Recent work in imaging genetics suggests high levels of genetic overlap within cortical regions for cortical thickness (CT) and surface area (SA). We model this multivariate system of genetic relationships by applying Genomic Structural Equation Modeling (Genomic SEM) and parsimoniously define five genomic brain factors underlying both CT and SA along with a general factor capturing genetic overlap across all brain regions. We validate these factors by demonstrating the generalizability of the model to a semi-independent sample and show that the factors align with biologically and functionally relevant parcellations of the cortex. We apply Stratified Genomic SEM to identify specific categories of genes (e.g., neuronal cell types) that are disproportionately associated with pleiotropy across specific subclusters of brain regions, as indexed by the genomic factors. Finally, we examine genetic associations with psychiatric and cognitive correlates, finding that broad aspects of cognitive function are associated with a general factor for SA and that psychiatric associations are null. These analyses provide key insights into the multivariate genomic architecture of two critical features of the cerebral cortex. <<<

Linn Sofie Sæther , Thor Ueland , Beathe Haatveit , Luigi Angelo Maglanoc , Attila Szabo , Srdjan Djurovic , Pål Aukrust , Daniel Roelfs , Christine Mohn , Monica Bettina Elkjaer Greenwood Ormerod , Trine Vik Lagerberg , Nils Eiel Steen , Ingrid Melle , Ole Andreas Andreassen , Torill Ueland <<<

AbstractA potential relationship between dysregulation of immune/inflammatory pathways and cognitive impairment has been suggested in severe mental illnesses (SMI), such as schizophrenia (SZ) and bipolar (BD) spectrum disorders. However, multivariate relationships between peripheral inflammatory/immune-related markers and cognitive domains are unclear, and many studies do not account for inter-individual variance in both cognitive functioning and inflammatory/immune status. This study aimed to investigate covariance patterns between inflammatory/immune-related markers and cognitive domains and further elucidate heterogeneity in a large SMI and healthy control (HC) cohort (SZ = 343, BD = 289, HC = 770). We applied canonical correlation analysis (CCA) to identify modes of maximum covariation between a comprehensive selection of cognitive domains and inflammatory/immune markers. We found that poor verbal learning and psychomotor processing speed was associated with higher levels of interleukin-18 system cytokines and beta defensin 2, reflecting enhanced activation of innate immunity, a pattern augmented in SMI compared to HC. Applying hierarchical clustering on covariance patterns identified by the CCA revealed a high cognition—low immune dysregulation subgroup with predominantly HC (24% SZ, 45% BD, 74% HC) and a low cognition—high immune dysregulation subgroup predominantly consisting of SMI patients (76% SZ, 55% BD, 26% HC). These subgroups differed in IQ, years of education, age, CRP, BMI (all groups), level of functioning, symptoms and defined daily dose (DDD) of antipsychotics (SMI cohort). Our findings suggest a link between cognitive impairment and innate immune dysregulation in a subset of individuals with severe mental illness. <<<

Linn Sofie Sæther, Thor Ueland, Beathe Haatveit, Luigi Angelo Maglanoc, Attila Szabo, Srdjan Djurovic, Pål Aukrust, Daniel Roelfs, Christine Mohn, Monica Bettina Elkjaer Greenwood Ormerod, Trine Vik Lagerberg, Nils Eiel Steen, Ingrid Melle, Ole Andreas Andreassen, Torill Ueland <<<

A potential relationship between dysregulation of immune/inflammatory pathways and cognitive impairment has been suggested in severe mental illnesses (SMI), such as schizophrenia (SZ) and bipolar (BD) spectrum disorders. However, multivariate relationships between peripheral inflammatory/immune-related markers and cognitive domains are unclear, and many studies do not account for inter-individual variance in both cognitive functioning and inflammatory/immune status. This study aimed to investigate covariance patterns between inflammatory/immune-related markers and cognitive domains and further elucidate heterogeneity in a large SMI and healthy control (HC) cohort (SZ = 343, BD = 289, HC = 770). We applied canonical correlation analysis (CCA) to identify modes of maximum covariation between a comprehensive selection of cognitive domains and inflammatory/immune markers. We found that poor verbal learning and psychomotor processing speed was associated with higher levels of interleukin-18 system cytokines and beta defensin 2, reflecting enhanced activation of innate immunity, a pattern augmented in SMI compared to HC. Applying hierarchical clustering on covariance patterns identified by the CCA revealed a high cognition-low immune dysregulation subgroup with predominantly HC (24% SZ, 45% BD, 74% HC) and a low cognition-high immune dysregulation subgroup predominantly consisting of SMI patients (76% SZ, 55% BD, 26% HC). These subgroups differed in IQ, years of education, age, CRP, BMI (all groups), level of functioning, symptoms and defined daily dose (DDD) of antipsychotics (SMI cohort). Our findings suggest a link between cognitive impairment and innate immune dysregulation in a subset of individuals with severe mental illness. <<<

