Diffusion-Guided Pretraining for Brain Graph Foundation Models
Department of Electrical and Computer Engineering, Lehigh University, Bethlehem, PA, USA（电气与计算机工程系，莱维大学）；Department of Computer Science and Engineering, Lehigh University, Bethlehem, PA, USA（计算机科学与工程系，莱维大学）；Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA（精神病学与行为科学系，斯坦福大学医学院）
With the growing interest in foundation models for brain signals, graph-based pretraining has emerged as a promising paradigm for learning transferable representations from connectome data. However, existing contrastive and masked autoencoder methods typically rely on naive random dropping or masking for augmentation, which is ill-suited for brain graphs and hypergraphs as it disrupts semantically meaningful connectivity patterns. Moreover, commonly used graph-level readout and reconstruction schemes fail to capture global structural information, limiting the robustness of learned representations. In this work, we propose a unified diffusion-based pretraining framework that addresses both limitations. First, diffusion is designed to guide structure-aware dropping and masking strategies, preserving brain graph semantics while maintaining effective pretraining diversity. Second, diffusion enables topology-aware graph-level readout and node-level global reconstruction by allowing graph embeddings and masked nodes to aggregate information from globally related regions. Extensive experiments across multiple neuroimaging datasets with over 25,000 subjects and 60,000 scans involving various mental disorders and brain atlases demonstrate consistent performance improvements.
EEG-VID: Task-Guided Latent Predictive Pretraining for EEG Decoding and Assistive Target Selection
School of Information and Control Engineering, China University of Mining and Technology（中国矿业大学信息与控制工程学院）；School of Automation and Intelligent Sensing, Shanghai Jiao Tong University（上海交通大学自动化与智能感知学院）；Shanghai Innovation Institute（上海创新研究院）
BrainWideBench: Benchmarking large-scale pretraining and across-animal transfer in multi-region neural recordings
University of Pennsylvania（宾夕法尼亚大学）；Mila（米拉研究所）；Université de Montréal（蒙特利尔大学）；Stanford University（斯坦福大学）；Columbia University（哥伦比亚大学）；Allen Institute（艾伦研究所）；William James Center for Research（威廉·詹姆斯研究中心）；ISPA - Instituto Universitário（ISPA 大学研究所）；University of Geneva（日内瓦大学）；Karolinska Institutet（卡罗林斯卡学院）；UCLA（加州大学洛杉矶分校）；University College London（伦敦大学学院）；Champalimaud Foundation（尚帕利莫基金会）；Lingang Laboratory（临港实验室）；The Chinese University of Hong Kong（香港中文大学）；Donders Institute（唐德斯研究所）；University of Minnesota（明尼苏达大学）；Princeton University（普林斯顿大学）；Leiden University（莱顿大学）；McGill University（麦吉尔大学）；IBM
LLM-Guided Diagnostic Evidence Alignment for Medical Vision-Language Pretraining under Limited Pairing
Institute of Intelligent Information Processing, Shanxi University（山西大学智能信息处理研究所）；Alliance Manchester Business School, The University of Manchester（曼彻斯特大学阿利安斯商学院）；Department of Computer Science and Engineering, The Hong Kong University of Science and Technology（香港科技大学计算机科学与工程系）
Deep reinforcement learning guided graph neural networks for brain network analysis
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Macquarie University（麦考瑞大学）；Beihang University（北京航空航天大学）；National Acoustic Laboratories（国家声学实验室）；CAEIT（中国电子科学研究院）
BrainOmni: A Brain Foundation Model for Unified EEG and MEG Signals
Shanghai Artificial Intelligence Laboratory, China（上海人工智能实验室）；Department of Electronic Engineering, Tsinghua University, China（清华大学电子工程系）；Department of Psychology, University of Cambridge, UK（剑桥大学心理学系）；Speech Hearing and Phonetic Sciences, University College London, UK（伦敦大学学院语音听力与语音科学系）；MRC Cognition and Brain Sciences Unit, University of Cambridge, UK（剑桥大学MRC认知与脑科学单位）
BrainSCL: Subtype-Guided Contrastive Learning for Brain Disorder Diagnosis
Computer Science and Engineering, Northeastern University, Shenyang, China（东北大学计算机科学与工程学院）；School of Computer Science and Artificial Intelligence, Shandong Normal University, Jinan, China（山东师范大学计算机科学与人工智能学院）；AiShiWeiLai AI Research, Beijing, China（艾世维来人工智能研究所）；Early Intervention Unit, Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China（南京医科大学附属南京脑科医院早期干预科）；Amii, University of Alberta, Edmonton, AB, Canada（阿尔伯塔大学Amii）
Beyond representational alignment with brain-guided language models for robust reasoning
State Key Lab of General AI, School of Intelligence Science and Technology, Peking University（北京大学通用人工智能国家重点实验室、智能科学与技术学院）；Department of Psychological and Cognitive Sciences, Tsinghua University（清华大学心理与认知科学系）；Microsoft Research Asia（微软亚洲研究院）
Better audio representations are more brain-like: linking model-brain alignment with performance in downstream auditory tasks
Instituto de Investigación en Ciencias de la Computación (ICC)（计算科学研究所（ICC））；CONICET-Universidad de Buenos Aires（CONICET-阿根廷布宜诺斯艾利斯大学）；Departamento de Computación, FCEyN（计算机系，FCEyN）；Universidad de Buenos Aires (UBA)（阿根廷布宜诺斯艾利斯大学（UBA））
A Prior Guided Adversarial Representation Learning and Hypergraph Perceptual Network for Predicting Abnormal Connections of Alzheimer's Disease
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Shenzhen University（深圳大学）；Renmin University of China（中国人民大学）；Central South University（中南大学）
REMEMBER: Retrieval-based Explainable Multimodal Evidence-guided Modeling for Brain Evaluation and Reasoning in Zero- and Few-shot Neurodegenerative Diagnosis
Lausanne University Hospital (CHUV)（洛桑大学医院（CHUV））；University of Lausanne (UNIL)（洛桑大学（UNIL））；Vietnam National University（越南国立大学）；University of Science, Vietnam National University Ho Chi Minh City（越南国立大学胡志明市科学大学）；International University, Vietnam National University Ho Chi Minh City（越南国立大学胡志明市国际大学）
