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.

Accelerated Aging after Traumatic Brain Injury: An ENIGMA Multi-Cohort Mega-Analysis.

OBJECTIVE: The long-term consequences of traumatic brain injury (TBI) on brain structure remain uncertain. Given evidence that a single significant brain injury event increases the risk of dementia, brain-age estimation could provide a novel and efficient indexing of the long-term consequences of TBI. Brain-age procedures use predictive modeling to calculate brain-age scores for an individual using structural magnetic resonance imaging (MRI) data. Complicated mild, moderate, and severe TBI (cmsTBI) is associated with a higher predicted age difference (PAD), but the progression of PAD over time remains unclear. We sought to examine whether PAD increases as a function of time since injury (TSI) and if injury severity and sex interacted to influence this progression. METHODS: Through the ENIGMA Adult Moderate and Severe (AMS)-TBI working group, we examine the largest TBI sample to date (n = 343), along with controls, for a total sample size of n = 540, to replicate and extend prior findings in the study of TBI brain age. Cross-sectional T1w-MRI data were aggregated across 7 cohorts, and brain age was established using a similar brain age algorithm to prior work in TBI. RESULTS: Findings show that PAD widens with longer TSI, and there was evidence for differences between sexes in PAD, with men showing more advanced brain age. We did not find strong evidence supporting a link between PAD and cognitive performance. INTERPRETATION: This work provides evidence that changes in brain structure after cmsTBI are dynamic, with an initial period of change, followed by relative stability in brain morphometry, eventually leading to further changes in the decades after a single cmsTBI. ANN NEUROL 2024;96:365-377.

Testing the sensitivity of diagnosis-derived patterns in functional brain networks to symptom burden in a Norwegian youth sample.

Aberrant brain network development represents a putative aetiological component in mental disorders, which typically emerge during childhood and adolescence. Previous studies have identified resting-state functional connectivity (RSFC) patterns reflecting psychopathology, but the generalisability to other samples and politico-cultural contexts has not been established. We investigated whether a previously identified cross-diagnostic case-control and autism spectrum disorder (ASD)-specific pattern of RSFC (discovery sample; aged 5-21 from New York City, USA; n = 1666) could be validated in a Norwegian convenience-based youth sample (validation sample; aged 9-25 from Oslo, Norway; n = 531). As a test of generalisability, we investigated if these diagnosis-derived RSFC patterns were sensitive to levels of symptom burden in both samples, based on an independent measure of symptom burden. Both the cross-diagnostic and ASD-specific RSFC pattern were validated across samples. Connectivity patterns were significantly associated with thematically appropriate symptom dimensions in the discovery sample. In the validation sample, the ASD-specific RSFC pattern showed a weak, inverse relationship with symptoms of conduct problems, hyperactivity and prosociality, while the cross-diagnostic pattern was not significantly linked to symptoms. Diagnosis-derived connectivity patterns in a developmental clinical US sample were validated in a convenience sample of Norwegian youth, however, they were not associated with mental health symptoms.

Dissecting unique and common variance across body and brain health indicators using age prediction.

Ageing is a heterogeneous multisystem process involving different rates of decline in physiological integrity across biological systems. The current study dissects the unique and common variance across body and brain health indicators and parses inter-individual heterogeneity in the multisystem ageing process. Using machine-learning regression models on the UK Biobank data set (N = 32,593, age range 44.6-82.3, mean age 64.1 years), we first estimated tissue-specific brain age for white and gray matter based on diffusion and T1-weighted magnetic resonance imaging (MRI) data, respectively. Next, bodily health traits, including cardiometabolic, anthropometric, and body composition measures of adipose and muscle tissue from bioimpedance and body MRI, were combined to predict 'body age'. The results showed that the body age model demonstrated comparable age prediction accuracy to models trained solely on brain MRI data. The correlation between body age and brain age predictions was 0.62 for the T1 and 0.64 for the diffusion-based model, indicating a degree of unique variance in brain and bodily ageing processes. Bayesian multilevel modelling carried out to quantify the associations between health traits and predicted age discrepancies showed that higher systolic blood pressure and higher muscle-fat infiltration were related to older-appearing body age compared to brain age. Conversely, higher hand-grip strength and muscle volume were related to a younger-appearing body age. Our findings corroborate the common notion of a close connection between somatic and brain health. However, they also suggest that health traits may differentially influence age predictions beyond what is captured by the brain imaging data, potentially contributing to heterogeneous ageing rates across biological systems and individuals.

