U of M researchers advance spinal cord stimulation treatment for better chronic pain relief

MINNEAPOLIS/ST. PAUL (05/08/2025) — Researchers at the University of Minnesota Medical School have developed a new approach to better understand and more accurately program spinal cord stimulators — devices often used to treat chronic pain. Their findings were recently published in the Journal of Neural Engineering.

By using advanced computer algorithms, the research team created a way to automatically detect real nerve responses in spinal cord recordings and filter out background noise and electrical interference. This improvement could allow for more personalized and effective treatments for patients.

“Our method offers a more precise, automated approach to detecting spinal cord signals, which could be a game-changer for how we program and optimize stimulators,” said David Darrow, MD, an assistant professor at the University of Minnesota Medical School and neurosurgeon with M Health Fairview. “This type of work has traditionally been proprietary, but now we’re opening the doors to broader collaboration.”

The study showed that even small changes in stimulation settings, such as the strength or direction of electrical pulses, can affect how well the spinal cord responds. Researchers found that looking at the measurement called the Area Under the Curve (AUC) helped best determine whether a nerve signal was real or just interference. 

“We found that even small changes in stimulation polarity and amplitude can dramatically shift whether we get reliable neural responses. Personalizing these parameters could lead to more effective pain relief,” said Seth König, Ph.D., corresponding author and staff scientist in the Herman-Darrow Lab at the University of Minnesota Medical School. 

The research also points to the possibility of designing implanted devices that can adjust themselves in real-time, based on how a patient’s nerves respond. This finding could not only improve pain relief but also extend the battery life of these devices and potentially expand their use to treat other conditions, such as paralysis. 

Future research will test these algorithms in long-term studies using fully implanted, closed-loop devices.
Funding was provided by Abbott Neuromodulation.

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