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.

Automated mood disorder symptoms monitoring from multivariate time-series sensory data: getting the full picture beyond a single number

Mood disorders (MDs) are among the leading causes of disease burden worldwide. Limited specialized care availability remains a major bottleneck thus hindering pre-emptive interventions. MDs manifest with changes in mood, sleep, and motor activity, observable in ecological physiological recordings thanks to recent advances in wearable technology. Therefore, near-continuous and passive collection of physiological data from wearables in daily life, analyzable with machine learning (ML), could mitigate this problem, bringing MDs monitoring outside the clinician's office. Previous works predict a single label, either the disease state or a psychometric scale total score. However, clinical practice suggests that the same label may underlie different symptom profiles, requiring specific treatments. Here we bridge this gap by proposing a new task: inferring all items in HDRS and YMRS, the two most widely used standardized scales for assessing MDs symptoms, using physiological data from wearables. To that end, we develop a deep learning pipeline to score the symptoms of a large cohort of MD patients and show that agreement between predictions and assessments by an expert clinician is clinically significant (quadratic Cohen's κ and macro-average F1 score both of 0.609). While doing so, we investigate several solutions to the ML challenges associated with this task, including multi-task learning, class imbalance, ordinal target variables, and subject-invariant representations. Lastly, we illustrate the importance of testing on out-of-distribution samples.

A comprehensive hierarchical comparison of structural connectomes in Major Depressive Disorder cases v. controls in two large population samples

BACKGROUND

The brain can be represented as a network, with nodes as brain regions and edges as region-to-region connections. Nodes with the most connections (hubs) are central to efficient brain function. Current findings on structural differences in Major Depressive Disorder (MDD) identified using network approaches remain inconsistent, potentially due to small sample sizes. It is still uncertain at what level of the connectome hierarchy differences may exist, and whether they are concentrated in hubs, disrupting fundamental brain connectivity.

METHODS

We utilized two large cohorts, UK Biobank (UKB, N = 5104) and Generation Scotland (GS, N = 725), to investigate MDD case-control differences in brain network properties. Network analysis was done across four hierarchical levels: (1) global, (2) tier (nodes grouped into four tiers based on degree) and rich club (between-hub connections), (3) nodal, and (4) connection.

RESULTS

In UKB, reductions in network efficiency were observed in MDD cases globally (d = -0.076, pFDR = 0.033), across all tiers (d = -0.069 to -0.079, pFDR = 0.020), and in hubs (d = -0.080 to -0.113, pFDR = 0.013-0.035). No differences in rich club organization and region-to-region connections were identified. The effect sizes and direction for these associations were generally consistent in GS, albeit not significant in our lower-N replication sample.

CONCLUSION

Our results suggest that the brain's fundamental rich club structure is similar in MDD cases and controls, but subtle topological differences exist across the brain. Consistent with recent large-scale neuroimaging findings, our findings offer a connectomic perspective on a similar scale and support the idea that minimal differences exist between MDD cases and controls.

Childhood cognitive ability and self-harm and suicide in later life

Background

Self-harm and suicide remain prevalent in later life. For younger adults, higher early-life cognitive ability appears to predict lower self-harm and suicide risk. Comparatively little is known about these associations among middle-aged and older adults.

Methods

This study examined the association between childhood (age 11) cognitive ability and self-harm and suicide risk among a Scotland-wide cohort (N = 53037), using hospital admission and mortality records to follow individuals from age 34 to 85. Multistate models examined the association between childhood cognitive ability and transitions between unaffected, self-harm, and then suicide or non-suicide death.

Results

After adjusting for childhood and adulthood socioeconomic conditions, higher childhood cognitive ability was significantly associated with reduced risk of self-harm among both males (451 events; HR = 0.90, 95% CI [0.82, 0.99]) and females (516 events; HR = 0.89, 95% CI [0.81, 0.98]). Childhood cognitive ability was not significantly associated with suicide risk among those with (Male: 16 events, HR = 1.05, 95% CI [0.61, 1.80]; Female: 13 events, HR = 1.08, 95% CI [0.55, 2.15]) or without self-harm events (Male: 118 events, HR = 1.17, 95% CI [0.84, 1.63]; Female: 31 events, HR = 1.30, 95% CI [0.70, 2.41]).

Limitations

The study only includes self-harm events that result in a hospital admission and does not account for self-harm prior to follow-up.

Conclusions

This extends work on cognitive ability and mental health, demonstrating that these associations can span the life course and into middle and older age.

Comprehensive assessment of sleep duration, insomnia, and brain structure within the UK Biobank cohort

STUDY OBJECTIVES

To assess for associations between sleeping more than or less than recommended by the National Sleep Foundation (NSF), and self-reported insomnia, with brain structure.

METHODS

Data from the UK Biobank cohort were analyzed (N between 9K and 32K, dependent on availability, aged 44 to 82 years). Sleep measures included self-reported adherence to NSF guidelines on sleep duration (sleeping between 7 and 9 hours per night), and self-reported difficulty falling or staying asleep (insomnia). Brain structural measures included global and regional cortical or subcortical morphometry (thickness, surface area, volume), global and tract-related white matter microstructure, brain age gap (difference between chronological age and age estimated from brain scan), and total volume of white matter lesions.

RESULTS

Longer-than-recommended sleep duration was associated with lower overall grey and white matter volumes, lower global and regional cortical thickness and volume measures, higher brain age gap, higher volume of white matter lesions, higher mean diffusivity globally and in thalamic and association fibers, and lower volume of the hippocampus. Shorter-than-recommended sleep duration was related to higher global and cerebellar white matter volumes, lower global and regional cortical surface areas, and lower fractional anisotropy in projection fibers. Self-reported insomnia was associated with higher global gray and white matter volumes, and with higher volumes of the amygdala, hippocampus, and putamen.

CONCLUSIONS

Sleeping longer than recommended by the NSF is associated with a wide range of differences in brain structure, potentially indicative of poorer brain health. Sleeping less than recommended is distinctly associated with lower cortical surface areas. Future studies should assess the potential mechanisms of these differences and investigate long sleep duration as a putative marker of brain health.

An unexpected ferromagnetic foreign body in a paediatric research participant undergoing 3T MRI

Metallic foreign bodies (FBs) are a safety risk during MRI. Here, we describe a boy in early childhood with an unexpected ferromagnetic FB discovered during a research brain MRI. Safety precautions included written and oral safety screening checklists and visual check during a structured safety pause. During introduction to the scanner, he was lifted to look at the bore. Staff became aware of an object flying into the bore. The child reached for his ear, and a 5 mm diameter ball bearing was found in the bore. The child had no external injury. We have introduced a 0.1 T handheld magnet to check for metallic FBs not known to the parent. FBs are a common paediatric emergency department presentation, particularly in younger children or those with cognitive or behavioural problems. This case highlights the importance of safety screening in paediatric MRI scanning, along with its fallibility.

