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This article describes the rationale, aims, and methodology of the Accelerating Medicines Partnership® Schizophrenia (AMP® SCZ). This is the largest international collaboration to date that will develop algorithms to predict trajectories and outcomes of individuals at clinical high risk (CHR) for psychosis and to advance the development and use of novel pharmacological interventions for CHR individuals. We present a description of the participating research networks and the data processing analysis and coordination center, their processes for data harmonization across 43 sites from 13 participating countries (recruitment across North America, Australia, Europe, Asia, and South America), data flow and quality assessment processes, data analyses, and the transfer of data to the National Institute of Mental Health (NIMH) Data Archive (NDA) for use by the research community. In an expected sample of approximately 2000 CHR individuals and 640 matched healthy controls, AMP SCZ will collect clinical, environmental, and cognitive data along with multimodal biomarkers, including neuroimaging, electrophysiology, fluid biospecimens, speech and facial expression samples, novel measures derived from digital health technologies including smartphone-based daily surveys, and passive sensing as well as actigraphy. The study will investigate a range of clinical outcomes over a 2-year period, including transition to psychosis, remission or persistence of CHR status, attenuated positive symptoms, persistent negative symptoms, mood and anxiety symptoms, and psychosocial functioning. The global reach of AMP SCZ and its harmonized innovative methods promise to catalyze the development of new treatments to address critical unmet clinical and public health needs in CHR individuals.

Functional neuroimaging emerged with great promise and has provided fundamental insights into the neurobiology of schizophrenia. However, it has faced challenges and criticisms, most notably a lack of clinical translation. This paper provides a comprehensive review and critical summary of the literature on functional neuroimaging, in particular functional magnetic resonance imaging (fMRI), in schizophrenia. We begin by reviewing research on fMRI biomarkers in schizophrenia and the clinical high risk phase through a historical lens, moving from case-control regional brain activation to global connectivity and advanced analytical approaches, and more recent machine learning algorithms to identify predictive neuroimaging features. Findings from fMRI studies of negative symptoms as well as of neurocognitive and social cognitive deficits are then reviewed. Functional neural markers of these symptoms and deficits may represent promising treatment targets in schizophrenia. Next, we summarize fMRI research related to antipsychotic medication, psychotherapy and psychosocial interventions, and neurostimulation, including treatment response and resistance, therapeutic mechanisms, and treatment targeting. We also review the utility of fMRI and data-driven approaches to dissect the heterogeneity of schizophrenia, moving beyond case-control comparisons, as well as methodological considerations and advances, including consortia and precision fMRI. Lastly, limitations and future directions of research in the field are discussed. Our comprehensive review suggests that, in order for fMRI to be clinically useful in the care of patients with schizophrenia, research should address potentially actionable clinical decisions that are routine in schizophrenia treatment, such as which antipsychotic should be prescribed or whether a given patient is likely to have persistent functional impairment. The potential clinical utility of fMRI is influenced by and must be weighed against cost and accessibility factors. Future evaluations of the utility of fMRI in prognostic and treatment response studies may consider including a health economics analysis.

Correction to: Molecular Psychiatry, published online 2 May 2023 In this article Honami Arai, Irene Bollettini, Rosa Calvo Escalona, Ana Coelho, Federica Colombo, Leila Darwich, Martine Fontaine, Toshikazu Ikuta, Jonathan C. Ipser, Asier Juaneda-Seguí, Hitomi Kitagawa, Gerd Kvale, Mafalda Machado-Sousa, Astrid Morer, Takashi Nakamae, Jin Narumoto, Joseph O’Neill, Sho Okawa, Eva Real, Veit Roessner, Joao R. Sato, Cinto Segalàs, Roseli G. Shavitt, Dick J. Veltman, Kei Yamada were missing from the author list indexed under the ENIGMA-OCD Working Group. Additionally, there was an error regarding Tokiko Yoshida’s name, where the first name and last name were written in the wrong order. The original article has been corrected.

