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...

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Autores principales:	Manuel Morante, Yannis Kopsinis, Christos Chatzichristos, Athanassios Protopapas, Sergios Theodoridis
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	fMRI Semi-blind Dictionary learning General linear model (GLM) Subject variability Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/70ac01dc43bf4b0e92b05540b2e8c22c
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id	oai:doaj.org-article:70ac01dc43bf4b0e92b05540b2e8c22c
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spelling	oai:doaj.org-article:70ac01dc43bf4b0e92b05540b2e8c22c2021-11-18T04:44:58ZEnhanced design matrix for task-related fMRI data analysis1095-957210.1016/j.neuroimage.2021.118719https://doaj.org/article/70ac01dc43bf4b0e92b05540b2e8c22c2021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1053811921009915https://doaj.org/toc/1095-9572In 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.Manuel MoranteYannis KopsinisChristos ChatzichristosAthanassios ProtopapasSergios TheodoridisElsevierarticlefMRISemi-blindDictionary learningGeneral linear model (GLM)Subject variabilityNeurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENNeuroImage, Vol 245, Iss , Pp 118719- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	fMRI Semi-blind Dictionary learning General linear model (GLM) Subject variability Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	fMRI Semi-blind Dictionary learning General linear model (GLM) Subject variability Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Manuel Morante Yannis Kopsinis Christos Chatzichristos Athanassios Protopapas Sergios Theodoridis Enhanced design matrix for task-related fMRI data analysis
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.
format	article
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
title	Enhanced design matrix for task-related fMRI data analysis
title_short	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_sort	enhanced design matrix for task-related fmri data analysis
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/70ac01dc43bf4b0e92b05540b2e8c22c
work_keys_str_mv	AT manuelmorante enhanceddesignmatrixfortaskrelatedfmridataanalysis AT yanniskopsinis enhanceddesignmatrixfortaskrelatedfmridataanalysis AT christoschatzichristos enhanceddesignmatrixfortaskrelatedfmridataanalysis AT athanassiosprotopapas enhanceddesignmatrixfortaskrelatedfmridataanalysis AT sergiostheodoridis enhanceddesignmatrixfortaskrelatedfmridataanalysis
_version_	1718425045521399808

Enhanced design matrix for task-related fMRI data analysis

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