Striatal correlates of Bayesian beliefs in self-efficacy in adolescents and their relation to mood and autonomy: a pilot study

Major depressive disorder often originates in adolescence and is associated with long-term functional impairment. Mechanistically characterizing this heterogeneous illness could provide important leads for optimizing treatment. Importantly, reward learning is known to be disrupted in depression. In this pilot fMRI study of 21 adolescents (16-20 years), we assessed how reward network disruption impacts specifically on Bayesian belief representations of self-efficacy (SE-B) and their associated uncertainty (SE-U), using a modified instrumental learning task probing activation induced by the opportunity to choose, and an optimal Hierarchical Gaussian Filter computational model. SE-U engaged caudate, nucleus accumbens (NAcc), precuneus, posterior parietal and dorsolateral prefrontal cortex (PFWE < 0.005). Sparse partial least squares analysis identified SE-U striatal activation as associating with one's sense of perceived choice and depressive symptoms, particularly anhedonia and negative feelings about oneself. As Bayesian uncertainty modulates belief flexibility and their capacity to steer future actions, this suggests that these striatal signals may be informative developmentally, longitudinally and in assessing response to treatment.

Pathway-Based Polygenic Risk Scores for Schizophrenia and Associations With Reported Psychotic-like Experiences and Neuroimaging Phenotypes in the UK Biobank

Background

Schizophrenia is a heritable psychiatric disorder with a polygenic architecture. Genome-wide association studies have reported that an increasing number of risk-associated variants and polygenic risk scores (PRSs) explain 17% of the variance in the disorder. Substantial heterogeneity exists in the effect of these variants, and aggregating them based on biologically relevant functions may provide mechanistic insight into the disorder.

Methods

Using the largest schizophrenia genome-wide association study conducted to date, we associated PRSs based on 5 gene sets previously found to contribute to schizophrenia pathophysiology-postsynaptic density of excitatory synapses, postsynaptic membrane, dendritic spine, axon, and histone H3-K4 methylation-along with respective whole-genome PRSs, with neuroimaging (n > 29,000) and reported psychotic-like experiences (n > 119,000) variables in healthy UK Biobank subjects.

Results

Several variables were significantly associated with the axon gene-set (psychotic-like communications, parahippocampal gyrus volume, fractional anisotropy thalamic radiations, and fractional anisotropy posterior thalamic radiations (β range -0.016 to 0.0916, false discovery rate-corrected p [pFDR] ≤ .05), postsynaptic density gene-set (psychotic-like experiences distress, global surface area, and cingulate lobe surface area [β range -0.014 to 0.0588, pFDR ≤ .05]), and histone gene set (entorhinal surface area: β = -0.016, pFDR = .035). From these, whole-genome PRSs were significantly associated with psychotic-like communications (β = 0.2218, pFDR = 1.34 × 10-7), distress (β = 0.1943, pFDR = 7.28 × 10-16), and fractional anisotropy thalamic radiations (β = -0.0143, pFDR = .036). Permutation analysis revealed that these associations were not due to chance.

Conclusions

Our results indicate that genetic variation in 3 gene sets relevant to schizophrenia may confer risk for the disorder through effects on previously implicated neuroimaging variables. Because associations were stronger overall for whole-genome PRSs, findings here highlight that selection of biologically relevant variants is not yet sufficient to address the heterogeneity of the disorder.

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.

Keeping up with the kids: the value of co-production in the study of irritability in youth depression and its underlying neural circuitry

Irritability is a core symptom of adolescent depression, characterized by an increased proneness to anger or frustration. Irritability in youth is associated with future mental health problems and impaired social functioning, suggesting that it may be an early indicator of emotion regulation difficulties. Adolescence is a period during which behavior is significantly impacted by one's environment. However, existing research on the neural basis of irritability typically use experimental paradigms that overlook the social context in which irritability occurs. Here, we bring together current findings on irritability in adolescent depression and the associated neurobiology and highlight directions for future research. Specifically, we emphasize the importance of co-produced research with young people as a means to improve the construct and ecological validity of research within the field. Ensuring that our research design and methodology accurately reflect to lives of young people today lays a strong foundation upon which to better understand adolescent depression and identify tractable targets for intervention.

Cognitive Function in People With Familial Risk of Depression

Importance

Cognitive impairment in depression is poorly understood. Family history of depression is a potentially useful risk marker for cognitive impairment, facilitating early identification and targeted intervention in those at highest risk, even if they do not themselves have depression. Several research cohorts have emerged recently that enable findings to be compared according to varying depths of family history phenotyping, in some cases also with genetic data, across the life span.

Objective

To investigate associations between familial risk of depression and cognitive performance in 4 independent cohorts with varied depth of assessment, using both family history and genetic risk measures.

Design, Setting, and Participants

This study used data from the Three Generations at High and Low Risk of Depression Followed Longitudinally (TGS) family study (data collected from 1982 to 2015) and 3 large population cohorts, including the Adolescent Brain Cognitive Development (ABCD) study (data collected from 2016 to 2021), National Longitudinal Study of Adolescent to Adult Health (Add Health; data collected from 1994 to 2018), and UK Biobank (data collected from 2006 to 2022). Children and adults with or without familial risk of depression were included. Cross-sectional analyses were conducted from March to June 2022.

Exposures

Family history (across 1 or 2 prior generations) and polygenic risk of depression.

Main Outcomes and Measures

Neurocognitive tests at follow-up. Regression models were adjusted for confounders and corrected for multiple comparisons.

Results

A total of 57 308 participants were studied, including 87 from TGS (42 [48%] female; mean [SD] age, 19.7 [6.6] years), 10 258 from ABCD (4899 [48%] female; mean [SD] age, 12.0 [0.7] years), 1064 from Add Health (584 [49%] female; mean [SD] age, 37.8 [1.9] years), and 45 899 from UK Biobank (23 605 [51%] female; mean [SD] age, 64.0 [7.7] years). In the younger cohorts (TGS, ABCD, and Add Health), family history of depression was primarily associated with lower performance in the memory domain, and there were indications that this may be partly associated with educational and socioeconomic factors. In the older UK Biobank cohort, there were associations with processing speed, attention, and executive function, with little evidence of education or socioeconomic influences. These associations were evident even in participants who had never been depressed themselves. Effect sizes between familial risk of depression and neurocognitive test performance were largest in TGS; the largest standardized mean differences in primary analyses were -0.55 (95% CI, -1.49 to 0.38) in TGS, -0.09 (95% CI, -0.15 to -0.03) in ABCD, -0.16 (95% CI, -0.31 to -0.01) in Add Health, and -0.10 (95% CI, -0.13 to -0.06) in UK Biobank. Results were generally similar in the polygenic risk score analyses. In UK Biobank, several tasks showed statistically significant associations in the polygenic risk score analysis that were not evident in the family history models.

Conclusions and Relevance

In this study, whether assessed by family history or genetic data, depression in prior generations was associated with lower cognitive performance in offspring. There are opportunities to generate hypotheses about how this arises through genetic and environmental determinants, moderators of brain development and brain aging, and potentially modifiable social and lifestyle factors across the life span.

