The Hierarchical INdependent component analysis Toolbox (HINT), is Matlab toolbox that serves as a platform for hierarchical ICA techniques. The toolbox currently supports hierarchical covariate-adjusted ICA as described in Shi and Guo (2016) and longitudinal covariate-adjusted hierarchical ICA as described in Wang and Guo (2019).
Supported by the National Institute of Mental Health under Award Number R01MH105561.
Run the HINT.m file in Matlab to start the Toolbox.
- [mtimesx] (https://www.mathworks.com/matlabcentral/fileexchange/25977-mtimesx-fast-matrix-multiply-with-multi-dimensional-support) - Used to speed up computation.
- [NIFTI Toolbox] (https://www.mathworks.com/matlabcentral/fileexchange/8797-tools-for-nifti-and-analyze-image) - Used for reading and writing nii files.
- [FASTICA] (https://research.ics.aalto.fi/ica/fastica/) - Used in initial pre-processing.
HINT's 2.0 release was 5/23/2022. This release includes longitudinal hc-ICA. A tutorial for this version of the toolbox can be found here.
This project is licensed under the MIT License - see the LICENSE file for details.
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