Prediction of gait neuromechanics following orthopedic interventions in children with cerebral palsy using computer simulations based on personalized models

Children with cerebral palsy (CP) suffer from a brain lesion that leads to impaired motor control, spasticity and muscle weakness. All these factors undermine the subjects' gait performance and, with time, will pose limitation to their mobility, independence and self-care. Orthopedic interventions aim at improving the walking performance. However, functional outcomes are not always as expected and, often, follow up surgery is needed to correct the treatment outcome, with an important socio-economic impact. My PhD will be part of the SimCP project, which aims at developing a computational platform that will support clinical decision making by allowing clinicians to a priori compare the functional outcome of orthopedic treatments based on subject-specific neuro-musculoskeletal models and predictive simulations. Specifically, I will quantify the lack of selective motor control expressed by CP subjects, characterized by a reduced number of independent motor modules. The modules will be determined by decomposing the experimental electromyography signals or computed muscle activations that reproduce observed joint torques, with the use of Non Negative Matrix Factorization. I will also analyze the effect of the detail of the musculoskeletal models on the extracted motor controls. I will then use these motor modules to drive subject-specific models during predictive simulations of the walking pattern after surgical intervention. To support the translation of these activities to the clinic, I will develop a Graphic User Interface that allows clinicians to directly interact with the computational models by translating information about an orthopedic treatment into changes in the models' parameters and the predicted outcome.

Publication year:2021

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