Immuno-epigenetic signature derived in saliva associates with the encephalopathy of prematurity and perinatal inflammatory disorders

BACKGROUND

Preterm birth is closely associated with a phenotype that includes brain dysmaturation and neurocognitive impairment, commonly termed Encephalopathy of Prematurity (EoP), of which systemic inflammation is considered a key driver. DNA methylation (DNAm) signatures of inflammation from peripheral blood associate with poor brain imaging outcomes in adult cohorts. However, the robustness of DNAm inflammatory scores in infancy, their relation to comorbidities of preterm birth characterised by inflammation, neonatal neuroimaging metrics of EoP, and saliva cross-tissue applicability are unknown.

METHODS

Using salivary DNAm from 258 neonates (n = 155 preterm, gestational age at birth 23.28 - 34.84 weeks, n = 103 term, gestational age at birth 37.00 - 42.14 weeks), we investigated the impact of a DNAm surrogate for C-reactive protein (DNAm CRP) on brain structure and other clinically defined inflammatory exposures. We assessed i) if DNAm CRP estimates varied between preterm infants at term equivalent age and term infants, ii) how DNAm CRP related to different types of inflammatory exposure (maternal, fetal and postnatal) and iii) whether elevated DNAm CRP associated with poorer measures of neonatal brain volume and white matter connectivity.

RESULTS

Higher DNAm CRP was linked to preterm status (-0.0107 ± 0.0008, compared with -0.0118 ± 0.0006 among term infants; p < 0.001), as well as perinatal inflammatory diseases, including histologic chorioamnionitis, sepsis, bronchopulmonary dysplasia, and necrotising enterocolitis (OR range |2.00 | to |4.71|, p < 0.01). Preterm infants with higher DNAm CRP scores had lower brain volume in deep grey matter, white matter, and hippocampi and amygdalae (β range |0.185| to |0.218|). No such associations were observed for term infants. Association magnitudes were largest for measures of white matter microstructure among preterms, where elevated epigenetic inflammation associated with poorer global measures of white matter integrity (β range |0.206| to |0.371|), independent of other confounding exposures.

CONCLUSIONS

Inflammatory-related DNAm captures the allostatic load of inflammatory burden in preterm infants. Such DNAm measures complement biological and clinical metrics when investigating the determinants of neurodevelopmental differences.

Large-scale analysis of structural brain asymmetries in schizophrenia via the ENIGMA consortium

Left-right asymmetry is an important organizing feature of the healthy brain that may be altered in schizophrenia, but most studies have used relatively small samples and heterogeneous approaches, resulting in equivocal findings. We carried out the largest case-control study of structural brain asymmetries in schizophrenia, with MRI data from 5,080 affected individuals and 6,015 controls across 46 datasets, using a single image analysis protocol. Asymmetry indexes were calculated for global and regional cortical thickness, surface area, and subcortical volume measures. Differences of asymmetry were calculated between affected individuals and controls per dataset, and effect sizes were meta-analyzed across datasets. Small average case-control differences were observed for thickness asymmetries of the rostral anterior cingulate and the middle temporal gyrus, both driven by thinner left-hemispheric cortices in schizophrenia. Analyses of these asymmetries with respect to the use of antipsychotic medication and other clinical variables did not show any significant associations. Assessment of age- and sex-specific effects revealed a stronger average leftward asymmetry of pallidum volume between older cases and controls. Case-control differences in a multivariate context were assessed in a subset of the data (N = 2,029), which revealed that 7% of the variance across all structural asymmetries was explained by case-control status. Subtle case-control differences of brain macrostructural asymmetry may reflect differences at the molecular, cytoarchitectonic, or circuit levels that have functional relevance for the disorder. Reduced left middle temporal cortical thickness is consistent with altered left-hemisphere language network organization in schizophrenia.

Predicting sex, age, general cognition and mental health with machine learning on brain structural connectomes

There is an increasing expectation that advanced, computationally expensive machine learning (ML) techniques, when applied to large population-wide neuroimaging datasets, will help to uncover key differences in the human brain in health and disease. We take a comprehensive approach to explore how multiple aspects of brain structural connectivity can predict sex, age, general cognitive function and general psychopathology, testing different ML algorithms from deep learning (DL) model (BrainNetCNN) to classical ML methods. We modelled N = 8183 structural connectomes from UK Biobank using six different structural network weightings obtained from diffusion MRI. Streamline count generally provided the highest prediction accuracies in all prediction tasks. DL did not improve on prediction accuracies from simpler linear models. Further, high correlations between gradient attribution coefficients from DL and model coefficients from linear models suggested the models ranked the importance of features in similar ways, which indirectly suggested the similarity in models' strategies for making predictive decision to some extent. This highlights that model complexity is unlikely to improve detection of associations between structural connectomes and complex phenotypes with the current sample size.

Childhood cognitive ability and self-harm and suicide in later life

Self-harm and suicide remain prevalent in later life. For younger adults, work has highlighted an association between higher early-life cognitive ability and lower self-harm and suicide risk. Comparatively little is known about its association with self-harm and suicide among older adults. Furthermore, most work has measured cognitive ability in early adulthood, raising issues of potential confounding by emerging psychiatric conditions. The present study examined the association between childhood (age 11) cognitive ability and self-harm and suicide risk among a Scotland-wide cohort of older adults (N = 53037), using health data linkage to follow individuals from age 34 to 85. Self-harm events were extracted from hospital admissions and suicide deaths were extracted from national mortality records. Multistate models were used to model transitions between unaffected, self-harm, and then suicide or non-suicide death, and to examine the association between childhood cognitive ability and each transition. After adjusting for childhood and adulthood socioeconomic conditions, higher childhood cognitive ability was significantly associated with reduced risk of self-harm among older females (N events = 516; HR = 0.90, 95% CI = [0.81, 0.99]). A similar, though non-significant, association was observed among older males (N events = 451; HR = 0.90, 95% CI = [0.82, 1.00]). Although suicide risk was higher among older adults experiencing self-harm, childhood cognitive ability was not significantly associated with suicide risk among either older adults experiencing no self-harm events (Male: N events = 118, HR = 1.17, 95% CI = [0.84, 1.63]; Female: N events = 31, HR = 1.30, 95% CI = [0.70, 2.41]) or those experiencing a self-harm event during follow-up (Male: N events = 16, HR = 1.05, 95% CI = [0.61, 1.80]; Female: N events = 13, HR = 1.08, 95% CI = [0.55, 2.14]). Higher suicide risk was significantly associated with covariates including higher adulthood deprivation and longer time in the self-harm state. These results extend work on cognitive ability and mental health, demonstrating that these associations can span across the life course and into older age.

Structural brain correlates of childhood trauma with replication across two large, independent community-based samples

INTRODUCTION

Childhood trauma and adversity are common across societies and have strong associations with physical and psychiatric morbidity throughout the life-course. One possible mechanism through which childhood trauma may predispose individuals to poor psychiatric outcomes is via associations with brain structure. This study aimed to elucidate the associations between childhood trauma and brain structure across two large, independent community cohorts.

