Disrupted limbic-prefrontal effective connectivity in response to fearful faces in lifetime depression.

BACKGROUND: Multiple brain imaging studies of negative emotional bias in major depressive disorder (MDD) have used images of fearful facial expressions and focused on the amygdala and the prefrontal cortex. The results have, however, been inconsistent, potentially due to small sample sizes (typically N<50). It remains unclear if any alterations are a characteristic of current depression or of past experience of depression, and whether there are MDD-related changes in effective connectivity between the two brain regions. METHODS: Activations and effective connectivity between the amygdala and dorsolateral prefrontal cortex (DLPFC) in response to fearful face stimuli were studied in a large population-based sample from Generation Scotland. Participants either had no history of MDD (N=664 in activation analyses, N=474 in connectivity analyses) or had a diagnosis of MDD during their lifetime (LMDD, N=290 in activation analyses, N=214 in connectivity analyses). The within-scanner task involved implicit facial emotion processing of neutral and fearful faces. RESULTS: Compared to controls, LMDD was associated with increased activations in left amygdala (PFWE=0.031,kE=4) and left DLPFC (PFWE=0.002,kE=33), increased mean bilateral amygdala activation (ß=0.0715,P=0.0314), and increased inhibition from left amygdala to left DLPFC, all in response to fearful faces contrasted to baseline. Results did not appear to be attributable to depressive illness severity or antidepressant medication status at scan time. LIMITATIONS: Most studied participants had past rather than current depression, average severity of ongoing depression symptoms was low, and a substantial proportion of participants were receiving medication. The study was not longitudinal and the participants were only assessed a single time. CONCLUSIONS: LMDD is associated with hyperactivity of the amygdala and DLPFC, and with stronger amygdala to DLPFC inhibitory connectivity, all in response to fearful faces, unrelated to depression severity at scan time. These results help reduce inconsistency in past literature and suggest disruption of 'bottom-up' limbic-prefrontal effective connectivity in depression.

Associations of negative affective biases and depressive symptoms in a community-based sample.

BACKGROUND: Major depressive disorder (MDD) was previously associated with negative affective biases. Evidence from larger population-based studies, however, is lacking, including whether biases normalise with remission. We investigated associations between affective bias measures and depressive symptom severity across a large community-based sample, followed by examining differences between remitted individuals and controls. METHODS: Participants from Generation Scotland (N = 1109) completed the: (i) Bristol Emotion Recognition Task (BERT), (ii) Face Affective Go/No-go (FAGN), and (iii) Cambridge Gambling Task (CGT). Individuals were classified as MDD-current (n = 43), MDD-remitted (n = 282), or controls (n = 784). Analyses included using affective bias summary measures (primary analyses), followed by detailed emotion/condition analyses of BERT and FAGN (secondary analyses). RESULTS: For summary measures, the only significant finding was an association between greater symptoms and lower risk adjustment for CGT across the sample (individuals with greater symptoms were less likely to bet more, despite increasingly favourable conditions). This was no longer significant when controlling for non-affective cognition. No differences were found for remitted-MDD v. controls. Detailed analysis of BERT and FAGN indicated subtle negative biases across multiple measures of affective cognition with increasing symptom severity, that were independent of non-effective cognition [e.g. greater tendency to rate faces as angry (BERT), and lower accuracy for happy/neutral conditions (FAGN)]. Results for remitted-MDD were inconsistent. CONCLUSIONS: This suggests the presence of subtle negative affective biases at the level of emotion/condition in association with depressive symptoms across the sample, over and above those accounted for by non-affective cognition, with no evidence for affective biases in remitted individuals.

Local CpG density affects the trajectory and variance of age-associated DNA methylation changes.

