Information decomposition of multichannel EMG to map functional interactions in the distributed motor system

Abstract

The central nervous system needs to coordinate multiple muscles during postural control. Here we used multivariate information decomposition to investigate the functional interactions in multichannel EMG during these tasks. A set of information measures were estimated from an instantaneous linear regression model and a timelagged VAR model fitted to the EMG envelopes of 36 muscles. We used network analysis to quantify the structure of functional interactions between muscles and compared them across experimental conditions. Conditional mutual information and transfer entropy revealed sparse networks dominated by local connections between muscles. When comparing the muscle networks across task conditions, we observed significant effects in the muscles involved in performing those tasks: pointing behavior affected upper body muscles and postural behavior mainly affected lower body muscles. However, information decomposition revealed distinct patterns underlying both effects. Manual and bimanual pointing were associated with reduced transfer to the pectoralis major muscles, but an increase in total information compared to no pointing, while postural instability resulted in increased information transfer, storage and information in the abductor longus muscles compared to normal stability. These findings show that directed interactions between muscles are widespread and task-dependent and can be assessed from surface EMG recorded during static postural tasks. We discuss the task-related effects in terms of gain modulations of spinal reflex pathways.

Muscle Synergies and Coherence Networks Reflect Different Modes of Coordination During Walking

“…We also have to admit that we did not directly assess the contribution of the supra-spinal inputs and it might be a “natural” step to evaluate these inputs using measures like partial directed coherence (e.g., Boonstra et al, 2015 ) or other directed information theoretic measures (e.g., Boonstra et al, 2019 ). While evidence about the functional role of intermuscular coherence is rapidly accumulating ( Farmer et al, 1993 ; Boonstra et al, 2015 , 2019 ; De Marchis et al, 2015 ), research on possible cortical contributions during whole-body movements comes with challenges ( Gwin et al, 2010 ). Several studies already revealed the phasic modulation of corticomuscular coherence ( Gwin et al, 2011 ; Gwin and Ferris, 2012 ; Roeder et al, 2018 ) and their importance of stabilizing modes of coordination ( Bruijn et al, 2015 ).…”

Perilaryngeal-Cranial Functional Muscle Network Differentiates Vocal Tasks: A Multi-Channel sEMG Approach

“…More recently, using coherence analysis, the fluency of corticomuscular connectivity has also been investigated to understand how the central nervous system communicates with the peripheral nervous system [15], [16]. Similarly, the functional muscle network is an emerging concept that uses simultaneous multichannel sEMG to decode how various muscle groups are synergistically synchronized during various motor tasks [17], [18]. Intermuscular coherence networks have been recently used to holistically investigate the muscular system during various gait tasks and uniquely discriminate subtle differences in lower limb functions in non-disabled adults [19], [20].…”

Non-parametric Functional Muscle Network as a Robust Biomarker of Fatigue

“…The concept of a functional muscle connectivity network has been recently investigated for non-fatiguing normal tasks and shown high sensitivity to subtle motor changes [33], [34], [31]. This level of task sensitivity to changes in (non-fatiguing) motor tasks has not been reported before using muscle synergy analysis or other forms of conventional spectrotemporal metrics.…”