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Background Neuropsychiatric disorders are common in 22q11.2 Deletion Syndrome (22q11DS) with about 25% of affected individuals developing schizophrenia spectrum disorders by young adulthood. Longitudinal evaluation of psychosis spectrum features and neurocognition can establish developmental trajectories and impact on functional outcome. Methods 157 youth with 22q11DS were assessed longitudinally for psychopathology focusing on psychosis spectrum symptoms, neurocognitive performance and global functioning. We contrasted the pattern of positive and negative psychosis spectrum symptoms and neurocognitive performance differentiating those with more prominent Psychosis Spectrum symptoms (PS+) to those without prominent psychosis symptoms (PS−). Results We identified differences in the trajectories of psychosis symptoms and neurocognitive performance between the groups. The PS+ group showed age associated increase in symptom severity, especially negative symptoms and general nonspecific symptoms. Correspondingly, their level of functioning was worse and deteriorated more steeply than the PS− group. Neurocognitive performance was generally comparable in PS+ and PS− groups and demonstrated a similar age-related trajectory. However, worsening executive functioning distinguished the PS+ group from PS− counterparts. Notably, of the three executive function measures examined, only working memory showed a significant difference between the groups in rate of change. Finally, structural equation modeling showed that neurocognitive decline drove the clinical change. Conclusions Youth with 22q11DS and more prominent psychosis features show worsening of symptoms and functional decline driven by neurocognitive decline, most related to executive functions and specifically working memory. The results underscore the importance of working memory in the developmental progression of psychosis.

The presence of a 22q11.2 microdeletion (22q11.2 deletion syndrome [22q11DS]) ranks among the greatest known genetic risk factors for the development of psychotic disorders. There is emerging evidence that the cerebellum is important in the pathophysiology of psychosis. However, there is currently limited information on cerebellar neuroanatomy in 22q11DS specifically.High-resolution 3T magnetic resonance imaging was acquired in 79 individuals with 22q11DS and 70 typically developing control subjects (N = 149). Lobar and lobule-level cerebellar volumes were estimated using validated automated segmentation algorithms, and subsequently group differences were compared. Hierarchical clustering, principal component analysis, and graph theoretical models were used to explore intercerebellar relationships. Cerebrocerebellar structural connectivity with cortical thickness was examined via linear regression models.Individuals with 22q11DS had, on average, 17.3% smaller total cerebellar volumes relative to typically developing subjects (p.0001). The lobules of the superior posterior cerebellum (e.g., VII and VIII) were particularly affected in 22q11DS. However, all cerebellar lobules were significantly smaller, even after adjusting for total brain volumes (all cerebellar lobules p.0002). The superior posterior lobule was disproportionately associated with cortical thickness in the frontal lobes and cingulate cortex, brain regions known be affected in 22q11DS. Exploratory analyses suggested that the superior posterior lobule, particularly Crus I, may be associated with psychotic symptoms in 22q11DS.The cerebellum is a critical but understudied component of the 22q11DS neuroendophenotype.

OBJECTIVE: The prevalence and significance of schizophrenia-related phenotypes at the population level is debated in the literature. Here, the authors assessed whether two recently reported neuroanatomical signatures of schizophrenia—signature 1, with widespread reduction of gray matter volume, and signature 2, with increased striatal volume—could be replicated in an independent schizophrenia sample, and investigated whether expression of these signatures can be detected at the population level and how they relate to cognition, psychosis spectrum symptoms, and schizophrenia genetic risk. METHODS: This cross-sectional study used an independent schizophrenia-control sample (N=347; ages 16–57 years) for replication of imaging signatures, and then examined two independent population-level data sets: typically developing youths and youths with psychosis spectrum symptoms in the Philadelphia Neurodevelopmental Cohort (N=359; ages 16–23 years) and adults in the UK Biobank study (N=836; ages 44–50 years). The authors quantified signature expression using support-vector machine learning and compared cognition, psychopathology, and polygenic risk between signatures. RESULTS: Two neuroanatomical signatures of schizophrenia were replicated. Signature 1 but not signature 2 was significantly more common in youths with psychosis spectrum symptoms than in typically developing youths, whereas signature 2 frequency was similar in the two groups. In both youths and adults, signature 1 was associated with worse cognitive performance than signature 2. Compared with adults with neither signature, adults expressing signature 1 had elevated schizophrenia polygenic risk scores, but this was not seen for signature 2. CONCLUSIONS: The authors successfully replicated two neuroanatomical signatures of schizophrenia and describe their prevalence in population-based samples of youths and adults. They further demonstrated distinct relationships of these signatures with psychosis symptoms, cognition, and genetic risk, potentially reflecting underlying neurobiological vulnerability.

