Researchers at the University of California, San Francisco (UCSF) have developed a new approach to help Parkinson’s disease patients improve their walking abilities by combining deep brain stimulation (DBS) with artificial intelligence (AI).
DBS is a treatment that involves implanting a device to send electrical signals to targeted areas of the brain. According to Doris Wang, MD, PhD, neurosurgeon and associate professor of Neurological Surgery at UCSF who led the study with postdoctoral researcher Hamid Fekri Azgomi, PhD, “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 explained how Parkinson’s disease affects walking: “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 treating gait disturbances has been challenging: “Among Parkinson’s patients’ major symptoms, gait has been quite difficult to treat. The most severe types of gait disorder are really challenging to treat with either medication or DBS. Although we use continuous high-frequency DBS to treat tremor and the slowness and stiffness of movement, it doesn’t work well for gait. That inspired me to think about different ways to stimulate the brain, by changing the timing and amount of energy delivered from the DBS simulation for gait specifically. That was the motivation for our study.”
The research team analyzed walking from both clinical and neurophysiological perspectives: “Our study looked at gait from two different perspectives — one is clinical and other neurophysiological.
From the clinical perspective, we wanted to know how we quantify good gait, or effective gait, versus poor gait. And then, how we could tune different stimulation parameters to change patients gait metrics.
From the neurophysiological perspective, we wanted to figure out what are the common effects of these gait-optimized stimulation parameters on brain activity.”
To find optimal settings for each patient’s needs during movement tests: “With each patient, we had to determine their baseline under their usual DBS settings and how we could make their gait better or worse by changing their DBS settings. Then we had them walk laps while we continuously streamed their neural data and gait mechanics.
We developed a Walking Performance Index, which is a comprehensive but also easily quantifiable set of measurements that indicated whether the person was actually walking better. We included four features that differentiate Parkinson’s gait from healthy subjects including arm swing amplitude, stride speed, stride length variability and stride symmetry.”
Machine learning played an important role: “From these sessions, we gathered data and used machine learning to identify DBS settings that improved each patients’ gait. AI helped predict settings that might be best for different patients. We found that for some patients a really high frequency worked better for their gait; for others a lower frequency worked best so not everyone’s optimized settings are same.”
Looking at changes in neural activity provided further insights: “By studying how DBS influences cerebral cortex’s motor network we identified brain waves associated with improved walking performance which can further guide programming in future.”
Personalized treatment resulted in meaningful improvements without worsening other symptoms: “The personalized settings for each patient led to meaningful improvements in walking such as faster more stable steps without worsening other symptoms,” said Wang.
The researchers plan further advancements using adaptive algorithms: “We are actively working on an adaptive or closed-loop DBS algorithm where patients switch this setting when walking but remain on standard DBS all other movement states,” Wang said.“We hope this can significantly improve symptoms…ultimately improve mobility reduce falls.”
