Researchers at the University of California, San Francisco (UCSF) have developed a new form of adaptive deep brain stimulation (aDBS) that adjusts therapy in real time as patients walk, improving gait stability and reducing falls in people with Parkinson’s disease.
Published in Nature Medicine, the study demonstrates for the first time that an implanted neurostimulator can detect brain signals associated with individual steps and modify stimulation within fractions of a second. The approach represents a significant advance in personalized neuromodulation and could help address one of the most difficult-to-treat symptoms of Parkinson’s disease.
“Difficulty walking is one of the most disabling symptoms of Parkinson’s disease and one of the hardest to treat,” said senior author Doris D. Wang, MD, PhD, associate professor of neurological surgery at UCSF. “Walking is a highly dynamic behavior that requires precise timing across both sides of the body. We developed a system that can recognize those movement patterns and respond in real time, effectively allowing the stimulation to work with the patient as they move.”
Addressing a major unmet need in Parkinson’s disease
More than 10 million people worldwide are living with Parkinson’s disease, a progressive neurodegenerative disorder characterized by tremor, rigidity, slowness of movement, and postural instability.
Deep brain stimulation has become an established treatment for many motor symptoms of the disease, particularly tremor and rigidity. However, gait impairment, freezing of gait, and falls often persist despite therapy and are major contributors to disability, hospitalization, and loss of independence.
One limitation of conventional DBS is that it delivers continuous stimulation regardless of a patient’s activity. While effective for relatively stable symptoms, this approach may not adequately support complex behaviors such as walking, which require rapid and constantly changing coordination between the brain, spinal cord, and muscles.
The UCSF team hypothesized that stimulation timed to a patient’s actual movements might provide more effective support for gait control.
Turning brain signals into therapeutic feedback
To develop the system, researchers identified neural activity patterns associated with movements of the left and right legs. These movement-related signals were then incorporated directly into an implanted neurostimulator capable of adjusting therapy automatically during different phases of walking.
Unlike previous adaptive DBS approaches that typically respond to slower changes in disease-related brain activity, the new system operates on a timescale of milliseconds and responds directly to behavior itself.
“The brain contains remarkably rich information about movement,” said first author Kenneth H. Louie, PhD, a UCSF postdoctoral scholar. “We found that we could identify neural signatures linked to each step and use them to guide stimulation in real time.”
The researchers liken the device to a cardiac pacemaker. Rather than delivering constant stimulation, the neurostimulator continuously monitors neural signals and dynamically modifies therapy to match the brain’s walking rhythm.
The system performs these adjustments internally without requiring an external computer, making it suitable for real-world use outside the laboratory.
Testing the adaptive system
The study enrolled five individuals with Parkinson’s disease who had previously undergone DBS surgery as part of a UCSF investigational research program.
In addition to therapeutic DBS leads implanted deep within the brain, participants received research electrodes positioned over motor-related cortical regions. These electrodes allowed researchers to record neural activity associated with walking and identify personalized movement signatures for each participant.
Using these individualized neural biomarkers, investigators programmed the implanted device to automatically adjust stimulation during walking.
Laboratory testing demonstrated improvements in several objective measures of gait performance, including increased gait symmetry and reduced variability between steps. Both metrics are associated with more stable and efficient walking patterns.
The researchers then evaluated the system in participants’ daily lives through a blinded, multi-day crossover study comparing periods with adaptive stimulation and periods with conventional stimulation settings.
During adaptive stimulation, participants experienced fewer falls while maintaining overall control of their Parkinson’s symptoms.
No serious adverse events were reported, and participants tolerated the rapid stimulation changes without difficulty.
Although the trial was small, the findings provide early evidence that matching stimulation to behavior may offer benefits beyond those achieved with continuous DBS.
A shift toward behavior-responsive neuromodulation
The study highlights an emerging trend in neuromodulation: moving from static therapies toward systems that continuously sense and respond to changing neural states.
Most adaptive DBS technologies under development focus on biomarkers that fluctuate over minutes or hours. The UCSF approach instead targets neural signals associated with immediate actions, enabling stimulation to respond almost instantaneously.
“This study is about more than walking,” said Wang. “It demonstrates that brain stimulation can adapt to what a person is doing in real time. That opens the door to future therapies that respond dynamically to movement, speech, mood, cognition, and other brain functions.”
The concept could ultimately extend beyond Parkinson’s disease to other neurological and psychiatric disorders in which symptoms vary throughout the day.
Toward intelligent brain implants
Researchers envision future implanted devices that function as closed-loop systems, continuously monitoring neural activity and delivering therapy only when needed.
Such systems could potentially improve treatment efficacy while reducing side effects and conserving device battery life. By tailoring stimulation to specific behaviors or symptoms, clinicians may be able to provide more precise and individualized care.
The work also represents an important technological milestone because the adaptive algorithms were embedded directly within the implanted device rather than relying on external processing hardware.
“This is an important step toward a new generation of brain therapies,” Wang said. “Instead of delivering the same stimulation all day long, future devices may continuously listen to the brain and immediately respond to a patient’s needs. Just as pacemakers transformed the treatment of heart disease, intelligent neurostimulators may transform how we treat disorders of the brain.”
While larger clinical studies will be needed to confirm the benefits of the approach, the results provide an early demonstration that real-time, behavior-responsive neuromodulation may offer a new way to address some of Parkinson’s disease’s most challenging symptoms.
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