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Principles of gait encoding in the subthalamic nucleus of people with Parkinson's disease.

Yohann ThenaisieKyuhwa LeeCharlotte MoermanStefano ScafaAndrea GálvezElvira PirondiniMorgane BurriJimmy RavierAlessandro PuiattiEttore A AccollaBenoit WickiAndré ZachariaMayté Castro JimenezJulien F BallyGrégoire CourtineJocelyne BlochEduardo Martin Moraud
Published in: Science translational medicine (2022)
Disruption of subthalamic nucleus dynamics in Parkinson's disease leads to impairments during walking. Here, we aimed to uncover the principles through which the subthalamic nucleus encodes functional and dysfunctional walking in people with Parkinson's disease. We conceived a neurorobotic platform embedding an isokinetic dynamometric chair that allowed us to deconstruct key components of walking under well-controlled conditions. We exploited this platform in 18 patients with Parkinson's disease to demonstrate that the subthalamic nucleus encodes the initiation, termination, and amplitude of leg muscle activation. We found that the same fundamental principles determine the encoding of leg muscle synergies during standing and walking. We translated this understanding into a machine learning framework that decoded muscle activation, walking states, locomotor vigor, and freezing of gait. These results expose key principles through which subthalamic nucleus dynamics encode walking, opening the possibility to operate neuroprosthetic systems with these signals to improve walking in people with Parkinson's disease.
Keyphrases
  • deep brain stimulation
  • parkinson disease
  • machine learning
  • lower limb
  • skeletal muscle
  • high throughput
  • artificial intelligence
  • deep learning
  • single cell