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 techniq...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2020-04-01
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| Series: | Frontiers in Neuroscience |
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| Online Access: | https://www.frontiersin.org/article/10.3389/fnins.2020.00221/full |
| _version_ | 1818414734133690368 |
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| author | Simon Wein Simon Wein Ana M. Tomé Markus Goldhacker Markus Goldhacker Mark W. Greenlee Elmar W. Lang |
| author_facet | Simon Wein Simon Wein Ana M. Tomé Markus Goldhacker Markus Goldhacker Mark W. Greenlee Elmar W. Lang |
| author_sort | Simon Wein |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-14T11:23:48Z |
| format | Article |
| id | doaj.art-8fdedcaca2fe4049b48c32d96f983827 |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-12-14T11:23:48Z |
| publishDate | 2020-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-8fdedcaca2fe4049b48c32d96f9838272022-12-21T23:03:38ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2020-04-011410.3389/fnins.2020.00221493061A Constrained ICA-EMD Model for Group Level fMRI AnalysisSimon Wein0Simon Wein1Ana M. Tomé2Markus Goldhacker3Markus Goldhacker4Mark W. Greenlee5Elmar W. Lang6CIML, Biophysics, University of Regensburg, Regensburg, GermanyExperimental Psychology, University of Regensburg, Regensburg, GermanyIEETA/DETI, Universidade de Aveiro, Aveiro, PortugalCIML, Biophysics, University of Regensburg, Regensburg, GermanyExperimental Psychology, University of Regensburg, Regensburg, GermanyExperimental Psychology, University of Regensburg, Regensburg, GermanyCIML, Biophysics, University of Regensburg, Regensburg, GermanyIndependent 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.https://www.frontiersin.org/article/10.3389/fnins.2020.00221/fullindependent component analysisICAempirical mode decompositionEMDGreen's-function - based EMDfMRI |
| spellingShingle | Simon Wein Simon Wein Ana M. Tomé Markus Goldhacker Markus Goldhacker Mark W. Greenlee Elmar W. Lang A Constrained ICA-EMD Model for Group Level fMRI Analysis Frontiers in Neuroscience independent component analysis ICA empirical mode decomposition EMD Green's-function - based EMD fMRI |
| title | A Constrained ICA-EMD Model for Group Level fMRI Analysis |
| title_full | A Constrained ICA-EMD Model for Group Level fMRI Analysis |
| title_fullStr | A Constrained ICA-EMD Model for Group Level fMRI Analysis |
| title_full_unstemmed | A Constrained ICA-EMD Model for Group Level fMRI Analysis |
| title_short | A Constrained ICA-EMD Model for Group Level fMRI Analysis |
| title_sort | constrained ica emd model for group level fmri analysis |
| topic | independent component analysis ICA empirical mode decomposition EMD Green's-function - based EMD fMRI |
| url | https://www.frontiersin.org/article/10.3389/fnins.2020.00221/full |
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