Current knowledge about functional connectivity in obsessive-compulsive disorder (OCD) is based on small-scale studies, limiting the generalizability of results. Moreover, the majority of studies have focused only on predefined regions or functional networks rather than connectivity throughout the entire brain. Here, we investigated differences in resting-state functional connectivity between OCD patients and healthy controls (HC) using mega-analysis of data from 1024 OCD patients and 1028 HC from 28 independent samples of the ENIGMA-OCD consortium. We assessed group differences in whole-brain functional connectivity at both the regional and network level, and investigated whether functional connectivity could serve as biomarker to identify patient status at the individual level using machine learning analysis. The mega-analyses revealed widespread abnormalities in functional connectivity in OCD, with global hypo-connectivity (Cohens d: -0.27 to -0.13) and few hyper-connections, mainly with the thalamus (Cohens d: 0.19 to 0.22). Most hypo-connections were located within the sensorimotor network and no fronto-striatal abnormalities were found. Overall, classification performances were poor, with area-under-the-receiver-operating-characteristic curve (AUC) scores ranging between 0.567 and 0.673, with better classification for medicated (AUC = 0.702) than unmedicated (AUC = 0.608) patients versus healthy controls. These findings provide partial support for existing pathophysiological models of OCD and highlight the important role of the sensorimotor network in OCD. However, resting-state connectivity does not so far provide an accurate biomarker for identifying patients at the individual level.

Reduced hippocampal volume (HV) is an established brain morphological feature of psychiatric conditions. HV is associated with brain connectivity in humans and non-human animals and altered connectivity is associated with risk for psychiatric illness. Associations between HV and connectivity remain poorly characterized in humans, and especially in phases of psychiatric illness that precede disease onset. This study examined associations between HV and hippocampal functional connectivity (FC) during rest in 141 healthy controls and 248 individuals at-risk for psychosis. Significant inverse associations between HV and hippocampal FC with the inferior parietal lobe (IPL) and thalamus were observed. Select associations between hippocampal FC and HV were moderated by diagnostic group. Significant moderation results shifted from implicating the IPL to the temporal pole after excluding participants on antipsychotic medication. Considered together, this work implicates hippocampal FC with the temporoparietal junction, within a specialized subsystem of the default mode network, as sensitive to HV.

Progressive grey matter loss has been demonstrated among clinical high-risk (CHR) individuals who convert to psychosis, but it is unknown whether these changes occur prior to psychosis onset. Identifying illness-related neurobiological mechanisms that occur prior to conversion is essential for targeted early intervention. Among participants in the third wave of the North American Prodrome Longitudinal Study (NAPLS3), this report investigated if steeper cortical thinning was observable prior to psychosis onset among CHR individuals who ultimately converted (CHR-C) and assessed the shortest possible time interval in which rates of cortical thinning differ between CHR-C, CHR non-converters (CHR-NC), and health controls (HC). 338 CHR-NC, 42 CHR-C, and 62 HC participants (age 19.3±4.2, 44.8% female, 52.5% racial/ethnic minority) completed up to 5 MRI scans across 8 months. Accelerated thinning among CHR-C compared to CHR-NC and HC was observed in multiple prefrontal, temporal, and parietal cortical regions. CHR-NC also exhibited accelerated cortical thinning compared to HC in several of these areas. Greater percent decrease in cortical thickness was observed among CHR-C compared to other groups across 2.9±1.8 months, on average, in several cortical areas. ROC analyses discriminating CHR-C from CHR-NC by percent thickness change in a left hemisphere region of interest, scanner, age, age2, and sex had an AUC of 0.74, with model predictive power driven primarily by percent thickness change. Findings indicate that accelerated cortical thinning precedes psychosis onset and differentiates CHR-C from CHR-NC and HC across short time intervals. Mechanisms underlying cortical thinning may provide novel treatment targets prior to psychosis onset.

