Abstract
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.
Highlights
In order to perform different actions/tasks, the brain is thought to rely on the simultaneous activation of several Functional Brain Networks (FBN) (Fair et al, 2009; Huettel et al, 2009), which are engaged in proper interaction to execute the tasks effectively
We prepared the Toolbox for Enhanced Design Matrix (TEDM) set-up as follows: First, the three task-related time courses employed in the standard design matrix were used as prior-information, in order to otbain the three assisted time courses, as explained above
We proposed a new methodology for constructing the design matrix within the GLM framework
Summary
In order to perform different actions/tasks, the brain is thought to rely on the simultaneous activation of several Functional Brain Networks (FBN) (Fair et al, 2009; Huettel et al, 2009), which are engaged in proper interaction to execute the tasks effectively Such networks, potentially distributed over the whole brain, constitute segregated regions that exhibit high functional connectivity (Fair et al, 2009; Huettel et al, 2009; Poldrack et al, 2011). One of the primary aims of fMRI data analysis consists of unmixing all those sources to reveal both their activation patterns and their corresponding areas of activation, referred to as spatial maps, in which each brain source of interest manifests itself in revealing its corresponding FBN
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