Biswal Bharat B, Uddin Lucina Q
The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China.
Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, USA.
Nature. 2025 May;641(8065):1121-1131. doi: 10.1038/s41586-025-08953-9. Epub 2025 May 28.
Since the discovery of resting-state functional connectivity in the human brain, this neuroimaging approach has revolutionized the study of neural architecture. Once considered noise, the functional significance of spontaneous low-frequency fluctuations across large-scale brain networks has now been investigated in more than 25,000 publications. In this Review, we provide a historical overview and thoughts regarding potential future directions for resting-state functional MRI (rsfMRI) research, highlighting the most informative analytic approaches that have been developed to reveal the brain's intrinsic spatiotemporal organization. We review the collaborative efforts that have led to the widespread use of rsfMRI in neuroscience, with an emphasis on methodological innovations that have been made possible by contributions from electrical and biomedical engineering, physics, mathematics and computer science. We focus on key theoretical and methodological advances that will be necessary for further progress in the field, highlighting the need for further integration with new developments in whole-brain computational modelling, more sophisticated approaches to brain-behaviour mapping, greater mechanistic insights from concurrent measurement of neurophysiology, and greater appreciation of the problem of generalization failure in machine learning applications. We propose that rsfMRI has the potential for even greater clinical relevance when it is fully integrated with population neuroscience and global health initiatives in the service of precision psychiatry.
自从在人类大脑中发现静息态功能连接以来，这种神经影像学方法彻底改变了神经结构的研究。曾经被视为噪声的全脑大规模网络中自发低频波动的功能意义，如今已在超过25000篇出版物中得到研究。在本综述中，我们提供了关于静息态功能磁共振成像（rsfMRI）研究的历史概述以及对潜在未来方向的思考，重点介绍了为揭示大脑内在时空组织而开发的最具信息量的分析方法。我们回顾了促使rsfMRI在神经科学中广泛应用的合作努力，强调了电气与生物医学工程、物理学、数学和计算机科学的贡献所带来的方法学创新。我们关注该领域进一步发展所需的关键理论和方法进展，强调需要与全脑计算建模的新发展进一步整合、采用更复杂的脑-行为映射方法、通过神经生理学的同步测量获得更深入的机制性见解，以及更深入认识机器学习应用中的泛化失败问题。我们认为，当rsfMRI与群体神经科学和全球健康倡议充分整合以服务于精准精神病学时，它具有更大的临床相关性潜力。