Subtle alterations in white matter microstructure are observed in youth at clinical high risk (CHR) for psychosis. However, the timing of these changes and their relationships to the emergence of psychosis remain unclear. Here, we track the evolution of white matter abnormalities in a large, longitudinal cohort of CHR individuals comprising the North American Prodrome Longitudinal Study (NAPLS-3). Multi-shell diffusion magnetic resonance imaging data were collected across multiple timepoints (1-5 over 1 year) in 286 subjects (aged 12-32 years): 25 CHR individuals who transitioned to psychosis (CHR-P; 61 scans), 205 CHR subjects with unknown transition outcome after the 1-year follow-up period (CHR-U; 596 scans), and 56 healthy controls (195 scans). Linear mixed effects models were fitted to infer the impact of age and illness-onset on variation in the fractional anisotropy of cellular tissue (FAT) and the volume fraction of extracellular free water (FW). Baseline measures of white matter microstructure did not differentiate between HC, CHR-U and CHR-P individuals. However, age trajectories differed between the three groups in line with a developmental effect: CHR-P and CHR-U groups displayed higher FAT in adolescence, and 4% lower FAT by 30 years of age compared to controls. Furthermore, older CHR-P subjects (20+ years) displayed 4% higher FW in the forceps major (p

BackgroundTransition to psychosis is among the most adverse outcomes of clinical high-risk (CHR) syndromes encompassing ultra-high risk (UHR) and basic symptom states. Clinical risk calculators may facilitate an early and individualized interception of psychosis, but their real-world implementation requires thorough validation across diverse risk populations, including young patients with depressive syndromes.MethodsWe validated the previously described NAPLS-2 (North American Prodrome Longitudinal Study 2) calculator in 334 patients (26 with transition to psychosis) with CHR or recent-onset depression (ROD) drawn from the multisite European PRONIA (Personalised Prognostic Tools for Early Psychosis Management) study. Patients were categorized into three risk enrichment levels, ranging from UHR, over CHR, to a broad-risk population comprising patients with CHR or ROD (CHR|ROD). We assessed how risk enrichment and different predictive algorithms influenced prognostic performance using reciprocal external validation.ResultsAfter calibration, the NAPLS-2 model predicted psychosis with a balanced accuracy (BAC) (sensitivity, specificity) of 68% (73%, 63%) in the PRONIA-UHR cohort, 67% (74%, 60%) in the CHR cohort, and 70% (73%, 66%) in patients with CHR|ROD. Multiple model derivation in PRONIA-CHR|ROD and validation in NAPLS-2-UHR patients confirmed that broader risk definitions produced more accurate risk calculators (CHR|ROD-based vs. UHR-based performance: 67% [68%, 66%] vs. 58% [61%, 56%]). Support vector machines were superior in CHR|ROD (BAC = 71%), while ridge logistic regression and support vector machines performed similarly in CHR (BAC = 67%) and UHR cohorts (BAC = 65%). Attenuated psychotic symptoms predicted psychosis across risk levels, while younger age and reduced processing speed became increasingly relevant for broader risk cohorts.ConclusionsClinical-neurocognitive machine learning models operating in young patients with affective and CHR syndromes facilitate a more precise and generalizable prediction of psychosis. Future studies should investigate their therapeutic utility in large-scale clinical trials.

While functional neuroimaging studies typically focus on a particular paradigm to investigate network connectivity, the human brain appears to possess an intrinsic "trait" architecture that is independent of any given paradigm. We have previously proposed the use of "cross-paradigm connectivity (CPC)" to quantify shared connectivity patterns across multiple paradigms and have demonstrated the utility of such measures in clinical studies. Here, using generalizability theory and connectome fingerprinting, we examined the reliability, stability, and individual identifiability of CPC in a group of highly-sampled healthy traveling subjects who received fMRI scans with a battery of five paradigms across multiple sites and days. Compared with single-paradigm connectivity matrices, the CPC matrices showed higher reliability in connectivity diversity, lower reliability in connectivity strength, higher stability, and higher individual identification accuracy. All of these assessments increased as a function of number of paradigms included in the CPC analysis. In comparisons involving different paradigm combinations and different brain atlases, we observed significantly higher reliability, stability, and identifiability for CPC matrices constructed from task-only data (versus those from both task and rest data), and higher identifiability but lower stability for CPC matrices constructed from the Power atlas (versus those from the AAL atlas). Moreover, we showed that multi-paradigm CPC matrices likely reflect the brain's "trait" structure that cannot be fully achieved from single-paradigm data, even with multiple runs. The present results provide evidence for the feasibility and utility of CPC in the study of functional "trait" networks and offer some methodological implications for future CPC studies.

