Signal and Image Processing Institute, University of Southern California, Los Angeles, USA.
Signal and Image Processing Institute, University of Southern California, Los Angeles, USA; Neuroscience Graduate Program, University of Southern California, Los Angeles, USA.
J Neurosci Methods. 2022 May 15;374:109566. doi: 10.1016/j.jneumeth.2022.109566. Epub 2022 Mar 17.
We present a new high-quality, single-subject atlas with sub-millimeter voxel resolution, high SNR, and excellent gray-white tissue contrast to resolve fine anatomical details. The atlas is labeled into two parcellation schemes: 1) the anatomical BCI-DNI atlas, which is manually labeled based on known morphological and anatomical features, and 2) the hybrid USCBrain atlas, which incorporates functional information to guide the sub-parcellation of cerebral cortex. In both cases, we provide consistent volumetric and cortical surface-based parcellation and labeling. The intended use of the atlas is as a reference template for structural coregistration and labeling of individual brains. A single-subject T1-weighted image was acquired five times at a resolution of 0.547 mm × 0.547 mm × 0.800 mm and averaged. Images were processed by an expert neuroanatomist using semi-automated methods in BrainSuite to extract the brain, classify tissue-types, and render anatomical surfaces. Sixty-six cortical and 29 noncortical regions were manually labeled to generate the BCI-DNI atlas. The cortical regions were further sub-parcellated into 130 cortical regions based on multi-subject connectivity analysis using resting fMRI (rfMRI) data from the Human Connectome Project (HCP) database to produce the USCBrain atlas. In addition, we provide a delineation between sulcal valleys and gyral crowns, which offer an additional set of 26 sulcal subregions per hemisphere. Lastly, a probabilistic map is provided to give users a quantitative measure of reliability for each gyral subdivision. Utility of the atlas was assessed by computing Adjusted Rand Indices (ARIs) between individual sub-parcellations obtained through structural-only coregistration to the USCBrain atlas and sub-parcellations obtained directly from each subject's resting fMRI data. Both atlas parcellations can be used with the BrainSuite, FreeSurfer, and FSL software packages.
我们提出了一种新的高质量、单个体素分辨率、高信噪比和出色的灰白质组织对比度的单个主体图谱，以解决精细的解剖细节。该图谱分为两种分割方案：1）解剖 BCI-DNI 图谱，它是根据已知的形态和解剖特征手动标记的，2）混合 USCBrain 图谱，它结合了功能信息来指导大脑皮层的亚分割。在这两种情况下，我们都提供了一致的体积和皮质表面分割和标记。该图谱的预期用途是作为个体大脑结构配准和标记的参考模板。单次采集的 T1 加权图像分辨率为 0.547mm×0.547mm×0.800mm，共采集了 5 次，并进行了平均处理。图像由一位神经解剖学专家使用 BrainSuite 中的半自动方法进行处理，以提取大脑、分类组织类型和呈现解剖表面。手动标记了 66 个皮质和 29 个非皮质区域，以生成 BCI-DNI 图谱。皮质区域根据来自人类连接组计划（HCP）数据库的静息 fMRI（rfMRI）数据的多主体连接分析进一步细分为 130 个皮质区域，以生成 USCBrain 图谱。此外，我们提供了脑回谷和脑回冠之间的划分，这为每个半球提供了另外 26 个脑回亚区。最后，提供了一个概率图，为每个脑回细分提供了一个定量的可靠性度量。通过计算通过结构仅配准到 USCBrain 图谱获得的个体亚分割与直接从每个受试者的静息 fMRI 数据获得的亚分割之间的调整兰德指数（ARI）来评估图谱的效用。该图谱的两个分区都可以与 BrainSuite、FreeSurfer 和 FSL 软件包一起使用。
