BACKGROUND:Childhood maltreatment (CM), encompassing abuse and neglect, is highly prevalent and associated with elevated risk for major depressive disorder (MDD), posttraumatic stress disorder (PTSD), and other related conditions. However, the extent to which neuroanatomical alterations in MDD and PTSD are attributable to CM is uncertain. METHODS:Here, we analyzed CM and whole-brain magnetic resonance imaging (MRI) data from 3711 participants in the ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) MDD and PTSD Working Groups (25 sites; mean age = 33.3 ± 13.0 years; 59.9% female). Normative modeling estimated deviation z scores for 14 subcortical volume, 68 cortical thickness (CT), and 68 surface area (SA) measures. To identify transdiagnostic effects, associations between CM and brain deviation scores were evaluated across all participants (patients and healthy control participants) stratified by sex and 3 age bins (pediatric, young adult, older adult). RESULTS:In young adults (ages 18-35), abuse was associated with larger volumes in the thalamus and pallidum, thinner isthmus cingulate and middle frontal regions, and thicker medial orbitofrontal cortex; there were no significant effects in pediatric (≤18 years) participants. The strongest effects were observed in young female adults (|β| = 0.07-0.22, q < .05): Greater abuse and neglect were correlated with smaller hippocampus and putamen volumes, thinner entorhinal cortex, and smaller SA in fusiform/inferior parietal regions and with larger SA in the orbitofrontal and occipital cortices. In males, abuse had widespread effects on CT and SA (|β| = 0.1-0.18, q < .05); effects for neglect were minimal. CONCLUSIONS:Our findings of age- and sex-specific instantiations of CM on brain morphometry highlight the importance of developmental context in understanding how adverse experiences shape neurobiological vulnerability to MDD and PTSD.

Major depressive disorder (MDD) is common and disabling, yet reported brain structural differences vary across studies. Here we performed a large vertex-wise (point-by-point) meta-analysis of cortical thickness and surface area using harmonized magnetic resonance imaging processing across 64 cohorts from the Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) MDD and Depression Imaging Research Consortium (DIRECT) consortia (5,736 patients; 6,538 controls). We show significantly lower cortical thickness in patients with MDD in multiple brain regions, including the inferior parietal, lateral occipital, superior parietal, medial and lateral orbitofrontal, anterior and posterior cingulate, and precentral gyri, with cortical surface area showing no significant differences. Effects were most pronounced in adults with acute depression, whereas adolescents showed no significant case-control differences. Antidepressant medication use at scanning was associated with more extensive thinning, although effect sizes remained modest (mostly |Cohen's d| < 0.20). This high-resolution, globally generalizable map can support studies of mechanisms and help evaluate structural markers of the clinical course and treatment response.

Autism Spectrum Disorder (ASD) is characterized by certain difficulties in emotion-related processing. Recent research using electroencephalography (EEG) to measure somatosensory evoked potentials during emotion perception has shown reduced embodiment of emotional expressions in autistic compared to neurotypical individuals, independently from differences in visual processing. However, the underlying neural dynamics are not clear. In this study, we use Dynamic Causal Modeling (DCM) on EEG data to investigate whether reduced embodiment during emotion processing in ASD individuals is caused by changes in intrinsic connectivity within the somatosensory cortex, or by top-down modulatory effects from higher-order frontal areas. We constructed a model involving the primary and secondary right somatosensory cortex, the right supplementary motor area and the right inferior frontal gyrus, and tested effective connectivity during emotion or gender discrimination tasks in two groups of ASD and typically developing (TD) participants (n = 38, male and female, 2 females). Our results reveal that task-related differences in electrocortical activity between the emotion and gender tasks are causally explained by changes in intrinsic activity within the right primary somatosensory cortex (rS1) in both TD and ASD. Importantly, these intrinsic changes in rS1 are significantly different between TD and ASD groups and individual task-related changes in rS1 significantly correlate with alexithymia traits. Our study provides novel evidence on the neural dynamics underlying difficulties in emotion processing in ASD individuals, highlighting that differential intrinsic activations of the rS1 are causally involved in such difficulties, and suggests that they are mediated by alexithymia.