BackgroundMalhi et al. in this issue critique the clinical high risk (CHR) syndrome for psychosis.MethodResponse to points of critique.ResultsWe agree that inconsistency in CHR nomenclature should be minimized. We respectfully disagree on other points. In our view: a) individuals with CHR and their families need help, using existing interventions, even though we do not yet fully understand disease mechanisms; b) substantial progress has been made in identification of biomarkers; c) symptoms used to identify CHR are specific to psychotic illnesses; d) CHR diagnosis is not "extremely difficult"; e) the pattern of progression, although heterogenous, is discernible; f) "psychosis-like symptoms" are common but are not used to identify CHR; and g) on the point described as 'the real risk,' CHR diagnosis does not frequently cause harmful stigma.DiscussionMalhi et al.'s arguments do not fairly characterize progress in the CHR field nor efforts to minimize stigma. That said, much work remains in areas of consistent nomenclature, mechanisms of disease, dissecting heterogeneity, and biomarkers. With regard to what the authors term the "real risk" of stigma associated with a CHR "label," however, our view is that avoiding words like "risk" and "psychosis" reinforces the stigma that both they and we mean to oppose. Moreover, patients and their families benefit from being given a term that describes what is happening to them.

Mounting evidence has shown disrupted brain network architecture across the psychosis spectrum. However, whether these changes relate to the development of psychosis is unclear. Here, we used graph theoretical analysis to investigate longitudinal changes in resting-state brain networks in samples of 72 subjects at clinical high risk (including 8 cases who converted to full psychosis) and 48 healthy controls drawn from the North American Prodrome Longitudinal Study (NAPLS) consortium. We observed progressive reduction in global efficiency (P = 0.006) and increase in network diversity (P = 0.001) in converters compared with non-converters and controls. More refined analysis separating nodes into nine key brain networks demonstrated that these alterations were primarily driven by progressively diminished local efficiency in the default-mode network (P = 0.004) and progressively enhanced node diversity across all networks (P

No diagnostic biomarkers are available for obsessive-compulsive disorder (OCD). Here, we aimed to identify magnetic resonance imaging (MRI) biomarkers for OCD, using 46 data sets with 2304 OCD patients and 2068 healthy controls from the ENIGMA consortium. We performed machine learning analysis of regional measures of cortical thickness, surface area and subcortical volume and tested classification performance using cross-validation. Classification performance for OCD vs. controls using the complete sample with different classifiers and cross-validation strategies was poor. When models were validated on data from other sites, model performance did not exceed chance-level. In contrast, fair classification performance was achieved when patients were grouped according to their medication status. These results indicate that medication use is associated with substantial differences in brain anatomy that are widely distributed, and indicate that clinical heterogeneity contributes to the poor performance of structural MRI as a disease marker.

ObjectiveAttention deficit hyperactivity disorder (ADHD), autism spectrum disorder (ASD), and obsessive-compulsive disorder (OCD) are common neurodevelopmental disorders that frequently co-occur. The authors sought to directly compare these disorders using structural brain imaging data from ENIGMA consortium data.MethodsStructural T1-weighted whole-brain MRI data from healthy control subjects (N=5,827) and from patients with ADHD (N=2,271), ASD (N=1,777), and OCD (N=2,323) from 151 cohorts worldwide were analyzed using standardized processing protocols. The authors examined subcortical volume, cortical thickness, and cortical surface area differences within a mega-analytical framework, pooling measures extracted from each cohort. Analyses were performed separately for children, adolescents, and adults, using linear mixed-effects models adjusting for age, sex, and site (and intracranial volume for subcortical and surface area measures).ResultsNo shared differences were found among all three disorders, and shared differences between any two disorders did not survive correction for multiple comparisons. Children with ADHD compared with those with OCD had smaller hippocampal volumes, possibly influenced by IQ. Children and adolescents with ADHD also had smaller intracranial volume than control subjects and those with OCD or ASD. Adults with ASD showed thicker frontal cortices compared with adult control subjects and other clinical groups. No OCD-specific differences were observed across different age groups and surface area differences among all disorders in childhood and adulthood.ConclusionsThe study findings suggest robust but subtle differences across different age groups among ADHD, ASD, and OCD. ADHD-specific intracranial volume and hippocampal differences in children and adolescents, and ASD-specific cortical thickness differences in the frontal cortex in adults, support previous work emphasizing structural brain differences in these disorders.