METHODS

The two samples comprised (i) a subsample of Generation Scotland (n=1,024); and (ii) individuals from UK Biobank (n=27,202). This comprised n=28,226 for mega-analysis. MRI scans were processed using Free Surfer, providing cortical, subcortical, and global brain metrics. Regression models were used to determine associations between childhood trauma measures and brain metrics and psychiatric phenotypes.

RESULTS

Childhood trauma associated with lifetime depression across cohorts (OR 1.06 GS, 1.23 UKB), and related to early onset and recurrent course within both samples. There was evidence for associations between childhood trauma and structural brain metrics. This included reduced global brain volume, and reduced cortical surface area with highest effects in the frontal (β=-0.0385, SE=0.0048, p(FDR)=5.43x10-15) and parietal lobes (β=-0.0387, SE=0.005, p(FDR)=1.56x10-14). At a regional level the ventral diencephalon (VDc) displayed significant associations with childhood trauma measures across both cohorts and at mega-analysis (β=-0.0232, SE=0.0039, p(FDR)=2.91x10-8). There were also associations with reduced hippocampus, thalamus, and nucleus accumbens volumes.

DISCUSSION

Associations between childhood trauma and reduced global and regional brain volumes were found, across two independent UK cohorts, and at mega-analysis. This provides robust evidence for a lasting effect of childhood adversity on brain structure.

AI-based dimensional neuroimaging system for characterizing heterogeneity in brain structure and function in major depressive disorder: COORDINATE-MDD consortium design and rationale

BACKGROUND

Efforts to develop neuroimaging-based biomarkers in major depressive disorder (MDD), at the individual level, have been limited to date. As diagnostic criteria are currently symptom-based, MDD is conceptualized as a disorder rather than a disease with a known etiology; further, neural measures are often confounded by medication status and heterogeneous symptom states.

METHODS

We describe a consortium to quantify neuroanatomical and neurofunctional heterogeneity via the dimensions of novel multivariate coordinate system (COORDINATE-MDD). Utilizing imaging harmonization and machine learning methods in a large cohort of medication-free, deeply phenotyped MDD participants, patterns of brain alteration are defined in replicable and neurobiologically-based dimensions and offer the potential to predict treatment response at the individual level. International datasets are being shared from multi-ethnic community populations, first episode and recurrent MDD, which are medication-free, in a current depressive episode with prospective longitudinal treatment outcomes and in remission. Neuroimaging data consist of de-identified, individual, structural MRI and resting-state functional MRI with additional positron emission tomography (PET) data at specific sites. State-of-the-art analytic methods include automated image processing for extraction of anatomical and functional imaging variables, statistical harmonization of imaging variables to account for site and scanner variations, and semi-supervised machine learning methods that identify dominant patterns associated with MDD from neural structure and function in healthy participants.

RESULTS

We are applying an iterative process by defining the neural dimensions that characterise deeply phenotyped samples and then testing the dimensions in novel samples to assess specificity and reliability. Crucially, we aim to use machine learning methods to identify novel predictors of treatment response based on prospective longitudinal treatment outcome data, and we can externally validate the dimensions in fully independent sites.

CONCLUSION

We describe the consortium, imaging protocols and analytics using preliminary results. Our findings thus far demonstrate how datasets across many sites can be harmonized and constructively pooled to enable execution of this large-scale project.

Early-life inflammatory markers and subsequent psychotic and depressive episodes between 10 to 28 years of age

Inflammation is implicated in depression and psychosis, including association of childhood inflammatory markers on the subsequent risk of developing symptoms. However, it is unknown whether early-life inflammatory markers are associated with the number of depressive and psychotic symptoms from childhood to adulthood. Using the prospective Avon Longitudinal Study of Children and Parents birth cohort (N = up-to 6401), we have examined longitudinal associations of early-life inflammation [exposures: interleukin-6 (IL-6), C-reactive protein (CRP) levels at age 9y; IL-6 and CRP DNA-methylation (DNAm) scores at birth and age 7y; and IL-6 and CRP polygenic risk scores (PRSs)] with the number of depressive episodes and psychotic experiences (PEs) between ages 10-28 years. Psychiatric outcomes were assessed using the Short Mood and Feelings Questionnaire and Psychotic Like Symptoms Questionnaires, respectively. Exposure-outcome associations were tested using negative binomial models, which were adjusted for metabolic and sociodemographic factors. Serum IL-6 levels at age 9y were associated with the total number of depressive episodes between 10 and 28y in the base model (n = 4835; β = 0.066; 95%CI:0.020-0.113; pFDR = 0.041) which was weaker when adjusting for metabolic and sociodemographic factors. Weak associations were observed between inflammatory markers (serum IL-6 and CRP DNAm scores) and total number of PEs. Other inflammatory markers were not associated with depression or PEs. Early-life inflammatory markers are associated with the burden of depressive episodes and of PEs subsequently from childhood to adulthood. These findings support a potential role of early-life inflammation in the aetiology of depression and psychosis and highlight inflammation as a potential target for treatment and prevention.

Systematic evaluation of machine learning algorithms for neuroanatomically-based age prediction in youth

Application of machine learning (ML) algorithms to structural magnetic resonance imaging (sMRI) data has yielded behaviorally meaningful estimates of the biological age of the brain (brain-age). The choice of the ML approach in estimating brain-age in youth is important because age-related brain changes in this age-group are dynamic. However, the comparative performance of the available ML algorithms has not been systematically appraised. To address this gap, the present study evaluated the accuracy (mean absolute error [MAE]) and computational efficiency of 21 machine learning algorithms using sMRI data from 2105 typically developing individuals aged 5-22 years from five cohorts. The trained models were then tested in two independent holdout datasets, one comprising 4078 individuals aged 9-10 years and another comprising 594 individuals aged 5-21 years. The algorithms encompassed parametric and nonparametric, Bayesian, linear and nonlinear, tree-based, and kernel-based models. Sensitivity analyses were performed for parcellation scheme, number of neuroimaging input features, number of cross-validation folds, number of extreme outliers, and sample size. Tree-based models and algorithms with a nonlinear kernel performed comparably well, with the latter being especially computationally efficient. Extreme Gradient Boosting (MAE of 1.49 years), Random Forest Regression (MAE of 1.58 years), and Support Vector Regression (SVR) with Radial Basis Function (RBF) Kernel (MAE of 1.64 years) emerged as the three most accurate models. Linear algorithms, with the exception of Elastic Net Regression, performed poorly. Findings of the present study could be used as a guide for optimizing methodology when quantifying brain-age in youth.

The nosological status of unipolar mania and hypomania within UK Biobank according to objective and subjective measures of diurnal rest and activity

BACKGROUND

There is uncertainty whether unipolar mania is a discrete sub-type of bipolar disorder. Disrupted rest/activity rhythms are a key feature of bipolar disorder (BD) but have not been well characterised in unipolar mania/hypomania (UM). We compared subjective and objective rest/activity patterns, demographic and mental health outcomes across BD, UM and control groups.