BACKGROUND: DNA methylation is an epigenetic mark associated with the repression of gene promoters. Its pattern in the genome is disrupted with age and these changes can be used to statistically predict age with epigenetic clocks. Altered rates of aging inferred from these clocks are observed in human disease. However, the molecular mechanisms underpinning age-associated DNA methylation changes remain unknown. Local DNA sequence can program steady-state DNA methylation levels, but how it influences age-associated methylation changes is unknown. RESULTS: We analyze longitudinal human DNA methylation trajectories at 345,895 CpGs from 600 individuals aged between 67 and 80 to understand the factors responsible for age-associated epigenetic changes at individual CpGs. We show that changes in methylation with age occur at 182,760 loci largely independently of variation in cell type proportions. These changes are especially apparent at 8322 low CpG density loci. Using SNP data from the same individuals, we demonstrate that methylation trajectories are affected by local sequence polymorphisms at 1487 low CpG density loci. More generally, we find that low CpG density regions are particularly prone to change and do so variably between individuals in people aged over 65. This differs from the behavior of these regions in younger individuals where they predominantly lose methylation. CONCLUSIONS: Our results, which we reproduce in two independent groups of individuals, demonstrate that local DNA sequence influences age-associated DNA methylation changes in humans in vivo. We suggest that this occurs because interactions between CpGs reinforce maintenance of methylation patterns in CpG dense regions.

Brain predictors of fatigue in rheumatoid arthritis: A machine learning study.

BACKGROUND: Fatigue is a common and burdensome symptom in Rheumatoid Arthritis (RA), yet is poorly understood. Currently, clinicians rely solely on fatigue questionnaires, which are inherently subjective measures. For the effective development of future therapies and stratification, it is of vital importance to identify biomarkers of fatigue. In this study, we identify brain differences between RA patients who improved and did not improve their levels of fatigue based on Chalder Fatigue Scale variation (ΔCFS≥ 2), and we compared the performance of different classifiers to distinguish between these samples at baseline. METHODS: Fifty-four fatigued RA patients underwent a magnetic resonance (MR) scan at baseline and 6 months later. At 6 months we identified those whose fatigue levels improved and those for whom it did not. More than 900 brain features across three data sets were assessed as potential predictors of fatigue improvement. These data sets included clinical, structural MRI (sMRI) and diffusion tensor imaging (DTI) data. A genetic algorithm was used for feature selection. Three classifiers were employed in the discrimination of improvers and non-improvers of fatigue: a Least Square Linear Discriminant (LSLD), a linear Support Vector Machine (SVM) and a SVM with Radial Basis Function kernel. RESULTS: The highest accuracy (67.9%) was achieved with the sMRI set, followed by the DTI set (63.8%), whereas classification performance using clinical features was at the chance level. The mean curvature of the left superior temporal sulcus was most strongly selected during the feature selection step, followed by the surface are of the right frontal pole and the surface area of the left banks of the superior temporal sulcus. CONCLUSIONS: The results presented evidence a superiority of brain metrics over clinical metrics in predicting fatigue changes. Further exploration of these methods may support clinicians to triage patients towards the most appropriate fatigue alleviating therapies.

Sexual dimorphism in the relationship between brain complexity, volume and general intelligence (g): a cross-cohort study.

Changes in brain morphology have been reported during development, ageing and in relation to different pathologies. Brain morphology described by the shape complexity of gyri and sulci can be captured and quantified using fractal dimension (FD). This measure of brain structural complexity, as well as brain volume, are associated with intelligence, but less is known about the sexual dimorphism of these relationships. In this paper, sex differences in the relationship between brain structural complexity and general intelligence (g) in two diverse geographic and cultural populations (UK and Indian) are investigated. 3D T1-weighted magnetic resonance imaging (MRI) data and a battery of cognitive tests were acquired from participants belonging to three different cohorts: Mysore Parthenon Cohort (MPC); Aberdeen Children of the 1950s (ACONF) and UK Biobank. We computed MRI derived structural brain complexity and g estimated from a battery of cognitive tests for each group. Brain complexity and volume were both positively corelated with intelligence, with the correlations being significant in women but not always in men. This relationship is seen across populations of differing ages and geographical locations and improves understanding of neurobiological sex-differences.

Investigation of the Inter- and Intrascanner Reproducibility and Repeatability of Radiomics Features in T1-Weighted Brain MRI.

