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Cognitive Task Performance During Titration Predicts Deep Brain Stimulation Treatment Efficacy: Evidence From a Case Study.

ABSTRACT: Device titration is a major challenge when using deep brain stimulation (DBS) to treat behavioral disorders. Unlike in movement disorders, there is no reliable real-time clinical feedback for changes in complex behaviors resulting from DBS. Here, a female patient receiving DBS of the nucleus accumbens for the treatment of morbid obesity underwent cognitive testing via the flanker task alongside traditional methods of device titration. One set of stimulation parameters administered during titration resulted in acute cognitive improvement (p = 0.033) and increased frontal engagement as measured by electroencephalography (left anterior: p = 0.007, right anterior: p = 0.005) relative to DBS-OFF. The same parameters resulted in the most weight-loss during long-term continuous stimulation (47.8 lbs lost in 129 days) compared to the results of other stimulation settings. Diffusion tensor imaging analyses showed increased connectivity to dorsal attention networks and decreased connectivity to the default mode network for optimal parameters (p < 0.01). Our results provide evidence that targeted cognitive testing is a potentially useful tool for capturing acute effects of DBS stimulation during titration and predicting long-term treatment outcomes. Clinical Trial Registration:www.ClinicalTrials.gov, identifier: NCT01512134.

SUBMITTER: Weichart ER

PROVIDER: S-EPMC7043267 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Cognitive Task Performance During Titration Predicts Deep Brain Stimulation Treatment Efficacy: Evidence From a Case Study.

Weichart Emily R ER Sederberg Per B PB Sammartino Francesco F Krishna Vibhor V Corrigan John D JD Rezai Ali R AR

Frontiers in psychiatry 20200219

Device titration is a major challenge when using deep brain stimulation (DBS) to treat behavioral disorders. Unlike in movement disorders, there is no reliable real-time clinical feedback for changes in complex behaviors resulting from DBS. Here, a female patient receiving DBS of the nucleus accumbens for the treatment of morbid obesity underwent cognitive testing <i>via</i> the flanker task alongside traditional methods of device titration. One set of stimulation parameters administered during ...[more]

PMID: 32140113

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Project description: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.

Dataset Information

Cognitive Task Performance During Titration Predicts Deep Brain Stimulation Treatment Efficacy: Evidence From a Case Study.

Publications

Cognitive Task Performance During Titration Predicts Deep Brain Stimulation Treatment Efficacy: Evidence From a Case Study.

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