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.
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