arXiv:2606. 03322v1 Announce Type: cross Abstract: The graphical representation of the brain offers critical insights into diagnosing and prognosing neurodegenerative disease via relationships between regions of interest (ROIs).
By Jaeyoon Sim, Minjae Lee, Guorong Wu, Won Hwa Kim
arXiv:2609.37220v1 Announce Type: new
Abstract: Exploring high-order correlations and long-range dependencies in brain networks holds significant value for both neuroscience research and clinical dia...
By Jingxi Feng, Xudong Chen, Yifan Zhang, Heming Xu, Hongcheng Han, Xijing Wang, Dong Zhang, Shaoyi Du
arXiv:2607. 01901v1 Announce Type: cross Abstract: Effective brain disease diagnosis requires the synergy of brain connectivity patterns and high-level semantic knowledge.
By Yidan Xu, Xiangmin Han, Rundong Xue, Huihui Ye
Functional brain networks exhibit a hierarchical organization across ROI, community, and whole-brain levels, supporting local processing, inter-community coordination, and global integration. Recent studies have demonstrated that brain community-aware modeling is beneficial for both diagnosis and biomarker identification of brain networks.
HyperAMS‑Net is a deep learning framework that classifies brain disorders from resting‑state fMRI or structural MRI data. It combines adaptive multi‑scale convolution, hypergraph attention, spatial‑channel attention, and adaptive feature fusion to capture complementary patterns across multiple scales and higher‑order dependencies. Evaluated on ABIDE, REST‑meta‑MDD, and ADNI datasets, it achieves state‑of‑the‑art accuracy and AUC, with ablation studies showing hypergraph attention as the most critical component.
By Proloy Kumar Mondal, Md Kamran Hussin Chowdhury, Hoi Leong Lee
arXiv:2603. 20452v2 Announce Type: replace Abstract: Longitudinal neuroimaging is essential for modeling disease progression in Alzheimer's disease (AD), yet irregular sampling and missing visits pose substantial challenges for learning reliable temporal representations.
By Ruiying Chen, Yutong Wang, Houliang Zhou, Wei Liang, Yong Chen, Lifang He