Deciphering the functional role of spatial and temporal muscle synergies in whole-body movements

Delis, Ioannis; Hilt, Pauline M.; Pozzo, Thierry; Panzeri, Stefano; Berret, Bastien

doi:10.1038/s41598-018-26780-z

Cited by 40 publications

(46 citation statements)

References 110 publications

(107 reference statements)

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“…For example, the quadriceps muscles vastus lateralis (VL) and vastus medialis (VM) have very similar contributions to task performance, with each muscle producing a similar extension torque at the knee [9][10][11]. Consistent with the proposal that co-variation patterns reflect muscles' contribution to task performance [7,[12][13][14][15], the activations of VM and VL are strongly correlated [15][16][17][18], suggesting that the CNS might control VM and VL as a single functional unit to simplify the achievement of task goals [19].…”

Section: Introductionmentioning

confidence: 73%

Coordination amongst quadriceps muscles suggests neural regulation of internal joint stresses, not simplification of task performance

Alessandro¹,

Prashara²,

Tentler³

et al. 2019

Preprint

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Many studies have demonstrated co-variation between muscle activations during behavior, suggesting that muscles are not controlled independently. According to one common proposal, this co-variation reflects simplification of task performance by the nervous system, so that muscles with similar contributions to task variables are controlled together. Alternatively, this co-variation might reflect regulation of low-level aspects of movements that are common across tasks, such as stresses within joints. We examined these issues by analyzing co-variation patterns in quadriceps muscle activity during locomotion in rats. The three mono-articular quadriceps muscles (vastus medialis, VM; vastus lateralis, VL; vastus intermedius, VI) produce knee extension and so have identical contributions to task performance; the bi-articular rectus femoris (RF) produces an additional hip flexion. Consistent with the proposal that muscle co-variation is related to similarity of muscle actions on task variables, we found that the co-variation between VM and VL was stronger than their co-variations with RF. However, co-variation between VM and VL was also stronger than their co-variations with VI. Since all vastii have identical actions on task variables, this finding suggests that co-variation between muscle activity is not solely driven by simplification of task performance. Instead, the preferentially strong covariation between VM and VL is consistent with the control of internal joint stresses: since VM and VL produce opposing mediolateral forces on the patella, the high positive correlation between their activation minimizes the net mediolateral patellar force. These results provide important insights into the interpretation of muscle co-variations and their role in movement control.

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Section: Introductionmentioning

confidence: 73%

Coordination amongst quadriceps muscles suggests neural regulation of internal joint stresses, not simplification of task performance

Alessandro¹,

Prashara²,

Tentler³

et al. 2019

Preprint

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“…CP decomposition is the simplest version of tensor decomposition; it is possible to apply a more sophisticated version of tensor decomposition. A popular alternative is Tucker decomposition [15], whose variant has been applied to EMG data [17, 18]. In Tucker decomposition, the number of spatial modules, the number of temporal modules, and the number of task components can differ from each other.…”

Section: Discussionmentioning

confidence: 99%

“…Despite the detailed description of mathematical and historical aspects of tensor decomposition, a description of movements and tasks is lacking; the significance of the tensor decomposition in discussing the task-dependent modulation of the spatiotemporal module is unclear. Other studies [17,18] applied a matrix tri-factorization to EMG data and revealed how each spatiotemporal module was modulated depending on the task. Although the tri-factorization algorithm and their new experimental paradigm [18] are sophisticated, the algorithm is a reduced version of tensor decomposition (a detailed description is given in [19]).…”

Section: Introductionmentioning

confidence: 99%

“…Other studies [17,18] applied a matrix tri-factorization to EMG data and revealed how each spatiotemporal module was modulated depending on the task. Although the tri-factorization algorithm and their new experimental paradigm [18] are sophisticated, the algorithm is a reduced version of tensor decomposition (a detailed description is given in [19]). Additionally, although the modulation patterns were estimated in an unsupervised manner (i.e., no experimental information is necessary), additional analysis for interpreting the modulation was based on a supervised method (i.e., experimental information should be required) [17,18].…”

Section: Introductionmentioning

confidence: 99%

“…Although the tri-factorization algorithm and their new experimental paradigm [18] are sophisticated, the algorithm is a reduced version of tensor decomposition (a detailed description is given in [19]). Additionally, although the modulation patterns were estimated in an unsupervised manner (i.e., no experimental information is necessary), additional analysis for interpreting the modulation was based on a supervised method (i.e., experimental information should be required) [17,18]. Although one advantage of tensor decomposition is its feature of unsupervised learning, the requirement of supervised learning reduces the advantage provided by this method.…”

Section: Introductionmentioning

confidence: 99%

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Detecting task-dependent modulation of spatiotemporal module via tensor decomposition: application to kinematics and EMG data for walking and running at various speed

