Accelerometry is a valid method to distinguish between healthy and 6-OHDA-lesioned parkinsonian rats.

In Parkinson's disease (PD), continuous sensor-based evaluation of motor symptom severity, e.g., using accelerometry, has become an emerging field of interest in clinical research. Continuous symptom monitoring would also be of interest in preclinical disease models; however, such devices are far less established in animal models, most likely due to additional requirements in size, energy consumption, and impairment-free attachment. In contrast, accelerometers manufactured in micro-electro-mechanical systems (MEMS) technology are promising sensor devices, which allow for space-saving and energy-efficient monitoring of movements. In the present study, we aim to extend the state of the art by establishing wireless accelerometer measurements as a simple and energy-efficient method to distinguish between healthy rats and the 6-hydroxydopamine (6-OHDA) PD animal model. Male Wistar-Han rats were assessed either three weeks after unilateral 6-OHDA or sham lesioning within their home cages with an extracorporeal accelerometer placed in a rodent backpack for 12 h during their active phase. The data was transmitted wirelessly to a computer, preprocessed, and a statistical analysis was performed to find differences between the datasets of 6-OHDA and sham-lesioned rats. The statistical analysis showed significant differences in the variances of the magnitude of the acceleration vectors between the two classes. In conclusion, accelerometry is a valid method to distinguish between 6-OHDA-lesioned rats with unilateral dopaminergic deficiency and their healthy counterparts. The presented method represents a first step towards automated symptom severity monitoring and provides a framework to expand the application to on-implant integrated accelerometers for continuous monitoring of symptom manifestations in rodent models of neurodegenerative diseases. Future studies are required to expand accelerometry to assess symptom severity to ultimately utilize it for preclinical research on adaptive therapies.