目的 越来越多的研究表明，基于静息态功能磁共振成像（rs-fMRI）的大脑功能分区比传统的大脑结构分区（如AAL分区、Brodmann分区等）在功能网络构建中功能一致性更高。但现阶段对于大脑功能模块的划分较粗糙，需要更精细准确的脑功能分区，明确宏观尺度的基本功能单元。为能使脑科学领域的研究者对基于静息态功能磁共振成像的脑功能分区进行有益的探索和应用，本文对其进行系统综述。方法 从rs-fMRI数据与大脑功能网络的关系出发，理清脑功能区分割的一般思路，对近几年来脑功能分区算法中出现的新思路、新方法以及对原有方法的改进做了较全面的阐述；最后总结该领域现阶段面临的问题并对未来的研究方向做了展望。结果 根据脑区情况，将脑功能分区分为全脑功能分区和局部脑功能分区，并分别阐释这两方面的优势与应用。同时，将脑功能分区算法归纳为基于数据驱动和基于模型驱动两大类，并展示了各类分区算法的优势以及面临的难点和挑战。结论 基于静息态功能磁共振成像的脑功能分区的研究已经取得了一些进展和有价值的研究成果，但是距离研究人脑机制，应用于脑部疾病的预防和诊断以及启示类脑科学的发展，还需要对脑功能分区方法进行更深入的研究和完善。后续研究中可将传统的分区算法和先验知识、空间领域信息、空间约束、稀疏编码、特征选择和采样学习等思想结合起来，形成融合性的脑功能分区算法，致力于更为细致准确的大脑功能分区和脑功能网络构建，解析脑的高级功能。

Objective Brain functional parcellation based on resting-state functional magnetic resonance imaging data has better functional consistency than traditional structural parcellation(such as AAL atlas and Brodmann atlas) in the construction of functional networks.However,a substantial part of parcellations of brain functional modules are rough at this stage,and it needs more precise and accurate brain functional parcellations,which can define the basic functional unit at macro scale.Considering its theoretical value,this study comprehensively reviews the existing brain functional parcellation methods and their applications in the field of brain science.Method With the widespread investigation and massive literature,we clear the general steps for the segmentation of brain functional areas based on the relationship between rs-fMRI data and brain functional network.The brain functional network can be further acquired only by defining the results of the brain parcellation.Then,the state-of-the-art ideas,methods,and the improvement of the original methods about brain functional parcellation are described in detail.This paper also classifies and analyses these algorithms of brain functional parcellation,whose good points,bad points,and sphere of application are stated in detail.In addition,the evaluation criteria of brain functional parcellation quality is introduced.Finally,the development trends and existing problems of this technology are highlighted.Result The brain functional parcellation can be divided into the whole brain functional parcellation and the regional brain functional parcellation according to the situation of brain regions.The whole brain functional parcellation can generate functional atlas and analyze the functional characteristics of the brain on the whole brain space scale.Also,according to the brain network,which can be constructed by functional atlases,it can promote the development of brain cognitive and brain-like artificial intelligence technology,occupying an important position in brain science research.The regional brain functional parcellation is aimed at dividing larger brain area with mixed functions into sub-regions with strong functional consistency,reflecting the functional distribution of the regional brain with more pertinence.Therefore,regional brain functional parcellation can be used to study brain-related diseases,and detection of abnormal changes in brain regions in time,which is significant for the prevention and diagnosis of brain diseases.The algorithms of brain functional parcellation are divided into two parts,namely,data driven and model driven,and the advantages,difficulties,and challenges of each algorithm are discussed.The data-driven parcellation methods always use limited parameters in the parcellation process and are less demanding on the shape of the image data to be processed.Most of the clustering methods have better performance on non-Gaussian data sets.Therefore,the application of data-driven parcellation methods are quite extensive,but the stability and accuracy of need improvement.Compared with the data-driven method,the model-driven method involves more parameters.During model establishment,the distribution of clusters and the model division of voxel signals require strong domain knowledge and reliable theoretical basis.These requirements limit the development of the model-driven methods to a certain extent.However,considering its uniqueness of the corresponding image,it can reflect individual differences and be applied to brain functional parcellation at group level.Moreover,the existing evaluation criteria can be a preliminary assessment of the functional parcellation results.For a more accurate evaluation,there is a lack of rigorous theoretical support for the overall evaluation system.Conclusion Some meaningful progress and valuable research results have been obtained in the study of brain functional parcellation based on resting-state fMRI.Therefore,researchers could use multimodal brain function data,and even combine with brain structure data,for brain functional parcellation.In addition,improving these methods is necessary so that more precise and accurate functional atlas could be studied in the mechanism of human brain,applied in early prevention,accurate diagnosis and curative effect evaluation of brain injury and neuropsychiatric diseases and brain-inspired and brain-computer interface intelligence technology.For example,based on a more detailed brain subregions,functional analysis of subregions can help to identify new biomarkers of specific diseases.Moreover,traditional partition algorithms can be combined with innovative ideas such as prior knowledge,spatial domain information,spatial constraint,sparse coding,feature selection,and sample learning in the follow-up research.These confluent brain functional parcellation algorithms are dedicated to finer brain functional regions and constructing more detailed and accurate brain functional networks to analyze advanced functions of the brain.