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 (β=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.

General and specific patterns of cortical gene expression as spatial correlates of complex cognitive functioning

Gene expression varies across the brain. This spatial patterning denotes specialised support for particular brain functions. However, the way that a given gene's expression fluctuates across the brain may be governed by general rules. Quantifying patterns of spatial covariation across genes would offer insights into the molecular characteristics of brain areas supporting, for example, complex cognitive functions. Here, we use principal component analysis to separate general and unique gene regulatory associations with cortical substrates of cognition. We find that the region-to-region variation in cortical expression profiles of 8235 genes covaries across two major principal components: gene ontology analysis suggests these dimensions are characterised by downregulation and upregulation of cell-signalling/modification and transcription factors. We validate these patterns out-of-sample and across different data processing choices. Brain regions more strongly implicated in general cognitive functioning (g; 3 cohorts, total meta-analytic N = 39,519) tend to be more balanced between downregulation and upregulation of both major components (indicated by regional component scores). We then identify a further 29 genes as candidate cortical spatial correlates of g, beyond the patterning of the two major components (|β| range = 0.18 to 0.53). Many of these genes have been previously associated with clinical neurodegenerative and psychiatric disorders, or with other health-related phenotypes. The results provide insights into the cortical organisation of gene expression and its association with individual differences in cognitive functioning.

General and specific patterns of cortical gene expression as spatial correlates of complex cognitive functioning

Gene expression varies across the brain. This spatial patterning denotes specialised support for particular brain functions. However, the way that a given gene's expression fluctuates across the brain may be governed by general rules. Quantifying patterns of spatial covariation across genes would offer insights into the molecular characteristics of brain areas supporting, for example, complex cognitive functions. Here, we use principal component analysis to separate general and unique gene regulatory associations with cortical substrates of cognition. We find that the region-to-region variation in cortical expression profiles of 8235 genes covaries across two major principal components : gene ontology analysis suggests these dimensions are characterised by downregulation and upregulation of cell-signalling/modification and transcription factors. We validate these patterns out-of-sample and across different data processing choices. Brain regions more strongly implicated in general cognitive functioning (g; 3 cohorts, total meta-analytic N = 39,519) tend to be more balanced between downregulation and upregulation of both major components (indicated by regional component scores). We then identify a further 41 genes as candidate cortical spatial correlates of g, beyond the patterning of the two major components (|β| range = 0.15 to 0.53). Many of these genes have been previously associated with clinical neurodegenerative and psychiatric disorders, or with other health-related phenotypes. The results provide insights into the cortical organisation of gene expression and its association with individual differences in cognitive functioning.

Patterns of brain atrophy in recently-diagnosed relapsing-remitting multiple sclerosis

Recurrent neuroinflammation in relapsing-remitting MS (RRMS) is thought to lead to neurodegeneration, resulting in progressive disability. Repeated magnetic resonance imaging (MRI) of the brain provides non-invasive measures of atrophy over time, a key marker of neurodegeneration. This study investigates regional neurodegeneration of the brain in recently-diagnosed RRMS using volumetry and voxel-based morphometry (VBM). RRMS patients (N = 354) underwent 3T structural MRI <6 months after diagnosis and 1-year follow-up, as part of the Scottish multicentre 'FutureMS' study. MRI data were processed using FreeSurfer to derive volumetrics, and FSL for VBM (grey matter (GM) only), to establish regional patterns of change in GM and normal-appearing white matter (NAWM) over time throughout the brain. Volumetric analyses showed a decrease over time (q<0.05) in bilateral cortical GM and NAWM, cerebellar GM, brainstem, amygdala, basal ganglia, hippocampus, accumbens, thalamus and ventral diencephalon. Additionally, NAWM and GM volume decreased respectively in the following cortical regions, frontal: 14 out of 26 regions and 16/26; temporal: 18/18 and 15/18; parietal: 14/14 and 11/14; occipital: 7/8 and 8/8. Left GM and NAWM asymmetry was observed in the frontal lobe. GM VBM analysis showed three major clusters of decrease over time: 1) temporal and subcortical areas, 2) cerebellum, 3) anterior cingulum and supplementary motor cortex; and four smaller clusters within the occipital lobe. Widespread GM and NAWM atrophy was observed in this large recently-diagnosed RRMS cohort, particularly in the brainstem, cerebellar GM, and subcortical and occipital-temporal regions; indicative of neurodegeneration across tissue types, and in accord with limited previous studies in early disease. Volumetric and VBM results emphasise different features of longitudinal lobar and loco-regional change, however identify consistent atrophy patterns across individuals. Atrophy measures targeted to specific brain regions may provide improved markers of neurodegeneration, and potential future imaging stratifiers and endpoints for clinical decision making and therapeutic trials.

Concurrent validity and reliability of suicide risk assessment instruments: A meta-analysis of 20 instruments across 27 international cohorts

OBJECTIVE

A major limitation of current suicide research is the lack of power to identify robust correlates of suicidal thoughts or behavior. Variation in suicide risk assessment instruments used across cohorts may represent a limitation to pooling data in international consortia.

METHOD

Here, we examine this issue through two approaches: (a) an extensive literature search on the reliability and concurrent validity of the most commonly used instruments and (b) by pooling data (N ∼ 6,000 participants) from cohorts from the Enhancing NeuroImaging Genetics Through Meta-Analysis (ENIGMA) Major Depressive Disorder and ENIGMA-Suicidal Thoughts and Behaviour working groups, to assess the concurrent validity of instruments currently used for assessing suicidal thoughts or behavior.

RESULTS

We observed moderate-to-high correlations between measures, consistent with the wide range (κ range: 0.15-0.97; r range: 0.21-0.94) reported in the literature. Two common multi-item instruments, the Columbia Suicide Severity Rating Scale and the Beck Scale for Suicidal Ideation were highly correlated with each other (r = 0.83). Sensitivity analyses identified sources of heterogeneity such as the time frame of the instrument and whether it relies on self-report or a clinical interview. Finally, construct-specific analyses suggest that suicide ideation items from common psychiatric questionnaires are most concordant with the suicide ideation construct of multi-item instruments.

CONCLUSIONS

Our findings suggest that multi-item instruments provide valuable information on different aspects of suicidal thoughts or behavior but share a modest core factor with single suicidal ideation items. Retrospective, multisite collaborations including distinct instruments should be feasible provided they harmonize across instruments or focus on specific constructs of suicidality. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Biogeographical survey of soil microbiomes across sub-Saharan Africa: structure, drivers, and predicted climate-driven changes

BACKGROUND

Top-soil microbiomes make a vital contribution to the Earth's ecology and harbor an extraordinarily high biodiversity. They are also key players in many ecosystem services, particularly in arid regions of the globe such as the African continent. While several recent studies have documented patterns in global soil microbial ecology, these are largely biased towards widely studied regions and rely on models to interpolate the microbial diversity of other regions where there is low data coverage. This is the case for sub-Saharan Africa, where the number of regional microbial studies is very low in comparison to other continents.