Brain-based gene expression of putative risk genes for anorexia nervosa.

The etiology of anorexia nervosa (AN) remains elusive. Recent genome-wide association studies identified the first genes liked to AN which reached genome-wide significance, although our understanding of how these genes confer risk remains preliminary. Here, we leverage the Allen Human Brain Atlas to characterize the spatially distributed gene expression patterns of genes linked to AN in the non-disordered human brain, developing whole-brain maps of AN gene expression. We found that genes associated with AN are most expressed in the brain, relative to all other body tissue types, and demonstrate gene-specific expression patterns which extend to cerebellar, temporal and basal ganglia structures in particular. fMRI meta-analyses reveal that AN gene expression maps correspond with functional brain activity involved in processing and anticipating appetitive and aversive cues. Findings offer novel insights around putative mechanisms through which genes associated with AN may confer risk.

Psychiatric disorders and brain white matter exhibit genetic overlap implicating developmental and neural cell biology.

Improved understanding of the shared genetic architecture between psychiatric disorders and brain white matter may provide mechanistic insights for observed phenotypic associations. Our objective is to characterize the shared genetic architecture of bipolar disorder (BD), major depression (MD), and schizophrenia (SZ) with white matter fractional anisotropy (FA) and identify shared genetic loci to uncover biological underpinnings. We used genome-wide association study (GWAS) summary statistics for BD (n = 413,466), MD (n = 420,359), SZ (n = 320,404), and white matter FA (n = 33,292) to uncover the genetic architecture (i.e., polygenicity and discoverability) of each phenotype and their genetic overlap (i.e., genetic correlations, overlapping trait-influencing variants, and shared loci). This revealed that BD, MD, and SZ are at least 7-times more polygenic and less genetically discoverable than average FA. Even in the presence of weak genetic correlations (range = -0.05 to -0.09), average FA shared an estimated 42.5%, 43.0%, and 90.7% of trait-influencing variants as well as 12, 4, and 28 shared loci with BD, MD, and SZ, respectively. Shared variants were mapped to genes and tested for enrichment among gene-sets which implicated neurodevelopmental expression, neural cell types, myelin, and cell adhesion molecules. For BD and SZ, case vs control tract-level differences in FA associated with genetic correlations between those same tracts and the respective disorder (rBD = 0.83, p = 4.99e-7 and rSZ = 0.65, p = 5.79e-4). Genetic overlap at the tract-level was consistent with average FA results. Overall, these findings suggest a genetic basis for the involvement of brain white matter aberrations in the pathophysiology of psychiatric disorders.

Genetic architecture of brain age and its causal relations with brain and mental disorders.

The difference between chronological age and the apparent age of the brain estimated from brain imaging data-the brain age gap (BAG)-is widely considered a general indicator of brain health. Converging evidence supports that BAG is sensitive to an array of genetic and nongenetic traits and diseases, yet few studies have examined the genetic architecture and its corresponding causal relationships with common brain disorders. Here, we estimate BAG using state-of-the-art neural networks trained on brain scans from 53,542 individuals (age range 3-95 years). A genome-wide association analysis across 28,104 individuals (40-84 years) from the UK Biobank revealed eight independent genomic regions significantly associated with BAG (p < 5 × 10-8) implicating neurological, metabolic, and immunological pathways - among which seven are novel. No significant genetic correlations or causal relationships with BAG were found for Parkinson's disease, major depressive disorder, or schizophrenia, but two-sample Mendelian randomization indicated a causal influence of AD (p = 7.9 × 10-4) and bipolar disorder (p = 1.35 × 10-2) on BAG. These results emphasize the polygenic architecture of brain age and provide insights into the causal relationship between selected neurological and neuropsychiatric disorders and BAG.

