Neuroimaging statistical modeling is a critical branch in the fields of neuroscience and medicine.This article provided an overview of recent methodological advances in neuroimaging statistical models.Firstly,the article introduced cognitive decoding models,focusing on how orthogonal decomposition methods and representational similarity analysis can be used to resolve the underlying cognitive processes in neuroimaging data.Secondly,the article discussed methods for individualized neuroimaging modeling,including normative modeling and individual brain functional parcellation,along with their applications in psychiatric research.Subsequently,the article explored data-driven disease progression models,elucidating how machine learning and statistical tools can be utilized to infer the progression patterns of disease biomarkers over time,and their applications in fields such as neurodegenerative diseases.Finally,the article reviewed artificial intelligence-based neuroimaging modeling methods,along with their applications in neuroimaging analysis.
维普
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