RESULTS

The aim of this study was to conduct an extensive biogeographical survey of sub-Saharan Africa's top-soil microbiomes, with a specific focus on investigating the environmental drivers of microbial ecology across the region. In this study, we sampled 810 sample sites across 9 sub-Saharan African countries and used taxonomic barcoding to profile the microbial ecology of these regions. Our results showed that the sub-Saharan nations included in the study harbor qualitatively distinguishable soil microbiomes. In addition, using soil chemistry and climatic data extracted from the same sites, we demonstrated that the top-soil microbiome is shaped by a broad range of environmental factors, most notably pH, precipitation, and temperature. Through the use of structural equation modeling, we also developed a model to predict how soil microbial biodiversity in sub-Saharan Africa might be affected by future climate change scenarios. This model predicted that the soil microbial biodiversity of countries such as Kenya will be negatively affected by increased temperatures and decreased precipitation, while the fungal biodiversity of Benin will benefit from the increase in annual precipitation.

CONCLUSION

This study represents the most extensive biogeographical survey of sub-Saharan top-soil microbiomes to date. Importantly, this study has allowed us to identify countries in sub-Saharan Africa that might be particularly vulnerable to losses in soil microbial ecology and productivity due to climate change. Considering the reliance of many economies in the region on rain-fed agriculture, this study provides crucial information to support conservation efforts in the countries that will be most heavily impacted by climate change. Video Abstract.

Integrated methylome and phenome study of the circulating proteome reveals markers pertinent to brain health

Characterising associations between the methylome, proteome and phenome may provide insight into biological pathways governing brain health. Here, we report an integrated DNA methylation and phenotypic study of the circulating proteome in relation to brain health. Methylome-wide association studies of 4058 plasma proteins are performed (N = 774), identifying 2928 CpG-protein associations after adjustment for multiple testing. These are independent of known genetic protein quantitative trait loci (pQTLs) and common lifestyle effects. Phenome-wide association studies of each protein are then performed in relation to 15 neurological traits (N = 1,065), identifying 405 associations between the levels of 191 proteins and cognitive scores, brain imaging measures or APOE e4 status. We uncover 35 previously unreported DNA methylation signatures for 17 protein markers of brain health. The epigenetic and proteomic markers we identify are pertinent to understanding and stratifying brain health.

Structural neuroimaging measures and lifetime depression across levels of phenotyping in UK biobank

Depression is assessed in various ways in research, with large population studies often relying on minimal phenotyping. Genetic results suggest clinical diagnoses and self-report measures of depression show some core similarities, but also important differences. It is not yet clear how neuroimaging associations depend on levels of phenotyping. We studied 39,300 UK Biobank imaging participants (20,701 female; aged 44.6 to 82.3 years, M = 64.1, SD = 7.5) with structural neuroimaging and lifetime depression data. Past depression phenotypes included a single-item self-report measure, an intermediate measure of 'probable' lifetime depression, derived from multiple questionnaire items relevant to a history of depression, and a retrospective clinical diagnosis according to DSM-IV criteria. We tested (i) associations between brain structural measures and each depression phenotype, and (ii) effects of phenotype on these associations. Depression-brain structure associations were small (β < 0.1) for all phenotypes, but still significant after FDR correction for many regional metrics. Lifetime depression was consistently associated with reduced white matter integrity across phenotypes. Cortical thickness showed negative associations with Self-reported Depression in particular. Phenotype effects were small across most metrics, but significant for cortical thickness in most regions. We report consistent effects of lifetime depression in brain structural measures, including reduced integrity of thalamic radiations and association fibres. We also observed significant differences in associations with cortical thickness across depression phenotypes. Although these results did not relate to level of phenotyping as expected, effects of phenotype definition are still an important consideration for future depression research.

Genetic variants associated with longitudinal changes in brain structure across the lifespan

Human brain structure changes throughout the lifespan. Altered brain growth or rates of decline are implicated in a vast range of psychiatric, developmental and neurodegenerative diseases. In this study, we identified common genetic variants that affect rates of brain growth or atrophy in what is, to our knowledge, the first genome-wide association meta-analysis of changes in brain morphology across the lifespan. Longitudinal magnetic resonance imaging data from 15,640 individuals were used to compute rates of change for 15 brain structures. The most robustly identified genes GPR139, DACH1 and APOE are associated with metabolic processes. We demonstrate global genetic overlap with depression, schizophrenia, cognitive functioning, insomnia, height, body mass index and smoking. Gene set findings implicate both early brain development and neurodegenerative processes in the rates of brain changes. Identifying variants involved in structural brain changes may help to determine biological pathways underlying optimal and dysfunctional brain development and aging.

Cohort profile for the STratifying Resilience and Depression Longitudinally (STRADL) study: A depression-focused investigation of Generation Scotland, using detailed clinical, cognitive, and neuroimaging assessments

STratifying Resilience and Depression Longitudinally (STRADL) is a population-based study built on the Generation Scotland: Scottish Family Health Study (GS:SFHS) resource. The aim of STRADL is to subtype major depressive disorder (MDD) on the basis of its aetiology, using detailed clinical, cognitive, and brain imaging assessments. The GS:SFHS provides an important opportunity to study complex gene-environment interactions, incorporating linkage to existing datasets and inclusion of early-life variables for two longitudinal birth cohorts. Specifically, data collection in STRADL included: socio-economic and lifestyle variables; physical measures; questionnaire data that assesses resilience, early-life adversity, personality, psychological health, and lifetime history of mood disorder; laboratory samples; cognitive tests; and brain magnetic resonance imaging. Some of the questionnaire and cognitive data were first assessed at the GS:SFHS baseline assessment between 2006-2011, thus providing longitudinal measures relevant to the study of depression, psychological resilience, and cognition. In addition, routinely collected historic NHS data and early-life variables are linked to STRADL data, further providing opportunities for longitudinal analysis. Recruitment has been completed and we consented and tested 1,188 participants.

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.

Comparing personalized brain-based and genetic risk scores for major depressive disorder in large population samples of adults and adolescents

Background

Major depressive disorder (MDD) is a polygenic disorder associated with brain alterations but until recently, there have been no brain-based metrics to quantify individual-level variation in brain morphology. Here, we evaluated and compared the performance of a new brain-based ‘Regional Vulnerability Index’ (RVI) with polygenic risk scores (PRS), in the context of MDD. We assessed associations with syndromal MDD in an adult sample (N = 702, age = 59 ± 10) and with subclinical depressive symptoms in a longitudinal adolescent sample (baseline N = 3,825, age = 10 ± 1; 2-year follow-up N = 2,081, age = 12 ± 1).

Methods

MDD-RVIs quantify the correlation of the individual’s corresponding brain metric with the expected pattern for MDD derived in an independent sample. Using the same methodology across samples, subject-specific MDD-PRS and six MDD-RVIs based on different brain modalities (subcortical volume, cortical thickness, cortical surface area, mean diffusivity, fractional anisotropy, and multimodal) were computed.

Results

In adults, MDD-RVIs (based on white matter and multimodal measures) were more strongly associated with MDD (β = 0.099–0.281, P_FDR = 0.001–0.043) than MDD-PRS (β = 0.056–0.152, P_FDR = 0.140–0.140). In adolescents, depressive symptoms were associated with MDD-PRS at baseline and follow-up (β = 0.084–0.086, p = 1.38 × 10⁻⁴−4.77 × 10⁻⁴) but not with any MDD-RVIs (β < 0.05, p > 0.05).

Conclusions

Our results potentially indicate the ability of brain-based risk scores to capture a broader range of risk exposures than genetic risk scores in adults and are also useful in helping us to understand the temporal origins of depression-related brain features. Longitudinal data, specific to the developmental period and on white matter measures, will be useful in informing risk for subsequent psychiatric illness.

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.

