A Deep Learning Prediction Model for Deep Brain Stimulation Optimization by fMRI

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.