BACKGROUND: Radiomics is the high throughput analysis of medical images using computer algorithms, which specifically assess textural features. It has increasingly been proposed as a tool for the development of imaging biomarkers. However, an important acknowledged limitation of radiomics is the lack of reproducibility of features produced. PURPOSE: To assess reproducibility and repeatability of radiomics variables in brain MRI through a multivisit, multicenter study. STUDY TYPE: Retrospective. POPULATION: Fourteen individuals visiting three institutions twice, 10 males with the mean age of 36.3 years and age range 25-51. FIELD STRENGTH: 3D T1W inversion recovery on three 1.5-T General Electric scanners. ASSESSMENT: Radiomics analysis by a consultant radiologist performed on the T1W images of the whole brain on all visits. All possible radiomics features were generated. STATISTICAL TEST: Concordance correlation coefficient (CCC) and dynamic range (DR) for all variables were calculated to assess the test-retest repeatability. Intraclass correlation coefficients (ICCs) were calculated to investigate the reproducibility of features across centers. RESULTS: Of 1596 features generated, 57 from center 1, 15 from center 2, and 22 from center 3 had a CCC > 0.9 and DR > 0.9. Eight variables had CCC > 0.9 and DR > 0.9 in all centers. Forty-one variables had an ICC of >0.9. No variables had CCC > 0.9, DR > 0.9, and ICC > 0.9. DATA CONCLUSION: Repeatability and reproducibility of variables is a significant limitation of radiomics analysis in 3DT1W brain MRI. Careful selection of radiomic features is required. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY STAGE: 2.

Cognitive Test Scores and Progressive Cognitive Decline in the Aberdeen 1921 and 1936 Birth Cohorts.

The Aberdeen birth cohorts of 1921 and 1936 (ABC21 and ABC36) were subjected to IQ tests in 1932 or 1947 when they were aged about 11y. They were recruited between 1997-2001 among cognitively healthy community residents and comprehensively phenotyped in a long-term study of brain aging and health up to 2017. Here, we report associations between baseline cognitive test scores and long-term cognitive outcomes. On recruitment, significant sex differences within and between the ABC21 and ABC36 cohorts supported advantages in verbal ability and learning among the ABC36 women that were not significant in ABC21. Comorbid physical disorders were self-reported in both ABC21 and ABC36 but did not contribute to differences in terms of performance in cognitive tests. When used alone without other criteria, cognitive tests scores which fell below the -1.5 SD criterion for tests of progressive matrices, namely verbal learning, digit symbol and block design, did not support the concept that Mild Cognitive Impairment (MCI) is a stable class of acquired loss of function with significant links to the later emergence of a clinical dementia syndrome. This is consistent with many previous reports. Furthermore, because childhood IQ-type data were available, we showed that a lower cognitive performance at about 64 or 78 y than that predicted by IQ at 11 ± 0.5 y did not improve the prediction of progress to MCI or greater cognitive loss. We used binary logistic regression to explore how MCI might contribute to the prediction of later progress to a clinical dementia syndrome. In a fully adjusted model using ABC21 data, we found that non-amnestic MCI, along with factors such as female sex and depressive symptoms, contributed to the prediction of later dementia. A comparable model using ABC36 data did not do so. We propose that (1) MCI criteria restricted to cognitive test scores do not improve the temporal stability of MCI classifications; (2) pathways towards dementia may differ according to age at dementia onset and (3) the concept of MCI may require measures (not captured here) that underly self-reported subjective age-related cognitive decline.

Blood-based epigenome-wide analyses of cognitive abilities.

BACKGROUND: Blood-based markers of cognitive functioning might provide an accessible way to track neurodegeneration years prior to clinical manifestation of cognitive impairment and dementia. RESULTS: Using blood-based epigenome-wide analyses of general cognitive function, we show that individual differences in DNA methylation (DNAm) explain 35.0% of the variance in general cognitive function (g). A DNAm predictor explains ~4% of the variance, independently of a polygenic score, in two external cohorts. It also associates with circulating levels of neurology- and inflammation-related proteins, global brain imaging metrics, and regional cortical volumes. CONCLUSIONS: As sample sizes increase, the ability to assess cognitive function from DNAm data may be informative in settings where cognitive testing is unreliable or unavailable.