Evaluating cone-beam CT in the diagnosis of suspected scaphoid fractures in the emergency department: preliminary findings

OBJECTIVES

Scaphoid fractures are challenging to accurately diagnose with delayed and missed diagnoses risking poor patient outcomes. Cone beam CT (CBCT) is an emerging technology facilitating alternative access to multi-planar imaging. The aim of this study was to evaluate the use of early CBCT in the diagnosis of suspected scaphoid fractures presenting via the Emergency Department (ED).

METHODS

In this single centre the imaging pathway was adapted to include early CBCT in adult patients with a persisting high index of clinical suspicion for scaphoid fracture despite normal radiographs. Evaluation of referrals between September 2019 and February 2020 was undertaken. Statistical analysis and temporal pathway metrics were assessed including interrater agreement for CBCT and radiography examinations.

RESULTS

Over the six-month timeframe 100 CBCT wrist scans were performed on 99 adult patients. 94% of CBCT scans were performed within 1 day of referral. 25% of scans demonstrated acute injuries including 12 radius, 6 trapezium and 2 scaphoid fractures. For fracture, CBCT had a sensitivity of 96.2% and specificity of 100%. There was a highly significant difference in interobserver variation between fracture detection on CBCT and radiographs.

CONCLUSION

CBCT heralds the potential for early accurate diagnosis of radiocarpal fractures, at lower cost, shortening clinical pathways and reducing clinical risk in the ED.

DNA Methylation and Protein Markers of Chronic Inflammation and Their Associations With Brain and Cognitive Aging

BACKGROUND AND OBJECTIVES

To investigate chronic inflammation in relation to cognitive aging by comparison of an epigenetic and serum biomarker of C-reactive protein and their associations with neuroimaging and cognitive outcomes.

METHODS

At baseline, participants (n = 521) were cognitively normal, around 73 years of age (mean 72.4, SD 0.716), and had inflammation, vascular risk (cardiovascular disease history, hypertension, diabetes, smoking, alcohol consumption, body mass index), and neuroimaging (structural and diffusion MRI) data available. Baseline inflammatory status was quantified by a traditional measure of peripheral inflammation-serum C-reactive protein (CRP)-and an epigenetic measure (DNA methylation [DNAm] signature of CRP). Linear models were used to examine the inflammation-brain health associations; mediation analyses were performed to interrogate the relationship between chronic inflammation, brain structure, and cognitive functioning.

RESULTS

We demonstrate that DNAm CRP shows significantly (on average 6.4-fold) stronger associations with brain health outcomes than serum CRP. DNAm CRP is associated with total brain volume (β = -0.197, 95% confidence interval [CI] -0.28 to -0.12, p _FDR = 8.42 × 10^-6), gray matter volume (β = -0.200, 95% CI -0.28 to -0.12, p _FDR = 1.66 × 10^-5), and white matter volume (β = -0.150, 95% CI -0.23 to -0.07, p _FDR = 0.001) and regional brain atrophy. We also find that DNAm CRP has an inverse association with global and domain-specific (speed, visuospatial, and memory) cognitive functioning and that brain structure partially mediates this CRP-cognitive association (up to 29.7%), dependent on lifestyle and health factors.

DISCUSSION

These results support the hypothesis that chronic inflammation may contribute to neurodegenerative brain changes that underlie differences in cognitive ability in later life and highlight the potential of DNAm proxies for indexing chronic inflammatory status.

CLASSIFICATION OF EVIDENCE

This study provides Class II evidence that a DNAm signature of CRP levels is more strongly associated with brain health outcomes than serum CRP levels.

Hair glucocorticoids are associated with childhood adversity, depressive symptoms and reduced global and lobar grey matter in Generation Scotland

Hypothalamic-pituitary-adrenal (HPA) axis dysregulation has been commonly reported in major depressive disorder (MDD), but with considerable heterogeneity of results; potentially due to the predominant use of acute measures of an inherently variable/phasic system. Chronic longer-term measures of HPA-axis activity have yet to be systematically examined in MDD, particularly in relation to brain phenotypes, and in the context of early-life/contemporaneous stress. Here, we utilise a temporally stable measure of cumulative HPA-axis function (hair glucocorticoids) to investigate associations between cortisol, cortisone and total glucocorticoids with concurrent measures of (i) lifetime-MDD case/control status and current symptom severity, (ii) early/current-life stress and (iii) structural neuroimaging phenotypes, in N = 993 individuals from Generation Scotland (mean age = 59.1 yrs). Increased levels of hair cortisol were significantly associated with reduced global and lobar brain volumes with reductions in the frontal, temporal and cingulate regions (βrange = -0.057 to -0.104, all PFDR < 0.05). Increased levels of hair cortisone were significantly associated with MDD (lifetime-MDD status, current symptoms, and severity; βrange = 0.071 to 0.115, all PFDR = < 0.05), with early-life adversity (β = 0.083, P = 0.017), and with reduced global and regional brain volumes (global: β = -0.059, P = 0.043; nucleus accumbens: β = -0.075, PFDR = 0.044). Associations with total glucocorticoids followed a similar pattern to the cortisol findings. In this large community-based sample, elevated glucocorticoids were significantly associated with MDD, with early, but not later-life stress, and with reduced global and regional brain phenotypes. These findings provide important foundations for future mechanistic studies to formally explore causal relationships between early adversity, chronic rather than acute measures of glucocorticoids, and neurobiological associations relevant to the aetiology of MDD.

Identification of plasma proteins relating to brain neurodegeneration and vascular pathology in cognitively normal individuals

INTRODUCTION

This study aims to first discover plasma proteomic biomarkers relating to neurodegeneration (N) and vascular (V) damage in cognitively normal individuals and second to discover proteins mediating sex-related difference in N and V pathology.

METHODS

Five thousand and thirty-two plasma proteins were measured in 1061 cognitively normal individuals (628 females and 433 males), nearly 90% of whom had magnetic resonance imaging measures of hippocampal volume (as N) and white matter hyperintensities (as V).

RESULTS

Differential protein expression analysis and co-expression network analysis revealed different proteins and modules associated with N and V, respectively. Furthermore, causal mediation analysis revealed four proteins mediated sex-related difference in N and one protein mediated such difference in V damage.

DISCUSSION

Once validated, the identified proteins could help to select cognitively normal individuals with N and V pathology for Alzheimer's disease clinical trials and provide targets for further mechanistic studies on brain sex differences, leading to sex-specific therapeutic strategies.

Spectral clustering based on structural magnetic resonance imaging and its relationship with major depressive disorder and cognitive ability

There is increasing interest in using data-driven unsupervised methods to identify structural underpinnings of common mental illnesses, including major depressive disorder (MDD) and associated traits such as cognition. However, studies are often limited to severe clinical cases with small sample sizes and most do not include replication. Here, we examine two relatively large samples with structural magnetic resonance imaging (MRI), measures of lifetime MDD and cognitive variables: Generation Scotland (GS subsample, N = 980) and UK Biobank (UKB, N = 8,900), for discovery and replication, using an exploratory approach. Regional measures of FreeSurfer derived cortical thickness (CT), cortical surface area (CSA), cortical volume (CV) and subcortical volume (subCV) were input into a clustering process, controlling for common covariates. The main analysis steps involved constructing participant K-nearest neighbour graphs and graph partitioning with Markov stability to determine optimal clustering of participants. Resultant clusters were (1) checked whether they were replicated in an independent cohort and (2) tested for associations with depression status and cognitive measures. Participants separated into two clusters based on structural brain measurements in GS subsample, with large Cohen's d effect sizes between clusters in higher order cortical regions, commonly associated with executive function and decision making. Clustering was replicated in the UKB sample, with high correlations of cluster effect sizes for CT, CSA, CV and subCV between cohorts across regions. The identified clusters were not significantly different with respect to MDD case-control status in either cohort (GS subsample: p_FDR  = .2239-.6585; UKB: p_FDR  = .2003-.7690). Significant differences in general cognitive ability were, however, found between the clusters for both datasets, for CSA, CV and subCV (GS subsample: d = 0.2529-.3490, p_FDR  < .005; UKB: d = 0.0868-0.1070, p_FDR  < .005). Our results suggest that there are replicable natural groupings of participants based on cortical and subcortical brain measures, which may be related to differences in cognitive performance, but not to the MDD case-control status.

