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Diffusion-weighted magnetic resonance imaging (dMRI) allows for non-invasive, detailed examination of the white matter structures of the brain. White matter tract-specific measures based on either the diffusion tensor model (e.g. FA, ADC, and MD) or tractography (e.g. volume, streamline count or density) are often compared between groups of subjects to localize differences within the white matter. Less commonly examined is the shape of the individual white matter tracts. In this paper, we propose to use the Laplace-Beltrami (LB) spectrum as a descriptor of the shape of white matter tracts. We provide an open, automated pipeline for the computation of the LB spectrum on segmented white matter tracts and demonstrate its efficacy through machine learning classification experiments. We show that the LB spectrum allows for distinguishing subjects diagnosed with bipolar disorder from age and sex-matched healthy controls, with classification accuracy reaching 95%. We further demonstrate that the results cannot be explained by traditional measures, such as tract volume, streamline count or mean and total length. The results indicate that there is valuable information in the anatomical shape of the human white matter tracts.
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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.
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- Award ID(s):
- 2210064
- PAR ID:
- 10613618
- Publisher / Repository:
- Frontiers
- Date Published:
- Journal Name:
- Frontiers in Neuroscience
- Volume:
- 18
- ISSN:
- 1662-453X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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The human brain is sexually dimorphic and these sex differences have shown to affect brain response to trauma. We investigated the sex differences in the tract structures by studying diffusion weighted (DW) images of 594 females and 506 males from the Human-Connectome-Project dataset. All the female and male DW images were reconstructed in the ICBM152 space using Q-Space diffeomorphic reconstruction technique and their mapped orientation distribution function images were averaged to generate the female- and male-DW-templates. The tract streamlines were generated through tractography for female and male templates and normalized to the total brain volume . The distributions of normalized tract lengths were significantly different between female- and male-templates and the female-template showed to have more longer normalized tracts compared to the male template. For the regional analysis, the templates were parcellated into sixteen regions of interests (ROI) including brain-stem, five subregions of corpus-callosum, and right and left hippocampus, thalamus, cerebellum white-matter (WM), cerebral WM, and cerebellum cortex using a FreeSurfer-based segmentation atlas. For all the ROIs, the average fractional anisotropy (0.5-5.7%) and normalized tract lengths (1.1-2.7%) were larger in female template while the average mean diffusion was larger (1.3-5.6%) in male-template. Quantifying brain connectivity by counting number of tracts passing through pairs of ROIs, showed more pairs with a higher connectivity in female-template, and one of the highest percentages of sex differences in right/left cerebellum WM/cortex connections. Our results reinforce the need to continue investigating the sex variations in axonal structure and their effects to brain trauma.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3389/fnins.2024.1389680
