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.

Background Acute stress induced (takotsubo) cardiomyopathy can result in a heart failure phenotype with a prognosis comparable to myocardial infarction. In this study, we hypothesized that inflammation is central to the pathophysiology and natural history of takotsubo cardiomyopathy. Methods In a multi-centre study, we prospectively recruited 55 patients with takotsubo cardiomyopathy and 51 age, sex and co-morbidity matched control subjects. During the index event and at 5 months follow-up, patients with takotsubo cardiomyopathy underwent multiparametric cardiac magnetic resonance imaging including ultrasmall superparamagnetic particles of iron oxide (USPIO) enhancement for detection of inflammatory macrophages in the myocardium. Blood monocyte subpopulations and serum cytokines were assessed as measures of systemic inflammation. Matched controls underwent investigation at a single time point. Results Subjects were predominantly middle aged (64±14years) women (90%). When compared to control subjects, patients with takotsubo cardiomyopathy had greater USPIO enhancement (expressed as the difference between pre-USPIO and post-USPIO T2*) in both ballooning (14.3±0.6 versus 10.5±0.9 ms, p<0.001) and non-ballooning (12.9±0.6 versus 10.5±0.9 ms, p=0.02) left ventricular myocardial segments. Serum interleukin-6 (23.1±4.5 versus 6.5±5.8 pg/mL, p< 0.001) and chemokine (C-X-C motif) ligand 1 (1903±168 versus 1272±177 pg/mL, p=0.01) concentrations, and classical CD14++CD16- monocytes (90±0.5 versus 87±0.9%, p=0.01) were also increased whilst intermediate CD14++CD16+ (5.4±0.3 versus 6.9±0.6%, p=0.01) and non-classical CD14+CD16++ (2.7±0.3% versus 4.2±0.5%, p=0.006) monocytes were reduced in patients with takotsubo cardiomyopathy. At 5 months, USPIO enhancement was no longer detectable in the left ventricular myocardium although there remained persistent elevations in serum interleukin-6 concentrations (p=0.009) and reductions in intermediate CD14++CD16+ monocytes (5.6±0.4 versus 6.9±0.6%, p=0.01). Conclusions We demonstrate for the first time that takotsubo cardiomyopathy is characterized by a myocardial macrophage inflammatory infiltrate, changes in the distribution of monocyte subsets and an increase in systemic pro-inflammatory cytokines. Many of these changes persisted for at least 5 months suggesting a low-grade chronic inflammatory state.

Background:LMTM is being developed as a treatment for AD based on inhibition of tau aggregation.Objectives:To examine the efficacy of LMTM as monotherapy in non-randomized cohort analyses as modified primary outcomes in an 18-month Phase III trial in mild AD.Methods:Mild AD patients (n = 800) were randomly assigned to 100 mg twice a day or 4 mg twice a day. Prior to unblinding, the Statistical Analysis Plan was revised to compare the 100 mg twice a day as monotherapy subgroup (n = 79) versus 4 mg twice a day as randomized (n = 396), and 4 mg twice a day as monotherapy (n = 76) versus 4 mg twice a day as add-on therapy (n = 297), with strong control of family-wise type I error.Results:The revised analyses were statistically significant at the required threshold of p < 0.025 in both comparisons for change in ADAS-cog, ADCS-ADL, MRI atrophy, and glucose uptake. The brain atrophy rate was initially typical of mild AD in both add-on and monotherapy groups, but after 9 months of treatment, the rate in monotherapy patients declined significantly to that reported for normal elderly controls. Differences in severity or diagnosis at baseline between monotherapy and add-on patients did not account for significant differences in favor of monotherapy.Conclusions:The results are consistent with earlier studies in supporting the hypothesis that LMTM might be effective as monotherapy and that 4 mg twice a day may serve as well as higher doses. A further suitably randomized trial is required to test this hypothesis.

A significant association between childhood SES and hippocampal volumes in late life is consistent with the established neurodevelopmental findings that early life conditions have an effect on structural brain development. This remains detectable more than 50 years later.

We investigated the differences in brain fMRI signal complexity in patients with schizophrenia while performing the Cyberball social exclusion task, using measures of Sample entropy and Hurst exponent (H). 13 patients meeting diagnostic and Statistical Manual of Mental Disorders, 4th Edition (DSM IV) criteria for schizophrenia and 16 healthy controls underwent fMRI scanning at 1.5 T. The fMRI data of both groups of participants were pre-processed, the entropy characterized and the Hurst exponent extracted. Whole brain entropy and H maps of the groups were generated and analysed. The results after adjusting for age and sex differences together show that patients with schizophrenia exhibited higher complexity than healthy controls, at mean whole brain and regional levels. Also, both Sample entropy and Hurst exponent agree that patients with schizophrenia have more complex fMRI signals than healthy controls. These results suggest that schizophrenia is associated with more complex signal patterns when compared to healthy controls, supporting the increase in complexity hypothesis, where system complexity increases with age or disease, and also consistent with the notion that schizophrenia is characterised by a dysregulation of the nonlinear dynamics of underlying neuronal systems.

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The use of curve-fitting and compartmental modelling for calculating physiological parameters from measured data has increased in popularity in recent years. Finding the 'best fit' of a model to data involves the minimization of a merit function. An example of a merit function is the sum of the squares of the differences between the data points and the model estimated points. This is facilitated by curve-fitting algorithms. Two curve-fitting methods, Levenberg-Marquardt and MINPACK-1, are investigated with respect to the search start points that they require and the accuracy of the returned fits. We have simulated one million dynamic contrast enhanced MRI curves using a range of parameters and investigated the use of single and multiple search starting points. We found that both algorithms, when used with a single starting point, return unreliable fits. When multiple start points are used, we found that both algorithms returned reliable parameters. However the MINPACK-1 method generally outperformed the Levenberg-Marquardt method. We conclude that the use of a single starting point when fitting compartmental modelling data such as this produces unsafe results and we recommend the use of multiple start points in order to find the global minima.