Connectome-constrained neural decoding reveals a representational hierarchy from perception to cognition to action  

脑认知建模新视角:脑图谱约束下的感知与认知神经表征

作　　者：Yu Zhang Lingzhong Fan Yongfu Hao Alain Dagher Tianzi Jiang Pierre Bellec 张瑜;樊令仲;郝永富;Alain Dagher;蒋田仔;Pierre Bellec

机构地区：[1]Zhejiang Lab,Hangzhou 311100,China [2]Brainnetome Center,Chinese Academy of Sciences,Beijing 100190,China [3]Institute of Automation,Chinese Academy of Sciences,Beijing 100190,China [4]McConnell Brain Imaging Center,Montreal Neurological Institute,McGill University,Montreal,QC,H3A 2B4,Canada [5]Centre de recherche de l’Institut universitaire de gériatrie de Montréal,Montreal,QC,H3W 1W6,Canada [6]Department of Psychology,University of Montreal,Montreal,QC,H3C 3J7,Canada

出　　处：《Science Bulletin》2025年第4期478-482,共5页科学通报(英文版)

基　　金：supported by the STI2030-Major Projects(2021ZD0200201,2022ZD0211500);the National Natural Science Foundation of China(62201519,52307259,62327805,and 82151307).

摘　　要：Understanding the neural substrates of human cognition is a central goal of neuroscience research.Modern imaging techniques,such as functional magnetic resonance imaging(fMRI),provide an opportunity to map cognitive function in vivo.To date,modeling shared information in task-evoked neural dynamics across individuals remains challenging,largely due to pronounced inter-subject variability in brain anatomy,function,and behaviors[1],[2].An emerging topic,known as hyperalignment or functional alignment,has been proposed recently[3],to map subject-specific neural responses onto a common representational space using either linear transformations of task-evoked neural activity[4]or resting-state connectivity profiles[5].However,these approaches often assume uniform neural responses across individuals,struggling to capture group heterogeneity and model functional interactions between brain areas[6].

关 键 词：representation alignment CONSTRAINED 

分 类 号：O17[理学—数学]