众脑之力:通过开放神经影像数据和大规模协作催化神经精神疾病的创新发现（英文）
作者机构：CAS Key Laboratory of Behavioral ScienceInstitute of Psychology Department of PsychologyUniversity of Chinese Academy of Sciences Department of Child and Adolescent PsychiatryNYU Grossman School of Medicine Nathan Kline Institute for Psychiatric Research Imaging Genetics CenterMark&Mary Stevens Institute for Neuroimaging&InformaticsKeck School of MedicineUniversity of Southern California Developmental Population Neuroscience Research CenterIDG/McGovern Institute for Brain ResearchBeijing Normal University National Basic Science Data Center Centre for Cognition and Brain DisordersThe Affiliated Hospital of Hangzhou Normal University Institute of Psychological ScienceHangzhou Normal University Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairment International Big-Data Center for Depression ResearchInstitute of PsychologyChinese Academy of Sciences Magnetic Resonance Imaging Research CenterInstitute of PsychologyChinese Academy of Sciences
出 版 物：《Science Bulletin》 (科学通报(英文版))
年 卷 期：2024年第10期
页 面：1536-1555页
核心收录：
学科分类：1002[医学-临床医学] 100205[医学-精神病与精神卫生学] 10[医学]
基 金：supported by the Sci-Tech Innovation 2030 Major Projects of Brain Science and Brain-inspired Intelligence Technology (2021ZD0200600) the National Natural Science Foundation of China (82122035, 81671774, 81630031, 32300933) the Key Research Program of the Chinese Academy of Sciences (ZDBS-SSW-JSC006) Beijing Nova Program of Science and Technology (Z191100001119104 and 20230484465) Beijing Natural Science Foundation (J230040) the Scientific Foundation of Institute of Psychology, Chinese Academy of Sciences (E3CX1425, E2CX4425YZ)
摘 要：Recent advances in open neuroimaging data are enhancing our comprehension of neuropsychiatric disorders. By pooling images from various cohorts, statistical power has increased, enabling the detection of subtle abnormalities and robust associations, and fostering new research methods. Global collaborations in imaging have furthered our knowledge of the neurobiological foundations of brain disorders and aided in imaging-based prediction for more targeted treatment. Large-scale magnetic resonance imaging initiatives are driving innovation in analytics and supporting generalizable psychiatric *** also emphasize the significant role of big data in understanding neural mechanisms and in the early identification and precise treatment of neuropsychiatric disorders. However, challenges such as data harmonization across different sites, privacy protection, and effective data sharing must be addressed. With proper governance and open science practices, we conclude with a projection of how large-scale imaging resources and collaborations could revolutionize diagnosis, treatment selection, and outcome prediction,contributing to optimal brain health.
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