A Review of Methods for Constructing Brain Functional Atlas Based on Neuroimaging Data and Machine Learning
Brain atlas is an important tool in brain science research,including brain function exploration,neuroscience and cognitive science,and clinical diagnosis and treatment,which can be constructed using machine learning based on neuroimaging data.The parcellation patterns generated by brain atlas provide the foundation for understanding brain structure and function,and are frequently used for defining nodes in brain networks to reduce the impact of imaging noise on analysis results.Compared to structural atlases,functional atlases have a later development but demonstrate higher functional consistency,gradually gaining widespread attention and application in various brain function-related studies.In order to reveal the development path of functional atlases,based on the investigation of different types and methods of brain functional atlases constructed using neuroimaging data and machine learning,this article first classified and summarized the atlases according to multiple attribute features such as cortical surface and voxel,individual and population,and imaging modalities,providing detailed information for each atlas.After that,according to machine learning methods,we reviewed the construction methods of brain atlases based on graph clustering and time series clustering separately.Finally,we outlined the challenges faced in the field of brain atlas research and prospects for future research directions.
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