Psychiatric disorders are complex, polygenic conditions characterized by patterned structural brain alterations. Whether these changes reflect transcriptional dysregulation driven by genetic risk remains unclear. We introduce a novel imaging-transcriptomics framework that integrates transcriptome-wide association studies (TWAS) with brain transcriptomic atlases to predict macroscale structural brain abnormalities across seven disorders: attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), anorexia nervosa (AN), bipolar disorder (BD), major depressive disorder (MDD), obsessive-compulsive disorder (OCD), and schizophrenia (SCZ). We generated disorder-related Gene Expression-based Disorder Associated Risk (GEDAR) maps and assessed their spatial correlation with observed brain alterations thereby establishing a structured approach to map polygenic transcriptional risk onto macroscale brain phenotypes. We found significant transcriptomic-anatomical correlations in MDD (cortical and subcortical), SCZ (subcortical), and ADHD (subcortical), indicating that regional transcriptional vulnerability might contribute to varying extents to the anatomical expression of genetic risk in these disorders. Pathway enrichment analysis on genetically predicted differentially expressed genes for those disorders where we found spatial correlations between GEDAR maps and observed structural changes revealed immune-related processes as dominant in MDD and SCZ, and neurodevelopmental pathways in ADHD. Importantly, spatial transcriptomic-anatomical alignment did not scale with between-disorder differences in heritability, pointing instead toward additional influences like developmental timing or environmental interactions. These findings underscore the potential and limitations of imaging transcriptomics as a framework for bridging the gap between genetic architecture and systems-level brain changes in psychiatric disorders.

The clinical and biological heterogeneity of major depressive disorder (MDD) may reflect the aggregation of different conditions with distinct pathologies under a single diagnostic label. Neuroanatomical heterogeneity in MDD was examined using a harmonized, age- and sex-matched sample from the ENIGMA MDD consortium (N = 5146; age range: 9-82 years; 64% female). Analyses of global neurostrucutral variability revealed greater cortical thickness heterogeneity in MDD compared with healthy controls (Cohen's d = -0.26). Regionally, increased variability in cortical thickness was most prominent in the cingulate (+6.1 to +6.6% more variation in MDD) and insular (+5.8%) cortices, as well as in the frontal (+5.7 to +6.8%) and temporal (+6.1 to +6.8%) lobes. Heterogeneity in cortical thickness was more pronounced among patients using antidepressant medication (Cohen's d = -0.39). Patient-specific analyses further showed that individuals with markedly increased cortical thickness variability (<5th percentile relative to the normative range) exhibited greater depressive symptom severity than those within the normative range (5th-95th percentile; Cohen's d = 0.19-0.36). Overall, the results indicate that neuroanatomical heterogeneity in MDD is primarily expressed in cortical thickness, offering refined insights into the neurobiological complexity of structural alterations associated with depression. These findings could guide future stratification efforts examining whether regionally confined changes in cortical thickness within areas of pronounced variability reflect clinically meaningful patient subgroups.

