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On the variability of dynamic functional connectivity assessment methods.

Mohammad Torabi1,2,3, Georgios D Mitsis2, Jean-Baptiste Poline3

  • 1Graduate Program in Biological and Biomedical Engineering, McGill University, Duff Medical Building, 3775 rue University, Montreal H3A 2B4, Canada.

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Summary

The choice of method significantly impacts dynamic functional connectivity (dFC) results, showing variability comparable to actual brain signal changes. Careful method selection and multianalysis approaches are crucial for reliable dFC studies.

Keywords:
analytical flexibilitydynamic functional connectivityfunctional magnetic resonance imagingneuroimagingreproducibility

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Area of Science:

  • Neuroscience
  • Brain Imaging
  • Computational Neuroscience

Background:

  • Dynamic functional connectivity (dFC) is vital for brain function analysis and biomarker development.
  • Numerous dFC assessment methods exist, leading to uncertainty regarding their impact on results.
  • Understanding variability in dFC methods is crucial for accurate brain function interpretation.

Purpose of the Study:

  • To investigate the variability of results from commonly used dynamic functional connectivity (dFC) assessment methods.
  • To quantify the impact of methodological choices on dFC estimates.
  • To identify patterns of similarity and dissimilarity across different dFC approaches.

Main Methods:

  • Implemented 7 distinct dFC assessment methods using Python.
  • Analyzed functional magnetic resonance imaging (fMRI) data from 395 Human Connectome Project subjects.
  • Quantified similarity between dFC results using metrics for overall, temporal, spatial, and intersubject agreement.

Main Results:

  • Observed weak to strong similarity across different dFC methods, indicating substantial variability.
  • dFC estimate variability was comparable to natural temporal fluctuations in functional connectivity.
  • Identified 3 distinct method groups with significant intergroup variability, each with unique assumptions.

Conclusions:

  • Methodological choices significantly influence dFC outcomes, necessitating careful selection and multianalysis strategies.
  • Distinguishing neural dFC variations from confounds and establishing validation frameworks are essential.
  • An open-source Python toolbox, PydFC, is provided to support multianalysis dFC assessment and improve study reliability.