Importance: Childhood poverty has been associated with increased internalizing and externalizing problems in adolescence, a period of peak onset for psychiatric problems. The underlying neural mechanisms remain unclear because longitudinal studies of poverty, brain structure, and changes in psychiatric symptoms are lacking.Objective: To examine whether structural differences in cortical regions mediate the association between household poverty and change in psychiatric symptoms in early adolescence.Design, Setting, and Participants: This longitudinal cohort study used baseline and 1-year follow-up data from the Adolescent Brain Cognitive Development Study. Children aged 9 to 10 years in the US were enrolled between September 1, 2016, and October 15, 2018. Data analysis was performed from August 13, 2021, to September 30, 2022.Exposures: Household poverty as measured by income-to-needs ratio, which incorporates family income and adjusts for family size as a percentage of the federal poverty level.Main Outcomes and Measures: Mediators were children's cortical surface area, thickness, and volume, obtained using magnetic resonance imaging. Internalizing and externalizing problems at 1-year follow-up were outcomes measured by maternal report using the Child Behavior Checklist. Analyses were adjusted for baseline psychiatric problems and sociodemographic variables, including sex, race and ethnicity, parental educational level, and study site.Results: Of the 7569 children (mean [SD] age, 9.91 [0.62] years; 3970 boys [52.5%]) included in the analysis, 1042 children (13.8%) lived below the poverty threshold between 2016 and 2018. Poverty was associated with increased externalizing symptoms score at 1-year follow-up (b = 1.57; 95% CI, 1.14-1.99), even after adjustment for baseline externalizing symptoms (b = 0.35; 95% CI, 0.06-0.64). The longitudinal associations of poverty with increases in externalizing problems over time were mediated by reductions in surface area in multiple cortical regions that support executive functioning (middle frontal gyrus), decision-making (lateral orbitofrontal cortex), visual processing (fusiform gyrus), auditory processing (transverse temporal gyrus), and emotion and language processing (superior temporal gyrus).Conclusions and Relevance: The findings of this study suggest that childhood poverty is associated with increases in externalizing problems, but not internalizing problems, over time in early adolescence. This association is mediated by reductions in cortical surface area across numerous brain regions. These findings highlight potential neurobiological mechanisms underlying the link between poverty and the emergence of externalizing problems during early adolescence. <<<

Tao Zhong , Jingkuan Wei , Kunhua Wu , Liangjun Chen , Fenqiang Zhao , Yuchen Pei , Ya Wang , Hongjiang Zhang , Zhengwang Wu , Ying Huang , Tengfei Li , Li Wang , Yongchang Chen , Weizhi Ji , Yu Zhang , Gang Li , Yuyu Niu <<<

Longitudinal brain imaging atlases with densely sampled time-points and ancillary anatomical information are of fundamental importance in studying early developmental characteristics of human and non-human primate brains during infancy, which feature extremely dynamic imaging appearance, brain shape and size. However, for non-human primates, which are highly valuable animal models for understanding human brains, the existing brain atlases are mainly developed based on adults or adolescents, denoting a notable lack of temporally densely-sampled atlases covering the dynamic early brain development. To fill this critical gap, in this paper, we construct a comprehensive set of longitudinal brain atlases and associated tissue probability maps (gray matter, white matter, and cerebrospinal fluid) with totally 12 time-points from birth to 4 years of age (i.e., 1, 2, 3, 4, 5, 6, 9, 12, 18, 24, 36, and 48 months of age) based on 175 longitudinal structural MRI scans from 39 typically-developing cynomolgus macaques, by leveraging state-of-the-art computational techniques tailored for early developing brains. Furthermore, to facilitate region-based analysis using our atlases, we also provide two popular hierarchy parcellations, i.e., cortical hierarchy maps (6 levels) and subcortical hierarchy maps (6 levels), on our longitudinal macaque brain atlases. These early developing atlases, which have the densest time-points during infancy (to the best of our knowledge), will greatly facilitate the studies of macaque brain development. <<<

Multi-atlas segmentation (MAS) is a promising framework for medical image segmentation. Generally, MAS methods register multiple atlases, i.e., medical images with corresponding labels, to a target image; and the transformed atlas labels can be combined to generate target segmentation via label fusion schemes. Many conventional MAS methods employed the atlases from the same modality as the target image. However, the number of atlases with the same modality may be limited or even missing in many clinical applications. Besides, conventional MAS methods suffer from the computational burden of registration or label fusion procedures. In this work, we design a novel cross-modality MAS framework, which uses available atlases from a certain modality to segment a target image from another modality. To boost the computational efficiency of the framework, both the image registration and label fusion are achieved by well-designed deep neural networks. For the atlas-to-target image registration, we propose a bi-directional registration network (BiRegNet), which can efficiently align images from different modalities. For the label fusion, we design a similarity estimation network (SimNet), which estimates the fusion weight of each atlas by measuring its similarity to the target image. SimNet can learn multi-scale information for similarity estimation to improve the performance of label fusion. The proposed framework was evaluated by the left ventricle and liver segmentation tasks on the MM-WHS and CHAOS datasets, respectively. Results have shown that the framework is effective for cross-modality MAS in both registration and label fusion. <<<

