What does science reveal about the invisible link between the brain and the muscles?

Responding quickly to something we see, such as braking when a car appears or grabbing an object that is falling, seems simple, but it requires extremely fine‑tuned coordination between the brain and the muscles. This coordination does not depend solely on sending “commands” to the muscles: it involves electrical rhythms in the brain that oscillate regularly and that, when they synchronise with muscle activity, can lead to faster or more accurate responses. To understand this process, a team led by Alice Tomassini studied the link between brain activity recorded via EEG and the force applied by the hand while participants tracked a moving target and reacted to unexpected changes in its trajectory.

The results showed that the brain uses two different rhythms to support two essential abilities: alpha rhythm – when the brain and the muscle oscillated in greater synchrony in this band, participants responded faster after the visual perturbation; beta rhythm – when this synchronization was stronger in this band, responses were more accurate and efficient, increasing the likelihood of achieving the goal and obtaining reward.

At an even finer level, the researchers found that performance improved when both signals oscillated at a phase considered “ideal,” suggesting that it is not only the strength of the oscillations that matters, but also how the brain and muscle “align” in time. These effects remained even after controlling for factors such as attention, applied force, or general variations in brain activity, reinforcing that the temporal synchronisation between brain and muscle plays its own autonomous role in motor control.

Overall, the study shows that the brain continuously adjusts how it coordinates with the body, alternating between rhythms that support speed and rhythms that support precision, depending on the demands of the moment. Understanding these mechanisms may inspire new approaches in motor rehabilitation, faster brain‑machine interfaces, and robotic systems that react in a more human‑like way. This study was published in the scientific journal Journal of Neuroscience, in the article Alpha and beta cortico-motor phase dynamics shape visuomotor control on a single-trial basis, as a part of research project 246/20 - The hidden rhythm of interpersonal (sub-)movement coordination, supported by the Bial Foundation.

Abstract

A central question in sensorimotor neuroscience is how sensory inputs are mapped onto motor outputs to enable swift and accurate responses, even in the face of unexpected environmental changes. In this study, we leverage cortico-motor coherence as a window into the dynamics of sensorimotor loops and explore how it relates to online visuomotor control. We recorded brain activity using electroencephalography (EEG) while human participants (of either sex) performed an isometric tracking task involving transient, unpredictable visual perturbations. Our results show that coherence between cortical activity and motor output (force) in the alpha band (8-13 Hz) is associated with faster motor responses, while beta-band coherence (18-30 Hz) promotes more accurate control, in turn linked to a higher likelihood of obtaining rewards. Both effects are most pronounced near the onset of the perturbation, underscoring the predictive value of cortico-motor coherence for sensorimotor performance. Single-trial analyses further reveal that deviations from the preferred cortico-motor phase relationship are associated with longer reaction times and larger errors, and these phase effects are independent of power effects. Thus, beta-band coherence may reflect a cautious, reward-efficient control strategy, while alpha-band coherence enables quicker, though not necessarily efficient, motor responses, indicating a complementary, reactive control mode. These results highlight the finely tuned nature of sensorimotor control, where different aspects of sensory-to-motor transformations are governed by frequency-specific neural synchronization on a moment-to-moment basis. By linking neural dynamics to motor output, this study sheds light on the spectrotemporal organization of sensorimotor networks and their distinct contribution to goal-directed behavior.