L-Dopa Induced Dyskinesia and cognitive impairment in Parkinson's Disease

Samuel Booth is a PhD Candidate in the Department of Human Anatomy and Cell Science at the University of Manitoba.

Title : Neuroanatomical substrate of L-Dopa Induced Dyskinesia and cognitive impairment in Parkinson's Disease: Limitations to effective treatment.

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 levodopa (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. Samuel's Ph.D. work has focused on investigating the neurophysiological and neuroanatomical substrate of LID and cognitive decline in Parkinson's disease patients using human patients and animal models.

First, he investigated 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 and non-stable L-DOPA responding animals. When he measured L-DOPA induced changes in relative cerebral metabolic rate (rCMR), key differences in LID and non-LID animals were observed. 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 this cluster. Our findings support the idea that plastic changes in striatal excitability underlie the expression of LID symptoms, and that these changes may be initiated in L-DOPA naïve animals.

He investigated the use of a supervised learning algorithm called Support Vector Machine (SVM) to retrospectively stratify PD patients with mild cognitive impairment based on brain fluorodeoxyglucose (FDG) –PET. The baseline scans were used to train a model which separated patients as PDD converters vs. stable MCI with high sensitivity and specificity. The model retained an accuracy of 73% in an external testing set. The metabolic pattern derived from 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; supporting the hypothesis that posterior cortical atrophy is a poor prognostic indicator in PD. These results indicate that FDG-PET-based SVM classifier has utility for predicting the cognitive prognosis of PD-MCI patients.