Personode: A Toolbox for ICA Map Classification and Individualized ROI Definition
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2020-06
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Journal Article
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Abstract
Canonical resting state networks (RSNs) can be obtained through independent component analysis (ICA). RSNs are reproducible across subjects but also present inter-individual differences, which can be used to individualize regions-of-interest (ROI) definition, thus making fMRI analyses more accurate. Unfortunately, no automatic tool for defining subject-specific ROIs exists, making the classification of ICAs as representatives of RSN time-consuming and largely dependent on visual inspection. Here, we present Personode, a user-friendly and open source MATLAB-based toolbox that semi-automatically performs the classification of RSN and allows for defining subject- and group-specific ROIs. To validate the applicability of our new approach and to assess potential improvements compared to previous approaches, we applied Personode to both task-related activation and resting-state data. Our analyses show that for task-related activation analyses, subject-specific spherical ROIs defined with Personode produced higher activity contrasts compared to ROIs derived from single-study and meta-analytic coordinates. We also show that subject-specific irregular ROIs defined with Personode improved ROI-to-ROI functional connectivity analyses. Hence, Personode might be a useful toolbox for ICA map classification into RSNs and group- as well as subject-specific ROI definitions, leading to improved analyses of task-related activation and functional connectivity. © 2020, Springer Science+Business Media, LLC, part of Springer Nature.
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published
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18 (3)
Pages / Article No.
339 - 349
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Springer
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Subject
ROI individualization; ICA map classification; Resting-state networks; Toolbox; Independent component analysis; Functional connectivity