Objective: The aim of our study was to characterize cortical neuronal network activity in levodopa-induced dyskinesia in order to better understand the underlying pathophysiology and to find new biomarkers that can be utilized for adaptive deep brain stimulation therapy.

Background: Levodopa is the most efficacious drug in the symptomatic therapy of motor symptoms in Parkinson´s disease (PD). The long-term treatment with levodopa is often complicated by the occurrence of troublesome involuntary hyperkinesia, so called levodopa-induced dyskinesia (LID), which severely affects the quality of life of patients. Recent evidence suggests that LID might be mediated by increased gamma oscillations in the cortico-basal ganglia loop.

Method: We recorded local field potentials from the primary motor cortex (M1) and videotaped motor behavior in freely moving rats before and after an unilateral 6-OHDA lesion as well as during a 3-week treatment with levodopa. In addition, the effects of coadminstration of levodopa and the dopamine receptor antagonists SCH-23390 (D1) and raclopride (D2) were studied. Power spectra and burst parameters were analyzed. Results were correlated with the abnormal involuntary movement score (AIMS) and used for generalized linear modeling (GLM).

Results: Following the 6-OHDA lesion, rats showed profound hemiakinesia and cortical beta oscillations. Levodopa reverted motor impairment, suppressed beta activity and, with repeated administration, led to a progressive enhancement of AIMS. Concurrently, we observed a highly significant increase in gamma oscillation power and peak frequency (range: 80-120 Hz). In addition, levodopa caused a shift towards longer and higher gamma bursts. While AIMS, gamma oscillations, and most gamma burst parameters reached their maximum following the 4th injection and remained on a stable plateau thereafter, the peak frequency of the gamma power continued to rise with every injection. Both SCH-23390 and raclopride suppressed gamma oscillations and AIMS to varying degrees. The electrophysiological parameter exhibiting the strongest correlation with AIMS proved to be the size of the fraction of longest gamma bursts. GLM performance in predicting AIMS above 3 reached a ROC-AUC of 0,97.

Conclusion: Gamma oscillations are related to LID and should be further studied as a network biomarker for adaptive deep brain stimulation.

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MDS Abstracts - https://www.mdsabstracts.org/abstract/cortical-gamma-oscillations-as-biomarkers-for-levodopa-induced-dyskinesia/