Imitating and exploring the human brain's resting and task-performing states via brain computing: scaling and architecture

作     者：Wenlian Lu Longbin Zeng Jiexiang Wang Shitong Xiang Yang Qi Qibao Zheng Ningsheng Xu Jianfeng Feng DTB Consortium Wenlian Lu;Longbin Zeng;Jiexiang Wang;Shitong Xiang;Yang Qi;Qibao Zheng;Ningsheng Xu;Jianfeng Feng;DTB Consortium

作者机构：Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence(Fudan University) Ministry of Education Fudan University Shanghai Center for Mathematical Sciences Fudan University Department of Computer ScienceUniversity of Warwick Zhangjiang Fudan International Innovation CenterFudan University 

出 版 物：《National Science Review》 (国家科学评论(英文版))

年 卷 期：2024年第11卷第5期

页      面：17-28页

核心收录：

学科分类：0710[理学-生物学] 12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 07[理学] 081104[工学-模式识别与智能系统] 08[工学] 071006[理学-神经生物学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the National Key R&D Program of China (2019YFA0709502) the Shanghai Municipal Science and Technology Major Project (2018SHZDZX01) the Zhejiang Lab,the Shanghai Center for Brain Science and Brain-Inspired Technology,the Programme of Introducing Talents of Discipline to Universities (B18015) the National Natural Science Foundation of China (62072111 and 62306078) 

主　　题：digital twin brain computational cortico-subcortical model fMRI data assimilation interoceptive circuit 

摘      要：A computational human brain model with the voxel-wise assimilation method was established based on individual structural and functional imaging data. We found that the more similar the brain model is to the biological counterpart in both scale and architecture, the more similarity was found between the assimilated model and the biological brain both in resting states and during tasks by quantitative metrics. The hypothesis that resting state activity reflects internal body states was validated by the interoceptive circuit s capability to enhance the similarity between the simulation model and the biological brain. We identified that the removal of connections from the primary visual cortex(V1) to downstream visual pathways significantly decreased the similarity at the hippocampus between the model and its biological counterpart,despite a slight influence on the whole brain. In conclusion, the model and methodology present a solid quantitative framework for a digital twin brain for discovering the relationship between brain architecture and functions, and for digitally trying and testing diverse cognitive, medical and lesioning approaches that would otherwise be unfeasible in real subjects.