BackgroundImpairments in spatial working memory (sWM) have been well documented in schizophrenia. Here we provide a comprehensive test of a microcircuit model of WM performance in schizophrenia that predicts enhanced effects of increasing delay duration and distractors based on a hypothesized imbalance of excitatory and inhibitory processes.MethodsModel predictions were tested in 41 people with schizophrenia (PSZ) and 32 healthy control subjects (HCS) performing an sWM task. In one condition, a single target location was followed by delays of 0, 2, 4, or 8 seconds. In a second condition, distractors were presented during the 4-second delay interval at 20°, 30°, 40°, 50°, or 90° from the original target location.ResultsPSZ showed less precise sWM representations than HCS, and the rate of memory drift over time was greater in PSZ than in HCS. Relative to HCS, the spatial recall responses of PSZ were more repelled by distractors presented close to the target location and more attracted by distractors presented far from the target location. The degree of attraction to distant distractors was correlated with the rate of memory drift in the absence of distractors.ConclusionsConsistent with the microcircuit model, PSZ exhibited both a greater rate of drift and greater attraction to distant distractors relative to HCS. These two effects were correlated, consistent with the proposal that they arise from a single underlying mechanism. However, the repulsion effects produced by nearby distractors were not predicted by the model and thus require an updated modeling framework.

BackgroundLateralized dysfunction has been suggested in obsessive-compulsive disorder (OCD). However, it is currently unclear whether OCD is characterized by abnormal patterns of brain structural asymmetry. Here we carried out what is by far the largest study of brain structural asymmetry in OCD.MethodsWe studied a collection of 16 pediatric datasets (501 patients with OCD and 439 healthy control subjects), as well as 30 adult datasets (1777 patients and 1654 control subjects) from the OCD Working Group within the ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) Consortium. Asymmetries of the volumes of subcortical structures, and of measures of regional cortical thickness and surface areas, were assessed based on T1-weighted magnetic resonance imaging scans, using harmonized image analysis and quality control protocols. We investigated possible alterations of brain asymmetry in patients with OCD. We also explored potential associations of asymmetry with specific aspects of the disorder and medication status.ResultsIn the pediatric datasets, the largest case-control differences were observed for volume asymmetry of the thalamus (more leftward; Cohen's d = 0.19) and the pallidum (less leftward; d = -0.21). Additional analyses suggested putative links between these asymmetry patterns and medication status, OCD severity, or anxiety and depression comorbidities. No significant case-control differences were found in the adult datasets.ConclusionsThe results suggest subtle changes of the average asymmetry of subcortical structures in pediatric OCD, which are not detectable in adults with the disorder. These findings may reflect altered neurodevelopmental processes in OCD.

Abstract Background Studies in individuals at clinical high risk (CHR) for psychosis provide a powerful means to predict outcomes and inform putative mechanisms underlying conversion to psychosis. In previous work, we applied advanced diffusion imaging methods to reveal that white matter pathology in a CHR population is characterized by cellular-specific changes in white matter, suggesting a preexisting neurodevelopmental anomaly. However, it remains unknown whether these deficits relate to clinical symptoms and/or conversion to frank psychosis. To address this gap, we examined cross-sectional and longitudinal white matter maturation in the largest imaging population of CHR individuals to date, obtained from the North American Prodrome Longitudinal Study (NAPLS-3). Methods Multi-shell diffusion magnetic resonance imaging (MRI) data were collected across multiple timepoints (1–6 at ~2 month intervals) in 286 subjects (age range=12–32 years). These were 230 unmedicated CHR subjects, including 11% (n=25) who transitioned to psychosis (CHR-converters), as well as 56 age and sex-matched healthy controls. Raw diffusion signals were harmonized to remove scanner/site-induced effects, yielding a unified imaging dataset. Fractional anisotropy of cellular tissue (FAt) and the volume fraction of extracellular free-water (FW) were assessed in 12 major tracts from the IIT Human Brain Atlas (v.5.0). Linear mixed effects (LME) models were fitted to infer developmental trajectories of FAt and FW across age for CHR-converters, CHR-nonconverters and control groups, while accounting for the repeated measurements on each individual. Results The rate at which FAt changed with age significantly differed between the three groups across commissural and association tracts (5 in total; p

