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Objectives: Data from neurocognitive assessments may not be accurate in the context of factors impacting validity, such as disengagement, unmotivated responding, or intentional underperformance. Performance validity tests (PVTs) were developed to address these phenomena and assess underperformance on neurocognitive tests. However, PVTs can be burdensome, rely on cutoff scores that reduce information, do not examine potential variations in task engagement across a battery, and are typically not well-suited to acquisition of large cognitive datasets. Here we describe the development of novel performance validity measures that could address some of these limitations by leveraging psychometric concepts using data embedded within the Penn Computerized Neurocognitive Battery (PennCNB). Methods: We first developed these validity measures using simulations of invalid response patterns with parameters drawn from real data. Next, we examined their application in two large, independent samples: 1) children and adolescents from the Philadelphia Neurodevelopmental Cohort (n = 9498); and 2) adult servicemembers from the Marine Resiliency Study-II (n = 1444). Results: Our performance validity metrics detected patterns of invalid responding in simulated data, even at subtle levels. Furthermore, a combination of these metrics significantly predicted previously established validity rules for these tests in both developmental and adult datasets. Moreover, most clinical diagnostic groups did not show reduced validity estimates. Conclusions: These results provide proof-of-concept evidence for multivariate, data-driven performance validity metrics. These metrics offer a novel method for determining the performance validity for individual neurocognitive tests that is scalable, applicable across different tests, less burdensome, and dimensional. However, more research is needed into their application.

Over the past few decades, neuroimaging has become a ubiquitous tool in basic research and clinical studies of the human brain. However, no reference standards currently exist to quantify individual differences in neuroimaging metrics over time, in contrast to growth charts for anthropometric traits such as height and weight1. Here we assemble an interactive open resource to benchmark brain morphology derived from any current or future sample of MRI data ( http://www.brainchart.io/ ). With the goal of basing these reference charts on the largest and most inclusive dataset available, acknowledging limitations due to known biases of MRI studies relative to the diversity of the global population, we aggregated 123,984 MRI scans, across more than 100 primary studies, from 101,457 human participants between 115 days post-conception to 100 years of age. MRI metrics were quantified by centile scores, relative to non-linear trajectories2 of brain structural changes, and rates of change, over the lifespan. Brain charts identified previously unreported neurodevelopmental milestones3, showed high stability of individuals across longitudinal assessments, and demonstrated robustness to technical and methodological differences between primary studies. Centile scores showed increased heritability compared with non-centiled MRI phenotypes, and provided a standardized measure of atypical brain structure that revealed patterns of neuroanatomical variation across neurological and psychiatric disorders. In summary, brain charts are an essential step towards robust quantification of individual variation benchmarked to normative trajectories in multiple, commonly used neuroimaging phenotypes., R.A.I.B. was supported by a British Academy Postdoctoral fellowship and by the Autism Research Trust. J. Seidlitz was supported by NIMH T32MH019112-29 and K08MH120564. S.R.W. was funded by UKRI Medical Research Council MC_UU_00002/2 and was supported by the NIHR Cambridge Biomedical Research Centre (BRC-1215-20014). E.T.B. was supported by an NIHR Senior Investigator award and the Wellcome Trust collaborative award for the Neuroscience in Psychiatry Network. A.F.A.-B. was supported by NIMH K08MH120564. Data were curated and analysed using a computational facility funded by an MRC research infrastructure award (MR/M009041/1) to the School of Clinical Medicine, University of Cambridge and supported by the mental health theme of the NIHR Cambridge Biomedical Research Centre.

Adolescence is hypothesized to be a critical period for the development of association cortex. A reduction of the excitation:inhibition (E:I) ratio is a hallmark of critical period development; however it has been unclear how to assess the development of the E:I ratio using non-invasive neuroimaging techniques. Here, we used pharmacological fMRI with a GABAergic benzodiazepine challenge to empirically generate a model of E:I ratio based on multivariate patterns of functional connectivity. In an independent sample of 879 youth (ages 8-22 years), this model predicted reductions in the E:I ratio during adolescence, which were specific to association cortex and related to psychopathology. These findings support hypothesized shifts in E:I balance of association cortices during a neurodevelopmental critical period in adolescence.TeaserInhibitory maturation of the association cortex reflects an adolescent critical period.

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 patient level when phenotypes are not necessarily already known. Hence, we investigated the stability of PGS in European-American (EUR)- and African-American (AFR)-ancestry individuals from the Philadelphia Neurodevelopmental Cohort (PNC) and the Adolescent Brain Cognitive Development (ABCD) cohort using different discovery GWAS 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 sameancestry and different-ancestry GWAS had correlations that ranged from th percentile of PGS overlapping from one height GWAS to another. The degree of overlap decreases sharply as higher quantiles, less heritable traits, and different-ancestry GWAS are considered. PGS computed from different discovery GWAS have only modest correlation at the level of the individual patient, underscoring the need to proceed cautiously with integrating PGS into precision medicine applications.

