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MICA-A toolbox for masked independent component analysis of fMRI data.

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

SUBMITTER: Moher Alsady T

PROVIDER: S-EPMC6867364 | biostudies-literature | 2016 Oct

REPOSITORIES: biostudies-literature

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MICA-A toolbox for masked independent component analysis of fMRI data.

Moher Alsady Tawfik T Blessing Esther M EM Beissner Florian F

Human brain mapping 20160511 10

Independent component analysis (ICA) is a widely used technique for investigating functional connectivity (fc) in functional magnetic resonance imaging data. Masked independent component analysis (mICA), that is, ICA restricted to a defined region of interest, has been shown to detect local fc networks in particular brain regions, including the cerebellum, brainstem, posterior cingulate cortex, operculo-insular cortex, hippocampus, and spinal cord. Here, we present the mICA toolbox, an open-sour ...[more]

PMID: 27168407

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Project description:IntroductionIndependent component analysis (ICA) has been extensively used for reducing task-free BOLD fMRI recordings into spatial maps and their associated time-courses. The spatially identified independent components can be considered as intrinsic connectivity networks (ICNs) of non-contiguous regions. To date, the spatial patterns of the networks have been analyzed with techniques developed for volumetric data.ObjectiveHere, we detail a graph building technique that allows these ICNs to be analyzed with graph theory.MethodsFirst, ICA was performed at the single-subject level in 15 healthy volunteers using a 3T MRI scanner. The identification of nine networks was performed by a multiple-template matching procedure and a subsequent component classification based on the network "neuronal" properties. Second, for each of the identified networks, the nodes were defined as 1,015 anatomically parcellated regions. Third, between-node functional connectivity was established by building edge weights for each networks. Group-level graph analysis was finally performed for each network and compared to the classical network.ResultsNetwork graph comparison between the classically constructed network and the nine networks showed significant differences in the auditory and visual medial networks with regard to the average degree and the number of edges, while the visual lateral network showed a significant difference in the small-worldness.ConclusionsThis novel approach permits us to take advantage of the well-recognized power of ICA in BOLD signal decomposition and, at the same time, to make use of well-established graph measures to evaluate connectivity differences. Moreover, by providing a graph for each separate network, it can offer the possibility to extract graph measures in a specific way for each network. This increased specificity could be relevant for studying pathological brain activity or altered states of consciousness as induced by anesthesia or sleep, where specific networks are known to be altered in different strength.

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MICA-A toolbox for masked independent component analysis of fMRI data.

Publications

MICA-A toolbox for masked independent component analysis of fMRI data.

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