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Abstract #1395

Robustness of Autoencoder-based Classifier for fMRI-based Optimization of Single-sided Deep Brain Stimulation

Afis Ajala1, Jianwei Qiu1, John Karigiannis1, Brendan Santyr2, Jurgen Germann2, Alexandre Boutet2, Luca Marinelli1, Chitresh Bhushan1, Radhika Madhavan1, Desmond Yeo1, and Andres Lozano2
1GE Global Research, Niskayuna, NY, United States, 2University Health Network and University of Toronto, Toronto, ON, Canada

Synopsis

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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Keywords