Cohort profile for the STratifying Resilience and Depression Longitudinally (STRADL) study: A depression-focused investigation of Generation Scotland, using detailed clinical, cognitive, and neuroimaging assessments

STratifying Resilience and Depression Longitudinally (STRADL) is a population-based study built on the Generation Scotland: Scottish Family Health Study (GS:SFHS) resource. The aim of STRADL is to subtype major depressive disorder (MDD) on the basis of its aetiology, using detailed clinical, cognitive, and brain imaging assessments. The GS:SFHS provides an important opportunity to study complex gene-environment interactions, incorporating linkage to existing datasets and inclusion of early-life variables for two longitudinal birth cohorts. Specifically, data collection in STRADL included: socio-economic and lifestyle variables; physical measures; questionnaire data that assesses resilience, early-life adversity, personality, psychological health, and lifetime history of mood disorder; laboratory samples; cognitive tests; and brain magnetic resonance imaging. Some of the questionnaire and cognitive data were first assessed at the GS:SFHS baseline assessment between 2006-2011, thus providing longitudinal measures relevant to the study of depression, psychological resilience, and cognition. In addition, routinely collected historic NHS data and early-life variables are linked to STRADL data, further providing opportunities for longitudinal analysis. Recruitment has been completed and we consented and tested 1,188 participants.

Association of Structural Magnetic Resonance Imaging Measures With Psychosis Onset in Individuals at Clinical High Risk for Developing Psychosis: An ENIGMA Working Group Mega-analysis

Importance

The ENIGMA clinical high risk (CHR) for psychosis initiative, the largest pooled neuroimaging sample of individuals at CHR to date, aims to discover robust neurobiological markers of psychosis risk.

Objective

To investigate baseline structural neuroimaging differences between individuals at CHR and healthy controls as well as between participants at CHR who later developed a psychotic disorder (CHR-PS+) and those who did not (CHR-PS-).

Design, Setting, and Participants

In this case-control study, baseline T1-weighted magnetic resonance imaging (MRI) data were pooled from 31 international sites participating in the ENIGMA Clinical High Risk for Psychosis Working Group. CHR status was assessed using the Comprehensive Assessment of At-Risk Mental States or Structured Interview for Prodromal Syndromes. MRI scans were processed using harmonized protocols and analyzed within a mega-analysis and meta-analysis framework from January to October 2020.

Main Outcomes and Measures

Measures of regional cortical thickness (CT), surface area, and subcortical volumes were extracted from T1-weighted MRI scans. Independent variables were group (CHR group vs control group) and conversion status (CHR-PS+ group vs CHR-PS- group vs control group).

Results

Of the 3169 included participants, 1428 (45.1%) were female, and the mean (SD; range) age was 21.1 (4.9; 9.5-39.9) years. This study included 1792 individuals at CHR and 1377 healthy controls. Using longitudinal clinical information, 253 in the CHR-PS+ group, 1234 in the CHR-PS- group, and 305 at CHR without follow-up data were identified. Compared with healthy controls, individuals at CHR exhibited widespread lower CT measures (mean [range] Cohen d = -0.13 [-0.17 to -0.09]), but not surface area or subcortical volume. Lower CT measures in the fusiform, superior temporal, and paracentral regions were associated with psychosis conversion (mean Cohen d = -0.22; 95% CI, -0.35 to 0.10). Among healthy controls, compared with those in the CHR-PS+ group, age showed a stronger negative association with left fusiform CT measures (F = 9.8; P < .001; q < .001) and left paracentral CT measures (F = 5.9; P = .005; q = .02). Effect sizes representing lower CT associated with psychosis conversion resembled patterns of CT differences observed in ENIGMA studies of schizophrenia (ρ = 0.35; 95% CI, 0.12 to 0.55; P = .004) and individuals with 22q11.2 microdeletion syndrome and a psychotic disorder diagnosis (ρ = 0.43; 95% CI, 0.20 to 0.61; P = .001).

Conclusions and Relevance

This study provides evidence for widespread subtle, lower CT measures in individuals at CHR. The pattern of CT measure differences in those in the CHR-PS+ group was similar to those reported in other large-scale investigations of psychosis. Additionally, a subset of these regions displayed abnormal age associations. Widespread disruptions in CT coupled with abnormal age associations in those at CHR may point to disruptions in postnatal brain developmental processes.

Aging-Sensitive Networks Within the Human Structural Connectome Are Implicated in Late-Life Cognitive Declines

BACKGROUND

Aging-related cognitive decline is a primary risk factor for Alzheimer's disease and related dementias. More precise identification of the neurobiological bases of cognitive decline in aging populations may provide critical insights into the precursors of late-life dementias.

METHODS

Using structural and diffusion brain magnetic resonance imaging data from the UK Biobank (n = 8185; age range, 45-78 years), we examined aging of regional gray matter volumes (nodes) and white matter structural connectivity (edges) within 9 well-characterized networks of interest in the human brain connectome. In the independent Lothian Birth Cohort 1936 (n = 534; all 73 years of age), we tested whether aging-sensitive connectome elements are enriched for key domains of cognitive function before and after controlling for early-life cognitive ability.

RESULTS

In the UK Biobank, age differences in individual connectome elements corresponded closely with principal component loadings reflecting connectome-wide integrity (|rnodes| = .420; |redges| = .583), suggesting that connectome aging occurs on broad dimensions of variation in brain architecture. In the Lothian Birth Cohort 1936, composite indices of node integrity were predictive of all domains of cognitive function, whereas composite indices of edge integrity were associated specifically with processing speed. Elements within the central executive network were disproportionately predictive of late-life cognitive function relative to the network's small size. Associations with processing speed and visuospatial ability remained after controlling for childhood cognitive ability.

CONCLUSIONS

These results implicate global dimensions of variation in the human structural connectome in aging-related cognitive decline. The central executive network may demarcate a constellation of elements that are centrally important to age-related cognitive impairments.

Structural brain correlates of serum and epigenetic markers of inflammation in major depressive disorder

Inflammatory processes are implicated in the aetiology of Major Depressive Disorder (MDD); however, the relationship between peripheral inflammation, brain structure and depression remains unclear, partly due to complexities around the use of acute/phasic inflammatory biomarkers. Here, we report the first large-scale study of both serological and methylomic signatures of CRP (considered to represent acute and chronic measures of inflammation respectively) and their associations with depression status/symptoms, and structural neuroimaging phenotypes (T1 and diffusion MRI) in a large community-based sample (Generation Scotland; NMDD cases = 271, Ncontrols = 609). Serum CRP was associated with overall MDD severity, and specifically with current somatic symptoms- general interest (β = 0.145, PFDR = 6 × 10-4) and energy levels (β = 0.101, PFDR = 0.027), along with reduced entorhinal cortex thickness (β = -0.095, PFDR = 0.037). DNAm CRP was significantly associated with reduced global grey matter/cortical volume and widespread reductions in integrity of 16/24 white matter tracts (with greatest regional effects in the external and internal capsules, βFA= -0.12 to -0.14). In general, the methylation-based measures showed stronger associations with imaging metrics than serum-based CRP measures (βaverage = -0.15 versus βaverage = 0.01 respectively). These findings provide evidence for central effects of peripheral inflammation from both serological and epigenetic markers of inflammation, including in brain regions previously implicated in depression. This suggests that these imaging measures may be involved in the relationship between peripheral inflammation and somatic/depressive symptoms. Notably, greater effects on brain morphology were seen for methylation-based rather than serum-based measures of inflammation, indicating the importance of such measures for future studies.