Crossed cerebellar diaschisis in Alzheimer's disease.

BACKGROUND: Crossed cerebellar diaschisis (CCD) is characterized by hypometabolism and hypoperfusion on molecular imaging in the cerebellum due to a supratentorial lesion on the contralateral side. CCD is a well-established phenomenon in acute or subacute conditions such as infarction but it has been less well described in chronic conditions such as neurodegenerative dementias. Here, we investigate CCD in a large sample of 830 people meeting research criteria for Alzheimer's disease (AD) using [18F]fluorodeoxyglucose-positron emission tomography (FDG-PET). MATERIALS AND METHODS: This study is based on FDG-PET data collected at baseline as part of two large-scale Phase III clinical trials of a novel tau aggregation inhibitor medication, methylthioninium in mild to moderate AD participants. Quantification of FDG-PET hypometabolism was carried out using standardized uptake value ratio (SUVR), with the pons as the comparison region. SUVR was compared in different regions of interest between the right and left hemispheres of the brain and cerebellum in people with mild AD (Mini-Mental State Examination score ≥ 20). RESULTS: Comparison of SUVR in different brain regions demonstrated significant differences in the temporal, occipital and cerebellar cortices. Right and left asymmetry was noted with lower SUVR in the left temporal and occipital regions, whereas SUVR was lower in the right side of the cerebellum. CONCLUSION: Here, we found robust evidence of CCD in a large sample of people with AD, a chronic neurodegenerative condition. The presence of this phenomenon in AD opens up a new avenue of research in AD pathogenesis and has the potential to change future diagnostic and therapeutic strategies.

Alzheimer's Dementia: The Emerging Role of Positron Emission Tomography.

Alzheimer's disease (AD) is the most common cause of dementia and accounts for approximately 50% to 80% of all cases of dementia. The diagnosis of probable AD is based on clinical criteria and overlapping clinical features pose a challenge to accurate diagnosis. However, neuroimaging has been included as a biomarker in various published criteria for the diagnosis of probable AD, in the absence of a confirmatory diagnostic test during life. Advances in neuroimaging techniques and their inclusion in diagnostic and research criteria for the diagnosis of AD includes the use of positron emission tomography (PET) imaging as a biomarker in various therapeutic and prognostic studies in AD. The development and application of a range of PET tracers will allow more detailed assessment of people with AD and will improve diagnostic specificity and targeted therapy of AD. The aim of this review is to summarize current evidence on PET imaging using the non-specific tracer [18F]fluorodeoxyglucose and specific tracers that target amyloid and tau pathology in people with AD.

Degeneration of basal and limbic networks is a core feature of behavioural variant frontotemporal dementia.

The behavioural variant of frontotemporal dementia is a clinical syndrome characterized by changes in behaviour, cognition and functional ability. Although atrophy in frontal and temporal regions would appear to be a defining feature, neuroimaging studies have identified volumetric differences distributed across large parts of the cortex, giving rise to a classification into distinct neuroanatomical subtypes. Here, we extended these neuroimaging studies to examine how distributed patterns of cortical atrophy map onto brain network hubs. We used baseline structural magnetic resonance imaging data collected from 213 behavioural variant of frontotemporal dementia patients meeting consensus diagnostic criteria and having definite evidence of frontal and/or temporal lobe atrophy from a global clinical trial conducted in 70 sites in Canada, United States of America, Australia, Asia and Europe. These were compared with data from 244 healthy elderly subjects from a well-characterized cohort study. We have used statistical methods of hierarchical agglomerative clustering of 68 regional cortical and subcortical volumes (34 in each hemisphere) to determine the reproducibility of previously described neuroanatomical subtypes in a global study. We have also attempted to link the structural findings to clinical features defined systematically using well-validated clinical scales (Addenbrooke's Cognitive Examination Revised, the Mini-Mental Status Examination, the Frontotemporal Dementia Rating Scale and the Functional Assessment Questionnaire) and subscales derived from them. Whilst we can confirm that the subtypes are robust, they have limited value in explaining the clinical heterogeneity of the syndrome. We have found that a common pattern of degeneration affecting a small number of subcortical, limbic and frontal nodes within highly connected networks (most previously identified as rich club members or functional binding nodes) is shared by all the anatomical subtypes. Degeneration in these core regions is correlated with cognitive and functional impairment, but less so with behavioural impairment. These findings suggest that degeneration in highly connected basal, limbic and frontal networks is a core feature of the behavioural variant of frontotemporal dementia phenotype irrespective of neuroanatomical and clinical heterogeneity, and may underly the impairment of integration in cognition, function and behaviour responsible for the loss of insight that characterizes the syndrome.