To explore modular organization of chromosomes in schizophrenia (SCZ) and bipolar disorder (BD), we applied ‘population-scale’ correlational structuring of 739 histone H3-lysine 27 acetylation and H3-lysine 4 trimethylation profiles, generated from the prefrontal cortex (PFC) of 568 cases and controls. Neuronal histone acetylomes and methylomes assembled as thousands of cis-regulatory domains (CRDs), revealing fine-grained, kilo-to megabase scale chromatin organization at higher resolution but firmly integrated into Hi-C chromosomal conformations. Large clusters of domains that were hyperacetylated in disease shared spatial positioning within the nucleus, predominantly regulating PFC projection neuron function and excitatory neurotransmission. Hypoacetylated domains were linked to inhibitory interneuron- and myelination-relevant genes. Chromosomal modular architecture is affected in SCZ and BD, with hyperacetylated domains showing unexpectedly strong convergences defined by cell type, nuclear topography, genetic risk, and active chromatin state across a wide developmental window.

Precisely how the anatomical structure of the brain supports a wide range of complex functions remains a question of marked importance in both basic and clinical neuroscience. Progress has been hampered by the lack of theoretical frameworks explaining how a structural network of relatively rigid inter-areal connections can produce a diverse repertoire of functional neural dynamics. Here, we address this gap by positing that the brain’s structural network architecture determines the set of accessible functional connectivity patterns according to predictions of network control theory. In a large developmental cohort of 823 youths aged 8 to 23 years, we found that the flexibility of a brain region’s functional connectivity was positively correlated with the proportion of its structural links extending to different cognitive systems. Notably, this relationship was mediated by nodes’ boundary controllability, suggesting that a region’s strategic location on the boundaries of modules may underpin the capacity to integrate information across different cognitive processes. Broadly, our study provides a mechanistic framework that illustrates how temporal flexibility observed in functional networks may be mediated by the controllability of the underlying structural connectivity.AUTHOR SUMMARYPrecisely how the relatively rigid white matter wiring of the human brain gives rise to a diverse repertoire of functional neural dynamics is not well understood. In this work, we combined tools from network science and control theory to address this question. Capitalizing on a large developmental cohort, we demonstrated that the ability of a brain region to flexibly change its functional module allegiance over time (i.e., its modular flexibility), was positively correlated with its proportion of anatomical edges projecting to multiple cognitive networks (i.e., its structural participation coefficient). Moreover, this relationship was strongly mediated by the region’s boundary controllability, a metric capturing its capacity to integrate information across multiple cognitive domains.

Schizophrenia is marked by deficits in facial affect processing associated with abnormalities in GABAergic circuitry, deficits also found in first-degree relatives. Facial affect processing involves a distributed network of brain regions including limbic regions like amygdala and visual processing areas like fusiform cortex. Pharmacological modulation of GABAergic circuitry using benzodiazepines like alprazolam can be useful for studying this facial affect processing network and associated GABAergic abnormalities in schizophrenia. Here, we use pharmacological modulation and computational modeling to study the contribution of GABAergic abnormalities toward emotion processing deficits in schizophrenia. Specifically, we apply principles from network control theory to model persistence energy – the control energy required to maintain brain activation states – during emotion identification and recall tasks, with and without administration of alprazolam, in a sample of first-degree relatives and healthy controls. Here, persistence energy quantifies the magnitude of theoretical external inputs during the task. We find that alprazolam increases persistence energy in relatives but not in controls during threatening face processing, suggesting a compensatory mechanism given the relative absence of behavioral abnormalities in this sample of unaffected relatives. Further, we demonstrate that regions in the fusiform and occipital cortices are important for facilitating state transitions during facial affect processing. Finally, we uncover spatial relationships (i) between regional variation in differential control energy (alprazolam versus placebo) and (ii) both serotonin and dopamine neurotransmitter systems, indicating that alprazolam may exert its effects by altering neuromodulatory systems. Together, these findings reveal differences in emotion-processing circuitry associated with genetic vulnerability to schizophrenia.

In multisite neuroimaging studies there is often unwanted technical variation across scanners and sites. These “scanner effects” can hinder detection of biological features of interest, produce inconsistent results, and lead to spurious associations. We assess scanner effects in two brain magnetic resonance imaging (MRI) studies where subjects were measured on multiple scanners within a short time frame, so that one could assume any differences between images were due to technical rather than biological effects. We propose mica (multisite image harmonization by CDF alignment), a tool to harmonize images taken on different scanners by identifying and removing within-subject scanner effects. Our goals in the present study were to (1) establish a method that removes scanner effects by leveraging multiple scans collected on the same subject, and, building on this, (2) develop a technique to quantify scanner effects in large multisite trials so these can be reduced as a preprocessing step. We found that unharmonized images were highly variable across site and scanner type, and our method effectively removed this variability by warping intensity distributions. We further studied the ability to predict intensity harmonization results for a scan taken on an existing subject at a new site using cross-validation.

