For the past decade, researchers at UC San Francisco (UCSF) have advanced the use of deep brain stimulation (DBS) to treat neurological and psychiatric conditions. Supported by funding from the National Institutes of Health (NIH), particularly through the BRAIN Initiative, UCSF scientists have developed new techniques that allow DBS devices to respond in real time to abnormal brain activity.
Deep brain stimulation involves implanting electrodes in specific areas of the brain to deliver electrical pulses that can interrupt problematic signals. This method has been used for years to manage movement disorders such as Parkinson’s disease. However, traditional DBS systems provide continuous stimulation and often fail to adapt to a patient’s changing symptoms.
Professors Philip Starr, MD, PhD, and Edward Chang, MD, at UCSF have led efforts to create personalized DBS approaches. These systems are designed to activate only when they detect patterns in brain activity associated with symptoms unique to each patient. The advancement is attributed in part to NIH support and has enabled clinical trials for adaptive DBS technology.
Shawn Connolly, who was diagnosed with Parkinson’s disease at age 39, participated in a clinical trial testing this adaptive approach. The system uses an algorithm that recognizes when symptoms are developing and delivers targeted stimulation accordingly. In February, the Food and Drug Administration approved two similar adaptive DBS algorithms—one based on research by Simon Little, MBBS, PhD—allowing for broader access to these technologies.
“It’s definitely changed my life,” Connolly said in 2024. “I can just go through the whole day feeling good.”
Starr and Chang’s work also includes exploring less invasive ways of achieving personalized results without surgery. Their use of electrocorticography allowed them to record detailed brain signals and develop more precise treatments for Parkinson’s patients.
Chronic pain is another area where UCSF researchers are applying personalized DBS strategies. Previous attempts using continuous stimulation had limited long-term success because patients’ brains adapted over time. In 2023, Prasad Shirvalkar, MD, PhD identified individual pain biomarkers by correlating recorded brain activity with patient-reported pain levels. Artificial intelligence was then used to predict pain episodes based on these biomarkers. This enabled clinical trials at UCSF for on-demand DBS systems that respond only when pain markers arise.
In mental health care, Professor Edward Chang led a team that mapped electrical patterns linked with mood states in depression patients. By 2020, this knowledge helped provide one participant named Sarah with a personalized DBS device that alleviated her treatment-resistant depression.
“I was at the end of the line. I was severely depressed,” Sarah said in 2021 about her experience before receiving treatment from UCSF specialists.
“In the early few months, the lessening of the depression was so abrupt… But it has lasted,” she added later about her ongoing recovery process after receiving therapy alongside her device.
Professor Andrew D. Krystal now leads a federally funded trial studying how deep brain stimulation may help others living with severe depression.
UCSF is also among roughly a dozen hospitals offering continuous deep brain stimulation as part of psychiatric care for severe obsessive-compulsive disorder (OCD). Andrew Moses Lee directs clinical trials aimed at identifying potential biomarkers for OCD symptoms within the brain—a step toward future personalized treatments for this condition as well.
“Tailoring these treatments to the person’s neural signature is really the key that allows DBS to be effective across many conditions,” Chang said regarding prospects for expanding applications beyond current indications.
