Keywords: Task/Intervention Based fMRI, Parkinson's Disease
Motivation: Maximization of clinical benefits in the treatment of Parkinson’s disease (PD) using deep brain stimulation (DBS) requires clinical parameter optimization with a time-to-optimization per patient of ~1year.
Goal(s): To build a deep-learning-based model for the prediction of optimal DBS parameters from a single functional MRI response map obtained during DBS.
Approach: Multilayer perceptron based optimal DBS parameter prediction model was trained and tested (five-fold cross-validation) using features extracted by an autoencoder model from DBS-fMRI responses.
Results: Accuracies of 79.1%, 84.5%, 81.7%, 83.3% and 70.2% (at 10% deviation from ground truth) were achieved in the prediction of voltage, frequency, and x-y-z contact locations respectively.
Impact: This study gives an initial evaluation of a prediction model for DBS parameter optimization, which has the potential to reduce the time-to-optimization per patient from ~1 year to few hours during a single clinical visit, thereby reducing patient’s financial burden.
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