Human short-latency reflexes show precise short-term gain adaptation after prior motion.

CNS motor feedback Compliant movements serotonergic neuromodulation short-latency refex adaptation

Journal

Journal of neurophysiology
ISSN: 1522-1598
Titre abrégé: J Neurophysiol
Pays: United States
ID NLM: 0375404

Informations de publication

Date de publication:
30 Oct 2024
Historique:
medline: 30 10 2024
pubmed: 30 10 2024
entrez: 30 10 2024
Statut: aheadofprint

Résumé

The central nervous system adapts the gain of short-latency reflex loops to changing conditions. Experiments on biomimetic robots showed that reflex modulation could substantially increase energy efficiency and stability of periodic motions if, unlike known mechanisms, the reflex modulation both acted precisely on the muscles involved and lasted after the motion. This study tests the presence of such a mechanism by having participants repeatedly rotate either their right elbow or shoulder joint, before perturbing either joint. The results demonstrate a mechanism that modulates short-latency reflex gains after prior motion with joint-specific precision. Enhanced gains were observed hundreds of milliseconds after movement cessation, a time scale well-suited to quickly adapt overall periodic motion cycles. A serotonin antagonist significantly decreased these post-movement gains diffusely across joints. But blocking serotonin did not affect the joint-specificity of the gain scaling more than a placebo, suggesting that serotonin sets the overall reflex gain across joints after movement by an effect that is modulated in a joint-specific manner by an unidentified neural circuit. These results confirm the existence of a new, joint-specific, fast, persistent adaptation of short-latency reflex loops induced by motion in human arms.

Identifiants

pubmed: 39475493
doi: 10.1152/jn.00212.2024
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Subventions

Organisme : EC | Horizon 2020 Framework Programme (H2020)
ID : 785907

Auteurs

Philipp Stratmann (P)

School of Computation, Information and Technology, Technical University of Munich, Germany.

Annika Schmidt (A)

School of Computation, Information and Technology, Technical University of Munich, Garching, Germany.

Hannes Höppner (H)

Berliner Hochschule für Technik, Berlin, Germany.

Patrick van der Smagt (P)

Machine Learning Research Lab, Eotvos Lorand University Budapest, ,, Hungary.

Tobias Meindl (T)

TUM University Hospital rechts der Isar, Technical University of Munich, Germany.

David W Franklin (DW)

TUM School of Medicine and Health, Technical University of Munich, Munich, Germany.

Alin Albu-Schäffer (A)

Institute of Robotics and Mechatronics, German Aerospace Center, Germany.

Classifications MeSH