Enhanced design matrix for task-related fMRI data analysis

• Published in: NeuroImage (Orlando, Fla.) Vol. 245; p. 118719
• Main Authors: Morante, Manuel, Kopsinis, Yannis, Chatzichristos, Christos, Protopapas, Athanassios, Theodoridis, Sergios
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 15.12.2021; Elsevier Limited; Elsevier
• Subjects: Algorithms; Brain mapping; Brain Mapping - methods; Brain research; Data analysis; Datasets as Topic; Dictionary learning; Experiments; fMRI; Functional magnetic resonance imaging; General linear model (GLM); Hemodynamics; Humans; Image Enhancement; Image Processing, Computer-Assisted - methods; Imaging, Three-Dimensional; Magnetic Resonance Imaging; Motion; Physiology; Scanners; Semi-blind; Sensitivity and Specificity; Statistical analysis; Subject variability; fMRI; General linear model (GLM); Subject variability; Semi-blind; Dictionary learning
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2021.118719

•A novel methodology to enhance the standard design matrix is proposed within the GLM framework for task-related fMRI data analysis.•The proposed methodology offers an enhanced potential: (a) It efficiently copes with uncertainties in modeling the hemodynamic response. (b) It accommodates 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. (c) It integrates external knowledge regarding the sparsity percentage associated with both the experimental design and brain atlases without the necessity of an explicit spatial template.•Results show the new approach is more sensitive to detecting significant activity than using the standard design matrix.•Results evidence the new approach provides more anatomically reliable activation clusters than using the standard design matrix.•A new toolbox extension for SPM with the proposed methodology was developed. 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.