Methodological Choices in Muscle Synergy Analysis Impact Differentiation of Physiological Characteristics Following Stroke

Abstract

Muscle synergy analysis (MSA) is a mathematical technique that reduces the dimensionality of electromyographic (EMG) data. Used increasingly in biomechanics research, MSA requires methodological choices at each stage of the analysis. Differences in methodological steps affect the overall outcome, making it difficult to compare results across studies. We applied MSA to EMG data collected from individuals post-stroke identified as either responders (RES) or non-responders (nRES) on the basis of a critical post-treatment increase in walking speed. Importantly, no clinical or functional indicators identified differences between the cohort of RES and nRES at baseline. For this exploratory study, we selected the five highest RES and five lowest nRES available from a larger sample. Our goal was to assess how the methodological choices made before, during, and after MSA affect the ability to differentiate two groups with intrinsic physiologic differences based on MSA results. We investigated 30 variations in MSA methodology to determine which choices allowed differentiation of RES from nRES at baseline. Trial-to-trial variability in time-independent synergy vectors (SVs) and time-varying neural commands (NCs) were measured as a function of: (1) number of synergies computed; (2) EMG normalization method before MSA; (3) whether SVs were held constant across trials or allowed to vary during MSA; and (4) synergy analysis output normalization method after MSA. MSA methodology had a strong effect on our ability to differentiate RES from nRES at baseline. Across all 10 individuals and MSA variations, two synergies were needed to reach an average of 90% variance accounted for (VAF). Based on effect sizes, differences in SV and NC variability between groups were greatest using two synergies with SVs that varied from trial-to-trial. Differences in SV variability were clearest using unit magnitude per trial EMG normalization, while NC variability was less sensitive to EMG normalization method. No outcomes were greatly impacted by output normalization method. MSA variability for some, but not all, methods successfully differentiated intrinsic physiological differences inaccessible to traditional clinical or biomechanical assessments. Our results were sensitive to methodological choices, highlighting the need for disclosure of all aspects of MSA methodology in future studies.

Evaluation of Synergy Extrapolation for Predicting Unmeasured Muscle Excitations from Measured Muscle Synergies

“…Muscle synergy analysis requires a number of methodological choices that can influence the results of the analysis (Banks et al, 2017). Methodological choices that have been studied include EMG processing approaches (e.g.…”

“…Another approach for estimating unmeasured muscle excitations is to use muscle synergy concepts. A muscle synergy is composed of a time-varying synergy excitation and a corresponding time-invariant synergy vector containing weights that define how each synergy excitation contributes to the excitation of each muscle (Tresch et al, 1999;Ting and Chvatal, 2010;Banks et al, 2017;Shourijeh and Fregly, 2020) Muscle synergies have been broadly used in descriptive research to analyze experimental muscle excitations during a large number of movement tasks (Ivanenko et al, 2005;Torres-Oviedo and Ting, 2007;Bowden et al, 2010;Walter et al, 2014;Kristiansen et al, 2015;Meyer et al, 2016a;Ruiz Garate et al, 2017;Sauder et al, 2019), but few studies have performed predictive analyses using muscle synergy information (Ajiboye and Weir, 2009;Bianco et al, 2018). Ajiboye and Weir demonstrated that subject-specific synergies extracted from muscle activities recorded for a subset of postures can be used to predict EMG patterns for the remaining postures.…”

“…number of synergies), and numerical analysis approaches (e.g. matrix decomposition algorithm (Tresch et al, 2006;Hug et al, 2012;Steele et al, 2013;Oliveira et al, 2014;Banks et al, 2017;Shuman et al, 2017;Ebied et al, This is a provisional file, not the final typeset article 2018; Gallina et al, 2018)), we also evaluated how each of these choices affects synergy extrapolation results. The evaluated methodological decisions for MSA process included matrix factorization algorithm (PCA and NMF), EMG normalization approach, and number of synergies.…”

A Review of EMG Techniques for Detection of Gait Disorders

“…A review cum research by Kieliba et al [47] supported that increase in the cut off frequency of the filter decreases the variance, accounts for a particular component and increases dimensional space of synergies to be extracted. EMG acquired from children with cerebral palsy and from individual's post-stroke has shown that the choice of preprocessing (filtering, normalization) had an effect on the number of synergies and differentiation of physiological traits [48,49]. Figure 3 displays how the choice of low pass filter (10 and 20 Hz), a second-order Butterworth filter, effects the dimensional space.…”

Análise da força de mordida, atividade eletromiográfica e espessura dos músculos masseter e temporal em nadadores de elite - modalidade Crawl

“…Estudos avaliaram o desempenho dos atletas desta modalidade em relação à análise biomecância por meio da braçada e respiração (Formosa et al, 2014;Ribeiro et al, 2017) Os dados eletromiográficos foram normalizados pelo apertamento dental em contração voluntária máxima com a finalidade de obter melhor interpretação pela comparação de diferentes indivíduos, músculos e períodos de coletas o qual diminuiu a diferença entre os participantes (Gerstner et al, 2017;Banks et al, 2017;Schwartz et al, 2017;Oliveira et al, 2017).…”