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Comparing test-retest reliability of dynamic functional connectivity methods.

Ann S Choe1, Mary Beth Nebel2, Anita D Barber3

  • 1Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, USA; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, USA.

Neuroimage
|July 9, 2017
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Summary
This summary is machine-generated.

Researchers evaluated methods for measuring dynamic functional connectivity (FC) in resting-state fMRI. The dynamic conditional correlations (DCC) method reliably captures FC fluctuations, outperforming sliding window approaches for summary statistics.

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Area of Science:

  • Neuroimaging
  • Cognitive Neuroscience
  • Biophysics

Background:

  • Dynamic functional connectivity (FC) estimation during resting-state functional magnetic resonance imaging (rs-fMRI) is gaining interest due to its condition-dependent nature.
  • Challenges in dynamic FC analysis include low signal-to-noise ratio (BOLD signal) and large datasets, necessitating reliable methods and summary measures.
  • Understanding dynamic FC is crucial for insights into brain function and individual differences.

Purpose of the Study:

  • To investigate the reliability of dynamic FC summary measures using three common estimation methods: sliding window (SW), tapered sliding window (TSW), and dynamic conditional correlations (DCC).
  • To assess the reliability of basic summary statistics and 'brain state'-derived measures from dynamic FC.
  • To compare the performance of different methods across two rs-fMRI test-retest datasets (Kirby and HCP).

Main Methods:

  • Application of SW, TSW, and DCC methods to two rs-fMRI test-retest datasets (Kirby and HCP).
  • Assessment of reliability for dynamic correlation summary statistics and summary measures derived from recurring whole-brain patterns ('brain states').
  • Statistical comparison of reliability across different estimation methods and measure categories.

Main Results:

  • Dynamic correlations were reliably detected across both datasets using all tested methods.
  • The DCC method demonstrated higher reliability for summary statistics compared to SW methods.
  • Reliability of 'brain state'-derived measures was consistently low across all estimation methods.
  • DCC-derived dynamic correlation variances were significantly more reliable than those from non-parametric methods.

Conclusions:

  • Dynamic FC fluctuations, particularly their variance, offer a reliable basis for summary measures.
  • The DCC method is recommended for estimating dynamic FC summary statistics due to its superior reliability.
  • Focusing on the variance of dynamic connectivity is crucial for developing robust summary measures for individual differences.