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

First-degree relatives of patients diagnosed with schizophrenia (SZ-FDRs) show similar patterns of brain abnormalities and cognitive alterations to patients, albeit with smaller effect sizes. First-degree relatives of patients diagnosed with bipolar disorder (BD-FDRs) show divergent patterns; on average, intracranial volume is larger compared to controls, and findings on cognitive alterations in BD-FDRs are inconsistent. Here, we performed a meta-analysis of global and regional brain measures (cortical and subcortical), current IQ, and educational attainment in 5,795 individuals (1,103 SZ-FDRs, 867 BD-FDRs, 2,190 controls, 942 schizophrenia patients, 693 bipolar patients) from 36 schizophrenia and/or bipolar disorder family cohorts, with standardized methods. Compared to controls, SZ-FDRs showed a pattern of widespread thinner cortex, while BD-FDRs had widespread larger cortical surface area. IQ was lower in SZ-FDRs (d = -0.42, p = 3 × 10-5 ), with weak evidence of IQ reductions among BD-FDRs (d = -0.23, p = .045). Both relative groups had similar educational attainment compared to controls. When adjusting for IQ or educational attainment, the group-effects on brain measures changed, albeit modestly. Changes were in the expected direction, with less pronounced brain abnormalities in SZ-FDRs and more pronounced effects in BD-FDRs. To conclude, SZ-FDRs and BD-FDRs show a differential pattern of structural brain abnormalities. In contrast, both had lower IQ scores and similar school achievements compared to controls. Given that brain differences between SZ-FDRs and BD-FDRs remain after adjusting for IQ or educational attainment, we suggest that differential brain developmental processes underlying predisposition for schizophrenia or bipolar disorder are likely independent of general cognitive impairment.

The hippocampus consists of anatomically and functionally distinct subfields that may be differentially involved in the pathophysiology of bipolar disorder (BD). Here we, the Enhancing NeuroImaging Genetics through Meta-Analysis Bipolar Disorder workinggroup, study hippocampal subfield volumetry in BD. T1-weighted magnetic resonance imaging scans from 4,698 individuals (BD = 1,472, healthy controls [HC] = 3,226) from 23 sites worldwide were processed with FreeSurfer. We used linear mixed-effects models and mega-analysis to investigate differences in hippocampal subfield volumes between BD and HC, followed by analyses of clinical characteristics and medication use. BD showed significantly smaller volumes of the whole hippocampus (Cohen's d = -0.20), cornu ammonis (CA)1 (d = -0.18), CA2/3 (d = -0.11), CA4 (d = -0.19), molecular layer (d = -0.21), granule cell layer of dentate gyrus (d = -0.21), hippocampal tail (d = -0.10), subiculum (d = -0.15), presubiculum (d = -0.18), and hippocampal amygdala transition area (d = -0.17) compared to HC. Lithium users did not show volume differences compared to HC, while non-users did. Antipsychotics or antiepileptic use was associated with smaller volumes. In this largest study of hippocampal subfields in BD to date, we show widespread reductions in nine of 12 subfields studied. The associations were modulated by medication use and specifically the lack of differences between lithium users and HC supports a possible protective role of lithium in BD.

Bipolar disorders (BDs) are among the leading causes of morbidity and disability. Objective biological markers, such as those based on brain imaging, could aid in clinical management of BD. Machine learning (ML) brings neuroimaging analyses to individual subject level and may potentially allow for their diagnostic use. However, fair and optimal application of ML requires large, multi-site datasets. We applied ML (support vector machines) to MRI data (regional cortical thickness, surface area, subcortical volumes) from 853 BD and 2167 control participants from 13 cohorts in the ENIGMA consortium. We attempted to differentiate BD from control participants, investigated different data handling strategies and studied the neuroimaging/clinical features most important for classification. Individual site accuracies ranged from 45.23% to 81.07%. Aggregate subject-level analyses yielded the highest accuracy (65.23%, 95% CI = 63.47-67.00, ROC-AUC = 71.49%, 95% CI = 69.39-73.59), followed by leave-one-site-out cross-validation (accuracy = 58.67%, 95% CI = 56.70-60.63). Meta-analysis of individual site accuracies did not provide above chance results. There was substantial agreement between the regions that contributed to identification of BD participants in the best performing site and in the aggregate dataset (Cohen's Kappa = 0.83, 95% CI = 0.829-0.831). Treatment with anticonvulsants and age were associated with greater odds of correct classification. Although short of the 80% clinically relevant accuracy threshold, the results are promising and provide a fair and realistic estimate of classification performance, which can be achieved in a large, ecologically valid, multi-site sample of BD participants based on regional neurostructural measures. Furthermore, the significant classification in different samples was based on plausible and similar neuroanatomical features. Future multi-site studies should move towards sharing of raw/voxelwise neuroimaging data.