Brain structural covariance networks reflect covariation in morphology of different brain areas and are thought to reflect common trajectories in brain development and maturation. Large-scale investigation of structural covariance networks in obsessive-compulsive disorder (OCD) may provide clues to the pathophysiology of this neurodevelopmental disorder. Using T1-weighted MRI scans acquired from 1616 individuals with OCD and 1463 healthy controls across 37 datasets participating in the ENIGMA-OCD Working Group, we calculated intra-individual brain structural covariance networks (using the bilaterally-averaged values of 33 cortical surface areas, 33 cortical thickness values, and six subcortical volumes), in which edge weights were proportional to the similarity between two brain morphological features in terms of deviation from healthy controls (i.e. z-score transformed). Global networks were characterized using measures of network segregation (clustering and modularity), network integration (global efficiency), and their balance (small-worldness), and their community membership was assessed. Hub profiling of regional networks was undertaken using measures of betweenness, closeness, and eigenvector centrality. Individually calculated network measures were integrated across the 37 datasets using a meta-analytical approach. These network measures were summated across the network density range of K = 0.10-0.25 per participant, and were integrated across the 37 datasets using a meta-analytical approach. Compared with healthy controls, at a global level, the structural covariance networks of OCD showed lower clustering (P

De novo and inherited rare genetic disorders (RGDs) are a major cause of human morbidity, frequently involving neuropsychiatric symptoms. Recent advances in genomic technologies and data sharing have revolutionized the identification and diagnosis of RGDs, presenting an opportunity to elucidate the mechanisms underlying neuropsychiatric disorders by investigating the pathophysiology of high-penetrance genetic risk factors. Here we seek out the best path forward for achieving these goals. We think future research will require consistent approaches across multiple RGDs and developmental stages, involving both the characterization of shared neuropsychiatric dimensions in humans and the identification of neurobiological commonalities in model systems. A coordinated and concerted effort across patients, families, researchers, clinicians and institutions, including rapid and broad sharing of data, is now needed to translate these discoveries into urgently needed therapies.

Memory deficits are a hallmark of psychotic disorders such as schizophrenia. However, whether the neural dysfunction underlying these deficits is present before the onset of illness and potentially predicts conversion to psychosis is unclear. In this study, we investigated brain functional alterations during memory processing in a sample of 155 individuals at clinical high risk (including 18 subjects who later converted to full psychosis) and 108 healthy controls drawn from the second phase of the North American Prodrome Longitudinal Study (NAPLS-2). All participants underwent functional magnetic resonance imaging with a paired-associate memory paradigm at the point of recruitment and were clinically followed up for approximately 2 years. We found that at baseline, subjects at high risk showed significantly higher activation during memory retrieval in the prefrontal, parietal, and bilateral temporal cortices (PFWE < .035). This effect was more pronounced in converters than nonconverters and was particularly manifested in unmedicated subjects (P < .001). The hyperactivation was significantly correlated with retrieval reaction time during scan in converters (P = .009) but not in nonconverters and controls, suggesting an exaggerated retrieval effort. These findings suggest that hyperactivation during memory retrieval may mark processes associated with conversion to psychosis, and such measures have potential as biomarkers for psychosis prediction.

