Article Title



Maddie Sayre1, Celeste Childs1, Libby Connolly1, Maddie Davis1, Quinlyn Shannehan1, Gabriela Simpson1, Sergi Garcia-Retortillo1, Plamen Ch Ivanov2. 1Wake Forest University, Winston-Salem, NC. 2Boston University, Boston, MA.

BACKGROUND: Skeletal muscles continuously coordinate to facilitate a wide range of movements. Muscle fiber composition and timing of activation account for distinct muscle functions and dynamics necessary to fine tune muscle coordination, generate movements, and adapt to fatigue. Here we investigate how distinct muscle fiber types dynamically synchronize and integrate as a network across muscles in response to fatigue. METHODS: Fourteen healthy adults performed three maximal body weight squat tests until exhaustion. Electromyography (EMG) signals from the following muscles were recorded simultaneously during the entire protocol: left and right vastus lateralis (LegL and LegR); left and right erector spinae (BackL and BackR). We first obtained 10 time series of EMG band power for each muscle, representing the dynamics of different muscle fiber types. To investigate cross-frequency interactions among EMG frequency bands that occur as a result of synchronous modulation of their spectral amplitudes, we calculated the bivariate equal-time Pearson’s cross-correlation for each pair of EMG band power time series across all Leg and Back muscles. RESULTS: Different muscle fiber types dynamically synchronize their activity across muscles following distinct patterns of cross-frequency communication. Specifically, with progression of fatigue, same-type muscle subnetworks (LegL-LegR and BackL-BackR) exhibit statically significant (i) global decline in links strength (p < 0.05) and (ii) increase in links strength stratification (p < 0.03), while (iii) preserving the general functional form of the network profile. In contrast, sub-networks of different-type muscles (Leg-Back) exhibit significant (i) global increase in links strength (p < 0.05) and (ii) decline in links strength stratification (p < 0.02), while (iii) changing the functional form of the network profile. CONCLUSION: This work addresses inter-muscular interactions among rhythms of myoelectrical activation, corresponding to the activity of different type muscle fibers, across muscles in response to fatigue. This dynamic network approach can lead to the development of network-based markers that will break new ground in the study of multilevel inter-muscular interactions, and will provide new understanding of diverse exercise-related phenomena such as performance, fatigue or muscle injuries.

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