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. 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. 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. 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.