The Hierarchical INdependent component analysis Toolbox (HINT), is Matlab toolbox that serves as a platform for hierarchical ICA techniques. The toolbox currently supports hierarchical ICA as described in Shi and Guo (2016).
Supported by the National Institute of Mental Health under Award Number R01MH105561.
Run the HINT.m file in Matlab to start the Toolbox. A user guide is provided in the docs folder.
- [GIFT] (http://mialab.mrn.org/software/gift/) - Used to obtain an initial guess for the EM algorithm.
- [BSMAC] (http://web1.sph.emory.edu/bios/CBIS/software.html#) - Used for several of the UI elements in the display window.
- [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 is currently in Beta.
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
Zhang, L., Agravat, S., Derado, G., Chen, S., McIntosh, B. J., & Bowman, F. D. (2012). BSMac: A MATLAB toolbox implementing a Bayesian spatial model for brain activation and connectivity. Journal of neuroscience methods, 204(1), 133-143.