Early life predictors of late life cerebral small vessel disease in four prospective cohort studies.

Development of cerebral small vessel disease, a major cause of stroke and dementia, may be influenced by early life factors. It is unclear whether these relationships are independent of each other, of adult socio-economic status or of vascular risk factor exposures. We examined associations between factors from birth (ponderal index, birth weight), childhood (IQ, education, socio-economic status), adult small vessel disease, and brain volumes, using data from four prospective cohort studies: STratifying Resilience And Depression Longitudinally (STRADL) (n = 1080; mean age = 59 years); the Dutch Famine Birth Cohort (n = 118; mean age = 68 years); the Lothian Birth Cohort 1936 (LBC1936; n = 617; mean age = 73 years), and the Simpson's cohort (n = 110; mean age = 78 years). We analysed each small vessel disease feature individually and summed to give a total small vessel disease score (range 1-4) in each cohort separately, then in meta-analysis, adjusted for vascular risk factors and adult socio-economic status. Higher birth weight was associated with fewer lacunes [odds ratio (OR) per 100 g = 0.93, 95% confidence interval (CI) = 0.88 to 0.99], fewer infarcts (OR = 0.94, 95% CI = 0.89 to 0.99), and fewer perivascular spaces (OR = 0.95, 95% CI = 0.91 to 0.99). Higher childhood IQ was associated with lower white matter hyperintensity burden (OR per IQ point = 0.99, 95% CI 0.98 to 0.998), fewer infarcts (OR = 0.98, 95% CI = 0.97 to 0.998), fewer lacunes (OR = 0.98, 95% CI = 0.97 to 0.999), and lower total small vessel disease burden (OR = 0.98, 95% CI = 0.96 to 0.999). Low education was associated with more microbleeds (OR = 1.90, 95% CI = 1.33 to 2.72) and lower total brain volume (mean difference = -178.86 cm3, 95% CI = -325.07 to -32.66). Low childhood socio-economic status was associated with fewer lacunes (OR = 0.62, 95% CI = 0.40 to 0.95). Early life factors are associated with worse small vessel disease in later life, independent of each other, vascular risk factors and adult socio-economic status. Risk for small vessel disease may originate in early life and provide a mechanistic link between early life factors and risk of stroke and dementia. Policies investing in early child development may improve lifelong brain health and contribute to the prevention of dementia and stroke in older age.

The use of positron emission tomography/magnetic resonance imaging in dementia: A literature review.

OBJECTIVES: Positron emission tomography-magnetic resonance imaging (PET/MRI) is an emerging hybrid imaging system in clinical nuclear medicine. Research demonstrates a comparative utility to current unimodal and hybrid methods, including PET-computed tomography (PET/CT), in several medical subspecialities such as neuroimaging. The aim of this review is to critically evaluate the literature from 2016 to 2021 using PET/MRI for the investigation of patients with mild cognitive impairment or dementia, and discuss the evidence base for widening its application into clinical practice. METHODS: A comprehensive literature search using the PubMed database was conducted to retrieve studies using PET/MRI in relation to the topics of mild cognitive impairment, dementia, or Alzheimer's disease between January 2016 and January 2021. This search strategy enabled studies on all dementia types to be included in the analysis. Studies were required to have a minimum of 10 human subjects and incorporate simultaneous PET/MRI. RESULTS: A total of 116 papers were retrieved, with 39 papers included in the final selection. These were broadly categorised into reviews (12), technical/methodological papers (11) and new data studies (16). For the current review, discussion focused on findings from the new data studies. CONCLUSIONS: PET/MRI offers additional insight into the underlying anatomical, metabolic and functional changes associated with dementia when compared with unimodal methods and PET/CT, particularly relating to brain regions including the hippocampus and default mode network. Furthermore, the improved diagnostic utility of PET/MRI, as reported by radiologists, offers improved classification of dementia patients, with important implications for clinical management.

