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.

Hypothalamic subunit volumes and relations to violence and psychopathy in male offenders with or without a psychotic disorder.

The hypothalamus is key to body homeostasis, including regulating cortisol, testosterone, vasopressin, and oxytocin hormones, modulating aggressive behavior. Animal studies have linked the morphology and function of the hypothalamus to aggression and affiliation, with a subregional pattern reflecting the functional division between the hypothalamic nuclei. We explored the relationship between hypothalamic subunit volumes in violent offenders with (PSY-V) and without (NPV) a psychotic disorder, and the association with psychopathy traits. 3T MRI scans (n = 628, all male 18-70 years) were obtained from PSY-V, n = 38, NPV, n = 20, non-violent psychosis patients (PSY-NV), n = 134, and healthy controls (HC), n = 436. The total hypothalamus volume and its eleven nuclei were delineated into five subunits using Freesurfer v7.3. Psychopathy traits were assessed with Psychopathy Checklist-revised (PCL-R). ANCOVAs and linear regressions were used to analyze associations with subunit volumes. Both groups with a history of violence exhibited smaller anterior-superior subunit volumes than HC (NPV Cohen's d = 0.56, p = 0.01 and PSY-V d = 0.38, p = 0.01). There were no significant differences between HC and PSY-NV. PCL-R scores were positively associated with the inferior tubular subunit on a trend level (uncorrected p = 0.045, Cohen's d = 0.04). We found distinct hypothalamic subunit volume reductions in persons with a history of violence independent of concomitant psychotic disorder but not in persons with psychosis alone. The results provide further information about the involvement of the hypothalamus in aggression, which ultimately may lead to the development of targeted treatment for the clinical and societal challenge of aggression and violent behavior.

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.

Polygenic risk for schizophrenia and bipolar disorder in relation to cardiovascular biomarkers.

Individuals with schizophrenia and bipolar disorder are at an increased risk of cardiovascular disease (CVD), and a range of biomarkers related to CVD risk have been found to be abnormal in these patients. Common genetic factors are a putative underlying mechanism, alongside lifestyle factors and antipsychotic medication. However, the extent to which the altered CVD biomarkers are related to genetic factors involved in schizophrenia and bipolar disorder is unknown. In a sample including 699 patients with schizophrenia, 391 with bipolar disorder, and 822 healthy controls, we evaluated 8 CVD risk biomarkers, including BMI, and fasting plasma levels of CVD biomarkers from a subsample. Polygenic risk scores (PGRS) were obtained from genome-wide associations studies (GWAS) of schizophrenia and bipolar disorder from the Psychiatric Genomics Consortium. The CVD biomarkers were used as outcome variables in linear regression models including schizophrenia and bipolar disorder PGRS as predictors, age, sex, diagnostic category, batch and 10 principal components as covariates, controlling for multiple testing by Bonferroni correction for the number of independent tests. Bipolar disorder PGRS was significantly (p = 0.03) negatively associated with BMI after multiple testing correction, and schizophrenia PGRS was nominally negatively associated with BMI. There were no other significant associations between bipolar or schizophrenia PGRS, and other investigated CVD biomarkers. Despite a range of abnormal CVD risk biomarkers in psychotic disorders, we only found a significant negative association between bipolar disorder PGRS and BMI. This has previously been shown for schizophrenia PGRS and BMI, and warrants further exploration.

Impulsivity across severe mental disorders: a cross-sectional study of immune markers and psychopharmacotherapy.

