The history and future of resting-state functional magnetic resonance imaging

• 成果类型: Review
• 署名作者: Biswal, Bharat B.; Uddin, Lucina Q.
• 署名单位: University of Electronic Science & Technology of China; New Jersey Institute of Technology; University of California System; University of California Los Angeles; University of California Los Angeles Medical Center; David Geffen School of Medicine at UCLA; University of California System; University of California Los Angeles
• 刊物名称: Nature
• ISSN/ISSBN: 0028-1680
• DOI: 10.1038/s41586-025-08953-9
• 发表日期: 2025-05-29
• 页码: 1121-1131
• 关键词: human brain; default mode; connectivity networks; cerebral-cortex; motor cortex; bold fmri; fluctuations; ORGANIZATION; oscillations; architecture

摘要

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.