Risk for schizophrenia peaks during early adulthood, a critical period for brain development. Although several influential theoretical models have been proposed for the developmental relationship between brain pathology and clinical onset, to our knowledge, no study has directly evaluated the predictions of these models for schizophrenia developmental genetic effects on brain structure. To address this question, we introduce a framework to estimate the effects of schizophrenia genetic variation on brain structure phenotypes across the life span. Five-hundred and six participants, including 30 schizophrenia probands, 200 of their relatives (aged 12-85 years) from 32 families with at least two first-degree schizophrenia relatives, and 276 unrelated controls, underwent MRI to assess regional cortical thickness (CT) and cortical surface area (CSA). Genetic variance decomposition analyses were conducted to distinguish among schizophrenia neurogenetic effects that are most salient before schizophrenia peak age-of-risk (i.e., early neurodevelopmental effects), after peak age-of-risk (late neurodevelopmental effects), and during the later plateau of age-of-risk (neurodegenerative effects). Genetic correlations between schizophrenia and cortical traits suggested early neurodevelopmental effects for frontal and insula CSA, late neurodevelopmental effects for overall CSA and frontal, parietal, and occipital CSA, and possible neurodegenerative effects for temporal CT and parietal CSA. Importantly, these developmental neurogenetic effects were specific to schizophrenia and not found with nonpsychotic depression. Our findings highlight the potentially dynamic nature of schizophrenia genetic effects across the lifespan and emphasize the utility of integrating neuroimaging methods with developmental behavior genetic approaches to elucidate the nature and timing of risk-conferring processes in psychopathology. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Validated screening tools are needed to detect subtle cognitive impairment in individuals at-risk for developing psychosis. Here, the utility of the Mini-Mental Status Examination (MMSE) and Penn Computerized Neurocognitive Battery (CNB) were evaluated for detecting cognitive impairment in individuals with psychosis spectrum (PS) symptoms.Participants (n = 229; 54 % female) completed the MMSE and CNB at baseline and two-year follow-up. PS (n = 91) and typically developing (TD; n = 138) participants were enrolled at baseline based on the presence or absence of PS symptoms. After two years, 65 participants remained PS, 104 participants remained TD, 23 participants had Emergent (EP) subthreshold PS symptoms, and 37 participants were experiencing Other Psychopathology (OP).Generally, those with PS had lower scores than TD on both the MMSE (p 0.0001) and CNB (p 0.0001). Additionally, OP participants performed lower on the MMSE than TD (p = 0.02). Receiver operating characteristic (ROC) analyses indicated similar area under the curve (AUCs) for the two instruments (0.67); the MMSE showed higher specificity (0.71 vs. 0.62), while the CNB showed higher sensitivity (0.66 vs 0.52). Use of the MMSE and CNB in combination provided the highest diagnostic classification.The MMSE and CNB can be used to screen for cognitive impairment in PS. The MMSE is better at ruling out PS-related cognitive impairment while the CNB is better at ruling in PS-related cognitive impairment. Overall, our results indicate that both tests are useful in screening for cognitive impairment, particularly in combination, in a PS population.

Evidence shows that reports of psychopathology symptoms by youth and their caregiver informants differ. To quantify youth-caregiver discrepancies in psychopathology symptoms and factors associated with such discrepancies, we investigated differences in how youth and their caregivers rated psychopathology symptoms. The sample (N = 5094) was extracted from the Philadelphia Neurodevelopmental Cohort, a community-based sample of youth and included participants ages 11-17 years old with both youth and caregiver reported symptom scores. Across psychopathology symptoms, youth-caregiver concordance was poor to fair (Cohens kappa for symptom items ranged between 0.03-0.41). Psychosis symptoms had the lowest concordance-Cohen's kappa ranged from 0.03 to 0.17 across psychosis symptoms. Discrepancies between youth and caregiver symptom reports were greater than average for Black youth and for youth of low socioeconomic status; discrepancies were also higher than average in youth with any psychiatric disorder when compared to typically developing youth. Network analysis of difference scores obtained by subtracting youth symptom scores from caregiver reported symptom scores showed that network connectivity (i.e., correlated difference scores) was sparsest for psychosis spectrum compared to other psychiatric disorders. Using a large sample, we show that youth and their caregiver informants tend to report psychopathology symptoms differently. Youth-caregiver discrepancies were the most pronounced for Black youth and youth of low socio-economic status. Race and socioeconomic status contribute to significant differences in how youth and their caregivers report such symptoms and are important factors that should be accounted for to facilitate accurate mental health symptom assessment and evaluation.

