5-min: 3306Consistent White Matter Parcellation in Adolescent Brain Cognitive Development (ABCD): A ~10k Harmonized Diffusion MRI Study

[Audio] Hello everyone, in this presentation, we will introduce our work on Consistent White Matter Parcellation in Adolescent Brain Cognitive Development, a 10 thousand Harmonized Diffusion MRI Study..

[Audio] The presenter has no financial interests or relationships to disclose..

[Audio] Diffusion MRI is the only non-invasive method that can map the living human brain's connections, which has been widely used to study the brain in health and disease. Several large studies, such as the ABCD Study, have acquired dMRI data from many thousands of subjects, providing huge scientific benefit to understand complex neural systems in neurodevelopment as well as across mental disorders. However, it is a challenging task to analyze data collected from different scanners due to large inter-scanner differences. A second challenge for large-scale data analysis is the need for automated extraction of white matter connections across different populations..

[Audio] In this work, we present a large-scale harmonized dMRI study where we have performed successful white matter tractography parcellation across about 10 thousand subjects from the ABCD study. There are four major computational steps. First, we perform dMRI harmonization to remove site-specific differences, using our novel machine learning algorithms that reconcile raw dMRI signals across disparate sites and acquisition parameters, while preserving inter-subject biological variability. Second, whole-brain tractography is performed to track fibers from the entire brain, using our advanced multi-tensor UKF algorithm that enables sensitively and consistently fiber tracking across various populations. Third, we perform tractography parcellation to identify white matter connections, using our WhiteMatterAnalysis pipeline in conjunction with an anatomical WM atlas, for automated fiber clustering and extraction of anatomical tracts. Fourth, multiple diffusion measurements are extracted from each parcellated cluster and anatomical tract using the SlicerDMRI software, including widely used measurements such as fractional anisotropy and the number of fibers..

[Audio] In the experiments, we first assess the effects of data harmonization on white matter parcellation, followed by an evaluation of white matter parcellation using the entire dataset..

[Audio] This figure shows the effects of data harmonization on white matter parcellation. Subfigures a and c show that, after harmonization, the identification rates in both cluster- and tract-level parcellations increase across all three example sites. Subfigures b and d show that the mean cluster and tract FA values are also closer to the corresponding reference data after harmonzation..

[Audio] Across all 10 thousand harmonized dMRI data from 21 sites in the ABCD study, we obtain a high identification rate in both tract- and cluster-level parcellation, 99.9% and 97.5%, respectively. We also obtain a low CoV of FA in both levels, 6.59% and 10.3%, respectively, which are low values indicative of consistent parcellation. The figure gives a visualization of example anatomical tracts across multiple subjects, showing visually consistent tract parcellation results..

[Audio] Overall, we show that after harmonization, more anatomical tracts are identified and their FA values are closer to the harmonization reference data. We also demonstrate highly successful WM parcellation, where overall 99.9% of tracts are identified. The parcellated WM tracts, as well as their diffusion measures, will be made available to the public. We believe that this will provide useful data sources for a large-scale data-intensive analysis of WM connections to study neurodevelopment..

[Audio] Thank you for your attention.. Thank you!.