Keywords: Parkinson's Disease, fMRISuccessful treatment of Parkinson’s disease using deep brain stimulation (DBS) of the sub-thalamic nucleus (STN) requires an optimal set of DBS parameters that involves time-consuming programming sessions (~1 year) by the current standard-of-care optimization protocol. Functional magnetic resonance imaging (fMRI) and deep learning with autoencoder-based feature extraction from DBS-fMRI responses have provided a way to rapidly optimize the DBS parameters. In this work, we examine the robustness of the unsupervised autoencoder-based feature extraction method to changes in the activation patterns of the DBS-fMRI responses, which may be caused by patient motion, difference in stimulation side and disease condition.
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