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Filtered correlation and allowed frequency spectra in dynamic functional connectivity.

Victor M Vergara1, Vince D Calhoun1

  • 1Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA.

Journal of Neuroscience Methods
|July 5, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces filtered sliding window correlation to accurately estimate dynamic functional connectivity by separating phase information from activation amplitudes. This novel method improves the analysis of brain connectivity across different frequencies, particularly in fMRI data.

Keywords:
Dynamic functional connectivityFilteringSliding window correlation

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Area of Science:

  • Neuroscience
  • Brain Imaging
  • Signal Processing

Background:

  • Dynamic functional connectivity analysis reveals brain network dynamics across various frequencies.
  • Sliding window correlation estimates time-varying connectivity but can be confounded by activation amplitudes.
  • Phase (cos θ) is a key feature of functional connectivity, distinct from amplitude.

Purpose of the Study:

  • To investigate conditions for separating time-varying correlation from amplitude-related noise.
  • To introduce filtered sliding window correlation for simultaneous time-varying phase estimation and nuisance filtering.
  • To enhance the accuracy of dynamic functional connectivity analysis.

Main Methods:

  • Developed filtered sliding window correlation (FSWC).
  • Applied mathematical models to identify and filter nuisance frequencies.
  • Utilized fMRI data to validate the method.

Main Results:

  • FSWC successfully separates phase-based connectivity from amplitude artifacts.
  • The method accurately estimates time-varying correlation (cos θ (t)).
  • Empirical results suggest relevant fMRI frequencies extend up to 0.05 Hz.

Conclusions:

  • Filtered sliding window correlation offers improved dynamic functional connectivity estimation.
  • The approach effectively controls for nuisance frequencies unrelated to phase.
  • This method enhances the reliability of analyzing brain connectivity dynamics.