BACKGROUND: Impulsivity is a transdiagnostic feature linked to severe clinical expression and a potential target for psychopharmacological strategies. Biological underpinnings are largely unknown, but involvement of immune dysregulation has been indicated, and the effects of psychopharmacological agents vary. We investigated if impulsivity was associated with circulating immune marker levels and with a range of psychopharmacological treatment regimens in severe mental disorders. METHODS: Impulsivity was assessed in a sample (N = 657) of patients with schizophrenia or schizophreniform disorder (SCZ) (N = 116) or bipolar disorder (BD) (N = 159) and healthy participants (N = 382) using the Barratt Impulsiveness Scale (BIS-11) questionnaire. Plasma levels of systemic immune markers (RANTES, IL-1RA, IL-18, IL-18BP, sTNFR-1) were measured by enzyme immunoassays. Patients underwent thorough clinical assessment, including evaluation of psychotropic medication. Associations were assessed using linear regressions. RESULTS: Impulsivity  was positively associated with SCZ (p < 0.001) and BD (p < 0.001) diagnosis and negatively associated with age (p < 0.05), but not significantly associated with any of the circulating immune markers independently of diagnostic status. Among patients, impulsivity was negatively associated with lithium treatment (p = 0.003) and positively associated with antidepressant treatment (p = 0.011) after controlling for diagnosis, psychotropic co-medications, manic symptoms, and depressive symptoms. CONCLUSIONS: We report elevated impulsivity across SCZ and BD but no associations to systemic immune dysregulation based on the current immune marker selection. The present study reveals associations between impulsivity in severe mental disorders and treatment with lithium and antidepressants, with opposite directions. Future studies are warranted to determine the causal directionality of the observed associations with psychopharmacotherapy.

Psychopathy subdomains in violent offenders with and without a psychotic disorder.

BACKGROUND: Violence in psychosis has been linked to antisocial behavior and psychopathy traits. Psychopathy comprises aspects of interpersonal, affective, lifestyle, and antisocial traits which may be differently involved in violent offending by persons with psychotic disorders. We explored psychopathy subdomains among violent offenders with and without a psychotic disorder. METHODS: 46 males, with a history of severe violence, with (n = 26; age 35.85 ± 10.34 years) or without (n = 20; age 39.10 ± 11.63 years) a diagnosis of a psychotic disorder, were assessed with the Psychopathy Checklist-Revised (PCL-R). PCL-R was split into subdomains following the four-facet model. Group differences in total and subdomain scores were analyzed with a general linear model with covariates. RESULTS: Total PCL-R scores did not differ between the groups (p = 0.61, Cohen's d = 0.17). The violent offenders without psychotic disorders had higher facet 2 scores than the patient group with psychotic disorders (p = 0.029, Cohen's d = 0.77). Facet 1, 3, or 4 scores did not differ between the groups. Controlling for age did not alter the results. CONCLUSION: Patients with a psychotic disorder and a history of severe violence have lower affective psychopathy scores than violent offenders without psychotic disorders. This observation may point toward distinct underlying mechanisms for violence and may provide a target for focused treatment and prevention.

Distributed genetic effects of the corpus callosum subregions suggest links to neuropsychiatric disorders and related traits.

