Li Chengyi, Yu Shan, Cui Yue
Laboratory of Brain Atlas and Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing, China; Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology, Chinese Academy of Sciences, Beijing, China.
Laboratory of Brain Atlas and Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing, China; Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China; School of Future Technology, University of Chinese Academy of Sciences, Beijing, China.
Neurosci Biobehav Rev. 2025 Jul;174:106172. doi: 10.1016/j.neubiorev.2025.106172. Epub 2025 Apr 21.
Individual brains vary greatly in morphology, connectivity, and organization. Group-level brain parcellations, which do not account for individual variations in brain parcels, are increasingly limited in their applicability, especially given the rapid development of precision medicine. Accurate individual-level brain functional mapping is pivotal for comprehending variations in brain functions and behaviors, the early and precise identification of brain abnormalities, and personalized treatments for neuropsychiatric disorders. Recent advances in neuroimaging and machine learning techniques have led to a surge in studies on the parcellation of individual brains. In this paper, we present an overview of recent advances in the methodologies of individual brain parcellation, including optimization- and learning-based methods. We then introduce comprehensive evaluation metrics to validate individual functional regions, and discuss how individual brain mapping advances neuroscience research and clinical medicine. Finally, major challenges and future directions of individual brain parcellation are summarized. In conclusion, we provide a comprehensive overview of individual brain parcellation methods, validations, and applications, highlighting current challenges and the urgent need for integrated platforms that encompass datasets, methods, and validations.
个体大脑在形态、连接性和组织结构上存在很大差异。群体水平的脑部分割并未考虑脑区的个体差异，其适用性日益受限，尤其是在精准医学迅速发展的背景下。准确的个体水平脑功能图谱对于理解脑功能和行为的差异、早期精确识别脑异常以及神经精神疾病的个性化治疗至关重要。神经成像和机器学习技术的最新进展促使个体脑部分割研究激增。在本文中，我们概述了个体脑部分割方法的最新进展，包括基于优化和学习的方法。然后我们引入综合评估指标来验证个体功能区，并讨论个体脑图谱如何推动神经科学研究和临床医学发展。最后总结了个体脑部分割的主要挑战和未来方向。总之，我们全面概述了个体脑部分割方法、验证及应用，强调了当前的挑战以及对涵盖数据集、方法和验证的集成平台的迫切需求。
