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Abstract Background Minor physical anomalies (MPAs) are congenital morphological abnormalities linked to disruptions of fetal development. MPAs are common in 22q11.2 deletion syndrome (22q11DS) and psychosis spectrum disorders (PS) and likely represent a disruption of early embryologic development that may help identify overlapping mechanisms linked to psychosis in these disorders. Methods Here, 2D digital photographs were collected from 22q11DS (n = 150), PS (n = 55), and typically developing (TD; n = 93) individuals. Photographs were analyzed using two computer-vision techniques: (1) DeepGestalt algorithm (Face2Gene (F2G)) technology to identify the presence of genetically mediated facial disorders, and (2) Emotrics—a semi-automated machine learning technique that localizes and measures facial features. Results F2G reliably identified patients with 22q11DS; faces of PS patients were matched to several genetic conditions including FragileX and 22q11DS. PCA-derived factor loadings of all F2G scores indicated unique and overlapping facial patterns that were related to both 22q11DS and PS. Regional facial measurements of the eyes and nose were smaller in 22q11DS as compared to TD, while PS showed intermediate measurements. Conclusions The extent to which craniofacial dysmorphology 22q11DS and PS overlapping and evident before the impairment or distress of sub-psychotic symptoms may allow us to identify at-risk youths more reliably and at an earlier stage of development.

Abstract Human cortical maturation has been posited to be organized along the sensorimotor-association axis, a hierarchical axis of brain organization that spans from unimodal sensorimotor cortices to transmodal association cortices. Here, we investigate the hypothesis that the development of functional connectivity during childhood through adolescence conforms to the cortical hierarchy defined by the sensorimotor-association axis. We tested this pre-registered hypothesis in four large-scale, independent datasets (total n = 3355; ages 5–23 years): the Philadelphia Neurodevelopmental Cohort (n = 1207), Nathan Kline Institute-Rockland Sample (n = 397), Human Connectome Project: Development (n = 625), and Healthy Brain Network (n = 1126). Across datasets, the development of functional connectivity systematically varied along the sensorimotor-association axis. Connectivity in sensorimotor regions increased, whereas connectivity in association cortices declined, refining and reinforcing the cortical hierarchy. These consistent and generalizable results establish that the sensorimotor-association axis of cortical organization encodes the dominant pattern of functional connectivity development.

Abstract Congenital heart disease (CHD) affecting the conotruncal region of the heart, occurs in 40–50% of patients with 22q11.2 deletion syndrome (22q11.2DS). This syndrome is a rare disorder with relative genetic homogeneity that can facilitate identification of genetic modifiers. Haploinsufficiency of TBX1, encoding a T-box transcription factor, is one of the main genes responsible for the etiology of the syndrome. We suggest that genetic modifiers of conotruncal defects in patients with 22q11.2DS may be in the TBX1 gene network. To identify genetic modifiers, we analyzed rare, predicted damaging variants in whole genome sequence of 456 cases with conotruncal defects and 537 controls, with 22q11.2DS. We then performed gene set approaches and identified chromatin regulatory genes as modifiers. Chromatin genes with recurrent damaging variants include EP400, KAT6A, KMT2C, KMT2D, NSD1, CHD7 and PHF21A. In total, we identified 37 chromatin regulatory genes, that may increase risk for conotruncal heart defects in 8.5% of 22q11.2DS cases. Many of these genes were identified as risk factors for sporadic CHD in the general population. These genes are co-expressed in cardiac progenitor cells with TBX1, suggesting that they may be in the same genetic network. The genes KAT6A, KMT2C, CHD7 and EZH2, have been previously shown to genetically interact with TBX1 in mouse models. Our findings indicate that disturbance of chromatin regulatory genes impact the TBX1 gene network serving as genetic modifiers of 22q11.2DS and sporadic CHD, suggesting that there are some shared mechanisms involving the TBX1 gene network in the etiology of CHD.

Summary: During adolescence, the brain undergoes extensive changes in white matter structure that support cognition. Data-driven approaches applied to cortical surface properties have led the field to understand brain development as a spatially and temporally coordinated mechanism that follows hierarchically organized gradients of change. Although white matter development also appears asynchronous, previous studies have relied largely on anatomical tract-based atlases, precluding a direct assessment of how white matter structure is spatially and temporally coordinated. Harnessing advances in diffusion modeling and machine learning, we identified 14 data-driven patterns of covarying white matter structure in a large sample of youth. Fiber covariance networks aligned with known major tracts, while also capturing distinct patterns of spatial covariance across distributed white matter locations. Most networks showed age-related increases in fiber network properties, which were also related to developmental changes in executive function. This study delineates data-driven patterns of white matter development that support cognition.

