Dataset Information

Preoperative Electroencephalography-Based Machine Learning Predicts Cognitive Deterioration After Subthalamic Deep Brain Stimulation.

ABSTRACT:

Background

Subthalamic 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.

Objective

To develop an automated machine learning model based on preoperative EEG data to predict cognitive deterioration 1 year after STN DBS.

Methods

Sixty 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.

Results

Tweny-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.

Conclusions

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. © 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

SUBMITTER: Geraedts VJ

PROVIDER: S-EPMC8596544 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Motor learning is defined as an improvement in performance through practice. The ability to learn new motor skills may be particularly challenged in patients with Parkinson's disease, in whom motor execution is impaired by the disease-defining motor symptoms such as bradykinesia. Subthalamic deep brain stimulation is an effective treatment in advanced Parkinson's disease, and its beneficial effects on Parkinsonian motor symptoms and motor execution have been widely demonstrated. Much less is known about whether deep brain stimulation directly interacts with motor learning independent of modulation of motor execution. We investigated motor sequence learning in 19 patients with Parkinson's disease treated with subthalamic deep brain stimulation and 19 age-matched controls. In a cross-over design, patients performed an initial motor sequence training session with active and inactive stimulation, respectively (experiments separated by ≥14 days). Performance was retested after 5 min and after a 6 h consolidation interval with active stimulation. Healthy controls performed a similar experiment once. We further investigated neural correlates underlying stimulation-related effects on motor learning by exploring the association of normative subthalamic deep brain stimulation functional connectivity profiles with stimulation-related differences in performance gains during training. Pausing deep brain stimulation during initial training resulted in the inhibition of performance gains that could have been indicative of learning at the behavioural level. Task performance improved significantly during training with active deep brain stimulation, but did not reach the level of learning dynamics of healthy controls. Importantly, task performance after the 6 h consolidation interval was similar across patients with Parkinson's disease independent of whether the initial training session had been performed with active or inactive deep brain stimulation. This indicates that early learning and subsequent consolidation were relatively intact despite severe impairments of motor execution during training with inactive deep brain stimulation. Normative connectivity analyses revealed plausible and significant connectivity of volumes of tissue activated by deep brain stimulation with several cortical areas. However, no specific connectivity profiles were associated with stimulation-dependent differences in learning during initial training. Our results show that motor learning in Parkinson's disease is independent of modulation of motor execution by subthalamic deep brain stimulation. This indicates an important role of the subthalamic nucleus in regulating general motor execution, whereas its role in motor learning appears negligible. Because longer-term outcomes were independent of performance gains during initial training, patients with Parkinson's disease may not need to wait for an optimal motor state to practice new motor skills.

Project description:Deep brain stimulation therapy is an effective symptomatic treatment for Parkinson's disease, yet the precise mechanisms responsible for its therapeutic effects remain unclear. Although the targets of deep brain stimulation are grey matter structures, axonal modulation is known to play an important role in deep brain stimulation's therapeutic mechanism. Several white matter structures in proximity to the subthalamic nucleus have been implicated in the clinical benefits of deep brain stimulation for Parkinson's disease. We assessed the connectivity patterns that characterize clinically beneficial electrodes in Parkinson's disease patients, after deep brain stimulation of the subthalamic nucleus. We evaluated 22 patients with Parkinson's disease (11 females, age 57 ± 9.1 years, disease duration 13.3 ± 6.3 years) who received bilateral deep brain stimulation of the subthalamic nucleus at the National Institutes of Health. During an initial electrode screening session, one month after deep brain stimulation implantation, the clinical benefits of each contact were determined. The electrode was localized by coregistering preoperative magnetic resonance imaging and postoperative computer tomography images and the volume of tissue activated was estimated from stimulation voltage and impedance. Brain connectivity for the volume of tissue activated of deep brain stimulation contacts was assessed using probabilistic tractography with diffusion-tensor data. Areas most frequently connected to clinically effective contacts included the thalamus, substantia nigra, brainstem and superior frontal gyrus. A series of discriminant analyses demonstrated that the strength of connectivity to the superior frontal gyrus and the thalamus were positively associated with clinical effectiveness. The connectivity patterns observed in our study suggest that the modulation of white matter tracts directed to the superior frontal gyrus and the thalamus is associated with favourable clinical outcomes and may contribute to the therapeutic effects of deep brain stimulation. Our method can be further developed to reliably identify effective deep brain stimulation contacts and aid in the programming process.