Longitudinal trajectories of brain age in young individuals at familial risk of mood disorder from the Scottish Bipolar Family Study

Background: Within young individuals, mood disorder onset may be related to changes in trajectory of brain structure development. To date, however, longitudinal prospective studies remain scarce and show partly contradictory findings, with a lack of emphasis on changes at the level of global brain patterns. Cross-sectional adult studies have applied such methods and show that mood disorders are associated with accelerated brain aging. Currently, it remains unclear whether young individuals show differential brain structure aging trajectories associated with onset of mood disorder and/or presence of familial risk. Methods: Participants included young individuals (15-30 years, 53%F) from the prospective longitudinal Scottish Bipolar Family Study with and without close family history of mood disorder. All were well at time of recruitment. Implementing a structural MRI-based brain age prediction model, we globally assessed individual trajectories of age-related structural change using the difference between predicted brain age and chronological age (brain-predicted age difference (brain-PAD)) at baseline and at 2-year follow-up. Based on follow-up clinical assessment, individuals were categorised into three groups: (i) controls who remained well (C-well, n = 93), (ii) high familial risk who remained well (HR-well, n = 74) and (iii) high familial risk who developed a mood disorder (HR-MD, n = 35). Results: At baseline, brain-PAD was comparable between groups. Results showed statistically significant negative trajectories of brain-PAD between baseline and follow-up for HR-MD versus C-well ( β = -0.60, p corrected < 0.001) and HR-well ( β = -0.36, p corrected = 0.02), with a potential intermediate trajectory for HR-well ( β = -0.24 years, p corrected = 0.06).   Conclusions: These preliminary findings suggest that within young individuals, onset of mood disorder and familial risk may be associated with a deceleration in brain structure aging trajectories. Extended longitudinal research will need to corroborate findings of emerging maturational lags in relation to mood disorder risk and onset.

Associations between vascular risk factors and brain MRI indices in UK Biobank

Aims

Several factors are known to increase risk for cerebrovascular disease and dementia, but there is limited evidence on associations between multiple vascular risk factors (VRFs) and detailed aspects of brain macrostructure and microstructure in large community-dwelling populations across middle and older age.

Methods and results

Associations between VRFs (smoking, hypertension, pulse pressure, diabetes, hypercholesterolaemia, body mass index, and waist–hip ratio) and brain structural and diffusion MRI markers were examined in UK Biobank (N = 9722, age range 44–79 years). A larger number of VRFs was associated with greater brain atrophy, lower grey matter volume, and poorer white matter health. Effect sizes were small (brain structural R² ≤1.8%). Higher aggregate vascular risk was related to multiple regional MRI hallmarks associated with dementia risk: lower frontal and temporal cortical volumes, lower subcortical volumes, higher white matter hyperintensity volumes, and poorer white matter microstructure in association and thalamic pathways. Smoking pack years, hypertension and diabetes showed the most consistent associations across all brain measures. Hypercholesterolaemia was not uniquely associated with any MRI marker.

Conclusion

Higher levels of VRFs were associated with poorer brain health across grey and white matter macrostructure and microstructure. Effects are mainly additive, converging upon frontal and temporal cortex, subcortical structures, and specific classes of white matter fibres. Though effect sizes were small, these results emphasize the vulnerability of brain health to vascular factors even in relatively healthy middle and older age, and the potential to partly ameliorate cognitive decline by addressing these malleable risk factors.

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 automated machine learning approach to predict brain age from cortical anatomical measures

The use of machine learning (ML) algorithms has significantly increased in neuroscience. However, from the vast extent of possible ML algorithms, which one is the optimal model to predict the target variable? What are the hyperparameters for such a model? Given the plethora of possible answers to these questions, in the last years, automated ML (autoML) has been gaining attention. Here, we apply an autoML library called Tree-based Pipeline Optimisation Tool (TPOT) which uses a tree-based representation of ML pipelines and conducts a genetic programming-based approach to find the model and its hyperparameters that more closely predicts the subject's true age. To explore autoML and evaluate its efficacy within neuroimaging data sets, we chose a problem that has been the focus of previous extensive study: brain age prediction. Without any prior knowledge, TPOT was able to scan through the model space and create pipelines that outperformed the state-of-the-art accuracy for Freesurfer-based models using only thickness and volume information for anatomical structure. In particular, we compared the performance of TPOT (mean absolute error [MAE]: 4.612 ± .124 years) and a relevance vector regression (MAE 5.474 ± .140 years). TPOT also suggested interesting combinations of models that do not match the current most used models for brain prediction but generalise well to unseen data. AutoML showed promising results as a data-driven approach to find optimal models for neuroimaging applications.

Longitudinal trajectories of brain age in young individuals at familial risk of mood disorder from the Scottish Bipolar Family Study

Background: Within young individuals, mood disorder onset may be related to changes in trajectory of brain structure development. To date, however, longitudinal prospective studies remain scarce and show partly contradictory findings, with a lack of emphasis on changes at the level of global brain patterns. Cross-sectional adult studies have applied such methods and show that mood disorders are associated with accelerated brain ageing. Currently, it remains unclear whether young individuals show differential brain structure aging trajectories associated with onset of mood disorder and/or presence of familial risk. Methods: Participants included young individuals (15-30 years, 53%F) from the prospective longitudinal Scottish Bipolar Family Study with and without close family history of mood disorder. All were well at time of recruitment. Implementing a structural MRI-based brain age prediction model, we globally assessed individual trajectories of age-related structural change using the difference between predicted brain age and chronological age (brain-predicted age difference (brain-PAD)) at baseline and at 2-year follow-up. Based on follow-up clinical assessment, individuals were categorised into three groups: (i) controls who remained well (C-well, n = 93), (ii) high familial risk who remained well (HR-well, n = 74) and (iii) high familial risk who developed a mood disorder (HR-MD, n = 35). Results: At baseline, brain-PAD was comparable between groups. Results showed statistically significant negative trajectories of brain-PAD between baseline and follow-up for HR-MD versus C-well ( β = -0.60, p corrected < 0.001) and HR-well ( β = -0.36, p corrected = 0.02), with a potential intermediate trajectory for HR-well ( β = -0.24 years, p corrected = 0.06).   Conclusions: These preliminary findings suggest that within young individuals, onset of mood disorder and familial risk may be associated with a deceleration in brain structure aging trajectories. Extended longitudinal research will need to corroborate findings of emerging maturational lags in relation to mood disorder risk and onset.

