26. What impairs information coding in the Basal Ganglia in Parkinson’s disease?

Ana V. Cruz1,2 a.cruz@ion.ac.ucl.uk Nicolas Mallet1,3 nicolas.mallet@pharm.ox.ac.uk Peter J. Maggil3 peter.magill@pharm.ox.ac.uk Peter Brown1 p.brown@ion.ucl.ac.uk Bruno B. Averbeck1 b.averbeck@ion.ucl.ac.uk

1Sobell Department of Motor Neuroscience and Movement Disorders, Institute of Neurology, London WC1N 3BG, United Kingdom
2Instituto Gulbenkian de Cincia, 2780-156 Oeiras, Portugal
3Medical Research Council Anatomical Neuropharmacology Unit, University of Oxford, Oxford OX1 3TH, United Kingdom

Recordings from deep brain stimulation electrodes in the basal ganglia of patients with Parkinson’s disease (PD) have shown that chronic dopamine loss is accompanied by increased beta frequency (13-30 Hz) oscillations at the single-cell and neuronal population levels. These oscillations are stronger when patients are off dopamine replacement medication than when they are on medication. Additionally, when on medication, the oscillations decrease even further when patients initiate movements. This suggests that the increased oscillations in the basal ganglia networks may be related to the movement deficits seen in the patients. How these excessive beta oscillations impact information processing in these circuits is unknown, but behavioral deficits might be due to decreased information coding capacity. To address this hypothesis, we recorded single-neuron activity from multiple sites in the external globus pallidus (GP) of control rats and 6-OHDA-lesioned rats, the latter of which is a PD model that shows exaggerated beta oscillations after chronic dopamine loss. We then characterized the effects of these oscillations on the network entropy, a measure of information coding capacity, by comparing entropy between neural ensemble activity in lesioned and normal animals.

Dopamine loss was associated with significant reductions in the firing rates of GP neurons and significant increases in single-cell beta oscillations and synchronized beta activity. We have used a logistic regression model to examine the relative effects of firing rate, oscillations and synchrony on the entropy in the network. The reduction in firing rates had the largest effect on entropy (86.3% decrease), the increased oscillations the second largest effect (11.9% decrease), and the excessive synchrony the smallest effect (1.8% decrease), at least for pairs of simultaneously-recorded GP neurons. However, effects of synchrony are likely to be higher when larger populations of neurons are considered rather than pairs. We are currently developing a model of the network effects of synchrony, at the population level, so its effects can be more directly compared to firing rates and oscillations. The results suggest that the excessive beta oscillations seen in basal ganglia circuits in PD may lead to a decrease in the entropy of these networks. However, the results also emphasize the importance of changes in firing rates in the Parkinsonian state.