Inter-Subject Analysis: A Partial Gaussian Graphical Model Approach

• Published in: Journal of the American Statistical Association Vol. 116; no. 534; pp. 746 - 755
• Main Authors: Ma, Cong, Lu, Junwei, Liu, Han
• Format: Journal Article
• Language: English
• Published: United States Taylor & Francis 03.04.2021; Taylor & Francis Ltd
• Subjects: Bias; Brain; Dependency; fMRI data; Functional connectivity; Gaussian graphical models; Graphical models; Neuroimaging; neurophysiology; normal distribution; Nuisance parameter; Parameter estimation; Regression analysis; Sample splitting; Sparsity; Statistical inference; Statistical methods; Statistics; Uncertainty assessment; Gaussian graphical models; Sample splitting; Uncertainty assessment; Nuisance parameter; fMRI data

Different from traditional intra-subject analysis, the goal of inter-subject analysis (ISA) is to explore the dependency structure between different subjects with the intra-subject dependency as nuisance. ISA has important applications in neuroscience to study the functional connectivity between bra...