Multi-Scale Structural MRI Features Reveal Task-Based Functional Connectivity and Its Alterations in Psychiatric Disorders: A Collaborative Graph Attention Network Approach
The Collaborative Graph Attention Multi-Task Network (CoGA-MTN), a deep learning framework that integrates multi-scale structural features to jointly predict task-based functional connectivity (FC) and identify potential disease-related network alterations, is introduced.
The template-aware Network-collaboration Model with Hierarchical Attention (TNA) provides an efficient and interpretable tool for pinpointing aberrant functional circuits, thereby contributing to the identification of potential network-based biomarkers and offering insights into the network pathophysiology of MDD.
Ting Chen, Binbin Zhou, Lin Sun· Discover Computing· 0 citations
BACKGROUND
Major depressive disorder (MDD) is a highly heterogeneous affective disorder, and the identification of reliable neuroimaging markers remains challenging. Resting-state functional magnetic resonance imaging offers a promising approach for characterizing altered brain functional organization in MDD. However,...
Ting Mei, Man-Yun Zhu, Hongbo Zhu et al.· Journal of Affective Disorde...· 0 citations
Results indicate MS patients exhibit impaired high-load connectivity patterns, with the proposed framework showing potential as an auxiliary diagnostic tool, and a technical blueprint for task-based network analysis.
Sareh Yousefi, M. Daliri· Brain Informatics· 0 citations
An Adaptive Multi-band Dynamic Functional-Connectivity Graph Attention Network (AMD-FCGAT) that represents resting-state electroencephalography as a set of band-resolved brain graphs and learns disease-specific connectivity signatures through attention offers an accurate and transparent tool for electroencephalography-...
Sumit Gupta, U. N. Ranjitha, J. G. S. Banu et al.· International journal of com...· 5 citations
Three representative graph neural network architectures for ASD classification based on resting-state functional MRI reveal that distinct GNN architectures converge on common neurobiological signatures of ASD, highlighting their potential for reliable brain network modeling and biomarker discovery in psychiatry researc...
