arXiv Machine Learning

Group ICA 2.0: Closing the Gap Between Subjects and Group Latent Decomposition with Copula-Linked Group ICA (CoLiG-ICA)

arXiv:2608. 16029v1 Announce Type: new Abstract: Group Independent Component Analysis (gICA) is widely used to decompose high-dimensional functional MRI data into interpretable brain networks.

arXiv Machine Learning

Aug 10

FedDOSE: Federated Learning Framework Decomposing Site Effects for Modeling Brain Dynamic Functional Connectivity

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

benchmarks safety

arXiv Machine Learning

Jul 21

RegionFM: Interpretable Region-Based Brain MRI Classification Using Foundation Model Embeddings

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

rag computer-vision fine-tuning

arXiv AI

Jun 12

BrainDINO: A Brain MRI Foundation Model for Generalizable Clinical Representation Learning

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

computer-vision fine-tuning

arXiv Machine Learning

Jun 5

Cross-scale spatially-aware generative modeling of transcriptomic programs underlying neurodegenerative brain organization

arXiv:2606. 05870v1 Announce Type: cross Abstract: Neurodegenerative disorders such as Alzheimer's disease exhibit highly organized patterns of regional brain vulnerability, yet the biological mechanisms underlying this spatial selectivity remain incompletely understood.

By Krishnakumar Vaithianathan (for the Alzheimer's Disease Neuroimaging Initiative)

arXiv AI

Jun 16

RECTOR: Masked Region-Channel-Temporal Modeling for Affective and Cognitive Representation Learning

arXiv:2606. 15278v1 Announce Type: cross Abstract: Affective and cognitive disorders manifest as distributed, time-varying brain network dynamics across regions, channels, and time, challenging robust representation learning from EEG/sEEG for clinical diagnosis.

By Jinhan Liu, Mahsa Shoaran

benchmarks

arXiv Machine Learning

Jul 27

Variational Low-rank Tensor Decomposition for Multisubject Spatiotemporal Data Analysis

arXiv:2607. 22262v1 Announce Type: cross Abstract: Modeling shared and subject-specific structure in multisubject spatiotemporal data remains challenging, particularly in neuroimaging, where both spatial and temporal patterns exhibit rich variability across subjects.

By Laura M. Montaldo, Ricardo A. Borsoi, Sebastian Miron, Tulay Adali

benchmarks

arXiv AI

Jun 9

NeuroAlign: Hierarchical Multimodal Fusion of Dynamic and Structural Neuroimaging for MCI Analysis

arXiv:2606. 07635v1 Announce Type: cross Abstract: Multimodal neuroimaging fusion of functional MRI (fMRI) and diffusion tensor imaging (DTI) provides complementary information for cognitive impairment analysis, but remains challenged by heterogeneous feature spaces and misaligned representations.

By Xiongri Shen, Zhenxi Song, Jiaqi wang, Yi Zhong, Leilei Zhao, Chenqi Xu, Linling Li, Yichen Wei, Lingyan Liang, Demao Deng, Luping Song, Ping Luan, Ahmed M. Anter, Shuqiang Wang, Baiying Lei, Zhiguo Zhang

rag diffusion multimodal safety

arXiv AI

Jun 10

Tractogram foundation model

arXiv:2606. 09893v1 Announce Type: cross Abstract: Diffusion MRI (dMRI) tractography is the only noninvasive approach for mapping white-matter pathways in the living human brain.

By Guikun Chen, Yuqian Chen, Yijie Li, Yogesh Rathi, Nikos Makris, Fan Zhang, Wenguan Wang, Lauren J. O'Donnell

rag diffusion

arXiv Machine Learning

Jun 5

Sparse Functional Singular Value Decomposition for Biclustering and Triclustering Longitudinal Data

arXiv:2606. 05488v1 Announce Type: cross Abstract: Identifying subtypes of complex conditions, such as Inflammatory Bowel Disease (IBD), often requires capturing latent patterns in longitudinal omics data.

By Yue Zhao, Thierry Chekouo, Sandra Safo

arXiv AI

Jun 19

BrainG3N: A Dual-Purpose Tokenizer for Controllable 3D Brain MRI Generation

arXiv:2606. 19651v1 Announce Type: new Abstract: Three-dimensional (3D) brain MRI is central to clinical neurology and neuro-oncology, where generative models could augment under-represented cohorts, simulate disease trajectories, and support privacy-preserving data sharing.

By Max Van Puyvelde, Ibrahim Gulluk, Wim Van Criekinge, Olivier Gevaert

llms rag diffusion nlp benchmarks safety

arXiv AI

Jun 2

Brain-Atlas-Guided Generative Counterfactual Attention for Explainable Cognitive Decline Diagnosis Using Multimodal Connectomes

arXiv:2606. 01237v1 Announce Type: new Abstract: Mild cognitive impairment (MCI) and subjective cognitive decline (SCD) are closely associated with the early Alzheimer's disease continuum, where accurate and explainable diagnosis is important for early risk assessment and intervention.

By Xiongri Shen, Jiaqi Wang, Zhenxi Song, Yi Zhong, Leilei Zhao, Xin He, Baiying Lei, Zhiguo Zhang

llms multimodal benchmarks safety

arXiv AI

Aug 3

MPP-GNN: Subject-Adaptive Community Detection for fMRI-Based Alzheimer's Disease Classification

arXiv:2607. 28681v1 Announce Type: cross Abstract: Functional magnetic resonance imaging (fMRI) is a widely used technique for studying the brain.

By Yang Zhang, Xiao Zhou, Jonathan Warrell, Avram Holmes, Xuan Zhang, Mark Gerstein

safety

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