Stratifying major depressive disorder by polygenic risk for schizophrenia in relation to structural brain measures

BACKGROUND

Substantial clinical heterogeneity of major depressive disorder (MDD) suggests it may group together individuals with diverse aetiologies. Identifying distinct subtypes should lead to more effective diagnosis and treatment, while providing more useful targets for further research. Genetic and clinical overlap between MDD and schizophrenia (SCZ) suggests an MDD subtype may share underlying mechanisms with SCZ.

METHODS

The present study investigated whether a neurobiologically distinct subtype of MDD could be identified by SCZ polygenic risk score (PRS). We explored interactive effects between SCZ PRS and MDD case/control status on a range of cortical, subcortical and white matter metrics among 2370 male and 2574 female UK Biobank participants.

RESULTS

There was a significant SCZ PRS by MDD interaction for rostral anterior cingulate cortex (RACC) thickness (β = 0.191, q = 0.043). This was driven by a positive association between SCZ PRS and RACC thickness among MDD cases (β = 0.098, p = 0.026), compared to a negative association among controls (β = -0.087, p = 0.002). MDD cases with low SCZ PRS showed thinner RACC, although the opposite difference for high-SCZ-PRS cases was not significant. There were nominal interactions for other brain metrics, but none remained significant after correcting for multiple comparisons.

CONCLUSIONS

Our significant results indicate that MDD case-control differences in RACC thickness vary as a function of SCZ PRS. Although this was not the case for most other brain measures assessed, our specific findings still provide some further evidence that MDD in the presence of high genetic risk for SCZ is subtly neurobiologically distinct from MDD in general.

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.

Global and Regional Development of the Human Cerebral Cortex: Molecular Architecture and Occupational Aptitudes

We have carried out meta-analyses of genome-wide association studies (GWAS) (n = 23 784) of the first two principal components (PCs) that group together cortical regions with shared variance in their surface area. PC1 (global) captured variations of most regions, whereas PC2 (visual) was specific to the primary and secondary visual cortices. We identified a total of 18 (PC1) and 17 (PC2) independent loci, which were replicated in another 25 746 individuals. The loci of the global PC1 included those associated previously with intracranial volume and/or general cognitive function, such as MAPT and IGF2BP1. The loci of the visual PC2 included DAAM1, a key player in the planar-cell-polarity pathway. We then tested associations with occupational aptitudes and, as predicted, found that the global PC1 was associated with General Learning Ability, and the visual PC2 was associated with the Form Perception aptitude. These results suggest that interindividual variations in global and regional development of the human cerebral cortex (and its molecular architecture) cascade-albeit in a very limited manner-to behaviors as complex as the choice of one's occupation.

The genetic architecture of the human cerebral cortex

The cerebral cortex underlies our complex cognitive capabilities, yet little is known about the specific genetic loci that influence human cortical structure. To identify genetic variants that affect cortical structure, we conducted a genome-wide association meta-analysis of brain magnetic resonance imaging data from 51,665 individuals. We analyzed the surface area and average thickness of the whole cortex and 34 regions with known functional specializations. We identified 199 significant loci and found significant enrichment for loci influencing total surface area within regulatory elements that are active during prenatal cortical development, supporting the radial unit hypothesis. Loci that affect regional surface area cluster near genes in Wnt signaling pathways, which influence progenitor expansion and areal identity. Variation in cortical structure is genetically correlated with cognitive function, Parkinson's disease, insomnia, depression, neuroticism, and attention deficit hyperactivity disorder.

Aberrant structural covariance networks in youth at high familial risk for mood disorder

OBJECTIVES

Current research suggests significant disruptions in functional brain networks in individuals with mood disorder, and in those at familial risk. Studies of structural brain networks provide important insights into synchronized maturational change but have received less attention. We aimed to investigate developmental relationships of large-scale brain networks in mood disorder using structural covariance (SC) analyses.

METHODS

We conducted SC analysis of baseline structural imaging data from 121 at the time of scanning unaffected high risk (HR) individuals (29 later developed mood disorder after a median time of 4.95 years), and 89 healthy controls (C-well) with no familial risk from the Scottish Bipolar Family Study (age 15-27, 64% female). Voxel-wise analyses of covariance were conducted to compare the associations between each seed region in visual, auditory, motor, speech, semantic, executive-control, salience and default-mode networks and the whole brain signal. SC maps were compared for (a) HR(all) versus C-well individuals, and (b) between those who remained well (HR-well), versus those who subsequently developed mood disorder (HR-MD), and C-well.

RESULTS

There were no significant differences between HR(all) and C-well individuals. On splitting the HR group based on subsequent clinical outcome, the HR-MD group however displayed greater baseline SC in the salience and executive-control network, and HR-well individuals showed less SC in the salience network, compared to C-well, respectively (P < .001).

CONCLUSIONS

These findings indicate differences in network-level inter-regional relationships, especially within the salience network, which precede onset of mood disorder in those at familial risk.

Cognitive functioning and lifetime major depressive disorder in UK Biobank

BACKGROUND

Cognitive impairment associated with lifetime major depressive disorder (MDD) is well-supported by meta-analytic studies, but population-based estimates remain scarce. Previous UK Biobank studies have only shown limited evidence of cognitive differences related to probable MDD. Using updated cognitive and clinical assessments in UK Biobank, this study investigated population-level differences in cognitive functioning associated with lifetime MDD.

METHODS

Associations between lifetime MDD and cognition (performance on six tasks and general cognitive functioning [g-factor]) were investigated in UK Biobank (N-range 7,457-14,836, age 45-81 years, 52% female), adjusting for demographics, education, and lifestyle. Lifetime MDD classifications were based on the Composite International Diagnostic Interview. Within the lifetime MDD group, we additionally investigated relationships between cognition and (a) recurrence, (b) current symptoms, (c) severity of psychosocial impairment (while symptomatic), and (d) concurrent psychotropic medication use.

RESULTS

Lifetime MDD was robustly associated with a lower g-factor (β = -0.10, PFDR = 4.7 × 10-5), with impairments in attention, processing speed, and executive functioning (β ≥ 0.06). Clinical characteristics revealed differential profiles of cognitive impairment among case individuals; those who reported severe psychosocial impairment and use of psychotropic medication performed worse on cognitive tests. Severe psychosocial impairment and reasoning showed the strongest association (β = -0.18, PFDR = 7.5 × 10-5).

CONCLUSIONS

Findings describe small but robust associations between lifetime MDD and lower cognitive performance within a population-based sample. Overall effects were of modest effect size, suggesting limited clinical relevance. However, deficits within specific cognitive domains were more pronounced in relation to clinical characteristics, particularly severe psychosocial impairment.

Magnetic Stimulus Responsive DDS Based on Chitosan Microbeads Embedded with Magnetic Nanoparticles

In this paper, we have presented a novel Drug Delivery Substrate (DDS) that that is responsive to external stimuli of high-frequency alternating magnetic fields. The DDS is constituted of chitosan crosslinked with PEGDMA (polyethylene glycol dimethacrylate), loaded with Fe₃O₄ magnetic nanoparticles and vancomycin. In another experiment, a 19-hour elution was observed where three magnetic stimuli of 25 mT, 109.9 kHz were given for 60 min to the test samples. The stimuli were separated by several hours. After excitation span, it was observed that the stimulated samples released a significantly higher amount of vancomycin by as much as 21% compared to non-stimulated samples. In another study, preliminary results showing the effect of different PEGDMA chain lengths have been discussed. These results show evidence of a smart, controllable DDS that allows modulation of its normal passive antibiotic elution by applying external stimuli per personalized needs.

