Beijing City Key Lab for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing, China; Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China; McGovern Institute for Brain Research, Peking University, Beijing, China.
Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, Croatia.
Neuroimage. 2020 Feb 1;206:116318. doi: 10.1016/j.neuroimage.2019.116318. Epub 2019 Nov 2.
Spatial normalization or deformation to a standard brain template is routinely used as a key module in various pipelines for the processing of magnetic resonance imaging (MRI) data. Brain templates are often constructed using MRI data from a limited number of subjects. Individual brains show significant variabilities in their morphology; thus, sample sizes and population differences are two key factors that influence brain template construction. To address these influences, we employed two independent groups from the Human Connectome Project (HCP) and the Chinese Human Connectome Project (CHCP) to quantify the impacts of sample sizes and population on brain template construction. We first assessed the effect of sample size on the construction of volumetric brain templates using data subsets from the HCP and CHCP datasets. We applied a voxel-wise index of the deformation variability and a logarithmically transformed Jacobian determinant to quantify the variability associated with the template construction and modeled the brain template variability as a power function of the sample size. At the system level, the frontoparietal control network and dorsal attention network demonstrated higher deformation variability and logged Jacobian determinants, whereas other primary networks showed lower variability. To investigate the population differences, we constructed Caucasian and Chinese standard brain atlases (namely, US200 and CN200). The two demographically matched templates, particularly the language-related areas, exhibited dramatic bilaterally in supramarginal gyri and inferior frontal gyri differences in their deformation variability and logged Jacobian determinant. Using independent data from the HCP and CHCP, we examined the segmentation and registration accuracy and observed significant reduction in the performance of the brain segmentation and registration when the population-mismatched templates were used in the spatial normalization. Our findings provide evidence to support the use of population-matched templates in human brain mapping studies. The US200 and CN200 templates have been released on the Neuroimage Informatics Tools and Resources Clearinghouse (NITRC) website (https://www.nitrc.org/projects/us200_cn200/).
空间标准化或变形到标准脑模板通常作为磁共振成像 (MRI) 数据处理的各种管道中的关键模块使用。脑模板通常使用来自少数对象的 MRI 数据构建。个体大脑在形态上表现出显著的可变性;因此,样本量和人群差异是影响脑模板构建的两个关键因素。为了解决这些影响,我们使用来自人类连接组计划 (HCP) 和中国人类连接组计划 (CHCP) 的两个独立组来量化样本量和人群对脑模板构建的影响。我们首先使用 HCP 和 CHCP 数据集的子数据集评估样本量对体积脑模板构建的影响。我们应用变形变异性的体素指标和对数变换的雅可比行列式来量化与模板构建相关的变异性,并将脑模板变异性建模为样本量的幂函数。在系统水平上,额顶控制网络和背侧注意网络显示出更高的变形变异性和对数雅可比行列式,而其他主要网络则显示出较低的变异性。为了研究人群差异,我们构建了白人和中国人的标准脑图谱(即 US200 和 CN200)。这两个人口统计学匹配的模板,特别是与语言相关的区域,在额下回和颞上回的变形变异性和对数雅可比行列式上表现出明显的双侧差异。使用来自 HCP 和 CHCP 的独立数据,我们检查了分割和注册准确性,并观察到当在空间标准化中使用人群不匹配的模板时,脑分割和注册的性能显著降低。我们的研究结果为在人类大脑映射研究中使用人群匹配的模板提供了证据支持。US200 和 CN200 模板已在神经影像学信息工具和资源知识库 (NITRC) 网站(https://www.nitrc.org/projects/us200_cn200/)上发布。