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The thresholding problem and variability in the EEG graph network parameters.

Timofey Adamovich1,2, Ilya Zakharov3,4, Anna Tabueva3,4

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Choosing graph thresholds in brain connectivity analysis significantly alters results. This study reveals how different thresholds impact global connectivity measures in resting-state EEG, urging a more rigorous approach to threshold selection for reliable research conclusions.

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Area of Science:

  • Neuroscience
  • Brain Connectivity Analysis
  • Graph Theory in Neuroscience

Background:

  • Graph thresholding is common in functional connectivity analysis to remove weak connections.
  • The arbitrary nature of threshold selection and its impact on results remain unclear.
  • Understanding threshold effects is crucial for accurate interpretation of brain network data.

Purpose of the Study:

  • To investigate how varying proportional thresholds affect global connectivity measures in resting-state EEG.
  • To analyze the dynamics of functional connectivity graphs under different thresholding strategies.
  • To highlight the influence of threshold choice on study outcomes and conclusions.

Main Methods:

  • Analysis of 146 resting-state EEG recordings.
  • Application of different proportional thresholds to functional connectivity graphs.
  • Evaluation of five synchronization measures (wPLI, ImCoh, Coherence, ciPLV, PPC) in sensor and source spaces.

Main Results:

  • Significant changes in global graph connectivity measures were observed as a function of the chosen threshold.
  • The dynamics of synchronization measures varied considerably with different threshold levels.
  • Threshold-dependent alterations can substantially influence the interpretation of brain connectivity patterns.

Conclusions:

  • The choice of threshold in functional connectivity analysis is not arbitrary and significantly impacts results.
  • Current thresholding practices may lead to divergent study conclusions.
  • Improved reasoning and alternative analytical approaches for thresholding are necessary in neuroimaging research.