Variability of trunk muscle synergies underlying the multidirectional movements and stability trunk motor tasks in healthy individuals

Abstract

Muscle synergy analysis is useful for investigating trunk coordination patterns based on the assumption that the central nervous system reduces the dimensionality of muscle activation to simplify movement. This study aimed to quantify the variability in trunk muscle synergy during various trunk motor tasks in healthy participants to provide reference data for evaluating trunk control strategies in patients and athletes. Sixteen healthy individuals performed 11 trunk movement and stability tasks with electromyography (EMG) recording of their spinal and abdominal muscles (6 bilaterally). Non-negative matrix factorization applied to the concatenated EMG of all tasks identified the five trunk muscle synergies (W) with their corresponding temporal patterns (C). The medians of within-cluster similarity defined by scalar products in W and rmax coefficient using the cross-correlation function in C were 0.73–0.86 and 0.64–0.75, respectively, while the inter-session similarities were 0.81–0.96 and 0.74–0.84, respectively. However, the lowest and highest values of both similarity indices were broad, reflecting the musculoskeletal system’s redundancy within and between participants. Furthermore, the significant differences in the degree of variability between the trunk synergies may represent the different neural features of synergy organization and strategies to overcome the various mechanical demands of a motor task.

Muscle synergy patterns as altered coordination strategies in individuals with chronic low back pain: a cross-sectional study

“…Theses trunk muscles present bilateral and unilateral connectivity defined by the anatomical constraints (e.g., common origin and insertion of muscles) and the neural circuitry that projects to functional groups of muscles for dynamic motor control [ 11 ]. Therefore, assessing multiple patterns of trunk EMG activity in an integrative fashion is required to accurately identify the overall picture of altered motor control in CLBP [ 12 ].…”

“…NMF employs linear extraction and separates EMG matrices into time-invariant weighted activations of a group of muscles (muscle weighting components) and time-variant activation profiles (temporal pattern components) [ 16 , 18 , 19 ]. For this method, large-scale and high-dimensional EMG data recorded during a variety of motor tasks are essential, as muscle synergies are the fundamental building blocks of neural constraints [ 20 ]; thus, it may address the question of how the CNS adapts in the presence of CLBP [ 12 ]. Furthermore, incorporating many trunk motor tasks is beneficial, as this increases the possibility of revealing the motor control strategies in CLBP that may have altered trunk motor control during different motor tasks [ 21 ].…”

“…We have previously revealed several trunk muscles synergies with unilateral and bilateral patterns of muscle activations, underlying locomotion and stability motor behaviors in healthy individuals [ 22 ]. We further found that theses functional trunk muscle synergies can be extracted from the EMG data of 11 trunk movement and stability tasks, which aimed to effectively capture the altered trunk muscle synergies in a clinical scenario such as CLBP [ 12 ]. In this context, the trunk motor tasks were symmetrically designed, and the EMG data of each task was time-interpolated to have the same data points, which would minimize the possibility that the extracted synergies were biased toward any certain directions.…”

Changes in muscle coordination patterns during 400‐m sprint: Impact of fatigue and performance decline

“…Considering the local minima inherent in NMF, each synergy extraction was repeated 300 times, and the VAF was calculated at each iteration. The iterations with the highest VAF were maintained (Saito et al, 2021(Saito et al, , 2022(Saito et al, , 2023Yokoyama et al, 2016Yokoyama et al, , 2021. VAFs >0.90% were used to identify the optimal number of synergies commonly used in the literature (Saito et al, 2021(Saito et al, , 2022(Saito et al, , 2023Yokoyama et al, 2016Yokoyama et al, , 2021.…”

“…The iterations with the highest VAF were maintained (Saito et al, 2021(Saito et al, , 2022(Saito et al, , 2023Yokoyama et al, 2016Yokoyama et al, , 2021. VAFs >0.90% were used to identify the optimal number of synergies commonly used in the literature (Saito et al, 2021(Saito et al, , 2022(Saito et al, , 2023Yokoyama et al, 2016Yokoyama et al, , 2021. It was suggested that the criterion VAF >0.90% ensures a sufficient representation of the data (Turpin et al, 2021), although this is still debated (Barradas et al, 2020).…”