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  3. Manifold Interactions Between The Action-mode Network And Sensorimotor Cortex During Human Motor Learning.
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  3. Manifold Interactions Between The Action-mode Network And Sensorimotor Cortex During Human Motor Learning.

Related Experiment Video

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Manifold Interactions between the Action-Mode Network and Sensorimotor Cortex during Human Motor Learning.

Maryam Ansari Esfeh1,2, Ali Rezaei1, Keanna Bamdad Rowchan1,2

  • 1Centre for Neuroscience Studies, Queen's University, Kingston, Ontario K7L 3N6, Canada.

The Journal of Neuroscience : the Official Journal of the Society for Neuroscience
|April 9, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

The Action-Mode Network (AMN) dynamically interacts with the Somatomotor Network (SMN) during motor learning. This coordination shifts from integration to segregation, forming a latent memory signature and guiding skill acquisition and relearning.

Keywords:
action-mode networkbrain connectivitymanifoldmotor controlmotor learningreaching

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Area of Science:

  • Neuroscience
  • Cognitive Science

Background:

  • Effective motor learning involves complex interactions between sensorimotor and higher-order control networks.
  • The precise coordination across learning phases (adaptation, consolidation, relearning) is not well understood.

Purpose of the Study:

  • To investigate the dynamic functional coupling between the Action-Mode Network (AMN) and Somatomotor Network (SMN) during multi-day visuomotor adaptation.
  • To elucidate the network reconfigurations underlying different stages of motor skill acquisition and memory.

Main Methods:

  • Human participants underwent a multi-day visuomotor adaptation task.
  • Manifold learning techniques were used to analyze changes in AMN-SMN functional connectivity.
  • Functional coupling was assessed across initial learning, plateaued performance, and relearning phases.

Main Results:

  • Initial learning showed AMN-SMN integration with broader cortices (manifold contraction).
  • As performance plateaued, the AMN disengaged (manifold expansion), while the SMN remained integrated, forming a memory signature.
  • This dynamic pattern was reinstated during subsequent relearning, and initial integration correlated with performance.

Conclusions:

  • The AMN dynamically modulates its coupling with the SMN to support motor learning, consolidation, and retrieval.
  • A hierarchical framework explains how large-scale sensorimotor networks guide motor memory.
  • Findings offer insights into the neural mechanisms of skill acquisition and memory formation.