How Parkinson’s disease affects cortical information flow: A multiscale model Cliff Kerr Complex Systems Group University of Sydney Neurosimulation Laboratory.

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Presentation transcript:

How Parkinson’s disease affects cortical information flow: A multiscale model Cliff Kerr Complex Systems Group University of Sydney Neurosimulation Laboratory State University of New York

2/15 Cliff Kerr | Multiscale model of Parkinson’s disease | Jan. 30 th, 2013 Parkinson’s disease Tremor (typically 3-6 Hz) Bradykinesia (slowness of movement) Bradyphrenia (slowness of thought)

3/15 Cliff Kerr | Multiscale model of Parkinson’s disease | Jan. 30 th, 2013 Spiking network model Event-driven integrate-and- fire model 6-layered cortex, 2 thalamic nuclei 15 cell types  5000 neurons

4/15 Cliff Kerr | Multiscale model of Parkinson’s disease | Jan. 30 th, 2013 Anatomy & physiology based on experimental data Adaptable to different brain regions based on cell populations/ connectivities Model generates realistic neuronal dynamics; demonstrated control of virtual arm Synaptic input: Synaptic plasticity: Spiking network model

5/15 Cliff Kerr | Multiscale model of Parkinson’s disease | Jan. 30 th, 2013 Spiking network model Connectivity matrix based on rat, cat, and macaque data Strong intralaminar and thalamocortical connectivity

6/15 Cliff Kerr | Multiscale model of Parkinson’s disease | Jan. 30 th, 2013 Neural field model Continuous firing rate model 9 neuronal populations 26 connections Field model activity drives network model

7/15 Cliff Kerr | Multiscale model of Parkinson’s disease | Jan. 30 th, 2013 Neural field model

8/15 Cliff Kerr | Multiscale model of Parkinson’s disease | Jan. 30 th, 2013 Neural field model GPi links basal ganglia to rest of brain:

9/15 Cliff Kerr | Multiscale model of Parkinson’s disease | Jan. 30 th, 2013 Firing rates in the field model drive an ensemble of Poisson processes, which then drive the network From field to network Network Field p1p1 p2p2 p3p3 Poisson

10/15 Cliff Kerr | Multiscale model of Parkinson’s disease | Jan. 30 th, 2013 Field model dynamics PD disrupts coherence between basal ganglia nuclei PD changes spectral power in beta/gamma bands

11/15 Cliff Kerr | Multiscale model of Parkinson’s disease | Jan. 30 th, 2013 Network model dynamics

12/15 Cliff Kerr | Multiscale model of Parkinson’s disease | Jan. 30 th, 2013 Network spectra

13/15 Cliff Kerr | Multiscale model of Parkinson’s disease | Jan. 30 th, 2013 Burst probability

14/15 Cliff Kerr | Multiscale model of Parkinson’s disease | Jan. 30 th, 2013 Granger causality

15/15 Cliff Kerr | Multiscale model of Parkinson’s disease | Jan. 30 th, 2013 Summary Model can reproduce many features of Parkinson’s disease (e.g. reduced cortical firing, increased coherence) Granger causality between cortical layers was markedly reduced in PD – possible explanation of bradyphrenia (…and bradykinesia?) Different input drives had a major effect on the model dynamics –Where possible, realistic inputs should be used instead of white noise for driving network models

16/15 Cliff Kerr | Multiscale model of Parkinson’s disease | Jan. 30 th, 2013 Acknowledgements Sacha J. van Albada Samuel A. Neymotin George L. Chadderdon III Peter A. Robinson William W. Lytton