Fluctuating asymmetry in brain structure and general intelligence in 73-year-olds

Fluctuating body asymmetry is theorized to indicate developmental instability, and to have small positive associations with low socioeconomic status (SES). Previous studies have reported small negative associations between fluctuating body asymmetry and cognitive functioning, but relationships between fluctuating brain asymmetry and cognitive functioning remain unclear. The present study investigated the association between general intelligence (a latent factor derived from a factor analysis on 13 cognitive tests) and the fluctuating asymmetry of four structural measures of brain hemispheric asymmetry: cortical surface area, cortical volume, cortical thickness, and white matter fractional anisotropy. The sample comprised members of the Lothian Birth Cohort 1936 (LBC1936, N = 636, mean age = 72.9 years). Two methods were used to calculate structural hemispheric asymmetry: in the first method, regions contributed equally to the overall asymmetry score; in the second method, regions contributed proportionally to their size. When regions contributed equally, cortical thickness asymmetry was negatively associated with general intelligence (β = −0.18,p < .001). There was no association between cortical thickness asymmetry and childhood SES, suggesting that other mechanisms are involved in the thickness asymmetry-intelligence association. Across all cortical metrics, asymmetry of regions identified by the parieto-frontal integration theory (P-FIT) was not more strongly associated with general intelligence than non-P-FIT asymmetry. When regions contributed proportionally, there were no associations between general intelligence and any of the asymmetry measures. The implications of these findings, and of different methods of calculating structural hemispheric asymmetry, are discussed.

•

Previous work has shown links between cortical asymmetry and intelligence.

•

In the current study, two methods of calculating global cortical hemispheric asymmetry were compared.

•

Equal region contribution: the association between cortical thickness asymmetry and general intelligence was β = −0.18.

•

Proportional region contribution: no associations between three measures of cortical asymmetry and general intelligence.

Longitudinal trajectories of brain age in young individuals at familial risk of mood disorder

Background: Within young individuals, mood disorder onset may be related to changes in trajectory of brain structure development. To date, however, longitudinal prospective studies remain scarce and show partly contradictory findings, with a lack of emphasis on changes at the level of global brain patterns. Cross-sectional adult studies have applied such methods and show that mood disorders are associated with accelerated brain ageing. Currently, it remains unclear whether young individuals show differential brain structure ageing trajectories associated with onset of mood disorder and/or presence of familial risk. Methods: Participants included young individuals (15-30 years, 53%F) from the prospective longitudinal Scottish Bipolar Family Study with and without close family history of mood disorder. All were well at time of recruitment. Implementing a structural MRI-based brain age prediction model, we globally assessed individual trajectories of age-related structural change using the difference between predicted brain age and chronological age (brain-predicted age difference (brain-PAD)) at baseline and at 2-year follow-up. Based on follow-up clinical assessment, individuals were categorised into three groups: (i) controls who remained well (C-well, n = 93), (ii) high familial risk who remained well (HR-well, n = 74) and (iii) high familial risk who developed a mood disorder (HR-MD, n = 35). Results: At baseline, brain-PAD was comparable between groups. Results showed statistically significant negative trajectories of brain-PAD between baseline and follow-up for HR-MD versus C-well ( β = -0.60, p corrected < 0.001) and HR-well ( β = -0.36, p corrected = 0.02), with a potential intermediate trajectory for HR-well ( β = -0.24 years, p corrected = 0.06).   Conclusions: These preliminary findings suggest that within young individuals, onset of mood disorder and familial risk may be associated with a deceleration in brain structure ageing trajectories. Extended longitudinal research will need to corroborate findings of emerging maturational lags in relation to mood disorder risk and onset.

No Alterations of Brain Structural Asymmetry in Major Depressive Disorder: An ENIGMA Consortium Analysis

OBJECTIVE

Asymmetry is a subtle but pervasive aspect of the human brain, and it may be altered in several psychiatric conditions. MRI studies have shown subtle differences of brain anatomy between people with major depressive disorder and healthy control subjects, but few studies have specifically examined brain anatomical asymmetry in relation to this disorder, and results from those studies have remained inconclusive. At the functional level, some electroencephalography studies have indicated left fronto-cortical hypoactivity and right parietal hypoactivity in depressive disorders, so aspects of lateralized anatomy may also be affected. The authors used pooled individual-level data from data sets collected around the world to investigate differences in laterality in measures of cortical thickness, cortical surface area, and subcortical volume between individuals with major depression and healthy control subjects.

METHODS

The authors investigated differences in the laterality of thickness and surface area measures of 34 cerebral cortical regions in 2,256 individuals with major depression and 3,504 control subjects from 31 separate data sets, and they investigated volume asymmetries of eight subcortical structures in 2,540 individuals with major depression and 4,230 control subjects from 32 data sets. T1-weighted MRI data were processed with a single protocol using FreeSurfer and the Desikan-Killiany atlas. The large sample size provided 80% power to detect effects of the order of Cohen's d=0.1.

RESULTS

The largest effect size (Cohen's d) of major depression diagnosis was 0.085 for the thickness asymmetry of the superior temporal cortex, which was not significant after adjustment for multiple testing. Asymmetry measures were not significantly associated with medication use, acute compared with remitted status, first episode compared with recurrent status, or age at onset.

CONCLUSIONS

Altered brain macro-anatomical asymmetry may be of little relevance to major depression etiology in most cases.

Sex Differences in the Adult Human Brain: Evidence from 5216 UK Biobank Participants

Sex differences in the human brain are of interest for many reasons: for example, there are sex differences in the observed prevalence of psychiatric disorders and in some psychological traits that brain differences might help to explain. We report the largest single-sample study of structural and functional sex differences in the human brain (2750 female, 2466 male participants; mean age 61.7 years, range 44-77 years). Males had higher raw volumes, raw surface areas, and white matter fractional anisotropy; females had higher raw cortical thickness and higher white matter tract complexity. There was considerable distributional overlap between the sexes. Subregional differences were not fully attributable to differences in total volume, total surface area, mean cortical thickness, or height. There was generally greater male variance across the raw structural measures. Functional connectome organization showed stronger connectivity for males in unimodal sensorimotor cortices, and stronger connectivity for females in the default mode network. This large-scale study provides a foundation for attempts to understand the causes and consequences of sex differences in adult brain structure and function.

Assistant radiographer practitioners: Creating capacity or challenging professional boundaries?

INTRODUCTION

Over the last 2 decades the assistant radiographer practitioner (ARP) role has been introduced into NHS diagnostic imaging departments as a strategy to expand the workforce and create capacity. This skill mix initiative has not been implemented in a standardised way and there is limited knowledge of the current role scope within general radiography (X-Ray).

METHOD

An electronic survey of ARPs working within UK diagnostic imaging departments was conducted. Both open and closed questions sought information regarding basic demographic data (age category; gender; geographic region), scope of practice (patient groups; anatomical regions; imaging outside of the diagnostic imaging department), limitations placed on practice, supervision and additional roles.

RESULTS

A total of 108 responses, including 13 trainees, were received. Most sites employ three or less ARPs in general radiography (n = 43/66; 65.2%), although 11 sites have five (range 1-15). The majority undertake imaging of both adults and children (n = 85/108; 78.7%), although limitations on age were described. Their scope of practice covers a broad anatomical range and included some non-ambulant patients. The level of supervision varied with some sites empowering ARPs to check the referral prior to examination (n = 25) or images post acquisition (n = 32) (both n = 20/66; χ² = 16.003; 1df; p = 0.000).