Linking menopause-related factors, history of depression, APOE ε4, and proxies of biological aging in the UK biobank cohort.

In a subset of females, postmenopausal status has been linked to accelerated aging and neurological decline. A complex interplay between reproductive-related factors, mental disorders, and genetics may influence brain function and accelerate the rate of aging in the postmenopausal phase. Using multiple regressions corrected for age, in this preregistered study we investigated the associations between menopause-related factors (i.e., menopausal status, menopause type, age at menopause, and reproductive span) and proxies of cellular aging (leukocyte telomere length, LTL) and brain aging (white and gray matter brain age gap, BAG) in 13,780 females from the UK Biobank (age range 39-82). We then determined how these proxies of aging were associated with each other, and evaluated the effects of menopause-related factors, history of depression (= lifetime broad depression), and APOE ε4 genotype on BAG and LTL, examining both additive and interactive relationships. We found that postmenopausal status and older age at natural menopause were linked to longer LTL and lower BAG. Surgical menopause and longer natural reproductive span were also associated with longer LTL. BAG and LTL were not significantly associated with each other. The greatest variance in each proxy of biological aging was most consistently explained by models with the addition of both lifetime broad depression and APOE ε4 genotype. Overall, this study demonstrates a complex interplay between menopause-related factors, lifetime broad depression, APOE ε4 genotype, and proxies of biological aging. However, results are potentially influenced by a disproportionate number of healthier participants among postmenopausal females. Future longitudinal studies incorporating heterogeneous samples are an essential step towards advancing female health.

Individual differences in internalizing symptoms in late childhood: A variance decomposition into cortical thickness, genetic and environmental differences.

The brain undergoes extensive development during late childhood and early adolescence. Cortical thinning is a prominent feature of this development, and some researchers have suggested that differences in cortical thickness may be related to internalizing symptoms, which typically increase during the same period. However, research has yielded inconclusive results. We utilized a new method that estimates the combined effect of individual differences in vertex-wise cortical thickness on internalizing symptoms. This approach allows for many small effects to be distributed across the cortex and avoids the necessity of correcting for multiple tests. Using a sample of 8763 children aged 8.9 to 11.1 from the ABCD study, we decomposed the total variation in caregiver-reported internalizing symptoms into differences in cortical thickness, additive genetics, and shared family environmental factors and unique environmental factors. Our results indicated that individual differences in cortical thickness accounted for less than 0.5% of the variation in internalizing symptoms. In contrast, the analysis revealed a substantial effect of additive genetics and family environmental factors on the different components of internalizing symptoms, ranging from 06% to 48% and from 0% to 34%, respectively. Overall, while this study found a minimal association between cortical thickness and internalizing symptoms, additive genetics, and familial environmental factors appear to be of importance for describing differences in internalizing symptoms in late childhood. RESEARCH HIGHLIGHTS: We utilized a new method for modelling the total contribution of vertex-wise individual differences in cortical thickness to internalizing symptoms in late childhood. The total contribution of individual differences in cortical thickness accounted for <0.5% of the variance in internalizing symptoms. Additive genetics and shared family environmental variation accounted for 17% and 34% of the variance in internalizing symptoms, respectively. Our results suggest that cortical thickness is not an important indicator for internalizing symptoms in childhood, whereas genetic and environmental differences have a substantial impact.

Spherical means-based free-water volume fraction from diffusion MRI increases non-linearly with age in the white matter of the healthy human brain.

