The earliest formulations of schizophrenia hypothesized that the formation of inappropriate associations between stimuli, thoughts, and percepts was a core disease process (Bleuler, 1911/1950; Schneider, 1930). Having developed an understanding of association formation both psychologically and physiologically in experimental animals, Pavlov attempted to apply what he had learned to psychiatric patients at the Balinskiy Psychiatric Hospital (Pavlov, 1928). This attempt is being realized through translational behavioral neuroscience studies of the role of dopaminergic neurotransmission in the midbrain, striatum, and prefrontal cortex in associative learning, implicating aberrant learning processes and their brain basis in the mesocorticolimbic dopamine system in the genesis of positive psychotic symptoms (Kapur , 2003) and in particular delusional beliefs (Corlett et al., 2006; Corlett et al., 2007; Corlett et al., 2007).
The work reported by Michael Frank and colleagues (Frank et al., 2007) contributes to this enterprise, shedding new light on the roles of dopamine in reinforcement learning through a combined computational and genetic analysis of healthy individuals’ behavior on a prediction error-driven reinforcement learning task. Given the link between reinforcement learning, dopamine, and psychosis, these data will likely aid our understanding of the pathophysiological processes underpinning the genesis of psychotic symptoms, especially since the genes of interest in the present analysis (DRD2, DARPP-32, and COMT) have all been associated with risk for schizophrenia (Talkowski et al., 2007).
However, there may be some inconsistencies between Frank and colleagues' results and prior data on the links between the functionality of the genes of interest, cognitive performance, brain structure and function, and risk for schizophrenia. In brief, variation in the gene that codes for DARPP-32 has also been associated with enhanced working memory task performance, as well as with striatal activity and frontostriatal structural and functional connectivity during a working memory task (Meyer-Lindenberg et al., 2007). Additionally, that same locus of variability was linked with increased risk for schizophrenia in a family association study. These data highlight the various biological and psychological processes that dopaminergic genes can impact upon and the importance of appreciating how interactions between brain structures can influence psychological processes. Put simply, the notion that genes act at the level of single neurotransmitters, single brain regions, and single psychological processes is likely overly simplistic.
Furthermore, the process of interaction between genes in subtending particular phenotypes (epistasis) may be particularly important in the case of the genes under examination in Frank and colleagues’ paper, since, ultimately, all of the genes impact in some way upon dopamine function across distinct but interacting brain regions. In particular, the possibility that COMT function in prefrontal cortex may impact upon dopamine levels and responsivity subcortically could well influence the neurobiological locus of the effects that Frank and colleagues report. That is, although COMT function has a direct impact upon dopamine levels in PFC, it may also have effects on subcortical responsivity through feedback projections to striatum (Bilder et al., 2004; Meyer-Lindenberg et al., 2002), a possibility not captured by Frank and colleagues’ interpretation of their results.
In addition, DARPP-32 has multiple functions—critically, it also modulates D2 function (Greengard, 2001), which might lead one to expect an impact upon both Go and NoGo learning, rather than the specific effect on Go learning that Frank and colleagues hypothesize. Indeed, Figure 2A seems to suggest that there was a trend towards an impact on NoGo learning, also. Additionally, DARPP-32 interacts with serotonin and acetylcholine signaling, amongst many other neurotransmitters and neuromodulators (Greengard, 2001). These relationships need to be taken into account, especially since both serotonin (Daw and Doya, 2006) and acetylcholine (Pauli and O'Reilly, 2007) have been implicated in reinforcement learning.
Ultimately, Frank and colleagues proffer an exciting new method of understanding the relationship between dopamine function and reinforcement learning. Combining this approach with functional neuroimaging, pharmacological manipulations, and studies of schizophrenic patients, whilst considering the role of genetic interactions, will aid our understanding of the neurobiology of learning and its dysfunction in schizophrenia.
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