For people with Parkinson’s disease, walking can be an unpredictable and exhausting task. Now, UCSF scientists have developed a method to fine-tune deep brain stimulation (DB) on each patient’s unique neural characteristics to provide stable progress, better equilibrium and new levels of accuracy for gait therapy.
The study was published in NPJ Parkinson’s Diseasemarking the transformation from a certain level of full DBS to a personalized data-driven approach. By recording brain signals as patients walk, the team created a “Walking Performance Index” (WPI), which enables them to optimize electrical stimulation settings in real time.
Track gait with brain and body sensors
Three people with Parkinson’s disease were implanted with neural devices that not only stimulated the brain’s ball pallidus, but also sowed brain activity continuously. During the clinic visit, the researchers had different DBS settings within the safety restrictions, while the patients walked a six-meter cycle. The sensor tracks step size, arm swing, pace speed and timing consistency.
These data are used to build WPI, a comprehensive score designed to reflect meaningful changes in walking function. Using machine learning, the team determined the best combination of amplitude, frequency, and pulse width for each patient.
Personalization brings real benefits
Compared with its standard clinical DBS setting, the patient showed:
- Stream speed up to 21%
- Step time variability decreases by nearly 47%
- Improve arm swing amplitude
- Better alignment between patient report and measurement performance
Crucially, these gait-specific DBS settings did not worsen other Parkinson’s symptoms, and one patient used the optimized settings at home every day.
Cracking the Neural Code of Better Walking
The researchers not only found a better setup, but also found how the brain performs on a good walk. Improvement of gait is associated with a decrease in the β-band activity of the ball pallidus during the critical stages of the step-by-step cycle, especially in the posture stages of the opposite leg.
First author Dr Hamid Fekri Azgomi said: “We addressed the problem of optimizing DBS settings as an engineering challenge, aiming to simulate the relationship between stimulation parameters, brain activity and walking performance.”
Each participant had a unique neural “biomarker” associated with better walking, highlighting the need for personalized treatment. Nevertheless, shared brain signatures (reduced pale beta during contralateral positions) point to possible common goals for future smart DBS systems.
Perform real-time, adaptive therapy
Senior author Dr. Doris Wang stressed the broader implication: “This work not only deepens our understanding of how DBS affects movement, but also highlights the hope for personalized neuromodulation for Parkinson’s disease and other neurological disorders.”
The team hopes to integrate WPI into a real-time DBS system using wearable sensors and motion capture, laying the foundation for fully adaptive neuromodulatory therapy.
Magazine: NPJ Parkinson’s Disease
doi: 10.1038/S41531-025-00510-0
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