The term free-water volume fraction (FWVF) refers to the signal fraction that could be found as the cerebrospinal fluid of the brain, which has been demonstrated as a sensitive measure that correlates with cognitive performance and various neuropathological processes. It can be quantified by properly fitting the isotropic component of the magnetic resonance (MR) signal in diffusion-sensitized sequences. Using N=287 healthy subjects (178F/109M) aged 25-94, this study examines in detail the evolution of the FWVF obtained with the spherical means technique from multi-shell acquisitions in the human brain white matter across the adult lifespan, which has been previously reported to exhibit a positive trend when estimated from single-shell data using the bi-tensor signal representation. We found evidence of a noticeably non-linear gain after the sixth decade of life, with a region-specific variate and varying change rate of the spherical means-based multi-shell FWVF parameter with age, at the same time, a heteroskedastic pattern across the adult lifespan is suggested. On the other hand, the FW corrected diffusion tensor imaging (DTI) leads to a region-dependent flattened age-related evolution of the mean diffusivity (MD) and fractional anisotropy (FA), along with a considerable reduction in their variability, as compared to the studies conducted over the standard (single-component) DTI. This way, our study provides a new perspective on the trajectory-based assessment of the brain and explains the conceivable reason for the variations observed in FA and MD parameters across the lifespan with previous studies under the standard diffusion tensor imaging.

Baseline long-term potentiation-like cortical plasticity is associated with longitudinal cortical thinning in healthy adults and in adults with bipolar disorder type II.

The precise neurobiological processes underlying cerebral cortical thinning in aging and psychiatric illnesses remain undetermined, yet aging- and synaptic dysfunction-related loss of synapses are potentially important mechanisms. We used long-term potentiation-like plasticity of the visual evoked potential as an index of synaptic function in the cortex and hypothesized that plasticity at baseline would be negatively associated with future cortical thinning in healthy adults and in adults with bipolar disorder type II. Thirty-two healthy adults and 15 adults with bipolar disorder type II underwent electroencephalography-based measurement of visual evoked potential plasticity and 3T magnetic resonance imaging of the brain at baseline and a follow-up brain scan on average 2.3 years later. The relationships between visual evoked potential plasticity at baseline and longitudinal cortical thickness changes were examined using Freesurfer and the Permutation Analysis of Linear Models tool. The analyses showed a negative association between the plasticity of the N1 visual evoked potential amplitude at baseline and thinning rate in the medial and lateral parietal and medial occipital cortices in healthy adults and in the right medial occipital cortex in the total sample of healthy adults and adults with bipolar disorder type II, indicating greater thinning over time in subjects with less N1 plasticity (pFWER < .05). Although preliminary, the results indicate an association between visual evoked potential plasticity and the future rate of cortical thinning in healthy adults and in bipolar disorder type II, supporting the hypothesis that cortical thinning might be related to synaptic dysfunction.

Benchmarking the generalizability of brain age models: Challenges posed by scanner variance and prediction bias.

Machine learning has been increasingly applied to neuroimaging data to predict age, deriving a personalized biomarker with potential clinical applications. The scientific and clinical value of these models depends on their applicability to independently acquired scans from diverse sources. Accordingly, we evaluated the generalizability of two brain age models that were trained across the lifespan by applying them to three distinct early-life samples with participants aged 8-22 years. These models were chosen based on the size and diversity of their training data, but they also differed greatly in their processing methods and predictive algorithms. Specifically, one brain age model was built by applying gradient tree boosting (GTB) to extracted features of cortical thickness, surface area, and brain volume. The other model applied a 2D convolutional neural network (DBN) to minimally preprocessed slices of T1-weighted scans. Additional model variants were created to understand how generalizability changed when each model was trained with data that became more similar to the test samples in terms of age and acquisition protocols. Our results illustrated numerous trade-offs. The GTB predictions were relatively more accurate overall and yielded more reliable predictions when applied to lower quality scans. In contrast, the DBN displayed the most utility in detecting associations between brain age gaps and cognitive functioning. Broadly speaking, the largest limitations affecting generalizability were acquisition protocol differences and biased brain age estimates. If such confounds could eventually be removed without post-hoc corrections, brain age predictions may have greater utility as personalized biomarkers of healthy aging.

The value of arterial spin labelling perfusion MRI in brain age prediction.

