Summary: UCSF researchers have recorded real-time brain signatures tied to movement symptoms and active deep brain stimulation in people with Parkinson’s disease. These long-duration neural recordings during everyday life could enable personalized stimulation therapies tailored to each patient’s brain activity.
Source: UCSF
UC San Francisco scientists have made pioneering neural recordings in people with Parkinson’s disease that pave the way for individualized brain stimulation therapies.
In a study published May 3 in Nature Biotechnology, researchers at the UCSF Weill Institute for Neurosciences implanted advanced neurostimulation devices capable of continuously monitoring brain activity for months at a time, both with and without active deep brain stimulation (DBS).
By combining these long-term recordings with wearable motion sensors, the team identified distinct brain wave patterns that correspond to particular motor abnormalities seen in Parkinson’s disease, such as slowing of movement and medication-induced involuntary movements.
This work provides the first evidence, gathered during normal daily activities, supporting the long-standing idea that Parkinson’s motor symptoms are linked to disordered brain rhythms. The findings also clarify how DBS appears to restore more orderly brain activity when it relieves symptoms.
“We can record hundreds of hours of brain activity wirelessly while patients go about their lives,” said Philip Starr, MD, PhD, the Dolores Cakebread Professor of Neurological Surgery at UCSF and the study’s senior author. “That level of real-world monitoring lets us precisely map the neural activity underlying specific neurological problems as they actually occur.”
Parkinson’s disease is a progressive neurological disorder characterized by slowed movement (bradykinesia), gait impairment, tremor, and a range of non-motor symptoms. While dopamine depletion is a hallmark of the disease, researchers have long suspected that abnormal brain oscillations also contribute to symptom fluctuations.
Previous recordings of neural activity in Parkinson’s patients were typically brief and limited to clinic or hospital settings, offering only a snapshot of brain dynamics that change throughout the day. To capture a fuller picture, study lead author Ro’ee Gilron, PhD, and colleagues implanted small sensing electrodes into the subthalamic nucleus and the motor cortex of five participants. These sensors linked to implantable pulse generators that could both sense ongoing electrical activity and deliver stimulation.
Over months of continuous monitoring, the devices produced a large dataset. The team developed algorithms to align neural recordings with data from wrist-worn movement trackers. This analysis revealed that episodes of dyskinesia (excessive involuntary movement often caused by medication) and bradykinesia corresponded to exaggerated activity in specific frequency bands in both the subthalamus and motor cortex.
Beyond observation, the researchers measured the immediate impact of active DBS on these neural signatures. Although DBS has been used for decades to lessen Parkinson’s symptoms, its precise mechanism has been incompletely understood because stimulation typically generates electrical artifacts that interfere with recording systems.
To overcome that barrier, the team applied a technique analogous to noise-cancelling technology: they generated an offsetting electrical signal that cancels the stimulation artifact, enabling clean recordings during continuous DBS. Using this approach, they directly observed that effective stimulation suppresses low-frequency oscillations that hinder movement and stabilizes higher-frequency activity that supports motor function.
“This is the first time we’ve been able to measure the effect of continuous stimulation on brain waves,” Gilron said. “Until now, clinicians have been delivering DBS without a direct, real-time readout of how stimulation affects the brain — it’s been like treating blood pressure without being able to measure it.”
The study’s findings underscore both shared features of Parkinson’s-related brain activity and the individuality of each patient’s neural patterns. Continuous, long-term neural recording is essential to capture that variability and to develop personalized control algorithms that can predict and respond to symptom onset.
With sufficiently large datasets and refined analytic methods, clinicians could anticipate the emergence of motor symptoms and adjust stimulation parameters automatically to prevent or reduce symptom episodes. The researchers compare the greater resolution and scope of these recordings to how the Hubble Space Telescope expanded our view of the cosmos: more detail and more data can lead to new discoveries.
The next step is a randomized clinical trial involving ten patients to test whether real-time monitoring combined with adaptive stimulation algorithms can accelerate individualized treatment development. Because these systems allow new algorithms to be evaluated immediately against recorded brain activity and patient movement, they could shorten the time required to translate discoveries into therapies compared with traditional drug development.
Beyond Parkinson’s disease, the approach has potential applications for other conditions driven by abnormal neural oscillations, including some forms of epilepsy, treatment-resistant depression, and chronic pain. Real-time neural monitoring paired with targeted neuromodulation could enable personalized interventions for a range of neurologic and psychiatric disorders.
“We’re using Parkinson’s as a model population to develop this platform,” Starr said. “Our hope is that this technology will eventually be applied to disorders where effective stimulation therapies do not yet exist.”
Authors: Philip A. Starr served as the study’s senior author and Ro’ee Gilron was the lead author. For the full list of contributors, see the published study.
Funding: NIH/NINDS UH3 NS100544 (BRAIN Initiative)
Disclosures: Implanted study devices were provided at no charge under a contract with Medtronic, Inc.
About this Parkinson’s disease research news
Source: UCSF
Contact: Alan Toth – UCSF
Image: The image is in the public domain
Original Research: Findings published in Nature Biotechnology