Zhao Lin
School of Computing, University of Georgia, Athens 30602 GA, USA.
Psychoradiology. 2025 Apr 29;5:kkaf007. doi: 10.1093/psyrad/kkaf007. eCollection 2025.
Functional magnetic resonance imaging (fMRI) provides a powerful tool for studying brain function by capturing neural activity in a non-invasive manner. Mapping brain function from fMRI data enables researchers to investigate the spatial and temporal dynamics of neural processes, providing insights into how the brain responds to various tasks and stimuli. In this review, we explore the evolution of deep learning-based methods for brain function mapping using fMRI. We begin by discussing various network architectures such as convolutional neural networks, recurrent neural networks, and transformers. We further examine supervised, unsupervised, and self-supervised learning paradigms for fMRI-based brain function mapping, highlighting the strengths and limitations of each approach. Additionally, we discuss emerging trends such as fMRI embedding, brain foundation models, and brain-inspired artificial intelligence, emphasizing their potential to revolutionize brain function mapping. Finally, we delve into the real-world applications and prospective impact of these advancements, particularly in the diagnosis of neural disorders, neuroscientific research, and brain-computer interfaces for decoding brain activity. This review aims to provide a comprehensive overview of current techniques and future directions in the field of deep learning and fMRI-based brain function mapping.
功能磁共振成像（fMRI）通过以非侵入性方式捕捉神经活动，为研究脑功能提供了一个强大的工具。从fMRI数据绘制脑功能图使研究人员能够研究神经过程的空间和时间动态，从而深入了解大脑对各种任务和刺激的反应方式。在本综述中，我们探讨了使用fMRI进行基于深度学习的脑功能映射方法的发展。我们首先讨论各种网络架构，如卷积神经网络、循环神经网络和变换器。我们进一步研究基于fMRI的脑功能映射的监督学习、无监督学习和自监督学习范式，突出每种方法的优点和局限性。此外，我们讨论了诸如fMRI嵌入、脑基础模型和受脑启发的人工智能等新兴趋势，强调它们在革新脑功能映射方面的潜力。最后，我们深入探讨这些进展在现实世界中的应用和预期影响，特别是在神经疾病诊断、神经科学研究以及用于解码脑活动的脑机接口方面。本综述旨在全面概述深度学习和基于fMRI的脑功能映射领域的当前技术和未来方向。