Current structural MRI-based brain age estimates and their difference from chronological age-the brain age gap (BAG)-are limited to late-stage pathological brain-tissue changes. The addition of physiological MRI features may detect early-stage pathological brain alterations and improve brain age prediction. This study investigated the optimal combination of structural and physiological arterial spin labelling (ASL) image features and algorithms. Healthy participants (n = 341, age 59.7 ± 14.8 years) were scanned at baseline and after 1.7 ± 0.5 years follow-up (n = 248, mean age 62.4 ± 13.3 years). From 3 T MRI, structural (T1w and FLAIR) volumetric ROI and physiological (ASL) cerebral blood flow (CBF) and spatial coefficient of variation ROI features were constructed. Multiple combinations of features and machine learning algorithms were evaluated using the Mean Absolute Error (MAE). From the best model, longitudinal BAG repeatability and feature importance were assessed. The ElasticNetCV algorithm using T1w + FLAIR+ASL performed best (MAE = 5.0 ± 0.3 years), and better compared with using T1w + FLAIR (MAE = 6.0 ± 0.4 years, p < .01). The three most important features were, in descending order, GM CBF, GM/ICV, and WM CBF. Average baseline and follow-up BAGs were similar (-1.5 ± 6.3 and - 1.1 ± 6.4 years respectively, ICC = 0.85, 95% CI: 0.8-0.9, p = .16). The addition of ASL features to structural brain age, combined with the ElasticNetCV algorithm, improved brain age prediction the most, and performed best in a cross-sectional and repeatability comparison. These findings encourage future studies to explore the value of ASL in brain age in various pathologies.

Brain-wide associations between white matter and age highlight the role of fornix microstructure in brain ageing.

Unveiling the details of white matter (WM) maturation throughout ageing is a fundamental question for understanding the ageing brain. In an extensive comparison of brain age predictions and age-associations of WM features from different diffusion approaches, we analyzed UK Biobank diffusion magnetic resonance imaging (dMRI) data across midlife and older age (N = 35,749, 44.6-82.8 years of age). Conventional and advanced dMRI approaches were consistent in predicting brain age. WM-age associations indicate a steady microstructure degeneration with increasing age from midlife to older ages. Brain age was estimated best when combining diffusion approaches, showing different aspects of WM contributing to brain age. Fornix was found as the central region for brain age predictions across diffusion approaches in complement to forceps minor as another important region. These regions exhibited a general pattern of positive associations with age for intra axonal water fractions, axial, radial diffusivities, and negative relationships with age for mean diffusivities, fractional anisotropy, kurtosis. We encourage the application of multiple dMRI approaches for detailed insights into WM, and the further investigation of fornix and forceps as potential biomarkers of brain age and ageing.

Associations between everyday activities and arterial spin labeling-derived cerebral blood flow: A longitudinal study in community-dwelling elderly volunteers.

Cerebral blood flow (CBF) is critical for brain metabolism and function. Age-related changes in CBF are associated with increased risk of neurocognitive disorders and vascular events such as stroke. Identifying correlates and positive modifiers of age-related changes in CBF before the emergence of incipient clinical decline may inform public health advice and clinical practice. Former research has been inconclusive regarding the association between regular physical activity and CBF, and there is a lack of studies on the association between level of everyday activities and CBF, in older adults. To investigate these relationships, 118 healthy community-dwelling adults (65-89 years) underwent pseudo-continuous arterial spin labeling (ASL) MRI, neurocognitive, physical, and activity assessments at baseline. Eighty-six participants completed a follow-up ASL MRI, on average 506 (SD = 113) days after the baseline scan. Cross-sectional analysis revealed credible evidence for positive associations between time spent on low intensity physical activity and CBF in multiple cortical and subcortical regions, time spent on moderate to vigorous intensity physical activity and accumbens CBF, participation in social activity and CBF in multiple cortical regions, and between reading and thalamic CBF, indicating higher regional CBF in more active adults. Longitudinal analysis revealed anecdotal evidence for an interaction between time and baseline level of gardening on occipital and parietal CBF, and baseline reading on pallidum CBF, indicating more change in CBF in adults with lower level of activity. The findings support that malleable lifestyle factors contribute to healthy brain aging, with relevance for public health guidelines.

