Developing Predictor Models of Postoperative Verbal Fluency After Deep Brain Stimulation Using Preoperative Neuropsychological Assessment

BACKGROUND:Deep brain stimulation (DBS) for Parkinson disease provides significant improvement of motor symptoms but can also produce neurocognitive side effects. A decline in verbal fluency (VF) is among the most frequently reported side effects. Preoperative factors that could predict VF decline have yet to be identified.OBJECTIVE:To develop predictive models of DBS postoperative VF decline using a machine learning approach.METHODS:We used a prospective database of patients who underwent neuropsychological and VF assessment before both subthalamic nucleus (n = 47, bilateral = 44) and globus pallidus interna (n = 43, bilateral = 39) DBS. We used a neurobehavioral rating profile as features for modeling postoperative VF. We constructed separate models for action, semantic, and letter VF. We used a leave-one-out scheme to test the accuracy of the predictive models using median absolute error and correlation with actual postoperative scores.RESULTS:The predictive models were able to predict the 3 types of VF with high accuracy ranging from a median absolute error of 0.92 to 1.36. Across all three models, higher preoperative fluency, digit span, education, and Mini-Mental State Examination were predictive of higher postoperative fluency scores. By contrast, higher frontal system deficits, age, Questionnaire for Impulsive-Compulsive Disorders in Parkinson's disease scored by the patient, disease duration, and Behavioral Inhibition/Behavioral Activation Scale scores were predictive of lower postoperative fluency scores.CONCLUSION:Postoperative VF can be accurately predicted using preoperative neurobehavioral rating scores above and beyond preoperative VF score and relies on performance over different aspects of executive function.

Automated summary

The study aimed to develop predictive models of postoperative VF decline in DBS using a machine learning approach.