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Generic dynamic causal modelling: An illustrative application to Parkinson's disease
Bernadette C M van Wijk1, Hayriye Cagnan2, Vladimir Litvak3
1Integrative Model-based Cognitive Neuroscience Research Unit, Department of Psychology, University of Amsterdam, The Netherlands; Department of Neurology, Charité - University Medicine Berlin, Germany; Wellcome Centre for Human Neuroimaging, UCL Institute of Neurology, London, UK.
Neuroimage
|August 22, 2018
Summary
This study introduces a flexible dynamic causal modeling framework for electrophysiological responses, enabling the integration of diverse neural mass models. The new approach reveals synaptic changes in Parkinson's disease patients treated with dopaminergic medication.
Area of Science:
- Computational neuroscience
- Systems neuroscience
Background:
- Dynamic causal modeling (DCM) is crucial for analyzing electrophysiological data.
- Existing DCM approaches have limitations in integrating diverse neural mass models.
Purpose of the Study:
- To develop a flexible DCM framework capable of integrating qualitatively different neural mass models.
- To apply this framework to investigate neural mechanisms underlying beta oscillation suppression in Parkinson's disease.
Main Methods:
- Developed a generic DCM framework allowing coupling of diverse cortical and subcortical neural mass models.
- Integrated a basal ganglia-thalamus model with a validated motor cortex model.
- Used experimental data from deep brain stimulation and magnetoencephalography in Parkinson's disease patients.
Main Results:
- Identified reduced synaptic efficacy in the subthalamic nucleus-external pallidum circuit.
- Observed reduced efficacy in the hyperdirect and indirect pathways leading to this circuit.
- Confirmed findings consistent with previous studies on dopaminergic medication effects.
Conclusions:
- The developed DCM framework offers enhanced flexibility for building complex neural models.
- The study provides insights into the synaptic pathophysiology of beta oscillation suppression in Parkinson's disease.
- This work lays the foundation for future modeling of event-related potentials and cross-spectral densities.