A common limitation of neuroimaging studies is their small sample sizes. To overcome this hurdle, the Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) Consortium combines neuroimaging data from many institutions worldwide. However, this introduces heterogeneity due to different scanning devices and sequences. ENIGMA projects commonly address this heterogeneity with random-effects meta-analysis or mixed-effects mega-analysis. Here we tested whether the batch adjustment method, ComBat, can further reduce site-related heterogeneity and thus increase statistical power. We conducted random-effects meta-analyses, mixed-effects mega-analyses and ComBat mega-analyses to compare cortical thickness, surface area and subcortical volumes between 2897 individuals with a diagnosis of schizophrenia and 3141 healthy controls from 33 sites. Specifically, we compared the imaging data between individuals with schizophrenia and healthy controls, covarying for age and sex. The use of ComBat substantially increased the statistical significance of the findings as compared to random-effects meta-analyses. The findings were more similar when comparing ComBat with mixed-effects mega-analysis, although ComBat still slightly increased the statistical significance. ComBat also showed increased statistical power when we repeated the analyses with fewer sites. Results were nearly identical when we applied the ComBat harmonization separately for cortical thickness, cortical surface area and subcortical volumes. Therefore, we recommend applying the ComBat function to attenuate potential effects of site in ENIGMA projects and other multi-site structural imaging work. We provide easy-to-use functions in R that work even if imaging data are partially missing in some brain regions, and they can be trained with one data set and then applied to another (a requirement for some analyses such as machine learning).

BackgroundSchizophrenia and bipolar disorder share genetic liability, and some structural brain abnormalities are common to both conditions. First-degree relatives of patients with schizophrenia (FDRs-SZ) show similar brain abnormalities to patients, albeit with smaller effect sizes. Imaging findings in first-degree relatives of patients with bipolar disorder (FDRs-BD) have been inconsistent in the past, but recent studies report regionally greater volumes compared with control subjects.MethodsWe performed a meta-analysis of global and subcortical brain measures of 6008 individuals (1228 FDRs-SZ, 852 FDRs-BD, 2246 control subjects, 1016 patients with schizophrenia, 666 patients with bipolar disorder) from 34 schizophrenia and/or bipolar disorder family cohorts with standardized methods. Analyses were repeated with a correction for intracranial volume (ICV) and for the presence of any psychopathology in the relatives and control subjects.ResultsFDRs-BD had significantly larger ICV (d = +0.16, q < .05 corrected), whereas FDRs-SZ showed smaller thalamic volumes than control subjects (d = -0.12, q < .05 corrected). ICV explained the enlargements in the brain measures in FDRs-BD. In FDRs-SZ, after correction for ICV, total brain, cortical gray matter, cerebral white matter, cerebellar gray and white matter, and thalamus volumes were significantly smaller; the cortex was thinner (d

The analysis of time-varying activity and connectivity patterns (i.e., the chronnectome) using resting-state magnetic resonance imaging has become an important part of ongoing neuroscience discussions. The majority of previous work has focused on variations of temporal coupling among fixed spatial nodes or transition of the dominant activity/connectivity pattern over time. Here, we introduce an approach to capture spatial dynamics within functional domains (FDs), as well as temporal dynamics within and between FDs. The approach models the brain as a hierarchical functional architecture with different levels of granularity, where lower levels have higher functional homogeneity and less dynamic behavior and higher levels have less homogeneity and more dynamic behavior. First, a high-order spatial independent component analysis is used to approximate functional units. A functional unit is a pattern of regions with very similar functional activity over time. Next, functional units are used to construct FDs. Finally, functional modules (FMs) are calculated from FDs, providing an overall view of brain dynamics. Results highlight the spatial fluidity within FDs, including a broad spectrum of changes in regional associations, from strong coupling to complete decoupling. Moreover, FMs capture the dynamic interplay between FDs. Patients with schizophrenia show transient reductions in functional activity and state connectivity across several FDs, particularly the subcortical domain. Activity and connectivity differences convey unique information in many cases (e.g., the default mode) highlighting their complementarity information. The proposed hierarchical model to capture FD spatiotemporal variations provides new insight into the macroscale chronnectome and identifies changes hidden from existing approaches.

