Beta-band activity in the subthalamic local field potential (LFP) is correlated with Parkinson's disease (PD) symptom severity and is the therapeutic target of deep brain stimulation (DBS). While beta fluctuations in PD patients are well characterized on shorter timescales, it is not known how beta activity evolves around the diurnal cycle, outside a clinical setting. Here, we obtained chronic recordings (34 ± 13 days) of subthalamic beta power in PD patients implanted with the Percept DBS device during high-frequency DBS and analysed their diurnal properties as well as sensitivity to artifacts. Time of day explained 41 ± 9% of the variance in beta power (p < 0.001 in all patients), with increased beta during the day and reduced beta at night. Certain movements affected LFP quality, which may have contributed to diurnal patterns in some patients. Future DBS algorithms may benefit from taking such diurnal and artifactual fluctuations in beta power into account.
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http://dx.doi.org/10.1038/s41531-022-00350-7 | DOI Listing |
Sleep
January 2025
Department of Neurology, University of Colorado Anschutz Medical Campus, Aurora, CO USA.
Study Objectives: Deep brain stimulation (DBS) of the subthalamic nucleus (STN) may improve sleep dysfunction, a common non-motor symptom of Parkinson disease (PD). Improvement in motor symptoms correlates with DBS-suppressed local field potential (LFP) activity, particularly in the beta frequency (13 - 30 Hz). Although well-characterized in the short term, little is known about the innate progression of these oscillations across the sleep-wake cycle.
View Article and Find Full Text PDFJ Neurosci
January 2025
Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, 40225 Germany
Recordings from Parkinson's disease (PD) patients typically show strong beta-band oscillations (13-35Hz), which can be modulated by deep brain stimulation (DBS). While high-frequency DBS (>100Hz) ameliorates motor symptoms and reduces beta activity in basal ganglia and motor cortex, the effects of low-frequency DBS (<30Hz) are less clear. Clarifying these effects is relevant for the debate about the role of beta oscillations in motor slowing, which might be causal or epiphenomenal.
View Article and Find Full Text PDFEur J Neurosci
January 2025
Laboratory of Human Cell Neurophysiology, N.N. Semenov Federal Research Center for Chemical Physics Russian Academy of Sciences, Moscow, Russia.
Excessive beta oscillations in the subthalamic nucleus are established as a primary electrophysiological biomarker for motor impairment in Parkinson's disease and are currently used as feedback signals in adaptive deep brain stimulation systems. However, there is still a need for optimization of stimulation parameters and the identification of optimal biomarkers that can accommodate varying patient conditions, such as ON and OFF levodopa medication. The precise boundaries of 'pathological' oscillatory ranges, associated with different aspects of motor impairment, are still not fully clarified.
View Article and Find Full Text PDFJ Affect Disord
January 2025
Center for Functional Neurosurgery, Department of Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. Electronic address:
Background: Parkinson's disease (PD) is primarily characterized by motor symptoms, but patients also experience a relatively high prevalence of non-motor symptoms, including emotional and cognitive impairments. While the subthalamic nucleus (STN) is a common target for deep brain stimulation to treat motor symptoms in PD, its role in emotion processing is still under investigation. This study examines the subthalamic neural oscillatory activities during facial emotion processing and its association with affective characteristics.
View Article and Find Full Text PDFNPJ Digit Med
January 2025
Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA.
Adaptive deep brain stimulation (DBS) provides individualized therapy for people with Parkinson's disease (PWP) by adjusting the stimulation in real-time using neural signals that reflect their motor state. Current algorithms, however, utilize condensed and manually selected neural features which may result in a less robust and biased therapy. In this study, we propose Neural-to-Gait Neural network (N2GNet), a novel deep learning-based regression model capable of tracking real-time gait performance from subthalamic nucleus local field potentials (STN LFPs).
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