Dynamic functional connectivity analysis based on time-varying partial correlation with a copula-DCC-GARCH model

• Published in: Neuroscience research 2021-08, Vol.169, p.27-39
• Main Authors: Lee, Namgil, Kim, Jong-Min
• Format: Article
• Language: English
• Subjects: Brain - diagnostic imaging; Brain Mapping; Cerebral Cortex; Connectome; Copula; DCC Receptor; Dynamic conditional correlation; Dynamic functional connectivity; Functional MRI; Generalized AutoRegressive Conditional Heteroscedastic (GARCH); Humans; Magnetic Resonance Imaging; Neuroimaging; Partial correlation
• ISSN: 0168-0102; 1872-8111
• DOI: 10.1016/j.neures.2020.06.006

•We suggest an improved method for the estimation of dynamic functional connectivity (dFC) for fMRI data.•The suggested time-varying partial correlation can avoid spurious correlations.•The suggested method is effective for the data having skewed non-normal distributions.•A recursive algorithm is explained for the efficient computation of the time-varying partial correlation.•Group analysis of real fMRI data demonstrate the robustness to the preprocessing steps for motion artifact removal. We suggest a time-varying partial correlation as a statistical measure of dynamic functional connectivity (dFC) in the human brain. Traditional statistical models often assume specific distributions on the measured data such as the Gaussian distribution, which prohibits their application to neuroimaging data analysis. First, we use the copula-based dynamic conditional correlation (DCC), which does not rely on a specific distribution assumption, for estimating time-varying correlation between regions-of-interest (ROIs) of the human brain. Then, we suggest a time-varying partial correlation based on the Gaussian copula-DCC-GARCH model as an effective method for measuring dFC in the human brain. A recursive algorithm is explained for computation of the time-varying partial correlation. Numerical simulation results demonstrate effectiveness of the partial correlation-based methods against pairwise correlation-based methods. In addition, a two-step procedure is described for the inference of sparse dFC structure using functional magnetic resonance imaging (fMRI) data. We illustrate the proposed method by analyzing an fMRI data set of human participants watching a Pixar animated movie. Based on twelve a priori selected brain regions in the cortex, we demonstrate that the proposed method is effective for inferring sparse dFC network structures and robust to noise distribution and a preprocessing step of fMRI data.