title: Image-guided preoperative prediction of pyramidal tract side effect in deep brain stimulation: proof of concept and application to the pyramidal tract side effect induced by pallidal stimulation.
abstract: Deep brain stimulation of the medial globus pallidus (GPm) is a surgical procedure for treating patients suffering from Parkinson's disease. Its therapeutic effect may be limited by the presence of pyramidal tract side effect (PTSE). PTSE is a contraction time-locked to the stimulation when the current spreading reaches the motor fibers of the pyramidal tract within the internal capsule. The objective of the study was to propose a preoperative predictive model of PTSE. A machine learning-based method called PyMAN (PTSE model based on artificial neural network) accounting for the current used in stimulation, the three-dimensional electrode coordinates and the angle of the trajectory, was designed to predict the occurrence of PTSE. Ten patients implanted in the GPm have been tested by a clinician to create a labeled dataset of the stimulation parameters that trigger PTSE. The kappa index value between the data predicted by PyMAN and the labeled data was 0.78. Further evaluation studies are desirable to confirm whether PyMAN could be a reliable tool for assisting the surgeon to prevent PTSE during the preoperative planning.
authors.affiliation: French Institute of Health and Medical Research, UMR 1099, 2 avenue du Pr. Léon Bernard, Rennes Cedex 35043, France; University of Rennes 1, Treatment of Signal and Imaging Laboratory, 2 avenue du Pr. Léon Bernard, Rennes Cedex 35043, France.
authors.forename: Clement
authors.initials: C
authors.lastname: Baumgarten
authors.affiliation: French Institute of Health and Medical Research, UMR 1099, 2 avenue du Pr. Léon Bernard, Rennes Cedex 35043, France; University of Rennes 1, Treatment of Signal and Imaging Laboratory, 2 avenue du Pr. Léon Bernard, Rennes Cedex 35043, France.
authors.forename: Yulong
authors.initials: Y
authors.lastname: Zhao
authors.affiliation: Rennes University Hospital , Department of Neurology, 2 rue Henri Le Guilloux, 35033 Rennes Cedex 9, France.
authors.forename: Paul
authors.initials: P
authors.lastname: Sauleau
authors.affiliation: Rennes University Hospital , Department of Neurology, 2 rue Henri Le Guilloux, 35033 Rennes Cedex 9, France.
authors.forename: Cecile
authors.initials: C
authors.lastname: Malrain
authors.affiliation: French Institute of Health and Medical Research, UMR 1099, 2 avenue du Pr. Léon Bernard, Rennes Cedex 35043, France; University of Rennes 1, Treatment of Signal and Imaging Laboratory, 2 avenue du Pr. Léon Bernard, Rennes Cedex 35043, France.
authors.forename: Pierre
authors.initials: P
authors.lastname: Jannin
authors.affiliation: French Institute of Health and Medical Research, UMR 1099, 2 avenue du Pr. Léon Bernard, Rennes Cedex 35043, France; University of Rennes 1, Treatment of Signal and Imaging Laboratory, 2 avenue du Pr. Léon Bernard, Rennes Cedex 35043, France; Rennes University Hospital, Department of Neurosurgery, 2 rue Henri Le Guilloux, 35033 Rennes Cedex 9, France.
authors.forename: Claire
authors.initials: C
authors.lastname: Haegelen
keywords: artificial neural network; computational modeling; computer-assisted surgery; deep brain stimulation; parkinson’s disease; patient-specific model; pyramidal tract side effect