A novel method for calculating beta band burst durations in Parkinson's disease using a physiological baseline.

J Neurosci Methods

Stanford University School of Medicine, Department of Neurology and Neurological Sciences, Stanford, CA, USA; Stanford University School of Medicine, Department of Neurosurgery, Stanford, CA, USA. Electronic address:

Published: September 2020

Background: Pathologically prolonged bursts of neural activity in the 8-30 Hz frequency range in Parkinson's disease have been measured using high power event detector thresholds.

New Method: This study introduces a novel method for determining beta bursts using a power baseline based on spectral activity that overlapped a simulated 1/f spectrum. We used resting state local field potentials from people with Parkinson's disease and a simulated 1/f signal to measure beta burst durations, to demonstrate how tuning parameters (i.e., bandwidth and center frequency) affect burst durations, to compare burst duration distributions with high power threshold methods, and to study the effect of increasing neurostimulation intensities on burst duration.

Results: The baseline method captured a broad distribution of resting state beta band burst durations. Mean beta band burst durations were significantly shorter on compared to off neurostimulation (p = 0.0046), and their distribution shifted towards that of the 1/f spectrum during increasing intensities of stimulation.

Comparison With Existing Methods: High power event detection methods, measure duration of higher power bursts and omit portions of the neural signal. The baseline method captured the broadest distribution of burst durations and was more sensitive than high power detection methods in demonstrating the effect of neurostimulation on beta burst duration.

Conclusions: The baseline method captured a broad range of fluctuations in beta band neural activity and demonstrated that subthalamic neurostimulation shortened burst durations in a dose (intensity) dependent manner, suggesting that beta burst duration is a useful control variable for closed loop algorithms.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7437353PMC
http://dx.doi.org/10.1016/j.jneumeth.2020.108811DOI Listing

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