Develop a Pipeline to Utilize Along-Tract Statistics Allowing Tractography Analysis

Student: Ahmad Basiri

Project Mentors: Dr. Sydney Y. Schaefer – SBHSE
Dr. Scott Beeman – SBHSE
Dr. Edward Ofori – College of Health Solutions

YouTube Link: View the video link below before joining the zoom meeting

Zoom link: https://asu.zoom.us/j/94675414261

Zoom meeting time: 9am - 11am.

Abstract

Since the late 1990s, diffusion magnetic resonance imaging (MRI) tractography methods have developed into a robust set of techniques to investigate white matter connectivity in the human brain. General tractography uses a “tract-averaged” approach to analysis by averaging the scalar values from the many streamline vertices in a tract dissection into a single point-spread estimate for each tract. There is prominent variation in diffusion imaging metrics (e.g., fractional anisotropy, FA) within tracts that is lost when a tract-averaged approach is used. New work is beginning to advance along-tract processing through a theoretical framework that models tracts as relatively tube-like structures with cross-sectional uniformity. With a spline-based resampling strategy of the tube-like structure, a large portion of within-tract variance along the tracts can be captured. The along-tract statistics can provide more detailed data about the nature of the diffusion than what is typically utilized by traditional tractography methods. The tools used to conduct along tract analysis are easily integrated into existing tractography studies and compatible with most existing imaging software. The purpose of this applied project was to create a standard operating procedure for a neuroimaging pipeline that utilizes existing toolkits to convert FSL files to a platform that enables along-tract-stats. The provided pipeline links multiple software such as MATLAB, DTK, and TrackVis, to create the necessary files for inclusion in along-tract stat analysis. This project yielded similar results to what was expected based on published literature, ensuring the quality of the method performed.