Importance:Major depressive disorder (MDD) is highly heterogeneous, with marked individual differences in clinical presentation and neurobiology, which may obscure identification of structural brain abnormalities in MDD. To explore this, we used normative modeling to index regional patterns of variability in cortical thickness (CT) across individual patients. Objective:To use normative modeling in a large dataset from the ENIGMA MDD consortium to obtain individualised CT deviations from the norm (relative to age, sex and site) and examine the relationship between these deviations and clinical characteristics. Design setting and participants:A normative model adjusting for age, sex and site effects was trained on 35 CT measures from FreeSurfer parcellation of 3,181 healthy controls (HC) from 34 sites (40 scanners). Individualised z-score deviations from this norm for each CT measure were calculated for a test set of 2,119 HC and 3,645 individuals with MDD. For each individual, each CT z-score was classified as being within the normal range (95% of individuals) or within the extreme range (2.5% of individuals with the thinnest or thickest cortices). Main outcome measures:Z-score deviations of CT measures of MDD individuals as estimated from a normative model based on HC. Results:Z-score distributions of CT measures were largely overlapping between MDD and HC (minimum 92%, range 92-98%), with overall thinner cortices in MDD. 34.5% of MDD individuals, and 30% of HC individuals, showed an extreme deviation in at least one region, and these deviations were widely distributed across the brain. There was high heterogeneity in the spatial location of CT deviations across individuals with MDD: a maximum of 12% of individuals with MDD showed an extreme deviation in the same location. Extreme negative CT deviations were associated with having an earlier onset of depression and more severe depressive symptoms in the MDD group, and with higher BMI across MDD and HC groups. Extreme positive deviations were associated with being remitted, of not taking antidepressants and less severe symptoms. Conclusions and relevance:Our study illustrates a large heterogeneity in the spatial location of CT abnormalities across patients with MDD and confirms a substantial overlap of CT measures with HC. We also demonstrate that individualised extreme deviations can identify protective factors and individuals with a more severe clinical picture. Key points: Question:Can z-scores derived from normative modelling shed light on the heterogeneous group-level findings of cortical thickness abnormalities in major depression and what characterises individuals at the extreme ends of cortical thickness abnormalities? Finding:We confirmed a large overlap in z-score distributions between depressed individuals and healthy controls and a heterogeneous spatial distribution of extreme z-deviations across brain regions across individual patients. Lower z-scores for cortical thickness were related to more severe clinical characteristics. Meaning:Our findings confirm the heterogeneity in individual variation in the location and extent of CT abnormalities across patients with MDD and stress the importance of individualised predictions when examining cortical thickness abnormalities.

The understanding of how antidepressant (AD) use is associated with brain structure in individuals with major depressive disorder (MDD) remains incomplete. We aimed to examine the association between AD medication use and brain morphology in relation to age and sex by pooling structural neuroimaging and clinical data from 32 cohorts within the ENIGMA-MDD working group. Interaction effects of group (2076 cases with current AD use (AD), 1495 cases not currently taking AD (nAD) and 5125 healthy controls (HC)) with age and sex, and main effects of group on regional brain structure (cortical surface area and thickness, and subcortical volume) were examined. Additionally, we examined the effect of AD type (SSRI, SNRI or mirtazapine) and duration of use on brain morphology. Younger individuals in the AD group showed lower bilateral middle temporal gyrus thickness compared to nAD and HC, but this was not seen in older individuals (crossover around 50 years). Lower hippocampal volume and thinner inferior temporal gyrus were shown in AD compared to nAD. These effects were independent of group differences in disease-course-related measures, but were driven by depressive symptom severity. Greater bilateral rostral anterior cingulate thickness was found in individuals older than approximately 40 years taking mirtazapine compared to individuals taking SSRIs or SNRIs. Evidence for subtle structural brain differences in temporal and limbic regions in individuals with MDD who currently use AD medication were found compared to those not currently taking AD medication. Future longitudinal studies are needed to determine the causality of these associations.

Subcortical ischemic vascular cognitive impairment (SIVCI) is the most common form of vascular cognitive impairment. Exercise is a potentially effective intervention for SIVCI. However, the mechanisms through which exercise promotes brain health and cognitive function are not well understood. Telomere length restoration and an increased capacity for cellular proliferation may provide a putative mechanism. Therefore, in this exploratory study we aimed to 1) assess the effect of a resistance training program on leukocyte telomere length; and 2) determine whether telomere length and/or change in telomere length is related to intervention response (i.e., change in cognitive function and/or brain structure). The sample consists of a subset of participants from a 12-month single-blinded, randomized controlled trial (ClinicalTrials.gov Identifier: NCT02669394). Participants (N = 91) were randomized to twice-weekly resistance training (RT) or an active control group (balance and tone exercises; BAT). Study eligibility included: 1) age 55 years and older; 2) magnetic resonance imaging (MRI) evidence of cerebral small vessel disease; 3) mild cognitive impairment; and 4) the absence of dementia. We measured participants’ leukocyte (blood) telomere length at baseline, 6 months, and 12 months, using a qPCR-based method. The intervention outcomes include cognitive function measured by the Alzheimer’s Disease Assessment Scale-Cognitive-Plus (ADAS-Cog-13 with additional cognitive tests) and structural brain magnetic resonance imaging (MRI) markers of SIVCI (e.g., white matter hyperintensities). We have generated telomere length data, demonstrating very good reproducibility with an intraclass correlation coefficient (ICC) of 0.853 [0.772, 0.907]. We will present findings that provide novel insights into the usefulness of telomere length as a predictive biomarker for intervention response. Furthermore, our work will contribute to the mechanistic understanding of exercise-induced benefits for cognitive and brain health in SIVCI. To our knowledge, this is the first study to investigate the role of telomere length in the efficacy of an exercise intervention for vascular cognitive impairment. This could inform the development of personalised interventions and aid the discovery of novel therapeutic targets.

