Enhanced design matrix for task-related fMRI data analysis
In this paper, we introduce a novel methodology for the analysis of task-related fMRI data. In particular, we propose an alternative way for constructing the design matrix, based on the newly suggested Information-Assisted Dictionary Learning (IADL) method. This technique offers an enhanced potentia...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2021-12-01
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| Series: | NeuroImage |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1053811921009915 |
| _version_ | 1818366534663274496 |
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| author | Manuel Morante Yannis Kopsinis Christos Chatzichristos Athanassios Protopapas Sergios Theodoridis |
| author_facet | Manuel Morante Yannis Kopsinis Christos Chatzichristos Athanassios Protopapas Sergios Theodoridis |
| author_sort | Manuel Morante |
| collection | DOAJ |
| description | In this paper, we introduce a novel methodology for the analysis of task-related fMRI data. In particular, we propose an alternative way for constructing the design matrix, based on the newly suggested Information-Assisted Dictionary Learning (IADL) method. This technique offers an enhanced potential, within the conventional GLM framework, (a) to efficiently cope with uncertainties in the modeling of the hemodynamic response function, (b) to accommodate unmodeled brain-induced sources, beyond the task-related ones, as well as potential interfering scanner-induced artifacts, uncorrected head-motion residuals and other unmodeled physiological signals, and (c) to integrate external knowledge regarding the natural sparsity of the brain activity that is associated with both the experimental design and brain atlases. The capabilities of the proposed methodology are evaluated via a realistic synthetic fMRI-like dataset, and demonstrated using a test case of a challenging fMRI study, which verifies that the proposed approach produces substantially more consistent results compared to the standard design matrix method. A toolbox extension for SPM is also provided, to facilitate the use and reproducibility of the proposed methodology. |
| first_indexed | 2024-12-13T22:37:41Z |
| format | Article |
| id | doaj.art-70ac01dc43bf4b0e92b05540b2e8c22c |
| institution | Directory Open Access Journal |
| issn | 1095-9572 |
| language | English |
| last_indexed | 2024-12-13T22:37:41Z |
| publishDate | 2021-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage |
| spelling | doaj.art-70ac01dc43bf4b0e92b05540b2e8c22c2022-12-21T23:28:56ZengElsevierNeuroImage1095-95722021-12-01245118719Enhanced design matrix for task-related fMRI data analysisManuel Morante0Yannis Kopsinis1Christos Chatzichristos2Athanassios Protopapas3Sergios Theodoridis4Dept. of Electronic Systems, Aalborg University, Denmark; Computer Technology Institutes & Press “Diophantus” (CTI), Patras, Greece; Corresponding author.LIBRA MLI Ltd, Edinburgh, UKDept. Electrical Engineering (ESAT), Dynamical Systems, Signal Processing and Data Analytics (STADIUS), KU Leuven, BelgiumDept. of Special Needs Education of the University of Oslo, NorwayDept. of Electronic Systems, Aalborg University, Denmark; Dept. of Informatics and Telecommunications of the National and Kapodistrian University of Athens, GreeceIn this paper, we introduce a novel methodology for the analysis of task-related fMRI data. In particular, we propose an alternative way for constructing the design matrix, based on the newly suggested Information-Assisted Dictionary Learning (IADL) method. This technique offers an enhanced potential, within the conventional GLM framework, (a) to efficiently cope with uncertainties in the modeling of the hemodynamic response function, (b) to accommodate unmodeled brain-induced sources, beyond the task-related ones, as well as potential interfering scanner-induced artifacts, uncorrected head-motion residuals and other unmodeled physiological signals, and (c) to integrate external knowledge regarding the natural sparsity of the brain activity that is associated with both the experimental design and brain atlases. The capabilities of the proposed methodology are evaluated via a realistic synthetic fMRI-like dataset, and demonstrated using a test case of a challenging fMRI study, which verifies that the proposed approach produces substantially more consistent results compared to the standard design matrix method. A toolbox extension for SPM is also provided, to facilitate the use and reproducibility of the proposed methodology.http://www.sciencedirect.com/science/article/pii/S1053811921009915fMRISemi-blindDictionary learningGeneral linear model (GLM)Subject variability |
| spellingShingle | Manuel Morante Yannis Kopsinis Christos Chatzichristos Athanassios Protopapas Sergios Theodoridis Enhanced design matrix for task-related fMRI data analysis NeuroImage fMRI Semi-blind Dictionary learning General linear model (GLM) Subject variability |
| title | Enhanced design matrix for task-related fMRI data analysis |
| title_full | Enhanced design matrix for task-related fMRI data analysis |
| title_fullStr | Enhanced design matrix for task-related fMRI data analysis |
| title_full_unstemmed | Enhanced design matrix for task-related fMRI data analysis |
| title_short | Enhanced design matrix for task-related fMRI data analysis |
| title_sort | enhanced design matrix for task related fmri data analysis |
| topic | fMRI Semi-blind Dictionary learning General linear model (GLM) Subject variability |
| url | http://www.sciencedirect.com/science/article/pii/S1053811921009915 |
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