Imprinting methylation predicts hippocampal volumes and hyperintensities and the change with age in later life.

Epigenetic imprinting is important for neurogenesis and brain function. Hippocampal volumes and brain hyperintensities in late life have been associated with early life circumstances. Epigenetic imprinting may underpin these associations. Methylation was measured at 982 sites in 13 imprinted locations in blood samples from a longitudinal cohort by bisulphite amplicon sequencing. Hippocampal volumes and hyperintensities were determined at age 64y and 72y using MRI. Hyperintensities were determined in white matter, grey matter and infratentorial regions. Permutation methods were used to adjust for multiple testing. At 64y, H19/IGF2 and NESPAS methylation predicted hippocampal volumes. PEG3 predicted hyperintensities in hippocampal grey matter, and white matter. GNASXL predicted grey matter hyperintensities. Changes with age were predicted for hippocampal volume (MEST1, KvDMR, L3MBTL, GNASXL), white matter (MEST1, PEG3) and hippocampal grey matter hyperintensities (MCTS2, GNASXL, NESPAS, L3MBTL, MCTS2, SNRPN, MEST1). Including childhood cognitive ability, years in education, or socioeconomic status as additional explanatory variables in regression analyses did not change the overall findings. Imprinting methylation in multiple genes predicts brain structures, and their change over time. These findings are potentially relevant to the development of novel tests of brain structure and function across the life-course, strategies to improve cognitive outcomes, and our understanding of early influences on brain development and function.

Automated classification of depression from structural brain measures across two independent community-based cohorts.

Major depressive disorder (MDD) has been the subject of many neuroimaging case-control classification studies. Although some studies report accuracies ≥80%, most have investigated relatively small samples of clinically-ascertained, currently symptomatic cases, and did not attempt replication in larger samples. We here first aimed to replicate previously reported classification accuracies in a small, well-phenotyped community-based group of current MDD cases with clinical interview-based diagnoses (from STratifying Resilience and Depression Longitudinally cohort, 'STRADL'). We performed a set of exploratory predictive classification analyses with measures related to brain morphometry and white matter integrity. We applied three classifier types-SVM, penalised logistic regression or decision tree-either with or without optimisation, and with or without feature selection. We then determined whether similar accuracies could be replicated in a larger independent population-based sample with self-reported current depression (UK Biobank cohort). Additional analyses extended to lifetime MDD diagnoses-remitted MDD in STRADL, and lifetime-experienced MDD in UK Biobank. The highest cross-validation accuracy (75%) was achieved in the initial current MDD sample with a decision tree classifier and cortical surface area features. The most frequently selected decision tree split variables included surface areas of bilateral caudal anterior cingulate, left lingual gyrus, left superior frontal, right precentral and paracentral regions. High accuracy was not achieved in the larger samples with self-reported current depression (53.73%), with remitted MDD (57.48%), or with lifetime-experienced MDD (52.68-60.29%). Our results indicate that high predictive classification accuracies may not immediately translate to larger samples with broader criteria for depression, and may not be robust across different classification approaches.

An overview of prevalence, determinants and health outcomes of polypharmacy.