BACKGROUND: The corpus callosum (CC) is a brain structure with a high heritability and potential role in psychiatric disorders. However, the genetic architecture of the CC and the genetic link with psychiatric disorders remain largely unclear. We investigated the genetic architectures of the volume of the CC and its subregions and the genetic overlap with psychiatric disorders. METHODS: We applied multivariate genome-wide association study (GWAS) to genetic and T1-weighted magnetic resonance imaging (MRI) data of 40,894 individuals from the UK Biobank, aiming to boost genetic discovery and to assess the pleiotropic effects across volumes of the five subregions of the CC (posterior, mid-posterior, central, mid-anterior and anterior) obtained by FreeSurfer 7.1. Multivariate GWAS was run combining all subregions, co-varying for relevant variables. Gene-set enrichment analyses were performed using MAGMA. Linkage disequilibrium score regression (LDSC) was used to determine Single nucleotide polymorphism (SNP)-based heritability of total CC volume and volumes of its subregions as well as their genetic correlations with relevant psychiatric traits. RESULTS: We identified 70 independent loci with distributed effects across the five subregions of the CC (p < 5 × 10-8). Additionally, we identified 33 significant loci in the anterior subregion, 23 in the mid-anterior, 29 in the central, 7 in the mid-posterior and 56 in the posterior subregion. Gene-set analysis revealed 156 significant genes contributing to volume of the CC subregions (p < 2.6 × 10-6). LDSC estimated the heritability of CC to (h2SNP = 0.38, SE = 0.03) and subregions ranging from 0.22 (SE = 0.02) to 0.37 (SE = 0.03). We found significant genetic correlations of total CC volume with bipolar disorder (BD, rg = -0.09, SE = 0.03; p = 5.9 × 10-3) and drinks consumed per week (rg = -0.09, SE = 0.02; p = 4.8 × 10-4), and volume of the mid-anterior subregion with BD (rg = -0.12, SE = 0.02; p = 2.5 × 10-4), major depressive disorder (MDD) (rg = -0.12, SE = 0.04; p = 3.6 × 10-3), drinks consumed per week (rg = -0.13, SE = 0.04; p = 1.8 × 10-3) and cannabis use (rg = -0.09, SE = 0.03; p = 8.4 × 10-3). CONCLUSIONS: Our results demonstrate that the CC has a polygenic architecture implicating multiple genes and show that CC subregion volumes are heritable. We found that distinct genetic factors are involved in the development of anterior and posterior subregions, consistent with their divergent functional specialisation. Significant genetic correlation between volumes of the CC and BD, drinks per week, MDD and cannabis consumption subregion volumes with psychiatric traits is noteworthy and deserving of further investigation.

Mind the gap: Performance metric evaluation in brain-age prediction.

Estimating age based on neuroimaging-derived data has become a popular approach to developing markers for brain integrity and health. While a variety of machine-learning algorithms can provide accurate predictions of age based on brain characteristics, there is significant variation in model accuracy reported across studies. We predicted age in two population-based datasets, and assessed the effects of age range, sample size and age-bias correction on the model performance metrics Pearson's correlation coefficient (r), the coefficient of determination (R2 ), Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). The results showed that these metrics vary considerably depending on cohort age range; r and R2 values are lower when measured in samples with a narrower age range. RMSE and MAE are also lower in samples with a narrower age range due to smaller errors/brain age delta values when predictions are closer to the mean age of the group. Across subsets with different age ranges, performance metrics improve with increasing sample size. Performance metrics further vary depending on prediction variance as well as mean age difference between training and test sets, and age-bias corrected metrics indicate high accuracy-also for models showing poor initial performance. In conclusion, performance metrics used for evaluating age prediction models depend on cohort and study-specific data characteristics, and cannot be directly compared across different studies. Since age-bias corrected metrics generally indicate high accuracy, even for poorly performing models, inspection of uncorrected model results provides important information about underlying model attributes such as prediction variance.

Genetic control of variability in subcortical and intracranial volumes.

Sensitivity to external demands is essential for adaptation to dynamic environments, but comes at the cost of increased risk of adverse outcomes when facing poor environmental conditions. Here, we apply a novel methodology to perform genome-wide association analysis of mean and variance in ten key brain features (accumbens, amygdala, caudate, hippocampus, pallidum, putamen, thalamus, intracranial volume, cortical surface area, and cortical thickness), integrating genetic and neuroanatomical data from a large lifespan sample (n = 25,575 individuals; 8-89 years, mean age 51.9 years). We identify genetic loci associated with phenotypic variability in thalamus volume and cortical thickness. The variance-controlling loci involved genes with a documented role in brain and mental health and were not associated with the mean anatomical volumes. This proof-of-principle of the hypothesis of a genetic regulation of brain volume variability contributes to establishing the genetic basis of phenotypic variance (i.e., heritability), allows identifying different degrees of brain robustness across individuals, and opens new research avenues in the search for mechanisms controlling brain and mental health.

Replicating extensive brain structural heterogeneity in individuals with schizophrenia and bipolar disorder.

