Chiang‐Shan R. Li

Cognitive control is a critical executive function. Many studies have combined general linear modeling and the stop signal task (SST) to delineate the component processes of cognitive control. For instance, by contrasting stop success (SS) and stop error (SE) trials in the SST, investigators examined regional responses to stop signal inhibition. In contrast to this parameterized approach, independent component analysis (ICA) elucidates brain networks subserving cognitive control. In our earlier work of 59 adults performing the SST during fMRI, we characterized six independent components (ICs). However, none of these ICs correlated with stop signal performance, raising questions about their behavioral validity. Here, in a larger sample (n = 100), we identified and explored 23 ICs for correlation with the stop signal reaction time (SSRT), a measure of the efficiency of response inhibition. At a corrected threshold (P < 0.0005), a paracentral lobule‐midcingulate network and a left inferior parietal‐supplementary motor‐somatomotor network showed a positive correlation between SE beta weight and SSRT. In contrast, a midline cerebellum–thalamus–pallidum network showed a negative correlation between SE beta weight and SSRT. These findings suggest that motor preparation and execution prolongs the SSRT, likely via an interaction between the go and stop processes as suggested by the race model. Behaviorally, consistent with this hypothesis, the difference in G and SE reaction times is positively correlated with SSRT across subjects. These new results highlight the importance of cognitive motor regions in response inhibition and support the utility of ICA in uncovering functional networks for cognitive control in the SST.Hum Brain Mapp 36:3289–3302, 2015. ©2015 Wiley Periodicals, Inc.

Background:  Alcohol abuse and dependence are common problems in the United States that stem from a variety of factors, one of which may be a period of high level social drinking during college and early adulthood. Extant study implicates risk taking as a cognitive factor that contributes to habitual and heavy drinking. We sought to examine the neural processes of risk taking in young, nondependent drinkers.Methods:  We compared 20 young adult social drinkers with a high level of alcohol use (AH), as defined by number of drinks per month, and 21 demographically matched drinkers with low to moderate alcohol use (ALM) in a functional magnetic resonance imaging study of the stop signal task. By contrasting risk taking (speeded) to risk aversion (slowed) trials, we examined the neural correlates of risk taking. Brain imaging data were analyzed with Statistical Parametric Mapping. Regions of interest were identified and corresponding effect sizes were examined for correlations with self‐reported alcohol use.Results:  The results showed that, compared with ALM, AH demonstrated decreased activation in right superior frontal gyrus and left caudate nucleus when contrasting risk taking and risk aversion trials at p < 0.001, uncorrected. Furthermore, examination of the effect size data showed that the extent of these decreased regional activations correlated with frequency of drinking in women, but not men.Conclusions:  These findings suggest a neural analog of nondependent, high level drinking. Specifically, high level social drinking is associated with altered activation of the caudate and superior frontal cortex, an association that appears to be stronger in women than in men and is strongly tied to the frequency of drinking. These results are relevant in understanding risk taking behavior in social drinking as well as in examining the potential path from high level social use in young adults to dangerous alcohol consumption later in life.

Cognitive control is a critical executive function of the human brain. Many studies have combined general linear modeling and the stop signal task (SST) to delineate the component processes of cognitive control. For instance, by contrasting stop success (SS) and stop error (SE) trials in the SST, investigators examined the neural processes underlying stop signal inhibition (SS > SE) and error processing (SE > SS). To complement this parameterized approach, here, we employed a data‐driven method—independent component analysis (ICA)—to elucidate neural networks and the relationship between neural networks subserving cognitive control. In 59 adults performing the SST during fMRI, we characterized six independent components with ICA. These functional networks, temporally sorted for go success, SS, and SE trials as the events of interest, included a motor cortical network for motor preparation and execution; a right fronto‐parietal network for attentional monitoring; a left fronto‐parietal network for response inhibition; a midline cortico‐subcortical network for error processing; a cuneus–precuneus network for behavioral engagement; and a “default” network for self‐referential processing. Across subjects the event‐associated weights of these functional networks showed a distinct pattern of correlation. These results provide new insight into the component processes of cognitive control. Hum Brain Mapp, 2012.