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Test-retest reliability of graph theoretic metrics in adolescent brains
Umeå University, Faculty of Medicine, Department of Clinical Sciences, Child and Adolescent Psychiatry. Department of Psychiatry and the Langley Porter Psychiatric Institute Weill Institute for Neurosciences University of California, San Francisco.
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2019 (English)In: Brain Connectivity, ISSN 2158-0014, E-ISSN 2158-0022, Vol. 9, no 2, p. 144-154Article in journal (Refereed) Published
Abstract [en]

Graph theory analysis of structural brain networks derived from diffusion tensor imaging (DTI) has become a popular analytical method in neuroscience, enabling advanced investigations of neurological and psychiatric disorders. The purpose of this study was to investigate: 1) the effects of edge weighting schemes, and 2) the effects of varying interscan periods on graph metrics' test-retest reliability within the adolescent brain. We compared a binary (B) network definition with three weighting schemes: fractional anisotropy (FA), streamline count (SC), and streamline count with density and length correction (SDL). Two commonly used global and two local graph metrics were examined. The analysis was conducted with two groups of adolescent volunteers who received DTI scans either 12 weeks apart (16.62±1.10yrs) or within the same scanning session (30 minutes apart) (16.65±1.14yrs). The intraclass correlation coefficient (ICC) was used to assess test-retest reliability and the coefficient of variation (CV) was used to assess precision. On average, each edge scheme produced reliable results at both time intervals. Weighted measures outperformed binary measures, with SDL-weights producing the most reliable metrics. All edge schemes except FA displayed high CV values, leaving FA as the only edge scheme that consistently showed high precision while also producing reliable results. Overall findings suggest that FA-weights are more suited for DTI connectome studies in adolescents.

Place, publisher, year, edition, pages
Mary Ann Liebert, 2019. Vol. 9, no 2, p. 144-154
Keywords [en]
connectome, diffusion tensor imaging (DTI), graph theory, human brain connectivity, diffusion MRI, network, edge weight, test–retest, adolescent brain
National Category
Psychiatry Neurosciences
Identifiers
URN: urn:nbn:se:umu:diva-153625DOI: 10.1089/brain.2018.0580ISI: 000463269400003PubMedID: 30398373OAI: oai:DiVA.org:umu-153625DiVA, id: diva2:1265783
Funder
Swedish Research Council, 350-2012-303
Note

Part 2. Special Issue.

Available from: 2018-11-26 Created: 2018-11-26 Last updated: 2019-05-23Bibliographically approved

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Henje Blom, Eva

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