Identifying brain processes involved in the risk and development of mental disorders is a major aim. We recently reported substantial interindividual heterogeneity in brain structural aberrations among patients with schizophrenia and bipolar disorder. Estimating the normative range of voxel-based morphometry (VBM) data among healthy individuals using a Gaussian process regression (GPR) enables us to map individual deviations from the healthy range in unseen datasets. Here, we aim to replicate our previous results in two independent samples of patients with schizophrenia (n1 = 94; n2 = 105), bipolar disorder (n1 = 116; n2 = 61), and healthy individuals (n1 = 400; n2 = 312). In line with previous findings with exception of the cerebellum our results revealed robust group level differences between patients and healthy individuals, yet only a small proportion of patients with schizophrenia or bipolar disorder exhibited extreme negative deviations from normality in the same brain regions. These direct replications support that group level-differences in brain structure disguise considerable individual differences in brain aberrations, with important implications for the interpretation and generalization of group-level brain imaging findings to the individual with a mental disorder.

Multimodal imaging improves brain age prediction and reveals distinct abnormalities in patients with psychiatric and neurological disorders.

The deviation between chronological age and age predicted using brain MRI is a putative marker of overall brain health. Age prediction based on structural MRI data shows high accuracy in common brain disorders. However, brain aging is complex and heterogenous, both in terms of individual differences and the underlying biological processes. Here, we implemented a multimodal model to estimate brain age using different combinations of cortical area, thickness and sub-cortical volumes, cortical and subcortical T1/T2-weighted ratios, and cerebral blood flow (CBF) based on arterial spin labeling. For each of the 11 models we assessed the age prediction accuracy in healthy controls (HC, n = 750) and compared the obtained brain age gaps (BAGs) between age-matched subsets of HC and patients with Alzheimer's disease (AD, n = 54), mild (MCI, n = 90) and subjective (SCI, n = 56) cognitive impairment, schizophrenia spectrum (SZ, n = 159) and bipolar disorder (BD, n = 135). We found highest age prediction accuracy in HC when integrating all modalities. Furthermore, two-group case-control classifications revealed highest accuracy for AD using global T1-weighted BAG, while MCI, SCI, BD and SZ showed strongest effects in CBF-based BAGs. Combining multiple MRI modalities improves brain age prediction and reveals distinct deviations in patients with psychiatric and neurological disorders. The multimodal BAG was most accurate in predicting age in HC, while group differences between patients and HC were often larger for BAGs based on single modalities. These findings indicate that multidimensional neuroimaging of patients may provide a brain-based mapping of overlapping and distinct pathophysiology in common disorders.

Brain scans from 21,297 individuals reveal the genetic architecture of hippocampal subfield volumes.

The hippocampus is a heterogeneous structure, comprising histologically distinguishable subfields. These subfields are differentially involved in memory consolidation, spatial navigation and pattern separation, complex functions often impaired in individuals with brain disorders characterized by reduced hippocampal volume, including Alzheimer's disease (AD) and schizophrenia. Given the structural and functional heterogeneity of the hippocampal formation, we sought to characterize the subfields' genetic architecture. T1-weighted brain scans (n = 21,297, 16 cohorts) were processed with the hippocampal subfields algorithm in FreeSurfer v6.0. We ran a genome-wide association analysis on each subfield, co-varying for whole hippocampal volume. We further calculated the single-nucleotide polymorphism (SNP)-based heritability of 12 subfields, as well as their genetic correlation with each other, with other structural brain features and with AD and schizophrenia. All outcome measures were corrected for age, sex and intracranial volume. We found 15 unique genome-wide significant loci across six subfields, of which eight had not been previously linked to the hippocampus. Top SNPs were mapped to genes associated with neuronal differentiation, locomotor behaviour, schizophrenia and AD. The volumes of all the subfields were estimated to be heritable (h2 from 0.14 to 0.27, all p < 1 × 10-16) and clustered together based on their genetic correlations compared with other structural brain features. There was also evidence of genetic overlap of subicular subfield volumes with schizophrenia. We conclude that hippocampal subfields have partly distinct genetic determinants associated with specific biological processes and traits. Taking into account this specificity may increase our understanding of hippocampal neurobiology and associated pathologies.

