Deep brain stimulation of the subthalamic nucleus is a well-established neuromodulation therapy for the symptomatic treatment of motor deficits in Parkinson\u2019s disease. For decades, this therapy has been optimized to alleviate symptoms such as tremor, bradykinesia (slowness of movements) and rigidity. However, deep brain stimulation often fails to improve, or can even aggravate gait deficits. To date, little is known about the neural activity patterns underlying gait deficits in Parkinson\u2019s disease, which has restricted the development of neuromodulation therapies better targeting these impairments.<\/p>\n
In this study, we leveraged a high-resolution gait platform established at CHUV to record the activity of the subthalamic nuclei, wirelessly and in real time, and to map it to whole-body movements and leg muscle activity while patients performed a series of walking tasks. We identified the neural activity patterns underlying basic walking, turning and freezing of gait. We then developed machine learning algorithms able to predict in real-time different aspects of walking, such as locomotor states, gait phases or effort modulations when avoiding obstacles, as well as pathological episodes such as freezing of gait.<\/p>\n
These results open new avenues for the development of adaptive neuromodulation therapies that employ predictions of leg motor states in real time to target and prevent gait and balance deficits in people with Parkinson\u2019s disease.<\/p>\n
Link to the article: https:\/\/www.science.org\/doi\/10.1126\/scitranslmed.abo1800<\/a><\/p>\n Contact information: <\/strong>Eduardo Martin Moraud eduardo.martin-moraud@chuv.ch<\/p>\n