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Measurement reliability for individual differences in multilayer network dynamics: Cautions and considerations.

Zhen Yang1, Qawi K Telesford2, Alexandre R Franco3

  • 1Center for Biomedical Imaging and Neuromodulation, The Nathan S. Kline Institute for Psychiatric Research, 140 Old Orangeburg Rd, Orangeburg, NY 10962, United States; Department of Psychiatry, NYU Grossman School of Medicine, 550 1st Avenue, New York, NY 10016, United States.

Neuroimage
|November 1, 2020
PubMed
Summary

Optimizing multilayer network analysis is crucial for reliable brain connectivity studies. Parameter selection and sufficient scan duration, not task type, significantly impact test-retest reliability for individual differences research.

Keywords:
FlexibilityModule allegianceMultilayer networksNaturalist viewingReliabilityScan duration

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Area of Science:

  • Neuroscience
  • Network Science
  • Computational Biology

Background:

  • Multilayer network models capture dynamic neural circuit configurations.
  • These models are increasingly used for studying individual differences in brain function.
  • Quantifying test-retest reliability of multilayer network measures is essential but underexplored.

Purpose of the Study:

  • To systematically evaluate factors influencing test-retest reliability of multilayer network measures.
  • To optimize multilayer community detection algorithms and parameters for neuroimaging.
  • To determine the impact of scan duration and task conditions on reliability.

Main Methods:

  • Evaluated multilayer community detection algorithms, including the generalized Louvain algorithm.
  • Assessed the influence of network parameter selection on reliability.
  • Investigated the effects of varying scan durations and task conditions (resting state, Inscapes, movie, flanker).

Main Results:

  • The default generalized Louvain algorithm can produce erroneous community detection results.
  • Parameter selection is critical for reliability and appears dataset-specific.
  • Scan duration is a stronger determinant of reliability than scan condition after parameter optimization.
  • Reliable measures were achieved with 20-30 minutes of data, with movie watching showing highest reliability.

Conclusions:

  • Caution against using default parameters in multilayer network analysis without dataset-specific optimization.
  • Optimized parameters and sufficient scan duration are key for reliable individual differences research using multilayer networks.
  • Updated algorithms and careful parameter selection are needed for robust neuroimaging studies.