We observed increasing levels of brain activation in a set of brain foci as the adolescent participants pumped up the balloons to seek larger and larger rewards. Consistent with earlier findings in normal adults (cf. Rao et al., 2008
), this network included the striatum bilaterally, thalamic regions, dorsal ACC (BA 32), the insula bilaterally, and other areas of the prefrontal cortex (BA 9, 10, 44, & 47). Addition areas in the frontal cortex, which are traditionally thought of as serving a role oriented more towards sensory-motor processing (e.g., BA 4 & 6), have not been reported to be involved in Rao and colleagues (2008)
. However, some of these differences are likely due to procedural factors (e.g., Rao and colleagues (2008)
imposed a random “rest” period of 1.5 to 2.5 s after each balloon pump and we did not, thereby increasing the amount of sensori-motor processing related to pumps). Using Monte Carlo simulations, we did not detect any foci that showed differences between the groups at conventional levels of significance, after controlling for the contribution of covariates. With respect to the functional role played by these brain areas, we have previously reported that adolescents with mild to moderate TBI showed higher levels of activation than healthy adolescents in several cortical regions related to cognitive control in a Counting Stroop task relative to non-injured controls despite comparable levels of performance (Tlustos et al., 2011
). Those regions included frontal and parietal regions such as BA 8 (with centroid coordinates at (4,21,47)) in the midline frontal area, ACC (4, 24, 39), and Inferior Parietal Lobule (BA 40) (40, −33, 35), which are quite close to the ones identified in , and may be expected to play a role of cognitive control in the BART.
Our current adaptation of the BART for fMRI is relatively fast-paced and of short duration, which is a distinct advantage for our younger participants for task engagement. On the other hand, our analyses are focused only on those periods during each trial before the occurrence of any events related to feedback or end-of-trial (i.e., participants chose to collect or the balloon exploded) for several reasons. First, the fMRI signals from these events of interest (e.g., outcome anticipation and processing, motor response preparation and maintenance, etc.) occurred very closely in time so it is difficult to resolve these signals individually. Second, certain events are naturally confounded in the BART (e.g., higher risk confounded with higher reward) that makes it difficult to distinguish the separate contribution of each. The limitations posed by these challenges need to be taken into account in future task modifications.
The small sample size and the issue of insufficient statistical power clearly limit the generalizability of the present results to the broader populations, particularly with respect to any failure to find a difference (either behaviorally or in fMRI) between groups, or whether or not we confine our analysis to the broader ROIs or limit them to specific brain regions such as the ACC, striatum or the OFC. Even though children with severe TBI often display poorer performance than less-severely injured or healthy control children across EF domains, participants with less severe injuries can perform relatively normally (e.g., Catroppa & Anderson, 2003
). We did not have any participants with severe TBI in the current study, so it may not be surprising that we did not find any behavioral differences between groups on the BART. It is noteworthy, however, that differences in other measures such as the BRIEF, WRAT and PPVT did reveal differences that approached or reached statistical significance. Second, with the exception of working memory index, performance on the BART was largely unrelated to other neuropsychological or cognitive measures in the current sample. Bornovalova and colleagues (2009
; also see Lejuez et al., 2002
) also reported age, gender, and family income as unrelated to any of the BART variables. Higher working memory load
have been observed to lead to greater discounting of delayed future rewards, greater preference for immediate rewards, and increased signs of impulsivity (Hinson, Jameson & Whitney, 2003
). By implication, those with a lower working memory index in the current study might have preferred then to pump less and opted for the immediate reward points in hand. Taken together, these observations provide no support yet for the claim that adolescents with TBI perform differently on RDM tasks (at least for the BART) than their non-injured counterparts.
Our neuroimaging findings related to anticipation of outcome and rewards are consistent with those reported by Van Leijenhorst and colleagues (2010b), who had healthy participants (age 10–12, 14–15, or 18–23) play a slot machine game. Van Leijenhorst and colleagues were interested in the differences in brain activation when participants either waited with greater anticipation and excitement to see if they won (cheeries-cheeries-???) or if they knew at this point that the trial would be a non-reward trial (cherries-pears-???). Van Leijenhorst and colleagues reported activation related to outcome anticipation in bilateral insula, striatum, dorsal cingulate cortex, parietal areas, and various areas in the frontal lobes in adolescents (age 10–12 and 14–15). They also reported that such differential activation was present in the striatum but not in the anterior insula for the adults. These results highlight the importance of the striatum, the insula, and parts of the medial prefrontal cortex in mediating outcome and reward anticipation in RDM. Importantly, while these areas are also implicated in the current study, there were no clear differences in the magnitude of the correlation across groups in these areas. Whether the basic appreciation and processing of risk and reward in the BART is different in adolescents with mild/moderate TBI and their non-injured counterparts remains to be determined in future, larger studies with more statistical power to detect potential group differences.