UCSF studies point the way toward continuous care for people with Parkinson’s disease

Two new studies from UC San Francisco point the way to 24-hour personalized care for people with Parkinson’s disease through an implanted device that can treat movement problems during the day and insomnia at night.

The approach, called adaptive deep brain stimulation or aDBS, uses methods derived from AI to monitor a patient’s brain activity for changes in symptoms.

When it notices them, it intervenes with precisely calibrated pulses of electricity. The therapy supplements the medications Parkinson’s patients take to control their symptoms, providing less stimulation when the drug is active to combat excessive movement and more stimulation when the drug wears off to relieve stiffness. prevent.

It is the first time that a so-called ‘closed loop’ brain implant technology has been shown to work in Parkinson’s patients in their daily lives. The device captures brain signals and creates a continuous feedback mechanism that can limit symptoms as they occur. Users can exit adaptive mode or turn off the treatment completely with a handheld device.

For the first study, researchers conducted a clinical trial with four people to test how well the approach worked during the day, comparing it to an earlier DBS brain implant technology known as Constant, or cDBS.

To ensure that the treatment would provide each participant with the maximum relief, the researchers asked them to identify their most bothersome symptom. The new technology reduced those symptoms by 50%. The results will be released on August 19 Naturopathy.

“This is the future of deep brain stimulation for Parkinson’s disease,” said Philip Starr, MD, PhD, Dolores Cakebread Professor of Neurological Surgery, co-director of the UCSF Movement Disorders and Neuromodulation Clinic and one of the study’s senior authors. .

Starr has been laying the foundation for this technology for more than ten years. In 2013, he developed a way to detect and then record the abnormal brain rhythms associated with Parkinson’s. In 2021, his team identified specific patterns in brain rhythms that correspond to motor symptoms.

“There has been a lot of interest in improving DBS therapy by making it adaptive and self-regulating, but only recently have the right tools and methods become available to enable people to use it long-term at home,” said Starr, who was recruited by UCSF in 1998 to start its DBS program.

Earlier this year, UCSF researchers led by Simon Little, MBBS, PhD, demonstrated in Nature communication that adaptive DBS has the potential to alleviate the insomnia that many patients with Parkinson’s suffer from.

“The big change we’ve made with adaptive DBS is that we can detect in real time where a patient is on the symptom spectrum and match this with the exact amount of stimulation he or she needs,” says Little, associate professor. of neurology and a senior author of both studies. Both Little and Starr are members of the UCSF Weill Institute for Neurosciences.

Restore movement

Parkinson’s disease affects approximately 10 million people around the world. It results from the loss of dopamine-producing neurons in deep parts of the brain responsible for controlling movement. The lack of these cells can also cause non-motor symptoms, which affect mood, motivation and sleep.

Treatment usually begins with levodopa, a drug that replaces the dopamine that these cells can no longer make. However, too much dopamine in the brain when the drug takes effect can cause uncontrolled movements called dyskinesia. As the medication wears off, tremors and stiffness occur again.

Some patients then choose to have a standard cDBS device implanted, which provides a constant level of electrical stimulation. Constant DBS can reduce the amount of medication required and partially reduce the fluctuations in symptoms. But the device can also over- or undercompensate, causing symptoms to change from one extreme to another throughout the day.

Closing the loop

To develop a DBS system that could adapt to a person’s changing dopamine levels, Starr and Little needed to enable the DBS to recognize the brain signals associated with different symptoms.

Previous research had identified patterns of brain activity linked to those symptoms in the subthalamic nucleus, or STN, the deep brain region that coordinates movement. This is the same area that cDBS stimulates, and Starr suspected that stimulation would dampen the signals they were supposed to pick up.

So he found alternative signals in another part of the brain, called the motor cortex, that would not be weakened by the DBS stimulation.

The next challenge was to figure out how to develop a system that could use these dynamic signals to monitor DBS in an environment outside the laboratory.

Building on findings from adaptive DBS studies he had conducted a decade earlier at the University of Oxford, Little worked with Starr and the team to develop an approach for detecting these highly variable signals across different drug and stimulation levels to.

Over the course of many months, postdoctoral scientists Carina Oehrn, MD, PhD, Stephanie Cernera, PhD, and Lauren Hammer, MD, PhD, created a data analytics pipeline that could turn it all into personalized algorithms to capture, analyze and respond to it. the unique brain activity associated with each patient’s symptom status.

John Ngai, PhD, who directs the Brain research by promoting innovative neurotechnologies® initiative (The BRAIN Initiative®) at the National Institutes of Health, said the study promises a marked improvement over current treatment for Parkinson’s.

“This personalized, adaptive DBS embodies the BRAIN Initiative’s core mission to revolutionize our understanding of the human brain,” he said.

A better night’s sleep

Continuous DBS aims to relieve daytime movement symptoms and does not typically relieve insomnia.

But over the past decade there has been increasing recognition of the impact that insomnia, mood disorders and memory problems have on Parkinson’s patients.

To help fill that gap, Little conducted a separate study that included four patients with Parkinson’s and one patient with dystonia, a related movement disorder. In their article published in Nature communicationFirst author Fahim Anjum, PhD, a postdoctoral researcher in the Department of Neurology at UCSF, showed that the device could recognize brain activity associated with different sleep states. He also showed that the system can recognize other patterns that indicate someone is likely to wake up in the middle of the night.

Little and Starr’s research teams, including their graduate student Clay Smyth, have begun testing new algorithms to help people sleep. Their first sleep ADBS study was published last year in Brain Stimulation.

Scientists are now developing similar closed DBS treatments for a range of neurological conditions.

We see it having a profound impact on patients, with potential not only in Parkinson’s disease, but probably also for psychiatric conditions such as depression and obsessive-compulsive disorder. We are at the dawn of a new era of neurostimulation therapies.”

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 study’s senior authors