Department of Statistics, University of Virginia, Charlottesville, VA 22904, USA.
Department of Statistics, University of Virginia, Charlottesville, VA 22904, USA.
Neuroimage. 2018 Jun;173:580-591. doi: 10.1016/j.neuroimage.2017.12.032. Epub 2017 Dec 27.
The focus of this paper is on evaluating brain responses to different stimuli and identifying brain regions with different responses using multi-subject, stimulus-evoked functional magnetic resonance imaging (fMRI) data. To jointly model many brain voxels' responses to designed stimuli, we present a new low-rank multivariate general linear model (LRMGLM) for stimulus-evoked fMRI data. The new model not only is flexible to characterize variation in hemodynamic response functions (HRFs) across different regions and stimulus types, but also enables information "borrowing" across voxels and uses much fewer parameters than typical nonparametric models for HRFs. To estimate the proposed LRMGLM, we introduce a new penalized optimization function, which leads to temporally and spatially smooth HRF estimates. We develop an efficient optimization algorithm to minimize the optimization function and identify the voxels with different responses to stimuli. We show that the proposed method can outperform several existing voxel-wise methods by achieving both high sensitivity and specificity. We apply the proposed method to the fMRI data collected in an emotion study, and identify anterior dACC to have different responses to a designed threat and control stimuli.
本文的重点是评估大脑对不同刺激的反应，并使用多主体、刺激诱发功能磁共振成像（fMRI）数据识别具有不同反应的大脑区域。为了联合建模许多大脑体素对设计刺激的反应，我们提出了一种新的低秩多变量广义线性模型（LRMGLM）用于刺激诱发 fMRI 数据。新模型不仅灵活，能够描述不同区域和刺激类型的血液动力学反应函数（HRF）的变化，而且能够在体素之间“借用”信息，并使用比典型的 HRF 非参数模型少得多的参数。为了估计所提出的 LRMGLM，我们引入了一个新的惩罚优化函数，该函数导致 HRF 估计在时间和空间上是平滑的。我们开发了一种有效的优化算法来最小化优化函数并识别对刺激有不同反应的体素。我们表明，所提出的方法可以通过实现高灵敏度和特异性来优于几种现有的体素方法。我们将所提出的方法应用于在情绪研究中收集的 fMRI 数据，并确定前扣带皮层（anterior dACC）对设计的威胁和控制刺激有不同的反应。