Lower circulating neuron-specific enolase concentrations in adults and adolescents with severe mental illness.

BACKGROUND: Both neurodegenerative and neurodevelopmental abnormalities have been suggested to be part of the etiopathology of severe mental illness (SMI). Neuron-specific enolase (NSE), mainly located in the neuronal cytoplasm, may indicate the process as it is upregulated after neuronal injury while a switch from non-neuronal enolase to NSE occurs during neuronal maturation. METHODS: We included 1132 adult patients with SMI [schizophrenia (SZ) or bipolar spectrum disorders], 903 adult healthy controls (HC), 32 adolescent patients with SMI and 67 adolescent HC. Plasma NSE concentrations were measured by enzyme immunoassay. For 842 adults and 85 adolescents, we used total grey matter volume (TGMV) based on T1-weighted magnetic resonance images processed in FreeSurfer v6.0. We explored NSE case-control differences in adults and adolescents separately. To investigate whether putative case-control differences in NSE were TGMV-dependent we controlled for TGMV. RESULTS: We found significantly lower NSE concentrations in both adult (p < 0.001) and adolescent patients with SMI (p = 0.007) compared to HC. The results remained significant after controlling for TGMV. Among adults, both patients with SZ spectrum (p < 0.001) and bipolar spectrum disorders (p = 0.005) had lower NSE than HC. In both patient subgroups, lower NSE levels were associated with increased symptom severity. Among adults (p < 0.001) and adolescents (p = 0.040), females had lower NSE concentrations than males. CONCLUSION: We found lower NSE concentrations in adult and adolescent patients with SMI compared to HC. The results suggest the lack of progressive neuronal injury, and may reflect abnormal neuronal maturation. This provides further support of a neurodevelopmental rather than a neurodegenerative mechanism in SMI.

Shared genetic architecture between schizophrenia and subcortical brain volumes implicates early neurodevelopmental processes and brain development in childhood.

Patients with schizophrenia have consistently shown brain volumetric abnormalities, implicating both etiological and pathological processes. However, the genetic relationship between schizophrenia and brain volumetric abnormalities remains poorly understood. Here, we applied novel statistical genetic approaches (MiXeR and conjunctional false discovery rate analysis) to investigate genetic overlap with mixed effect directions using independent genome-wide association studies of schizophrenia (n = 130,644) and brain volumetric phenotypes, including subcortical brain and intracranial volumes (n = 33,735). We found brain volumetric phenotypes share substantial genetic variants (74-96%) with schizophrenia, and observed 107 distinct shared loci with sign consistency in independent samples. Genes mapped by shared loci revealed (1) significant enrichment in neurodevelopmental biological processes, (2) three co-expression clusters with peak expression at the prenatal stage, and (3) genetically imputed thalamic expression of CRHR1 and ARL17A was associated with the thalamic volume as early as in childhood. Together, our findings provide evidence of shared genetic architecture between schizophrenia and brain volumetric phenotypes and suggest that altered early neurodevelopmental processes and brain development in childhood may be involved in schizophrenia development.

Use of High-Dose Androgens Is Associated with Reduced Brain-Derived Neurotrophic Factor in Male Weightlifters.

