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Induction and Assessment of Class Switch Recombination in Purified Murine B Cells
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Assessing reliability in neuroimaging research through intra-class effect decomposition (ICED).

Andreas M Brandmaier1,2, Elisabeth Wenger1, Nils C Bodammer1

  • 1Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany.

Elife
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PubMed
Summary

We introduce intra-class effect decomposition (ICED), a new method to assess magnetic resonance imaging (MRI) reliability by breaking down measurement errors. This approach helps researchers understand and improve brain imaging study designs.

Keywords:
G theorycoefficient of variationhumanindividual differencesintra-class correlationneurosciencereliabilitystructural equation modeling

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Area of Science:

  • Neuroimaging
  • Psychometrics

Background:

  • Magnetic resonance imaging (MRI) is crucial for brain-behavior research.
  • Current standards for assessing MRI reliability are insufficient.

Purpose of the Study:

  • Introduce intra-class effect decomposition (ICED) for assessing MRI reliability.
  • Provide a standardized method for reporting measurement error sources.

Main Methods:

  • Utilize structural equation modeling (SEM) with repeated-measures data.
  • Decompose reliability into orthogonal sources of measurement error (e.g., session, day, site).

Main Results:

  • ICED quantifies distinct error components in MRI measurements.
  • Demonstrated application on myelin content and resting-state functional connectivity data.
  • Showcased leveraging longitudinal and cross-sectional data for improved reliability estimates.

Conclusions:

  • ICED offers a robust framework for evaluating MRI reliability.
  • Enables researchers to identify and mitigate sources of measurement error.
  • Informs the design of future, more reliable neuroimaging studies.