Wein, Simon and Tome, Ana M. and Goldhacker, Markus and Greenlee, Mark W. and Lang, Elmar W. (2020) A Constrained ICA-EMD Model for Group Level fMRI Analysis. FRONTIERS IN NEUROSCIENCE, 14: 221. ISSN , 1662-453X
Full text not available from this repository. (Request a copy)Abstract
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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | INDEPENDENT COMPONENT ANALYSIS; BLIND SOURCE SEPARATION; FUNCTIONAL MRI DATA; TIME-SERIES; DECOMPOSITION; CONNECTIVITY; ALGORITHMS; INFERENCES; NETWORK; INFOMAX; independent component analysis; ICA; empirical mode decomposition; EMD; Green's-function; based EMD; fMRI |
| Subjects: | 100 Philosophy & psychology > 150 Psychology 500 Science > 570 Life sciences |
| Divisions: | Human Sciences > Institut für Psychologie > Lehrstuhl für Psychologie I (Allgemeine Psychologie I und Methodenlehre) - Prof. Dr. Mark W. Greenlee Biology, Preclinical Medicine > Institut für Biophysik und physikalische Biochemie > Prof. Dr. Elmar Lang |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 26 Mar 2021 11:29 |
| Last Modified: | 26 Mar 2021 11:29 |
| URI: | https://pred.uni-regensburg.de/id/eprint/44738 |
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