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Test–retest reliability of brain morphology estimates

Metrics of brain morphology are increasingly being used to examine inter-individual differences, making it important to evaluate the reliability of these structural measures. Here we used two open-access datasets to assess the intersession reliability of three cortical measures (thickness, gyrificat...

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Autores principales: Madan, Christopher R., Kensinger, Elizabeth A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5413592/
https://www.ncbi.nlm.nih.gov/pubmed/28054317
http://dx.doi.org/10.1007/s40708-016-0060-4
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author Madan, Christopher R.
Kensinger, Elizabeth A.
author_facet Madan, Christopher R.
Kensinger, Elizabeth A.
author_sort Madan, Christopher R.
collection PubMed
description Metrics of brain morphology are increasingly being used to examine inter-individual differences, making it important to evaluate the reliability of these structural measures. Here we used two open-access datasets to assess the intersession reliability of three cortical measures (thickness, gyrification, and fractal dimensionality) and two subcortical measures (volume and fractal dimensionality). Reliability was generally good, particularly with the gyrification and fractal dimensionality measures. One dataset used a sequence previously optimized for brain morphology analyses and had particularly high reliability. Examining the reliability of morphological measures is critical before the measures can be validly used to investigate inter-individual differences.
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spelling pubmed-54135922017-05-19 Test–retest reliability of brain morphology estimates Madan, Christopher R. Kensinger, Elizabeth A. Brain Inform Article Metrics of brain morphology are increasingly being used to examine inter-individual differences, making it important to evaluate the reliability of these structural measures. Here we used two open-access datasets to assess the intersession reliability of three cortical measures (thickness, gyrification, and fractal dimensionality) and two subcortical measures (volume and fractal dimensionality). Reliability was generally good, particularly with the gyrification and fractal dimensionality measures. One dataset used a sequence previously optimized for brain morphology analyses and had particularly high reliability. Examining the reliability of morphological measures is critical before the measures can be validly used to investigate inter-individual differences. Springer Berlin Heidelberg 2017-01-05 /pmc/articles/PMC5413592/ /pubmed/28054317 http://dx.doi.org/10.1007/s40708-016-0060-4 Text en © The Author(s) 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Article
Madan, Christopher R.
Kensinger, Elizabeth A.
Test–retest reliability of brain morphology estimates
title Test–retest reliability of brain morphology estimates
title_full Test–retest reliability of brain morphology estimates
title_fullStr Test–retest reliability of brain morphology estimates
title_full_unstemmed Test–retest reliability of brain morphology estimates
title_short Test–retest reliability of brain morphology estimates
title_sort test–retest reliability of brain morphology estimates
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5413592/
https://www.ncbi.nlm.nih.gov/pubmed/28054317
http://dx.doi.org/10.1007/s40708-016-0060-4
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