Castruccio Stefano, Ombao Hernando, Genton Marc G
Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, 153 Hurley Hall, Notre Dame, Indiana 46556, U.S.A.
Statistics Program, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia.
Biometrics. 2018 Sep;74(3):823-833. doi: 10.1111/biom.12844. Epub 2018 Jan 22.
Functional Magnetic Resonance Imaging (fMRI) is a primary modality for studying brain activity. Modeling spatial dependence of imaging data at different spatial scales is one of the main challenges of contemporary neuroimaging, and it could allow for accurate testing for significance in neural activity. The high dimensionality of this type of data (on the order of hundreds of thousands of voxels) poses serious modeling challenges and considerable computational constraints. For the sake of feasibility, standard models typically reduce dimensionality by modeling covariance among regions of interest (ROIs)-coarser or larger spatial units-rather than among voxels. However, ignoring spatial dependence at different scales could drastically reduce our ability to detect activation patterns in the brain and hence produce misleading results. We introduce a multi-resolution spatio-temporal model and a computationally efficient methodology to estimate cognitive control related activation and whole-brain connectivity. The proposed model allows for testing voxel-specific activation while accounting for non-stationary local spatial dependence within anatomically defined ROIs, as well as regional dependence (between-ROIs). The model is used in a motor-task fMRI study to investigate brain activation and connectivity patterns aimed at identifying associations between these patterns and regaining motor functionality following a stroke.
功能磁共振成像（fMRI）是研究大脑活动的主要方式。在不同空间尺度上对成像数据的空间依赖性进行建模是当代神经成像的主要挑战之一，它能够对神经活动的显著性进行准确检验。这类数据的高维度（达数十万体素量级）带来了严峻的建模挑战和相当大的计算限制。为了可行性，标准模型通常通过对感兴趣区域（ROI，即更粗糙或更大的空间单元）之间的协方差进行建模，而非对体素之间的协方差进行建模，来降低维度。然而，忽略不同尺度的空间依赖性可能会大幅降低我们检测大脑激活模式的能力，从而产生误导性结果。我们引入了一种多分辨率时空模型和一种计算高效的方法，以估计与认知控制相关的激活和全脑连通性。所提出的模型能够在考虑解剖学定义的ROI内非平稳局部空间依赖性以及区域间依赖性（ROI之间）的同时，对体素特异性激活进行检验。该模型用于一项运动任务fMRI研究，以调查大脑激活和连通性模式，旨在识别这些模式与中风后恢复运动功能之间的关联。
