The present results clearly demonstrate that brain/behavior correlation analyses benefit from the use of dimensionality reduction in comparison to voxelwise analyses. In particular, we found that whereas voxelwise analyses with appropriate statistical corrections did not detect robust correlations between behavior and activation, even in a well-powered sample, significant correlations were detected between behavior and activation in a set of distributed networks identified using independent components analysis. When ICA was applied to statistical maps (rather than timeseries), it provided a set of components that were strongly concordant with results from previous voxelwise analyses, including components that reflect networks commonly associated with motor response execution and also regions known to be critical for response inhibition. It also identified regions that are commonly deactivated during task performance, including the midline regions commonly known as the “default mode” network (Raichle et al., 2001
Analyses of the correlation between engagement of these components and behavioral measures of response inhibition provide a resolution to inconsistencies in previous studies of the relation between inhibitory control behavior and fMRI signals. First, our results demonstrate that response inhibition ability is positively related to engagement of networks that include regions that have been consistently shown to be active in the Stop-signal paradigm, including right IFG/anterior insula, pre-SMA, and basal ganglia structures. These findings are highly consistent with previous reports not only of activation seen during successful inhibition during performance of the Stop-signal task, but also with previous reports of the relationship between activation in these regions and individual differences in performance across stopping tasks (Forstmann et al., 2008
; Goghari and MacDonald, 2009
; Li et al., 2006
). Second, our results demonstrate that response inhibition ability is negatively related to the engagement of the default mode network across individuals.
A novel aspect of the present results is the significant association between response inhibition ability and components derived from ICA applied to statistical maps, although the activation in stopping-related components is in line with previous reports. Component 14 included key regions associated with response inhibition, including the pre-SMA/SFG/paracingulate, the right IFC/opercular/insular cortices, and bilateral basal ganglia and thalamus. Component 2 included these same regions, but also included the right frontal orbital cortex/MFG/frontal pole, left MFG, posterior cingulate, and right posterior parietal through occipital cortex. The correlation between SSRT and activation in component 14 was significant, even after correction for multiple comparisons, further demonstrating that the regions included in component 14 represent critical regions underlying response inhibition. Results of our voxel-wise regression analyses revealed activation in many of these same regions, although at an uncorrected threshold, providing tentative evidence of whole-brain correlations. These findings are consistent with previous imaging studies that have taken both whole-brain and region of interest approaches, as well as results from DTI analyses (Aron et al., 2007
; Li et al., 2006
) and loss-of-function studies (Aron et al., 2003
; Chambers et al., 2006
; Chen et al., 2009
; Floden and Stuss, 2006
The use of component-based analysis also allowed us to find interesting negative relationships between brain activity and inhibitory performance. First, SSRT positively correlated with activation in the default mode network, which was identified from task activation maps through the use of ICA. The default-mode network comprised a network of brain regions, including the medial prefrontal cortex, the medial, lateral, and inferior parietal cortex, and the precuneus/posterior cingulate cortex, which are consistently deactivated during performance of cognitive tasks and activated during rest (Biswal et al., 2010
; Raichle et al., 2001
Our ICA analysis of Stop-signal data revealed distinct components of activation throughout the default-mode network for both StopInhibit-Go and Go-Null contrasts. However, only default-mode network activation during successful Stop trials positively correlated with SSRT. This is an intriguing finding and is in line with suggestions that increased default-mode network activity during task performance may underlie impaired attentional control (Mason et al., 2007
; Sonuga-Barke and Castellanos, 2007
). As an increase of activation, or an attenuation of deactivation, in this network is suggested to interfere with task-specific attention and goal-directed action, our finding of a positive correlation between default-mode network activation and SSRT suggests that default-mode network activation in individuals with poorer response inhibition may reflect another mechanism of impaired response inhibition. Alternatively, the relative engagement of default mode versus task-related components may reflect the degree to which each subject is cognitively engaged in the task.
Second, SSRT positively correlated with activation in a motor pathway, including the SMA through bilateral pre- and postcentral gyri, the posterior cingulate, and the left putamen. Although this correlation did not survive correction, it is suggestive that individuals with poorer response inhibition (longer SSRT) had increased activation in regions responsible for the execution of a motor response in comparison to individuals with better response inhibition. It has been reported in a TMS study that successful Stop trials are associated with suppression of cortico-motoneuronal excitability as compared to baseline (Badry et al., 2009
). An incomplete motor suppression response may therefore represent another potential mechanism influencing poor response inhibition.
It is interesting that only the correlations between components from the StopInhibit-Go contrast and SSRT survived correction. Although there were significant correlations between go-task performance and components of go-task activation, these did not survive correction. There is a considerable body of evidence supporting the relationship between individual differences in stopping activation and performance, specifically in the right IFC, preSMA, and right STN, and much less supporting a relationship between individual differences in going activation and performance. However, there is reason to expect to see a relationship between performance on Go trials and neural activation. For example, a negative correlation between Go RT and bilateral insula activation during successful Stop trials has been reported (Garavan et al., 2006
). The reason for specificity of correlations between stopping activation and performance may be that there is greater variability associated with the neural mechanisms underlying the response inhibition as opposed to execution.
