PT  - JOURNAL ARTICLE
AU  - John Kruper
AU  - Jason D. Yeatman
AU  - Adam Richie-Halford
AU  - David Bloom
AU  - Mareike Grotheer
AU  - Sendy Caffarra
AU  - Gregory Kiar
AU  - Iliana I. Karipidis
AU  - Ethan Roy
AU  - Bramsh Q. Chandio
AU  - Eleftherios Garyfalldis
AU  - Ariel Rokem
TI  - Evaluating the reliability of human brain white matter tractometry
AID  - 10.1101/2021.02.24.432740
DP  - 2021 Jan 01
TA  - bioRxiv
PG  - 2021.02.24.432740
4099  - http://biorxiv.org/content/early/2021/06/28/2021.02.24.432740.short
4100  - http://biorxiv.org/content/early/2021/06/28/2021.02.24.432740.full
AB  - The validity of research results depends on the reliability of analysis methods. In recent years, there have been concerns about the validity of research that uses diffusion-weighted MRI (dMRI) to understand human brain white matter connections in vivo, in part based on reliability of the analysis methods used in this field. We defined and assessed three dimensions of reliability in dMRI-based tractometry, an analysis technique that assesses the physical properties of white matter pathways: (1) reproducibility, (2) test-retest reliability and (3) robustness. To facilitate reproducibility, we provide software that automates tractometry (https://yeatmanlab.github.io/pyAFQ). In measurements from the Human Connectome Project, as well as clinical-grade measurements, we find that tractometry has high test-retest reliability that is comparable to most standardized clinical assessment tools. We find that tractometry is also robust: showing high reliability with different choices of analysis algorithms. Taken together, our results suggest that tractometry is a reliable approach to analysis of white matter connections. The overall approach taken here both demonstrates the specific trustworthiness of tractometry analysis and outlines what researchers can do to demonstrate the reliability of computational analysis pipelines in neuroimaging.Competing Interest StatementThe authors have declared no competing interest.