arXiv Machine Learning

A foundation-model approach to pediatric headache classification from rs-fMRI

arXiv:2608. 07287v1 Announce Type: new Abstract: Headache is the most common neurological disorder in children and substantially affects quality of life.

arXiv Machine Learning

Jul 30

An Attention-Based Framework for Alzheimers Disease Classification Using Resting-State fMRI

arXiv:2607. 26746v1 Announce Type: cross Abstract: Accurate identification of Alzheimers disease (AD) using resting-state functional magnetic resonance imaging (rs-fMRI) remains challenging due to the high dimensionality, noise, and complex inter-regional dependencies inherent in functional brain connectivity, which limit the effectiveness of traditional approaches based on handcrafted connectivity features or conventional machine learning models.

By Harshiddhi Pathak, Gowtham Reddy N, Mrinal Acharya, Manjunatha Mahadevappa

llms

arXiv Machine Learning

1d ago

BrainLinear: A Linear Model for Brain Network Analysis in Sparse Tangent Subspaces

arXiv:2608. 15266v1 Announce Type: cross Abstract: Functional connectome analysis examines brain-region interactions to understand and identify disorders such as autism spectrum disorder and Alzheimer's disease.

By Sijing Wu, Dongyuan Li, Miaoting Huang, Weiwei Ye, Ying Zhang, Feng Xia, Renhe Jiang

llms safety

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 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

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

Jul 9

STST-JEPA: Shallow-Target Spatio-Temporal Joint Embedding Prediction Architecture For EEG Self-Supervised Learning

arXiv:2607. 06629v1 Announce Type: new Abstract: Brain age -- the age inferred from a physiological recording -- is an emerging biomarker whose deviation from chronological age tracks neurological and psychiatric burden, and EEG is an attractive substrate for it because it is cheap, portable, and temporally rich.

By Roy Segal, Yoni Svechinsky, Tomer Fekete

llms rag nlp fine-tuning benchmarks

arXiv Machine Learning

Jun 19

Alzheimer's Disease Diagnosis using a Multimodal Approach with 3D MRI and PET

arXiv:2606. 20037v1 Announce Type: new Abstract: Alzheimer's disease (AD) is an irreversible neurodegenerative disorder and a leading cause of death worldwide.

By Loukas Ilias, Anthi-Maria Vozinaki, Christos Ntanos, Dimitris Askounis

multimodal safety

arXiv AI

Jun 12

Fusion Learning from Dynamic Functional Connectivity: Combining the Amplitude and Phase of fMRI Signals to Identify Brain Disorders

arXiv:2603. 24603v2 Announce Type: replace-cross Abstract: Dynamic functional connectivity (dFC) derived from resting-state functional magnetic resonance imaging (fMRI) has been extensively utilized in brain science research.

By Jinlong Hu, Jiatong Huang, Zijian Cai

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

Jun 16

EEG-FM-Bench: A Comprehensive Benchmark for the Systematic Evaluation and Diagnostic Analyses of EEG Foundation Models

arXiv:2508. 17742v3 Announce Type: replace-cross Abstract: Electroencephalography foundation models (EEG-FMs) have advanced brain signal analysis, but the lack of standardized evaluation benchmarks impedes model comparison and scientific progress.

By Wei Xiong, Jiangtong Li, Jie Li, Kun Zhu, Changjun Jiang

fine-tuning benchmarks safety

arXiv Machine Learning

1d ago

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.

By Oktay Agcaoglu

safety

arXiv Machine Learning

Jul 7

Interpretable machine learning predicts Parkinson's disease severity using motion-corrected QSM MRI and multiband multiecho fMRI features

arXiv:2607. 02553v1 Announce Type: cross Abstract: Introduction: Objective neuroimaging biomarkers may improve Parkinson's disease motor assessment by capturing brain variation not directly observable from clinical examination.

By Aixa X. Andrade

multimodal

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