BackgroundThe profile of cortical neuroanatomical abnormalities in schizophrenia is not fully understood, despite hundreds of published structural brain imaging studies. This study presents the first meta-analysis of cortical thickness and surface area abnormalities in schizophrenia conducted by the ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) Schizophrenia Working Group.MethodsThe study included data from 4474 individuals with schizophrenia (mean age, 32.3 years; range, 11-78 years; 66% male) and 5098 healthy volunteers (mean age, 32.8 years; range, 10-87 years; 53% male) assessed with standardized methods at 39 centers worldwide.ResultsCompared with healthy volunteers, individuals with schizophrenia have widespread thinner cortex (left/right hemisphere: Cohen's d = -0.530/-0.516) and smaller surface area (left/right hemisphere: Cohen's d = -0.251/-0.254), with the largest effect sizes for both in frontal and temporal lobe regions. Regional group differences in cortical thickness remained significant when statistically controlling for global cortical thickness, suggesting regional specificity. In contrast, effects for cortical surface area appear global. Case-control, negative, cortical thickness effect sizes were two to three times larger in individuals receiving antipsychotic medication relative to unmedicated individuals. Negative correlations between age and bilateral temporal pole thickness were stronger in individuals with schizophrenia than in healthy volunteers. Regional cortical thickness showed significant negative correlations with normalized medication dose, symptom severity, and duration of illness and positive correlations with age at onset.ConclusionsThe findings indicate that the ENIGMA meta-analysis approach can achieve robust findings in clinical neuroscience studies; also, medication effects should be taken into account in future genetic association studies of cortical thickness in schizophrenia.

Background: It is well established that people with an intellectual disability have high rates of mental health problems, yet rates of uptake of services do not match need. Aim: To identify the current literature pertaining to the barriers and facilitators to access to mental health services for people with an intellectual disability. Method: A systematic search identified English-language articles that addressed barriers or enablers to access, mental health services, and intellectual disability from 2005 to 2016. Results were synthesized according to Gulliford et al.'s four dimensions of access: availability, utilization, relevance and effectiveness, and equity. Results: Barriers and enablers were identified across all the dimensions. Organizational barriers, lack of services, and poor-quality services related to deficits in knowledge were among the barriers discussed in the literature. Facilitators included emphasis on interagency collaboration, and training and education. Substantial gaps were also identified, particularly in relation to the lived experience of these barriers. Conclusions: Further research and evaluation across all aspects of access to mental health care for people with an intellectual disability is needed.

Background and Hypotheses Previous studies revealed innate immune system activation in people with schizophrenia (SZ), potentially mediated by endogenous pathogen recognition receptors, notably Toll-like receptors (TLR). TLRs are activated by pathogenic molecules like bacterial lipopolysaccharides (TLR1 and TLR4), viral RNA (TLR3), or both (TLR8). Furthermore, the complement system, another key component of innate immunity, has previously been linked to SZ. Study Design Peripheral mRNA levels of TLR1, TLR3, TLR4, and TLR8 were compared between SZ and healthy controls (HC). We investigated their relationship with immune activation through complement expression and cortical thickness of the cingulate gyrus, a region susceptible to immunological hits. TLR mRNA levels and peripheral complement receptor mRNA were extracted from 86 SZ and 77 HC white blood cells; structural MRI scans were conducted on a subset. Study Results We found significantly higher TLR4 and TLR8 mRNA levels and lower TLR3 mRNA levels in SZ compared to HC. TLRs and complemental factors were significantly associated in SZ and HC, with the strongest deviations of TLR mRNA levels in the SZ subgroup having elevated complement expression. Cortical thickness of the cingulate gyrus was inversely associated with TLR8 mRNA levels in SZ, and with TLR4 and TLR8 levels in HC. Conclusions The study underscores the role of innate immune activation in schizophrenia, indicating a coordinated immune response of TLRs and the complement system. Our results suggest there could be more bacterial influence (based on TLR 4 levels) as opposed to viral influence (based on TLR3 levels) in schizophrenia. Specific TLRs were associated with brain cortical thickness reductions of limbic brain structures. [ABSTRACT FROM AUTHOR]

Programs for early detection of psychosis help identify individuals experiencing emerging psychosis and link them with appropriate services, thereby reducing the duration of untreated psychosis (DUP). The authors used the cascade-of-care framework to identify various care stages between screening and enrollment in coordinated specialty care (CSC) and to determine attrition at each stage, with the goal of identifying points in the referral process that may affect DUP. Project partners included a college counseling center and CSC program. All college students seeking mental health services at a counseling center between 2020 and 2022 (N=1,945) completed the Prodromal Questionnaire–Brief (PQ-B) at intake. Students who met the distress cutoff score were referred for a phone screening. Those who met criteria on the basis of this screening were referred for assessment and possible enrollment into CSC. Six stages in the cascade of care for early detection were identified. Of the students who completed the PQ-B as part of intake (stage 1), 547 (28%) met the PQ-B cutoff score (stage 2). Counselors referred 428 (78%) students who met the PQ-B cutoff score (stage 3), and 212 (50%) of these students completed the phone screening (stage 4). Seventy-two (34%) students completed a CSC eligibility assessment (stage 5), 21 (29%) of whom were enrolled in CSC (stage 6). The cascade-of-care framework helped conceptualize the flow within a program for early psychosis detection in order to identify stages that may contribute to lengthier DUP. Future research is warranted to better understand the factors that contribute to DUP at these stages. [ABSTRACT FROM AUTHOR]