Summary: Human brain size changes dynamically through early development, peaks in adolescence, and varies up to 2-fold among adults. However, the molecular genetic underpinnings of interindividual variation in brain size remain unknown. Here, we leveraged postmortem brain RNA sequencing and measurements of brain weight (BW) in 2,531 individuals across three independent datasets to identify 928 genome-wide significant associations with BW. Genes associated with higher or lower BW showed distinct neurodevelopmental trajectories and spatial patterns that mapped onto functional and cellular axes of brain organization. Expression of BW genes was predictive of interspecies differences in brain size, and bioinformatic annotation revealed enrichment for neurogenesis and cell-cell communication. Genome-wide, transcriptome-wide, and phenome-wide association analyses linked BW gene sets to neuroimaging measurements of brain size and brain-related clinical traits. Cumulatively, these results represent a major step toward delineating the molecular pathways underlying human brain size variation in health and disease.

Background: Impairment in intrinsic motivation (IM), the drive to satisfy internal desires like mastery, may play a key role in disability in psychosis. However, we have limited knowledge regarding relative impairments in IM compared to extrinsic motivation (EM) or general motivation (GM), in part due to limitations in existing measures. Methods: Here we address this gap using a novel Trait Intrinsic and Extrinsic Motivation self-report scale in a sample of n = 243 participants including those with schizophrenia, psychosis-risk, and healthy controls. Each of the 7 IM and 6 EM items used a 7-point Likert scale assessing endorsement of dispositional statements. Bifactor analyses of these items yielded distinct IM, EM, and GM factor scores. Convergent and discriminant validity were examined in relation to General Causality Orientation Scale (GCOS-CP) and Quality of Life 3-item IM measure (QLS-IM). Utility was assessed in relation to psychosis-spectrum (PS) status and CAINS clinical amotivation. Results: IM and EM showed acceptable inter-item consistency (IM: α = 0.88; EM: α = 0.66); the bifactor model exhibited fit that varied from good to borderline to inadequate depending on the specific fit metric (SRMR = 0.038, CFI = 0.94, RMSEA = 0.106 ± 0.014). IM scores correlated with established IM measures: GCOS-CP Autonomy (rho = 0.38, p 0.05). Two-year stability in an available longitudinal subset (n = 35) was strong (IM: rho = 0.64, p

Delay discounting is a measure of impulsive choice relevant in adolescence as it predicts many real-life outcomes, including obesity and academic achievement. However, resting-state functional networks underlying individual differences in delay discounting during youth remain incompletely described. Here we investigate the association between multivariate patterns of functional connectivity and individual differences in impulsive choice in a large sample of children, adolescents, and adults. A total of 293 participants (9–23 years) completed a delay discounting task and underwent 3T resting-state fMRI. A connectome-wide analysis using multivariate distance-based matrix regression was used to examine whole-brain relationships between delay discounting and functional connectivity. These analyses revealed that individual differences in delay discounting were associated with patterns of connectivity emanating from the left dorsal prefrontal cortex, a default mode network hub. Greater delay discounting was associated with greater functional connectivity between the dorsal prefrontal cortex and other default mode network regions, but reduced connectivity with regions in the dorsal and ventral attention networks. These results suggest delay discounting in children, adolescents, and adults is associated with individual differences in relationships both within the default mode network and between the default mode and networks involved in attentional and cognitive control.

ABSTRACT: Diffusion MRI is the dominant non-invasive imaging method used to characterize white matter organization in health and disease. Increasingly, fiber-specific properties within a voxel are analyzed using fixels. While tools for conducting statistical analyses of fixel-wise data exist, currently available tools support only a limited number of statistical models. Here we introduce ModelArray, an R package for mass-univariate statistical analysis of fixel-wise data. At present, ModelArray supports linear models as well as generalized additive models (GAMs), which are particularly useful for studying nonlinear effects in lifespan data. In addition, ModelArray also aims for scalable analysis. With only several lines of code, even large fixel-wise datasets can be analyzed using a standard personal computer. Detailed memory profiling revealed that ModelArray required only limited memory even for large datasets. As an example, we applied ModelArray to fixel-wise data derived from diffusion images acquired as part of the Philadelphia Neurodevelopmental Cohort (n = 938). ModelArray revealed anticipated nonlinear developmental effects in white matter. Moving forward, ModelArray is supported by an open-source software development model that can incorporate additional statistical models and other imaging data types. Taken together, ModelArray provides a flexible and efficient platform for statistical analysis of fixel-wise data.

Studies of brain network development typically focus on a single scale. Here, the authors derived personalized functional networks across scales, and find that network development systematically adheres to and strengthens hierarchical cortical organization.