When individual subjects are imaged with multiple modalities, biological information is present not only within each modality, but also between modalities – that is, in how modalities covary at the voxel level. Previous studies have shown that local covariance structures between modalities, or intermodal coupling (IMCo), can be summarized for two modalities, and that two-modality IMCo reveals otherwise undiscovered patterns in neurodevelopment and certain diseases. However, previous IMCo methods are based on the slopes of local weighted linear regression lines, which are inherently asymmetric and limited to the two-modality setting. Here, we present a generalization of IMCo estimation which uses local covariance decompositions to define a symmetric, voxel-wise coupling coefficient that is valid for two or more modalities. We use this method to study coupling between cerebral blood flow, amplitude of low frequency fluctuations, and local connectivity in 803 subjects ages 8 through 22. We demonstrate that coupling is spatially heterogeneous, varies with respect to age and sex in neurodevelopment, and reveals patterns that are not present in individual modalities. As availability of multi-modal data continues to increase, principal-component-based IMCo (pIMCo) offers a powerful approach for summarizing relationships between multiple aspects of brain structure and function. An R package for estimating pIMCo is available at: https://github.com/hufengling/pIMCo.

Using machine learning, we recently decomposed the neuroanatomical heterogeneity of established schizophrenia to discover two volumetric subgroups—a ‘lower brain volume’ subgroup (SG1) and an ‘higher striatal volume’ subgroup (SG2) with otherwise normal brain structure. In this study, we investigated whether the MRI signatures of these subgroups were also already present at the time of the first-episode of psychosis (FEP) and whether they were related to clinical presentation and clinical remission over 1-, 3-, and 5-years. We included 572 FEP and 424 healthy controls (HC) from 4 sites (Sao Paulo, Santander, London, Melbourne) of the PHENOM consortium. Our prior MRI subgrouping models (671 participants; USA, Germany, and China) were applied to both FEP and HC. Participants were assigned into 1 of 4 categories: subgroup 1 (SG1), subgroup 2 (SG2), no subgroup membership (‘None’), and mixed SG1 + SG2 subgroups (‘Mixed’). Voxel-wise analyses characterized SG1 and SG2 subgroups. Supervised machine learning analyses characterized baseline and remission signatures related to SG1 and SG2 membership. The two dominant patterns of ‘lower brain volume’ in SG1 and ‘higher striatal volume’ (with otherwise normal neuromorphology) in SG2 were identified already at the first episode of psychosis. SG1 had a significantly higher proportion of FEP (32%) vs. HC (19%) than SG2 (FEP, 21%; HC, 23%). Clinical multivariate signatures separated the SG1 and SG2 subgroups (balanced accuracy = 64%; p

