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Dynamic causal modelling the 'resting' Parkinsonian brain, and network discovery

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If you have a question about this talk, please contact Mikail Rubinov.

Starved of dopamine, the dynamics of the Parkinsonian brain impact on both ‘action’ and ‘resting’ motor behaviour. Deep brain stimulation (DBS) has become an established means of managing these symptoms, although its mechanisms of action remain unclear. Non-invasive characterisations of induced brain responses, and the effective connectivity underlying them, generally appeals to dynamic causal modelling of neuroimaging data. When the brain is at rest however, this sort of characterisation has been limited to correlations (functional connectivity). Stochastic dynamic causal modelling for fMRI is a recent extension capable of modelling BOLD data collected during task-free periods. In this talk I will introduce dynamic causal modelling and its stochastic variant, and discuss how this can be used to model dynamics of the resting state. I will present some recent work where we model the effective connectivity underlying low frequency blood oxygen level dependent (BOLD) fluctuations in the resting Parkinsonian motor network – disclosing the distributed effects of deep brain stimulation on cortico-subcortical connections, and how these relate to clinical severity. The talk will also briefly touch upon recent developments in deterministic schemes for modelling the resting state, as well as using model optimisation techniques to search large model spaces to identify functional networks.

This talk is part of the Brain Mapping Unit Networks Meeting and the Cambridge Connectome Consortium series.

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