Along-Tract Statistics Allow for Enhanced Tractography Analysis

John B. Colby^1,2, Lindsay Soderberg¹, Catherine Lebel¹, Ivo D. Dinov^1,3, Paul M. Thompson^1,2, Elizabeth R. Sowell¹

Despite the large amount of within-tract variability in diffusion imaging indices like FA, the vast majority of neuroscience applications still utilize a traditional tract-averaged approach for their deterministic tractography analyses. Here, we lay out a straightforward workflow for conducting one type of within-tract analysis that attains an economical balance between accessibility and improved modeling ability. We then demonstrate the advantages of this approach over traditional tract-averaged methods by looking at both within-subject and between-group examples.