Copula directional dependence for inference and statistical analysis of whole-brain connectivity from fMRI data

Namgil Lee, Jong Min Kim

    Research output: Contribution to journalArticlepeer-review

    3 Scopus citations

    Abstract

    Introduction: Inferring connectivity between brain regions has been raising a lot of attention in recent decades. Copula directional dependence (CDD) is a statistical measure of directed connectivity, which does not require strict assumptions on probability distributions and linearity. Methods: In this work, CDDs between pairs of local brain areas were estimated based on the fMRI responses of human participants watching a Pixar animation movie. A directed connectivity map of fourteen predefined local areas was obtained for each participant, where the network structure was determined by the strengths of the CDDs. A resampling technique was further applied to determine the statistical significance of the connectivity directions in the networks. In order to demonstrate the effectiveness of the suggested method using CDDs, statistical group analysis was conducted based on graph theoretic measures of the inferred directed networks and CDD intensities. When the 129 fMRI participants were grouped by their age (3–5 year-old, 7–12 year-old, adult) and gender (F, M), nonparametric two-way analysis of variance (ANOVA) results could identify which cortical regions and connectivity structures correlated with the two physiological factors. Results: Especially, we could identify that (a) graph centrality measures of the frontal eye fields (FEF), the inferior temporal gyrus (ITG), and the temporopolar area (TP) were significantly affected by aging, (b) CDD intensities between FEF and the primary motor cortex (M1) and between ITG and TP were highly significantly affected by aging, and (c) CDDs between M1 and the anterior prefrontal cortex (aPFC) were highly significantly affected by gender. Software: The R source code for fMRI data preprocessing, estimation of directional dependences, network visualization, and statistical analyses are available at https://github.com/namgillee/CDDforFMRI.

    Original languageEnglish (US)
    Article numbere01191
    JournalBrain and Behavior
    Volume9
    Issue number1
    DOIs
    StatePublished - Jan 2019

    Bibliographical note

    Funding Information:
    This study was supported by a 2018 research grant from Kangwon National University and by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No.

    Funding Information:
    This study was supported by a 2018 research grant from Kangwon National University and by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. 2017R1C1B5076912).

    Funding Information:
    Kangwon National University, Grant/ Award Number: 2018; National Research Foundation of Korea, Grant/Award Number:

    Publisher Copyright:
    © 2018 The Authors. Brain and Behavior published by Wiley Periodicals, Inc.

    Copyright:
    Copyright 2019 Elsevier B.V., All rights reserved.

    Keywords

    • Brodmann area
    • connectivity
    • cortex
    • directional dependence
    • functional magnetic resonance imaging (fMRI)
    • group analysis

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