ImportanceAutism spectrum disorder (ASD) is associated with significant clinical, neuroanatomical, and genetic heterogeneity that limits precision diagnostics and treatment.ObjectiveTo assess distinct neuroanatomical dimensions of ASD using novel semisupervised machine learning methods and to test whether the dimensions can serve as endophenotypes also in non-ASD populations.Design, Setting, and ParticipantsThis cross-sectional study used imaging data from the publicly available Autism Brain Imaging Data Exchange (ABIDE) repositories as the discovery cohort. The ABIDE sample included individuals diagnosed with ASD aged between 16 and 64 years and age- and sex-match typically developing individuals. Validation cohorts included individuals with schizophrenia from the Psychosis Heterogeneity Evaluated via Dimensional Neuroimaging (PHENOM) consortium and individuals from the UK Biobank to represent the general population. The multisite discovery cohort included 16 internationally distributed imaging sites. Analyses were performed between March 2021 and March 2022.Main Outcomes and MeasuresThe trained semisupervised heterogeneity through discriminative analysis models were tested for reproducibility using extensive cross-validations. It was then applied to individuals from the PHENOM and the UK Biobank. It was hypothesized that neuroanatomical dimensions of ASD would display distinct clinical and genetic profiles and would be prominent also in non-ASD populations.ResultsHeterogeneity through discriminative analysis models trained on T1-weighted brain magnetic resonance images of 307 individuals with ASD (mean [SD] age, 25.4 [9.8] years; 273 [88.9%] male) and 362 typically developing control individuals (mean [SD] age, 25.8 [8.9] years; 309 [85.4%] male) revealed that a 3-dimensional scheme was optimal to capture the ASD neuroanatomy. The first dimension (A1: aginglike) was associated with smaller brain volume, lower cognitive function, and aging-related genetic variants (FOXO3; Z = 4.65; P = 1.62 × 10−6). The second dimension (A2: schizophrenialike) was characterized by enlarged subcortical volumes, antipsychotic medication use (Cohen d = 0.65; false discovery rate–adjusted P = .048), partially overlapping genetic, neuroanatomical characteristics to schizophrenia (n = 307), and significant genetic heritability estimates in the general population (n = 14 786; mean [SD] h2, 0.71 [0.04]; P −4). The third dimension (A3: typical ASD) was distinguished by enlarged cortical volumes, high nonverbal cognitive performance, and biological pathways implicating brain development and abnormal apoptosis (mean [SD] β, 0.83 [0.02]; P = 4.22 × 10−6).Conclusions and RelevanceThis cross-sectional study discovered 3-dimensional endophenotypic representation that may elucidate the heterogeneous neurobiological underpinnings of ASD to support precision diagnostics. The significant correspondence between A2 and schizophrenia indicates a possibility of identifying common biological mechanisms across the 2 mental health diagnoses.

Rare genomic disorders (RGDs) confer elevated risk for neurodevelopmental psychiatric disorders. In this era of intense genomics discoveries, the landscape of RGDs is rapidly evolving. However, there has not been comparable progress to date in scalable, harmonized phenotyping methods. As a result, beyond associations with categorical diagnoses, the effects on dimensional traits remain unclear for many RGDs. The nature and specificity of RGD effects on cognitive and behavioral traits is an area of intense investigation: RGDs are frequently associated with more than one psychiatric condition, and those studied to date affect, to varying degrees, a broad range of developmental and cognitive functions. Although many RGDs have large effects, phenotypic expression is typically influenced by additional genomic and environmental factors. There is emerging evidence that using polygenic risk scores in individuals with RGDs offers opportunities to refine prediction, thus allowing for the identification of those at greatest risk of psychiatric illness. However, translation into the clinic is hindered by roadblocks, which include limited genetic testing in clinical psychiatry, and the lack of guidelines for following individuals with RGDs, who are at high risk of developing psychiatric symptoms. The Genes to Mental Health Network (G2MH) is a newly funded National Institute of Mental Health initiative that will collect, share, and analyze large-scale data sets combining genomics and dimensional measures of psychopathology spanning diverse populations and geography. The authors present here the most recent understanding of the effects of RGDs on dimensional behavioral traits and risk for psychiatric conditions and discuss strategies that will be pursued within the G2MH network, as well as how expected results can be translated into clinical practice to improve patient outcomes.

Chromosomal organization, scaling from the 147-base pair (bp) nucleosome to megabase-ranging domains encompassing multiple transcriptional units, including heritability loci for psychiatric traits, remains largely unexplored in the human brain. In this study, we constructed promoter- and enhancer-enriched nucleosomal histone modification landscapes for adult prefrontal cortex from H3-lysine 27 acetylation and H3-lysine 4 trimethylation profiles, generated from 388 controls and 351 individuals diagnosed with schizophrenia (SCZ) or bipolar disorder (BD) (n = 739). We mapped thousands of cis-regulatory domains (CRDs), revealing fine-grained, 104-106-bp chromosomal organization, firmly integrated into Hi-C topologically associating domain stratification by open/repressive chromosomal environments and nuclear topography. Large clusters of hyper-acetylated CRDs were enriched for SCZ heritability, with prominent representation of regulatory sequences governing fetal development and glutamatergic neuron signaling. Therefore, SCZ and BD brains show coordinated dysregulation of risk-associated regulatory sequences assembled into kilobase- to megabase-scaling chromosomal domains.