The components extracted from the StopInhibit-StopRespond contrast also did not correlate with performance after correction. This reflects the substantial overlap in the engagement of the right-lateralized fronto-basal ganglia network between StopInhibit and StopRespond trials. We have focused on the StopInhibit-Go contrast because this is the contrast that should most directly index inhibitory function according to the race model of stop-signal inhibition. Although it is not immediately intuitive, the difference between StopRespond and StopInhibit trials in the stop signal task is not actually thought to reflect differences in inhibition according to this model. Rather, it should instead reflect differences in the speed of the Go process that is racing against the Stop process.
Consistent with our analyses of ICA components, activation during StopInhibit-StopRespond that negatively correlated with SSRT did not survive correction in our voxel-wide analyses. In contrast, the activation during StopInhibit-StopRespond that positively correlated with SSRT did survive correction. Although one might interpret this difference in findings from ICA vs. voxel-wise analyses as reflecting the sensitivity of ICA to broad vs. specific constructs, respectively, we believe that the lack of significant correlations between StopInhibit-StopRespond components and SSRT instead reflects the limited set of regions for the StopInhibit-StopRespond (or StopRespond-StopInhibit) comparison, combined with the use of a relatively small number of independent components, which biases the analysis towards finding broader components.
A strength of the present study is that we used Group ICA to analyze functional imaging data acquired during the performance of a Stop-signal task in a large sample of healthy adults. An advantage of using Group ICA to isolate components, which we can then correlate with task performance, is that we are able to tease apart patterns of activation in regions that would otherwise not be distinct in group-level activation maps. A common approach is to compare successful Stop trials to successful Go or unsuccessful Stop trials in an attempt to isolate activation specific to the stopping process. Both of these contrasts, however, involve more than just response inhibition. For example, for successful Stop as compared to Go trials, there is the additional perceptual processing associated with the stop-signal. Furthermore, this contrast still captures aspects of motor planning and processing. This is apparent in the results of our ICA analyses as component 9, which positively correlated with SSRT, included widespread activation in a motor planning and execution network. It is also noteworthy that we were able to identify activation in the “default-mode” network (component 1) even though we analyzed statistical maps, as opposted to timeseries data.
An additional strength of this analysis is that we were able to take advantage of data collected across five separate studies and combine them into one mega-analysis (Costafreda, 2009
). Rather than combining results from separate studies, we were able to combine the data and perform a new series of analyses. Furthermore, even though different scanners were used, we were able to control for group membership in our group-level analyses, as well as in our correlations, in order to control for scanning- and study-related differences.
Indeed, we believe that a particular strength of our study is the sufficiently large sample size needed in order to compare this data dimensionality reduction approach to mass univariate analyses for the purposes of individual differences research. One of the main reasons for the inconsistency in results regarding the relationship between inhibitory control (or better yet, any measure of individual differences) and fMRI signals has been the use of small samples with low power to detect correlations of reasonable size. It has become increasingly clear that sample sizes on the order of 100 subjects are necessary in order to obtain sufficient power to find correlations of reasonable size (e.g., Yarkoni (2009)
). As such, our sample is novel in demonstrating the nature of SSRT-activation correlations in a well powered sample and likely more accurately reflects the relationship between individual differences in performance and activation than previously reported.
We did limit our sample to only include individuals over the age of 18. Age has been shown to have a significant influence not only on Stop-signal performance, but also on neural activation during response inhibition (Bunge et al., 2002
; Rubia et al., 2006
). Indeed, there is reason to believe that the brain regions underlying successful response inhibition are not yet fully matured and are still undergoing cortical differentiation before the age of 18 and therefore drastically alter patterns of activation as compared to adults. Furthermore, SSRT has been reported to decrease with age (Williams et al., 1999
; Ridderinkhof et al., 1999
). Our results are therefore specific to adults and the relationship between components from Group ICA and performance may differ considerably in children and adolescents.
These results do not directly speak to the debate over whether the right IFC is directly involved in the suppression of a motor response as part of a hyperdirect pathway (Aron, 2007
; Swann et al., 2009
), or whether the right IFC plays a signal monitoring role (Chao et al., 2009
). These results do however highlight the extent of activation seen throughout a right frontal cluster which includes activation in the IFC, frontal operculum, insula, frontal orbital cortex, and frontal pole. In line with this, it has been reported that right inferior frontal activation during complete response inhibition did not overlap with inferior frontal activation while preparing to inhibit an upcoming automatic response (Goghari and MacDonald, 2009
). In the present study, we found that activation in one component from the StopInhibit-Go contrast was significantly correlated with SSRT after correction, while another was not, even though both included overlapping activation in right frontal, as well as superior frontal and paracingulate, regions. The amount and degree of activation seen in these regions is large, and further work will be needed to attempt to identify the roles within each of these regions during response inhibition.