神经影像学
数据科学
联营
大数据
比例(比率)
数据共享
模式
心理学
医学
计算机科学
神经科学
人工智能
数据挖掘
社会学
社会科学
物理
替代医学
病理
量子力学
作者
Bin Lü,Xiao Chen,F. Xavier Castellanos,Paul M. Thompson,Xi‐Nian Zuo,Yu‐Feng Zang,Chao‐Gan Yan
标识
DOI:10.1016/j.scib.2024.03.006
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 studies. We 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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