Background: Impulse control behaviors (ICB) are frequently observed in patients with Parkinson’s disease (PD) and are characterized by compulsive and repetitive behavior resulting from the inability to resist internal drives. Objectives: In this study, we aimed to provide a better understanding of structural and functional brain alterations and clinical parameters related to ICB in PD patients. Methods: We utilized a dataset from the Parkinson’s Progression Markers Initiative including 36 patients with ICB (PDICB+) compared to 76 without ICB (PDICB-) and 61 healthy controls (HC). Using multimodal MRI data we assessed gray matter brain volume, white matter integrity, and graph topological properties at rest. Results: Compared with HC, PDICB+ showed reduced gray matter volume in the bilateral superior and middle temporal gyrus and in the right middle occipital gyrus. Compared with PDICB-, PDICB+ showed volume reduction in the left anterior insula. Depression and anxiety were more prevalent in PDICB+ than in PDICB- and HC. In PDICB+, lower gray matter volume in the precentral gyrus and medial frontal cortex, and higher axial diffusivity in the superior corona radiata were related to higher depression score. Both PD groups showed disrupted functional topological network pattern within the cingulate cortex compared with HC. PDICB+ vs PDICB- displayed reduced topological network pattern in the anterior cingulate cortex, insula, and nucleus accumbens. Conclusions: Our results suggest that structural alterations in the insula and abnormal topological connectivity pattern in the salience network and the nucleus accumbens may lead to impaired decision making and hypersensitivity towards reward in PDICB+. Moreover, PDICB+ are more prone to suffer from depression and anxiety.

Abstract The goal of this study was to compare brain structure between individuals with generalized anxiety disorder (GAD) and healthy controls. Previous studies have generated inconsistent findings, possibly due to small sample sizes, or clinical/analytic heterogeneity. To address these concerns, we combined data from 28 research sites worldwide through the ENIGMA-Anxiety Working Group, using a single, pre-registered mega-analysis. Structural magnetic resonance imaging data from children and adults (5–90 years) were processed using FreeSurfer. The main analysis included the regional and vertex-wise cortical thickness, cortical surface area, and subcortical volume as dependent variables, and GAD, age, age-squared, sex, and their interactions as independent variables. Nuisance variables included IQ, years of education, medication use, comorbidities, and global brain measures. The main analysis (1020 individuals with GAD and 2999 healthy controls) included random slopes per site and random intercepts per scanner. A secondary analysis (1112 individuals with GAD and 3282 healthy controls) included fixed slopes and random intercepts per scanner with the same variables. The main analysis showed no effect of GAD on brain structure, nor interactions involving GAD, age, or sex. The secondary analysis showed increased volume in the right ventral diencephalon in male individuals with GAD compared to male healthy controls, whereas female individuals with GAD did not differ from female healthy controls. This mega-analysis combining worldwide data showed that differences in brain structure related to GAD are small, possibly reflecting heterogeneity or those structural alterations are not a major component of its pathophysiology.

With the increasing availability of neuroimaging data from multiple modalities—each providing a different lens through which to study brain structure or function—new techniques for comparing, integrating, and interpreting information within and across modalities have emerged. Recent developments include hypothesis tests of associations between neuroimaging modalities, which can be used to determine the statistical significance of intermodal associations either throughout the entire brain or within anatomical subregions or functional networks. While these methods provide a crucial foundation for inference on intermodal relationships, they cannot be used to answer questions about where in the brain these associations are most pronounced. In this paper, we introduce a new method, called CLEAN-R, that can be used both to test intermodal correspondence throughout the brain and also to localize this correspondence. Our method involves first adjusting for the underlying spatial autocorrelation structure within each modality before aggregating information within small clusters to construct a map of enhanced test statistics. Using structural and functional magnetic resonance imaging data from a subsample of children and adolescents from the Philadelphia Neurodevelopmental Cohort, we conduct simulations and data analyses where we illustrate the high statistical power and nominal type I error levels of our method. By constructing an interpretable map of group-level correspondence using spatially-enhanced test statistics, our method offers insights beyond those provided by earlier methods.

The Brain Imaging Data Structure (BIDS) is a specification accompanied by a software ecosystem that was designed to create reproducible and automated workflows for processing neuroimaging data. BIDS Apps flexibly build workflows based on the metadata detected in a dataset. However, even BIDS valid metadata can include incorrect values or omissions that result in inconsistent processing across sessions. Additionally, in large-scale, heterogeneous neuroimaging datasets, hidden variability in metadata is difficult to detect and classify. To address these challenges, we created a Python-based software package titled “Curation of BIDS” (CuBIDS), which provides an intuitive workflow that helps users validate and manage the curation of their neuroimaging datasets. CuBIDS includes a robust implementation of BIDS validation that scales to large samples and incorporates DataLad––a version control software package for data––as an optional dependency to ensure reproducibility and provenance tracking throughout the entire curation process. CuBIDS provides tools to help users perform quality control on their images’ metadata and identify unique combinations of imaging parameters. Users can then execute BIDS Apps on a subset of participants that represent the full range of acquisition parameters that are present, accelerating pipeline testing on large datasets.

