Computational models describing possible mechanisms for generation of excessive beta oscillations in Parkinson’s disease

Alexander Pavlides, John Hogan, Rafal Bogacz

Research output: Contribution to journalArticle (Academic Journal)peer-review

65 Citations (Scopus)
240 Downloads (Pure)

Abstract

In Parkinson’s disease, an increase in beta oscillations within the basal ganglia nuclei has
been shown to be associated with difficulty in movement initiation. An important role in the
generation of these oscillations is thought to be played by the motor cortex and by a network
composed of the subthalamic nucleus (STN) and the external segment of globus pallidus
(GPe). Several alternative models have been proposed to describe the mechanisms for
generation of the Parkinsonian beta oscillations. However, a recent experimental study of
Tachibana and colleagues yielded results which are challenging for all published computational
models of beta generation. That study investigated how the presence of beta oscillations
in a primate model of Parkinson’s disease is affected by blocking different connections
of the STN-GPe circuit. Due to a large number of experimental conditions, the study provides
strong constraints that any mechanistic model of beta generation should satisfy. In
this paper we present two models consistent with the data of Tachibana et al. The first
model assumes that Parkinsonian beta oscillation are generated in the cortex and the STNGPe
circuits resonates at this frequency. The second model additionally assumes that the
feedback from STN-GPe circuit to cortex is important for maintaining the oscillations in the
network. Predictions are made about experimental evidence that is required to differentiate
between the two models, both of which are able to reproduce firing rates, oscillation frequency
and effects of lesions carried out by Tachibana and colleagues. Furthermore, an
analysis of the models reveals how the amplitude and frequency of the generated oscillations
depend on parameters.
Original languageEnglish
Article numbere1004609
Number of pages29
JournalPLoS Computational Biology
Volume11
Issue number12
DOIs
Publication statusPublished - 18 Dec 2015

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