Chronic treatment with levodopa (LD) in Parkinson's disease (PD) can cause drug induced dyskinesias. Mucuna pruriens endocarp powder (MPEP) contains several compounds including natural LD and has been reported to not cause drug-induced dyskinesias. We evaluated the effects of Mucuna pruriens to determine if its underlying mechanistic actions are exclusively due to LD. We first compared MPEP with and without carbidopa (CD), and LD+CD in hemiparkinsonian (HP) monkeys. Each treatment ameliorated parkinsonism. We then compared the neuronal firing properties of the substantia nigra reticulata (SNR) and subthalamic nucleus (STN) in HP monkeys with MPEP+CD and LD+CD to evaluate basal ganglia circuitry alterations. Both treatments decreased SNR firing rate compared to HP state. However, LD+CD treatments significantly increased SNR bursting firing patterns that were not seen with MPEP+CD treatments. No significant changes were seen in STN firing properties. We then evaluated the effects of a water extract of MPEP. Oral MPWE ameliorated parkinsonism without causing drug-induced dyskinesias. The distinctive neurophysiological findings in the basal ganglia and the ability to ameliorate parkinsonism without causing dyskinesias strongly suggest that Mucuna pruriens acts through a novel mechanism that is different from that of LD.

Dopamine replacement therapy with levodopa (LD) is currently the most effective pharmacological treatment for Parkinson's disease (PD), a neurodegenerative disorder characterized by dysfunction of basal ganglia electrophysiology. The effects of chronic LD treatments on the electrophysiological activity of the subthalamic nucleus (STN) and the substantia nigra reticulata (SNR) in parkinsonism are not clear. In the present study we examined the effects of chronic LD treatments on the firing rate and firing pattern of STN and SNR neurons in the stable hemiparkinsonian monkey model of PD. We also evaluated local field potentials of both nuclei before and after LD treatments. In a stable hemiparkinsonian state, STN and SNR had a mean firing rate of 42.6 ± 3.5H z (mean ± SEM) and 52.1 ± 5.7 Hz, respectively. Chronic intermittent LD exposure induced marked amelioration of parkinsonism with no apparent drug-induced motor complications. LD treatments did not significantly change the mean firing rate of STN neurons (41.3 ± 3.3 Hz) or bursting neuronal firing patterns. However, LD treatments induced a significant reduction of the mean firing rates of SNR neurons to 36.2 ± 3.3 Hz (p<0.05) and a trend toward increased burstiness. The entropy of the spike sequences from STN and SNR was unchanged by LD treatment, while there was a shift of spectral power into higher frequency bands in the LFPs. The inability of chronic LD treatments to reduce the bursty firing patterns in the STN and SNR should be further examined as a potential pathophysiological mechanism for PD symptoms that are refractory to LD treatments.

A simple method is described for using principal component analysis (PCA) to score rat sleep recordings as awake, rapid-eye-movement (REM) sleep, or non-REM (NREM) sleep. PCA was used to reduce the dimensionality of the features extracted from each epoch to three, and the projections were then graphed in a scatterplot where the clusters were visually apparent. The clusters were then directly manually selected, classifying the entire recording at once. The method was tested in a set of ten 24-hour rat sleep electroencephalogram (EEG) and electromyogram (EMG) recordings. Classifications by two human raters performing traditional epoch-by-epoch scoring were blindly compared with classifications by another two human raters using the new PCA method. Overall interrater median percent agreements ranged between 93.7% and 94.9%. Median Cohen's kappa coefficient ranged from 0.890 to 0.909. The PCA method on average required about five minutes for classification of each 24-hour recording. The combination of good accuracy and reduced time compared to traditional sleep scoring suggests that the method may be useful for sleep research.