ArticleMovement disorders : official journal of the Movement Disorder Society2021
Preoperative Electroencephalography-Based Machine Learning Predicts Cognitive Deterioration After Subthalamic Deep Brain Stimulation.
Article in Movement disorders : official journal of the Movement Disorder Society, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 3 of them syntheses that pooled it.
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Who cites it
14 citing papers in PubMed, 3 syntheses or guidelines pooled it, 19 citations in OpenAlex.
- Houston, We Have AI Problem! Quality Issues with Neuroimaging-Based Artificial Intelligence in Parkinson's Disease: A Systematic Review.Movement disorders : official journal of the Movement Disorder Society · 2024Pooled it
- Machine learning for the detection and diagnosis of cognitive impairment in Parkinson's Disease: A systematic review.PloS one · 2024Pooled it
- Prediction models for postoperative cognitive dysfunction in adults: a systematic review of methodological quality and clinical applicability.Frontiers in neurologyPooled it
- Computational Insights Into Smart Bioelectronics in Digital Health Care (2020-2024): Topic Modeling Study.JMIR medical informatics · 2026Review
- Subthalamic stimulation modulates working memory-related cortical dynamics in Parkinson's disease.Brain communications · 2026Article
- Development and application of a multi-task fusion model using susceptibility-weighted imaging-based substantia nigra and adjacent structures for prognostic prediction of subthalamic nucleus deep brain stimulation in Parkinson's disease.Brain communications · 2026Article
- Article
- Bibliometric analysis of electroencephalogram research in Parkinson's disease from 2004 to 2023.Frontiers in neuroscience · 2024Article
- Postoperative cognitive dysfunction in elderly postcardiac surgery patients: progress in rehabilitation application research.Frontiers in rehabilitation sciences · 2024Review
- Machine learning for adaptive deep brain stimulation in Parkinson's disease: closing the loop.Journal of neurology · 2023Review
- Machine learning-based prediction of longitudinal cognitive decline in early Parkinson's disease using multimodal features.Scientific reports · 2023Article
- The effect of deep brain stimulation in Parkinson's disease reflected in EEG microstates.NPJ Parkinson's disease · 2023Article
- Predicting Motor Outcome and Quality of Life After Subthalamic Deep Brain Stimulation for Parkinson's Disease: The Role of Standard Screening Measures and Wearable-Data.Journal of Parkinson's disease · 2023Article
- Bibliometric Analysis of Quantitative Electroencephalogram Research in Neuropsychiatric Disorders From 2000 to 2021.Frontiers in psychiatry · 2022Article
Corrections and comments
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Authors and funding
11 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSubthalamic 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.
objectiveTo develop an automated machine learning model based on preoperative EEG data to predict cognitive deterioration 1 year after STN DBS.
methodsSixty 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.
resultsTweny-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.
conclusionsPreoperative 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.
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