METHODS

UK residents aged 37-73 years were recruited into UK Biobank from 2006 to 2010. BD, UM and control groups were identified via a mental health questionnaire. Demographic, mental health and subjective sleep outcomes were self-reported. Accelerometery data were available for a subset of participants, and objective measures of sleep and activity were derived.

RESULTS

A greater proportion of males met UM criteria, and more females were in the BD group. Both BD and UM groups had poor mental health outcomes vs. controls. Objectively measured activity differed between all three groups: UM had highest levels of activity and BD lowest. The UM group had shorter sleep duration compared to controls. Subjective rest/activity measures showed that both mood disorder groups (compared to controls) had later chronotype preference, more disturbed sleep and increased difficulty getting up in the morning. However, the UM group were more likely to report an early chronotype compared to BD and control groups.

CONCLUSIONS

BD and UM share features in common, but key differences support the proposition that UM may be a distinct and more clinically homogenous disorder. UM was characterised by a higher proportion of males, early chronotype, increased activity and shorter sleep duration.

Sex differences in predictors and regional patterns of brain age gap estimates

The brain-age-gap estimate (brainAGE) quantifies the difference between chronological age and age predicted by applying machine-learning models to neuroimaging data and is considered a biomarker of brain health. Understanding sex differences in brainAGE is a significant step toward precision medicine. Global and local brainAGE (G-brainAGE and L-brainAGE, respectively) were computed by applying machine learning algorithms to brain structural magnetic resonance imaging data from 1113 healthy young adults (54.45% females; age range: 22-37 years) participating in the Human Connectome Project. Sex differences were determined in G-brainAGE and L-brainAGE. Random forest regression was used to determine sex-specific associations between G-brainAGE and non-imaging measures pertaining to sociodemographic characteristics and mental, physical, and cognitive functions. L-brainAGE showed sex-specific differences; in females, compared to males, L-brainAGE was higher in the cerebellum and brainstem and lower in the prefrontal cortex and insula. Although sex differences in G-brainAGE were minimal, associations between G-brainAGE and non-imaging measures differed between sexes with the exception of poor sleep quality, which was common to both. While univariate relationships were small, the most important predictor of higher G-brainAGE was self-identification as non-white in males and systolic blood pressure in females. The results demonstrate the value of applying sex-specific analyses and machine learning methods to advance our understanding of sex-related differences in factors that influence the rate of brain aging and provide a foundation for targeted interventions.

Neuroinflammation in HIV-associated depression: evidence and future perspectives

People living with HIV face a high risk of mental illness, especially depression. We do not yet know the precise neurobiological mechanisms underlying HIV-associated depression. Depression severity in the general population has been linked to acute and chronic markers of systemic inflammation. Given the associations between depression and peripheral inflammation, and since HIV infection in the brain elicits a neuroinflammatory response, it is possible that neuroinflammation contributes to the high prevalence of depression amongst people living with HIV. The purpose of this review was to synthesise existing evidence for associations between inflammation, depression, and HIV. While there is strong evidence for independent associations between these three conditions, few preclinical or clinical studies have attempted to characterise their interrelationship, representing a major gap in the literature. This review identifies key areas of debate in the field and offers perspectives for future investigations of the pathophysiology of HIV-associated depression. Reproducing findings across diverse populations will be crucial in obtaining robust and generalisable results to elucidate the precise role of neuroinflammation in this pathophysiology.

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.

Complex trait methylation scores in the prediction of major depressive disorder

BACKGROUND

DNA methylation (DNAm) is associated with time-varying environmental factors that contribute to major depressive disorder (MDD) risk. We sought to test whether DNAm signatures of lifestyle and biochemical factors were associated with MDD to reveal dynamic biomarkers of MDD risk that may be amenable to lifestyle interventions.

METHODS

Here, we calculated methylation scores (MS) at multiple p-value thresholds for lifestyle (BMI, smoking, alcohol consumption, and educational attainment) and biochemical (high-density lipoprotein (HDL) and total cholesterol) factors in Generation Scotland (GS) (N=9,502) and in a replication cohort (ALSPAC_adults, N=565), using CpG sites reported in previous well-powered methylome-wide association studies. We also compared their predictive accuracy for MDD to a MDD MS in an independent GS sub-sample (N=4,432).

FINDINGS

Each trait MS was significantly associated with its corresponding phenotype in GS (β_range=0.089-1.457) and in ALSPAC (β_range=0.078-2.533). Each MS was also significantly associated with MDD before and after adjustment for its corresponding phenotype in GS (β_range=0.053-0.145). After accounting for relevant lifestyle factors, MS for educational attainment (β=0.094) and alcohol consumption (MS_p-value<0.01-0.5; β_range=-0.069-0.083) remained significantly associated with MDD in GS. Smoking (AUC=0.569) and educational attainment (AUC=0.585) MSs could discriminate MDD from controls better than the MDD MS (AUC=0.553) in the independent GS sub-sample. Analyses implicating MDD did not replicate across ALSPAC, although the direction of effect was consistent for all traits when adjusting for the MS corresponding phenotypes.

INTERPRETATION

We showed that lifestyle and biochemical MS were associated with MDD before and after adjustment for their corresponding phenotypes (p_nominal<0.05), but not when smoking, alcohol consumption, and BMI were also included as covariates. MDD results did not replicate in the smaller, female-only independent ALSPAC cohort (N_ALSPAC=565; N_GS=9,502), potentially due to demographic differences or low statistical power, but effect sizes were consistent with the direction reported in GS. DNAm scores for modifiable MDD risk factors may contribute to disease vulnerability and, in some cases, explain additional variance to their observed phenotypes.

FUNDING

Wellcome Trust.

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.

Brain charts for the human lifespan

Over the past few decades, neuroimaging has become a ubiquitous tool in basic research and clinical studies of the human brain. However, no reference standards currently exist to quantify individual differences in neuroimaging metrics over time, in contrast to growth charts for anthropometric traits such as height and weight1. Here we assemble an interactive open resource to benchmark brain morphology derived from any current or future sample of MRI data ( http://www.brainchart.io/ ). With the goal of basing these reference charts on the largest and most inclusive dataset available, acknowledging limitations due to known biases of MRI studies relative to the diversity of the global population, we aggregated 123,984 MRI scans, across more than 100 primary studies, from 101,457 human participants between 115 days post-conception to 100 years of age. MRI metrics were quantified by centile scores, relative to non-linear trajectories2 of brain structural changes, and rates of change, over the lifespan. Brain charts identified previously unreported neurodevelopmental milestones3, showed high stability of individuals across longitudinal assessments, and demonstrated robustness to technical and methodological differences between primary studies. Centile scores showed increased heritability compared with non-centiled MRI phenotypes, and provided a standardized measure of atypical brain structure that revealed patterns of neuroanatomical variation across neurological and psychiatric disorders. In summary, brain charts are an essential step towards robust quantification of individual variation benchmarked to normative trajectories in multiple, commonly used neuroimaging phenotypes.

