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DCM, Conductance Based Models and Clinical Applications

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Marreiros,  AC
Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Marreiros, A., Pinotsis, D., Brown, P., & Friston, K. (2015). DCM, Conductance Based Models and Clinical Applications. In B. Bhattacharya, & F. Chowdhury (Eds.), Validating Neuro-Computational Models of Neurological and Psychiatric Disorders (pp. 43-70). Cham, Switzerland: Springer International Publishing.


Cite as: http://hdl.handle.net/11858/00-001M-0000-002A-4433-9
Abstract
This chapter reviews some recent advances in dynamic causal modelling (DCM) of electrophysiology, in particular with respect to conductance based models and clinical applications. DCM addresses observed responses of complex neuronal systems by looking at the neuronal interactions that generate them and how these responses reflect the underlying neurobiology. DCM is a technique for inferring the biophysical properties of cortical sources and their directed connectivity based on distinct neuronal and observation models. The DCM framework uses mathematical formalisms of neural masses, neural fields and mean-fields as forward or generative models for observed neuronal activity. We here consider conductance based neural mass, mean-field and field models—and review their latest technical developments. We use dynamically rich conductance based models to generate responses in laminar-specific populations of excitatory and inhibitory cells. These models allow for the evaluation of neuronal connections and high-order statistics of neuronal states, using Bayesian estimation and inference. We also discuss recent clinical applications of DCM for convolution based neural mass models, in particular for the study of Parkinson’s disease. We present a study of data from Parkinsonian patients, and model the large-scale network changes underlying the pathological excess of beta oscillations that characterise the Parkinsonian state.