arXiv AI

MAGNET: Manifold-Aware Graph Diffusion Network for Connectome Generation

arXiv Machine Learning

Aug 18

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

Jun 30

BrainRiem: Riemannian Prototype Learning for Source-Free Cross-Site Brain Network Diagnosis

arXiv:2606. 29200v1 Announce Type: new Abstract: Multi-site functional MRI (fMRI) studies are essential for robust neuropsychiatric diagnosis yet suffer severe domain shifts from scanner heterogeneity, demographics, and site-specific acquisition protocols.

By Kunyu Zhang, Tianxiang Xu

fine-tuning benchmarks safety

arXiv Machine Learning

Sep 1

Benchmarking External Generalization of SPD Matrix Learning for Resting-State fMRI Connectome Prediction

arXiv:2608.30418v1 Announce Type: cross Abstract: Resting-state functional magnetic resonance imaging (rs-fMRI) functional connectivity (FC) matrices are widely used for individual-level prediction,...

By Ce Ju, Antoine Collas, Florent Bouchard, Bertrand Thirion

benchmarks

arXiv Machine Learning

Aug 11

A continually expandable foundation model for brain MRI

arXiv:2608. 08319v1 Announce Type: cross Abstract: Brain magnetic resonance imaging (MRI) is central to neuroscience and clinical assessment, but models are commonly developed for individual diseases, populations or imaging protocols.

By Michail Mamalakis, Carmen Jimenez-Mesa, Yonghao Li, Hao Chen, Chao Li, Antonios Mamalakis, John Suckling, Richard Bethlehem, Stephen J. Price, Richard J. Gilbertson, Pietro Lio

diffusion efficiency

arXiv Machine Learning

Oct 9

BrainATCL: Adaptive Temporal Brain Connectivity Learning for Functional Link Prediction and Age Estimation

BrainATCL is an unsupervised, nonparametric framework that learns adaptive temporal brain connectivity from resting‑state fMRI data. It dynamically adjusts the lookback window for each snapshot based on newly added edges and encodes graph sequences with a GINE‑Mamba2 backbone, incorporating brain‑structure and function‑informed edge attributes. The method is evaluated on functional link prediction and age estimation, showing superior performance and strong generalization, even across sessions.

By Yiran Huang, Amirhossein Nouranizadeh, Christine Ahrends, Mengjia Xu

arXiv AI

Jun 15

GMN4AD: Graph Matching Network for Alzheimer's Disease Diagnosis with Test-Time Domain Adaptation using Multi-centered Structure Magnetic Resonance Imaging

arXiv:2606. 13919v1 Announce Type: cross Abstract: Alzheimer's Disease (AD) is a progressive neurodegenerative disorder that affects millions of older adults, with prevalence expected to rise significantly in the coming years.

By Chen Zhao, Huan Huang, Yixin Xie, Jiajing Huang, Weihua Zhou, Nandakumar Narayanan

fine-tuning benchmarks

arXiv Machine Learning

Jul 14

MVMGNN;Multi-View Masked Graph Neural Network for Alzheimer's Disease Diagnosis using Structural MRI

arXiv:2607. 09788v1 Announce Type: cross Abstract: Alzheimer's disease (AD) is a common neurodegenerative disorder, and early diagnosis is of great significance for delaying disease progression and enabling timely intervention.

By Ni Yao, Zhenxu Wang, Danyang Sun, Chuang Han, Yanting Li, Jiaofen Nan, Fubao Zhu, Chen Zhao, Weihua Zhou

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 Machine Learning

Sep 30

High-Resolution Dynamic Functional Connectivity Generation with Graph-Variate Flow Matching

arXiv:2609.37037v1 Announce Type: new Abstract: High-resolution dynamic functional connectivity (DFC) can reveal rapidly evolving brain-network interactions, but short temporal windows yield noisy, o...

By Om Roy, Yashar Moshfeghi, Keith Malcolm Smith

llms diffusion

arXiv Computer Vision

Sep 2

Conditional Flow Matching for Cross-Field MRI Harmonisation

arXiv:2609.00960v1 Announce Type: new Abstract: Magnetic resonance images of the same subject look markedly different across field strengths, which complicates the comparison and pooling of data acro...

By Baris Imre, Aram Salehi, Levente Baljer, Andrew Webb, Marius Staring, Efe Ilicak

diffusion fine-tuning safety

arXiv AI

Jun 2

A physics-informed foundation model for quantitative diffusion MRI

arXiv:2606. 00156v1 Announce Type: cross Abstract: Understanding the human brain requires access to its microscopic tissue architecture.

By Zihan Li, Jialan Zheng, Ziyu Li, Xun Yuan, Kasidit Anmahapong, Ziang Wang, Mingxuan Liu, Hongjia Yang, Yifei Chen, Zhuhao Wang, Yuhang He, Fang Chen, Rui Li, Huaiqiang Sun, Yi Liao, Congyu Liao, Yang Yang, Haibo Qu, Xue Zhang, Hongen Liao, Qiyuan Tian

diffusion

arXiv AI

Jul 9

Latent graph encoding of multimodal neuroimaging features with generative AI architectures

arXiv:2607. 07027v1 Announce Type: cross Abstract: While generative models enable encoding of complex neuroimaging data for feature generation and reconstruction, developing optimal architectural frameworks with appropriate encoding and latent space processes is crucial for studying structural and functional properties of the brain.

By Ishaan Batta, Meenu Ajith, Vince Calhoun

llms diffusion multimodal safety

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