Deviations from normative brain ageing trajectories are linked to a wide range of adverse health outcomes. A number of brain age prediction models have been developed, based on various neuroimaging modalities, machine learning algorithms, training samples, and age ranges. However, it remains unknown whether these models converge on a shared genetic liability, and whether capturing this shared signal could provide a more sensitive marker of brain health than any single model alone. We first conducted a new brain age gap (BAG) GWAS in a sample of 60,735 individuals across 29 cohorts worldwide, and then applied genomic structural equation modelling to examine the shared genetic variance between five prior BAG GWASs and our new analysis, using a single latent BAG factor (30 cohorts overall). All six BAG GWASs loaded onto a single factor, explaining 63% of the total genetic variance. We identified 19 independent SNPs associated with the BAG factor, including four novel associations. Genetically, the BAG factor was positively correlated with multiple traits, with blood pressure, smoking, longevity, autism, and sleep showing putatively causal effects. A polygenic score (PGS) for the BAG factor showed associations with phenotypic BAGs already in childhood, with stronger links observed in adulthood. Phenome-wide association analyses indicated that BAG factor PGS captured associations with more health traits than individual BAG PGSs. Our findings underscore the importance of considering the shared variance across different BAG constructs to identify robust correlates of poor brain health.

Machine learning (ML) techniques have gained popularity in the neuroimaging field due to their potential for classifying neuropsychiatric disorders. However, the diagnostic predictive power of the existing algorithms has been limited by small sample sizes, lack of representativeness, data leakage, and/or overfitting. Here, we overcome these limitations with the largest multi-site sample size to date (N = 5365) to provide a generalizable ML classification benchmark of major depressive disorder (MDD) using shallow linear and non-linear models. Leveraging brain measures from standardized ENIGMA analysis pipelines in FreeSurfer, we were able to classify MDD versus healthy controls (HC) with a balanced accuracy of around 62%. But after harmonizing the data, e.g., using ComBat, the balanced accuracy dropped to approximately 52%. Accuracy results close to random chance levels were also observed in stratified groups according to age of onset, antidepressant use, number of episodes and sex. Future studies incorporating higher dimensional brain imaging/phenotype features, and/or using more advanced machine and deep learning methods may yield more encouraging prospects.

