Dept. of Electronic Systems, Aalborg University, Denmark; Computer Technology Institutes & Press "Diophantus" (CTI), Patras, Greece.
LIBRA MLI Ltd, Edinburgh, UK.
Neuroimage. 2021 Dec 15;245:118719. doi: 10.1016/j.neuroimage.2021.118719. Epub 2021 Nov 12.
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.
在本文中，我们介绍了一种新的方法来分析与任务相关的 fMRI 数据。特别是，我们提出了一种替代的构建设计矩阵的方法，基于新提出的信息辅助字典学习（IADL）方法。该技术在传统的 GLM 框架内提供了一种增强的潜力，（a）有效地处理血流动力学响应函数建模中的不确定性，（b）适应任务相关以外的未建模的大脑诱导源，以及潜在的干扰扫描器诱导的伪影、未校正的头部运动残差和其他未建模的生理信号，以及（c）整合与实验设计和大脑图谱相关的大脑活动自然稀疏性的外部知识。所提出的方法的能力通过一个现实的合成 fMRI 样数据集进行评估，并通过一个具有挑战性的 fMRI 研究的测试案例进行演示，该案例验证了与标准设计矩阵方法相比，所提出的方法产生了更一致的结果。还提供了一个用于 SPM 的工具箱扩展，以方便所提出的方法的使用和可重复性。
