Back

Autogenic spinal excitatory circuit ensures skilled hand movements in primates

Kim, G.; Tomatsu, S.; Umeda, T.; Takei, T.; Funato, T.; Seki, K.

2025-04-01 neuroscience
10.1101/2025.03.31.646263 bioRxiv
Show abstract

Skillful hand movements are a hallmark of primates, including humans, requiring sophisticated motor planning and execution. Challenging the longstanding paradigm that emphasizes the dominance of the cerebral cortex1, our study highlights a pivotal role for spinal excitatory reflex circuits in both planning and executing skillful hand movements. Using a combination of experimental approaches with behaving non-human primates and predictive simulation, we identified a group of excitatory spinal interneurons that orchestrate a closed-loop, positive feedback mechanism during voluntary wrist movements. This mechanism is characterized by a bidirectional interaction between inter-neuronal spiking and muscle activity, mediated by motoneuronal efferent signals and proprioceptive afferent signals from the same agonistic muscles. Furthermore, we demonstrate that the temporal profile of muscle activity during movement execution, including amplitude and duration, is pre-determined during motor planning at the spinal interneurons, functioning as a force-feedback gain within the excitatory circuit. These findings suggest that autogenic spinal excitatory circuits play a predominant role in shaping overall muscle activation during motor execution, provided the proper reflex gain is pre-set by higher neural systems during motor planning. Together, our findings provide a cellular-level perspective on how spinal reflex loops contribute to skilled voluntary movements in primates, extending over a century of spinal reflex research2.

Published in Proceedings of the National Academy of Sciences (predicted rank #5) · training set

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.