Objective: A downside of Deep Brain Stimulation (DBS) for Parkinson's Disease (PD) is that cognitive function may deteriorate postoperatively. Electroencephalography (EEG) was explored as biomarker... Show moreObjective: A downside of Deep Brain Stimulation (DBS) for Parkinson's Disease (PD) is that cognitive function may deteriorate postoperatively. Electroencephalography (EEG) was explored as biomarker of cognition using a Machine Learning (ML) pipeline.Methods: A fully automated ML pipeline was applied to 112 PD patients, taking EEG time-series as input and predicted class-labels as output. The most extreme cognitive scores were selected for class differentiation, i.e. best vs. worst cognitive performance (n = 20 per group). 16,674 features were extracted per patient; feature-selection was performed using a Boruta algorithm. A random forest classifier was modelled; 10-fold cross-validation with Bayesian optimization was performed to ensure generalizability. The predicted class-probabilities of the entire cohort were compared to actual cognitive performance.Results: Both groups were differentiated with a mean accuracy of 0.92; using only occipital peak frequency yielded an accuracy of 0.67. Class-probabilities and actual cognitive performance were negatively linearly correlated (b =-0.23 (95% confidence interval (-0.29,-0.18))).Conclusions: Particularly high accuracies were achieved using a compound of automatically extracted EEG biomarkers to classify PD patients according to cognition, rather than a single spectral EEG feature.Significance: Automated EEG assessment may have utility for cognitive profiling of PD patients during the DBS screening. (c) 2021 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). Show less
Patients with Parkinson's Disease may be eligible for Deep Brain Stimulation (DBS) in case of severe motor complications. This thesis provides indications for improving patient selection for DBS,... Show morePatients with Parkinson's Disease may be eligible for Deep Brain Stimulation (DBS) in case of severe motor complications. This thesis provides indications for improving patient selection for DBS, as well as describing new biomarkers based on Electroencephalography (EEG) to aid during the DBS selection process. Show less
Objective: In Parkinson’s Disease (PD), measures of non-dopaminergic systems involvement may reflect disease severity and therefore contribute to patient-selection for Deep Brain Stimulation (DBS).... Show moreObjective: In Parkinson’s Disease (PD), measures of non-dopaminergic systems involvement may reflect disease severity and therefore contribute to patient-selection for Deep Brain Stimulation (DBS). There is currently no determinant for non-dopaminergic disease severity. In this exploratory study, we investigated whether quantitative EEG reflects non-dopaminergic disease severity in PD. Methods: Sixty-three consecutive PD patients screened for DBS were included (mean age 62.4 ± 7.2 years, 32% females). Relative spectral powers and the Phase-Lag-Index (PLI) reflecting functional connectivity were analysed on routine EEGs. Non-dopaminergic disease severity was quantified using the SENS-PD score and its subdomains; motor-severity was quantified using the MDS-UPDRS III. Results: The SENS-PD composite score correlated with a spectral ratio ((d + h)/(a1 + a2 + b) powers) (global spectral ratio Pearson’s r = 0.4, 95% Confidence Interval (95%CI) 0.1–0.6), and PLI in the a2 band (10–13 Hz) (r = 0.3, 95%CI 0.5 to 0.1). These correlations seem driven by the subdomains cognition and psychotic symptoms. MDS-UPDRS III was not significantly correlated with EEG parameters. Conclusions: EEG slowing and reduced functional connectivity in the a2 band were associated with non-dopaminergic disease severity in PD. Significance: The described EEG parameters may have complementary utility as determinants of non-dopaminergic involvement in PD. Show less