A high rate of polypharmacy is, in part, a consequence of the increasing proportion of multimorbidity in the ageing population worldwide. Our understanding of the potential harm of taking multiple medications in an older, multi-morbid population, who are likely to be on a polypharmacy regime, is limited. This is a narrative literature review that aims to appraise and summarise recent studies published about polypharmacy. We searched MEDLINE using the search terms polypharmacy (and its variations, e.g. multiple prescriptions, inappropriate drug use, etc.) in titles. Systematic reviews and original studies in English published between 2003 and 2018 were included. In this review, we provide current definitions of polypharmacy. We identify the determinants and prevalence of polypharmacy reported in different studies. Finally, we summarise some of the findings regarding the association between polypharmacy and health outcomes in older adults, with a focus on frailty, hospitalisation and mortality. Polypharmacy was most often defined in terms of the number of medications that are being taken by an individual at any given time. Our review showed that the prevalence of polypharmacy varied between 10% to as high as around 90% in different populations. Chronic conditions, demographics, socioeconomics and self-assessed health factors were independent predictors of polypharmacy. Polypharmacy was reported to be associated with various adverse outcomes after adjusting for health conditions. Optimising care for polypharmacy with valid, reliable measures, relevant to all patients, will improve the health outcomes of older adult population.

Blunted medial prefrontal cortico-limbic reward-related effective connectivity and depression.

Major depressive disorder is a leading cause of disability and significant mortality, yet mechanistic understanding remains limited. Over the past decade evidence has accumulated from case-control studies that depressive illness is associated with blunted reward activation in the basal ganglia and other regions such as the medial prefrontal cortex. However it is unclear whether this finding can be replicated in a large number of subjects. The functional anatomy of the medial prefrontal cortex and basal ganglia has been extensively studied and the former has excitatory glutamatergic projections to the latter. Reduced effect of glutamatergic projections from the prefrontal cortex to the nucleus accumbens has been argued to underlie motivational disorders such as depression, and many prominent theories of major depressive disorder propose a role for abnormal cortico-limbic connectivity. However, it is unclear whether there is abnormal reward-linked effective connectivity between the medial prefrontal cortex and basal ganglia related to depression. While resting state connectivity abnormalities have been frequently reported in depression, it has not been possible to directly link these findings to reward-learning studies. Here, we tested two main hypotheses. First, mood symptoms are associated with blunted striatal reward prediction error signals in a large community-based sample of recovered and currently ill patients, similar to reports from a number of studies. Second, event-related directed medial prefrontal cortex to basal ganglia effective connectivity is abnormally increased or decreased related to the severity of mood symptoms. Using a Research Domain Criteria approach, data were acquired from a large community-based sample of subjects who participated in a probabilistic reward learning task during event-related functional MRI. Computational modelling of behaviour, model-free and model-based functional MRI, and effective connectivity dynamic causal modelling analyses were used to test hypotheses. Increased depressive symptom severity was related to decreased reward signals in areas which included the nucleus accumbens in 475 participants. Decreased reward-related effective connectivity from the medial prefrontal cortex to striatum was associated with increased depressive symptom severity in 165 participants. Decreased striatal activity may have been due to decreased cortical to striatal connectivity consistent with glutamatergic and cortical-limbic related theories of depression and resulted in reduced direct pathway basal ganglia output. Further study of basal ganglia pathophysiology is required to better understand these abnormalities in patients with depressive symptoms and syndromes.

Smoking-by-genotype interaction in type 2 diabetes risk and fasting glucose.

Smoking is a potentially causal behavioral risk factor for type 2 diabetes (T2D), but not all smokers develop T2D. It is unknown whether genetic factors partially explain this variation. We performed genome-environment-wide interaction studies to identify loci exhibiting potential interaction with baseline smoking status (ever vs. never) on incident T2D and fasting glucose (FG). Analyses were performed in participants of European (EA) and African ancestry (AA) separately. Discovery analyses were conducted using genotype data from the 50,000-single-nucleotide polymorphism (SNP) ITMAT-Broad-CARe (IBC) array in 5 cohorts from from the Candidate Gene Association Resource Consortium (n = 23,189). Replication was performed in up to 16 studies from the Cohorts for Heart Aging Research in Genomic Epidemiology Consortium (n = 74,584). In meta-analysis of discovery and replication estimates, 5 SNPs met at least one criterion for potential interaction with smoking on incident T2D at p<1x10-7 (adjusted for multiple hypothesis-testing with the IBC array). Two SNPs had significant joint effects in the overall model and significant main effects only in one smoking stratum: rs140637 (FBN1) in AA individuals had a significant main effect only among smokers, and rs1444261 (closest gene C2orf63) in EA individuals had a significant main effect only among nonsmokers. Three additional SNPs were identified as having potential interaction by exhibiting a significant main effects only in smokers: rs1801232 (CUBN) in AA individuals, rs12243326 (TCF7L2) in EA individuals, and rs4132670 (TCF7L2) in EA individuals. No SNP met significance for potential interaction with smoking on baseline FG. The identification of these loci provides evidence for genetic interactions with smoking exposure that may explain some of the heterogeneity in the association between smoking and T2D.

