MICA—A toolbox for masked independent component analysis of fMRI data

摘要: Independent component analysis (ICA) is a widely used technique for investigating functional connectivity (fc) in magnetic resonance imaging data. Masked independent (mICA), that is, ICA restricted to defined region of interest, has been shown detect local fc networks particular brain regions, including the cerebellum, brainstem, posterior cingulate cortex, operculo-insular hippocampus, and spinal cord. Here, we present mICA toolbox, an open-source GUI toolbox based on FSL command line tools performs related analyses integrated way. Functions include automated mask generation from atlases, essential preprocessing, mICA-based parcellation, back-reconstruction whole-brain ones, reproducibility analysis. Automated slice-wise calculation cropping are additional functions reduce computational time memory requirements large analyses. To validate our tested these different using resting-state task-based data Human Connectome Project. In detected six together with their counterparts, closely replicating previous results. MICA-based parcellation hippocampus showed longitudinally discrete configuration greater heterogeneity anterior consistent animal human literature. Finally, brainstem motor sensory nuclei involved task tongue movement, thereby extending earlier Hum Brain Mapp 37:3544-3556, 2016. © 2016 Wiley Periodicals, Inc.