Researchers at UC San Francisco have introduced a new approach to help patients with Parkinson’s disease improve their walking. The technique combines deep brain stimulation (DBS) with artificial intelligence (AI) to tailor treatment for individual needs.
DBS involves implanting a device that sends electrical signals to specific areas of the brain. Doris Wang, MD, PhD, neurosurgeon and associate professor at UCSF, explained the process: “DBS uses an implanted device. This is done with a minimally invasive surgery. I drill two very small holes in the skull, and then insert really thin wires or electrodes, which are the size of angel hair spaghetti and very flexible. The wires run from the side of the head all the way down to the chest under the skin. In the chest, these wires are connected to an electrical pulse generator. You can think of it as a pacemaker for the brain.”
Wang described how DBS works for Parkinson’s disease: “In Parkinson’s disease, the destruction of dopamine neurons in brain’s basal ganglia area causes of variety of motor issues, including ‘Parkinson’s gait.’ People with the disease tend to shuffle when they walk and take many mini steps when they turn. They also have different step lengths between the left and right foot, and some patients freeze in place. These symptoms often lead to falls. These walking problems are reflective a change in the pattern of their brain waves, making it harder for them to change their movements. DBS works by changing these patterns.”
She noted that traditional treatments such as medication or continuous high-frequency DBS do not work well for severe gait disorders associated with Parkinson’s disease.
The research team developed a Walking Performance Index that measures aspects such as arm swing amplitude, stride speed, stride length variability, and stride symmetry—features distinguishing Parkinson’s gait from healthy movement.
Using AI and machine learning techniques allowed researchers to analyze data collected during patient walking sessions. “From these sessions, we gathered data and used machine learning to identify the DBS settings that improved each patients’ gait. AI helped predict the settings that might be best for different patients,” Wang said.
The study found that optimal settings varied among patients; some benefited from higher frequencies while others responded better to lower ones.
On studying neurophysiological effects, Wang stated: “By studying how DBS influences the cerebral cortex’s motor network, we identified brain waves associated with improved walking performance, which can further guide programming in the future.”
Patients participating in this research experienced improvements such as faster and more stable steps without worsening other symptoms. The team is now working on adaptive algorithms so that patients can automatically switch between standard DBS settings and those optimized for walking depending on activity.
Wang concluded: “The personalized settings for each patient led to meaningful improvements in walking, such as faster more stable steps, without worsening other symptoms. We are actively working on an adaptive, or closed-loop, DBS algorithm where patients switch to this gait-optimized setting when walking but remain on their standard DBS at all other movement states. We hope that this can significantly improve gait symptoms for Parkinson’s patients and ultimately improve their mobility and reduce falls.”
