Dissecting muscle synergies in the task space

Abstract

The muscle synergy is a guiding concept in motor control research that relies on the general notion of muscles 'working together' towards task performance. However, although the synergy concept has provided valuable insights into motor coordination, muscle interactions have not been fully characterised with respect to task performance. Here, we address this research gap by proposing a novel perspective to the muscle synergy that assigns a specific functional role to each muscle coupling by characterising its task-relevance. Crucially, our novel perspective provides nuance to the muscle synergy concept, demonstrating how muscular interactions can 'work together' in different ways: a) irrespective of the task at hand but also b) redundantly or c) complementarily towards common task-goals. To establish this perspective, we leverage information- and network-theory and dimensionality reduction methods to include discrete and continuous task parameters directly during muscle synergy extraction. Specifically, we introduce co-information as a measure of the task relevance of muscle interactions and use it to categorise such interactions as task-irrelevant (present across tasks), redundant (sharing the same task information) or synergistic (representing different aspects of the task). To demonstrate these types of interactions in real data, we apply the framework to large-scale datasets of human movements and extract generalizable and scale-invariant representations consisting of subnetworks of synchronised muscle couplings and distinct temporal patterns. The representations effectively capture the functional interplay between task end-goals and biomechanical affordances and the concurrent processing of functionally similar (redundant) and complementary (synergistic) task information. The proposed framework unifies the capabilities of current approaches in capturing distinct motor features while providing novel insights and research opportunities through a nuanced perspective to the muscle synergy.

Quantifying the diverse contributions of hierarchical muscle interactions to motor function

“…We found an almost perfect concordance between the presented intermuscular components and those extracted from a subset of the data (~0.99 average correlation). The robustness of these components exceeds previous implementations of the NIF [19,29], where a high level of concordance was also found.…”

“…Following this decomposition, we then ran these separate PID atoms through an established pipeline [19,29], referred to as the Network-Information framework (NIF) (Fig.2(A-E)) (see Materials and methods for detailed breakdown). The purpose of the NIF is to produce functionally and physiologically relevant and interpretable low-dimensional representations of muscular interactions underlying coordinated movements.…”

“…Following this, in previous applications of the NIF [19,29], to determine the optimal number of clusters to extract with dimensionality reduction, we have implemented community detection protocols suitable for multiplexed networks [30,64,65]. In the current study however, due to the highly nested structure found in A , just a single cluster was identified using these established methods.…”

“…Next, to elucidate the functional interactions within muscles underlying whole-body reaching movements, we applied the proposed methodology to pairwise combinations of amplitude signals from six frequency-bands ([𝑓 𝑥 , 𝑓 𝑦 ]) (Delta [0.1-4 Hz], Theta [4][5][6][7][8], Alpha [8][9][10][11][12], Beta [12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30], Low-Gamma (Piper rhythm) , High Gamma (Gamma) [60-80 Hz]) (see 'Quantifying diverse muscular interactions in the task space' and 'Data pre-processing' sections of the Materials and methods) extracted from the right anterior deltoid muscle. This computation serves as a novel, nonlinear measure of coherence decomposed into its task-relevant informational constituents, which we used here to determine the multifarious effects of [𝑓 𝑥 , 𝑓 𝑦 ] on the right, reaching finger anteroposterior kinematic (Fig.…”

“…( a ) EMG data along with corresponding task parameters are recorded from human participants performing naturalistic movements. ( b ) PID is applied to all [m x , m y ] and τ combinations, with the resulting information atoms then input into an established pipeline [19,29]. ( c ) The output of this framework is low-dimensional representations consisting of pairwise dependencies between muscles and their task- and participant-specific activations.…”