Deviations from a typical ageing trajectory are an important risk factor for poor health outcomes. The difference between chronological and brain-predicted age (i.e., brain-predicted age difference [PAD]) is one such measure of deviation from healthy ageing. Brain-PAD has been linked to over 40 traits and is generally thought to be heritable. However, specific genetic loci that influence brain-PAD are still largely unknown. Three recent genome-wide association studies (GWASs) on brain-PAD in the UK Biobank (n up to 28,104; age range: 40 to 84) identified a small number of associated genetic variants. This small number might be explained by the narrow age range and moderate sample size used in these studies. Larger samples covering the complete adult lifespan are needed to elucidate the genes implicated in brain-PAD, their impact on other biological systems in the brain and peripheral tissues, and the causal relationship between PAD and mental health.Brain-PAD was derived using a ridge regression model with 77 FreeSurfer-derived structural brain imaging features of surface area, cortical thickness and subcortical volume as an input in a total of n=47,167 participants from 28 datasets within the ENIGMA consortium. For a subset of these (n=34,112), we carried out a genome-wide association meta-analysis (GWAS) of brain-PAD. Additive effects of genetic variants on brain-PAD were tested, adjusting for age, age2, sex, total intracranial volume, genetic ancestry, imaging covariates (e.g. multiple scanners) and disease status (for case-control studies). We applied linear (mixed) models using BOLT-LMM, RareMetalWorker or PLINK2. Preliminary results were meta-analysed in METAL, weighing each cohort according to sample size. Ancestry-specific analyses were performed to disentangle universal versus population-specific genetic influences.A total of n=47,167 participants were included in the phenotypic analysis (age range 18-75 years; 52.8% females). Brain age was predicted with mean absolute error of 9.58 years (range 4.67-21.29). For the subset of datasets included in the GWAS, the mean absolute error was slightly larger (14.25 years; range 6.30-21.29; age-bias corrected=9.08). Fixed effect meta-analysis using METAL identified 66 genome-wide significant variants associated with brain-PAD at P=5 × 10. Three of these variants (on chromosomes 2, 15 and 16) were independent using r=0.1 and 500 kb window size. Two out of three variants identified had been previously implicated in brain-related phenotypes. SNP-based heritability was estimated at 0.1923 (SE=0.0167).Our findings indicate that brain age deviations in adulthood might be moderately heritable. Genetic loci overlapped partially with previous studies using overlapping data (e.g., UK Biobank), but different brain age estimation methods, suggesting a degree of consistency across methods. Identifying the underlying genetic loci can help to shed light on the causal risk factors involved in brain ageing, aiding in the prevention and treatment of age-related poor health outcomes, such as schizophrenia, Alzheimer's disease, and other cognitive impairments.

BackgroundTelomere length (TL) has been linked to cognitive function, decline and dementia. This study aimed to explore whether both measured TL and genetic disposition for TL predict dimensions of cognitive performance in a longitudinal sample of older UK adults.MethodsWe analysed data from PROTECT study participants aged ≥50 years without a dementia diagnosis, who had completed longitudinal cognitive testing. We calculated polygenic scores for telomere length (PGS-TL) for 7,877 participants and measured relative telomere length (RTL) in a subgroup of 846 participants using DNA extracted from saliva samples collected within 6 months either side of their baseline cognitive testing. Latent growth models were used to examine whether RTL and PGS-TL predict both baseline and longitudinal changes in cognitive performance (4 time-points, annually).ResultsIn the whole sample, we did not observe significant associations between either measure of telomere length and initial or longitudinal changes in cognitive performance. Stratifying by median age, in older adults (≥ ∼62 years), longer baseline RTL showed a nominal association with poorer baseline verbal reasoning performance (n = 423, Mintercept = 47.58, B = −1.05, p = .011) and PGS-TL was associated with performance over time (n = 3,939; slope factor, Mslope = 3.23, B = −0.45, p = .001; slope2 factor, Mslope2 = 0.21, B = 0.13, p = .002).ConclusionOur findings suggest either the absence of a significant relationship between telomere length (RTL and PGS-TL) and cognitive performance (baseline and change over time), or possibly a weak age-dependent and domain-specific relationship, in older adults of European ancestry. More research is needed in representative and ancestrally diverse samples over a longer assessment period. Alternative biological ageing indicators may still provide utility in the early detection of individuals at risk for cognitive decline (e.g., pace-of ageing epigenetic clocks).

Psychotic symptoms are relatively common in children and adolescents attending mental health services. On most occasions, their presence is not associated with a primary psychotic disorder, and their clinical significance remains understudied. No studies to date have evaluated the prevalence and clinical correlates of psychotic symptoms in children requiring inpatient mental health treatment. All children aged 6 to 12 years admitted to an inpatient children's unit over a 9-year period were included in this naturalistic study. Diagnosis at discharge, length of admission, functional impairment, and medication use were recorded. Children with psychotic symptoms without a childhood-onset schizophrenia spectrum disorder (COSS) were compared with children with COSS and children without psychotic symptoms using Chi-square and linear regressions. A total of 211 children were admitted during this period with 62.4% experiencing psychotic symptoms. The most common diagnosis in the sample was autism spectrum disorder (53.1%). Psychotic symptoms were not more prevalent in any diagnosis except for COSS (100%) and intellectual disability (81.8%). Psychotic symptoms were associated with longer admissions and antipsychotic medication use. The mean length of admission of children with psychotic symptoms without COSS seems to lie in between that of children without psychotic symptoms and that of children with COSS. We concluded that psychotic symptoms in children admitted to the hospital may be a marker of severity. Screening for such symptoms may have implications for treatment and could potentially contribute to identifying more effective targeted interventions and reducing overall morbidity.

