Prediction of Clinical Deep Brain Stimulation Target for Essential Tremor From 1.5 Tesla MRI Anatomical Landmarks - Engelhardt, Julien; Cuny, Emmanuel; Guehl, Dominique; Burbaud, Pierre; Damon-Perrière, Nathalie; Dallies-Labourdette, Camille; Thomas, Juliette; Branchard, Olivier; Schmitt, Louise-Amélie; Gassa, Narimane; Zemzemi, Nejib (Frontiers in Neurology, 2021)
Background Deep brain stimulation is an efficacious treatment for refractory essential tremor; though targeting the intra-thalamic nuclei remains challenging. Objectives We sought to develop an inverse approach to retrieve the position of the leads in a cohort of patients operated on with optimal clinical outcomes from anatomical landmarks identifiable by 1.5 tesla Magnetic Resonance Imaging. Methods The learning database included clinical outcomes and postoperative imaging from which the coordinates of the active contacts and those of anatomical landmarks were extracted. We used machine learning regression methods to build three different prediction models. External validation was performed according to a leave-one-out cross-validation. Results Fifteen patients (29 leads) were included, with a median tremor improvement of 72% on the Fahn-Tolosa-Marin scale. Kernel Ridge Regression, deep neural networks and Support Vector Regression (SVR) were used. SVR gave the best results with a mean error of 1.33 +/- 1.64 mm between the predicted target and the active contact position. Conclusion We report an original method for the targeting in Deep brain stimulation for essential tremor based on patients’ radio-anatomical features. This approach will be tested in a prospective clinical trial.