Internal states as a source of subject-dependent movement variability and their representation by large-scale networks

Abstract

AbstractA human’s ability to adapt and learn relies on reflecting on past performance. Such reflections form latent factors called internal states that induce variability of movement and behavior to improve performance. Internal states are critical for survival, yet their temporal dynamics and neural substrates are less understood. Here, we link internal states with motor performance and neural activity using state-space models and local field potentials captured from depth electrodes in over 100 brain regions. Ten human subjects performed a goal-directed center-out reaching task with perturbations applied to random trials, causing subjects to fail goals and reflect on their performance. Using computational methods, we identified two internal states, indicating that subjects kept track of past errors and perturbations, that predicted variability in reaction times and speed errors. These states granted access to latent information indicative of how subjects strategize learning from trial history, impacting their overall performance. We further found that large-scale brain networks differentially encoded these internal states. The dorsal attention network encoded past errors in frequencies above 100 Hz, suggesting a role in modulating attention based on tracking recent performance in working memory. The default network encoded past perturbations in frequencies below 15 Hz, suggesting a role in achieving robust performance in an uncertain environment. Moreover, these networks more strongly encoded internal states and were more functionally connected in higher performing subjects, whose learning strategy was to respond by countering with behavior that opposed accumulating error. Taken together, our findings suggest large-scale brain networks as a neural basis of strategy. These networks regulate movement variability, through internal states, to improve motor performance.Key pointsMovement variability is a purposeful process conjured up by the brain to enable adaptation and learning, both of which are necessary for survival.The culmination of recent experiences—collectively referred to as internal states—have been implicated in variability during motor and behavioral tasks.To investigate the utility and neural basis of internal states during motor control, we estimated two latent internal states using state-space representation that modeled motor behavior during a goal-directed center-out reaching task in humans with simultaneous whole-brain recordings from intracranial depth electrodes.We show that including these states—based on error and environment uncertainty—improves the predictability of subject-specific variable motor behavior and reveals latent information related to task performance and learning strategies where top performers counter error scaled by trial history while bottom performers maintain error tendencies.We further show that these states are encoded by the large-scale brain networks known as the dorsal attention network and default network in frequencies above 100 Hz and below 15 Hz but found neural differences between subjects where network activity closely modulates with states and exhibits stronger functional connectivity for top performers.Our findings suggest the involvement in large-scale brain networks as a neural basis of motor strategy that orchestrates movement variability to improve motor performance.