Expected value and prediction error abnormalities in depression and schizophrenia.
ABSTRACT The dopamine system has been linked to anhedonia in depression and both the positive and negative symptoms of schizophrenia, but it remains unclear how dopamine dysfunction could mechanistically relate to observed symptoms. There is considerable evidence that phasic dopamine signals encode prediction error (differences between expected and actual outcomes), with reinforcement learning theories being based on prediction error-mediated learning of associations. It has been hypothesized that abnormal encoding of neural prediction error signals could underlie anhedonia in depression and negative symptoms in schizophrenia by disrupting learning and blunting the salience of rewarding events, and contribute to psychotic symptoms by promoting aberrant perceptions and the formation of delusions. To test this, we used model based functional magnetic resonance imaging and an instrumental reward-learning task to investigate the neural correlates of prediction errors and expected-reward values in patients with depression (n=15), patients with schizophrenia (n=14) and healthy controls (n=17). Both patient groups exhibited abnormalities in neural prediction errors, but the spatial pattern of abnormality differed, with the degree of abnormality correlating with syndrome severity. Specifically, reduced prediction errors in the striatum and midbrain were found in depression, with the extent of signal reduction in the bilateral caudate, nucleus accumbens and midbrain correlating with increased anhedonia severity. In schizophrenia, reduced prediction error signals were observed in the caudate, thalamus, insula and amygdala-hippocampal complex, with a trend for reduced prediction errors in the midbrain, and the degree of blunting in the encoding of prediction errors in the insula, amygdala-hippocampal complex and midbrain correlating with increased severity of psychotic symptoms. Schizophrenia was also associated with disruption in the encoding of expected-reward values in the bilateral amygdala-hippocampal complex and parahippocampal gyrus, with the degree of disruption correlating with psychotic symptom severity. Neural signal abnormalities did not correlate with negative symptom severity in schizophrenia. These findings support the suggestion that a disruption in the encoding of prediction error signals contributes to anhedonia symptoms in depression. In schizophrenia, the findings support the postulate of an abnormality in error-dependent updating of inferences and beliefs driving psychotic symptoms. Phasic dopamine abnormalities in depression and schizophrenia are suggested by our observation of prediction error abnormalities in dopamine-rich brain areas, given the evidence for dopamine encoding prediction errors. The findings are consistent with proposals that psychiatric syndromes reflect different disorders of neural valuation and incentive salience formation, which helps bridge the gap between biological and phenomenological levels of understanding.
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ABSTRACT: Schizophrenia is an illness with a remarkably complex symptom presentation that has thus far been out of reach of neuroscientific explanation. This presents a fundamental problem for developing better treatments that target specific symptoms or root causes. One promising path forward is the incorporation of computational neuroscience, which provides a way to formalize experimental observations and, in turn, make theoretical predictions for subsequent studies. We review three complementary approaches: (a) biophysically based models developed to test cellular-level and synaptic hypotheses, (b) connectionist models that give insight into large-scale neural-system-level disturbances in schizophrenia, and (c) models that provide a formalism for observations of complex behavioral deficits, such as negative symptoms. We argue that harnessing all of these modeling approaches represents a productive approach for better understanding schizophrenia. We discuss how blending these approaches can allow the field to progress toward a more comprehensive understanding of schizophrenia and its treatment.05/2015; 3(3). DOI:10.1177/2167702614562041
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ABSTRACT: We used the Iowa Gambling Test (IGT) to examine the relationship of reward learning to both neuropsychological functioning and symptom formation in 65 individuals with schizophrenia. Results indicated that compared to controls, participants with schizophrenia showed significantly reduced reward learning, which in turn correlated with reduced intelligence, memory and executive function, and negative symptoms. The current findings suggested that a disease-related disturbance in reward learning may underlie both cognitive and motivation deficits, as expressed by neuropsychological impairment and negative symptoms in schizophrenia.Schizophrenia Research 09/2014; 159(2-3). DOI:10.1016/j.schres.2014.08.028 · 4.43 Impact Factor
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ABSTRACT: Background. Altered corticostriatothalamic encoding of reinforcement is a core feature of depression. Here we examine reinforcement learning in late-life depression in the theoretical framework of the vascular depression hypothesis. This hypothesis attributes the co-occurrence of late-life depression and poor executive control to prefrontal/cingulate disconnection by vascular lesions. Methods. Our fMRI study compared 31 patients with major depression aged 60+ to 16 controls. Using a computational model, we estimated neural and behavioral responses to reinforcement in an uncertain, changing environment (probabilistic reversal learning). Results. Poor executive control and depression each explained distinct variance in corticostriatothalamic response to unexpected rewards. Depression, but not poor executive control, predicted disrupted functional connectivity between the striatum and prefrontal cortex. White-matter hyperintensities predicted diminished corticostriatothalamic responses to reinforcement, but did not mediate effects of depression or executive control. In two independent samples, poor executive control predicted a failure to persist with rewarded actions, an effect distinct from depressive oversensitivity to punishment. The findings were unchanged in a subsample of participants with vascular disease. Results were robust to effects of confounders including psychiatric comorbidities, physical illness, depressive severity, and psychotropic exposure. Conclusions. Contrary to the predictions of the vascular depression hypothesis, altered encoding of rewards in late-life depression is dissociable from impaired contingency learning associated with poor executive control. Functional connectivity and behavioral analyses point to a disruption of ascending mesostriatocortical reward signals in late-life depression and a failure of cortical contingency encoding in elderly with poor executive control.Psychological Medicine 01/2015; 45(07). DOI:10.1017/S0033291714002517 · 5.43 Impact Factor