Masked ICA is your tool of choice if you are interested in the local connectivity networks in a particular brain region, such as the brainstem, hippocampus, cerebellum, or spinal cord. Such networks often remain undiscovered in whole-brain ICAs because their contribution to the variance are to small compared to that of cortical large-scale signals.
We made the mICA Toolbox to make masked ICA user-friendly. It is based on command line tools from FSL to perform the ICA and related analyses (e.g. specific data preprocessing, atlas-based mask generation, mICA-based parcellation, dual-regression and direct back reconstruction). Various options provide flexible control of the analysis.
The toolbox furthermore offers an easy way to calculate ICA reproducibility over a range of decomposition dimensions, which can be used to overcome the common problem of dimensionality estimation.
- License: GNU General Public License (GPL)
Programming Language: Python, Tcl/Tk
- Language: English
- Operating System: Linux, MacOS
- Supported Data Format: NIfTI-1
Download, Documentation and Support
The latest version of the mICA Toolbox, including documentation, version history and a support forum can be found here. Please follow the link for downloading mICA Toolbox and support requests.
If you use the mICA toolbox for your research, please cite the following publications:
Moher Alsady T, Blessing EM, Beissner F – MICA-A toolbox for masked independent component analysis of fMRI data – Hum Brain Mapp. 2016;37(10):3544-56 – DOI: 10.1002/hbm.23258