Two recent research studies highlight a new direction for ongoing personalized treatment for individuals with Parkinson’s disease, utilizing an implanted device that can address movement issues during the day and difficulty sleeping at night.
UCSF research indicates that tailored, self-regulating neuromodulation could improve both movement and sleep quality.
New findings from UC San Francisco demonstrate a method for continuous, personalized care for individuals with Parkinson’s disease, using an implanted device designed to alleviate daytime movement challenges and nighttime insomnia.
This innovative technique, known as adaptive deep brain stimulation (aDBS), employs AI-based methods to track changes in a patient’s brain activity related to their symptoms.
Upon detecting changes, the device sends carefully adjusted electrical pulses. This therapy works alongside the medications used by Parkinson’s patients, delivering less stimulation while the medication is effective to avoid excessive movements, and increasing stimulation as the medication’s effects diminish to counteract stiffness.
This is the first instance of a “closed loop” brain implant technology effectively operating in Parkinson’s patients throughout their daily activities. The device captures brain signals to establish a feedback system that effectively manages symptoms as they occur. Users have the option to exit adaptive mode or completely turn off the treatment using a handheld device.
In the initial study, researchers conducted a clinical trial involving four participants to evaluate the effectiveness of this method during the day, comparing it to an earlier DBS technology termed constant or cDBS.
To maximize relief for each participant, the researchers asked them to pinpoint their most troublesome symptom. The new technology achieved a 50% reduction in those symptoms. The findings were published on August 19 in Nature Medicine.
“This represents the future of deep brain stimulation for treating Parkinson’s disease,” stated Philip Starr, MD, PhD, the Dolores Cakebread Professor of Neurological Surgery, co-director of the UCSF Movement Disorders and Neuromodulation Clinic, and one of the lead authors of the study.
Starr has been developing this technology for over a decade. He pioneered a method to identify and record abnormal brain rhythms linked to Parkinson’s in 2013. By 2021, his team had pinpointed specific brain rhythm patterns that corresponded to motor symptoms.
“There has been significant interest in enhancing DBS therapy by making it adaptable and self-regulatory, but only recently have we obtained the necessary tools and methods for prolonged use in home settings,” said Starr, who joined UCSF in 1998 to launch its DBS program.
Earlier this year, a team of UCSF researchers led by Simon Little, MBBS, PhD, demonstrated in Nature Communications that aDBS could alleviate the insomnia experienced by many Parkinson’s patients.
“The major advancement we’ve accomplished with aDBS is our ability to detect, in real-time, where a patient falls on the symptom spectrum and provide the exact stimulation they require,” said Little, who holds a senior faculty position in neurology and contributed to both studies. Both Little and Starr are part of the UCSF Weill Institute for Neurosciences.
Enhancing Movement
Parkinson’s disease affects approximately 10 million individuals globally. It is caused by the deterioration of dopamine-producing neurons in the brain’s deeper regions, which are crucial for movement control. The loss of these cells also leads to non-motor symptoms that impact mood, motivation, and sleep.
Treatment typically begins with levodopa, a medication that compensates for the dopamine that is deficient due to the dying neurons. However, excessive dopamine can result in dyskinesia, which involves uncontrolled movements as the medication sets in. Conversely, as the medication’s effects wane, tremors and stiffness can return.
Some patients opt for a standard cDBS implant, which provides a steady level of electrical stimulation. While cDBS can reduce the frequency of medication needed and somewhat smooth out symptom fluctuations, it may also lead to over- or under-correction, resulting in symptom swings throughout the day.
Creating a Feedback Loop
To devise a DBS system that could adapt to an individual’s varying dopamine levels, Starr and Little needed to enable the DBS to recognize the brain signals corresponding to different symptoms.
Earlier research had highlighted brain activity patterns associated with these symptoms within the subthalamic nucleus (STN), the deep brain region that regulates movement and which cDBS targets. Starr suspected that the stimulation here might dampen the signals they needed to observe.
Therefore, he identified alternative signals from the motor cortex, a different brain area, which would not be diminished by DBS stimulation.
The next challenge was to create a system capable of leveraging these dynamic signals to manage DBS in real-life situations.
Building on insights from previously conducted adaptive DBS research at Oxford University a decade ago, Little collaborated with Starr and the team to develop a method to identify highly variable signals across different medication and stimulation doses.
Over several months, postdoctoral researchers Carina Oehrn, MD, PhD, Stephanie Cernera, PhD, and Lauren Hammer, MD, PhD, constructed an analysis pipeline to create customized algorithms for recording, analyzing, and responding to each patient’s unique brain activity related to their symptoms.
John Ngai, PhD, who leads the Brain Research Through Advancing Innovative Neurotechnologies® initiative (The BRAIN Initiative®) at the National Institutes of Health, remarked that this study could significantly enhance current Parkinson’s treatments.
“This personalized, adaptive DBS aligns with The BRAIN Initiative’s essential goal to transform our understanding of the human brain,” he stated.
Improving Sleep Quality
Constant DBS concentrates on alleviating daytime movement issues and does not typically help with insomnia.
However, in recent years, there has been a growing acknowledgment of the effects that sleep disorders, mood issues, and memory problems have on patients with Parkinson’s.
To address this, Little conducted a separate trial involving four individuals with Parkinson’s and one with dystonia, a similar movement disorder. In their paper published in Nature Communications, lead author Fahim Anjum, PhD, a postdoctoral scholar in UCSF’s Neurology Department, illustrated that the device could detect brain activity linked to various stages of sleep and identify patterns suggesting a person might awaken during the night.
The research teams led by Little and Starr, which also includes graduate student Clay Smyth, have begun evaluating new algorithms to support sleep. Their first sleep-related aDBS study was published last year in Brain Stimulation.
Scientists are now exploring similar closed-loop DBS therapies for an array of neurological disorders.
“We observe a substantial impact on patients, showing potential not only for Parkinson’s but also for mental health issues like depression and obsessive-compulsive disorder,” said Starr. “We are on the brink of a new era in neurostimulation therapies.”
