Abstract
The serotonin system is heavily involved in cognitive and emotional control processes. Previous work has typically investigated this system’s role in control processes separately for cognitive and emotional domains, yet it has become clear the two are linked. The present study, therefore, examined whether variation in a serotonin receptor gene (HTR2A, rs6313) moderated effects of emotion on inhibitory control. An emotional antisaccade task was used in which participants looked toward (prosaccade) or away (antisaccade) from a target presented to the left or right of a happy, angry, or neutral face. Overall, antisaccade latencies were slower for rs6313 C allele homozygotes than T allele carriers, with no effect of genotype on prosaccade latencies. Thus, C allele homozygotes showed relatively weak inhibitory control but intact reflexive control. Importantly, the emotional stimulus was either present during target presentation (overlap trials) or absent (gap trials). The gap effect (slowed latency in overlap versus gap trials) in antisaccade trials was larger with angry versus neutral faces in C allele homozygotes. This impairing effect of negative valence on inhibitory control was larger in C allele homozygotes than T allele carriers, suggesting that angry faces disrupted/competed with the control processes needed to generate an antisaccade to a greater degree in these individuals. The genotype difference in the negative valence effect on antisaccade latency was attenuated when trial N-1 was an antisaccade, indicating top-down regulation of emotional influence. This effect was reduced in C/C versus T/_ individuals, suggesting a weaker capacity to downregulate emotional processing of task-irrelevant stimuli.
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Notes
Emotional face stimuli were selected from the NimStim set of facial expressions (Tottenham et al. 2009). Model IDs were the following: happy (01F_HA_O), angry (01F_AN_O), neutral (01F_NE_C).
Latency models were estimated within SAS PROC MIXED using restricted maximum likelihood estimation and Satterthwaite denominator degrees of freedom. The latency model included random intercepts for mean differences between subjects, −2ΔLL(1) = 798.0, p < .001, and between trials, −2ΔLL(1) = 30.1, p < .001, as well as random slopes for mean differences across subjects in the effects saccade, −2ΔLL(2) = 577.7, p < .001, gap, −2ΔLL(3) = 130.2, p < .001, and their interaction, −2ΔLL(5) = 126.3, p < .001. Given that accuracy is a dichotomous outcome (correct or incorrect saccade), for analysis of errors, a generalized linear function modeling the logit of the probability of an errant saccade was selected. Parameter estimates, therefore, are on a logit scale, which is unbounded and symmetric around zero. A logit of zero means that a saccade was equally likely to be incorrect as correct—i.e., a logit of zero is equivalent to a probability p of .50, where p = exp(logit)/(1 + exp(logit)). To facilitate interpretation, we transformed the mean logit of an error in each condition back onto the probability scale for plotting purposes (Fig. 2) using the equation above. Error models were estimated within SAS PROC GLIMMIX using pseudo-maximum likelihood estimation and Satterthwaite denominator degrees of freedom. The model included random intercepts for mean differences between subjects, −2ΔLL(1) = 2133.9, p < .001, and trials, −2ΔLL(1) = 84.7, p < .001, as well as random slopes for mean differences between subjects in the effects saccade, −2ΔLL(2) = 824.1, p < .001, and gap, −2ΔLL(3) = 135.4, p < .001.
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Acknowledgments
This research was supported by a University of Nebraska-Lincoln Substance Abuse and Violence Initiative (SAVI) seed grant to S.S. and M.D. We thank Grace Sullivan for her helpful comments throughout the development of the manuscript. Correspondence may be sent to Mark Mills, the University of Nebraska-Lincoln, 238 Burnett Hall, Lincoln, NE, USA 68588 (mark.mills2@huskers.unl.edu).
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Mills, M., Wieda, O., Stoltenberg, S.F. et al. Emotion moderates the association between HTR2A (rs6313) genotype and antisaccade latency. Exp Brain Res 234, 2653–2665 (2016). https://doi.org/10.1007/s00221-016-4669-6
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DOI: https://doi.org/10.1007/s00221-016-4669-6