Contains fulltext : 248376.pdf (Publisher’s version ) (Open Access) For many traits, males show greater variability than females, with possible implications for understanding sex differences in health and disease. Here, the ENIGMA (Enhancing Neuro Imaging Genetics through Meta-Analysis) Consortium presents the largest-ever mega-analysis of sex differences in variability of brain structure, based on international data spanning nine decades of life. Subcortical volumes, cortical surface area and cortical thickness were assessed in MRI data of 16,683 healthy individuals 1-90 years old (47% females). We observed significant patterns of greater male than female between-subject variance for all subcortical volumetric measures, all cortical surface area measures, and 60% of cortical thickness measures. This pattern was stable across the lifespan for 50% of the subcortical structures, 70% of the regional area measures, and nearly all regions for thickness. Our findings that these sex differences are present in childhood implicate early life genetic or gene-environment interaction mechanisms. The findings highlight the importance of individual differences within the sexes, that may underpin sex-specific vulnerability to disorders.

Background: Normative modeling is a statistical approach to quantify the degree to which a particular individual-level measure deviates from the pattern observed in a normative reference population. When applied to human brain morphometric measures it has the potential to inform about the significance of normative deviations for health and disease. Normative models can be implemented using a variety of algorithms that have not been systematically appraised. Methods: To address this gap, eight algorithms were compared in terms of performance and computational efficiency using brain regional morphometric data from 37,407 healthy individuals (53% female; aged 3-90 years) collated from 87 international MRI datasets. Performance was assessed with the mean absolute error (MAE) and computational efficiency was inferred from central processing unit (CPU) time. The algorithms evaluated were Ordinary Least Squares Regression (OLSR), Bayesian Linear Regression (BLR), Generalized Additive Models for Location, Scale, and Shape (GAMLSS), Parametric Lambda, Mu, Sigma (LMS), Gaussian Process Regression (GPR), Warped Bayesian Linear Regression (WBLG), Hierarchical Bayesian Regression (HBR), and Multivariable Fractional Polynomial Regression (MFPR). Model optimization involved testing nine covariate combinations pertaining to acquisition features, parcellation software versions, and global neuroimaging measures (i.e., total intracranial volume, mean cortical thickness, and mean cortical surface area). Findings: Statistical comparisons across models at PFDR

Delay discounting is a measure of impulsive choice relevant in adolescence as it predicts many real-life outcomes, including substance use disorders, obesity, and academic achievement. However, the 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 youth. A total of 293 youth (9-23 years) completed a delay discounting task and underwent resting-state fMRI at 3T. A connectome-wide analysis using multivariate distance-based matrix regression was used to examine whole-brain relationships between delay discounting and functional connectivity was then performed. These analyses revealed that individual differences in delay discounting were associated with patterns of connectivity emanating from the left dorsal prefrontal cortex, a hub of the default mode network. Delay discounting was associated with greater functional connectivity between the dorsal prefrontal cortex and other parts of the default mode network, and reduced connectivity with regions in the dorsal and ventral attention networks. These results suggest that delay discounting in youth 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.

BackgroundBrain MRIs acquired in clinical settings represent a valuable and underutilized scientific resource for investigating neurodevelopment. Utilization of these clinical scans has been limited because of their clinical acquisition and technical heterogeneity. These barriers have curtailed the interpretability and scientific value of retrospective studies of clinically acquired brain MRIs, compared to studies of prospectively acquired research quality brain MRIs.PurposeTo develop a scalable and rigorous approach to generate clinical brain growth chart models, to benchmark neuroanatomical differences in clinical MRIs, and to validate clinically-derived brain growth charts against those derived from large-scale research studies.Materials and MethodsWe curated a set of clinical MRIScans withLimitedImagingPathology (SLIP) – so-called “clinical controls” – from an urban pediatric healthcare system acquired between 2005 and 2020. The curation process included manual review of signed radiology reports, as well as automated and manual quality review of images without gross pathology. We measured global and regional volumetric imaging phenotypes in the SLIP sample using two alternative, advanced image processing pipelines, and quantitatively compared clinical brain growth charts to research brain growth charts derived from >123,000 MRIs.ResultsThe curated SLIP dataset included 372 patients scanned between the ages of 28 days post-birth and 22.2 years across nine 3T MRI scanners. Clinical brain growth charts were highly similar to growth charts derived from large-scale research datasets, in terms of the normative developmental trajectories predicted by the models. The clinical indication of the scans did not significantly bias the output of clinical brain charts. Tens of thousands of additional healthcare system scans meet inclusion criteria to be included in future brain growth charts.ConclusionBrain charts derived from clinical-controls are highly similar to brain charts from research-controls, suggesting that curated clinical scans could be used to supplement research datasets.Summary StatementBrain growth charts of pediatric clinical MRIs with limited imaging pathology (N=372) are highly correlated with charts from a large aggregated set of research controls (N>120,000).Key ResultsA cohort of brain MRI scans with limited reported imaging pathology (N=372, 186 female; ages 0.07 - 22.2 years, median = 10.2) were identified using signed radiology reports and processed using two segmentation pipelines. Growth charts generated from these scans are highly correlated with growth charts from a large aggregated set of research controls (r range 0.990 - 0.999). There was no evidence of bias due to the reason for each scan.

