To this point, this review has focused on how variability in task-related activation might give clues to the neural implementation of CR. This approach relies on the idea that CR can be mediated by differential expression of the networks typically used to mediate task performance, or by differential recruitment of new compensatory networks in the face of brain damage. Since this approach centers on the networks that directly underlie task performance, it does not address an alternate concept of how CR is neurally mediated. The epidemiologic data suggest that CR allows someone to maintain functioning across a wide range of tasks for a longer time in the face of aging or AD pathology. This suggests that the brain networks subserving cognitive reserve are not equivalent to those required to perform any one particular task. Rather, it is quite likely that a more general “cognitive reserve network” would be elicited by many tasks. By subserving some general as opposed to task-specific function, cognitive reserve might allow someone to cope with pathology and maintain effective functioning for a longer period of time across many domains.
Since the nature and underlying cognitive operations of such a network are not clear, the task at hand might be described as studying the relationship between task-related brain activation and proxies for cognitive reserve, as opposed to the relationship to performance of the task itself. We have addressed this question by using CR proxies as covariates in our imaging analyses. In each case, we tried to identify patterns of load-related activation that are expressed as a function of CR. This allows us to study what aspects of task-related activation vary as a function of CR, whether or not this activation would be identified if we simply looked at task-related activation itself. Further, in one study we sought to determine whether such CR-related activation could be noted across two tasks with different processing demands. That is, could we derive a generic CR network that might be operating across multiple tasks?
Two analyses of data from our continuous nonverbal recognition task investigated activation that correlated with measures of CR. The first study (, Study 7) (Stern et al., 2003
) used 19 healthy young adults between the ages of 18 and 30. The raw score of the NART was used as a proxy measure for cognitive reserve. A GLM analysis sought brain areas where the change in fMRI response amplitude from low to titrated demand conditions correlated with an individual subject's NART scores. During the study phase of the task, positive correlations between load-related activation and NART were seen in left middle frontal gyrus and negative correlations were seen at right superior frontal gyrus, middle frontal gyrus, precentral gyrus, medial frontal gyrus, and insula. We also found brain areas that showed correlations between task-related activation and NART scores during the recognition phase of the task. In summary, the primary finding of this study was that, both during study and during subsequent retrieval, brain areas were noted where there was a systematic relationship between CR and brain activation. These correlations point to aspects of processing that differ as a function of CR in healthy young adults. This study can be contrasted with Study 2 described above. Recall that in Study 2 we sought to identify a network that increased in expression from the low to the titrated demand condition. This network was presumed to represent aspects of task processing that change with task load. We treated individual differences in increase in network expression as a measure differential efficiency and related these changes t measures if CR. Thus that study focused on how the neural substrates of performing the task itself and how they may be related to CR. In contrast, the current study directly explored the neural substrates of CR itself by asking whether there are aspects of load related activation that correlate with CR, whether or not they directly mediate task performance. Another study that used a similar approach (, Study 8) was described above in the section entitled Different networks underlying task performance in young and old. That study used the same nonverbal serial learning task, PET as the imaging modality, and both old and young subjects were included. As described above, that study used a GLM approach that searched for voxels in which there was a correlation between the CR measure and the change in activation from the low to the titrated condition. We found brain areas in young and old where such correlations were observed. Again this approach is aimed at identifying activation associated with CR as opposed to that associated with task performance.
The final study reviewed here (, Study 9) (Stern et al., 2007
) sought to determine whether there is a generic network that subserves CR across multiple tasks. Our strategy was to see if we could find a single network that showed increased load-related activation as a function of CR across two tasks with differing cognitive processing demands. Young and elder subjects were scanned with fMRI while performing either the letter or the shape Sternberg task. Load-dependent fMRI signal corresponding to each trial component (i.e., stimulus presentation, retention delay, and probe) and task (letter or shape) were regressed onto putative CR variables. We then used MLM to summarize the imaging data – CR relationships. We wished to determine if there were patterns of CR-related brain activity whose latent predictors had similar contributions from both the letter and shape tasks. Such a pattern, expressed across two tasks with divergent processing demands, would be a likely candidate for a generic neural substrate underlying CR. We identified a pattern like this in the young group: a spatial pattern expressed during the stimulus presentation phase manifested similar relationships between CR and load-related activation across both the letter and shape WM tasks. Thus, in the young subjects we identified a common CR network that was expressed across both tasks. Elders expressed the network in a manner similar to the younger subjects when performing the letter task, but not the shape task.
The analysis did not concern itself with whether differential expression of this CR-related spatial pattern was associated with better or worse performance. In fact, we explicitly eliminated the possibility of such a relationship in the analytic design. This reduces the chance that CR-related network expression is influenced by differences in performance across individuals. We relied on the effortful processing in these tasks (by looking at load-related activation) to elicit CR-related networks, independent of performance. This means that a high-CR young person, although badly performing in a high-demand task, will still show a pure instantiation of the CR-network. The inference that we wish to draw is that this network might represent the neural instantiation of CR, or alternately that the ability to invoke this network might underlie the benefits that CR imparts. Because this pattern reflects a CR-related network that is used by healthy individuals it meets the proposed criteria for neural reserve.
It is of interest that in the elders, pattern expression was not consistent across the two tasks. Notably, the directionality of pattern expression was similar to that in the young subjects for the letter but not the shape task. Follow up studies are needed to test the idea that the CR-related pattern can be used by elders in the simpler letter task but not in the more challenging shape task.
The common CR pattern noted here consisted of bilateral superior frontal gyrus (BA 10), left medial frontal gyrus 9, right medial frontal gyrus (6, 8), and left Middle frontal gyrus (8). Many of the areas included in the common CR pattern here have been noted in studies of control processes such as task switching (Braver, Reynolds, & Donaldson, 2003
; Wager, Jonides, & Reading, 2003
), as well as in some studies of working memory (Wager & Smith, 2006
). In the fMRI study described above (, Study 7), which used the nonverbal serial recognition task (Stern et al., 2003
), we found several of the same areas noted here were differentially activated as a function of CR during both the encoding and retrieval phases of the task. As in the current study, increased expression of these areas was associated with higher measured CR. These consistent findings across studies and tasks provide a preliminary suggestion that control processes may be an important component of some aspects of CR.
This study raises two important sets of questions that must be addressed in the future. First, it will be of interest to see if expression of this CR-related network by younger subjects can be detected during the performance of tasks not used in the current study. If the network is expressed across multiple tasks it would support the idea that it mediates a general feature of CR. In the future, it will be even more important to determine whether differential expression of any putative CR pattern actually imparts reserve against the neural effects of aging. One way to address this question would be to measure expression of such a network in a set of younger subjects and then follow them over time, with the prediction that higher expression will predict slower progression of age-related cognitive changes.