Brain Implant Detects Parkinson's Patients' Walking States During Daily Activities

An experimental brain implant can capture the signals of movement-related brain regions while Parkinson's patients perform daily activities like walking to the kitchen or strolling through a park, researchers reported Feb. 13 in the journal Science Advances. This is the first demonstration that a fully implanted device can be used to detect a specific movement state in humans during real-world activity.

Researchers from UC San Francisco recruited four Parkinson's disease patients who were slated to receive deep brain stimulation implants. These implants can reduce Parkinson's symptoms by sending electrical pulses to brain regions that control movement. The four patients received implants that not only emit electrical pulses but also can record brain activity.

The research team then tracked the patients through more than 80 hours of unsupervised daily activity. During this time, the patients also wore a sensor on their ankle that captured their walking gait, so researchers could compare that data to the brain waves occurring during movement. The bidirectional investigational DBS system recorded neural activity from movement-related brain regions, including the motor cortex and globus pallidus.

Results showed that walking could be distinguished from non-walking states based on brain waves alone, using patterns that varied between individuals. By analyzing synchronized neural and movement data collected during unsupervised daily activity, researchers identified individualized patterns of brain activity associated with walking. These neural signatures allowed the implanted deep brain stimulation device to classify movement states using signals generated during natural, at-home activities.

Researchers identified personalized neural biomarkers associated with gait and demonstrated that these signals can be used for real-time movement state classification within the constraints of an implanted device. This establishes a framework for future adaptive DBS systems that could adjust stimulation in response to a patient's activity state.

Based on this feedback from a person's implant, doctors might be able to tweak the deep brain stimulation they receive to fit whether they are walking, sitting or performing some other activity. For example, the implants might be set up to provide stimulation that's optimized for walking, whenever they sense that a Parkinson's patient is up and about.

Movement problems are a major symptom of Parkinson's — short and shuffling steps, stiffness, instability, tremors and involuntary actions. Gait impairment is one of the most disabling symptoms of Parkinson's disease. Patients often experience short, shuffling steps, difficulty initiating movement, and instability during turning. These changes increase fall risk and can significantly affect independence and quality of life. Current DBS therapy delivers continuous stimulation, but symptoms such as walking difficulty can fluctuate throughout the day and often do not respond to DBS settings that treat tremor, slowness, or stiffness.

The study was designed to demonstrate feasibility rather than clinical efficacy. The sample size was small, and additional studies will be required to determine whether movement-state detection can improve clinical outcomes. The research team is now planning future trials to evaluate whether stimulation settings optimized for walking can be dynamically applied using these neural biomarkers.

By enabling the study of brain activity during natural behavior, the approach may ultimately expand the reach of brain-computer interfaces and adaptive neuromodulation beyond controlled laboratory environments and into everyday life.