Importance: Certain psychiatric and immune-related disorders are reciprocal risk factors. However, the nature of these associations is unclear.Objective: To characterize the pleiotropy between psychiatric and immune-related traits, as well as risk factors of hypothesized relevance.Design, Setting, and Participants: This genetic association study was conducted from July 10, 2020, to January 15, 2022. Analyses used genome-wide association (GWA) statistics related to 14 psychiatric traits; 13 immune-related phenotypes, ie, allergic, autoimmune, and inflammatory disorders; and 15 risk factors related to health-related behaviors, social determinants of health, and stress response. Genetically correlated psychiatric-immune pairs were assessed using 2-sample mendelian randomization (MR) with sensitivity analyses and multivariable adjustment for genetic associations of third variables. False discovery rate correction (Q value < .05) was applied for each analysis.Exposures: Genetic associations.Main Outcomes and Measures: Genetic correlations and MR association estimates with SEs and P values. A data-driven approach was used that did not test a priori planned hypotheses.Results: A total of 44 genetically correlated psychiatric-immune pairs were identified, including 31 positive correlations (most consistently involving asthma, Crohn disease, hypothyroidism, and ulcerative colitis) and 13 negative correlations (most consistently involving allergic rhinitis and type 1 diabetes). Correlations with third variables were especially strong for psychiatric phenotypes. MR identified 7 associations of psychiatric phenotypes on immune-related phenotypes that were robust to multivariable adjustment, including the positive association of (1) the psychiatric cross-disorder phenotype with asthma (odds ratio [OR], 1.04; 95% CI, 1.02-1.06), Crohn disease (OR, 1.09; 95% CI, 1.05-1.14), and ulcerative colitis (OR, 1.09; 95% CI, 1.05-1.14); (2) major depression with asthma (OR, 1.25; 95% CI, 1.13-1.37); (3) schizophrenia with Crohn disease (OR, 1.12; 95% CI, 1.05-1.18) and ulcerative colitis (OR, 1.14; 95% CI, 1.07-1.21); and a negative association of risk tolerance with allergic rhinitis (OR, 0.77; 95% CI, 0.67-0.92).Conclusions and Relevance: Results of this genetic association study suggest that genetic liability for psychiatric disorders was associated with liability for several immune disorders, suggesting that vertical pleiotropy related to behavioral traits (or correlated third variables) contributes to clinical associations observed in population-scale data. <<<

BACKGROUND: Although the American Academy of Sleep Medicine suggests at least 9 h of sleep per day for 6-12-year-olds, children in recent generations often report sleeping less than this amount. Because early adolescence is a crucial period for neurocognitive development, we aimed to investigate how insufficient sleep affects children's mental health, cognition, brain function, and brain structure over 2 years.METHODS: In this propensity score matched, longitudinal, observational cohort study, we obtained data from a population-based sample of 9-10-year-olds from 21 US study sites in the ongoing Adolescent Brain Cognitive Development (ABCD) study. Participants were categorised as having sufficient sleep or insufficient sleep on the basis of a cutoff of 9 h sleep per day. Using propensity score matching, we matched these two groups of participants on 11 key covariates, including sex, socioeconomic status, and puberty status. Participants were excluded from our analysis if they did not pass a baseline resting-state functional MRI quality check or had missing data for the covariates involved in propensity score matching. Outcome measures retrieved from the ABCD study were behavioural problems, mental health, cognition, and structural and resting-state functional brain measures, assessed at baseline and at 2-year follow-up. We examined group differences on these outcomes over those 2 years among all eligible participants. We then did mediation analyses of the neural correlates of behavioural changes induced by insufficient sleep.FINDINGS: Between Sept 1, 2016, and Oct 15, 2018, 11 878 individuals had baseline data collected for the ABCD study, of whom 8323 were eligible and included in this study (4142 participants in the sufficient sleep group and 4181 in the insufficient sleep group). Follow-up data were collected from July 30, 2018, to Jan 15, 2020. We identified 3021 matched sufficient sleep-insufficient sleep pairs at baseline and 749 matched pairs at 2-year follow-up, and observed similar differences between the groups in behaviour and neural measures at both timepoints; the effect sizes of between-group differences in behavioural measures at these two timepoints were significantly correlated with each other (r=0·85, 95% CI 0·73-0·92; p<0·0001). A similar pattern was observed in resting-state functional connectivity (r=0·54, 0·45-0·61; p<0·0001) and in structural measures (eg, in grey matter volume r=0·61, 0·51-0·69; p<0·0001). We found that cortico-basal ganglia functional connections mediate the effects of insufficient sleep on depression, thought problems, and crystallised intelligence, and that structural properties of the anterior temporal lobe mediate the effect of insufficient sleep on crystallised intelligence.INTERPRETATION: These results provide population-level evidence for the long-lasting effect of insufficient sleep on neurocognitive development in early adolescence. These findings highlight the value of early sleep intervention to improve early adolescents' long-term developmental outcomes.FUNDING: National Institutes of Health. <<<