Network structure of the human musculoskeletal system shapes neural interactions on multiple time scales - PubMed (original) (raw)

Network structure of the human musculoskeletal system shapes neural interactions on multiple time scales

Jennifer N Kerkman et al. Sci Adv. 2018.

Abstract

Human motor control requires the coordination of muscle activity under the anatomical constraints imposed by the musculoskeletal system. Interactions within the central nervous system are fundamental to motor coordination, but the principles governing functional integration remain poorly understood. We used network analysis to investigate the relationship between anatomical and functional connectivity among 36 muscles. Anatomical networks were defined by the physical connections between muscles, and functional networks were based on intermuscular coherence assessed during postural tasks. We found a modular structure of functional networks that was strongly shaped by the anatomical constraints of the musculoskeletal system. Changes in postural tasks were associated with a frequency-dependent reconfiguration of the coupling between functional modules. These findings reveal distinct patterns of functional interactions between muscles involved in flexibly organizing muscle activity during postural control. Our network approach to the motor system offers a unique window into the neural circuitry driving the musculoskeletal system.

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Figures

Fig. 1

Fig. 1. Community structure of the anatomical muscle network.

(A) Topological representation of the anatomical network. The nodes of the network represent the muscles, and the edges represent the anatomical connections between muscles that are attached to the same bone or connective tissue. The five modules are color-coded. (B) Spatial representation of anatomical muscle network displayed on the human body (53). The size of each node represents the number of other nodes it is connected to.

Fig. 2

Fig. 2. Community structure of multiplex functional muscle networks.

(A) Frequency spectra of the four components obtained using NNMF. (B) Multiplex community structure of functional muscle network across frequencies and conditions. The dominant hand of all participants is displayed on the right side of the human body. (C) Spatial representation of the average muscle network displayed on the human body (53). The size of the nodes represents the number of other nodes it is connected to and the width of the edges the number of edges across layers. (D) Binary muscle networks for each layer.

Fig. 3

Fig. 3. Relationship between functional connectivity and anatomical distance.

(A) Adjacency and distance matrix of the anatomical muscle network. The maximum anatomical distance (path length) is 4. (B) Percentage of functional edges of thresholded networks across experimental conditions as a function of anatomical distance. (C) Distribution of edge weights of functional networks as a function of anatomical distance for each layer. Weights were averaged across experimental conditions. Edges connecting muscles within the same module are color-coded (rUA, right upper arm; FA, bilateral forearms; T, torso; rUL, right upper leg; lUL, left upper leg; and LL, bilateral lower legs), and gray dots represent edges between modules.

Fig. 4

Fig. 4. Clustered graphs of functional muscle networks across conditions.

(A) Clustered graphs in the nine experimental conditions (columns) and the four frequency components (rows). The nodes are the modules identified using multiplex modularity analysis. Node size represents the network density within, and the width of the edges represents the connection density between modules. (B) Spatial representation of the functional modules on the human body: right upper arm (rUA), bilateral forearms (FA), torso (T), right upper leg (rUL), left upper leg (lUL), and bilateral lower legs (LL). We used toolboxes for geometry processing to generate the colored meshes (54) and display them on the human body (53). (C) Significant differences in the connectivity of the clustered graphs between the stability conditions. Two contrasts were assessed: normal stability–anterior-posterior instability and normal stability–medial-lateral instability. A permutation test was used, and family-wise error control was maintained using Bonferroni correction (84 comparisons). Significant differences (_P_corrected < 0.05) are color-coded: Red depicts an increase and blue depicts a decrease in the average weights. Colored edges and nodes depict significant changes in connectivity between and within modules, respectively. (D) Significant differences in the connectivity of the clustered graphs between the pointing conditions. Two contrasts were assessed: no pointing–unimanual pointing and no pointing–bimanual pointing.

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