Maturation of cortical microstructure and cognitive development in childhood and adolescence: A T1w/T2w ratio MRI study.

The restructuring and optimization of the cerebral cortex from early childhood and through adolescence is an essential feature of human brain development, underlying immense cognitive improvements. Beyond established morphometric cortical assessments, the T1w/T2w ratio quantifies partly separate biological processes, and might inform models of typical neurocognitive development and developmental psychopathology. In the present study, we computed vertex-wise T1w/T2w ratio across the cortical surface in 621 youths (3-21 years) sampled from the Pediatric Imaging, Neurocognition, and Genetics (PING) study and tested for associations with individual differences in age, sex, and both general and specific cognitive abilities. The results showed a near global linear age-related increase in T1w/T2w ratio across the brain surface, with a general posterior to anterior increasing gradient in association strength. Moreover, results indicated that boys in late adolescence had regionally higher T1w/T2w ratio as compared to girls. Across individuals, T1w/T2w ratio was negatively associated with general and several specific cognitive abilities mainly within anterior cortical regions. Our study indicates age-related differences in T1w/T2w ratio throughout childhood, adolescence, and young adulthood, in line with the known protracted myelination of the cortex. Moreover, the study supports T1w/T2w ratio as a promising surrogate measure of individual differences in intracortical brain structure in neurodevelopment.

Hippocampal subfield and amygdala nuclei volumes in schizophrenia patients with a history of violence.

Schizophrenia (SCZ) is associated with an increased risk of violence compared to the general population. Previous studies have indicated smaller hippocampal and amygdala volumes in violent than non-violent psychotic patients. However, little is known about volumetric differences at the subdivision level of these structures. In the present study, hippocampal subfields and amygdala nuclei volumes were estimated with FreeSurfer from 3 T MRI of SCZ patients with (SCZ-V, n = 24) and without (SCZ-NV, n = 51) a history of severe violence and 90 healthy controls (HC). Volumetric differences between groups were explored with a general linear model covarying for confounders, in addition to follow-up analyses in patient groups controlling for clinical characteristics such as antipsychotic medication, duration of illness and illicit substance use. SCZ-V had smaller total hippocampal volume and smaller CA1, HATA, fimbria, and molecular layer of DG volumes compared to HC. Total amygdala volume together with basal nucleus, accessory basal nucleus, CTA, and paralaminar nucleus volumes were smaller in SCZ-V compared to HC. In SCZ-NV, compared to HC, the observed smaller volumes were limited to basal and paralaminar nucleus. There were no significant differences in hippocampal subfield and amygdala nuclei volumes between SCZ-V and SCZ-NV. Follow-up analyses showed that the results in patient groups were not affected by clinical characteristics. The results suggest that smaller hippocampal subfield and amygdala nuclei volumes may be relevant to violence risk in SCZ. However, the neurobiological signature of violence in SCZ should be further investigated in larger cohorts.

Resting-state basal ganglia network codes a motor musical skill and its disruption From dystonia.

BACKGROUND: Musician's dystonia critically impacts professional musicians' careers as they may lose musical skills, which have been acquired through long and intensive training. Yet the pathophysiology of musician's dystonia and its link to the neural mechanisms supporting musical skills is poorly understood. We tested if resting-state functional connectivity might reflect an aspect of musical skill linked to the pathophysiology of musician's dystonia. We also tested a second hypothesis that the region with altered resting-state functional connectivity might be correlated with a quantitative measure of musical skills. METHODS: We studied 21 patients with musician's dystonia affecting their hands and 34 healthy musicians, using resting-state functional magnetic resonance imaging and behavioral assessment. We tested between-group differences of resting-state functional connectivity throughout the whole brain using independent component analysis. RESULTS: We found abnormal basal ganglia resting-state functional connectivity in the putamina of patients with musician's dystonia compared with those of healthy musicians (P = 0.035 corrected for multiple comparisons). We also found that the temporal precision of keystrokes was correlated with basal ganglia functional connectivity in the putamina of healthy pianists (r = 0.72, P = 0.0005), but not in pianists with musician's dystonia (r = -0.11, P = 0.64). CONCLUSIONS: We show that abnormalities of the putamen exist even at rest in musician's dystonia, whereas putaminal abnormality has previously been reported during a task. Moreover, basal ganglia resting-state functional connectivity in the putamen represented training levels in healthy musicians, and its disruption was associated with musician's dystonia. This novel finding hints at the pathophysiological mechanisms by which musician's dystonia follows extensive musical training. © 2018 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.

