Abstract
ObjectiveThe 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.BackgroundCurrently, 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.MethodsNineteen 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.ResultsThe 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).ConclusionsThis 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.
Highlights
Neurodegenerative diseases are major causes of physical and cognitive dysfunction leading to declining function and quality of life in older people
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)
This pilot study found that Brain Network Analytics (BNA) can identify patients with early Parkinson’s disease (PD) using an advanced analysis of event-related potentials (ERPs)
Summary
Neurodegenerative diseases are major causes of physical and cognitive dysfunction leading to declining function and quality of life in older people. Disease-modifying therapy (DMT) is not yet available for these disorders, but when developed, it should be administered early in the neurodegenerative process to minimize irreversible accumulative brain damage, preferably during the prodromal or preclinical phases to maximize their effect [1,2,3,4]. A major barrier to DMT development is the lack of effective tools for early diagnosis and for objective monitoring of disease activity during clinical trials. In Parkinson’s disease (PD), CSF- or blood-based biomarkers have not yet been proven specific enough for clinical utility for diagnosis or longitudinal monitoring [5]. There is need for biomarkers that detect PD at an earlier stage to allow intervention and monitoring of potential disease-modifying therapies. 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
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