Along-tract statistics allow for enhanced tractography analysis

Abstract

Diffusion imaging tractography is a valuable tool for neuroscience researchers because it allows the generation of individualized virtual dissections of major white matter tracts in the human brain. It facilitates between-subject statistical analyses tailored to the specific anatomy of each participant. There is prominent variation in diffusion imaging metrics (e.g., fractional anisotropy, FA) within tracts, but most tractography studies use 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. Here we describe a complete workflow needed to conduct an along-tract analysis of white matter streamline tract groups. This consists of 1) A flexible MATLAB toolkit for generating along-tract data based on B-spline resampling and compilation of scalar data at different collections of vertices along the curving tract spines, and 2) Statistical analysis and rich data visualization by leveraging tools available through the R platform for statistical computing. We demonstrate the effectiveness of such an along-tract approach over the tract-averaged approach in an example analysis of 10 major white matter tracts in a single subject. We also show that these techniques easily extend to between-group analyses typically used in neuroscience applications, by conducting an along-tract analysis of differences in FA between 9 individuals with fetal alcohol spectrum disorders (FASDs) and 11 typically-developing controls. This analysis reveals localized differences between FASD and control groups that were not apparent using a tract-averaged method. Finally, to validate our approach and highlight the strength of this extensible software framework, we implement 2 other methods from the literature and leverage the existing workflow tools to conduct a comparison study.

White matter tractography in early psychosis: clinical and neurocognitive associations

“…Finally, while DTI tractography has the advantage of being able to quantify white matter tract integrity along the entire fibre bundle, the averaging of markers over large volumes may suppress focal pathologies. Future studies could use alongtract white mat ter statistics of the reconstructed tracts, 63 though these inves tigations must take into consideration how normal white matter maturation over time influences neurocognition, and their association with medication and substance abuse.…”

White matter microstructure in boys with persistent depressive disorder

“…However, analysis was concentrated on tracts that exhibit sheet-like structure excluding tube-like tracts such as, for example, the fornix and cingulum. DTI-TK's tract-specific analysis currently does not provide adequate means to examine these tube-like tracts, therefore future work may extend the current findings by drawing upon geometrical models for tubelike structures as developed by others (Colby et al, 2012;Jones, Travis, Eden, Pierpaoli, & Basser, 2005).…”

Fiber up‐sampling and quality assessment of tractograms – towards quantitative brain connectivity

“…Fiber‐specific metrics are quantified by the generally unreliable fiber‐count (Jones et al., 2012) or ROI‐based approaches. The evaluation of diffusion metrics along segmented tractography bundles was introduced by (Colby et al., 2012) and (Yeatman, Dougherty, Myall, Wandell, & Feldman, 2012). The Automated Fiber Quantification (AFQ) framework allows the automatic identification and segmentation of major white matter tracts and evaluates scalar diffusion measures such as fractional anisotropy (FA) along these trajectories to quantify changes within the tract diffusion profiles among different subjects or groups (Yeatman et al., 2012).…”