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A Constrained ICA-EMD Model for Group Level fMRI Analysis

  • Independent component analysis (ICA), being a data-driven method, has been shown to be a powerful tool for functional magnetic resonance imaging (fMRI) data analysis. One drawback of this multivariate approach is that it is not, in general, compatible with the analysis of group data. Various techniques have been proposed to overcome this limitation of ICA. In this paper, a novel ICA-based workflow for extracting resting-state networks from fMRI group studies is proposed. An empirical mode decomposition (EMD) is used, in a data-driven manner, to generate reference signals that can be incorporated into a constrained version of ICA (cICA), thereby eliminating the inherent ambiguities of ICA. The results of the proposed workflow are then compared to those obtained by a widely used group ICA approach for fMRI analysis. In this study, we demonstrate that intrinsic modes, extracted by EMD, are suitable to serve as references for cICA. This approach yields typical resting-state patterns that are consistent over subjects. By introducing these reference signals into the ICA, our processing pipeline yields comparable activity patterns across subjects in a mathematically transparent manner. Our approach provides a user-friendly tool to adjust the trade-off between a high similarity across subjects and preserving individual subject features of the independent components.

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Metadaten
Author:Simon Wein, Ana Maria ToméORCiD, Markus GoldhackerORCiD, Mark W. GreenleeORCiD, Elmar Wolfgang LangORCiD
DOI:https://doi.org/10.3389/fnins.2020.00221
ISSN:1662-453X
ISSN:1662-4548
Parent Title (English):Frontiers in Neuroscience
Publisher:frontiers
Document Type:Article
Language:English
Year of first Publication:2020
Release Date:2022/08/11
Volume:14
Article Number:221
First Page:1
Last Page:10
Institutes:Fakultät Maschinenbau
Begutachtungsstatus:peer-reviewed
OpenAccess Publikationsweg:Gold Open Access- Erstveröffentlichung in einem/als Open-Access-Medium
research focus:Digitalisierung
Licence (German):Creative Commons - CC BY - Namensnennung 4.0 International