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

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.