Background Major psychiatric disorders are complex with a strong genetic component. We have a limited comprehension of the molecular and neuroanatomical factors contributing to them, making clinical management and developing effective treatments challenging. Recent advances in imaging transcriptomics have changed our understanding of polygenic diseases. The primary aim of this study is to address how much of brain structural changes seen in psychiatric disorders are shaped by transcriptomics and genetics. Methods Regional microarray expression data were obtained from the Allen Human Brain Atlas and processed using the Abagen toolbox. Data were mapped on the Desikany-Killiany atlas and only genes available in the Allen Human Protein Atlas were retained. GWAS data from the PGC for seven psychiatric disorders (ADHD, anorexia-AN, autism-ASD, bipolar disorder-BD, major depression disorder-MDD, OCD, and schizophrenia-SCZ) were used to perform TWAS prediction with S-PrediXcan and S-MultiXcan using 11 brain-specific elastic-net eqtl models. The top 10, 5, and 1 % of the TWAS-predicted genes (according to pvalue) were used to calculate the transcriptomic poligenic risk scores (TPRS), as weighted average, with the TWAS predicted Z-score as weight, of all top dysregulated genes, the top upregulated (Z>0) and top down-regulated (Z < 0) genes. Structural brain differences between healthy controls and patients were calculated using the Cohens’ d statistical test obtained by the ENIGMA consortium. Correspondence between TPRS scores and disorder structural brain differences was assessed via null-model permutation test, separately for cortical and subcortical regions. Statistical significance was set at pspin < 0.05. Results Among the disorders investigated, several showed significant correlations between TPRS and neuroimaging measures. ADHD showed significant correlations with subcortical regions for scores calculated with top 10 % of all genes (r=0.91, pspin < 0.05). Additional correlations were found with the top 1 % of upregulated genes in subcortical regions (r=-0.77, pspin < 0.05) and with the top 10 % (r=-0.30, pspin < 0.05) and 5 % (r=-0.40, pspin < 0.05) of downregulated genes in cortical regions. BD showed significant correlations between top 5 % of dysregulated genes in cortical regions (r=0.46, pspin < 0.05). MDD showed significant correlations with dysregulated genes in all thresholds (r=0.53±0.009, pspin < 0.05), top 1 % of upregulated cortical genes (r=0.40, pspin < 0.05) and top 10 % of downregulated genes (r=-0.42, pspin < 0.05). Lastly, SCZ showed significant correlations only in subcortical regions; between top 10 % (r=0.70, pspin < 0.05) and 5 % (r=-0.66, pspin < 0.05) of dysregulated genes and between top 10 % and 5 % of downregulated genes (r=0.71±0.01, pspin < 0.05). Discussion Our results show that, at the group level, cortical thickness differences seen in ADHD, BD and MDD; and subcortical volumes in ADHD, MDD and SCZ can be predicted based on a simple transposition of the genetic risk into the brain space informed by spatial transcriptomics. AN, ASD and OCD show no correlations. Next steps will be to explore links with heritability.

IntroductionPrevious studies have shown associations between cognitive function and C-reactive protein (CRP) levels in older adults. Few studies have considered the extent to which a genetic predisposition for higher CRP levels contributes to this association.MethodsData was analyzed from 7,817 UK participants aged >50 years as part of the PROTECT study, within which adults without dementia completed a comprehensive neuropsychological battery. We constructed a polygenic risk score (PRS-CRP) that explained 9.61% of the variance in serum CRP levels (p = 2.362 × 10−7) in an independent cohort. Regressions were used to explore the relationship between PRS-CRP and cognitive outcomes.ResultsWe found no significant associations between PRS-CRP and any cognitive measures in the sample overall. In older participants (>62 years), we observed a significant positive association between PRS-CRP and self-ordered search score (i.e., spatial working memory).ConclusionWhilst our results indicate a weak positive relationship between PRS-CRP and spatial working memory that is specific to older adults, overall, there appears to be no strong effects of PRS-CRP on cognitive function.

