This therapeutic algorithm and tool processes neural signals to determine optimal and personalized deep brain stimulation (DBS) to treat and reduce symptoms of movement disorders. Deep brain stimulation (DBS) is an invasive neurological therapy for treating movement and neuropsychiatric disorders such as Parkinson’s disease (PD), essential tremor (ET), dystonia, epilepsy, and obsessive-compulsive disorder. In conventional deep brain stimulation therapies, the stimulation target includes brain regions such as the subthalamic nucleus (STN) and the globus pallidus internus (GPi) regions. However, the targets to improve patient symptoms are in the subregions, often not identifiable on imaging, and are different per patient. Clinicians must use a trial-and-error approach, which is often laborious and time-consuming, testing different stimulation parameters and visually assessing their therapeutic effect on a patient over time.
Researchers at the University of Florida have designed a deep brain stimulation (DBS) algorithm and tool for analyzing neural signals in near real-time, drastically speeding up the adjustment process by opening a direct window into the patient’s brain. The information gathered is highly personalized and immediately available to the provider. The algorithm, designed to take advantage of the causal relationship between optimal therapeutic parameters and the resulting brain signals, predicts the stimulation parameters that provide the best suppression of pathological signals. The prediction process is conveniently swift, requiring only one session, and delivers personalized DBS therapy decisions to the clinicians on a web platform after processing on a cloud computing server.
Analyzes brain activity to quickly identify the optimal deep brain stimulation parameters for each patient
Deep brain stimulation (DBS) operates through electrical contacts in the brain that deliver electrical stimulation to interfere with symptomatic neural signals. This stimulation consists of square voltage pulses whose amplitude, width, and frequency parameters can vary to personalize the treatment. Personalization is critical because the sub-regions of the subthalamic nucleus (STN) and globus pallidus internus (GPi) responsible for therapeutic relief from movement disorder symptoms such as slow, halting movement (bradykinesia) are small, patient-specific, and essentially invisible to brain imaging. Typically, DBS personalization proceeds by trial-and-error, with the provider adjusting the stimulation parameters in response to the patient’s symptoms, a lengthy process with associated side effects including reduced balance, vision, and speech abilities. This tool visualizes and analyzes neural signals in real-time, enabling the provider to identify personalized stimulation parameters quickly and easily. The processor applies a range of stimulation parameters and measures the frequency and power of the brain signals in response to this stimulation, using these to construct a prediction without requiring repeated visual determination of symptoms.
