AAN 2026 | Optimizing deep brain stimulation targets to improve gait function

This body of work addresses gait dysfunction in Parkinson’s disease. We chose this because it’s typically a very difficult symptom that’s very common after deep brain stimulation or just in general in Parkinson’s. It’s commonly refractory to medications as well as refractory or maybe even gets worse after deep brain stimulation. So recently, two of my colleagues, Len Lowe, a neurologist at Harvard Medical School, as well as Nanditha Rajamani, a postdoctoral fellow at Harvard Medical School, identified two brain circuits that are related to gait dysfunction. So Dr. Lowe identified a stroke network associated with gait dysfunction after a stroke. And Nanditha Rajamani identified a set of brain fibers that were associated with either gait improvement or impairment after someone got DBS for Parkinson’s disease. And so the idea is now that we’ve localized them, maybe these could actually be stimulation targets for gait dysfunction in patients. However, the technology to actually do that didn’t exist. So what we did was we developed an optimization algorithm that would take a patient’s electrode kind of floating in space and we would then relate that to one of our two targets. So we’ve got the functional brain network or those little fibers that sort of run around the DBS electrode and we ask how can we organize the electrical current across that electrode in order to maximally hit either the functional brain network or the fibers. This algorithm that we developed ended up working and we found that we could significantly increase our ability to hit either functional brain network targets or tract-based targets, whether it was gait or tremor or rigidity or really what have you, we could hit them. That’s meaningful because you can’t really see those when you’re a clinician sitting in the clinic. It’s hard to keep in your mind where this nebulous concept of a brain network lives for an individual patient or where those fibers live. So it’s hard to intentionally program to that manually. So this allows us to translate a lot of these unique kind of circuit targets right into the clinic. When we went back and we looked at a hundred patients who had received deep brain stimulation at Harvard Medical School and had their pre-deep brain stimulation gait measured and then their post-deep brain stimulation gait measured a year later, we wondered, did patients who just randomly got closer to their kind of ideal gait settings do better than ones who got or who were farther away get worse. We found that was actually the case. So the more patients hit kind of the optimized settings using their brain networks or their gait network, the better they did. And same thing for the fibers, the better they did. But the interesting part was the patients who were very, very far away from their ideal settings actually had post-DBS gait decline. We then wondered if, you know, this was all just retrospective, so does this actually hold merit when we go prospectively and evaluate in real people? So we brought back about 10 patients and prospectively reprogrammed them and then just assigned them to get either their best clinical settings done by an expert movement disorders neurologist or our gait-optimized settings for either the network or the fibers. We found that patients who were assigned to receive their gait-optimized settings actually required, they all noted significant subjective benefit. And when we took them, when we did a three-meter walk test to just investigate what objective components of gait might be correlating with that subjective benefit, we found that it was primarily driven by decreased time required to walk and roughly 75% decreased time spent frozen. We found that all of these patients actually went home on their gait-optimized settings. People would come in, say, using canes and leave without canes. We’re hoping to move forward with a prospective proper randomized controlled trial in the future.

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