Takiyama

Yokoyama

Kaneko

et al. 2019

Preprint

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1How the central nervous system (CNS) controls many joints and muscles is a fundamental question 2 in motor neuroscience and related research areas. An attractive hypothesis is the module hypothesis: 3 the CNS controls groups of joints or muscles (i.e., spatial modules) while providing time-varying motor 4 commands (i.e., temporal modules) to the spatial modules rather than controlling each joint or muscle 5 separately. Another fundamental question is how the CNS generates numerous repertories of movement 6 patterns. One hypothesis is that the CNS modulates the spatial and/or temporal modules depending 7 on the required tasks. It is thus essential to quantify the spatial module, the temporal module, and 8 the task-dependent modulation of those modules. Although previous methods attempted to quantify 9 these aspects, they considered the modulation in only the spatial or temporal module. These limitations 10 were possibly due to the constraints inherent to conventional methods for quantifying the spatial and 11 temporal modules. Here, we demonstrate the effectiveness of tensor decomposition in quantifying the 12 spatial module, the temporal module, and the task-dependent modulation of these modules without such 13 limitations. We further demonstrate that the tensor decomposition provides a new perspective on the 14 task-dependent modulation of spatiotemporal modules: in switching from walking to running, the CNS 15 modulates the peak timing in the temporal module while recruiting proximal muscles in the corresponding 16 spatial module. 17Author summary 18 There are at least two fundamental questions in motor neuroscience and related research areas: 1) how 19 does the central nervous system (CNS) control many joints and muscles and 2) how does the CNS 20 generate numerous repertories of movement patterns. One possible answer to question 1) is that the 21 CNS controls groups of joints or muscles (i.e., spatial modules) while providing time-varying motor 22 factors inherent to joint angle and electromyographic (EMG) data (i.e., spatial and temporal modules), 54 there can be limitations to considering more than three factors such as spatial modules, temporal modules, 55 and the task-dependent modulations of those modules. For example, with matrix decomposition, the task-56 dependent modulation of the temporal module between two tasks has been discussed under the constraint 57 of the same spatial module; on the other hand, the modulation of the spatial module has been discussed 58 without considering the temporal module. These constraints can thus provide diverse and non-unified 59 perspectives on the task-dependent modulation of the spatiotemporal modules. 60Here, we demonstrate the effectiveness of tensor decomposition in extracting the spatial module, the 61 temporal module, and the task-dependent modulations of the spatiotemporal modules. Tensor decompo-62 sition is a generalized version of matrix decomposition. By constructing tensor data that combine matrix 63 data in the third dimension, tensor decomposition e...

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

Kakehata,

Saito,

Takei

et al. 2024

European Journal of Sport Science

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The purpose of this study was to compare the muscle synergies extracted from 14 unilateral lower‐limb and trunk muscles between the first and final parts of a 400‐m sprint in experienced sprinters to understand neuromuscular coordination of multiple muscles in the fatigued condition sprint. Nine male 400‐m sprinters (400‐m personal record: 48.11 ± 1.6 s) performed 400‐m sprints as with the real competition strategy. We defined the first part (100–150 m section) and the final part (350–400 m section), and obtained mean spatiotemporal variables (e.g., running speed, step frequency, and step length) for both parts. Electromyography (EMG) signals were obtained using wireless EMG sensors (2000 Hz) from 14 lower‐limb and trunk muscles. Non‐negative matrix factorization was performed to extract the muscle synergies for both parts. We observed significantly declined spatiotemporal variables in the final part induced by fatigue. The extracted number of synergies was 7.0 ± 0.7 (mean ± SD) for the first part and 7.2 ± 0.4 for the final part with no significant differences between parts. However, we identified specific muscle synergy, and alterations in the individual muscle weightings of several hip muscles (rectus femoris: RF, tensor fasciae latae: TFL, and glutes maximus: Gmax muscles) while there was no change in the muscle weighting of shank muscles and the temporal patterns of all muscles even following fatigue in the 400‐m sprint. Fatigue‐induced performance decline in a 400‐m sprint corresponds to alterations in muscle synergies, particularly in hip muscles, with notable shifts in RF, TFL, and Gmax activation.

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Deciphering the functional role of spatial and temporal muscle synergies in whole-body movements

Cited by 40 publications

References 110 publications

Coordination amongst quadriceps muscles suggests neural regulation of internal joint stresses, not simplification of task performance

Coordination amongst quadriceps muscles suggests neural regulation of internal joint stresses, not simplification of task performance

Detecting task-dependent modulation of spatiotemporal module via tensor decomposition: application to kinematics and EMG data for walking and running at various speed

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

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