Circulating asymmetric dimethylarginine and cognitive decline: A 4-year follow-up study of the 1936 Aberdeen Birth Cohort.

BACKGROUND: The underlying mechanisms leading to dementia and Alzheimer's disease (AD) are unclear. Asymmetric dimethylarginine (ADMA), an endogenous inhibitor of nitric oxide synthase, may be associated with cognitive decline, but population-based evidence is lacking. METHODS: Change in cognitive performance was assessed in participants of the Aberdeen Birth Cohort of 1936 using longitudinal Raven's progressive matrices (RPM) between 2000 and 2004. Multiple linear regression was used to estimate the association between ADMA concentrations in 2000 and change in cognitive performance after adjustment for potential confounders. RESULTS: A total of 93 participants had complete information on cognitive performance between 2000 and 2004. Mean plasma ADMA concentrations were approximately 0.4 µmol/L lower in those participants with stable or improved RPM scores over follow-up compared with participants whose cognitive performance worsened. In confounder-adjusted analysis, one SD (0.06 µmol/L) increase in ADMA at 63 years of age was associated with an average reduction in RPM of 1.26 points (95% CI 0.14-2.26) after 4 years. CONCLUSION: Raised plasma ADMA concentrations predicted worsening cognitive performance after approximately 4 years in this cohort of adults in late-middle age. These findings have implications for future research, including presymptomatic diagnosis or novel therapeutic targets for dementia and AD.

Concentration-Dependent Activity of Hydromethylthionine on Clinical Decline and Brain Atrophy in a Randomized Controlled Trial in Behavioral Variant Frontotemporal Dementia.

BACKGROUND: Hydromethylthionine is a potent inhibitor of pathological aggregation of tau and TDP-43 proteins. OBJECTIVE: To compare hydromethylthionine treatment effects at two doses and to determine how drug exposure is related to treatment response in bvFTD. METHODS: We undertook a 52-week Phase III study in 220 bvFTD patients randomized to compare hydromethylthionine at 200 mg/day and 8 mg/day (intended as a control). The principal outcomes were change on the Addenbrookes Cognitive Examination - Revised (ACE-R), the Functional Activities Questionnaire (FAQ), and whole brain volume. Secondary outcomes included Modified Clinical Global Impression of Change (Modified-CGIC). A population pharmacokinetic exposure-response analysis was undertaken in 175 of the patients with available blood samples and outcome data using a discriminatory plasma assay for the parent drug. RESULTS: There were no significant differences between the two doses as randomized. There were steep concentration-response relationships for plasma levels in the range 0.3-0.6 ng/ml at the 8 mg/day dose on clinical and MRI outcomes. There were significant exposure-dependent differences at 8 mg/day for FAQ, Modified-CGIC, and whole brain atrophy comparing patients with plasma levels greater than 0.346 ng/ml with having minimal drug exposure. The exposure-response is biphasic with worse outcomes at the high concentrations produced by 200 mg/day. CONCLUSIONS: Hydromethylthionine has a similar concentration-response profile for effects on clinical decline and brain atrophy at the 8 mg/day dose in bvFTD as recently reported in AD. Treatment responses in bvFTD are predicted to be maximal at doses in the range 20-60 mg/day. A confirmatory placebo-controlled trial is now planned.