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Nonlinear dynamic causal models for fMRI.

Klaas Enno Stephan1, Lars Kasper, Lee M Harrison

  • 1Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, 12 Queen Square, London WC1N 3BG, UK. k.stephan@fil.ion.ucl.ac.uk

Neuroimage
|June 21, 2008
PubMed
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This study introduces a nonlinear extension to Dynamic Causal Modelling (DCM) to better explain neuronal population interactions, particularly gating mechanisms, using fMRI data.

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Systems Neuroscience

Background:

  • Effective connectivity models neuronal population interactions.
  • Current models like Dynamic Causal Modelling (DCM) use bilinear equations to describe experimental modulation.
  • These bilinear models cannot capture neuronal gating mechanisms observed in electrophysiology.

Purpose of the Study:

  • To present a nonlinear extension of DCM capable of modeling neuronal gating processes.
  • To assign modulation of network interactions to specific neuronal populations.
  • To demonstrate the model's validity and utility with simulations and empirical data.

Main Methods:

  • Developed a second-order nonlinear extension of Dynamic Causal Modelling (DCM).
  • Utilized synthetic data to differentiate nonlinear from bilinear mechanisms.

Related Experiment Videos

  • Applied the extended DCM to fMRI data from attention and binocular rivalry paradigms.

    • Main Results:

    • The nonlinear DCM successfully distinguished nonlinear from bilinear mechanisms in synthetic data.
    • Attention-related increases in V5 responses were explained by posterior parietal cortex gating the V1-V5 connection.
    • Interactions in visual areas during binocular rivalry were modulated by the middle frontal gyrus.

    Conclusions:

    • The nonlinear DCM provides a more biologically plausible account of effective connectivity, including gating.
    • Bayesian model selection favored the nonlinear models over bilinear ones for empirical fMRI data.
    • This extended framework enhances understanding of neurobiological processes like attention and perception.