Probabilistic White Matter Atlases of Human Auditory, Basal Ganglia, Language, Precuneus, Sensorimotor, Visual and Visuospatial Networks.
Probabilistic White Matter Atlases of Human Auditory, Basal Ganglia, Language, Precuneus, Sensorimotor, Visual and Visuospatial Networks.
复制标题
DOI：
10.3389/fnhum.2017.00306
复制
发表时间：
2017
影响因子：
2.9
通讯作者：
Figley CR
中科院分区：
文献类型：
--
作者：
Figley TD;Mortazavi Moghadam B;Bhullar N;Kornelsen J;Courtney SM;Figley CR
背景：尽管功能连通性分析和几个固有皮质网络的广为人知的拓扑结构，但对这些网络背后的白质区域(即结构连通性)知之甚少。因此，在目前的研究中，我们进行了fMRI引导的扩散张量成像(DTI)脑束成像，以创建先前识别的八个脑功能网络的概率白质图谱，包括听觉、基底节、语言、楔前核、感觉运动、初级视觉、高级视觉和视觉空间网络。方法：从32名健康志愿者的队列中获取全脑弥散成像数据，并使用两阶段、高维、非线性空间归一化过程将数据扭曲到ICBM模板。然后，使用连续跟踪纤维关联(FACT)算法和多感兴趣区域方法来识别感兴趣的纤维束，进行确定性的纤维束成像，其分数各向异性(FA)≥为0.15，偏差角度为50°。八个网络中每个网络的感兴趣区域(ROI)取自预先存在的功能定义区域的地图集，以探索每个网络内所有ROI到ROI的连接，所有产生的流线被保存为二进制掩码，以创建每个ROI到ROI对之间的区域的概率地图集(跨参与者)。结果：得到的功能定义的白质地图集(即每个区域和每个网络作为一个整体)被保存为立体定位ICBM坐标中的NIFTI图像，并已被添加到UManitoba-JHU功能定义的人类白质地图集(http://www.nitrc.org/projects/uofm_jhu_atlas/).结论：据我们所知，这项工作是首次尝试全面识别和定位听觉、基底节、语言、楔前、感觉运动、初级视觉、高级视觉和视觉空间网络的白质连接。因此，由此产生的概率图谱为未来的神经成像研究提供了一个独特的工具，希望将体素或基于ROI的变化(即DTI或其他定量白质成像信号)归因于这些功能大脑网络。
Background: Despite the popularity of functional connectivity analyses and the well-known topology of several intrinsic cortical networks, relatively little is known about the white matter regions (i.e., structural connectivity) underlying these networks. In the current study, we have therefore performed fMRI-guided diffusion tensor imaging (DTI) tractography to create probabilistic white matter atlases for eight previously identified functional brain networks, including the Auditory, Basal Ganglia, Language, Precuneus, Sensorimotor, Primary Visual, Higher Visual and Visuospatial Networks. Methods: Whole-brain diffusion imaging data were acquired from a cohort of 32 healthy volunteers, and were warped to the ICBM template using a two-stage, high-dimensional, non-linear spatial normalization procedure. Deterministic tractography, with fractional anisotropy (FA) ≥0.15 and deviation angle <50°, was then performed using the Fiber Association by Continuous Tracking (FACT) algorithm, and a multi-ROI approach to identify tracts of interest. Regions-of-interest (ROIs) for each of the eight networks were taken from a pre-existing atlas of functionally defined regions to explore all ROI-to-ROI connections within each network, and all resulting streamlines were saved as binary masks to create probabilistic atlases (across participants) for tracts between each ROI-to-ROI pair. Results: The resulting functionally-defined white matter atlases (i.e., for each tract and each network as a whole) were saved as NIFTI images in stereotaxic ICBM coordinates, and have been added to the UManitoba-JHU Functionally-Defined Human White Matter Atlas (http://www.nitrc.org/projects/uofm_jhu_atlas/). Conclusion: To the best of our knowledge, this work represents the first attempt to comprehensively identify and map white matter connectomes for the Auditory, Basal Ganglia, Language, Precuneus, Sensorimotor, Primary Visual, Higher Visual and Visuospatial Networks. Therefore, the resulting probabilistic atlases represent a unique tool for future neuroimaging studies wishing to ascribe voxel-wise or ROI-based changes (i.e., in DTI or other quantitative white matter imaging signals) to these functional brain networks.
登录
查看更多内容
影响因子：
4.3
作者：
Figley CR;Asem JS;Levenbaum EL;Courtney SM
通讯作者：
Courtney SM
DOI：
10.1023/b:visi.0000043755.93987.aa
发表时间：
2005-02-01
影响因子：
19.5
作者：
Beg, MF;Miller, MI;Younes, L
通讯作者：
Younes, L
影响因子：
5.4
作者：
Lanciego, Jose L.;Luquin, Natasha;Obeso, Jose A.
通讯作者：
Obeso, Jose A.
影响因子：
10.6
作者：
Daducci, Alessandro;Jorge Canales-Rodriguez, Erick;Thiran, Jean-Philippe
通讯作者：
Thiran, Jean-Philippe
影响因子：
5.7
作者：
Landman, Bennett A.;Farrell, Jonathan A. D.;Mori, Susumu
通讯作者：
Mori, Susumu