While graph theoretical modeling has dramatically advanced our understanding of complex brain systems, the feasibility of aggregating connectomic data in large imaging consortia remains unclear. Here, using a battery of cognitive, emotional and resting fMRI paradigms, we investigated the generalizability of functional connectomic measures across sites and sessions. Our results revealed overall fair to excellent reliability for a majority of measures during both rest and tasks, in particular for those quantifying connectivity strength, network segregation and network integration. Processing schemes such as node definition and global signal regression (GSR) significantly affected resulting reliability, with higher reliability detected for the Power atlas (vs. AAL atlas) and data without GSR. While network diagnostics for default-mode and sensori-motor systems were consistently reliable independently of paradigm, those for higher-order cognitive systems were reliable predominantly when challenged by task. In addition, based on our present sample and after accounting for observed reliability, satisfactory statistical power can be achieved in multisite research with sample size of approximately 250 when the effect size is moderate or larger. Our findings provide empirical evidence for the generalizability of brain functional graphs in large consortia, and encourage the aggregation of connectomic measures using multisite and multisession data.

The 22q11.2 deletion syndrome (22q11DS) is a recurrent copy number variant with high penetrance for developmental neuropsychiatric disorders. Study of individuals with 22q11DS therefore may offer key insights into neural mechanisms underlying such complex illnesses. Resting-state functional connectivity MRI studies in idiopathic schizophrenia have consistently revealed disruption of thalamic and hippocampal circuitry. Here, we sought to test whether this circuitry is similarly disrupted in the context of this genetic high-risk condition. To this end, resting-state functional connectivity patterns were assessed in a sample of human youth with 22q11DS (n = 42; 59.5% female) and demographically matched healthy controls (n = 39; 53.8% female). Neuroimaging data were acquired via single-band protocols and analyzed in line with methods provided by the Human Connectome Project. We computed functional relationships between individual-specific anatomically defined thalamic and hippocampal seeds and all gray matter voxels in the brain. Whole-brain Type I error protection was achieved through nonparametric permutation-based methods. The 22q11DS patients displayed dissociable disruptions in thalamic and hippocampal functional connectivity relative to control subjects. Thalamocortical coupling was increased in somatomotor regions and reduced across associative networks. The opposite effect was observed for the hippocampus in regards to somatomotor and associative network connectivity. The thalamic and hippocampal dysconnectivity observed in 22q11DS suggests that high genetic risk for psychiatric illness is linked with disruptions in large-scale corticosubcortical networks underlying higher-order cognitive functions. These effects highlight the translational importance of large-effect copy number variants for informing mechanisms underlying neural disruptions observed in idiopathic developmental neuropsychiatric disorders.SIGNIFICANCE STATEMENT Investigation of neuroimaging biomarkers in highly penetrant genetic syndromes represents a more biologically tractable approach to identify neural circuit disruptions underlying developmental neuropsychiatric conditions. The 22q11.2 deletion syndrome confers particularly high risk for psychotic disorders and is thus an important translational model in which to investigate systems-level mechanisms implicated in idiopathic illness. Here, we show resting-state fMRI evidence of large-scale sensory and executive network disruptions in youth with 22q11DS. In particular, this study provides the first evidence that these networks are disrupted in a dissociable fashion with regard to the functional connectivity of the thalamus and hippocampus, suggesting circuit-level dysfunction.

Objective: Brain imaging communities focusing on different diseases have increasingly started to collaborate and to pool data to perform well-powered meta- and mega-analyses. Some methodologists claim that a one-stage individual-participant data (IPD) mega-analysis can be superior to a two-stage aggregated data meta-analysis, since more detailed computations can be performed in a mega-analysis. Before definitive conclusions regarding the performance of either method can be drawn, it is necessary to critically evaluate the methodology of, and results obtained by, meta- and mega-analyses. Methods: Here, we compare the inverse variance weighted random-effect meta-analysis model with a multiple linear regression mega-analysis model, as well as with a linear mixed-effects random-intercept mega-analysis model, using data from 38 cohorts including 3,665 participants of the ENIGMA-OCD consortium. We assessed the effect sizes and standard errors, and the fit of the models, to evaluate the performance of the different methods. Results: The mega-analytical models showed lower standard errors and narrower confidence intervals than the meta-analysis. Similar standard errors and confidence intervals were found for the linear regression and linear mixed-effects random-intercept models. Moreover, the linear mixed-effects random-intercept models showed better fit indices compared to linear regression mega-analytical models. Conclusions: Our findings indicate that results obtained by meta- and mega-analysis differ, in favor of the latter. In multi-center studies with a moderate amount of variation between cohorts, a linear mixed-effects random-intercept mega-analytical framework appears to be the better approach to investigate structural neuroimaging data.

