ArticleeLife2018
Assessing reliability in neuroimaging research through intra-class effect decomposition (ICED).
Article in eLife, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
40 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A decade of test-retest reliability of functional connectivity: A systematic review and meta-analysis.NeuroImage · 2019Pooled it
- Coupled cognitive changes in adulthood: A meta-analysis.Psychological bulletin · 2019Pooled it
- Beyond classical metrics: Generalizability theory across psychophysiological modalities.International journal of psychophysiology : official journal of the International Organization of Psychophysiology · 2026Review
- Assessing the reliability of hippocampal single-voxel proton magnetic resonance spectroscopy through intra-class effect decomposition.Imaging neuroscience (Cambridge, Mass.) · 2026Article
- Effects of measurement errors on relationships between resting-state functional connectivity and psychological phenotypes.Scientific reports · 2025Article
- Developmental Changes in Brain Cellular Membrane and Energy Metabolism: A Multi-occasionThe Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- Distinct genetic underpinnings of inter-individual differences in the sensorimotor-association axis of cortical organisation.bioRxiv : the preprint server for biology · 2025Article
- Comparing repeatability metrics for quantitative susceptibility mapping in the head and neck.Magma (New York, N.Y.) · 2025Article
- Reliability of structural brain change in cognitively healthy adult samples.bioRxiv : the preprint server for biology · 2025Article
- How much is "enough"? Considerations for functional connectivity reliability in pediatric naturalistic fMRI.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Challenges in measuring individual differences of brain function.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Reliability of structural brain change in cognitively healthy adult samples.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Why experimental variation in neuroimaging should be embraced.Nature communications · 2024Review
- A (sub)field guide to quality control in hippocampal subfield segmentation on high-resolution THuman brain mapping · 2024Review
- The psychometric upgrade psychophysiology needs.Psychophysiology · 2024Review
- Reliability of task-based fMRI in the dorsal horn of the human spinal cord.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Longitudinal stability of cortical grey matter measures varies across brain regions, imaging metrics, and testing sites in the ABCD study.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Experiment-based calibration in psychology: Optimal design considerations.Journal of mathematical psychology · 2023Article
- A (Sub)field Guide to Quality Control in Hippocampal Subfield Segmentation on Highresolution TbioRxiv : the preprint server for biology · 2023Article
- Enhancing precision in human neuroscience.eLife · 2023Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.