bioRxivPathologyPreprint
Identification of neural and non-neural origins of joint hyper-resistance based on a novel neuromechanical model.
J. Willaert, K. Desloovere, A. Van Campenhout, L. H. Ting, F. De Groote
Abstract
Joint hyper-resistance is a common symptom in neurological disorders. It has both neural and nonneural origins, but it has been challenging to distinguish different origins based on clinical tests alone. Combining instrumented tests with parameter identification based on a neuromechanical model may allow us to dissociate the different origins of joint hyper-resistance in individual patients. However, this requires that the model captures the underlying mechanisms. Here, we propose a neuromechanical model that, in contrast to previously proposed models, accounts for muscle shortrange stiffness and its interaction with muscle tone and reflex activity. We collected knee angle trajectories during the pendulum test in 15 children with cerebral palsy (CP) and 5 typically developing children. We did the test in two conditions - hold and pre-movement - that have been shown to alter knee movement. We modeled the lower leg as an inverted pendulum actuated by two antagonistic Hill-type muscles extended with SRS. Reflex activity was modeled as delayed, linear feedback from muscle force. We estimated neural and non-neural parameters by optimizing the fit between simulated and measured knee angle trajectories during the hold condition. The model could fit a wide range of knee angle trajectories in the hold condition. The model with personalized parameters predicted the effect of pre-movement demonstrating that the model captured the underlying mechanism and subject-specific deficits. Our model thus allows us to determine subject-specific origins of joint hyper-resistance and thereby opens perspectives for improved diagnosis and consequently treatment selection in children with spastic CP.
- organism: human
- assay: imaging
- research focus: cell type annotation
- research focus: clustering
- approach: deep learning
- approach: statistical model
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