Understanding the fundamental alterations in brain functioning that lead to psychotic disorders remains a major challenge in clinical neuroscience. In particular, it is unknown whether any state-independent biomarkers can potentially predict the onset of psychosis and distinguish patients from healthy controls, regardless of paradigm. Here, using multi-paradigm fMRI data from the North American Prodrome Longitudinal Study consortium, we show that individuals at clinical high risk for psychosis display an intrinsic "trait-like" abnormality in brain architecture characterized as increased connectivity in the cerebello-thalamo-cortical circuitry, a pattern that is significantly more pronounced among converters compared with non-converters. This alteration is significantly correlated with disorganization symptoms and predictive of time to conversion to psychosis. Moreover, using an independent clinical sample, we demonstrate that this hyperconnectivity pattern is reliably detected and specifically present in patients with schizophrenia. These findings implicate cerebello-thalamo-cortical hyperconnectivity as a robust state-independent neural signature for psychosis prediction and characterization.

ObjectiveBrain imaging studies of structural abnormalities in OCD have yielded inconsistent results, partly because of limited statistical power, clinical heterogeneity, and methodological differences. The authors conducted meta- and mega-analyses comprising the largest study of cortical morphometry in OCD ever undertaken.MethodT1-weighted MRI scans of 1,905 OCD patients and 1,760 healthy controls from 27 sites worldwide were processed locally using FreeSurfer to assess cortical thickness and surface area. Effect sizes for differences between patients and controls, and associations with clinical characteristics, were calculated using linear regression models controlling for age, sex, site, and intracranial volume.ResultsIn adult OCD patients versus controls, we found a significantly lower surface area for the transverse temporal cortex and a thinner inferior parietal cortex. Medicated adult OCD patients also showed thinner cortices throughout the brain. In pediatric OCD patients compared with controls, we found significantly thinner inferior and superior parietal cortices, but none of the regions analyzed showed significant differences in surface area. However, medicated pediatric OCD patients had lower surface area in frontal regions. Cohen's d effect sizes varied from -0.10 to -0.33.ConclusionsThe parietal cortex was consistently implicated in both adults and children with OCD. More widespread cortical thickness abnormalities were found in medicated adult OCD patients, and more pronounced surface area deficits (mainly in frontal regions) were found in medicated pediatric OCD patients. These cortical measures represent distinct morphological features and may be differentially affected during different stages of development and illness, and possibly moderated by disease profile and medication.

Objective: Brain imaging communities focusing on different diseases have increasingly started to collaborate and to pool data to perform well-powered meta- and mega-analyses. Some methodologists claim that a one-stage individual-participant data (IPD) mega-analysis can be superior to a two-stage aggregated data meta-analysis, since more detailed computations can be performed in a mega-analysis. Before definitive conclusions regarding the performance of either method can be drawn, it is necessary to critically evaluate the methodology of, and results obtained by, meta- and mega-analyses. Methods: Here, we compare the inverse variance weighted random-effect meta-analysis model with a multiple linear regression mega-analysis model, as well as with a linear mixed-effects random-intercept mega-analysis model, using data from 38 cohorts including 3,665 participants of the ENIGMA-OCD consortium. We assessed the effect sizes and standard errors, and the fit of the models, to evaluate the performance of the different methods. Results: The mega-analytical models showed lower standard errors and narrower confidence intervals than the meta-analysis. Similar standard errors and confidence intervals were found for the linear regression and linear mixed-effects random-intercept models. Moreover, the linear mixed-effects random-intercept models showed better fit indices compared to linear regression mega-analytical models. Conclusions: Our findings indicate that results obtained by meta- and mega-analysis differ, in favor of the latter. In multi-center studies with a moderate amount of variation between cohorts, a linear mixed-effects random-intercept mega-analytical framework appears to be the better approach to investigate structural neuroimaging data.