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An adaptive directed transfer function approach for detecting dynamic causal interactions.

Christopher Wilke1, Lei Ding, Bin He

  • 1Department of Biomedical Engineering, University of Minnesota, 7-105 NHH, 312 Church Street SE, Minneapolis, MN 55455, USA.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|November 16, 2007
PubMed
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The adaptive directed transfer function (ADTF) effectively identifies dynamic causality changes in complex systems. This new method, using time-varying coefficients, surpasses conventional directed transfer function (DTF) in tracking causal interactions.

Area of Science:

  • Neuroscience
  • Complex Systems Analysis
  • Time Series Analysis

Background:

  • Causality detection in multivariate time series is crucial for understanding complex systems.
  • Conventional methods like the directed transfer function (DTF) assume stationary interactions.
  • Limitations exist in capturing dynamic, time-varying causal relationships.

Purpose of the Study:

  • To introduce and validate the adaptive directed transfer function (ADTF) for time-varying causality.
  • To compare the efficacy of ADTF against the conventional DTF in detecting dynamic causal changes.
  • To assess ADTF's capability in identifying the primary source of information outflow in evolving systems.

Main Methods:

  • Application of the directed transfer function (DTF) to time-varying coefficients from a multivariate adaptive autoregressive (MVAAR) model.

Related Experiment Videos

  • Development of the adaptive directed transfer function (ADTF) as a novel measure of time-varying causality.
  • Simulation using two multivariate models with predefined, altered interaction patterns over time.

    • Main Results:

    • The ADTF successfully discerned dynamic changes in causal interaction patterns in both simulated models.
    • ADTF demonstrated capability in identifying the primary source of information outflow.
    • Results suggest ADTF provides more insightful information on dynamic causality compared to conventional DTF.

    Conclusions:

    • The adaptive directed transfer function (ADTF) is a promising tool for analyzing time-varying causality.
    • ADTF offers enhanced ability to detect evolving causal structures in complex systems.
    • This method advances the understanding of dynamic interactions in multivariate time series data.