# Interdependence patterns of multifrequency oscillations predict visuomotor behavior

## TL;DR

This study shows that brain oscillations can predict sensorimotor behavior by analyzing how different brain regions coordinate during tasks.

## Contribution

The paper introduces high-dimensional oscillatory portraits to capture interdependence patterns of brain oscillations for predicting visuomotor behavior.

## Key findings

- Oscillatory portraits better separate trial categories than individual oscillatory elements.
- Movement accuracy is linked to the adaptability of oscillatory coordination architecture.
- Interdependence networks show stable structure across tasks but adapt to constraints.

## Abstract

We show that sensorimotor behavior can be reliably predicted from single-trial EEG oscillations fluctuating in a coordinated manner across brain regions, frequency bands, and movement time epochs. We define high-dimensional oscillatory portraits to capture the interdependence between basic oscillatory elements, quantifying oscillations occurring in single trials at specific frequencies, locations, and time epochs. We find that the general structure of the element interdependence networks (effective connectivity) remains stable across task conditions, reflecting an intrinsic coordination architecture and responds to changes in task constraints by subtle but consistently distinct topological reorganizations. Trial categories are reliably and significantly better separated using oscillatory portraits than from the information contained in individual oscillatory elements, suggesting an interelement coordination-based encoding. Furthermore, single-trial oscillatory portrait fluctuations are predictive of fine trial-to-trial variations in movement kinematics. Remarkably, movement accuracy appears to be reflected in the capacity of the oscillatory coordination architecture to flexibly update as an effect of movement-error integration.

This study demonstrates that sensorimotor behavior can be accurately predicted from single-trial EEG oscillations that exhibit coordinated fluctuations across various brain regions, frequency bands, and movement time epochs. We introduce high-dimensional oscillatory portraits to capture the relationships among basic oscillatory elements, quantifying oscillations at specific frequencies and times during individual trials. Our findings indicate that the overall structure of these interdependence networks, or effective connectivity, remains stable across different task conditions, showcasing an intrinsic coordination architecture that adapts to task constraints through subtle topological changes. Additionally, fluctuations in single-trial oscillatory portraits can predict variations in movement kinematics, with movement accuracy reflecting the oscillatory architecture’s ability to adapt in response to movement errors.