Abstract Background Pathways leading to psychosis in 22q11.2 deletion syndrome (22q11.2DS) have been the focus of intensive research during the last two decades. One of the common clinical risk factors for the evolution of psychosis in 22q11.2DS is the presence of positive and negative subthreshold psychotic symptoms. The gold standard for measuring subthreshold symptoms is the Structured Interview for Prodromal Syndromes (SIPS) and its accompanying Scale of Prodromal Symptoms (SOPS) ratings. Although the scale has been used by many centers studying 22q11.2DS, the inter-site reliability of the scale in this population has never been established. Methods In the present study, experienced clinical assessors from three large international centers studying 22q11.2DS independently rated video recordings of 18 adolescents and young adults with 22q11.2DS. Results The intraclass correlations coefficients (ICCs) among three raters for the SOPS total scores, as well as for the positive, negative, and disorganization subscale scores, were good-to-excellent (ICCs range 0.73–0.93). The raters were also able to reliably determine the subjects’ subthreshold syndrome status (ICC = 0.71). The reliability of individual items was good-to-excellent for all items, ranging from 0.61 for motor disturbances [G3] to 0.95 for bizarre thinking. Conclusions Our results show that trained clinicians can reliably screen for subthreshold psychotic symptoms in individuals with 22q11.2DS. To increase assessment reliability, we suggest specific clarifications and simplifications to the standard SIPS interview for future studies.

Abstract Computerized natural language processing (NLP) allows for objective and sensitive detection of speech disturbance, a hallmark of schizophrenia spectrum disorders (SSD). We explored several methods for characterizing speech changes in SSD (n = 20) compared to healthy control (HC) participants (n = 11) and approached linguistic phenotyping on three levels: individual words, parts-of-speech (POS), and sentence-level coherence. NLP features were compared with a clinical gold standard, the Scale for the Assessment of Thought, Language and Communication (TLC). We utilized Bidirectional Encoder Representations from Transformers (BERT), a state-of-the-art embedding algorithm incorporating bidirectional context. Through the POS approach, we found that SSD used more pronouns but fewer adverbs, adjectives, and determiners (e.g., “the,” “a,”). Analysis of individual word usage was notable for more frequent use of first-person singular pronouns among individuals with SSD and first-person plural pronouns among HC. There was a striking increase in incomplete words among SSD. Sentence-level analysis using BERT reflected increased tangentiality among SSD with greater sentence embedding distances. The SSD sample had low speech disturbance on average and there was no difference in group means for TLC scores. However, NLP measures of language disturbance appear to be sensitive to these subclinical differences and showed greater ability to discriminate between HC and SSD than a model based on clinical ratings alone. These intriguing exploratory results from a small sample prompt further inquiry into NLP methods for characterizing language disturbance in SSD and suggest that NLP measures may yield clinically relevant and informative biomarkers.

Abstract Psychopathology is rooted in neurodevelopment. However, clinical and biological heterogeneity, together with a focus on case-control approaches, have made it difficult to link dimensions of psychopathology to abnormalities of neurodevelopment. Here, using the Philadelphia Neurodevelopmental Cohort, we built normative models of cortical volume and tested whether deviations from these models better predicted psychiatric symptoms compared to raw cortical volume. Specifically, drawing on the p-factor hypothesis, we distilled 117 clinical symptom measures into six orthogonal psychopathology dimensions: overall psychopathology, anxious-misery, externalizing disorders, fear, positive psychosis symptoms, and negative psychosis symptoms. We found that multivariate patterns of deviations yielded improved out-of-sample prediction of psychopathology dimensions compared to multivariate patterns of raw cortical volume. We also found that correlations between overall psychopathology and deviations in ventromedial prefrontal, inferior temporal, and dorsal anterior cingulate cortices were stronger than those observed for specific dimensions of psychopathology (e.g., anxious-misery). Notably, these same regions are consistently implicated in a range of putatively distinct disorders. Finally, we performed conventional case-control comparisons of deviations in a group of individuals with depression and a group with attention-deficit hyperactivity disorder (ADHD). We observed spatially overlapping effects between these groups that diminished when controlling for overall psychopathology. Together, our results suggest that modeling cortical brain features as deviations from normative neurodevelopment improves prediction of psychiatric symptoms in out-of-sample testing, and that p-factor models of psychopathology may assist in separating biomarkers that are disorder-general from those that are disorder-specific.

Summary: Polygenic scores (PGS) are commonly evaluated in terms of their predictive accuracy at the population level by the proportion of phenotypic variance they explain. To be useful for precision medicine applications, they also need to be evaluated at the individual level when phenotypes are not necessarily already known. We investigated the stability of PGS in European American (EUR) and African American (AFR)-ancestry individuals from the Philadelphia Neurodevelopmental Cohort and the Adolescent Brain Cognitive Development study using different discovery genome-wide association study (GWAS) results for post-traumatic stress disorder (PTSD), type 2 diabetes (T2D), and height. We found that pairs of EUR-ancestry GWAS for the same trait had genetic correlations >0.92. However, PGS calculated from pairs of same-ancestry and different-ancestry GWAS had correlations that ranged from

