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Sliding window correlation analysis: Modulating window shape for dynamic brain connectivity in resting state.

Fatemeh Mokhtari1, Milad I Akhlaghi2, Sean L Simpson3

  • 1Laboratory for Complex Brain Networks, Department of Radiology, Wake Forest University School of Medicine, Winston Salem, NC, USA; Virginia Tech-Wake Forest University School of Biomedical Engineering and Sciences, Wake Forest University School of Medicine, Winston Salem, NC, USA.

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Summary
This summary is machine-generated.

A new modulated rectangular (mRect) window improves sliding window correlation analysis for dynamic functional connectivity. This method better tracks brain network states compared to conventional windows, enhancing connectivity estimations.

Keywords:
Connectivity network statesDynamic brain connectivityModulated rectangular windowNetwork states transitionResting stateSliding window correlation analysis

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Data Analysis

Background:

  • Sliding window correlation (SWC) is widely used for dynamic functional connectivity analysis.
  • Conventional windows (rectangular, tapered) can suppress fast dynamic correlations and introduce spectral modulations.
  • Existing methods struggle to accurately capture rapid changes in brain network states.

Purpose of the Study:

  • Introduce a novel modulated rectangular (mRect) window function.
  • Address the limitations of conventional windows in SWC analysis.
  • Improve the estimation of dynamic functional connectivity and network state tracking.

Main Methods:

  • Frequency domain analysis of simulated time series to assess spectral properties.
  • Simulation of dynamic network data with changing states using fMRI time series.
  • Quantification of window performance using the Jaccard index for state identification.

Main Results:

  • Conventional windows can cause unwanted spectral modulations, suppressing dynamic correlations.
  • The mRect window effectively alleviates spectral modulation issues.
  • mRect demonstrated superior performance in tracking simulated dynamic network states compared to conventional windows.

Conclusions:

  • The proposed mRect window enhances SWC estimations by reducing suppression of dynamic correlations.
  • mRect improves the accuracy of identifying changing brain network states.
  • This novel window function offers better insights into dynamic functional connectivity analyses.