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Neuroimage. 2019 Feb 2;189:655-666. doi: 10.1016/j.neuroimage.2019.02.001. [Epub ahead of print]

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

Author information

1
Laboratory 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. Electronic address: mfatemeh@vt.edu.
2
Independent Researcher, Cupertino, CA, USA.
3
Laboratory 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; Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston Salem, NC, USA.
4
Biomedical Research Imaging Center, Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
5
Laboratory 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; Translational Science Center, Wake Forest University, Winston Salem, NC, USA.

Abstract

The sliding window correlation (SWC) analysis is a straightforward and common approach for evaluating dynamic functional connectivity. Despite the fact that sliding window analyses have been long used, there are still considerable technical issues associated with the approach. A great effort has recently been dedicated to investigate the window setting effects on dynamic connectivity estimation. In this direction, tapered windows have been proposed to alleviate the effect of sudden changes associated with the edges of rectangular windows. Nevertheless, the majority of the windows exploited to estimate brain connectivity tend to suppress dynamic correlations, especially those with faster variations over time. Here, we introduced a window named modulated rectangular (mRect) to address the suppressing effect associated with the conventional windows. We provided a frequency domain analysis using simulated time series to investigate how sliding window analysis (using the regular window functions, e.g. rectangular and tapered windows) may lead to unwanted spectral modulations, and then we showed how this issue can be alleviated through the mRect window. Moreover, we created simulated dynamic network data with altering states over time using simulated fMRI time series, to examine the performance of different windows in tracking network states. We quantified the state identification rate of different window functions through the Jaccard index, and observed superior performance of the mRect window compared to the conventional window functions. Overall, the proposed window function provides an approach that improves SWC estimations, and thus the subsequent inferences and interpretations based on the connectivity network analyses.

KEYWORDS:

Connectivity network states; Dynamic brain connectivity; Modulated rectangular window; Network states transition; Resting state; Sliding window correlation analysis

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