Limitations in effective treatment of Parkinson’s Disease: neuroanatomical substrate of L-Dopa induced dyskinesia and cognitive impairment

Parkinson’s disease (PD) is the fastest growing neurodegenerative disease, and in an increasingly aged population the care and management of PD is an increasing global concern. PD symptoms are managed well by L-DOPA treatment in the early stages of the disease, however the efficacy is reduced as the disease progresses due to an increase in troublesome L-DOPA induced dyskinesias (LID), as well as the development of cognitive decline. We investigated the neurophysiological and neuroanatomical substrate of LID in a 6-OHDA lesioned rat model of PD, showing that chronic L-DOPA treatment induces an exaggerated vasomotor response in LID animals. Remodeling of the microvasculature, on the other hand, is dose dependent and not evident in animals with low-dose progressive onset LID. An L-DOPA induced increase in relative cerebral blood flow (rCBF) in the dorsolateral striatum is evident in LID animals, but not in stable L-DOPA responding animals. When we measured L-DOPA induced changes in relative cerebral metabolic rate (rCMR), we observed key differences in LID and non-LID animals. L-DOPA reduced striatal rCMR in non-LID animals both from the first dose and after 21 days of treatment, consistent with the theory that L-DOPA therapeutically reduces striatal hyperexcitability. Conversely, L-DOPA failed to show consistent reduction in rCMR in LID animals, and after symptoms had developed, L-DOPA markedly increased rCMR in the striatum. Our findings support the idea that plastic changes in striatal excitability underly the expression of LID symptoms, and that these changes may be initiated in L-DOPA naïve animals. We investigated the use of a supervised machine learning algorithm called Support Vector Machine (SVM) to retrospectively stratify patients based on brain fluorodeoxyglucose (FDG) –PET. The baseline scans were used to train a model which separated PD patients with mild cognitive impairment (MCI) as dementia converters vs. stable MCI with 95% sensitivity and 91% specificity. The model retained an accuracy of 73% in an external testing set. The SVM model was topographically characterized by hypometabolism in the temporal and parietal lobes and hypermetabolism in the anterior cingulum, putamen, insular, mesiotemporal, and postcentral gyrus. These results indicate that FDG-PET-based SVM classifier has utility for predicting the cognitive prognosis of PD-MCI patients.

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