A neurocomputational theory of action regulation predicts motor behavior in neurotypical individuals and patients with Parkinson's disease

Surviving in an uncertain environment requires not only the ability to select the best action, but also the flexibility to withhold inappropriate actions when the environmental conditions change. Although selecting and withholding actions have been extensively studied in both human and animals, there is still lack of consensus on the mechanism underlying these action regulation functions, and more importantly, how they inter-relate. A critical gap impeding progress is the lack of a computational theory that will integrate the mechanisms of action regulation into a unified framework. The current study aims to advance our understanding by developing a neurodynamical computational theory that models the mechanism of action regulation that involves suppressing responses, and predicts how disruption of this mechanism can lead to motor deficits in Parkinson's disease (PD) patients. We tested the model predictions in neurotypical individuals and PD patients in three behavioral tasks that involve free action selection between two opposed directions, action selection in the presence of conflicting information and abandoning an ongoing action when a stop signal is presented. Our results and theory suggest an integrated mechanism of action regulation that affects both action initiation and inhibition. When this mechanism is disrupted, motor behavior is affected, leading to longer reaction times and higher error rates in action inhibition. Author summary Humans can rapidly regulate actions according to updated demands of the environment. A key component of action regulation is action inhibition, the failure of which contributes to various neuropsychiatric disorders. Despite extensive efforts to understand how the brain selects, pauses, and abandons actions, the mechanisms underlying these functions and how they inter-relate remain elusive. The current study introduces a large-scale model that characterizes the computations of action regulation functions, how they are implemented within brain networks and how disruption of these circuits can lead to deficits in motor behavior seen in Parkinson's disease (PD). The model was developed by studying the motor behavior of healthy individuals and PD patients in three motor tasks that involve action inhibition. Overall, the model explains many aspects on motor behavior when people select between multiple actions, make decisions when conflicting information is present, and stop ongoing actions. It explains many key features of PD patients, including longer responses in generating movements even without competing actions and conflicting information, and lower probability to stop an action. Our neurocomputational theory provides significant insights on the circuit computations underlying action regulation, opening new avenues for improving and developing therapeutic interventions for diseases that may involve this circuit.

Automated summary

This study develops a computational theory that models the mechanism of action regulation and explains how disruption of this mechanism can lead to motor deficits in Parkinson's disease patients. The results suggest an integrated mechanism of action regulation that affects both action initiation and inhibition. The model provides insights into the circuit computations underlying action regulation and has implications for therapeutic interventions for diseases involving this circuit.