INTRODUCTION: Use of high-dose androgens causes drastic changes in hormonal milieu and is associated with adverse medical, psychological, and cognitive effects. Brain-derived neurotrophic factor (BDNF), a member of the neurotrophin family of growth factors plays a critical role in neuroplasticity, with implications for cognitive function and mental health. The impact of long-term, high-dose androgen use on BDNF in a natural setting has not been investigated. This study examined the association between long-term androgen exposure and BDNF levels, and the links between BDNF, heavy resistance exercise, hormones, androgens, and mental health. METHODS: We measured serum levels of BDNF and sex steroid hormones in male weightlifters (N = 141) with a history of current (n = 59), past (n = 29), or no (n = 52) androgen use. All participants completed questionnaires assessing maximum strength and measures of anxiety and depression. Group differences in BDNF were tested using general linear models adjusting for age and associations between BDNF and strength, anxiety, and depression using Pearson's or Kendall's correlations. RESULTS: Both current (mean: 44.1 ng/mL [SD: 12.7]) and past (39.5 ng/mL [SD: 13.9]) androgen users showed lower serum BDNF levels compared to nonusing controls (51.5 [SD: 15.3], p < 0.001, ηp2 = 0.10). BDNF levels were negatively related to maximal strength, and with hormonal status in past androgen users, but no significant associations were found with measures of depression and anxiety. CONCLUSION: Lower circulating BDNF concentrations in current and past androgen users suggest that high-dose androgen exposure triggers persistent changes in BDNF expression. Further studies are needed to verify the relationship and its potential clinical implications.

Investigating anabolic-androgenic steroid dependence and muscle dysmorphia with network analysis among male weightlifters.

BACKGROUND: Anabolic-androgenic steroid (AAS) dependence has numerous adverse health consequences, and may be driven in part by body image concerns, primarily muscle dysmorphia. This study aims to further understand and identify potential clinical targets using network analyses of AAS dependence and muscle dysmorphia symptoms in males who used AAS and weightlifting controls. METHODS: A sample of 153 men who currently or previously used AAS and 88 weight-lifting controls were recruited through social media and relevant online forums, and via posters and flyers distributed in select gyms in Oslo, Norway. Symptoms of AAS dependence and muscle dysmorphia were assessed using clinical interviews and standardized questionnaires. Severity of muscle dysmorphia symptoms were compared between the groups using independent samples t-tests. The following symptom networks were computed using Gaussian graphical modeling or mixed graphical modeling: (1) AAS dependence symptoms among men with AAS use (2) muscle dysmorphia symptoms among men with AAS use and weight-lifting controls in two separate networks, which were compared using a network comparison test, and (3) AAS dependence and muscle dysmorphia symptoms among men with AAS use. RESULTS: In a network of AAS dependence symptoms, continuing use despite physical and mental side effects, using longer than planned, tolerance, and work/life interference were the most central symptoms. When comparing symptom structures of muscle dysmorphia between those who used AAS and controls, the most central symptoms in each group were exercise dependence and size/symmetry concerns, respectively. Men with AAS use demonstrated elevated muscle dysmorphia symptoms compared to controls, indicating that both the severity and structure of symptoms differ between these groups. In a network including both AAS dependence and muscle dysmorphia symptoms, no significant connections between symptom groups were identified. CONCLUSIONS: AAS dependence is complex, with correlated somatic and psychological challenges driving the symptom network, indicating that alleviating physical and mental health concerns during both AAS use and cessation is an important clinical target. Muscle dysmorphia symptoms related to taking action (diet, exercise, and supplement use) appear to cluster together more for those who use AAS than those who do not.

Shared genetic architecture between mental health and the brain functional connectome in the UK Biobank.

Psychiatric disorders are complex clinical conditions with large heterogeneity and overlap in symptoms, genetic liability and brain imaging abnormalities. Building on a dimensional conceptualization of mental health, previous studies have reported genetic overlap between psychiatric disorders and population-level mental health, and between psychiatric disorders and brain functional connectivity. Here, in 30,701 participants aged 45-82 from the UK Biobank we map the genetic associations between self-reported mental health and resting-state fMRI-based measures of brain network function. Multivariate Omnibus Statistical Test revealed 10 genetic loci associated with population-level mental symptoms. Next, conjunctional FDR identified 23 shared genetic variants between these symptom profiles and fMRI-based brain network measures. Functional annotation implicated genes involved in brain structure and function, in particular related to synaptic processes such as axonal growth (e.g. NGFR and RHOA). These findings provide further genetic evidence of an association between brain function and mental health traits in the population.