Decoding Motor Control: Multimodal Approaches to Cortical Plasticity and Learning

Dear Colleagues,

Motor control and cortical plasticity have long been central themes in neuroscience, linking perception, action, and adaptation. Over the past decades, major advances in neuroimaging, electrophysiology, and computational modeling have deepened our understanding of how the brain encodes, coordinates, and refines movement. These insights have also transformed fields such as rehabilitation, sports science, and neuroengineering.

This Special Issue aims to integrate diverse perspectives on how neural circuits adapt and reorganize in response to training, injury, and sensory feedback. By fostering dialog between basic and applied sciences, we aim to illuminate the mechanisms underlying motor learning and cortical reorganization across species, contexts, and methodological approaches.

We encourage the submission of contributions that employ multimodal methodologies—combining neurophysiological, behavioral, biomechanical, and computational data—to decode the dynamic interactions within motor networks. Studies using neuroimaging, non-invasive brain stimulation, machine learning, and wearable sensing technologies are particularly welcome.

We invite original research articles, systematic or narrative reviews, and methodological papers addressing motor control, neural plasticity, motor learning, and their applications to neurorehabilitation, human performance, and brain–machine interfaces.

Prof. Dr. Gonzalo Marquez
Dr. David Colomer-Poveda
Guest Editors

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