Elucidating neuromusculoskeletal mechanisms underlying gait deviations in children with cerebral palsy: The power of computer simulations

Cerebral palsy (CP) is the most common motor disorder among children, affecting around 1 per 500 newborns. Due to brain damage before the first birthday, children with CP often develop neural, muscular, and skeletal impairments during growth. Common impairments are spasticity, weakness, contractures, and bony torsions. These impairments result in gait deviations, but the underlying mechanisms are not entirely clear, whereas these are relevant for adequate treatment selection. Neuromusculoskeletal modelling is a type of biocomputational simulations that can enhance our mechanistic understanding of how impairments cause gait deviations. However, current common modelling approaches lack personalisation, and predictive simulation approaches with modelled impairments currently lack comparisons with pathology-specific experimental data, limiting the evaluations of the effects of these impairments on gait. Therefore, the overall aim of this thesis was to develop, personalise and apply biocomputational simulations to elucidate how neuromusculoskeletal impairments affect gait in children with CP. The first part of this thesis focused on personalisation of common neuromusculoskeletal modelling methods (Study 1, 2 & 3). The skeletal geometry, musculotendon behaviour, and neural control were each personalised. Each of the personalisation approaches resulted in a patient-specific model that was more consistent with experimental measures than a generic model, and, therefore, is more likely to yield more physiologically representative results. In the second part of this thesis predictive simulations were developed and applied to evaluate the effects of neuromuscular impairments on gait (Study 4 up to 8). A predictive simulation framework was developed, in which cost function criteria were combined and weighted in a stepwise approach to best predict healthy gait. Subsequently, plantarflexor weakness was implemented into the framework for healthy gait. Our simulation framework predicted most gait changes present in patients with bilateral plantarflexor weakness. Furthermore, a plantarflexor contracture was added to the developed predictive simulations framework. Predicted gait was compared against experimental gait data from children with idiopathic toe walking (ITW) that had a plantarflexor contracture. The simulation showed a good match with ITW, but only when a penalty on high fibre lengths was applied. Also, spasticity was implemented into the predictive simulations framework to predict three gait patterns that were distinguished in children with spasticity. Different muscles were spastic within each gait pattern. Thus, predictive simulations with neuromusculoskeletal impairments were used to predict pathology-specific gait patterns. In conclusion, within this thesis, we developed, personalised and applied biocomputational simulations to elucidate how neuromusculoskeletal impairments affect gait in children with CP. Model geometry, musculotendon behaviour, and neural control were personalised by adjusting model parameters and optimisations to better agree with patient-specific experimental data. Also, pathology-specific predictive simulations allowed evaluation of the effects of contracture, weakness, and spasticity on gait. This evaluation elucidated how specific gait deviations could be caused by specific impairments. Further studies should work towards enhancing clinical applicability of biocomputational simulations, by investigating what simplest level of personalisation is accurate enough for clinical use. Ultimately, this will enhance the clinical application of neuromusculoskeletal modelling to optimise patient-specific treatment outcomes.