Author:
Chandio Bramsh Qamar,Risacher Shannon Leigh,Pestilli Franco,Bullock Daniel,Yeh Fang-Cheng,Koudoro Serge,Rokem Ariel,Harezlak Jaroslaw,Garyfallidis Eleftherios
Abstract
AbstractTractography has created new horizons for researchers to study brain connectivity in vivo. However, tractography is an advanced and challenging method that has not been used so far for medical data analysis at a large scale in comparison to other traditional brain imaging methods. This work allows tractography to be used for large scale and high-quality medical analytics. BUndle ANalytics (BUAN) is a fast, robust, and flexible computational framework for real-world tractometric studies. BUAN combines tractography and anatomical information to analyze the challenging datasets and identifies significant group differences in specific locations of the white matter bundles. Additionally, BUAN takes the shape of the bundles into consideration for the analysis. BUAN compares the shapes of the bundles using a metric called bundle adjacency which calculates shape similarity between two given bundles. BUAN builds networks of bundle shape similarities that can be paramount for automating quality control. BUAN is freely available in DIPY. Results are presented using publicly available Parkinson’s Progression Markers Initiative data.
Funder
National Institutes of Health
National Institute of Mental Health
University of Washington from the Gordon
Publisher
Springer Science and Business Media LLC
Cited by
69 articles.
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