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Updated: Dec 8, 2025

Controlling Parkinson's Disease With Adaptive Deep Brain Stimulation
Published on: July 16, 2014
Gait-related frequency modulation of beta oscillatory activity in the subthalamic nucleus of parkinsonian patients
Andrea Canessa1, Chiara Palmisano2, Ioannis U Isaias2
1Department of Neurology, University Hospital and Julius Maximilian University, Würzburg, Germany; Department of Informatics, Bioengineering, Robotics and System Engineering, University of Genoa, Genoa, Italy.
Background:
Abnormal beta band activity in the subthalamic nucleus (STN) is known to be exaggerated in patients with Parkinson's disease, and the amplitude of such activity has been associated with akinetic rigid symptoms. New devices for deep brain stimulation (DBS) that operate by adapting the stimulation parameters generally rely on the detection of beta activity amplitude modulations in these patients. Movement-related frequency modulation of beta oscillatory activity has been poorly investigated, despite being an attractive variable for extracting information about basal ganglia activity.
Objective:
We studied the STN oscillatory activity associated with locomotion and proposed a new approach to extract movement related information from beta band activity.
Methods:
We recorded bilateral local field potential of the STN in eight parkinsonian patients implanted with DBS electrodes during upright quiet standing and unperturbed walking. Neurophysiological recordings were combined with kinematic measurements and individual molecular brain imaging studies. We then determined the information carried by the STN oscillatory activity about locomotion and we identified task-specific biomarkers.
Results:
We found a gait-related peak frequency modulation of the beta band of STN recordings of parkinsonian patients. This novel biomarker and the associated power modulations were highly informative to detect the walking state (with respect to standing) in each single patient.
Conclusion:
Frequency modulation in the human STN represents a fundamental aspect of information processing of locomotion. Our information-driven approach could significantly enrich the spectrum of Parkinson's neural markers, with input signals encoding ongoing tasks execution for an appropriate online tuning of DBS delivery.
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