Tractometry of the Human Connectome Project: resources and insights

Kruper, John; Hagen, McKenzie P.; Rheault, François; Crane, Isaac; Gilmore, Asa; Narayan, Manjari; Motwani, Keshav; Lila, Eardi; Rorden, Chris; Yeatman, Jason D.; Rokem, Ariel

doi:10.3389/fnins.2024.1389680

Tractometry of the Human Connectome Project: resources and insights

Journal Article · Wed Jun 12 00:00:00 EDT 2024 · Frontiers in Neuroscience (Online)

DOI:https://doi.org/10.3389/fnins.2024.1389680· OSTI ID:2471924

Kruper, John ^[1]; Hagen, McKenzie P. ^[2]; Rheault, François ^[3]; Crane, Isaac ^[4]; Gilmore, Asa ^[2]; Narayan, Manjari ^[5]; Motwani, Keshav ^[2]; Lila, Eardi ^[2]; Rorden, Chris ^[6]; Yeatman, Jason D. ^[5]; Rokem, Ariel ^[2]

University of Washington, Seattle, WA (United States); OSTI
University of Washington, Seattle, WA (United States)
Universitè de Sherbrooke, QC (Canada)
University of Chicago, IL (United States)
Stanford University, CA (United States)
University of South Carolina, Columbia, SC (United States)

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.

View Accepted Manuscript (DOE)

Research Organization:: Krell Institute, Ames, IA (United States)

Sponsoring Organization:: National Institutes of Health (NIH); National Science Foundation (NSF); USDOE Office of Science (SC)

Grant/Contract Number:: SC0023112

OSTI ID:: 2471924

Journal Information:: Frontiers in Neuroscience (Online), Journal Name: Frontiers in Neuroscience (Online) Vol. 18; ISSN 1662-453X

Publisher:: Frontiers Research FoundationCopyright Statement

Country of Publication:: United States

Language:: English

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