Identification of an early-stage Parkinson's disease neuromarker using event-related potentials, brain network analytics and machine-learning.

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 funct

SUBMITTER: Hassin-Baer S 

PROVIDER: S-EPMC8741046 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature