Tailored deep brain stimulation improves walking in Parkinson disease

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For patients with Parkinson disease, changes in their ability to walk can be dramatic. “Parkinson’s gait,” as it is often called, can include changes in step length and asymmetry between legs. This gait dysfunction reduces a person’s mobility, increases fall risk, and significantly impacts a patient’s quality of life.

Although high-frequency deep brain stimulation is highly effective for lessening symptoms of tremors, rigidity, and bradykinesia (the slowing of movement), its impact on gait has been more variable and less predictable among patients with advanced gait-related problems. Significant challenges in enhancing deep brain stimulation outcomes for advanced gait disorders have included the lack of a standardized gait metric for clinicians to use during programming, as well as understanding the impact of different stimulation factors on gait.

In a new study, researchers at UCSF developed a systematic way to quantify key aspects of gait relevant to Parkinson disease and used machine learning to identify the best deep brain stimulation settings for each individual. These personalized settings led to meaningful improvements in walking, such as faster and more stable steps, without worsening other symptoms.

Their study results were published June 18th in the Nature publication, npj Parkinson’s Disease.

“We approached the problem of optimizing deep brain stimulation settings as an engineering challenge, aiming to model the relationship between stimulation parameters, brain activity, and walking performance,” said study first author Hamid Fekri Azgomi PhD, a postdoctoral scholar in the Wang Lab at UCSF.

How to optimize gait performance

In the study, patients with Parkinson disease were implanted with a deep brain stimulation device that both stimulates the brain and records neural activity during walking. During clinic visits, patients’ deep brain stimulation settings were altered within safety ranges to examine their impacts on gait functions. In response to each set of deep brain stimulation settings, participants walked in a loop of approximately six meters with continuous streaming of their neural data and gait kinematics.

The researchers then developed a walking performance index to assess gait metrics, such as step length, stride velocity, and arm swing amplitude, and to provide insight into gait consistency. By combining these metrics, the walking performance index offered a comprehensive assessment of gait, addressing multiple dimensions of motor function affected by Parkinson disease.

“Our results confirmed that changes in deep brain stimulation settings were effectively captured by the walking performance index, aligning with patient and clinician evaluations during each visit,” said Azgomi. “This validation supports that the walking performance index is an effective metric for assessing and targeting gait improvements in people with Parkinson disease. Using these techniques, we were able to predict and identify personalized deep brain stimulation settings that improved the walking performance index.”

First, the researchers also identified brain activity patterns linked to better walking. By using multivariate models, the authors then identified distinct neural dynamics that differentiate optimal gait performance from less effective patterns. Improved gait correlated with reduced beta-band brainwave activity during specific phases of the gait cycle in the globus pallidus, which is a part of the brain associated with a loss of muscle movement in people with Parkinson disease.

These findings, along with identified person-specific neural biomarkers, underscore the importance of personalized, data-driven interventions for gait enhancement for people living with Parkinson disease.

“This work not only deepens our understanding of how deep brain stimulation affects movement but also highlights the promise of personalized neuromodulation for Parkinson’s and other neurological disorders, bringing us closer to smarter, more effective neuromodulation therapies,” said senior study author Doris Wang, MD, PhD, a neurosurgeon and UCSF associate professor of Neurological Surgery.

The authors say that future directions for this research include developing automated systems for real-time gait analysis and integrating the walking performance index with deep brain stimulation programming software. Technologies such as gait mats, wearable sensors, and advanced motion capture systems could enable continuous and precise monitoring of gait, allowing for more accurate deep brain stimulation adjustments.

Source: News Release
University of California, San Francisco (UCSF)
July 22, 2025