Abstract Psychotic symptoms are not only an important feature of severe neuropsychiatric disorders, but are also common in the general population, especially in youth. The genetic etiology of psychosis symptoms in youth remains poorly understood. To characterize genetic risk for psychosis spectrum symptoms (PS), we leverage a community-based multiethnic sample of children and adolescents aged 8–22 years, the Philadelphia Neurodevelopmental Cohort (n = 7225, 20% PS). Using an elastic net regression model, we aim to classify PS status using polygenic scores (PGS) based on a range of heritable psychiatric and brain-related traits in a multi-PGS model. We also perform univariate PGS associations and evaluate age-specific effects. The multi-PGS analyses do not improve prediction of PS status over univariate models, but reveal that the attention deficit hyperactivity disorder (ADHD) PGS is robustly and uniquely associated with PS (OR 1.12 (1.05, 1.18) P = 0.0003). This association is driven by subjects of European ancestry (OR = 1.23 (1.14, 1.34), P = 4.15 × 10−7) but is not observed in African American subjects (P = 0.65). We find a significant interaction of ADHD PGS with age (P = 0.01), with a stronger association in younger children. The association is independent of phenotypic overlap between ADHD and PS, not indirectly driven by substance use or childhood trauma, and appears to be specific to PS rather than reflecting general psychopathology in youth. In an independent sample, we replicate an increased ADHD PGS in 328 youth at clinical high risk for psychosis, compared to 216 unaffected controls (OR 1.06, CI(1.01, 1.11), P = 0.02). Our findings suggest that PS in youth may reflect a different genetic etiology than psychotic symptoms in adulthood, one more akin to ADHD, and shed light on how genetic risk can be investigated across early disease trajectories.

Background: Research participant feedback is rarely collected; therefore, investigators have limited understanding regarding stakeholders’ (affected individuals/caregivers) motivation to participate. Members of the Genes to Mental Health Network (G2MH) surveyed stakeholders affected by copy number variants (CNVs) regarding perceived incentives for study participation, opinions concerning research priorities, and the necessity for future funding. Respondents were also asked about feelings of preparedness, research burden, and satisfaction with research study participation. Methods: Modified validated surveys were used to assess stakeholders´ views across three domains: (1) Research Study Enrollment, Retainment, Withdrawal, and Future Participation; (2) Overall Research Experience, Burden, and Preparedness; (3) Research Priorities and Obstacles. Top box score analyses were performed. Results: A total of 704 stakeholders´ responded from 29 countries representing 55 CNVs. The top reasons for initial participation in the research included reasons related to education and altruism. The top reasons for leaving a research study included treatment risks and side effects. The importance of sharing research findings and laboratory results with stakeholders was underscored by participants. Most stakeholders reported positive research experiences. Conclusions: This study provides important insight into how individuals and families affected with a rare CNV feel toward research participation and their overall experience in rare disease research. There are clear targets for areas of improvement for study teams, although many stakeholders reported positive research experiences. Key findings from this international survey may help advance collaborative research and improve the experience of participants, investigators, and other stakeholders moving forward.

Abstract Intelligence quotient (IQ) testing is standard for evaluating cognitive abilities in genomic studies but requires professional expertise in administration and interpretation, and IQ scores do not translate into insights on implicated brain systems that can link genes to behavior. Individuals with 22q11.2 deletion syndrome (22q11.2DS) often undergo IQ testing to address special needs, but access to testing in resource‐limited settings is challenging. The brief Penn Computerized Neurocognitive Battery (CNB) provides measures of cognitive abilities related to brain systems and can screen for cognitive dysfunction. To examine the relation between CNB measures and IQ, we evaluated participants with the 22q11.2DS from Philadelphia and Tel Aviv (N = 117; 52 females; mean age 18.8) who performed both an IQ test and the CNB with a maximum of 5 years between administrations and a subsample (n = 24) who had both IQ and CNB assessments at two time points. We estimated domain‐level CNB scores using exploratory factor analysis (including bifactor for overall scores) and related those scores (intraclass correlations (ICCs)) to the IQ scores. We found that the overall CNB accuracy score showed similar correlations between time 1 and time 2 as IQ (0.775 for IQ and 0.721 for CNB accuracy), correlated well with the IQ scores (ICC = 0.565 and 0.593 for time 1 and time 2, respectively), and correlated similarly with adaptive functioning (0.165 and 0.172 for IQ and CNB, respectively). We provide a crosswalk (from linear equating) between standardized CNB and IQ scores. Results suggest that one can substitute the CNB for IQ testing in future genetic studies that aim to probe specific domains of brain‐behavior relations beyond IQ.

