Assessing Reliability in Neuroimaging Research Through Intra-Class Effect Decomposition

Andreas M. Brandmaier, Elisabeth Wenger, Nils C. Bodammer, Simone Kühn, Naftali Raz and Ulman Lindenberger: Assessing Reliability in Neuroimaging Research Through Intra-Class Effect Decomposition (ICED)

E-life 2nd July, 2018

 

Abstract

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.

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Published July 10, 2018 3:31 PM - Last modified Aug. 30, 2018 3:51 PM