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