The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.
Neuroimage. 2012 Jul 2;61(3):613-21. doi: 10.1016/j.neuroimage.2012.03.078. Epub 2012 Apr 3.
Resting state functional connectivity MRI (rsfc-MRI) reveals a wealth of information about the functional organization of the brain, but poses unique challenges for quantitative image analysis, mostly related to the large number of voxels with low signal-to-noise ratios. In this study, we tested the idea of using a prior spatial parcellation of the entire brain into various structural units, to perform an analysis on a structure-by-structure, rather than voxel-by-voxel, basis. This analysis, based upon atlas parcels, potentially offers enhanced SNR and reproducibility, and can be used as a common anatomical framework for cross-modality and cross-subject quantitative analysis. We used Large Deformation Diffeomorphic Metric Mapping (LDDMM) and a deformable brain atlas to parcel each brain into 185 regions. To investigate the precision of the cross-subject analysis, we computed inter-parcel correlations in 20 participants, each of whom was scanned twice, as well as the consistency of the connectivity patterns inter- and intra-subject, and the intersession reproducibility. We report significant inter-parcel correlations consistent with previous findings, and high test-retest reliability, an important consideration when the goal is to compare clinical populations. As an example of the cross-modality analysis, correlation with anatomical connectivity is also examined.
静息态功能磁共振连接成像（rsfc-MRI）揭示了大量有关大脑功能组织的信息，但对定量图像分析提出了独特的挑战，主要与大量信噪比低的体素有关。在这项研究中，我们测试了使用大脑整体的先验空间分割成各种结构单元的想法，以便在结构上而不是体素上进行分析。这种基于图谱的分析方法可能具有更高的信噪比和可重复性，并且可以作为跨模态和跨个体的定量分析的通用解剖学框架。我们使用大变形 diffeomorphic 度量映射（LDDMM）和可变形脑图谱将每个大脑分割成 185 个区域。为了研究跨个体分析的精度，我们在 20 名参与者中计算了每个参与者两次扫描的体素间相关性，以及个体内和个体间连接模式的一致性，以及跨会话的可重复性。我们报告了与先前发现一致的显著的体素间相关性，以及高的测试-重测可靠性，这在比较临床人群时是一个重要的考虑因素。作为跨模态分析的一个例子，还检查了与解剖连接的相关性。
