Communications for Statistical Applications and Methods

The Korean Statistical Society (한국통계학회)
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Dynamic bivariate correlation methods comparison study in fMRI

  • Jaehee Kim (Department of Statistics, Duksung Women's University)
  • Received : 2023.06.07
  • Accepted : 2023.10.30
  • Published : 2024.01.31
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Abstract

Most functional magnetic resonance imaging (fMRI) studies in resting state have assumed that the functional connectivity (FC) between time series from distinct brain regions is constant. However, increased interest has recently been in quantifying possible dynamic changes in FC during fMRI experiments. FC study may provide insight into the fundamental workings of brain networks to brain activity. In this work, we focus on the specific problem of estimating the dynamic behavior of pairwise correlations between time courses extracted from two different brain regions. We compare the sliding-window techniques such as moving average (MA) and exponentially weighted moving average (EWMA), dynamic causality with vector autoregressive (VAR) model, dynamic conditional correlation (DCC) based on volatility, and the proposed alternative methods to use differencing and recursive residuals. We investigate the properties of those techniques in a series of simulation studies. We also provide an application with major depressive disorder (MDD) patient fMRI data to demonstrate studying dynamic correlations.

Keywords

Acknowledgement

This research was supported by 2023 Duksung Women's University Research Fund.

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