Contains fulltext : 288062.pdf (Publisher’s version ) (Closed access) There is limited convergence in neuroimaging investigations into volumes of subcortical brain regions in social anxiety disorder (SAD). The inconsistent findings may arise from variations in methodological approaches across studies, including sample selection based on age and clinical characteristics. The ENIGMA-Anxiety Working Group initiated a global mega-analysis to determine whether differences in subcortical volumes can be detected in adults and adolescents with SAD relative to healthy controls. Volumetric data from 37 international samples with 1115 SAD patients and 2775 controls were obtained from ENIGMA-standardized protocols for image segmentation and quality assurance. Linear mixed-effects analyses were adjusted for comparisons across seven subcortical regions in each hemisphere using family-wise error (FWE)-correction. Mixed-effects d effect sizes were calculated. In the full sample, SAD patients showed smaller bilateral putamen volume than controls (left: d = -0.077, pFWE = 0.037; right: d = -0.104, pFWE = 0.001), and a significant interaction between SAD and age was found for the left putamen (r = -0.034, pFWE = 0.045). Smaller bilateral putamen volumes (left: d = -0.141, pFWE

Cortical variations in cytoarchitecture form a sensory-fugal axis that systematically shapes regional profiles of extrinsic connectivity. Additionally, this axis is thought to guide signal propagation and integration across the cortical hierarchy. While human neuroimaging work has shown that this axis constrains local properties of the human connectome, it remains unclear whether it also shapes the asymmetric signaling that arises from higher-order connectome topology. Here, we used network control theory to examine the amount of energy required to propagate dynamics across the sensory-fugal axis. Our results revealed an asymmetry in this energy indicating that bottom-up transitions were easier to complete compared to top-down transitions. Supporting analyses demonstrated that this asymmetry was underpinned by a connectome topology that is wired to support efficient bottom-up signaling. Finally, we found that this asymmetry correlated with changes in intrinsic neuronal timescales and lessened throughout youth. Our results show that cortical variation in cytoarchitecture may guide the formation of macroscopic connectome topology.

Neuroimaging studies point to neurostructural abnormalities in youth with anxiety disorders. Yet, findings are based on small-scale studies, often with small effect sizes, and have limited generalizability and clinical relevance. These issues have prompted a paradigm shift in the field towards highly powered (i.e., big data) individual-level inferences, which are data-driven, transdiagnostic, and neurobiologically informed. Here, we built and validated neurostructural machine learning (ML) models for individual-level inferences based on the largest-ever multi-site neuroimaging sample of youth with anxiety disorders (age: 10-25 years, N=3,343 individuals from 32 global sites), as compiled by three ENIGMA Anxiety Working Groups: Panic Disorder (PD), Generalized Anxiety Disorder (GAD), and Social Anxiety Disorder (SAD). ML classifiers were trained on MRI-derived regional measures of cortical thickness, surface area, and subcortical volumes to classify patients and healthy controls (HC) for each anxiety disorder separately and across disorders (transdiagnostic classification). Modest, yet robust, classification performance was achieved for PD vs. HC (AUC=0.62), but other disorder-specific and transdiagnostic classifications were not significantly different from chance. However, above chance-level transdiagnostic classifications were obtained in exploratory subgroup analyses of male patients vs. male HC, unmedicated patients vs. HC, and patients with low anxiety severity vs. HC (AUC 0.59-0.63). The above chance-level classifications were based on plausible and specific neuroanatomical features in fronto-striato-limbic and temporo-parietal regions. This study provides a realistic estimate of classification performance in a large, ecologically valid, multi-site sample of youth with anxiety disorders, and may as such serve as a benchmark.

