A recent feasibility study funded by the National Institutes of Health (NIH) suggests that an implantable device which adjusts its operation based on real-time brain activity may help better control Parkinson's Disease (PD) symptoms in some patients.

This approach, known as Adaptive Deep Brain Stimulation (aDBS), improves upon traditional Deep Brain Stimulation (DBS) used for treating PD and other neurological disorders. Research indicates that aDBS can better manage PD symptoms compared to conventional DBS.

DBS involves placing small electrodes in specific areas of the brain to send electrical signals and alleviate symptoms of diseases like PD. Traditional DBS provides continuous stimulation, which can sometimes cause side effects because the brain does not always need the same level of stimulation. In contrast, aDBS uses real-time feedback from the brain, combined with machine learning algorithms, to dynamically adjust the stimulation intensity based on the patient’s changing needs.

In the study, four participants who had already undergone traditional DBS were asked to identify their most troublesome symptoms, often related to involuntary movements or difficulty initiating movement. The aDBS system was then set up to target these specific symptoms. After a few months of algorithm training, participants were sent home to compare the effectiveness of aDBS with their existing treatment. The study used a crossover design, with participants alternating between aDBS and traditional DBS every two to seven days.

The results showed that aDBS improved the most bothersome symptoms by about 50% compared to traditional DBS. Interestingly, even though participants were unaware of which treatment they were receiving at any given time, three-quarters were able to identify when they were using aDBS because their symptoms showed noticeable improvement.

This study builds on years of research by Dr. Philip Starr and his team at the University of California, San Francisco. In 2018, they introduced an early version of the aDBS system, which adjusts its operation based on feedback from brain activity—this system is known as "closed-loop" DBS. By 2021, advancements made it possible to record brain activity during normal daily activities. The current research combines these developments, using data from everyday brain activity to guide the aDBS system. However, they discovered that DBS itself significantly alters brain activity, making it difficult to detect the expected signals. As a result, they used a data-driven approach to find new signals in the brains of PD patients undergoing DBS.

Parkinson’s Disease is typically treated with levodopa, which compensates for dopamine loss in the brain. However, the effectiveness of this medication fluctuates, peaking shortly after administration and gradually diminishing as it is metabolized. aDBS can help smooth out these fluctuations by increasing stimulation when medication levels are high and decreasing it when levels are low, making it a good option for those who require high doses of levodopa.

Despite these promising findings, there are still challenges to making aDBS more widely available. Setting up the device initially requires significant input from specialized clinicians. However, researchers hope that in the future, the device can better manage itself, reducing the need for frequent visits to adjust settings. Automation is crucial for making aDBS accessible to more patients in clinical settings.

Addressing the accessibility and training challenges of DBS is important because this therapy requires expertise that is not widespread. Automating aDBS could make this treatment more accessible to a larger number of patients.

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