CONCLUSION

ARPs are helping to maintain capacity in imaging departments but we suggest there is further scope for expansion. The practice described by the post holders suggests that many are working beyond the scope envisaged by the radiography professional body.

Personality stability from age 14 to age 77 years

There is evidence for differential stability in personality trait differences, even over decades. The authors used data from a sample of the Scottish Mental Survey, 1947 to study personality stability from childhood to older age. The 6-Day Sample (N = 1,208) were rated on six personality characteristics by their teachers at around age 14. In 2012, the authors traced as many of these participants as possible and invited them to take part in a follow-up study. Those who agreed (N = 174) completed a questionnaire booklet at age 77 years, which included rating themselves and asking someone who knew them well to rate them on the same 6 characteristics on which they were rated in adolescence. Each set of 6 ratings was reduced to the same single underlying factor, denoted dependability, a trait comparable to conscientiousness. Participants' and others' older-age personality characteristic ratings were moderately correlated with each other, and with other measures of personality and wellbeing, but correlations suggested no significant stability of any of the 6 characteristics or their underlying factor, dependability, over the 63-year interval. However, a more complex model, controlling rater effects, indicated significant 63-year stability of 1 personality characteristic, Stability of Moods, and near-significant stability of another, Conscientiousness. Results suggest that lifelong differential stability of personality is generally quite low, but that some aspects of personality in older age may relate to personality in childhood. (PsycINFO Database Record

Stress in childhood, adolescence and early adulthood, and cortisol levels in older age

The glucocorticoid hypothesis suggests that overexposure to stress may cause permanent upregulation of cortisol. Stress in youth may therefore influence cortisol levels even in older age. Using data from the 6-Day Sample, we investigated the effects of high stress in childhood, adolescence and early adulthood - as well as individual variables contributing to these measures; parental loss, social deprivation, school and home moves, illness, divorce and job instability - upon cortisol levels at age 77 years. Waking, waking +45 min (peak) and evening salivary cortisol samples were collected from 159 participants, and the 150 who were not using steroid medications were included in this study. After correcting for multiple comparisons, the only significant association was between early-adulthood job instability and later-life peak cortisol levels. After excluding participants with dementia or possible mild cognitive impairment, early-adulthood high stress showed significant associations with lower evening and mean cortisol levels, suggesting downregulation by stress, but these results did not survive correction for multiple comparisons. Overall, our results do not provide strong evidence of a relationship between stress in youth and later-life cortisol levels, but do suggest that some more long-term stressors, such as job instability, may indeed produce lasting upregulation of cortisol, persisting into the mid-to-late seventies.

Associations among height, body mass index and intelligence from age 11 to age 78 years

Background

Intelligence is related to both height and body mass index (BMI) at various stages of life. Several studies have demonstrated longitudinal relationships between these measures, but none has established whether height and intelligence, or BMI and intelligence are linked from childhood through to older age.

Methods

We assessed the relations between these measures over an interval of up to 67 years using data from the 36-Day Sample, an initially-representative sample of Scottish people born in 1936, assessed at age 11 years (N = 6,291) and again at 77–78 years (N = 722). This paper focuses on the 423 participants (6.7 % of the original sample) who provided relevant data in late adulthood.

Results

Height and intelligence were significantly positively associated in childhood (β = .23) and late adulthood (β = .21–.29). Longitudinal correlations also showed that childhood intelligence predicted late-adulthood height (β = .20), and childhood height predicted late-adulthood cognitive ability (β = .12–.14). We observed no significant relationship between BMI and intelligence either in childhood or in late adulthood, nor any longitudinal association between the two in this sample.

Conclusions

Our results on height and intelligence are the first to demonstrate that their relationship spans almost seven decades, from childhood through to late adulthood, and they call for further investigation into the mechanisms underlying this lifelong association.

Electronic supplementary material

The online version of this article (doi:10.1186/s12877-016-0340-0) contains supplementary material, which is available to authorized users.

Personality and Other Lifelong Influences on Older-Age Health and Wellbeing: Preliminary Findings in Two Scottish Samples

Recent observations that personality traits are related to later-life health and wellbeing have inspired considerable interest in exploring the mechanisms involved. Other factors, such as cognitive ability and education, also show longitudinal influences on health and wellbeing, but it is not yet clear how all these early-life factors together contribute to later-life health and wellbeing. In this preliminary study, we assessed hypothesised relations among these variables across the life course, using structural equation modelling in a sample assessed on dependability (a personality trait related to conscientiousness) in childhood, cognitive ability and social class in childhood and older age, education, and health and subjective wellbeing in older age. Our models indicated that both health and subjective wellbeing in older age were influenced by childhood IQ and social class, via education. Some older-age personality traits mediated the effects of early-life variables, on subjective wellbeing in particular, but childhood dependability did not show significant associations. Our results therefore did not provide evidence that childhood dependability promotes older-age health and wellbeing, but did highlight the importance of other early-life factors, particularly characteristics that contribute to educational attainment. Further, personality in later life may mediate the effects of early-life factors on health and subjective wellbeing. © 2016 The Authors. European Journal of Personality published by John Wiley & Sons Ltd on behalf of European Association of Personality Psychology.

Comparable rest-related promotion of spatial memory consolidation in younger and older adults

Flexible spatial navigation depends on cognitive mapping, a function that declines with increasing age. In young adults, a brief period of postnavigation rest promotes the consolidation and integration of spatial memories into accurate cognitive maps. We examined (1) whether rest promotes spatial memory consolidation and integration in older adults; and (2) whether the magnitude of the rest benefit changes with increasing age. Young and older adults learned a route through a virtual environment, followed by a 10-minute delay comprising either wakeful rest or a perceptual task, and a subsequent cognitive mapping task, requiring the pointing to landmarks from different locations. Pointing accuracy was lower in the older than younger adults. However, there was a comparable rest-related enhancement in pointing accuracy in the 2 age groups. Together our findings suggest that (1) the age-related decline in cognitive mapping cannot be explained by increased consolidation interference in older adults; and (2) as we grow older, rest continues to support the consolidation and integration of spatial memories.

Early-life predictors of resilience and related outcomes up to 66 years later in the 6-day sample of the 1947 Scottish mental survey

PURPOSE

Psychological resilience, the ability to manage and quickly recover from stress and trauma, is associated with a range of health and wellbeing outcomes. Resilience is known to relate to personality, self-esteem and positive affect, and may also depend upon childhood experience and stress. In this study, we investigated the role of early-life contributors to resilience and related factors in later life.

METHODS

We used data from the 6-day sample of the Scottish mental survey 1947, an initially representative sample of Scottish children born in 1936. They were assessed on a range of factors between the ages of 11 and 27 years, and resilience and other outcomes at 77 years.

RESULTS

Higher adolescent dependability unexpectedly predicted lower resilience in older-age, as did childhood illnesses, while a count of specific stressors experienced throughout early life significantly predicted higher later-life resilience. We also observed significant cross-sectional correlations between resilience and measures of physical health, mental health, wellbeing and loneliness. Some of the associations between early-life predictors and later-life outcomes were significantly mediated by resilience.

CONCLUSIONS

Our results support the hypothesis that stress throughout early life may help to build resilience in later-life, and demonstrate the importance of resilience as a mediator of other influences on health and wellbeing in older age. We suggest that the mechanisms determining how early-life stress leads to higher resilience are worthy of further investigation, and that psychological resilience should be a focus of research and a target for therapeutic interventions aiming to improve older-age health and wellbeing.