Cerebral perfusion imaging predicts levodopa-induced dyskinesia in Parkinsonian rat model.
Authors
Affiliations (6)
Affiliations (6)
- Graduate Program in Biomedical Engineering, Price Faculty of Engineering, University of Manitoba, 75 Chancellor's Circle, Winnipeg, MB, R3T 5V6, Canada.
- PrairieNeuro Research Centre, Kleysen Institute for Advanced Medicine, Health Sciences Centre, 710 William Avenue, Winnipeg, MB, R3E 3J7, Canada.
- Department of Human Anatomy and Cell Science, Rady Faculty of Health Sciences, University of Manitoba, 130-745 Bannatyne Avenue, Winnipeg, MB, Canada.
- Graduate Program in Biomedical Engineering, Price Faculty of Engineering, University of Manitoba, 75 Chancellor's Circle, Winnipeg, MB, R3T 5V6, Canada. [email protected].
- PrairieNeuro Research Centre, Kleysen Institute for Advanced Medicine, Health Sciences Centre, 710 William Avenue, Winnipeg, MB, R3E 3J7, Canada. [email protected].
- Department of Human Anatomy and Cell Science, Rady Faculty of Health Sciences, University of Manitoba, 130-745 Bannatyne Avenue, Winnipeg, MB, Canada. [email protected].
Abstract
Many Parkinson's disease (PD) patients manifest complications related to treatment called levodopa-induced dyskinesia (LID). Preventing the onset of LID is crucial to the management of PD, but the reasons why some patients develop LID are unclear. The ability to prognosticate predisposition to LID would be valuable for the investigation of mitigation strategies. Thirty rats received 6-hydroxydopamine to induce Parkinsonism-like behaviors before treatment with levodopa (2 mg/kg) daily for 22 days. Fourteen developed LID-like behaviors. Fluorodeoxyglucose PET, T<sub>2</sub>-weighted MRI and cerebral perfusion imaging were collected before treatment. Support vector machines were trained to classify prospective LID vs. non-LID animals from treatment-naïve baseline imaging. Volumetric perfusion imaging performed best overall with 86.16% area-under-curve, 86.67% accuracy, 92.86% sensitivity, and 81.25% specificity for classifying animals with LID vs. non-LID in leave-one-out cross-validation. We have demonstrated proof-of-concept for imaging-based classification of susceptibility to LID of a Parkinsonian rat model using perfusion-based imaging and a machine learning model.