Biophysical modeling of frontocentral ERP generation links circuit-level mechanisms of action-stopping to a behavioral race model

Abstract

AbstractHuman frontocentral event-related potentials (FC-ERPs) are ubiquitous neural correlates of cognition and control, but their generating multiscale mechanisms remain mostly unknown. We used the Human Neocortical Neurosolver(HNN)’s biophysical model of a canonical neocortical circuit under exogenous thalamic and cortical drive to simulate the cell and circuit mechanisms underpinning the P2, N2, and P3 features of the FC-ERP observed after Stop-Signals in the Stop-Signal task (SST). We demonstrate that a sequence of simulated external thalamocortical and cortico-cortical drives can produce the FC-ERP, similar to what has been shown for primary sensory cortices. We used this model of the FC-ERP to examine likely circuit-mechanisms underlying FC-ERP features that distinguish between successful and failed action-stopping. We also tested their adherence to the predictions of the horse-race model of the SST, with specific hypotheses motivated by theoretical links between the P3 and Stop process. These simulations revealed that a difference in P3 onset between successful and failed Stops is most likely due to a later arrival of thalamocortical drive in failed Stops, rather than, for example, a difference in effective strength of the input. In contrast, the same model predicted that early thalamocortical drives underpinning the P2 and N2 differed in both strength and timing across stopping accuracy conditions. Overall, this model generates novel testable predictions of the thalamocortical dynamics underlying FC-ERP generation during action-stopping. Moreover, it provides a detailed cellular and circuit-level interpretation that supports links between these macroscale signatures and predictions of the behavioral race model.Significance statementThe frontocentral event-related potential (FC-ERP) is an easily-measurable neural correlate of cognition and control. However, the cortical dynamics that produce this signature in humans are complex, limiting the ability of researchers to make predictions about its underlying mechanisms. In this study, we used the biophysical model included in the open-source Human Neocortical Neurosolver software to simulate and evaluate the likely cellular and circuit mechanisms that underlie the FC-ERP in the Stop-Signal task. We modeled mechanisms of the FC-ERP during successful and unsuccessful stopping, generating testable predictions regarding Stop-associated computations in human frontal cortex. Moreover, the resulting model parameters provide a starting point for simulating mechanisms of the FC-ERP and other frontal scalp EEG signatures in other task conditions and contexts.