The Coronavirus Disease 2019 (COVID-19) pandemic brought unprecedented socially isolating measures to mitigate the spread of disease, heightening the importance of public outdoor urban greenspace. Here, we investigated the association between tree-rich greenspace and mental health in a large opportunity sample surveyed using a crowdsourcing research website (www.covid19resilience.org) between April 6th and October 12th, 2020 during the pandemic in the United States. Participants living in the U.S. (N = 2,089, 83.1% females, mean age 42 years, age range 18–90 years) were mapped to 1,080 unique ZIP Codes and mean tree canopy density was calculated with a 250 m buffer around each ZIP Code grouped by quartile as a proxy for nearby greenspace. Four mental health parameters were assessed: (1) COVID-19-related worries; (2) anxiety symptoms; (3) depression symptoms; and (4) a standardized and weighted composite mental health score of all three parameters. Multivariate regression analyses with multilevel models were used to study the association between nearby greenspace and the four mental health outcomes, controlling for participant demographics and ZIP Code urbanicity. In the entire cohort, increased nearby greenspace showed a significant protective effect for depression (Coef. = −0.27; p = 0.0499), and composite mental health scores (Coef. = −0.19; p = 0.038) when comparing ZIP Codes with the most greenspace to ZIP Codes with the least, with no observed effects on COVID-19 related worries or anxiety individually. Stratifying by age suggested protective trends of greenspace on mental health in older subsets of the population (top age quartile, over 51 years old) experiencing less depression (Coef. = −0.45; p = 0.048) and lower composite mental health scores (Coef. = −0.34; – = 0.032) as a function of nearby greenspace. Additionally, younger subsets of the population (second youngest age quartile, 31–38 years) experienced lower COVID-19 related worries (Coef. = −1.34; p = 0.022) as a function of greenspace. These findings may indicate that tree-rich greenspace plays a protective role on mental health during the COVID-19 pandemic, specifically in certain age groups, supporting the use of greenspace-related strategies to help mitigate mental health burden during this challenging and isolating time.

Environment (E) is pivotal in explaining variability in brain and behavior development, including suicidal ideation (SI) and behavior. It is therefore critical to systematically study relationships among environmental exposures (i.e., exposome) and suicidal phenotypes. Here, we evaluated the role of individual-level adversity and neighborhood environment and their interaction (E x E) in association with youth SI. Sample included youth (N = 7,054, ages 11–21) from the Philadelphia Neurodevelopmental Cohort, which investigated clinical phenotypes in a diverse US community population. We examined cross-sectional associations of environmental exposures with lifetime history of SI (n = 671), focusing on interactions between individual-level exposures to assaultive trauma (n = 917) and neighborhood-level socioeconomic status (SES) quantified using geocoded Census data. Models included potential confounds and overall psychopathology. Results showed that assaultive trauma was strongly associated with SI (OR = 3.3, 95%CI 2.7–4, p

AbstractBackgroundQuestions remain regarding whether genetic influences on early life psychopathology overlap with cognition and show developmental variation.MethodsUsing data from 9,421 individuals aged 8–21 from the Philadelphia Neurodevelopmental Cohort, factors of psychopathology were generated using a bifactor model of item-level data from a psychiatric interview. Five orthogonal factors were generated: anxious-misery (mood and anxiety), externalizing (attention deficit hyperactivity and conduct disorder), fear (phobias), psychosis-spectrum, and a general factor. Genetic analyses were conducted on a subsample of 4,662 individuals of European American ancestry. A genetic relatedness matrix was used to estimate heritability of these factors, and genetic correlations with executive function, episodic memory, complex reasoning, social cognition, motor speed, and general cognitive ability. Gene × Age analyses determined whether genetic influences on these factors show developmental variation.ResultsExternalizing was heritable (h2 = 0.46, p = 1 × 10−6), but not anxious-misery (h2 = 0.09, p = 0.183), fear (h2 = 0.04, p = 0.337), psychosis-spectrum (h2 = 0.00, p = 0.494), or general psychopathology (h2 = 0.21, p = 0.040). Externalizing showed genetic overlap with face memory (ρg = −0.412, p = 0.004), verbal reasoning (ρg = −0.485, p = 0.001), spatial reasoning (ρg = −0.426, p = 0.010), motor speed (ρg = 0.659, p = 1x10−4), verbal knowledge (ρg = −0.314, p = 0.002), and general cognitive ability (g)(ρg = −0.394, p = 0.002). Gene × Age analyses revealed decreasing genetic variance (γg = −0.146, p = 0.004) and increasing environmental variance (γe = 0.059, p = 0.009) on externalizing.ConclusionsCognitive impairment may be a useful endophenotype of externalizing psychopathology and, therefore, help elucidate its pathophysiological underpinnings. Decreasing genetic variance suggests that gene discovery efforts may be more fruitful in children than adolescents or young adults.

