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Single-scale time-dependent window-sizes in sliding-window dynamic functional connectivity analysis: A validation

Xiaowei Zhuang1, Zhengshi Yang1, Virendra Mishra1

  • 1Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, USA.

Neuroimage
|July 3, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces novel data-driven window sizes for dynamic functional connectivity analysis. These single-scale time-dependent window sizes improve the accuracy of predicting cognitive impairment and behavioral variance.

Keywords:
Dynamic functional connectivity (FC)Empirical mode decomposition (EMD)Regression and classification analysisSingle-scale time-dependent (SSTD) window-sizesSliding-window analysis

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Signal Processing

Background:

  • Dynamic functional connectivity (FC) analysis commonly uses the sliding-window method.
  • Optimal window size selection remains a challenge due to heuristic choices and lack of ground truth.
  • Existing methods may not fully capture the temporal dynamics of brain activity.

Purpose of the Study:

  • To introduce and validate a novel data-driven approach for determining window sizes in dynamic FC analysis.
  • To assess the performance of time-dependent window sizes compared to fixed window sizes.
  • To enhance the sensitivity of dynamic FC measures for detecting cognitive and behavioral variations.

Main Methods:

  • Computed single-scale time-dependent (SSTD) window sizes based on the frequency content of time series at each time point.
  • Utilized simulated sinusoidal time series with frequency shifts to validate SSTD window size accuracy.
  • Applied SSTD-derived dynamic FC matrices to fMRI data for classification and regression analyses.

Main Results:

  • SSTD window sizes accurately captured time-dependent frequency information in simulated data.
  • Dynamic FC analysis using SSTD window sizes yielded higher classification accuracy for cognitive impairment in fighters.
  • Improved explained behavioral variance in healthy young adults using SSTD-derived dynamic FC features.

Conclusions:

  • SSTD window sizes offer a data-driven and time-dependent alternative to fixed window sizes for dynamic FC analysis.
  • The proposed method enhances the ability to capture relevant temporal dynamics in brain activity.
  • This approach shows promise for more sensitive detection of cognitive and behavioral states using neuroimaging data.