Starting today, individuals with Parkinson’s disease have a new treatment option made possible by U.S. Food and Drug Administration (FDA) approval of innovative technology known as adaptive deep brain stimulation (aDBS). This therapy employs an implanted device that continuously monitors the brain for signs of developing symptoms.
When specific patterns of brain activity are detected, the device delivers precisely calibrated electric pulses to mitigate symptom onset. The FDA has approved two treatment algorithms designed by Medtronic, a medical device company. Both algorithms target the same region in the brain called the subthalamic nucleus but respond differently:
- The “fast” algorithm identifies patterns linked with symptoms and immediately suppresses them.
- The “slow” algorithm provides continuous stimulation to maintain brain activity within a range that minimizes symptoms.
Developed in 2013 by UC San Francisco neurologist Simon Little, MBBS, PhD, while he was a Wellcome Trust clinical research fellow at Oxford University with Peter Brown, MBBS. The fast approach distinguishes itself from traditional continuous deep brain stimulation (cDBS), which delivers constant brain stimulation since its FDA approval in 1999.
In contrast to cDBS, aDBS is more sensitive and can adapt when patients’ brain activity changes – often due to medication adjustments. Its constant monitoring also smooths out irregularities in brain activity, preventing symptoms such as stiffness and involuntary movements before they manifest. Patients will be able to select or switch between the two adaptive algorithms using Bluetooth-enabled software.
As more people use these early aDBS algorithms, healthcare providers are gaining insights into how different patients experience them. This knowledge may guide future prescriptions based on individual patient needs. The approved aDBS methods represent only an initial step in what is possible with this technology.
In 2019, Simon Little joined UCSF and has continued to develop newer algorithms for motor symptoms like stiffness and tremors as well as non-motor issues such as mood disorders and insomnia that Parkinson’s patients often face. In a study published last August, along with neurosurgeon Philip Starr, MD, PhD, they created an algorithm targeting brain signals in the cerebral cortex – different from where previously approved algorithms were aimed.
This new approach improved symptoms and reduced side effects more effectively than cDBS in their research. The trial was a “double-blind” design: participants lived at home with changing settings while neither they nor researchers knew what configurations were active, ensuring unbiased results.
The UCSF team’s algorithm is even more advanced than those approved by the FDA on Monday. While further development continues, it has shown promising outcomes in small clinical trials and has allowed some patients to return to activities like professional skateboarding that had been compromised due to their condition.
Little envisions future advancements where artificial intelligence will help customize aDBS algorithms for individual needs. Additionally, ongoing research explores the potential of personalized DBS therapy for conditions such as depression, chronic pain, and obsessive-compulsive disorder.
This approval provides impetus for further development in deep brain stimulation technology to treat these additional health issues effectively.
