TY - JOUR TI - Assessing reliability in neuroimaging research through intra-class effect decomposition (ICED) AU - Brandmaier, Andreas M AU - Wenger, Elisabeth AU - Bodammer, Nils C AU - Kühn, Simone AU - Raz, Naftali AU - Lindenberger, Ulman A2 - Johansen-Berg, Heidi A2 - Kastner, Sabine VL - 7 PY - 2018 DA - 2018/07/02 SP - e35718 C1 - eLife 2018;7:e35718 DO - 10.7554/eLife.35718 UR - https://doi.org/10.7554/eLife.35718 AB - Magnetic 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. KW - reliability KW - intra-class correlation KW - structural equation modeling KW - G theory KW - coefficient of variation KW - individual differences JF - eLife SN - 2050-084X PB - eLife Sciences Publications, Ltd ER -