Researchers at UC San Francisco have introduced a new approach to help Parkinson’s disease patients improve their walking abilities. The method combines deep brain stimulation (DBS) with artificial intelligence (AI) to tailor electrical impulses in the brain for each individual, resulting in better gait performance without worsening other symptoms.
Deep brain stimulation involves implanting a device that sends electric signals to targeted areas of the brain. Doris Wang, MD, PhD, neurosurgeon and associate professor at UCSF, led the research team alongside postdoctoral researcher Hamid Fekri Azgomi, PhD. Wang explained the procedure: “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.”
Parkinson’s disease often leads to motor issues such as shuffling steps, uneven stride lengths between feet, and freezing in place—symptoms that frequently cause falls. Wang noted: “In Parkinson’s disease, the destruction of dopamine neurons in brain’s basal ganglia area causes a 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.” DBS works by altering patterns in brain waves associated with movement.
Traditional treatments like medication or continuous high-frequency DBS have had limited success addressing severe gait disorders among Parkinson’s patients. “Among Parkinson’s patients’ major symptoms, gait has been quite difficult to treat,” said Wang. She added that standard approaches did not adequately address walking problems: “Although we use continuous high-frequency DBS to treat tremor and the slowness and stiffness of movement, it doesn’t work well for gait.”
To address this gap, researchers focused on changing both timing and energy levels delivered by DBS specifically for walking difficulties. They assessed patient gait clinically—quantifying good versus poor walking—and neurophysiologically—examining how changes affected brain activity.
Each patient underwent testing under their usual DBS settings before adjustments were made based on neural data collected during walking sessions. Researchers developed a Walking Performance Index—a set of measurable features including arm swing amplitude, stride speed, stride length variability, and stride symmetry—to determine if patients’ walking improved.
Artificial intelligence played a key role by analyzing data from these sessions through machine learning algorithms that identified optimal DBS settings for each participant. According to Wang: “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.” The study found that optimal frequencies varied across individuals.
The research also explored how tailored stimulation influenced activity in motor networks within participants’ brains: “By studying how DBS influences the cerebral cortex’s motor network, we identified brain waves associated with improved walking performance,” Wang said.
For those involved in this NIH-supported study at UCSF—the personalized approach resulted in faster and more stable steps without aggravating other symptoms linked with Parkinson’s disease. Looking ahead, Wang described efforts toward developing an adaptive algorithm so patients could automatically switch between standard settings for general movement states and optimized ones when walking: “We hope that this can significantly improve gait symptoms for Parkinson’s patients and ultimately improve their mobility and reduce falls.”
