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Citation |
Rueda-Delgado LM, O'Halloran L, Enz N, Ruddy KL, Kiiski H, Bennett M, Farina F, Jollans L, Vahey N, Whelan R. Int. J. Psychophysiol. 2019; ePub(ePub): ePub. |
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Affiliation |
School of Psychology, Trinity College Dublin, Dublin 2, Ireland; Global Brain Health Institute, Trinity College Dublin, Dublin 2, Ireland. Electronic address: Robert.whelan@tcd.ie. |
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Copyright |
(Copyright © 2019, Elsevier Publishing) |
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DOI |
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PMID |
30936044 |
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Abstract |
Stop-signal reaction time (SSRT), the time needed to cancel an already-initiated motor response, quantifies individual differences in inhibitory control. Electrophysiological correlates of SSRT have primarily focused on late event-related potential (ERP) components over midline scalp regions from successfully inhibited stop trials. SSRT is robustly associated with the P300, there is mixed evidence for N200 involvement, and there is little information on the role of early ERP components. Here, machine learning was first used to interrogate ERPs during both successful and failed stop trials from 64 scalp electrodes at 4 ms resolution (n = 148). The most predictive model included data from both successful and failed stop trials, with a cross-validated Pearson's r of 0.32 between measured and predicted SSRT significantly higher than null models. From successful stop trials, spatiotemporal features overlapping the N200 in right frontal areas and the P300 in frontocentral areas predicted SSRT, as did early ERP activity (<200 ms). As a demonstration of the reproducibility of these findings, the application of this model to a separate dataset of 97 participants was also significant (r = 0.29). These results show that ERPs during failed stops are relevant to SSRT, and that both early and late ERP activity contribute to individual differences in SSRT. Notably, the right lateralized N200, which predicted SSRT here, is not often observed in neurotypical adults. Both the ascending slope and peak of the P300 component predicted SSRT. These results were replicable, both within the training sample and when applied to ERPs from a separate dataset. Language: en |