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Independent component analysis (ICA) is a popular tool for investigatingbrain organization in neuroscience research. In fMRI studies, an important goalis to study how brain networks are modulated by subjects' clinical anddemographic variables. Existing ICA methods and toolboxes don't incorporatesubjects' covariates effects in ICA estimation of brain networks, whichpotentially leads to loss in accuracy and statistical power in detecting brainnetwork differences between subjects' groups. We introduce a Matlab toolbox,HINT (Hierarchical INdependent component analysis Toolbox), that provides ahierarchical covariate-adjusted ICA (hc-ICA) for modeling and testing covariateeffects and generates model-based estimates of brain networks on both thepopulation- and individual-level. HINT provides a user-friendly Matlab GUI thatallows users to easily load images, specify covariate effects, monitor modelestimation via an EM algorithm, specify hypothesis tests, and visualizeresults. HINT also has a command line interface which allows users toconveniently run and reproduce the analysis with a script. HINT implements anew multi-level probabilistic ICA model for group ICA. It provides astatistically principled ICA modeling framework for investigating covariateeffects on brain networks. HINT can also generate and visualize model-basednetwork estimates for user-specified subject groups, which greatly facilitatesgroup comparisons.

HINT: A Hierarchical Independent Component Analysis Toolbox for Investigating Brain Functional Networks using Neuroimaging Data