Assessing reliability in neuroimaging research through intra-class effect decomposition (ICED)

  1. Andreas M Brandmaier  Is a corresponding author
  2. Elisabeth Wenger
  3. Nils C Bodammer
  4. Simone Kühn
  5. Naftali Raz
  6. Ulman Lindenberger
  1. Max Planck Institute for Human Development, Germany
  2. University Clinic Hamburg-Eppendorf, Germany

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.

Data availability

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/.

The following previously published data sets were used
    1. Arshad M
    2. Stanley J A
    3. Raz
    4. N
    (2018) Reliability of Myelin Water Fraction in ALIC
    Publicly available at Open Science Framework.

Article and author information

Author details

  1. Andreas M Brandmaier

    Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany
    For correspondence
    brandmaier@mpib-berlin.mpg.de
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8765-6982
  2. Elisabeth Wenger

    Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany
    Competing interests
    The authors declare that no competing interests exist.
  3. Nils C Bodammer

    Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany
    Competing interests
    The authors declare that no competing interests exist.
  4. Simone Kühn

    Clinic and Policlinic for Psychiatry and Psychotherapy, University Clinic Hamburg-Eppendorf, Hamburg, Germany
    Competing interests
    The authors declare that no competing interests exist.
  5. Naftali Raz

    Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany
    Competing interests
    The authors declare that no competing interests exist.
  6. Ulman Lindenberger

    Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany
    Competing interests
    The authors declare that no competing interests exist.

Funding

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.

Copyright

© 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.

Metrics

  • 2,334
    views
  • 267
    downloads
  • 47
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Andreas M Brandmaier
  2. Elisabeth Wenger
  3. Nils C Bodammer
  4. Simone Kühn
  5. Naftali Raz
  6. Ulman Lindenberger
(2018)
Assessing reliability in neuroimaging research through intra-class effect decomposition (ICED)
eLife 7:e35718.
https://doi.org/10.7554/eLife.35718

Share this article

https://doi.org/10.7554/eLife.35718

Further reading

    1. Neuroscience
    Zeming Fang, Meihua Zhao ... Ru-Yuan Zhang
    Research Article

    Previous studies on reinforcement learning have identified three prominent phenomena: (1) individuals with anxiety or depression exhibit a reduced learning rate compared to healthy subjects; (2) learning rates may increase or decrease in environments with rapidly changing (i.e. volatile) or stable feedback conditions, a phenomenon termed learning rate adaptation; and (3) reduced learning rate adaptation is associated with several psychiatric disorders. In other words, multiple learning rate parameters are needed to account for behavioral differences across participant populations and volatility contexts in this flexible learning rate (FLR) model. Here, we propose an alternative explanation, suggesting that behavioral variation across participant populations and volatile contexts arises from the use of mixed decision strategies. To test this hypothesis, we constructed a mixture-of-strategies (MOS) model and used it to analyze the behaviors of 54 healthy controls and 32 patients with anxiety and depression in volatile reversal learning tasks. Compared to the FLR model, the MOS model can reproduce the three classic phenomena by using a single set of strategy preference parameters without introducing any learning rate differences. In addition, the MOS model can successfully account for several novel behavioral patterns that cannot be explained by the FLR model. Preferences for different strategies also predict individual variations in symptom severity. These findings underscore the importance of considering mixed strategy use in human learning and decision-making and suggest atypical strategy preference as a potential mechanism for learning deficits in psychiatric disorders.

    1. Neuroscience
    Minsik Yun, Do-Hyoung Kim ... Young-Joon Kim
    Research Article

    In birds and insects, the female uptakes sperm for a specific duration post-copulation known as the ejaculate holding period (EHP) before expelling unused sperm and the mating plug through sperm ejection. In this study, we found that Drosophila melanogaster females shortens the EHP when incubated with males or mated females shortly after the first mating. This phenomenon, which we termed male-induced EHP shortening (MIES), requires Or47b+ olfactory and ppk23+ gustatory neurons, activated by 2-methyltetracosane and 7-tricosene, respectively. These odorants raise cAMP levels in pC1 neurons, responsible for processing male courtship cues and regulating female mating receptivity. Elevated cAMP levels in pC1 neurons reduce EHP and reinstate their responsiveness to male courtship cues, promoting re-mating with faster sperm ejection. This study established MIES as a genetically tractable model of sexual plasticity with a conserved neural mechanism.