Trevor S. Ahearn

Expected value and prediction error abnormalities in depression and schizophrenia

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.

Abnormal temporal difference reward-learning signals in major depression

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.

Childhood Socioeconomic Status and Adult Brain Size: Childhood Socioeconomic Status Influences Adult Hippocampal Size

To investigate in older adults without dementia the relationships between socioeconomic status (SES) in childhood and magnetic resonance imaging (MRI)‐derived brain volume measures typical of brain aging and Alzheimers disease (AD).

The accuracy of pharmacokinetic parameter measurement in DCE-MRI of the breast at 3 T.

The purpose of this work is to quantify the accuracy of pharmacokinetic parameter measurement in DCE-MRI of breast cancer at 3 T in relation to three sources of error. Individually, T1 measurement error, temporal resolution and transmitted RF field inhomogeneity are considered. Dynamic contrast enhancement curves were simulated using standard acquisition parameters of a DCE-MRI protocol. Errors on pre-contrast T1 due to incorrect RF spoiling were considered. Flip angle errors were measured and introduced into the fitting routine, and temporal resolution was also varied. The error in fitted pharmacokinetic parameters, K(trans) and v(e), was calculated. Flip angles were found to be reduced by up to 55% of the expected value. The resultant errors in our range of K(trans) and v(e) were found to be up to 66% and 74%, respectively. Incorrect T1 estimation results in K(trans) and v(e) errors up to 531% and 233%, respectively. When the temporal resolution is reduced from 10 to 70 s K(trans) drops by up to 48%, while v(e) shows negligible variation. In combination, uncertainties in tissue T1 map and applied flip angle were shown to contribute to errors of up to 88% in K(trans) and 73% in v(e). These results demonstrate the importance of high temporal resolution, accurate T1 measurement and good B1 homogeneity.

The relationship between vascular and metabolic characteristics of primary breast tumours

The objective of this study was to investigate the relationship between vascular and metabolic characteristics of breast tumours in vivo, using contrast-enhanced dynamic MRI and 2-[18F] fluoro-2-deoxy-d-glucose (FDG) PET imaging. Twenty patients with large or locally advanced primary breast cancers were imaged prior to therapy. MRI data were acquired using a dynamic gradient echo sequence and analysed using two pharmacokinetic models. Static PET data were acquired in 2D mode. A significant association (P<0.05) was observed between the calculated exchange rate constants of both pharmacokinetic models and calculated PET FDG dose uptake ratios (DUR). Statistical analysis showed that the exchange rate constants can explain between 27 and 44% of the variance observed in the PET FDG uptake ratios. A relationship was demonstrated between the vascular and metabolic characteristics of primary breast tumours showing that any assessment of tumour metabolic activity using PET may be controlled at least in part by delivery of uptake agent due to the vascular characteristics of the tumour. MRI and PET provide methods of assessing breast tumour vascularity and metabolism in vivo using the exchange rate constants of dynamic MRI, and DUR of PET, respectively, these measures being related but not equivalent.

B1 transmission-field inhomogeneity and enhancement ratio errors in dynamic contrast-enhanced MRI (DCE-MRI) of the breast at 3T.

To quantify B1 transmission‐field inhomogeneity in breast imaging of normal volunteers at 3T using 3D T1‐weighted spoiled gradient echo and to assess the resulting errors in enhancement ratio (ER) measured in dynamic contrast‐enhanced MRI (DCE‐MRI) studies of the breast.

Cerebellar brain volume accounts for variance in cognitive performance in older adults

INTRODUCTION Frontal lobe atrophy is implicated in patterns of age-related cognitive decline. However, other brain areas, including the cerebellum, support the work of the frontal lobes and are also sensitive to the effects of ageing. A relationship between cerebellar brain volume and cognitive function in older adults is reported, but no study has separated variance associated with cerebellar gray matter volume and cerebellar white matter volume; and no study has examined whether or not brain volume in the cerebellum is related to cognitive function in older adults after statistical control for frontal lobe volume of gray and white matter. METHOD We used voxel based morphometry (VBM) and structural equation modelling (SEM) to analyse relations between general cognitive ability (G) and volume of GM and WM in frontal areas and cerebellum in a sample of 228 older adults (121 males and 107 females). RESULTS Results indicate that GM volume in the cerebellum predicts G, even when total intracranial volume (TICV) and GM gray and WM volumes in frontal lobes are statistically controlled. However, results differ for males and females, with males showing a stronger relationship between brain volume in the cerebellum and G. CONCLUSIONS Results are discussed in light of neurological models of cognitive ageing and the significance of the cerebellum in models of cognitive functioning.

Prospective multi-centre Voxel Based Morphometry study employing scanner specific segmentations: procedure development using CaliBrain structural MRI data.

BackgroundStructural Magnetic Resonance Imaging (sMRI) of the brain is employed in the assessment of a wide range of neuropsychiatric disorders. In order to improve statistical power in such studies it is desirable to pool scanning resources from multiple centres. The CaliBrain project was designed to provide for an assessment of scanner differences at three centres in Scotland, and to assess the practicality of pooling scans from multiple-centres.MethodsWe scanned healthy subjects twice on each of the 3 scanners in the CaliBrain project with T1-weighted sequences. The tissue classifier supplied within the Statistical Parametric Mapping (SPM5) application was used to map the grey and white tissue for each scan. We were thus able to assess within scanner variability and between scanner differences. We have sought to correct for between scanner differences by adjusting the probability mappings of tissue occupancy (tissue priors) used in SPM5 for tissue classification. The adjustment procedure resulted in separate sets of tissue priors being developed for each scanner and we refer to these as scanner specific priors.ResultsVoxel Based Morphometry (VBM) analyses and metric tests indicated that the use of scanner specific priors reduced tissue classification differences between scanners. However, the metric results also demonstrated that the between scanner differences were not reduced to the level of within scanner variability, the ideal for scanner harmonisation.ConclusionOur results indicate the development of scanner specific priors for SPM can assist in pooling of scan resources from different research centres. This can facilitate improvements in the statistical power of quantitative brain imaging studies.

Nonlinear complexity analysis of brain fMRI signals in schizophrenia

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.

Inter-individual Differences in fMRI Entropy Measurements in Old Age

We investigated the association between individual differences in cognitive performance in old age and the approximate entropy (ApEn) measured from functional magnetic resonance imaging (fMRI) data acquired from 40 participants of the Aberdeen Birth Cohort 1936 (ABC1936), while undergoing a visual information processing task: inspection time (IT). Participants took a version of the Moray House Test (MHT) No. 12 at age 11, a valid measure of childhood intelligence. The same individuals completed a test of non-verbal reasoning (Ravens Standard Progressive Matrices [RPM]) aged about 68 years. The IT, MHT and RPM scores were used as indicators of cognitive performance. Our results show that higher regional signal entropy is associated with better cognitive performance. This finding was independent of ability in childhood but not independent of current cognitive ability. ApEn is used for the first time to identify a potential source of individual differences in cognitive ability using fMRI data.