DNA methylome-wide association study of genetic risk for depression implicates antigen processing and immune responses

BACKGROUND

Depression is a disabling and highly prevalent condition where genetic and epigenetic, such as DNA methylation (DNAm), differences contribute to disease risk. DNA methylation is influenced by genetic variation but the association between polygenic risk of depression and DNA methylation is unknown.

METHODS

We investigated the association between polygenic risk scores (PRS) for depression and DNAm by conducting a methylome-wide association study (MWAS) in Generation Scotland (N = 8898, mean age = 49.8 years) with replication in the Lothian Birth Cohorts of 1921 and 1936 and adults in the Avon Longitudinal Study of Parents and Children (ALSPAC) (Ncombined = 2049, mean age = 79.1, 69.6 and 47.2 years, respectively). We also conducted a replication MWAS in the ALSPAC children (N = 423, mean age = 17.1 years). Gene ontology analysis was conducted for the cytosine-guanine dinucleotide (CpG) probes significantly associated with depression PRS, followed by Mendelian randomisation (MR) analysis to infer the causal relationship between depression and DNAm.

RESULTS

Widespread associations (NCpG = 71, pBonferroni < 0.05, p < 6.3 × 10-8) were found between PRS constructed using genetic risk variants for depression and DNAm in CpG probes that localised to genes involved in immune responses and neural development. The effect sizes for the significant associations were highly correlated between the discovery and replication samples in adults (r = 0.79) and in adolescents (r = 0.82). Gene Ontology analysis showed that significant CpG probes are enriched in immunological processes in the human leukocyte antigen system. Additional MWAS was conducted for each lead genetic risk variant. Over 47.9% of the independent genetic risk variants included in the PRS showed associations with DNAm in CpG probes located in both the same (cis) and distal (trans) locations to the genetic loci (pBonferroni < 0.045). Subsequent MR analysis showed that there are a greater number of causal effects found from DNAm to depression than vice versa (DNAm to depression: pFDR ranged from 0.024 to 7.45 × 10-30; depression to DNAm: pFDR ranged from 0.028 to 0.003).

CONCLUSIONS

PRS for depression, especially those constructed from genome-wide significant genetic risk variants, showed methylome-wide differences associated with immune responses. Findings from MR analysis provided evidence for causal effect of DNAm to depression.

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.

A functional MRI facial emotion-processing study of autism in individuals with special educational needs

This study aimed to investigate the functional imaging associations of autism in individuals with special educational needs and demonstrate the feasibility of such research. The study included 18 individuals (3 female,15 male; mean age 24.3; mean IQ 69.7) with special educational needs (SEN), of whom 9 met criteria for autism. The task examined the Blood-oxygen-level dependant response to fearful and neutral faces. Individuals in the autism group had 2 clusters of significantly reduced activity centred on the left superior frontal gyrus and left angular gyrus compared to those with SEN alone in response to the fearful faces. In the response to neutral faces, individuals in the autism group also had a cluster of significantly greater activity centred on the right precentral gyrus compared to those with SEN alone. We suggest that autistic characteristics in individuals with SEN are associated with changes in fearful facial emotion processing analogous to those previously reported in autistic individuals without SEN, and who are of average or above average cognitive ability. The finding of enhanced response to neutral facial stimuli needs further investigation, although we speculate this may relate to reports of the experience of 'hyper-mentalisation' in social situations as reported by some autistic individuals.

Methylome-wide association study of antidepressant use in Generation Scotland and the Netherlands Twin Register implicates the innate immune system

Antidepressants are an effective treatment for major depressive disorder (MDD), although individual response is unpredictable and highly variable. Whilst the mode of action of antidepressants is incompletely understood, many medications are associated with changes in DNA methylation in genes that are plausibly linked to their mechanisms. Studies of DNA methylation may therefore reveal the biological processes underpinning the efficacy and side effects of antidepressants. We performed a methylome-wide association study (MWAS) of self-reported antidepressant use accounting for lifestyle factors and MDD in Generation Scotland (GS:SFHS, N = 6428, EPIC array) and the Netherlands Twin Register (NTR, N = 2449, 450 K array) and ran a meta-analysis of antidepressant use across these two cohorts. We found ten CpG sites significantly associated with self-reported antidepressant use in GS:SFHS, with the top CpG located within a gene previously associated with mental health disorders, ATP6V1B2 (β = -0.055, pcorrected = 0.005). Other top loci were annotated to genes including CASP10, TMBIM1, MAPKAPK3, and HEBP2, which have previously been implicated in the innate immune response. Next, using penalised regression, we trained a methylation-based score of self-reported antidepressant use in a subset of 3799 GS:SFHS individuals that predicted antidepressant use in a second subset of GS:SFHS (N = 3360, β = 0.377, p = 3.12 × 10-11, R2 = 2.12%). In an MWAS analysis of prescribed selective serotonin reuptake inhibitors, we showed convergent findings with those based on self-report. In NTR, we did not find any CpGs significantly associated with antidepressant use. The meta-analysis identified the two CpGs of the ten above that were common to the two arrays used as being significantly associated with antidepressant use, although the effect was in the opposite direction for one of them. Antidepressants were associated with epigenetic alterations in loci previously associated with mental health disorders and the innate immune system. These changes predicted self-reported antidepressant use in a subset of GS:SFHS and identified processes that may be relevant to our mechanistic understanding of clinically relevant antidepressant drug actions and side effects.

Accelerated Global and Local Brain Aging Differentiate Cognitively Impaired From Cognitively Spared Patients With Schizophrenia

BACKGROUND

Accelerated aging has been proposed as a mechanism underlying the clinical and cognitive presentation of schizophrenia. The current study extends the field by examining both global and regional patterns of brain aging in schizophrenia, as inferred from brain structural data, and their association with cognitive and psychotic symptoms.

METHODS

Global and local brain-age-gap-estimates (G-brainAGE and L-brainAGE) were computed using a U-Net Model from T₁-weighted structural neuroimaging data from 84 patients (aged 16-35 years) with early-stage schizophrenia (illness duration <5 years) and 1,169 healthy individuals (aged 16-37 years). Multidomain cognitive data from the patient sample were submitted to Heterogeneity through Discriminative Analysis (HYDRA) to identify cognitive clusters.

RESULTS

HYDRA classified patients into a cognitively impaired cluster (n = 69) and a cognitively spared cluster (n = 15). Compared to healthy individuals, G-brainAGE was significantly higher in the cognitively impaired cluster (+11.08 years) who also showed widespread elevation in L-brainAGE, with the highest deviance observed in frontal and temporal regions. The cognitively spared cluster showed a moderate increase in G-brainAGE (+8.94 years), and higher L-brainAGE localized in the anterior cingulate cortex. Psychotic symptom severity in both clusters showed a positive but non-significant association with G-brainAGE.

DISCUSSION

Accelerated aging in schizophrenia can be detected at the early disease stages and appears more closely associated with cognitive dysfunction rather than clinical symptoms. Future studies replicating our findings in multi-site cohorts with larger numbers of participants are warranted.

