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cs.LG2026
Isolating Nonlinear Independent Sources in fMRI with -TCVAE Models
Qiang Li, Shujian Yu, Jesus Malo +3
Learning meaningful latent representations from nonlinear fMRI data remains a fundamental challenge in neuroimaging analysis. Traditional independent component analysis, widely use…
cs.LG2026
Modeling Higher-Order Brain Interactions via a Multi-View Information Bottleneck Framework for fMRI-based Psychiatric Diagnosis
Kunyu Zhang, Qiang Li, Vince D. Calhoun +1
Resting-state functional magnetic resonance imaging (fMRI) has emerged as a cornerstone for psychiatric diagnosis, yet most approaches rely on pairwise brain cortical or sub-cortic…
cs.LG2026
PIME: Prototype-based Interpretable MCTS-Enhanced Brain Network Analysis for Disorder Diagnosis
Kunyu Zhang, Yanwu Yang, Jing Zhang +2
Recent deep learning methods for fMRI-based diagnosis have achieved promising accuracy by modeling functional connectivity networks. However, standard approaches often struggle wit…