Assessing reliability in neuroimaging research through intra-class effect decomposition (ICED)
Magnetic resonance imaging has become an indispensable tool for studying associations between structural and functional properties of the brain and 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.
The dataset on myelin water fraction measurements is freely available at https://osf.io/t68my/ and the link-wise resting state functional connectivity data is available at https://osf.io/8n24x/.
Reliability of Myelin Water Fraction in ALICPublicly available at Open Science Framework.
Resting-state fMRI correlations: from link-wise unreliability to whole brain stabilityPublicly available at Open Science Framework.
Article and author information
National Institutes of Health (R01-AG011230)
- Naftali Raz
Horizon 2020 Framework Programme (732592)
- Andreas M Brandmaier
- Simone Kühn
- Ulman Lindenberger
Max-Planck-Gesellschaft (Open-access funding)
- Andreas M Brandmaier
- Elisabeth Wenger
- Nils C Bodammer
- Naftali Raz
- Ulman Lindenberger
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
- Heidi Johansen-Berg, University of Oxford, United Kingdom
- Received: February 6, 2018
- Accepted: July 1, 2018
- Accepted Manuscript published: July 2, 2018 (version 1)
- Version of Record published: July 13, 2018 (version 2)
© 2018, Brandmaier et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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