Muscle Synergy Constraints Do Not Improve Estimates of Muscle Activity From Static Optimization During Gait for Unimpaired Children or Children With Cerebral Palsy

Abstract

Neuromusculoskeletal simulation provides a promising platform to inform the design of assistive devices or inform rehabilitation. For these applications, a simulation must be able to accurately represent the person of interest, such as an individual with a neurologic injury. If a simulation fails to predict how an individual recruits and coordinates their muscles during movement, it will have limited utility for informing design or rehabilitation. While inverse dynamic simulations have previously been used to evaluate anticipated responses from interventions, like orthopedic surgery or orthoses, they frequently struggle to accurately estimate muscle activations, even for tasks like walking. The simulated muscle activity often fails to represent experimentally measured muscle activity from electromyographic (EMG) recordings. Research has theorized that the nervous system may simplify the range of possible activations used during dynamic tasks, by constraining activations to weighted groups of muscles, referred to as muscle synergies. Synergies are altered after neurological injury, such as stroke or cerebral palsy (CP), and may provide a method for improving subject-specific models of neuromuscular control. The aim of this study was to test whether constraining simulation to synergies could improve estimated muscle activations compared to EMG data. We evaluated modeled muscle activations during gait for six typically developing (TD) children and six children with CP. Muscle activations were estimated with: (1) static optimization (SO), minimizing muscle activations squared, and (2) synergy SO (SynSO), minimizing synergy activations squared using the weights identified from EMG data for two to five synergies. While SynSO caused changes in estimated activations compared to SO, the correlation to EMG data was not higher in SynSO than SO for either TD or CP groups. The correlations to EMG were higher in CP than TD for both SO (CP: 0.48, TD: 0.36) and SynSO (CP: 0.46, TD: 0.26 for five synergies). Constraining activations to SynSO caused

Maturation of the Locomotor Circuitry in Children With Cerebral Palsy

“…To end with, the final neural output of spinal locomotor circuitry is represented by the spatiotemporal modulation of alpha-motoneuron (MN) activity, which can be assessed by mapping the activity patterns from a large number of simultaneously recorded muscles onto the anatomical rostrocaudal location of the MN pools in the spinal cord ( Yakovenko et al, 2002 ; Ivanenko et al, 2013 ; Wenger et al, 2016 ), and by decomposing the coordinated muscle activation profiles into a small set of common factors as a means to look backward from the periphery to the CNS ( Davis and Vaughan, 1993 ; Lacquaniti et al, 2012b ). There are now several studies that evaluated the spatiotemporal organization of the spinal locomotor output in CP ( Steele et al, 2015 , 2019 ; Tang et al, 2015 ; Cappellini et al, 2016 ; Shuman et al, 2016 , 2017 , 2018 , 2019a , b ; Hashiguchi et al, 2018 ; Kim et al, 2018 ; Booth et al, 2019 ; Yu et al, 2019 ; Falisse et al, 2020 ; Pitto et al, 2020 ; Short et al, 2020 ).…”

Clarify Sit-to-Stand Muscle Synergy and Tension Changes in Subacute Stroke Rehabilitation by Musculoskeletal Modeling

“…Inverse dynamics is a common approach to calculating joint torques and muscle activation. However, simulated muscle activation often shows poor conformity to measured EMG (Hicks et al, 2015 ; Shuman et al, 2019 ). Forward dynamics, on the other hand, was widely used for muscle activation simulation in prior studies of human movement (Neptune et al, 2009 ; An et al, 2014 , 2015 ; Hicks et al, 2015 ; Mehrabi et al, 2019 ).…”

A muscle synergy-based method to estimate muscle activation patterns of children with cerebral palsy using data collected from typically developing children

“…Muscle synergy extrapolation methods to estimate unmeasured EMG could also inform muscle activation patterns used for computational modelling, such as neuromusculoskeletal modelling 23,24 . Neuromusculoskeletal models enable understanding the internal biomechanics of musculoskeletal tissues and are becoming popular tools to create personalised rehabilitation and training interventions [25][26][27] .…”