Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, United States.
Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, United States; Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, United States; Department of Diagnostic Radiology and Nuclear Medicine, Rush University Medical Center, Chicago, IL, United States.
Neuroimage. 2021 Jan 15;225:117462. doi: 10.1016/j.neuroimage.2020.117462. Epub 2020 Oct 16.
Reporting white matter findings in voxel-wise neuroimaging studies typically lacks specificity in terms of brain connectivity. Therefore, the purpose of this work was to develop an approach for rapidly extracting standardized brain connectivity information for white matter regions with significant findings in voxel-wise neuroimaging studies. The new approach was named regionconnect and is based on precalculated average healthy adult brain connectivity information stored in standard space in a fashion that allows fast retrieval and integration. Towards this goal, the present work first generated and evaluated the white matter connectome of the IIT Human Brain Atlas v.5.0. It was demonstrated that the edges of the atlas connectome are representative of those of individual participants of the Human Connectome Project in terms of the spatial organization of streamlines and spatial patterns of track-density. Next, the new white matter connectome was used to develop multi-layer, connectivity-based labels for each white matter voxel of the atlas, consistent with the fact that each voxel may contain axons from multiple connections. The regionconnect algorithm was then developed to rapidly integrate information contained in the multi-layer labels across voxels of a white matter region and to generate a list of the most probable connections traversing that region. Usage of regionconnect does not require high angular resolution diffusion MRI or any MRI data. The regionconnect algorithm as well as the white matter tractogram and connectome, multi-layer, connectivity-based labels, and associated resources developed for the IIT Human Brain Atlas v.5.0 in this work are available at www.nitrc.org/projects/iit. An interactive, online version of regionconnect is also available at www.iit.edu/~mri.
在体素水平的神经影像学研究中报告白质发现通常缺乏脑连接的特异性。因此，这项工作的目的是开发一种方法，用于快速提取体素水平神经影像学研究中具有显著发现的白质区域的标准化脑连接信息。新方法命名为 regionconnect，它基于预先计算的、以标准空间存储的健康成人大脑连接信息，以便快速检索和集成。为此，本工作首先生成和评估了 IIT 人类大脑图谱 v.5.0 的白质连接组。结果表明，图谱连接组的边缘在流线的空间组织和轨迹密度的空间模式方面代表了人类连接组计划个体参与者的边缘。接下来，使用新的白质连接组为图谱中的每个白质体素开发基于连接的多层标签，这与每个体素可能包含来自多个连接的轴突的事实一致。然后开发了 regionconnect 算法，以快速整合图谱中白质区域内各体素的多层标签信息，并生成穿越该区域的最可能连接列表。使用 regionconnect 不需要高角度分辨率扩散 MRI 或任何 MRI 数据。regionconnect 算法以及在这项工作中为 IIT 人类大脑图谱 v.5.0 开发的白质束图、连接组、基于连接的多层标签以及相关资源可在 www.nitrc.org/projects/iit 上获得。regionconnect 的在线交互式版本也可在 www.iit.edu/~mri 上获得。
