Category: Parkinson's Disease: Neurophysiology
Objective: To develop an automated Machine Learning model based on preoperative electroencephalography data to predict cognitive deterioration one year after subthalamic Deep Brain Stimulation (STN DBS) in Parkinson’s Disease (PD) patients.
Background: STN DBS may relieve refractory motor complications in PD patients. Despite careful screening, it remains difficult to determine severity of alpha-synucleopathy involvement which influences the risk of post-operative complications including cognitive deterioration. Quantitative EEG reflects cognitive dysfunction in PD and may provide biomarkers of postoperative cognitive decline.
Method: Sixty DBS candidates were included; 42 patients had available preoperative EEGs to compute a fully automated Machine Learning model. Movement Disorders Society criteria classified patients as cognitively stable or deteriorated at one-year follow-up. 16674 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: Twenty-five patients were classified as cognitively stable, 17 patients demonstrated cognitive decline. The model differentiated classes with a mean (SD) accuracy of 0.88 (0.05), with a positive predicted value of 91.4% (95%CI 82.9, 95.9) and negative predicted 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.
Conclusion: 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.
To cite this abstract in AMA style:
V. Geraedts, M. Koch, R. Kuiper, M. Kefalas, T. Bäck, J. van Hilten, H. Wang, H. Middelkoop, N. vd Gaag, MF. Contarino, M. Tannemaat. Preoperative EEG-based Machine Learning predicts cognitive deterioration after STN DBS in Parkinson’s Disease patients [abstract]. Mov Disord. 2021; 36 (suppl 1). https://www.mdsabstracts.org/abstract/preoperative-eeg-based-machine-learning-predicts-cognitive-deterioration-after-stn-dbs-in-parkinsons-disease-patients/. Accessed June 15, 2025.« Back to MDS Virtual Congress 2021
MDS Abstracts - https://www.mdsabstracts.org/abstract/preoperative-eeg-based-machine-learning-predicts-cognitive-deterioration-after-stn-dbs-in-parkinsons-disease-patients/