2022/10/17 by Hongtu Zhu, Tengfei Li, Zhu, Hongtu +3 · 1 citation
Biochemistry, Genetics and Molecular Biology · #Applications (stat.AP) #FOS: Biological sciences #FOS: Computer and information sciences #Gene expression and cancer classification #Neurons and Cognition (q-bio.NC)
paper · pdf · doi:10.48550/arxiv.2210.09217
openalex publication_date 2022/10/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The aim of this paper is to provide a comprehensive review of statistical challenges in neuroimaging data analysis from neuroimaging techniques to large-scale neuroimaging studies to statistical learning methods. We briefly review eight popular neuroimaging techniques and their potential applications in neuroscience research and clinical translation. We delineate the four common themes of neuroimaging data and review major image processing analysis methods for processing neuroimaging data at the individual level. We briefly review four large-scale neuroimaging-related studies and a consortium on imaging genomics and discuss four common themes of neuroimaging data analysis at the population level. We review nine major population-based statistical analysis methods and their associated statistical challenges and present recent progress in statistical methodology to address these challenges.