PDF(Pubmed)
Abstract：
UNASSIGNED: The Human Connectome Project (HCP) has become a keystone dataset in human neuroscience, with a plethora of important applications in advancing brain imaging methods and an understanding of the human brain. We focused on tractometry of HCP diffusion-weighted MRI (dMRI) data.
UNASSIGNED: We used an open-source software library (pyAFQ; https://yeatmanlab.github.io/pyAFQ) to perform probabilistic tractography and delineate the major white matter pathways in the HCP subjects that have a complete dMRI acquisition (n = 1,041). We used diffusion kurtosis imaging (DKI) to model white matter microstructure in each voxel of the white matter, and extracted tract profiles of DKI-derived tissue properties along the length of the tracts. We explored the empirical properties of the data: first, we assessed the heritability of DKI tissue properties using the known genetic linkage of the large number of twin pairs sampled in HCP. Second, we tested the ability of tractometry to serve as the basis for predictive models of individual characteristics (e.g., age, crystallized/fluid intelligence, reading ability, etc.), compared to local connectome features. To facilitate the exploration of the dataset we created a new web-based visualization tool and use this tool to visualize the data in the HCP tractometry dataset. Finally, we used the HCP dataset as a test-bed for a new technological innovation: the TRX file-format for representation of dMRI-based streamlines.
UNASSIGNED: We released the processing outputs and tract profiles as a publicly available data resource through the AWS Open Data program\'s Open Neurodata repository. We found heritability as high as 0.9 for DKI-based metrics in some brain pathways. We also found that tractometry extracts as much useful information about individual differences as the local connectome method. We released a new web-based visualization tool for tractometry-\"Tractoscope\" (https://nrdg.github.io/tractoscope). We found that the TRX files require considerably less disk space-a crucial attribute for large datasets like HCP. In addition, TRX incorporates a specification for grouping streamlines, further simplifying tractometry analysis.
UNASSIGNED: We used an open-source software library (pyAFQ; https://yeatmanlab.github.io/pyAFQ) to perform probabilistic tractography and delineate the major white matter pathways in the HCP subjects that have a complete dMRI acquisition (n = 1,041). We used diffusion kurtosis imaging (DKI) to model white matter microstructure in each voxel of the white matter, and extracted tract profiles of DKI-derived tissue properties along the length of the tracts. We explored the empirical properties of the data: first, we assessed the heritability of DKI tissue properties using the known genetic linkage of the large number of twin pairs sampled in HCP. Second, we tested the ability of tractometry to serve as the basis for predictive models of individual characteristics (e.g., age, crystallized/fluid intelligence, reading ability, etc.), compared to local connectome features. To facilitate the exploration of the dataset we created a new web-based visualization tool and use this tool to visualize the data in the HCP tractometry dataset. Finally, we used the HCP dataset as a test-bed for a new technological innovation: the TRX file-format for representation of dMRI-based streamlines.
UNASSIGNED: We released the processing outputs and tract profiles as a publicly available data resource through the AWS Open Data program\'s Open Neurodata repository. We found heritability as high as 0.9 for DKI-based metrics in some brain pathways. We also found that tractometry extracts as much useful information about individual differences as the local connectome method. We released a new web-based visualization tool for tractometry-\"Tractoscope\" (https://nrdg.github.io/tractoscope). We found that the TRX files require considerably less disk space-a crucial attribute for large datasets like HCP. In addition, TRX incorporates a specification for grouping streamlines, further simplifying tractometry analysis.
摘要：
■HumanConnectomeProject（HCP）已成为人类神经科学的重要数据集，在推进脑成像方法和对人脑的理解方面有过多的重要应用。我们专注于HCP扩散加权MRI（dMRI）数据的示踪法。
■我们使用了一个开源软件库（pyAFQ;https://yeatmanlab。github.io/pyAFQ）进行概率示踪成像，并描绘具有完整dMRI采集的HCP受试者的主要白质途径（n=1,041）。我们使用扩散峰度成像（DKI）来模拟白质每个体素中的白质微观结构，以及沿着管道长度提取的DKI衍生的组织特性的管道轮廓。我们探讨了数据的经验性质：首先，我们使用HCP样本中大量双胞胎对的已知遗传连锁评估了DKI组织特性的遗传力.第二,我们测试了示差法作为个体特征预测模型基础的能力(例如，年龄,结晶/流体智能，阅读能力,等。),与局部连接体特征相比。为了促进对数据集的探索，我们创建了一个新的基于网络的可视化工具，并使用该工具将HCP示差测量数据集中的数据可视化。最后,我们使用HCP数据集作为新技术创新的试验平台:用于表示基于dMRI的流线的TRX文件格式.
■我们通过AWSOpenData计划的OpenNeurodata存储库发布了处理输出和区域配置文件，作为公开可用的数据资源。我们发现，在一些大脑通路中，基于DKI的指标的遗传力高达0.9。我们还发现，示踪法提取了与局部连接体方法一样多的有关个体差异的有用信息。我们发布了一个新的基于Web的tractometry可视化工具-\"Tractoscope\"(https://nrdg.github.io/tractoscope)。我们发现TRX文件需要的磁盘空间要少得多，这对于像HCP这样的大型数据集来说是一个至关重要的属性。此外,TRX包含了对流线型进行分组的规范，进一步简化示差分析。
■我们使用了一个开源软件库（pyAFQ;https://yeatmanlab。github.io/pyAFQ）进行概率示踪成像，并描绘具有完整dMRI采集的HCP受试者的主要白质途径（n=1,041）。我们使用扩散峰度成像（DKI）来模拟白质每个体素中的白质微观结构，以及沿着管道长度提取的DKI衍生的组织特性的管道轮廓。我们探讨了数据的经验性质：首先，我们使用HCP样本中大量双胞胎对的已知遗传连锁评估了DKI组织特性的遗传力.第二,我们测试了示差法作为个体特征预测模型基础的能力(例如，年龄,结晶/流体智能，阅读能力,等。),与局部连接体特征相比。为了促进对数据集的探索，我们创建了一个新的基于网络的可视化工具，并使用该工具将HCP示差测量数据集中的数据可视化。最后,我们使用HCP数据集作为新技术创新的试验平台:用于表示基于dMRI的流线的TRX文件格式.
■我们通过AWSOpenData计划的OpenNeurodata存储库发布了处理输出和区域配置文件，作为公开可用的数据资源。我们发现，在一些大脑通路中，基于DKI的指标的遗传力高达0.9。我们还发现，示踪法提取了与局部连接体方法一样多的有关个体差异的有用信息。我们发布了一个新的基于Web的tractometry可视化工具-\"Tractoscope\"(https://nrdg.github.io/tractoscope)。我们发现TRX文件需要的磁盘空间要少得多，这对于像HCP这样的大型数据集来说是一个至关重要的属性。此外,TRX包含了对流线型进行分组的规范，进一步简化示差分析。
