John A Kruper, McKenzie P. Hagen, François Rheault, Isaac Crane, Asa Gilmore, Manjari Narayan, Keshav Motwani, Eardi Lila, Chris Rorden, Jason D. Yeatman, A. Rokem
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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.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. 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引用次数: 0
摘要
人类连接组计划(HCP)已成为人类神经科学的关键数据集,在推进脑成像方法和了解人类大脑方面有着大量重要应用。我们重点研究了HCP弥散加权核磁共振成像(dMRI)数据的牵引成像。我们使用一个开源软件库(pyAFQ; https://yeatmanlab.github.io/pyAFQ)进行概率牵引成像,并在具有完整dMRI采集的HCP受试者(n = 1,041)中划分出主要的白质通路。我们使用扩散峰度成像(DKI)对白质每个体素的白质微观结构进行建模,并沿着束的长度提取 DKI 衍生组织属性的束剖面。我们探索了数据的经验特性:首先,我们利用在 HCP 中采样的大量双胞胎的已知遗传联系评估了 DKI 组织特性的遗传性。其次,与局部连接组特征相比,我们测试了牵引力测量作为个体特征(如年龄、结晶/流体智力、阅读能力等)预测模型基础的能力。为了便于探索数据集,我们创建了一个新的基于网络的可视化工具,并使用该工具对 HCP 牵引测量数据集中的数据进行可视化。最后,我们将 HCP 数据集作为一项新技术创新的试验平台:TRX 文件格式,用于表示基于 dMRI 的流线型数据。我们通过 AWS 开放数据计划的开放神经数据存储库发布了处理输出和牵引剖面,作为可公开获取的数据资源。我们发现,在某些大脑通路中,基于 DKI 的指标的遗传率高达 0.9。我们还发现,牵引测量法与局部连接组法一样能提取关于个体差异的有用信息。我们发布了一个新的基于网络的牵引测量可视化工具--"Tractoscope" (https://nrdg.github.io/tractoscope)。我们发现,TRX 文件所需的磁盘空间要少得多--这对于像 HCP 这样的大型数据集来说至关重要。此外,TRX 还集成了流线分组规范,进一步简化了牵引测量分析。
Tractometry of the Human Connectome Project: resources and insights
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.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.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.