Aim: This exploratory study aimed to examine differences in rates of self and clinician‐reports of trauma in youth at clinical high risk for psychosis (CHR) and whether rates of reporting differed by ethnicity. Methods: Self‐reported history of trauma was collected at intake amongst youth at CHR enrolled in Coordinated Specialty Care (CSC) services (N = 52). A structured chart review was conducted for the same sample to identify clinician‐reported history of trauma throughout treatment in CSC. Results: For all patients, frequency of self‐reported trauma at intake to CSC (56%) was lower compared to clinician‐reports of trauma throughout treatment (85%). Hispanic patients self‐reported trauma at intake (35%) less frequently than non‐Hispanics (69%) (p =.02). No differences were found in clinician reported exposure to trauma by ethnicity throughout treatment. Conclusion: Whilst further research is needed, these findings suggest the need for formalised, repeated, and culturally appropriate assessments of trauma within CSC. [ABSTRACT FROM AUTHOR]

Poor academic performance during childhood predicts later adverse outcomes, and could be targeted for improvement if detected early. This study used population-based record linkage to examine the association between early life risk factors and academic achievement at two different stages of development using two different cohorts: a kindergarten (~age 5 years) and a grade 3 cohort (~age 8 years). Similar factors were predictive of academic performance in both age groups, including positive effects of increasing maternal age and lack of maternal prenatal smoking. Female sex was associated with higher scores for literacy. The results suggest that children with less developed academic skills can be identified earlier, with effective programmes to enhance academic skills needed during the first year of school to enhance subsequent results.

Biological development is driven by a complex dance between nurture and nature, determined not only by the specific features of the interacting genetic and environmental influences but also by the timing of their rendezvous. The initiation of large-scale longitudinal studies, ever-expanding knowledge of genetics, and increasing availability of neuroimaging data to provide endophenotypic bridges between molecules and behavior are beginning to provide some insight into interactions of developmental stage, genes, and the environment, although daunting challenges remain. Prominent amongst these challenges are difficulties in identifying and quantifying relevant environmental factors, discerning the relative contributions to multiply determined outcomes, and the likelihood that brain development is a non-linear dynamic process in which small initial differences may yield large later effects. Age-sensitive mechanisms include developmental changes in gene expression, epigenetic modifications, synaptic arborization/pruning, and maturational improvements in our capacity to seek out environments of our choosing. Greater understanding of how genetic and environmental factors interact differently across ages is an important step toward elucidating the mechanisms by which phenotypes are created--and how they may differ in health and disease. This knowledge may also provide clues to guide the type and timing of interventions to maximize outcomes.

Adolescence is a time of increased divergence between males and females in physical characteristics, behavior, and risk for psychopathology. Here we will review data regarding sex differences in brain structure and function during this period of the lifespan. The most consistent sex difference in brain morphometry is the 9-12% larger brain size that has been reported in males. Individual brain regions that have most consistently been reported as different in males and females include the basal ganglia, hippocampus, and amygdala. Diffusion tensor imaging and magnetization transfer imaging studies have also shown sex differences in white matter development during adolescence. Functional imaging studies have shown different patterns of activation without differences in performance, suggesting male and female brains may use slightly different strategies for achieving similar cognitive abilities. Longitudinal studies have shown sex differences in the trajectory of brain development, with females reaching peak values of brain volumes earlier than males. Although compelling, these sex differences are present as group averages and should not be taken as indicative of relative capacities of males or females. (Contains 1 figure.)

Variation in the number of sex chromosomes is a relatively common genetic condition, affecting as many as 1/400 individuals. The sex chromosome aneuploidies (SCAs) are associated with characteristic behavioral and cognitive phenotypes, although the degree to which specific individuals are affected can fall within a wide range. Understanding the effects of different dosages of sex chromosome genes on brain development may help to understand the basis for functional differences in affected individuals. It may also be informative regarding how sex chromosomes contribute to typical sexual differentiation. Studies of 47,XXY males make up the bulk of the current literature of neuroimaging studies in individuals with supernumerary sex chromosomes, with a few small studies or case reports of the other SCAs. Findings in 47,XXY males typically include decreased gray and white matter volumes, with most pronounced effects in the frontal and temporal lobes. Functional studies have shown evidence of decreased lateralization. Although the hypogonadism typically found in 47,XXY males may contribute to the decreased brain volume, the observation that 47,XXX females also show decreased brain volume in the presence of normal pubertal maturation suggests a possible direct dosage effect of X chromosome genes. Additional X chromosomes, such as in 49,XXXXY males, are associated with more markedly decreased brain volume and increased incidence of white matter hyperintensities. The limited data regarding effects of having two Y chromosomes (47,XYY) do not find significant differences in brain volume, although there are some reports of increased head size. (Contains 1 table and 1 figure.)