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A Whole Brain Atlas with Sub-parcellation of Cortical Gyri using Resting fMRI

机译:使用静息功能磁共振成像的大脑皮层回碎的全脑图谱

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The new hybrid-BCI-DNI atlas is a high-resolution MPRAGE, single-subject atlas, constructed using both anatomical and functional information to guide the parcellation of the cerebral cortex. Anatomical labeling was performed manually on coronal single-slice images guided by sulcal and gyral landmarks to generate the original (non-hybrid) BCI-DNI atlas. Functional sub-parcellations of the gyral ROIs were then generated from 40 minimally preprocessed resting fMRI datasets from the HCP database. Gyral ROIs were transferred from the BCI-DNI atlas to the 40 subjects using the HCP grayordinate space as a reference. For each subject, each gyral ROI was subdivided using the fMRI data by applying spectral clustering to a similarity matrix computed from the fMRI time-series correlations between each vertex pair. The sub-parcellations were then transferred back to the original cortical mesh to create the sub-parcellated hBCI-DNI atlas with a total of 67 cortical regions per hemisphere. To assess the stability of the gyral subdivisons, a separate set of 60 HCP datasets were processed as follows: 1) coregistration of the structural scans to the hBCI-DNI atlas; 2) coregistration of the anatomical BCI-DNI atlas without functional subdivisions, followed by sub-parcellation of each subject's resting fMRI data as described above. We then computed consistency between the anatomically-driven delineation of each gyral subdivision and that obtained per subject using individual fMRI data. The gyral sub-parcellations generated by atlas-based registration show variable but generally good overlap of the confidence intervals with the resting fMRI-based subdivisions. These consistency measures will provide a quantitative measure of reliability of each subdivision to users of the atlas.
机译:新的混合BCI-DNI地图集是高分辨率MPRAGE单主题地图集,它使用解剖和功能信息来指导大脑皮质的分裂。人工标记在冠状窦和龈沟引导下的冠状单切片图像上进行,以生成原始的(非混合型)BCI-DNI图集。然后从HCP数据库的40个经过最少预处理的静息fMRI数据集中生成回旋ROI的功能子部分。使用HCP灰度空间作为参考,将ROI从BCI-DNI图集转移到40位受试者。对于每个受试者,通过将光谱聚类应用于根据每个顶点对之间的fMRI时间序列相关性计算的相似度矩阵,可以使用fMRI数据对每个回旋ROI进行细分。然后将这些亚散细胞转移回原始皮质网孔,以创建每个半球共有67个皮质区域的亚散细胞hBCI-DNI地图集。为了评估回旋细分的稳定性,按以下方式处理了60个HCP数据集的单独集合:1)将结构扫描归纳到hBCI-DNI地图集。 2)不进行功能细分的BCI-DNI解剖图谱的整体分布,然后如上所述对每个受试者的静息fMRI数据进行细分。然后,我们使用单个功能磁共振成像数据计算了每个回旋细分的解剖驱动轮廓与每个受试者获得的轮廓之间的一致性。通过基于图谱的配准生成的回旋子细分显示出可变的但通常与静置的基于fMRI的细分的置信区间重叠良好。这些一致性度量将为图集的用户提供每个细分的可靠性的定量度量。

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