We have seen that a proportion of patients with schizophrenia perform differently from control subjects in some tests of associative learning and that this form of learning may relate to psychotic symptoms. Moreover, the neural circuitry underlying associative learning appears to be substantially altered in psychosis. These points prompt an important residual question: namely, if the neurophysiology of learning is so different in psychosis, how do patients learn successfully?
There are a number of possible answers to this question. Perhaps, the standard patterns of activation that underpin successful learning are present, but the signal-to-noise ratio of the recorded neural responses is lower, leading to significant group differences. One way of addressing this question is to examine patterns of neural activation at a low statistical threshold in patients to see whether a “normal” pattern of activation is present at a lower grade.
A second possible explanation is that overt behavioral responses (which quantify learning) are a cruder measure than that provided by imaging, which is multidimensional. Under more demanding conditions (outside the scanner), patients may begin to fail but, using reasonably simple tasks in the scanner, sensitive neural measures are able to show group differences not detectable in behavioral outcome variables.
A third explanation is that patients may be engaging alternative neural systems to achieve a level of performance comparable to that of control subjects. Such a possibility could be explored by capitalizing on the whole-brain information available with functional neuroimaging. This allows us to look beyond the regions of interest and determine whether patients are engaging brain regions beyond the normal circuitry. That is, we ignore responses in the traditional mesocorticolimbic circuitry, which we know is engaged during prediction error–driven causal learning and reward learning, and focus instead on activity outside of these circuits of interest. Care must be taken here, however. This analysis will not only reveal regions whose activity is compensatory but also regions whose responses may be causing the learning dysfunction, ie, brain areas whose activation interferes with and is deleterious for successful learning. These possibilities can be explored by relating responses in these regions to learning competence.
Below, we consider each of these 3 proposed explanations in more detail, using reexamination of imaging data during reward learning24
and causal learning26
to establish evidence supporting them.
Do Patients Activate the Normal Neural Circuitry During Learning, Albeit to a Lesser Degree Than Control Subjects?
We reexamined our data and first found support for the signal-to-noise ratio hypothesis. For example, at a more lenient statistical threshold than we used previously, we found clusters of activity in patients in bilateral ventral striatum and medial prefrontal cortex during reward learning—suggesting that patients were activating these areas, just not as robustly as control subjects (). It is interesting that these classic learning-related regions are identified at a lower threshold, indicative of less robust activation. Perhaps this modest activity was sufficient for our fairly simple task.
Fig. 1. Reward Prediction Error in Psychosis Patients, Revealing Activation in Bilateral Ventral Striatum and Medial Prefrontal Cortex (P < .005 Uncorrected, Minimum Cluster Size 10 Voxels). See online supplementary material for a color version of this (more ...)
An analogous result was found in the dorsal striatum by Weickert et al35
using an implicit associative learning task—“the weather prediction” task. Here, while patients did activate dorsal striatum in this nonrewarding learning task and their performance did not significantly differ from control subjects, nevertheless, in patients, the dorsal, associative striatum, was significantly less active than in control subjects.
How can patients with reduced or noisy activity responses show preserved learning? One might ask: Does the degree of brain activation matter for behavior? Initial evidence from studies of healthy humans suggests that reinforcement learning prediction error signal strength in the dorsal striatum does indeed distinguish better from worse learners during probabilistic learning. For example, Schonberg et al36
examined reward-based decision making in a sample of more than 30 healthy control subjects and considered the relationship between learning performance and brain activation. They found that striatal prediction error signals during learning differentiated learners from nonlearners and that, across subjects, the magnitude of prediction error signals in the dorsal striatum correlated significantly with behavioral performance. We further explored the role of “signal” and “noise” in the effects we observed, by comparing brain responses during causal learning in the system of interest in patients who learned well with responses in those subjects who were poor learners. We noted that the better learning patients engaged frontal cortex and dorsal striatum (head of caudate) to a greater extent than did poorer learners (). In this respect, a closer look at the data suggests that patients do show measurable activations and that these activations, being smaller, may be sufficient only to sustain weaker levels of behavioral performance.
Fig. 2. During Causal Learning, Better Performing Patients More Strongly Activate the A Priori Network of Interest of Head of Caudate and Right Prefrontal Cortex Compared With Worse Performing Patients (P < .005 Uncorrected). See online supplementary (more ...)
Are Brain Signals More Sensitive to Group Differences Than Behavioral Learning Measures?
There is some evidence in favor of this explanation. In the causal learning task, the nonsignificant difference in learning measures between patients and control subjects () is consistent with the notion that fMRI measures may indeed represent a more sensitive multidimensional assay of learning. Indeed, in other work, we have exploited this sensitivity to adjudicate between competing mechanistic accounts of causal learning.17
In our reward learning task,24
on closer inspection, there was a trend for control subjects to learn the reward task better than patients (with a mean of 80% correct choices as opposed to 67% in patients), but this difference was not statistically significant (, left panel). Furthermore, if reaction times (as opposed to choice behavior) were used as an index of learning, there were indeed statistically significant differences between case subjects and control subjects (, right panel). Thus, when viewed across both experiments, behavioral results in these tasks are less sensitive than imaging results at differentiating diagnostic groups, but they do reveal some evidence suggestive of group differences.
Equivalent Learning Rates in First-Episode Psychosis Patients and Control Subjects During Causal Learning. Error bars represent SEs.
Fig. 4. Behavioral Results for Reward Learning. Left panel shows that although control subjects made more correct choices than patients, this difference was not statistically significant. Right panel shows an adaptive reinforcement-related speeding effect in (more ...)
Do Patients Learn Using Alternative Strategies?
Learning in patients may not be so much impaired as different, at least in a proportion of patients. Here, neuroimaging offers the unique opportunity of building up an overall picture of not just how patients fail in the task but how they succeed. It is possible, eg, that patients may engage additional or alternative neural strategies in order to achieve behavioral success. If we look at those patients who are successful (ie, perhaps more likely to be applying compensatory or alternative mechanisms successfully), we can tell whether good performance in patients is upheld by differing neural systems to those responsible for good performance in control subjects. If patients engage in additional/alternative neural activity, and if this extra activity is associated with preserved performance, then we could infer that the extra neural activation represents a strategic or compensatory change. Thus, as above, this might explain why patients show (relative) failure of activation in the traditional neural system in the face of apparently preserved performance.
We examined whether the high-performing patients (defined using a median split) specifically activated extra regions making the assumption that a criterion for identifying such compensatory activity would be that it would involve regions that were active neither in the control subjects (where compensatory activity was not required) nor in the low-performing patients (where failing performance suggests that compensatory activity is not occurring or is less prevalent). Patients who were better learners did not differ from worse learners in symptomatology but did have higher estimated premorbid IQ. In our causal learning data,26
we identified regions (outside of our a priori circuit of interest) that were more active in competent learning patients (compared with their poor learning groupmates) and furthermore that were not engaged preferentially by better learning compared with worse learning control subjects. This combination of contrasts enables us to rule out activity that is generically related to better performance, identifying areas specifically related to performance boost underpinned by an “extra” or compensatory activation. We identified significant foci in the parietal lobe and the anterior cingulate cortex (see , upper and middle panels). We found that during reward learning,24
the same analytical approach revealed clusters in visual cortex, frontal pole, and right parietal lobe (, lower panel).
Fig. 5. Regions in Which Better Performing Patients Activate More Strongly Than Worse Performing Patients, Masking Out Areas Activated by Control Subjects, So Indicating Patient Specificity. Upper and middle panels show causal learning; lower panels show reward (more ...)