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Updated: Sep 7, 2025

Controlling Parkinson's Disease With Adaptive Deep Brain Stimulation
Published on: July 16, 2014
Subthalamic low beta bursts differ in Parkinson's disease phenotypes
Arnaldo Fim Neto1, Julia Baldi de Luccas2, Bruno Leonardo Bianqueti2
1Center for Engineering, Modeling and Applied Social Sciences, Federal University of ABC, São Bernardo do Campo, Brazil; Brazilian Institute of Neuroscience and Neurotechnology, Campinas, São Paulo, Brazil; Department of Cosmic Rays and Chronology, Institute of Physics, University of Campinas, Campinas, Brazil.
Objective:
Parkinson's disease (PD) patients may be categorized into tremor-dominant (TD) and postural-instability and gait disorder (PIGD) motor phenotypes, but the dynamical aspects of subthalamic nucleus local field potentials (STN-LFP) and the neural correlates of this phenotypical classification remain unclear.
Methods:
35 STN-LFP (20 PIGD and 15 TD) were investigated through continuous wavelet transform and machine-learning-based methods. The beta oscillation - the main band associated with motor impairment in PD - dynamics was characterized through beta burst parameters across phenotypes and burst intervals under specific proposed criteria for optimal burst threshold definition.
Results:
Low-frequency (13-22 Hz) beta burst probability was the best predictor for PD phenotypes (75% accuracy). PIGD patients presented higher average burst duration (p = 0.018), while TD patients exhibited higher burst probability (p = 0.014). Categorization into shorter and longer than 400 ms bursts led to significant interaction between burst length categories and the phenotypes (p < 0.050) as revealed by mixed-effects models. Long burst durations and short bursts probability positively correlated, respectively, with rigidity-bradykinesia (p = 0.029) and tremor (p = 0.038) scores.
Conclusions:
Subthalamic low-frequency beta bursts differed between TD and PIGD phenotypes and correlated with motor symptoms.
Significance:
These findings improve the PD phenotypes' electrophysiological characterization and may define new criteria for adaptive deep brain stimulation.
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