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

Namgil Lee, Jong Min Kim

    Research output: Contribution to journalArticlepeer-review

    1 Scopus citations

    Abstract

    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.

    Original languageEnglish (US)
    JournalNeuroscience Research
    Early online dateJul 3 2020
    DOIs
    StateE-pub ahead of print - Jul 3 2020

    Bibliographical note

    Funding Information:
    This study was supported by a National Research Foundation of Korea (NRF) grant funded by the Korea government ( MSIT) (No. 2017R1C1B5076912) .

    Publisher Copyright:
    © 2020 Elsevier B.V. and Japan Neuroscience Society

    Copyright:
    Copyright 2020 Elsevier B.V., All rights reserved.

    Keywords

    • Copula
    • Dynamic conditional correlation
    • Dynamic functional connectivity
    • Functional MRI
    • Generalized AutoRegressive Conditional Heteroscedastic (GARCH)
    • Partial correlation

    PubMed: MeSH publication types

    • Journal Article

    Fingerprint Dive into the research topics of 'Dynamic functional connectivity analysis based on time-varying partial correlation with a copula-DCC-GARCH model'. Together they form a unique fingerprint.

    Cite this