One of the least well understood regions of the human brain is rostral prefrontal cortex, approximating Brodmann's area 10. Here, we investigate the possibility that there are functional subdivisions within this region by conducting a meta-analysis of 104 functional neuroimaging studies (using positron emission tomography/functional magnetic resonance imaging). Studies involving working memory and episodic memory retrieval were disproportionately associated with lateral activations, whereas studies involving mentalizing (i.e., attending to one's own emotions and mental states or those of other agents) were disproportionately associated with medial activations. Functional variation was also observed along a rostral-caudal axis, with studies involving mentalizing yielding relatively caudal activations and studies involving multiple-task coordination yielding relatively rostral activations. A classification algorithm was trained to predict the task, given the coordinates of each activation peak. Performance was well above chance levels (74% for the three most common tasks; 45% across all eight tasks investigated) and generalized to data not included in the training set. These results point to considerable functional segregation within rostral prefrontal cortex.

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.

Anhedonia is a core symptom of major depressive disorder (MDD), long thought to be associated with reduced dopaminergic function. However, most antidepressants do not act directly on the dopamine system and all antidepressants have a delayed full therapeutic effect. Recently, it has been proposed that antidepressants fail to alter dopamine function in antidepressant unresponsive MDD. There is compelling evidence that dopamine neurons code a specific phasic (short duration) reward-learning signal, described by temporal difference (TD) theory. There is no current evidence for other neurons coding a TD reward-learning signal, although such evidence may be found in time. The neuronal substrates of the TD signal were not explored in this study. Phasic signals are believed to have quite different properties to tonic (long duration) signals. No studies have investigated phasic reward-learning signals in MDD. Therefore, adults with MDD receiving long-term antidepressant medication, and comparison controls both unmedicated and acutely medicated with the antidepressant citalopram, were scanned using fMRI during a reward-learning task. Three hypotheses were tested: first, patients with MDD have blunted TD reward-learning signals; second, controls given an antidepressant acutely have blunted TD reward-learning signals; third, the extent of alteration in TD signals in major depression correlates with illness severity ratings. The results supported the hypotheses. Patients with MDD had significantly reduced reward-learning signals in many non-brainstem regions: ventral striatum (VS), rostral and dorsal anterior cingulate, retrosplenial cortex (RC), midbrain and hippocampus. However, the TD signal was increased in the brainstem of patients. As predicted, acute antidepressant administration to controls was associated with a blunted TD signal, and the brainstem TD signal was not increased by acute citalopram administration. In a number of regions, the magnitude of the abnormal signals in MDD correlated with illness severity ratings. The findings highlight the importance of phasic reward-learning signals, and are consistent with the hypothesis that antidepressants fail to normalize reward-learning function in antidepressant-unresponsive MDD. Whilst there is evidence that some antidepressants acutely suppress dopamine function, the long-term action of virtually all antidepressants is enhanced dopamine agonist responsiveness. This distinction might help to elucidate the delayed action of antidepressants. Finally, analogous to recent work in schizophrenia, the finding of abnormal phasic reward-learning signals in MDD implies that an integrated understanding of symptoms and treatment mechanisms is possible, spanning physiology, phenomenology and pharmacology.

Depressive illness is associated with sustained widespread cognitive deficits, in addition to repeated experience of distressing emotions. An accepted theory, which broadly accounts for features of the syndrome, and its delayed response to antidepressant medication, is lacking. One possibility, which has received considerable attention, is that depressive illness is associated with a specific underlying deficit: a blunted or impaired ability to respond to feedback information. Unlike healthy controls, if patients with a depressive illness commit an error, they can be at increased risk of committing a subsequent error, possibly due to a failure to adjust performance in order to reduce the risk of error. In some speeded tasks, performance adjustment in humans is reliably associated with trial-to-trial change in reaction times (RTs), such as 'post-error slowing'. Previous studies of abnormal response to feedback have not investigated RT change in any detail. We used a combination of quantitative modelling of RTs and fMRI in 15 patients and 14 matched controls to test the hypothesis that depressive illness was associated with a blunted behavioural and neural response to feedback information during a gambling task. The results supported the hypothesis. Controls responded to negative ('lose') feedback by an increase in RT and activation of the anterior cingulate, the extent of which correlated with RT change. Patients did not significantly increase their RTs, nor activate the anterior cingulate. Controls responded to positive ('win') feedback by a reduction in RT and activation of the ventral striatum, the extent of which correlated with RT change. Patients neither reduced their RT nor activated the ventral striatum. RT adjustment correlated with self-reported anhedonia for both patients and controls. This behavioural deficit, together with its associated pattern of abnormal neural activity, implies that the anterior midline cortical substrate for error correction, which includes projections from the monoamine systems, is dysfunctional in depressive illness. Many studies have reported abnormalities of the medial frontal cortex in depressive illness; however, the mechanism by which antidepressant medication acts via the monoamine systems remains elusive. Our results suggest a direct link between the core subjective symptom of anhedonia, replicated neuropsychological deficits, electrophysiological and imaging abnormalities, and hypothesized dysfunction of the error correction system.