Conduct disorder - a comprehensive exploration of comorbidity patterns, genetic and environmental risk factors.

Conduct disorder (CD), a common mental disorder in children and adolescents, is characterized by antisocial behavior. Despite similarities with antisocial personality disorder (ASPD) and possible diagnostic continuity, CD has been shown to precede a range of adult-onset mental disorders. Additionally, little is known about the putative shared genetic liability between CD and adult-onset mental disorders and the underlying gene-environment interplay. Here, we interrogated comorbidity between CD and other mental disorders from the Norwegian Mother, Father and Child Cohort Study (n = 114 500) and investigated how polygenic risk scores (PRS) for mental health traits were associated with CD/CD traits in childhood and adolescence. Gene-environment interplay patterns for CD was explored with data on bullying and parental education. We found CD to be comorbid with several child and adult-onset mental disorders. This phenotypic overlap corresponded with associations between PRS for mental disorders and CD. Additionally, our findings support an additive gene-environment model. Previously conceptualized as a precursor of ASPD, we found that CD was associated with polygenic risk for several child- and adult-onset mental disorders. High comorbidity of CD with other psychiatric disorders reflected on the genetic level should inform research studies, diagnostic assessments and clinical follow-up of this heterogenous group.

Parental education and income are linked to offspring cortical brain structure and psychopathology at 9-11 years.

Background: A child's socioeconomic environment can shape central aspects of their life, including vulnerability to mental disorders. Negative environmental influences in youth may interfere with the extensive and dynamic brain development occurring at this time. Indeed, there are numerous yet diverging reports of associations between parental socioeconomic status (SES) and child cortical brain morphometry. Most of these studies have used single metric- or unimodal analyses of standard cortical morphometry that downplay the probable scenario where numerous biological pathways in sum account for SES-related cortical differences in youth. Methods: To comprehensively capture such variability, using data from 9758 children aged 8.9-11.1 years from the ABCD Study®, we employed linked independent component analysis (LICA) and fused vertex-wise cortical thickness, surface area, curvature and grey-/white-matter contrast (GWC). LICA revealed 70 uni- and multimodal components. We then assessed the linear relationships between parental education, parental income and each of the cortical components, controlling for age, sex, genetic ancestry, and family relatedness. We also assessed whether cortical structure moderated the negative relationships between parental SES and child general psychopathology. Results: Parental education and income were both associated with larger surface area and higher GWC globally, in addition to local increases in surface area and to a lesser extent bidirectional GWC and cortical thickness patterns. The negative relation between parental income and child psychopathology were attenuated in children with a multimodal pattern of larger frontal- and smaller occipital surface area, and lower medial occipital thickness and GWC. Conclusion: Structural brain MRI is sensitive to SES diversity in childhood, with GWC emerging as a particularly relevant marker together with surface area. In low-income families, having a more developed cortex across MRI metrics, appears beneficial for mental health.

Multimodal workflows optimally predict response to repetitive transcranial magnetic stimulation in patients with schizophrenia: a multisite machine learning analysis.

