# Gamma oscillations in patients with Parkinson’s Disease using chronic invasive brain recording: detection, entrainment, and functional relevance

> **NIH NIH F31** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2024 · $6,023

## Abstract

PROJECT SUMMARY
As Parkinson’s disease (PD) progresses, patients develop slowness of movement (bradykinesia), tremor and
rigidity. The first-line treatment for PD is the dopamine precursor levodopa. However, long-term use can lead to
excessive movement (dyskinesia). To address levodopa-induced dyskinesia, medication dose is adjusted, and
deep brain stimulation (DBS) of the basal ganglia is utilized. Without sufficiently lowering the medication dose,
DBS can exacerbate dyskinesia. However, the mechanisms with which DBS causes dyskinesia, and how they
are modulated by the dopaminergic cycle, are not fully understood. Excessive gamma band (65-90Hz)
synchronization in the motor network correlates with dyskinesia in preliminary human studies. This signal has
the potential to predict the onset of a dyskinetic episode when identified across a complete set of spectral
features with continuous measures of motor fluctuations. During therapeutic DBS of either the subthalamic
nucleus (STN) or globus pallidus (GP), the gamma oscillation is often entrained (amplified and modulated) to
one-half the stimulation frequency. However, the clinical relevance of entrainment, and how it may interact with
across stimulation settings, has not yet been investigated. We predict that stimulation-induced 1:2 entrainment
of cortical oscillations (oscillations detected at one-half the stimulation frequency) will therapeutically increase
movement, while simultaneously decreasing excessive/pathological movement, otherwise known as
dyskinesia. The goals of this study are to use multisite, chronically implanted neurostimulators and continuous
wrist-wearable sensors to identify correlates of dyskinesia across a complete set of spectral features and to
assess the possible clinical benefit, as it relates to dyskinesia scores measured by the wearable sensors, of
stimulation-induced entrainment of these spectral features. First, we will characterize correlates of dyskinesia
using hundreds of hours of chronic neural recordings collected in a naturalistic environment across typical
dopaminergic cycles with objective dyskinesia monitoring using wearables prior to the initialization of
stimulation. The majority of these patients experience dyskinetic motor signs while taking their clinically
recommended medications. Then, we will determine the most predictive features of dyskinesia using machine
learning models to predict the continuous dyskinesia scores derived from validated wearable algorithms of the
sensors worn on the wrist. We hypothesize that periodic gamma components will be most predictive of
dyskinetic episodes prior to the onset of stimulation. Next, we will determine the relationship between gamma
entrainment, dyskinesia severity, and deep brain stimulation amplitude. To do this, we will record neural data in
a similar setting as before. However, during this process, patients will experience deep brain stimulation at the
clinically recommended stimulation frequency, and patient...

## Key facts

- **NIH application ID:** 10995195
- **Project number:** 1F31NS131043-01A1
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** Maria Olaru
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $6,023
- **Award type:** 1
- **Project period:** 2024-07-01 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10995195

## Citation

> US National Institutes of Health, RePORTER application 10995195, Gamma oscillations in patients with Parkinson’s Disease using chronic invasive brain recording: detection, entrainment, and functional relevance (1F31NS131043-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10995195. Licensed CC0.

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