arXiv:2608. 07393v1 Announce Type: new Abstract: Functional Magnetic Resonance Imaging ( fMRI ) data are often pooled into collaborative multi-site consortia, as deep learning models for analyses require large datasets to generalize well.
By Deepank Girish, Yi Hao Chan, Yubin Zheng, Sukrit Gupta, Jagath C. Rajapakse
The paper introduces MSR-IVA, a state-aware fusion method that combines a shared structural representation with state‑specific residual adaptations and masks for incomplete state expression. Applied to an Alzheimer’s Disease Neuroimaging Initiative cohort, MSR-IVA increased matched source coupling by 6.5% and decreased unmatched dependence by 15.7% compared to a baseline IVA approach. For subjects expressing both states, the method achieved a mean absolute cross‑state structural source correlation of 0.9177, far higher than the 0.2978 obtained without sharing, indicating effective preservation of source correspondence while allowing state‑specific adaptation.
By Victor Solomon, Zening Fu, Rafal Angryk, Vince D. Calhoun, Jingyu Liu
Rhamba is a region‑aware pretraining framework for resting‑state fMRI that combines anatomically guided masking with hybrid Attention‑Mamba architectures. The study pretrained models on the ABIDE dataset using three masking strategies (Any, Majority, Pure) and evaluated four architectural variants, finding that the Mamba‑Attention (MA) hybrid achieved the best average AUROC on downstream schizophrenia and ADHD classification tasks. Explainable AI via Integrated Gradients highlighted that performance depends on the interaction between masking strategy and architecture rather than a single dominant configuration.
By Pankaj Pandey, Ruthwik Reddy Doodipala, Pratheek Eranki, Carolina Torres-Rojas, Manob Jyoti Saikia, Ranganatha Sitaram
arXiv:2607. 16325v1 Announce Type: cross Abstract: Foundation models provide powerful representations for brain MRI analysis, but their predictions remain difficult to interpret in anatomically meaningful terms.
By Wei Zhang
arXiv:2604. 27277v3 Announce Type: replace-cross Abstract: Brain MRI underpins a wide range of neuroscientific and clinical applications, yet most learning-based methods remain task-specific and require substantial labeled data.
By Yizhou Wu, Shansong Wang, Yuheng Li, Mojtaba Safari, Mingzhe Hu, Chih-Wei Chang, Harini Veeraraghavan, Xiaofeng Yang
arXiv:2609.26867v1 Announce Type: cross
Abstract: This article is motivated by an imaging application from the Adolescent Brain Cognitive Development (ABCD) study, aiming to predict task-based brain...
By Rajarshi Guhaniyogi, Pritam Dey, Krishnendu Chandra, Aaron Scheffler, Bani K. Mallick