Improvement of Pyramidal Tract Side Effect Prediction Using a Data-Driven Method in Subthalamic Stimulation.

OBJECTIVE: subthalamic nucleus deep brain stimulation (STN DBS) is limited by the occurrence of a pyramidal tract side effect (PTSE) induced by electrical activation of the pyramidal tract. Predictive models are needed to assist the surgeon during the electrode trajectory preplanning. The objective of the study was to compare two methods of PTSE prediction based on clinical assessment of PTSE induced by STN DBS in patients with Parkinson's disease.

METHODS: two clinicians assessed PTSE postoperatively in 20 patients implanted for at least three months in the STN. The resulting dataset of electroclinical tests was used to evaluate two methods of PTSE prediction. The first method was based on the volume of tissue activated (VTA) modeling and the second one was a data-driven-based method named Pyramidal tract side effect Model based on Artificial Neural network (PyMAN) developed in our laboratory. This method was based on the nonlinear correlation between the PTSE current threshold and the 3-D electrode coordinates. PTSE prediction from both methods was compared using Mann-Whitney U test.

RESULTS: 1696 electroclinical tests were used to design and compare the two methods. Sensitivity, specificity, positive- and negative-predictive values were significantly higher with the PyMAN method than with the VTA-based method (P < 0.05).

CONCLUSION: the PyMAN method was more effective than the VTA-based method to predict PTSE.

SIGNIFICANCE: this data-driven tool could help the neurosurgeon in predicting adverse side effects induced by DBS during the electrode trajectory preplanning.