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Evaluating the reliability of human brain white matter tractometry

View ORCID ProfileJohn Kruper, View ORCID ProfileJason D. Yeatman, View ORCID ProfileAdam Richie-Halford, View ORCID ProfileDavid Bloom, View ORCID ProfileMareike Grotheer, View ORCID ProfileSendy Caffarra, View ORCID ProfileGregory Kiar, View ORCID ProfileIliana I. Karipidis, View ORCID ProfileEthan Roy, View ORCID ProfileBramsh Q. Chandio, View ORCID ProfileEleftherios Garyfalldis, View ORCID ProfileAriel Rokem
doi: https://doi.org/10.1101/2021.02.24.432740
John Kruper
aDepartment of Psychology, University of Washington, Seattle, WA, 98195, United States of America
beScience Institute, University of Washington, Seattle, WA, 98195, United States of America
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Jason D. Yeatman
cGraduate School of Education, Stanford University, Stanford, CA, 94305, United States of America
dDivision of Developmental-Behavioral Pediatrics, Stanford University School of Medicine, Stanford, CA, 94305, United States of America
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Adam Richie-Halford
beScience Institute, University of Washington, Seattle, WA, 98195, United States of America
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David Bloom
aDepartment of Psychology, University of Washington, Seattle, WA, 98195, United States of America
beScience Institute, University of Washington, Seattle, WA, 98195, United States of America
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Mareike Grotheer
eCenter for Mind, Brain and Behavior - CMBB, Hans-Meerwein-Straße 6, Marburg 35032, Germany
fDepartment of Psychology, University of Marburg, Marburg 35039, Germany
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Sendy Caffarra
cGraduate School of Education, Stanford University, Stanford, CA, 94305, United States of America
dDivision of Developmental-Behavioral Pediatrics, Stanford University School of Medicine, Stanford, CA, 94305, United States of America
gBasque Center on Cognition, Brain and Language, BCBL, 20009, Spain
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Gregory Kiar
hDepartment of Biomedical Engineering, McGill University, Montreal, H3A 0E9, Canada
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Iliana I. Karipidis
iCenter for Interdisciplinary Brain Sciences Research, Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, 94305, United States of America
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Ethan Roy
cGraduate School of Education, Stanford University, Stanford, CA, 94305, United States of America
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Bramsh Q. Chandio
jDepartment of Intelligent Systems Engineering, Luddy School of Informatics, Computing and Engineering, Indiana University Bloomington, Bloomington, IN, 47408, United States of America
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Eleftherios Garyfalldis
jDepartment of Intelligent Systems Engineering, Luddy School of Informatics, Computing and Engineering, Indiana University Bloomington, Bloomington, IN, 47408, United States of America
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Ariel Rokem
aDepartment of Psychology, University of Washington, Seattle, WA, 98195, United States of America
beScience Institute, University of Washington, Seattle, WA, 98195, United States of America
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  • For correspondence: arokem@uw.edu
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Abstract

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 Statement

The authors have declared no competing interest.

Footnotes

  • There have been a variety of minor changes done during the review process with the Aperture journal. Large changes include: profile reliability is now calculated using ICC instead of PCC; subject reliability now uses spearman's rho instead of PCC; the RecoBundles section has been updated after more reasonable parameters were used.

  • https://github.com/36000/Tractometry_TRR_and_robustness

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted June 28, 2021.
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Evaluating the reliability of human brain white matter tractometry
John Kruper, Jason D. Yeatman, Adam Richie-Halford, David Bloom, Mareike Grotheer, Sendy Caffarra, Gregory Kiar, Iliana I. Karipidis, Ethan Roy, Bramsh Q. Chandio, Eleftherios Garyfalldis, Ariel Rokem
bioRxiv 2021.02.24.432740; doi: https://doi.org/10.1101/2021.02.24.432740
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Evaluating the reliability of human brain white matter tractometry
John Kruper, Jason D. Yeatman, Adam Richie-Halford, David Bloom, Mareike Grotheer, Sendy Caffarra, Gregory Kiar, Iliana I. Karipidis, Ethan Roy, Bramsh Q. Chandio, Eleftherios Garyfalldis, Ariel Rokem
bioRxiv 2021.02.24.432740; doi: https://doi.org/10.1101/2021.02.24.432740

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