study, we develop a multi-connectivity representation learning framework to integrate multi-connectivity topological representation from structural connectivity, functional connectivity and dynamic functional connectivities for automatic diagnosis of MDD. Briefly, structural graph, static functional graph and dynamic functional graphs are first computed from the diffusion magnetic resonance imaging (dMRI) and resting state functional magnetic resonance imaging (rsfMRI). Secondly, a novel Multi-Connectivity Representation Learning Network (MCRLN) approach is developed to integrate the multiple graphs with modules of structural-functional fusion and static-dynamic fusion. We innovatively design a Structural-Functional Fusion (SFF) module, which decouples graph convolution to capture modality-specific features and modality-shared features separately for an accurate brain region representation. To further integrate the static graphs and dynamic functional graphs, a novel Static-Dynamic Fusion (SDF) module is developed to pass the important connections from static graphs to dynamic graphs via attention values. Finally, the performance of the proposed approach is comprehensively examined with large cohorts of clinical data, which demonstrates its effectiveness in classifying MDD patients. The sound performance suggests the potential of the MCRLN approach for the clinical use in diagnosis. The code is available at https://github.com/LIST-KONG/MultiConnectivity-master.
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