Prior studies have demonstrated that patients with chromosome 22q11.2 deletion syndrome (22q11.2DS) have lower platelet counts (PC) compared to non-deleted populations. They also have an increased mean platelet volume. The mechanism for this has been postulated to be haploinsufficiency of the GPIBB gene. We examined platelet parameters, deletion size and factors known to influence counts, including status of thyroid hormone and congenital heart disease (CHD), in a population of 825 patients with 22q11.2DS. We also measured surface expression of GPIB-IX complex by flow cytometry. The major determinant of PC was deletion status of GP1BB, regardless of surface expression or other factors. Patients with nested distal chromosome 22q11.2 deletions (those with GP1BB present) had higher PCs than those with proximal deletions where GP1BB is deleted. Patients with 22q11.2DS also demonstrated an accelerated PC decrease with age, occurring in childhood. These data demonstrate that genes within the proximal deletion segment drive PC differences in 22q11.2DS and suggest that PC reference ranges may need to be adjusted for age and deletion size in 22q11.2DS populations. Bleeding did not correlate with either platelet count or GPIb expression. Further studies into drivers of expression of GPIb and associations with severe thrombocytopenia and immune thrombocytopenia are needed to inform clinical care.

Animal studies of neurodevelopmental plasticity have shown that intrinsic brain activity evolves from high amplitude and globally synchronized to suppressed and sparse as plasticity declines and the cortex matures. Leveraging resting-state functional MRI data from 1033 individuals (8-23 years), we reveal that this stereotyped refinement of intrinsic activity occurs during human development and provides evidence for a cortical gradient of neurodevelopmental plasticity during childhood and adolescence. Specifically, we demonstrate that declines in the amplitude of intrinsic activity are initiated heterochronously across regions, coupled to the maturation of a plasticity-restricting structural feature, and temporally staggered along a hierarchical sensorimotor-association axis from ages 8 to 18. Youth from disadvantaged environments exhibit reduced intrinsic activity in regions further up the sensorimotor-association axis, suggestive of a reduced level of plasticity in late-maturing cortices. Our results uncover a hierarchical axis of neurodevelopment and offer insight into the temporal sequence of protracted neurodevelopmental plasticity in humans.

Prior work has shown that there is substantial interindividual variation in the spatial distribution of functional networks across the cerebral cortex, or functional topography. However, it remains unknown whether there are sex differences in the topography of individualized networks in youth. Here, we leveraged an advanced machine learning method (sparsity-regularized non-negative matrix factorization) to define individualized functional networks in 693 youth (ages 8 to 23 y) who underwent functional MRI as part of the Philadelphia Neurodevelopmental Cohort. Multivariate pattern analysis using support vector machines classified participant sex based on functional topography with 82.9% accuracy ( P < 0.0001). Brain regions most effective in classifying participant sex belonged to association networks, including the ventral attention, default mode, and frontoparietal networks. Mass univariate analyses using generalized additive models with penalized splines provided convergent results. Furthermore, transcriptomic data from the Allen Human Brain Atlas revealed that sex differences in multivariate patterns of functional topography were spatially correlated with the expression of genes on the X chromosome. These results highlight the role of sex as a biological variable in shaping functional topography.

Background Individuals with autism spectrum disorder (ASD) are challenged not only by the defining features of social-communication deficits and restricted repetitive behaviors, but also by a myriad of psychopathology varying in severity. Different cognitive deficits underpin these psychopathologies, which could be subjected to intervention to alter the course of the disorder. Understanding domain-specific mediating effects of cognition is essential for developing targeted intervention strategies. However, the high degree of inter-correlation among different cognitive functions hinders elucidation of individual effects. Methods In the Philadelphia Neurodevelopmental Cohort, 218 individuals with ASD were matched with 872 non-ASD controls on sex, age, race, and socioeconomic status. Participants of this cohort were deeply and broadly phenotyped on neurocognitive abilities and dimensional psychopathology. Using structural equation modeling, inter-correlation among cognitive domains were adjusted before mediation analysis on outcomes of multi-domain psychopathology and functional level. Results While social cognition, complex cognition, and memory each had a unique pattern of mediating effect on psychopathology domains in ASD, none had significant effects on the functional level. In contrast, executive function was the only cognitive domain that exerted a generalized negative impact on every psychopathology domain (p factor, anxious-misery, psychosis, fear, and externalizing), as well as functional level. Conclusions Executive function has a unique association with the severity of comorbid psychopathology in ASD, and could be a target of interventions. As executive dysfunction occurs variably in ASD, our result also supports the clinical utility of assessing executive function for prognostic purposes.