We used the auditory roving oddball to investigate whether individual differences in self-reported anxiety influence event-related potential (ERP) activity related to sensory gating and mismatch negativity (MMN). The state-trait anxiety inventory (STAI) was used to assess the effects of anxiety on the ERPs for auditory change detection and information filtering in a sample of thirty-six healthy participants. The roving oddball paradigm involves presentation of stimulus trains of auditory tones with certain frequencies followed by trains of tones with different frequencies. Enhanced negative mid-latency response (130–230 ms post-stimulus) was marked at the deviant (first tone) and the standard (six or more repetitions) tone at Fz, indicating successful mismatch negativity (MMN). In turn, the first and second tone in a stimulus train were subject to sensory gating at the Cz electrode site as a response to the second stimulus was suppressed at an earlier latency (40–80 ms). We used partial correlations and analyses of covariance to investigate the influence of state and trait anxiety on these two processes. Higher trait anxiety exhibited enhanced MMN amplitude (more negative) (F(1,33) = 14.259, p = 6.323 × 10−6, ηp2 = 0.302), whereas state anxiety reduced sensory gating (F(1,30) = 13.117, p = 0.001, ηp2 = 0.304). Our findings suggest that high trait-anxious participants demonstrate hypervigilant change detection to deviant tones that appear more salient, whereas increased state anxiety associates with failure to filter out irrelevant stimuli.

Mental health-related stigma is poorly understood, and minimal research has focused on the experience of stigma from children’s perspectives. We sought to investigate whether children treated as inpatients and outpatients had different experiences of stigma over time and whether stigma is linked to global functioning cross-sectionally and longitudinally. Children, aged 8–12 years, receiving treatment within a national specialist mental health inpatient unit were matched for age, gender and diagnosis with children receiving outpatient treatment (N = 64). Validated measures of stigma, global functioning and symptom severity were collected at the start of treatment and upon discharge from the ward for inpatients, and a similar timeframe for their individually matched outpatients. Latent change score models and partial correlation coefficients were employed to test our hypotheses. No differences in most aspects of stigma between children treated as inpatients and outpatients were observed, except for personal rejection at baseline and self-stigma at follow-up favouring outpatients. A reduction in stigma was observed in societal devaluation, personal rejection and secrecy for inpatients, and self-stigma and secrecy for outpatients between the two assessments. Societal devaluation declined at a higher rate among inpatients compared to outpatients, albeit reductions in stigma were comparable for all remaining measures. No association was found between the change in stigma and change in global functioning. Future research may offer further insights into the development and maintenance of stigma and identify key targets for anti-stigma interventions to reduce its long-term impact.

COPYRIGHT © 2023 Han, Dima, Zhu and Schmaal. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Editorial: Women in psychiatry 2022: computational psychiatry

Mechanisms underpinning age-related variations in cortical thickness in the human brain remain poorly understood. We investigated whether inter-regional age-related variations in cortical thinning (in a multicohort neuroimaging dataset from the ENIGMA Lifespan Working Group totalling 14,248 individuals, aged 4-89 years) depended on cell-specific marker gene expression levels. We found differences amidst early-life (<20 years), mid-life (20-60 years), and late-life (>60 years) in the patterns of association between inter-regional profiles of cortical thickness and expression profiles of marker genes for CA1 and S1 pyramidal cells, astrocytes, and microglia. Gene ontology and enrichment analyses indicated that each of the three life-stages was associated with different biological processes and cellular components: synaptic modeling in early life, neurotransmission in mid-life, and neurodegeneration in late-life. These findings provide mechanistic insights into age-related cortical thinning during typical development and aging.