Objective: The limbic system is involved in memory and in processing of emotional stimuli. We measured volume of the hippocampus, amygdala, and thalamus, and assessed their relative contribution to episodic memory and emotion identification in POMS. Method: Sixty-five POMS participants (Mage = 18.3 ± 3.9 years; 48 female (73.8%)), average disease duration = 3.8 ± 3.8 years) and 76 age- and sex-matched controls (Mage = 18.1 ± 4.6 years; 49 female (64.5%)) completed the Penn Computerized Neurocognitive Battery (PCNB); 59 of 65 POMS participants and 69 out of 76 controls underwent 3 T MRI scanning. We derived age-adjusted Z-scores on accuracy and response time (RT) measures of episodic memory and emotion identification of the PCNB. Magnetic resonance imaging (MRI) volumetrics were normalized using the scaling factor computed by SIENAx. On PCNB tests that differed between groups, we used multiple linear regression to assess relationships between regional brain volumes and either episodic memory or emotion identification outcomes controlling for age, sex, accuracy/RT, and parental education. Results: POMS participants were slower and less accurate than controls on the episodic memory domain but did not differ from controls on emotion outcomes. At the subtest level, POMS participants showed reduced accuracy on Word Memory (p = .002) and slower performance on Face Memory (p = .04) subtests. POMS participants had smaller total and regional brain volumes of the hippocampus, amygdala, and thalamus (p values ≤ 0.01). Collapsing across groups, both hippocampal and thalamic volume were significant predictors of Word Memory accuracy; hippocampal volume (B = 0.24, SE = 0.10, p = .02) was more strongly associated with Word Memory performance than thalamic volume (B = 0.16, SE = 0.05, p = .003), though the estimate with was less precise. Conclusions: POMS participants showed reduced episodic memory performance compared to controls. Aspects of episodic memory performance were associated with hippocampal and thalamic volume. Emotion identification was intact, despite volume loss in the amygdala.

Alterations in resting-state functional connectivity (rsFC) have been demonstrated in Posttraumatic Stress Disorder (PTSD). However, such reports have primarily focused on adult participants, whereas findings in adolescents with PTSD are mixed and not entirely consistent with the adult literature. Here, we examined rsFC in a non-treatment seeking adolescent sample with posttraumatic stress symptoms (PTSS; n = 59) relative to asymptomatic controls (n = 226). We also examined differences between trauma-exposed and non-exposed control subgroups (TEC n = 73 and Non-TEC n = 153) to examine alterations associated with more general trauma exposure. Finally, we compared the PTSS and TEC groups, to confirm that the reported alterations in PTSS were not driven by trauma exposure. Using a seed-based approach, we examined connectivity of default-mode (DMN) and salience (SN) networks, where alterations have been previously reported. Results suggest that PTSS are associated with less within-DMN connectivity and greater SN-DMN connectivity, as well as altered connectivity with attention regions. Trauma exposure is associated with greater within-SN connectivity. Additionally, we report findings from exploratory connectome-based analysis, which demonstrate a number of topological alterations within DMN in the PTSS group. Overall, our findings replicate prior reports of altered rsFC in PTSD and extend them to non-treatment seeking, trauma-exposed adolescents, who did or did not report PTSS. They specifically highlight SN-DMN desegregation, lower within-DMN and greater within-SN connectivity, as well as altered connectivity with attention regions, in trauma-exposed adolescents. Future research is required to confirm that adolescents with diagnosed PTSD have similar/exacerbated connectivity patterns.

Co-morbidity and symptom overlap make it difficult to associate psychiatric disorders with unique neural signatures. Here, the authors use a data-driven approach to show that the symptom dimensions of mood, psychosis, fear and externalizing behavior exhibit unique patterns of functional dysconnectivity.

To date, most transcriptome studies of schizophrenia focus on the analysis of protein-coding genes. Long noncoding RNAs (lncRNAs) are emerging as key tissue-specific regulators of cellular and disease processes. The amygdala brain region has been implicated in the pathophysiology of schizophrenia. We performed unbiased whole transcriptome profiling of amygdala tissues from 22 schizophrenia patients and 24 non-psychiatric controls using RNA-seq. We reconstructed amygdala transcriptome and employed systems biology approaches to annotating the functional roles of lncRNAs. As a result, we identified 839 novel lncRNAs in amygdala. We found in amygdala lncRNAs are more subtype-specific than protein-coding genes. We identified functional modules associated with “synaptic transmission”, “ribosome”, and “immune responses” which were related to schizophrenia pathophysiology that involved lncRNAs. Integrative functional analyses associating individual lncRNAs with specific pathways and functions further show that amygdala lncRNAs are connected with all of these pathways. Our study presents the first systematic landscape of lncRNAs in amygdala tissue from schizophrenia cases. Keywords: Schizophrenia, Next-generation sequencing, Transcriptome, Systems biology

