bioRxivNeurosciencePreprint
The Human Rectus Femoris Muscle Receives an Independent Common Synaptic Input During Isometric Leg Extensions
Finja BeermanniD, Daniel HalleriD, Luca Hofbeck, Simone ZaccaroniD, Marcel BetschiD, Alessandro Del VecchioiD
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Abstract
The distribution of common synaptic inputs across spinal motor neuron pools within and across synergist muscles reveals direct insight in the neural control of movement. We previously documented two dominant common inputs in the human vastus lateralis (VL) and medialis (VM) muscle during isometric leg extensions. Whether these inputs are also shared with rectus femoris (RF), a synergist muscle that contributes substantially to leg extension but is biarticular, remains unknown. We simultaneously recorded motor unit activity from VM, RF, and VL using multiple targeted intramuscular electromyographic sensors during isometric knee extension. Decomposed motor unit spike trains were analyzed using discharge characteristics, pairwise correlation, spectral coherence, explained variance, and factor analysis to characterize the structure of common synaptic input within and across muscles. Despite their shared mechanical output via a common patellar tendon, the three muscles exhibited distinct patterns of neural organization. RF consistently received a strong, low-dimensional common input that was largely independent of the drive to VM and VL, as evidenced by elevated within-muscle correlation and coherence, low between-muscle coupling, and an invariant dedicated latent factor across all participants. VM and VL shared a substantial proportion of common synaptic input, but the degree of coupling and the underlying factor structure varied across individuals, ranging from a single shared factor to largely independent muscle-specific inputs. These findings indicate that the quadriceps are organized in a muscle- and subject-specific manner, with RF receiving a dedicated independent input.
- organism: human
- assay: electronic health records
- research focus: cell type annotation
- approach: statistical model
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