MIT Yes Center for Biomedical Imaging Statistics (CBIS) NITRC HINT: Hierarchical Independent Component Analysis Toolbox The Hierarchical Independent Component Analysis Toolbox (HINT) is a Matlab toolbox serving as a user-friendly platform for conducting analyses under the hierarchical ICA framework. At this time, the toolbox implements the hc-ICA technique of Shi and Guo, 2016 and the longitudinal technique of Wang and Guo, 2019. Highlights: Model based estimation and hypothesis testing of covariate effects Visualization windows to examine covariate effects, contrasts, and to compare sub-populations Shi, R., & Guo, Y. (2016). Investigating differences in brain functional networks using hierarchical covariate-adjusted independent component analysis. The Annals of Applied Statistics, 10(4), 1930–1957. http://doi.org/10.1214/16-AOAS946 Wang, Y., & Guo, Y. (2019). A hierarchical independent component analysis model for longitudinal neuroimaging studies. NeuroImage, 189, 380-400. https://doi.org/10.1016/j.neuroimage.2018.12.024 Video Tutorial: https://www.youtube.com/watch?v=lacy1bnKTYA Mac/Windows/Linux/HPC 2023-7-30 2.0 2018-11-21 Beta Version 1 HINT: Hierarchical Independent Component Analysis Toolbox Connectivity Analysis, Modeling, Independent Component Analysis, Regression, Multivariate Analysis, Visualization, MR, MIT http://www.nitrc.org/projects/hint/, http://https://github.com/Emory-CBIS/HINT