Preoperative Electroencephalography‐Based Machine Learning Predicts Cognitive Deterioration After Subthalamic Deep Brain Stimulation

• Published in: Movement disorders Vol. 36; no. 10; pp. 2324 - 2334
• Main Authors: Geraedts, Victor J., Koch, Milan, Kuiper, Roy, Kefalas, Marios, Bäck, Thomas H.W., Hilten, Jacobus J., Wang, Hao, Middelkoop, Huub A.M., Gaag, Niels A., Contarino, Maria Fiorella, Tannemaat, Martijn R.
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.10.2021; Wiley Subscription Services, Inc
• Subjects: Automation; Bayesian analysis; Biomarkers; cognition; Cognitive ability; Deep brain stimulation; EEG; Electroencephalography; Learning algorithms; Machine learning; Movement disorders; Neurodegenerative diseases; Parkinson's disease; prediction; quantitative EEG; Regular Issue; Solitary tract nucleus; subthalamic nucleus
• ISSN: 0885-3185; 1531-8257
• DOI: 10.1002/mds.28661

ABSTRACT Background Subthalamic deep brain stimulation (STN DBS) may relieve refractory motor complications in Parkinson's disease (PD) patients. Despite careful screening, it remains difficult to determine severity of alpha‐synucleinopathy involvement which influences the risk of postoperative complications including cognitive deterioration. Quantitative electroencephalography (qEEG) reflects cognitive dysfunction in PD and may provide biomarkers of postoperative cognitive decline. Objective To develop an automated machine learning model based on preoperative EEG data to predict cognitive deterioration 1 year after STN DBS. Methods Sixty DBS candidates were included; 42 patients had available preoperative EEGs to compute a fully automated machine learning model. Movement Disorder Society criteria classified patients as cognitively stable or deteriorated at 1‐year follow‐up. A total of 16,674 EEG‐features were extracted per patient; a Boruta algorithm selected EEG‐features to reflect representative neurophysiological signatures for each class. A random forest classifier with 10‐fold cross‐validation with Bayesian optimization provided class‐differentiation. Results Tweny‐five patients were classified as cognitively stable and 17 patients demonstrated cognitive decline. The model differentiated classes with a mean (SD) accuracy of 0.88 (0.05), with a positive predictive value of 91.4% (95% CI 82.9, 95.9) and negative predictive value of 85.0% (95% CI 81.9, 91.4). Predicted probabilities between classes were highly differential (hazard ratio 11.14 [95% CI 7.25, 17.12]); the risk of cognitive decline in patients with high probabilities of being prognosticated as cognitively stable (>0.5) was very limited. Conclusions Preoperative EEGs can predict cognitive deterioration after STN DBS with high accuracy. Cortical neurophysiological alterations may indicate future cognitive decline and can be used as biomarkers during the DBS screening. © 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society