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 asneurodegenerative diseases. Finally

the article reviewed artificial intelligence-based neuroimaging modeling methods

along with their applications in neuroimaging analysis.