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Identification of an early-stage Parkinson’s disease neuromarker using event-related potentials, brain network analytics and machine-learning
- Source :
- PLoS ONE, PLoS ONE, Vol 17, Iss 1, p e0261947 (2022), PLoS ONE, Vol 17, Iss 1 (2022)
- Publication Year :
- 2022
- Publisher :
- Public Library of Science, 2022.
-
Abstract
- Objective The purpose of this study is to explore the possibility of developing a biomarker that can discriminate early-stage Parkinson’s disease from healthy brain function using electroencephalography (EEG) event-related potentials (ERPs) in combination with Brain Network Analytics (BNA) technology and machine learning (ML) algorithms. Background Currently, diagnosis of PD depends mainly on motor signs and symptoms. However, there is need for biomarkers that detect PD at an earlier stage to allow intervention and monitoring of potential disease-modifying therapies. Cognitive impairment may appear before motor symptoms, and it tends to worsen with disease progression. While ERPs obtained during cognitive tasks performance represent processing stages of cognitive brain functions, they have not yet been established as sensitive or specific markers for early-stage PD. Methods Nineteen PD patients (disease duration of ≤2 years) and 30 healthy controls (HC) underwent EEG recording while performing visual Go/No-Go and auditory Oddball cognitive tasks. ERPs were analyzed by the BNA technology, and a ML algorithm identified a combination of features that distinguish early PD from HC. We used a logistic regression classifier with a 10-fold cross-validation. Results The ML algorithm identified a neuromarker comprising 15 BNA features that discriminated early PD patients from HC. The area-under-the-curve of the receiver-operating characteristic curve was 0.79. Sensitivity and specificity were 0.74 and 0.73, respectively. The five most important features could be classified into three cognitive functions: early sensory processing (P50 amplitude, N100 latency), filtering of information (P200 amplitude and topographic similarity), and response-locked activity (P-200 topographic similarity preceding the motor response in the visual Go/No-Go task). Conclusions This pilot study found that BNA can identify patients with early PD using an advanced analysis of ERPs. These results need to be validated in a larger PD patient sample and assessed for people with premotor phase of PD.
- Subjects :
- Male
Computer and Information Sciences
Neural Networks
Physiology
Imaging Techniques
Science
Cognitive Neuroscience
Event-Related Potentials
Neurophysiology
Social Sciences
Neuroimaging
Research and Analysis Methods
Machine Learning
Medical Conditions
Cognition
Medicine and Health Sciences
Psychology
Humans
Evoked Potentials
Aged
Clinical Neurophysiology
Cognitive Impairment
Brain Mapping
Brain Diseases
Multidisciplinary
Movement Disorders
Cognitive Neurology
Electrophysiological Techniques
Cognitive Psychology
Biology and Life Sciences
Brain
Neurodegenerative Diseases
Parkinson Disease
Electroencephalography
Middle Aged
Electrophysiology
Bioassays and Physiological Analysis
Neurology
Brain Electrophysiology
Medicine
Cognitive Science
Perception
Sensory Perception
Female
Clinical Medicine
Research Article
Neuroscience
Subjects
Details
- Language :
- English
- ISSN :
- 19326203
- Volume :
- 17
- Issue :
- 1
- Database :
- OpenAIRE
- Journal :
- PLoS ONE
- Accession number :
- edsair.doi.dedup.....671fd07067d7500a107c512c98d478fe