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eLife Volume 7 ,2018-07-01
Assessing reliability in neuroimaging research through intra-class effect decomposition (ICED)
Neuroscience
Andreas M Brandmaier 1 , 2 Elisabeth Wenger 1 Nils C Bodammer 1 Simone Kühn 3 Naftali Raz 1 , 4 Ulman Lindenberger 1 , 2
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DOI:10.7554/eLife.35718
Received 2018-02-06, accepted for publication 2018-07-01, Published 2018-07-01
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摘要

10.7554/eLife.35718.001Magnetic resonance imaging has become an indispensable tool for studying associations of structural and functional properties of the brain with behavior in humans. However, generally recognized standards for assessing and reporting the reliability of these techniques are still lacking. Here, we introduce a new approach for assessing and reporting reliability, termed intra-class effect decomposition (ICED). ICED uses structural equation modeling of data from a repeated-measures design to decompose reliability into orthogonal sources of measurement error that are associated with different characteristics of the measurements, for example, session, day, or scanning site. This allows researchers to describe the magnitude of different error components, make inferences about error sources, and inform them in planning future studies. We apply ICED to published measurements of myelin content and resting state functional connectivity. These examples illustrate how longitudinal data can be leveraged separately or conjointly with cross-sectional data to obtain more precise estimates of reliability.

关键词

Human;individual differences;coefficient of variation;G theory;structural equation modeling;intra-class correlation;reliability

授权许可

© 2018, Brandmaier et al
http://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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Andreas M Brandmaier,Elisabeth Wenger,Nils C Bodammer,Simone Kühn,Naftali Raz,Ulman Lindenberger. Assessing reliability in neuroimaging research through intra-class effect decomposition (ICED). eLife ,Vol.7(2018)

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