Owing to the low signal-to-noise ratio of functional magnetic resonance imaging, it is difficult to get a good estimate of neural activity elicited by task novelty: by the time one has collected enough trials for a good estimate, the task is no longer novel! However, a recent J Neurosci paper from Cole, Bagic, Kass & Schneider circumvents this problem through a clever design. And the design pays off: the results indicate that the widely-hypothesized anterior-to-posterior flow of information through prefrontal cortex may actually be reversed when unpracticed novel tasks need to be prepared and performed. This result could have profound implications for our understanding what aspects of the prefrontal “division of labor” are dynamic based on abstract task features like novelty.
The study itself is a tour de force. Cole et al used a task where the subject’s actual behavior is a combination of three independent factors: what kind of semantic judgement they will have to make to a pair of stimuli, what fingers they will ultimately use to respond, and what logic they will use in determining the precise stimulus-response mapping. A specific example will help: subjects would first be instructed they needed to make a judgment about whether items are sweet or not (the semantic judgment), that they will respond with their left hand (the response demand), and that they will respond with the index finger if both items are congruent (i.e., both either sweet or not sweet) but with their middle finger if incongruent (this is the stimulus-response mapping rule). They would next be presented with a series of trials, each consisting of a pair of words; in this example, if they saw “apple” and “grape” the correct response would be to respond with the index finger. Subjects also got practice with other semantic judgments (whether items were green or not, loud or not, etc) other stimulus-response rules (whether one and only one item matches the feature of interest; whether the second item matches the feature of interest; or whether the second item does not match the feature of interest), and other response demands (with the right, as opposed to left hand).
The bottom line here is that after practicing a few examples, Cole et al could use novel combinations of these demands to produce 60 completely novel tasks in the scanner – enough to allow a reliable estimate of the hemodynamic response to such novel tasks – and contrast that with the hemodynamic response to more well-practiced tasks built from the same basic demands.
The results showed that the dorsolateral prefrontal cortex (DLPFC) was more active when subjects were being instructed on what novel task to perform than when being instructed on what more well-practiced task to perform. Conversely, an area more anterior to this (so-called “anterior prefrontal cortex” or APFC) was more active during this instruction phase for the well-practiced tasks, relative to the novel ones. Incredibly, this double dissociation reversed during the performance of the first trial of any given task, such that APFC was more active for the novel tasks than the well-practiced ones, but DLPFC more active for the well-practiced than the novel tasks. These results were then replicated in a second experiment, using a magnetic rather than hemodynamic measure of neural activity (via magnetoencephalography, or MEG).
The use of MEG had an additional advantage; its superior temporal resolution enables a finer-grained estimate of how fluctuations in activity in these areas may mutually influence one another. Through two different forms of effective connectivity modeling (Granger causality and phase slope index, or PSI) Cole et al demonstrate that the causal influence is from DLPFC to APFC during the encoding and performance of a novel task. Practiced tasks, by contrast, were associated with a complete reversal of these effects, with APFC primarily influencing DLPFC activation during preparation and performance.
These results are somewhat discrepant with some hypotheses regarding the operation of hierarchical systems capable of this kind of “dynamic reconfiguration.” Consider the view of cortico-striatal loops as hierarchically arrayed, such that prefrontal areas support the active maintenance of information that is increasingly “abstract” (e.g., perhaps in terms of policy abstraction) as one moves anteriorly in PFC. A correspondingly hierarchical set of striatal areas may flexibly gate this information (see here for evidence in support of this view, and here for a model). These models could predict that DLPFC would become more active during the instruction phase of a novel task because that area will track the constituent parts of the upcoming task – its stimulus-side processing (the semantic judgment), its stimulus-response mapping (the response rule), and its response-side processing (which hand to use). Some of this information may then be “shuttled” to APFC, so that APFC can guide subsequent performance based on this relatively abstract response policy (i.e., together the rules specify how to respond, but not exactly what response should be made). The problem here is that Cole et al actually find primarily bottom-up influences from DLPFC to APFC even during performance of the task – precisely the time where the models would seem to predict that top-down influences should predominate.
I should say that these models are extraordinarily complex, and it is difficult to predict what they will do without actually running them. It is therefore worth considering what kinds of processing within these models could give rise to the Cole et al results, before claiming that the models are really fundamentally in conflict with this study.
At the same time, it is also worth being very clear about what Cole et al found. In only a fairly restricted set of cases did they really see an asymmetric interaction between APFC and DLPFC. During instruction of a practiced task, APFC had significantly more influence on DLPFC than the converse only at one time point during the instruction phase; the other two time points were associated with no significant differences in directionality (and one of these time points seems to go in the opposite direction). Similarly, during instruction of a novel task, DLPFC had significantly more influence on APFC than the converse only at one out of three time points, and again one of the other two appears to go in the opposite direction. Finally, during performance of these tasks, there was no significant asymmetry in the directionality for either novel or practiced tasks, and these effects were only reported for the first (of three total) trials.
There are other important caveats here as well. Some of the univariate results are descriptive of comparatively tiny swaths of cortex – in some cases just a few dozen voxels. This is really a double edged sword, and so it’s not quite fair to criticize Cole et al on these grounds; had they observed these patterns in large areas of cortex it would appear to be rather nonspecific and uninformative, but now that they’ve found these patterns in a very restricted area, one can argue that their results do not capture general principles of frontal function.
In summary, the results are intriguing, but their representativeness and consistency is worth of consideration. In addition, they seem to have an uncomfortable contrast with some hierarchical models. In principle, these results could prove to be the key to hierarchical frontal lobe function, and prompt a revision of extant computational models. On the other hand, a few replications and extensions of these results would probably be an important first step.