What we learn about bipolar disorder from large-scale neuroimaging: Findings and future directions from the ENIGMA Bipolar Disorder Working Group

MRI-derived brain measures offer a link between genes, the environment and behavior and have been widely studied in bipolar disorder (BD). However, many neuroimaging studies of BD have been underpowered, leading to varied results and uncertainty regarding effects. The Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) Bipolar Disorder Working Group was formed in 2012 to empower discoveries, generate consensus findings and inform future hypothesis-driven studies of BD. Through this effort, over 150 researchers from 20 countries and 55 institutions pool data and resources to produce the largest neuroimaging studies of BD ever conducted. The ENIGMA Bipolar Disorder Working Group applies standardized processing and analysis techniques to empower large-scale meta- and mega-analyses of multimodal brain MRI and improve the replicability of studies relating brain variation to clinical and genetic data. Initial BD Working Group studies reveal widespread patterns of lower cortical thickness, subcortical volume and disrupted white matter integrity associated with BD. Findings also include mapping brain alterations of common medications like lithium, symptom patterns and clinical risk profiles and have provided further insights into the pathophysiological mechanisms of BD. Here we discuss key findings from the BD working group, its ongoing projects and future directions for large-scale, collaborative studies of mental illness.

Associations between alcohol use and accelerated biological ageing

Harmful alcohol use is a leading cause of premature death and is associated with age-related disease. Biological ageing is highly variable between individuals and may deviate from chronological ageing, suggesting that biomarkers of biological ageing (derived from DNA methylation or brain structural measures) may be clinically relevant. Here, we investigated the relationships between alcohol phenotypes and both brain and DNA methylation age estimates. First, using data from UK Biobank and Generation Scotland, we tested the association between alcohol consumption (units/week) or hazardous use (Alcohol Use Disorders Identification Test [AUDIT] scores) and accelerated brain and epigenetic ageing in 20,258 and 8051 individuals, respectively. Second, we used Mendelian randomisation (MR) to test for a causal effect of alcohol consumption levels and alcohol use disorder (AUD) on biological ageing. Alcohol use showed a consistent positive association with higher predicted brain age (AUDIT-C: β = 0.053, p = 3.16 × 10-13 ; AUDIT-P: β = 0.052, p = 1.6 × 10-13 ; total AUDIT score: β = 0.062, p = 5.52 × 10-16 ; units/week: β = 0.078, p = 2.20 × 10-16 ), and two DNA methylation-based estimates of ageing, GrimAge (units/week: β = 0.053, p = 1.48 × 10-7 ) and PhenoAge (units/week: β = 0.077, p = 2.18x10-10 ). MR analyses revealed limited evidence for a causal effect of AUD on accelerated brain ageing (β = 0.118, p = 0.044). However, this result should be interpreted cautiously as the significant effect was driven by a single genetic variant. We found no evidence for a causal effect of alcohol consumption levels on accelerated biological ageing. Future studies investigating the mechanisms associating alcohol use with accelerated biological ageing are warranted.

Greater male than female variability in regional brain structure across the lifespan

For many traits, males show greater variability than females, with possible implications for understanding sex differences in health and disease. Here, the ENIGMA (Enhancing Neuro Imaging Genetics through Meta-Analysis) Consortium presents the largest-ever mega-analysis of sex differences in variability of brain structure, based on international data spanning nine decades of life. Subcortical volumes, cortical surface area and cortical thickness were assessed in MRI data of 16,683 healthy individuals 1-90 years old (47% females). We observed significant patterns of greater male than female between-subject variance for all subcortical volumetric measures, all cortical surface area measures, and 60% of cortical thickness measures. This pattern was stable across the lifespan for 50% of the subcortical structures, 70% of the regional area measures, and nearly all regions for thickness. Our findings that these sex differences are present in childhood implicate early life genetic or gene-environment interaction mechanisms. The findings highlight the importance of individual differences within the sexes, that may underpin sex-specific vulnerability to disorders.

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.

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.

Brain structural associations with depression in a large early adolescent sample (the ABCD study®)

BACKGROUND

Depression is the leading cause of disability worldwide with > 50% of cases emerging before the age of 25 years. Large-scale neuroimaging studies in depression implicate robust structural brain differences in the disorder. However, most studies have been conducted in adults and therefore, the temporal origins of depression-related imaging features remain largely unknown. This has important implications for understanding aetiology and informing timings of potential intervention.

METHODS

Here, we examine associations between brain structure (cortical metrics and white matter microstructural integrity) and depression ratings (from caregiver and child), in a large sample (N = 8634) of early adolescents (9 to 11 years old) from the US-based, Adolescent Brain and Cognitive Development (ABCD) Study®. Data was collected from 2016 to 2018.

FINDINGS

We report significantly decreased global cortical and white matter metrics, and regionally in frontal, limbic and temporal areas in adolescent depression (Cohen's d = -0⋅018 to -0⋅041, β = -0·019 to -0⋅057). Further, we report consistently stronger imaging associations for caregiver-reported compared to child-reported depression ratings. Divergences between reports (caregiver vs child) were found to significantly relate to negative socio-environmental factors (e.g., family conflict, absolute β = 0⋅048 to 0⋅169).

INTERPRETATION

Depression ratings in early adolescence were associated with similar imaging findings to those seen in adult depression samples, suggesting neuroanatomical abnormalities may be present early in the disease course, arguing for the importance of early intervention. Associations between socio-environmental factors and reporter discrepancy warrant further consideration, both in the wider context of the assessment of adolescent psychopathology, and in relation to their role in aetiology.

FUNDING

Wellcome Trust (References: 104036/Z/14/Z and 220857/Z/20/Z) and the Medical Research Council (MRC, Reference: MC_PC_17209).

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.

Associations between major psychiatric disorder polygenic risk scores and blood-based markers in UK biobank

Major depressive disorder (MDD), schizophrenia (SCZ), and bipolar disorder (BD) have both shared and discrete genetic risk factors, and are associated with peripheral abnormalities. The relationships between such genetic architectures and blood-based markers are, however, unclear. We investigated relationships between polygenic risk scores (PRS) for these disorders and peripheral markers in the UK Biobank cohort. We calculated polygenic risk scores for n = 367,329 (MDD PRS), n = 366,465 (SCZ PRS), and n = 366,383 (BD PRS) UK Biobank cohort subjects. We then examined associations between disorder PRS and 58 inflammatory/immune, hematological, bone, cardiovascular, hormone, liver, renal and diabetes-associated blood markers using two generalized linear regression models: 'minimally adjusted' controlling for variables such as age and sex, and 'fully adjusted' including additional lifestyle covariates: BMI, alcohol and smoking status, and medication intake. There were 38/58 MDD PRS, 32/58 SCZ PRS, and 20/58 BD PRS-blood marker associations detected for our minimally adjusted model. Of these, 13/38 (MDD PRS), 14/32 (SCZ PRS), and 10/20 (BD PRS) associations remained significant after controlling for lifestyle factors. Many were disorder-specific, with 8/13 unique MDD PRS associations identified. Several disorder-specific associations for MDD and SCZ were immune-related, with mostly positive and negative associations identified for MDD and SCZ PRS respectively. This study suggests that MDD, SCZ and BD have both shared and distinct peripheral markers associated with disorder-specific genetic risk. The results also implicate inflammatory dysfunction in MDD and SCZ, albeit with differences in patterns between the two conditions, and enrich our understanding of potential underlying pathophysiological mechanisms in major psychiatric disorders.

