According to the race model of stop-signal response inhibition (Logan and Cowan, 1984
), SSRT is a measure of the time needed to engage an inhibitory process. Individual differences in this quantity have long been linked to development (Williams et al., 1999
) and impulsivity (Logan et al., 1997
); they have also shown significant impairments in impulse control disorders such as ADHD (Logan et al., 2000
; Lijffijt et al., 2005
), substance use (Fillmore and Rush, 2002
; Monterosso et al., 2005
), and OCD (Chamberlain et al., 2006
). However, some of these groups also appear to show increased variability in their performance on the primary (go) task as well (Williams et al., 1999
; Lijffijt et al., 2005
), which could inflate the estimated SSRT value. We strove to demonstrate that SSRT, not SDRT, is critically related to successful response inhibition. We found that both SSRT and SDRT were correlated with neural activity during successful inhibition, but only SSRT was predictable from that neural activity. This supports the claim that SSRT is the only behavioral variable critically related to the neural mechanisms of response inhibition. Moreover, we sought to extend previous findings that age and SSRT are related to the neural correlates of successful response inhibition by demonstrating that neural activity is predictive of these variables. Our results supported this claim as well, given that age and SSRT were the only two variables that could be predicted by neural activity, and that they were specifically predicted by neural activity during successful response inhibition, but not successful response execution.
Using a standard GLM analysis, we found much of the variability in neural activity in regions typically involved in successful response inhibition performance, such as the anterior cingulate gyrus, superior frontal gyrus, striatum and STN, was uniquely correlated with SSRT, while a widespread network of regions not specific to response inhibition were correlated with SDRT, including much of the left prefrontal cortex and primary sensory and motor areas. One region, a portion of the rostral anterior cingulate gyrus, showed activity related to age. Additionally, only one region, which included the superior, middle, and transverse temporal gyri, showed activity related to GoRT. During successful Go task performance, only response time behavior was related to neural activity (both GoRT and SDRT).
While the GLM analysis was useful in determining where neural activity was related to age or performance, we could not use those results as an indication of how neural activity may predict those variables. Thus, we implemented three methods of predictive analysis (Gaussian process regression with a linear kernel, Gaussian process regression with a squared exponential kernel, and support vector regression) to determine which aspects of the stop-signal task caused neural activity that was predictive of age or behavioral performance across individuals.
We found that neural activity during successful response inhibition as defined by contrasting successful stopping vs. successful going was predictive of age and SSRT, but not GoRT or SDRT. This was the case even though there were significant correlations between SDRT and activation during successful inhibition in our standard GLM analysis. This discrepancy between significant correlations and unsuccessful prediction may reflect the fact that the correlations were driven by a small number of observations. It also highlights the need to be careful regarding the conflation of correlation and prediction, as is common in the neuroimaging literature.
Additionally, we found that neural activity during successful Go task performance was not predictive of age or any behavioral variables. Taken together, these results imply that individual differences in SSRT are specifically related to response inhibition processes as opposed to response execution processes. Further evidence for a linkage between developmental changes in SSRT and changes in inhibitory processing was seen in that similar neural regions were predictive of both age and SSRT, including regions known to be utilized during successful motor inhibition, such as the right IFG, the striatum and the right STN. Lastly, individual differences in SSRT were related to inhibitory processes and not variability of Go task performance.
It is important to note that any conclusions from the present results must be qualified by the fact that only a small portion of the possible space of statistical learning methods was chosen. In particular, though we examined a range of statistical machines (linear and non-linear support vector machines and linear Gaussian processes), it could be the case that other methods would be more sensitive to specific effects. In addition, we did not employ any feature selection but rather included whole-brain data in the analyses. Finally, additional parameter optimization could have potentially improved predictive performance. However, the presence of significant results across multiple methods suggests that the significant prediction observed here is at least somewhat robust to methodological choices.
The current findings support and extend much previous developmental and response inhibition research. It has been demonstrated that cortical and subcortical neural regions involved in response inhibition are not fully developed structurally (Giedd et al., 1999
; Sowell et al., 1999
) or functionally (Casey et al., 2000
) until adulthood. More specific to response inhibition, previous studies have demonstrated that adults have greater activity in the right IFG, a brain region known to be critical for successful response inhibition (Bunge et al., 2002
; Durston et al., 2002
; Rubia et al., 2007
), and that response inhibition ability increases with increasing age (Casey et al., 1997
; Williams et al., 1999
; Durston et al., 2002
). Even in fully developed, healthy adults, right IFG activity has been shown to be related to response inhibition ability (Aron and Poldrack, 2006
). Therefore, the current study extends these correlational findings by demonstrating that neural activity during successful response inhibition in the right IFG, the striatum and the right STN is predictive of age and SSRT. It is important, however, to qualify that even though we extended the results of previous research from correlational to predictive, without a direct manipulation of neural functioning (i.e., a lesion or TMS), we cannot make strong claims about the causal relationship between regional brain function and behavior.
A debate in developmental neuroscience centers around how researchers can tease apart age-related neural differences from performance-related differences, especially when performance improves with age, as occurs in the stop-signal task (Durston and Casey, 2006
). The results from our predictive analysis indicate that patterns of neural activity during successful response inhibition predicted both age and SSRT equally well in our participants. This implies that in a task where age and performance are inextricably linked, a combination of age effects and performance effects underlies neural activity. Future research could further enhance our knowledge of the relationship between age and SSRT by more specifically exploring whether age influences SSRT directly or if the two variables are related via a separate, mediating factor. A combination of predictive analyses and formal modeling of the causal relationships between these and other variables may help to elucidate this relationship.