With the advent of Big Data Imaging Analytics applied to neuroimaging, datasets from multiple sites need to be pooled into larger samples. However, heterogeneity across different scanners, protocols and populations, renders the task of finding underlying disease signatures challenging. The current work investigates the value of multi-task learning in finding disease signatures that generalize across studies and populations. Herein, we present a multi-task learning type of formulation, in which different tasks are from different studies and populations being pooled together. We test this approach in an MRI study of the neuroanatomy of schizophrenia (SCZ) by pooling data from 3 different sites and populations: Philadelphia, Sao Paulo and Tianjin (50 controls and 50 patients from each site), which posed integration challenges due to variability in disease chronicity, treatment exposure, and data collection. Some existing methods are also tested for comparison purposes. Experiments show that classification accuracy of multi-site data outperformed that of single-site data and pooled data using multi-task feature learning, and also outperformed other comparison methods. Several anatomical regions were identified to be common discriminant features across sites. These included prefrontal, superior temporal, insular, anterior cingulate cortex, temporo-limbic and striatal regions consistently implicated in the pathophysiology of schizophrenia, as well as the cerebellum, precuneus, and fusiform, middle temporal, inferior parietal, postcentral, angular, lingual and middle occipital gyri. These results indicate that the proposed multi-task learning method is robust in finding consistent and reliable structural brain abnormalities associated with SCZ across different sites, in the presence of multiple sources of heterogeneity. Keywords: Multi-task learning, Multi-site classification, Imaging heterogeneity, Sparsity, Schizophrenia, MRI

Human brain development is characterized by an increased control of neural activity, but how this happens is not well understood. Here, authors show that white matter connectivity in 882 youth, aged 8-22, becomes increasingly specialized locally and is optimized for network control.

Abstract Background Neurodevelopmental and neuropsychiatric disorders represent a wide spectrum of heterogeneous yet inter-related disease conditions. The overlapping clinical presentations of these diseases suggest a shared genetic etiology. We aim to identify shared structural variants spanning the spectrum of five neuropsychiatric disorders. Methods We investigated copy number variations (CNVs) in five cohorts, including schizophrenia (SCZ), bipolar disease (BD), autism spectrum disorders (ASD), attention deficit hyperactivity disorder (ADHD), and depression, from 7849 cases and 10,799 controls. CNVs were called based on intensity data from genome-wide SNP arrays and CNV frequency was compared between cases and controls in each disease cohort separately. Meta-analysis was performed via a gene-based approach. Quantitative PCR (qPCR) was employed to validate novel significant loci. Results In our meta-analysis, two genes containing CNVs with exonic overlap reached genome-wide significance threshold of meta P value

Cognitive function evolves significantly over development, enabling flexible control of human behavior. Yet, how these functions are instantiated in spatially distributed and dynamically interacting networks, or graphs, that change in structure from childhood to adolescence is far from understood. Here we applied a novel machine-learning method to track continuously overlapping and time-varying subgraphs in the brain at rest within a sample of 200 healthy youth (ages 8–11 and 19–22) drawn from the Philadelphia Neurodevelopmental Cohort. We uncovered a set of subgraphs that capture surprisingly integrated and dynamically changing interactions among known cognitive systems. We observed that subgraphs that were highly expressed were especially transient, flexibly switching between high and low expression over time. This transience was particularly salient in a subgraph predominantly linking frontoparietal regions of the executive system, which increases in both expression and flexibility from childhood to young adulthood. Collectively, these results suggest that healthy development is accompanied by an increasing precedence of executive networks and a greater switching of the regions and interactions subserving these networks.

One of the most significant effects of neural plasticity manifests in the case of sensory deprivation when cortical areas that were originally specialized for the functions of the deprived sense take over the processing of another modality. Vision and audition represent two important senses needed to navigate through space and time. Therefore, the current systematic review discusses the cross-modal behavioral and neural consequences of deafness and blindness by focusing on spatial and temporal processing abilities, respectively. In addition, movement processing is evaluated as compiling both spatial and temporal information. We examine whether the sense that is not primarily affected changes in its own properties or in the properties of the deprived modality (i.e., temporal processing as the main specialization of audition and spatial processing as the main specialization of vision). References to the metamodal organization, supramodal functioning, and the revised neural recycling theory are made to address global brain organization and plasticity principles. Generally, according to the reviewed studies, behavioral performance is enhanced in those aspects for which both the deprived and the overtaking senses provide adequate processing resources. Furthermore, the behavioral enhancements observed in the overtaking sense (i.e., vision in the case of deafness and audition in the case of blindness) are clearly limited by the processing resources of the overtaking modality. Thus, the brain regions that were previously recruited during the behavioral performance of the deprived sense now support a similar behavioral performance for the overtaking sense. This finding suggests a more input-unspecific and processing principle-based organization of the brain. Finally, we highlight the importance of controlling for and stating factors that might impact neural plasticity and the need for further research into visual temporal processing in deaf subjects.

The 22q11.2 deletion syndrome (22q11DS) is an uncommon genetic disorder with an increased risk of psychosis. Although the neural substrates of psychosis and schizophrenia are not well understood, aberrations in cortical networks represent intriguing potential mechanisms. Investigations of anatomic networks within 22q11DS are sparse. We investigated group differences in anatomic network structure in 48 individuals with 22q11DS and 370 typically developing controls by analyzing covariance patterns in cortical thickness among 68 regions of interest using graph theoretical models. Subjects with 22q11DS had less robust geographic organization relative to the control group, particularly in the occipital and parietal lobes. Multiple global graph theoretical statistics were decreased in 22q11DS. These results are consistent with prior studies demonstrating decreased connectivity in 22q11DS using other neuroimaging methodologies.