EEG data could offer a non-invasive biomarker for Parkinson’s

EEG data could offer a non-invasive biomarker for Parkinson’s

Nicko Jackson et al have successfully demonstrated a non-invasive method to detect Parkinson’s disease (PD) using electroencephalography (EEG) to measure waveform shape of beta waves. The researchers unveiled the features of beta waveform shape that could help distinguish PD patients on and off medication. Neural activity in the beta frequency range (13-30 Hz) is excessively synchronized in Parkinson’s Disease (PD). The study evaluated non-invasive recordings in a dataset of 15 PD patients with resting scalp EEG. Specifically, beta oscillations over sensorimotor electrodes in PD patients off medication showed greater sharpness asymmetry and steepness asymmetry than on medication. The researchers showed that beta oscillations over sensorimotor cortex most often had a canonical shape, and that using this prototypical shape as an inclusion-criteria increased the effect size of the findings. The change in waveform shape reflected hypersynchronous input, possibly originating from basal ganglia. The findings suggest novel ways of measuring beta synchrony that incorporates waveform shape that could improve detection of PD pathophysiology in non-invasive recordings. Thus, waveform shape may be considered as a non-invasive electrophysiology biomarker of PD state with potential utility for assessing treatments, monitoring disease, or diagnosis.

Source: eNeuro 20 May 2019, ENEURO.0151-19.2019; DOI: https://doi.org/10.1523/ENEURO.0151-19.2019

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