The response variability to repetitive transcranial magnetic stimulation (rTMS) challenges the effective use of this treatment option in patients with schizophrenia. This variability may be deciphered by leveraging predictive information in structural MRI, clinical, sociodemographic, and genetic data using artificial intelligence. We developed and cross-validated rTMS response prediction models in patients with schizophrenia drawn from the multisite RESIS trial. The models incorporated pre-treatment sMRI, clinical, sociodemographic, and polygenic risk score (PRS) data. Patients were randomly assigned to receive active (N = 45) or sham (N = 47) rTMS treatment. The prediction target was individual response, defined as ≥20% reduction in pre-treatment negative symptom sum scores of the Positive and Negative Syndrome Scale. Our multimodal sequential prediction workflow achieved a balanced accuracy (BAC) of 94% (non-responders: 92%, responders: 95%) in the active-treated group and 50% in the sham-treated group. The clinical, clinical + PRS, and sMRI-based classifiers yielded BACs of 65%, 76%, and 80%, respectively. Apparent sadness, inability to feel, educational attainment PRS, and unemployment were most predictive of non-response in the clinical + PRS model, while grey matter density reductions in the default mode, limbic networks, and the cerebellum were most predictive in the sMRI model. Our sequential modelling approach provided superior predictive performance while minimising the diagnostic burden in the clinical setting. Predictive patterns suggest that rTMS responders may have higher levels of brain grey matter in the default mode and salience networks which increases their likelihood of profiting from plasticity-inducing brain stimulation methods, such as rTMS. The future clinical implementation of our models requires findings to be replicated at the international scale using stratified clinical trial designs.

Clustering Schizophrenia Genes by Their Temporal Expression Patterns Aids Functional Interpretation.

BACKGROUND: Schizophrenia is a highly heritable brain disorder with a typical symptom onset in early adulthood. The 2-hit hypothesis posits that schizophrenia results from differential early neurodevelopment, predisposing an individual, followed by a disruption of later brain maturational processes that trigger the onset of symptoms. STUDY DESIGN: We applied hierarchical clustering to transcription levels of 345 genes previously linked to schizophrenia, derived from cortical tissue samples from 56 donors across the lifespan. We subsequently calculated clustered-specific polygenic risk scores for 743 individuals with schizophrenia and 743 sex- and age-matched healthy controls. STUDY RESULTS: Clustering revealed a set of 183 genes that was significantly upregulated prenatally and downregulated postnatally and 162 genes that showed the opposite pattern. The prenatally upregulated set of genes was functionally annotated to fundamental cell cycle processes, while the postnatally upregulated set was associated with the immune system and neuronal communication. We found an interaction between the 2 scores; higher prenatal polygenic risk showed a stronger association with schizophrenia diagnosis at higher levels of postnatal polygenic risk. Importantly, this finding was replicated in an independent clinical cohort of 3233 individuals. CONCLUSIONS: We provide genetics-based evidence that schizophrenia is shaped by disruptions of separable biological processes acting at distinct phases of neurodevelopment. The modeling of genetic risk factors that moderate each other's effect, informed by the timing of their expression, will aid in a better understanding of the development of schizophrenia.

An evolutionary timeline of the oxytocin signaling pathway.

Oxytocin is a neuropeptide associated with both psychological and somatic processes like parturition and social bonding. Although oxytocin homologs have been identified in many species, the evolutionary timeline of the entire oxytocin signaling gene pathway has yet to be described. Using protein sequence similarity searches, microsynteny, and phylostratigraphy, we assigned the genes supporting the oxytocin pathway to different phylostrata based on when we found they likely arose in evolution. We show that the majority (64%) of genes in the pathway are 'modern'. Most of the modern genes evolved around the emergence of vertebrates or jawed vertebrates (540 - 530 million years ago, 'mya'), including OXTR, OXT and CD38. Of those, 45% were under positive selection at some point during vertebrate evolution. We also found that 18% of the genes in the oxytocin pathway are 'ancient', meaning their emergence dates back to cellular organisms and opisthokonta (3500-1100 mya). The remaining genes (18%) that evolved after ancient and before modern genes were classified as 'medium-aged'. Functional analyses revealed that, in humans, medium-aged oxytocin pathway genes are highly expressed in contractile organs, while modern genes in the oxytocin pathway are primarily expressed in the brain and muscle tissue.