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.

Epigenetic prediction of major depressive disorder

Variation in DNA methylation (DNAm) is associated with lifestyle factors such as smoking and body mass index (BMI) but there has been little research exploring its ability to identify individuals with major depressive disorder (MDD). Using penalised regression on genome-wide CpG methylation, we tested whether DNAm risk scores (MRS), trained on 1223 MDD cases and 1824 controls, could discriminate between cases (n = 363) and controls (n = 1417) in an independent sample, comparing their predictive accuracy to polygenic risk scores (PRS). The MRS explained 1.75% of the variance in MDD (β = 0.338, p = 1.17 × 10-7) and remained associated after adjustment for lifestyle factors (β = 0.219, p = 0.001, R2 = 0.68%). When modelled alongside PRS (β = 0.384, p = 4.69 × 10-9) the MRS remained associated with MDD (β = 0.327, p = 5.66 × 10-7). The MRS was also associated with incident cases of MDD who were well at recruitment but went on to develop MDD at a later assessment (β = 0.193, p = 0.016, R2 = 0.52%). Heritability analyses found additive genetic effects explained 22% of variance in the MRS, with a further 19% explained by pedigree-associated genetic effects and 16% by the shared couple environment. Smoking status was also strongly associated with MRS (β = 0.440, p ≤ 2 × 10-16). After removing smokers from the training set, the MRS strongly associated with BMI (β = 0.053, p = 0.021). We tested the association of MRS with 61 behavioural phenotypes and found that whilst PRS were associated with psychosocial and mental health phenotypes, MRS were more strongly associated with lifestyle and sociodemographic factors. DNAm-based risk scores of MDD significantly discriminated MDD cases from controls in an independent dataset and may represent an archive of exposures to lifestyle factors that are relevant to the prediction of MDD.

Brain Correlates of Suicide Attempt in 18,925 Participants Across 18 International Cohorts

BACKGROUND

Neuroimaging studies of suicidal behavior have so far been conducted in small samples, prone to biases and false-positive associations, yielding inconsistent results. The ENIGMA-MDD Working Group aims to address the issues of poor replicability and comparability by coordinating harmonized analyses across neuroimaging studies of major depressive disorder and related phenotypes, including suicidal behavior.

METHODS

Here, we pooled data from 18 international cohorts with neuroimaging and clinical measurements in 18,925 participants (12,477 healthy control subjects and 6448 people with depression, of whom 694 had attempted suicide). We compared regional cortical thickness and surface area and measures of subcortical, lateral ventricular, and intracranial volumes between suicide attempters, clinical control subjects (nonattempters with depression), and healthy control subjects.

RESULTS

We identified 25 regions of interest with statistically significant (false discovery rate < .05) differences between groups. Post hoc examinations identified neuroimaging markers associated with suicide attempt including smaller volumes of the left and right thalamus and the right pallidum and lower surface area of the left inferior parietal lobe.

CONCLUSIONS

This study addresses the lack of replicability and consistency in several previously published neuroimaging studies of suicide attempt and further demonstrates the need for well-powered samples and collaborative efforts. Our results highlight the potential involvement of the thalamus, a structure viewed historically as a passive gateway in the brain, and the pallidum, a region linked to reward response and positive affect. Future functional and connectivity studies of suicidal behaviors may focus on understanding how these regions relate to the neurobiological mechanisms of suicide attempt risk.

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.

Glutamate and functional connectivity - support for the excitatory-inhibitory imbalance hypothesis in autism spectrum disorders

It has been proposed that the Glutamate (Glu) system is implicated in autism spectrum disorders (ASD) via an imbalance between excitatory and inhibitory brain circuits, which impacts on brain function. Here, we investigated the excitatory-inhibitory imbalance theory by measuring Glu-concentrations and the relationship with resting-state function. Nineteen adult males with ASD and 19 age and sex-matched healthy controls (HC) (23 - 58 years) underwent Proton Magnetic Resonance Spectroscopy of the dorsal anterior cingulate cortex (dACC) and resting-state functional Magnetic Resonance Imaging (fMRI). Glu and Glx concentrations were compared between groups. Seed-based functional connectivity was analyzed with a priori seeds of the right and left dACC. Finally, metabolite concentrations were related to functional connectivity coefficients and compared between both groups. Individuals with ASD showed significantly negative associations between increased Glx concentrations and reduced functional connectivity between the dACC and insular, limbic and parietal regions. In contrast, HC displayed a positive relationship between the same metabolite and connectivity measures. We provided new evidence to support the excitatory-inhibitory imbalance theory, where excitatory Glx concentrations were related to functional dysconnectivity in ASD. Future research is needed to investigate large-scale functional networks in association with both excitatory and inhibitory metabolites in subpopulations of ASD.

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.

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.

Grey and white matter associations of psychotic-like experiences in a general population sample (UK Biobank)

There has been a substantial amount of research reporting the neuroanatomical associations of psychotic symptoms in people with schizophrenia. Comparatively little attention has been paid to the neuroimaging correlates of subclinical psychotic symptoms, so-called "psychotic-like experiences" (PLEs), within large healthy populations. PLEs are relatively common in the general population (7-13%), can be distressing and negatively affect health. This study therefore examined gray and white matter associations of four different PLEs (auditory or visual PLEs, and delusional ideas about conspiracies or communications) in subjects of the UK Biobank study with neuroimaging data (N = 21,390, mean age = 63 years). We tested for associations between any PLE (N = 768) and individual PLEs with gray and white matter brain structures, controlling for sex, age, intracranial volume, scanning site, and position in the scanner. Individuals that reported having experienced auditory hallucinations (N = 272) were found to have smaller volumes of the caudate, putamen, and accumbens (β = -0.115-0.134, pcorrected = 0.048-0.036), and reduced temporal lobe volume (β = -0.017, pcorrected = 0.047) compared to those that did not. People who indicated that they had ever believed in unreal conspiracies (N = 111) had a larger volume of the left amygdala (β = 0.023, pcorrected = 0.038). Individuals that reported a history of visual PLEs (N = 435) were found to have reduced white matter microstructure of the forceps major (β = -0.029, pcorrected = 0.009), an effect that was more marked in participants who reported PLEs as distressing. These associations were not accounted for by diagnoses of psychotic or depressive illness, nor the known risk factors for psychotic symptoms of childhood adversity or cannabis use. These findings suggest altered regional gray matter volumes and white matter microstructure in association with PLEs in the general population. They further suggest that these alterations may appear more frequently with the presentation of different psychotic symptoms in the absence of clinically diagnosed psychotic disorders.