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Capsulotomy is effective in reducing OCD symptoms. There is a substantial risk of adverse effects, and the risk may vary between surgical methods. Our findings suggest that smaller lesions are safer and that high radiation doses and multiple procedures should be avoided.

To date, electroconvulsive therapy (ECT) is the most potent treatment in severe depression. Although ECT has been successfully applied in clinical practice for over 70 years, the underlying mechanisms of action remain unclear. We used functional MRI and a unique data-driven analysis approach to examine functional connectivity in the brain before and after ECT treatment. Our results show that ECT has lasting effects on the functional architecture of the brain. A comparison of pre-and posttreatment functional connectivity data in a group of nine patients revealed a significant cluster of voxels in and around the left dorsolateral prefrontal cortical region (Brodmann areas 44, 45, and 46), where the average global functional connectivity was considerably decreased after ECT treatment (P < 0.05, family-wise error-corrected). This decrease in functional connectivity was accompanied by a significant improvement (P < 0.001) in depressive symptoms; the patients' mean scores on the Montgomery Asberg Depression Rating Scale pre-and posttreatment were 36.4 (SD = 4.9) and 10.7 (SD = 9.6), respectively. The findings reported here add weight to the emerging "hyperconnectivity hypothesis" in depression and support the proposal that increased connectivity may constitute both a biomarker for mood disorder and a potential therapeutic target. T he treatment of depressive disorder is one of the most pressing issues in contemporary medical practice: The illness is a leading cause of significant disability worldwide (1). Current therapeutic strategies are imperfect, and there is an urgent need to develop more consistently effective and rapidly acting treatment solutions. Unfortunately, our understanding of the etiopathology of mood disorder is incomplete. As a result, the elucidation of the mechanisms of action of effective treatment has necessarily been less than secure and advance has been slow. Available treatments (e.g., chemical antidepressant therapy) were discovered serendipitously rather than designed, and insight into the biology of depressive disorder by study of antidepressant actions has been limited. In particular, the effectiveness of electroconvulsive therapy (ECT), the most potent and rapidly acting of all antidepressant agents (2), has eluded a coherent explanatory framework despite its regular use for more than 70 years. However, if the effects of ECT could be reproduced in a less invasive way, and with a more benign side-effect profile, the treatment of severe depression would be significantly enhanced. To achieve this, we need first to understand better how ECT influences brain function. Here, we show that ECT alters the functional architecture of frontal systems by strongly down-regulating connectivity in key circuits implicated in mood disorder.Efforts to delineate the neural substrate of mood disorder using functional brain imaging technology have generally identified abnormal frontal cortical and limbic activity in depressed patients (3-9). Although such regional findings tend to correspond anatomically to tho...

Quantitative abnormalities of brain structure in patients with major depressive disorder have been reported at a group level for decades. However, these structural differences appear subtle in comparison with conventional radiologically defined abnormalities, with considerable inter-subject variability. Consequently, it has not been possible to readily identify scans from patients with major depressive disorder at an individual level. Recently, machine learning techniques such as relevance vector machines and support vector machines have been applied to predictive classification of individual scans with variable success. Here we describe a novel hybrid method, which combines machine learning with feature selection and characterization, with the latter aimed at maximizing the accuracy of machine learning prediction. The method was tested using a multi-centre dataset of T(1)-weighted 'structural' scans. A total of 62 patients with major depressive disorder and matched controls were recruited from referred secondary care clinical populations in Aberdeen and Edinburgh, UK. The generalization ability and predictive accuracy of the classifiers was tested using data left out of the training process. High prediction accuracy was achieved (~90%). While feature selection was important for maximizing high predictive accuracy with machine learning, feature characterization contributed only a modest improvement to relevance vector machine-based prediction (~5%). Notably, while the only information provided for training the classifiers was T(1)-weighted scans plus a categorical label (major depressive disorder versus controls), both relevance vector machine and support vector machine 'weighting factors' (used for making predictions) correlated strongly with subjective ratings of illness severity. These results indicate that machine learning techniques have the potential to inform clinical practice and research, as they can make accurate predictions about brain scan data from individual subjects. Furthermore, machine learning weighting factors may reflect an objective biomarker of major depressive disorder illness severity, based on abnormalities of brain structure.