Intraoperative vs. postoperative side-effects-thresholds during pallidal and thalamic DBS

Background: It is currently unknown whether results from intraoperative test stimulation of two types of Deep Brain Stimulation (DBS), either during awake pallidal (GPi) or thalamic (Vim), are comparable to the results generated by chronic stimulation through the definitive lead.Objective: To determine whether side-effects-thresholds from intraoperative test stimulation are indicative of postoperative stimulation findings.Methods: Records of consecutive patients who received GPi or Vim were analyzed. Thresholds for the induction of either capsular or non-capsular side-effects were compared at matched depths and at group-level.Results: Records of fifty-two patients were analyzed (20 GPis, 75 Vims). The induction of side-effects was not significantly different between intraoperative and postoperative assessments at matched depths, although a large variability was observed (capsular: GPi DBS: p = 0.79; Vim DBS: p = 0.68); non-capsular: GPi DBS: p = 0.20; and Vim DBS: p = 0.35). Linear mixed-effect models revealed no differences between intraoperative and postoperative assessments, although the Vim had significantly lower thresholds (capsular side-effects p = 0.01, non-capsular side-effects p < 0.01). Unpaired survival analyses demonstrated lower intraoperative than postoperative thresholds for capsular side-effects in patients under GPi DBS (p = 0.01), while higher intraoperative thresholds for non-capsular side-effects in patients under Vim DBS (p = 0.01).Conclusion: There were no significant differences between intraoperative and postoperative assessments of GPi and Vim DBS, although thresholds cannot be directly extrapolated at an individual level due to high variability.Scientific Assessment and Innovation in Neurosurgical Treatment Strategie

Machine learning for automated EEG-based biomarkers of cognitive impairment during Deep Brain Stimulation screening in patients with Parkinson's Disease

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 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/).Neurological Motor Disorder

Machine learning for automated EEG-based biomarkers of cognitive impairment during Deep Brain Stimulation screening in patients with Parkinson’s Disease

Algorithms and the Foundations of Software technolog

Distinguishing normal, neuropathic and myopathic EMG with an automated machine learning approach

ObjectiveDistinguishing normal, neuropathic and myopathic electromyography (EMG) traces can be challenging. We aimed to create an automated time series classification algorithm.MethodsEMGs of healthy controls (HC, n = 25), patients with amyotrophic lateral sclerosis (ALS, n = 20) and inclusion body myositis (IBM, n = 20), were retrospectively selected based on longitudinal clinical follow-up data (ALS and HC) or muscle biopsy (IBM). A machine learning pipeline was applied based on 5-second EMG fragments of each muscle. Diagnostic yield expressed as area under the curve (AUC) of a receiver-operator characteristics curve, accuracy, sensitivity, and specificity were determined per muscle (muscle-level) and per patient (patient-level).ResultsDiagnostic yield of the classification ALS vs. HC was: AUC 0.834 ± 0.014 at muscle-level and 0.856 ± 0.009 at patient-level. For the classification HC vs. IBM, AUC was 0.744 ± 0.043 at muscle-level and 0.735 ± 0.029 at patient-level. For the classification ALS vs. IBM, AUC was 0.569 ± 0.024 at muscle-level and 0.689 ± 0.035 at patient-level.ConclusionsAn automated time series classification algorithm can distinguish EMGs from healthy individuals from those of patients with ALS with a high diagnostic yield. Using longer EMG fragments with different levels of muscle activation may improve performance.Neurological Motor Disorder

Distinguishing normal, neuropathic and myopathic EMG with an automated machine learning approach

OBJECTIVE\nMETHODS\nRESULTS\nCONCLUSIONS\nSIGNIFICANCE\nDistinguishing normal, neuropathic and myopathic electromyography (EMG) traces can be challenging. We aimed to create an automated time series classification algorithm.\nEMGs of healthy controls (HC, n = 25), patients with amyotrophic lateral sclerosis (ALS, n = 20) and inclusion body myositis (IBM, n = 20), were retrospectively selected based on longitudinal clinical follow-up data (ALS and HC) or muscle biopsy (IBM). A machine learning pipeline was applied based on 5-second EMG fragments of each muscle. Diagnostic yield expressed as area under the curve (AUC) of a receiver-operator characteristics curve, accuracy, sensitivity, and specificity were determined per muscle (muscle-level) and per patient (patient-level).\nDiagnostic yield of the classification ALS vs. HC was: AUC 0.834 ± 0.014 at muscle-level and 0.856 ± 0.009 at patient-level. For the classification HC vs. IBM, AUC was 0.744 ± 0.043 at muscle-level and 0.735 ± 0.029 at patient-level. For the classification ALS vs. IBM, AUC was 0.569 ± 0.024 at muscle-level and 0.689 ± 0.035 at patient-level.\nAn automated time series classification algorithm can distinguish EMGs from healthy individuals from those of patients with ALS with a high diagnostic yield. Using longer EMG fragments with different levels of muscle activation may improve performance.\nIn the future, machine learning algorithms may help improve the diagnostic accuracy of EMG examinations.Algorithms and the Foundations of Software technolog

Preoperative electroencephalography‐based machine learning predicts cognitive deterioration after subthalamic deepbrain stimulation

Algorithms and the Foundations of Software technolog

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

Background:Standardized screening for subthalamic deep brain stimulation (STN DBS) in Parkinson’s disease (PD) patients is crucial to determine eligibility, but its utility to predict postoperative outcomes in eligible patients is inconclusive. It is unknown whether wearable data can contribute to this aim.Objective:To evaluate the utility of universal components incorporated in the DBS screening, complemented by a wearable sensor, to predict motor outcomes and Quality of life (QoL) one year after STN DBS surgery.Methods:Consecutive patients were included in the OPTIMIST cohort study from two DBS centers. Standardized assessments included a preoperative Levodopa Challenge Test (LCT), and questionnaires on QoL and non-motor symptoms including cognition, psychiatric symptoms, impulsiveness, autonomic symptoms, and sleeping problems. Moreover, an ambulatory wearable sensor (Parkinson Kinetigraph (PKG)) was used. Postoperative assessments were similar and also included a Stimulation Challenge Test to determine DBS effects on motor function.Results:Eighty-three patients were included (median (interquartile range) age 63 (56–68) years, 36% female). Med-OFF (Stim-OFF) motor severity deteriorated indicating disease progression, but patients significantly improved in terms of Med-ON (Stim-ON) motor function, motor fluctuations, QoL, and most non-motor domains. Motor outcomes were not predicted by preoperative tests, including covariates of either LCT or PKG. Postoperative QoL was predicted by better preoperative QoL, lower age, and more preoperative impulsiveness scores in multivariate models.Conclusion:Data from the DBS screening including wearable data do not predict postoperative motor outcome at one year. Post-DBS QoL appears primarily driven by non-motor symptoms, rather than by motor improvement.Scientific Assessment and Innovation in Neurosurgical Treatment Strategie

Right on track: Towards improving DBS patient selection and care

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, as well as describing new biomarkers based on Electroencephalography (EEG) to aid during the DBS selection process. LUMC / Geneeskund

Unravelling the Parkinson's disease network: Taking the connectome beyond the brain

Neurological Motor Disorder

What predicts quality of life after subthalamic deep brain stimulation in Parkinson's disease? A systematic review

Neurological Motor Disorder