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 data exist, currently available tools are memory intensive, difficult to scale to large datasets, and support only a limited number of statistical models. Here we introduce ModelArray, a memory-efficient R package for mass-univariate statistical analysis of fixel data. With only several lines of code, even large fixel datasets can be analyzed using a standard personal computer. At present, ModelArray supports linear models as well as generalized additive models (GAMs), which are particularly useful for studying nonlinear effects in lifespan data. Detailed memory profiling revealed that ModelArray required only limited memory even for large datasets. As an example, we applied ModelArray to fixel data derived from diffusion images acquired as part of the Philadelphia Neurodevelopmental Cohort (n=938). ModelArray required far less memory than existing tools and 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 an efficient and flexible platform for statistical analysis of fixel data.HIGHLIGHTSModelArray is an R package for mass-univariate statistical analysis of fixel dataModelArray is memory-efficient even for large-scale datasetsModelArray supports linear and nonlinear modeling and is extensible to more modelsModelArray facilitates easy statistical analysis of large-scale fixel dataGraphical abstract

ObjectiveThe authors used multimodal data collected during pre/early-adolescence in research settings to predict self-report of past suicide attempt (SA) by mid-late adolescence in pediatric care settings. The study further aimed to determine types of data that contribute to the prediction; and to test generalizability of the prediction in an independent sample.MethodsThe authors applied machine learning methods on clinical, neurocognitive and geocoded neighborhood environmental data from the Philadelphia Neurodevelopmental Cohort study (PNC, Mean age 11.1, SD=2.2, 52.3% female and 51.4% Black participants) to predict SA reported ∼5 years later in a community pediatric clinic (n=922, 5.3% SA) or emergency department (n=497, 8.2% SA). The authors compared prediction performance when using all data versus individual data types, then used feature selection algorithms (Lasso, Relief and Random Forest) to identify important predictors and compared performance of models relying on feature subsets.ResultsIn the training dataset, SA prediction was good, reaching AUC=0.75, sensitivity/specificity 0.76/0.77 when relying on feature subsets identified using feature selection pipelines. Use of highest-ranking feature subsets from the training dataset yielded similar prediction in the testing dataset with AUC=0.74, sensitivity/specificity 0.66/0.70. Different algorithms showed different high-ranking features, but overall multiple data domains were represented among the highest-ranking predictors selected by each algorithm.ConclusionsThese findings provide evidence for feasibility of predicting youth SA using data collected at a single timepoint early in life in a diverse cohort. Results encourage incorporation of multiple data types including neurocognitive and geocoded environmental measures in machine learning SA prediction pipelines.

BackgroundImpaired emotion processing constitutes a key dimension of schizophrenia and a possible endophenotype of this illness. Empirical studies consistently report poorer emotion recognition performance in patients with schizophrenia as well as in individuals at enhanced risk of schizophrenia (“at risk”). fMRI studies also report consistent patterns of abnormal brain activation in response to emotional stimuli in patients, in particular decreased amygdala activation. In contrast, brain-level abnormalities in at-risk individuals are more elusive. We address this gap using an image-based meta-analysis of the fMRI literature.MethodsfMRI studies investigating brain responses to negative emotional stimuli and reporting a comparison between at-risk individuals and healthy controls were identified. Frequentist and Bayesian voxel-wise meta-analyses were performed separately, by implementing a random effect model with unthresholded group-level T-maps from individual studies as input.ResultsSeventeen studies with a cumulative total of 677 at-risk individuals and 805 healthy controls were included. Frequentist analyses did not reveal significant differences between at-risk individuals and healthy controls. Similar results were observed with Bayesian analyses, which provided strong evidence for the absence of meaningful brain activation differences across the entire brain. Region of interest analyses specifically focusing on the amygdala confirmed the lack of group differences in this region.ConclusionsThese results suggest that brain activation patterns in response to emotional stimuli are unlikely to constitute a reliable endophenotype of schizophrenia. We suggest that future studies rather focus on impaired functional connectivity as an alternative and promising endophenotype.

A novel self-supervised deep learning (DL) method is developed for computing bias-free, personalized brain functional networks (FNs) that provide unique opportunities to better understand brain function, behavior, and disease. Specifically, convolutional neural networks with an encoder-decoder architecture are employed to compute personalized FNs from resting-state fMRI data without utilizing any external supervision by optimizing functional homogeneity of personalized FNs in a self-supervised setting. We demonstrate that a DL model trained on fMRI scans from the Human Connectome Project can identify canonical FNs and generalizes well across four different datasets. We further demonstrate that the identified personalized FNs are informative for predicting individual differences in behavior, brain development, and schizophrenia status. Taken together, self-supervised DL allows for rapid, generalizable computation of personalized FNs.