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

Namgil Lee, Jong-Min Kim

    Research output: Contribution to journalArticle

    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 1 2019

    Fingerprint

    Magnetic Resonance Imaging
    Frontal Lobe
    Temporal Lobe
    Brain
    Motor Cortex
    Motion Pictures
    Prefrontal Cortex
    Analysis of Variance
    Software
    Direction compound

    Keywords

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

    PubMed: MeSH publication types

    • Journal Article
    • Research Support, Non-U.S. Gov't

    Cite this

    Copula directional dependence for inference and statistical analysis of whole-brain connectivity from fMRI data. / Lee, Namgil; Kim, Jong-Min.

    In: Brain and Behavior, Vol. 9, No. 1, e01191, 01.01.2019.

    Research output: Contribution to journalArticle

    @article{22be3bea0ef04c138e284c9c860d5382,
    title = "Copula directional dependence for inference and statistical analysis of whole-brain connectivity from fMRI data",
    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.",
    keywords = "Brodmann area, connectivity, cortex, directional dependence, functional magnetic resonance imaging (fMRI), group analysis",
    author = "Namgil Lee and Jong-Min Kim",
    year = "2019",
    month = "1",
    day = "1",
    doi = "10.1002/brb3.1191",
    language = "English (US)",
    volume = "9",
    journal = "Brain and Behavior",
    issn = "2157-9032",
    publisher = "John Wiley and Sons Inc.",
    number = "1",

    }

    TY - JOUR

    T1 - Copula directional dependence for inference and statistical analysis of whole-brain connectivity from fMRI data

    AU - Lee, Namgil

    AU - Kim, Jong-Min

    PY - 2019/1/1

    Y1 - 2019/1/1

    N2 - 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.

    AB - 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.

    KW - Brodmann area

    KW - connectivity

    KW - cortex

    KW - directional dependence

    KW - functional magnetic resonance imaging (fMRI)

    KW - group analysis

    UR - http://www.scopus.com/inward/record.url?scp=85059128115&partnerID=8YFLogxK

    UR - http://www.scopus.com/inward/citedby.url?scp=85059128115&partnerID=8YFLogxK

    U2 - 10.1002/brb3.1191

    DO - 10.1002/brb3.1191

    M3 - Article

    C2 - 30592175

    AN - SCOPUS:85059128115

    VL - 9

    JO - Brain and Behavior

    JF - Brain and Behavior

    SN - 2157-9032

    IS - 1

    M1 - e01191

    ER -