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Causality Analysis of fMRI Data Based on the Directed Information Theory Framework.

Zhe Wang, Ahmed Alahmadi, David C Zhu

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    |September 29, 2015
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    Directed Information (DI) analysis offers a powerful, model-free method for brain connectivity research. This approach effectively captures both linear and nonlinear causal relationships, outperforming Granger causality (GC) in complex brain dynamics.

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    Area of Science:

    • Neuroscience
    • Information Theory
    • Computational Biology

    Background:

    • Brain connectivity analysis is crucial for understanding brain function.
    • Existing methods like Granger causality (GC) have limitations in capturing nonlinear dynamics.
    • Directed Information (DI) theory offers a promising alternative framework for causality analysis.

    Purpose of the Study:

    • To introduce and detail the Directed Information (DI) framework for fMRI-based causality analysis.
    • To compare the efficacy of DI-based causality analysis against Granger causality (GC) using simulated and real fMRI data.
    • To explore the dynamics of information transmission in brain connectivity.

    Main Methods:

    • Introduction to the core concepts of the DI theory framework.
    • Detailed procedure for calculating DI measures between two time series, including optimal bin size selection and probability estimation.
    • Application and comparison of DI-based causality analysis with GC using simulated and fMRI datasets.

    Main Results:

    • GC analysis effectively detects linear causal relationships but struggles with nonlinear ones.
    • DI-based causality analysis demonstrates superior performance in capturing both linear and nonlinear causal relationships.
    • Brain connectivity exhibits dynamic, bidirectional information transmission, which DI quantifies more effectively than GC, especially under bidirectional flow.

    Conclusions:

    • DI theory provides a robust, model-free framework for assessing causality in brain connectivity from fMRI data.
    • DI analysis is more comprehensive than GC, accurately reflecting complex linear and nonlinear interactions.
    • The findings highlight the importance of considering bidirectional information flow in brain networks and the advantage of DI in quantifying it.