Dopamine Neurons Respond to Errors in the Prediction of Sensory Features of Expected Rewards.

Midbrain dopamine neurons have been proposed to signal prediction errors as defined in model-free reinforcement learning algorithms. While these algorithms have been extremely powerful in interpreting dopamine activity, these models do not register any error unless there is a difference between the value of what is predicted and what is received. Yet learning often occurs in response to changes in the unique features that characterize what is received, sometimes with no change in its value at all. Here, we show that classic error-signaling dopamine neurons also respond to changes in value-neutral sensory features of an expected reward. This suggests that dopamine neurons have access to a wider variety of information than contemplated by the models currently used to interpret their activity and that, while their firing may conform to predictions of these models in some cases, they are not restricted to signaling errors in the prediction of value.

Neural correlates of sad faces predict clinical remission to cognitive behavioural therapy in depression.

Currently, there are no neurobiological markers of clinical response for cognitive behavioural therapy (CBT) used in clinical practice. We investigated the neural pattern of activity to implicit processing of sad facial expressions as a predictive marker of clinical response. Sixteen medication-free patients in an acute episode of major depression underwent functional magnetic resonance imaging scans before treatment with CBT. Nine patients showed a full clinical response. The pattern of activity, which predicted clinical response, was analysed with support vector machine and leave-one-out cross-validation. The functional neuroanatomy of sad faces at the lowest and highest intensities identified patients, before the initiation of therapy, who had a full clinical response to CBT (sensitivity 71%, specificity 86%, P = 0.029).

Pattern classification of sad facial processing: toward the development of neurobiological markers in depression.

BACKGROUND: Methods of analysis that examine the pattern of cerebral activity over the whole brain have been used to identify and predict neurocognitive states in healthy individuals. Such methods may be applied to functional neuroimaging data in patient groups to aid in the diagnosis of psychiatric disorders and the prediction of treatment response. We sought to examine the sensitivity and specificity of whole brain pattern classification of implicit processing of sad facial expressions in depression. METHODS: Nineteen medication-free patients with depression and 19 healthy volunteers had been recruited for a functional magnetic resonance imaging (fMRI) study involving serial scans. The fMRI paradigm entailed incidental affective processing of sad facial stimuli with modulation of the intensity of the emotional expression (low, medium, and high intensity). The fMRI data were analyzed at each level of affective intensity with a support vector machine pattern classification method. RESULTS: The pattern of brain activity during sad facial processing correctly classified up to 84% of patients (sensitivity) and 89% of control subjects (specificity), corresponding to an accuracy of 86% (p < .0001). Classification of patients' clinical response at baseline, prior to the initiation of treatment, showed a trend toward significance. CONCLUSIONS: Significant classification of patients in an acute depressive episode was achieved with whole brain pattern analysis of fMRI data. The prediction of treatment response showed a trend toward significance due to the reduced power of the subsample. Such methods may provide the first steps toward developing neurobiological markers in psychiatry.