BackgroundTo test the functional implications of impaired white matter (WM) connectivity among patients with schizophrenia and their relatives, we examined the heritability of fractional anisotropy (FA) measured on diffusion tensor imaging data acquired in Pittsburgh and Philadelphia, and its association with cognitive performance in a unique sample of 175 multigenerational non-psychotic relatives of 23 multiplex schizophrenia families and 240 unrelated controls (total = 438).MethodsWe examined polygenic inheritance (h2r) of FA in 24 WM tracts bilaterally, and also pleiotropy to test whether heritability of FA in multiple WM tracts is secondary to genetic correlation among tracts using the Sequential Oligogenic Linkage Analysis Routines. Partial correlation tests examined the correlation of FA with performance on eight cognitive domains on the Penn Computerized Neurocognitive Battery, controlling for age, sex, site and mother's education, followed by multiple comparison corrections.ResultsSignificant total additive genetic heritability of FA was observed in all three-categories of WM tracts (association, commissural and projection fibers), in total 33/48 tracts. There were significant genetic correlations in 40% of tracts. Diagnostic group main effects were observed only in tracts with significantly heritable FA. Correlation of FA with neurocognitive impairments was observed mainly in heritable tracts.Conclusions:Our data show significant heritability of all three-types of tracts among relatives of schizophrenia. Significant heritability of FA of multiple tracts was not entirely due to genetic correlations among the tracts. Diagnostic group main effect and correlation with neurocognitive performance were mainly restricted to tracts with heritable FA suggesting shared genetic effects on these traits.

The high comorbidity among neuropsychiatric disorders suggests a possible common neurobiological phenotype. Resting-state regional cerebral blood flow (CBF) can be measured noninvasively with magnetic resonance imaging (MRI) and abnormalities in regional CBF are present in many neuropsychiatric disorders. Regional CBF may also provide a useful biological marker across different types of psychopathology. To investigate CBF changes common across psychiatric disorders, we capitalized upon a sample of 1042 youths (ages 11-23 years) who completed cross-sectional imaging as part of the Philadelphia Neurodevelopmental Cohort. CBF at rest was quantified on a voxelwise basis using arterial spin labeled perfusion MRI at 3T. A dimensional measure of psychopathology was constructed using a bifactor model of item-level data from a psychiatric screening interview, which delineated four factors (fear, anxious-misery, psychosis and behavioral symptoms) plus a general factor: overall psychopathology. Overall psychopathology was associated with elevated perfusion in several regions including the right dorsal anterior cingulate cortex (ACC) and left rostral ACC. Furthermore, several clusters were associated with specific dimensions of psychopathology. Psychosis symptoms were related to reduced perfusion in the left frontal operculum and insula, whereas fear symptoms were associated with less perfusion in the right occipital/fusiform gyrus and left subgenual ACC. Follow-up functional connectivity analyses using resting-state functional MRI collected in the same participants revealed that overall psychopathology was associated with decreased connectivity between the dorsal ACC and bilateral caudate. Together, the results of this study demonstrate common and dissociable CBF abnormalities across neuropsychiatric disorders in youth.

Background Resting-state fMRI (rs-fMRI) has emerged as a prominent tool for the study of functional connectivity. The identification of the regions associated with the different brain functions has received significant interest. However, most of the studies conducted so far have focused on the definition of a common set of regions, valid for an entire population. The variation of the functional regions within a population has rarely been accounted for. New method In this paper, we propose sGraSP, a graph-based approach for the derivation of subject-specific functional parcellations. Our method generates first a common parcellation for an entire population, which is then adapted to each subject individually. Results Several cortical parcellations were generated for 859 children being part of the Philadelphia Neurodevelopmental Cohort. The stability of the parcellations generated by sGraSP was tested by mixing population and subject rs-fMRI signals, to generate subject-specific parcels increasingly closer to the population parcellation. We also checked if the parcels generated by our method were better capturing a development trend underlying our data than the original parcels, defined for the entire population. Comparison with existing methods We compared sGraSP with a simpler and faster approach based on a Voronoi tessellation, by measuring their ability to produce functionally coherent parcels adapted to the subject data. Conclusions Our parcellations outperformed the Voronoi tessellations. The parcels generated by sGraSP vary consistently with respect to signal mixing, the results are highly reproducible and the neurodevelopmental trend is better captured with the subject-specific parcellation, under all the signal mixing conditions.

Cognitive functioning in schizophrenia is characterized by a generalized impairment in current cognitive ability based on traditional population-based norms. However, these norms assume a normal cognitive trajectory and do not directly account for illness-related declines from expected cognitive potential. Indeed, schizophrenia patients exhibit even greater deviation between their observed and expected cognitive functioning based on expanded norms that leverage premorbid variables resistant to illness-related features. The current study further quantified the extent to which illness-related features account for this deviation from expectation and assessed its relationship to neurophysiologic (mismatch negativity, P3a, theta oscillations), clinical, and psychosocial functioning in schizophrenia patients. Expected cognitive ability (PENN-CNB global cognition) in patients (n = 684) was calculated using healthy comparison subject (n = 660) weighted regression based on premorbid variables resistant to illness-related decline (demographics, single-word reading, parental education). The magnitude of any deviation between current (observed) and regression-predicted (expected) cognitive ability was calculated. Results indicated that 24% (n = 164) of the total patient population exhibited significant ( ≥ -1.96 SD) deviation between observed and expected global cognitive ability. Interestingly, 20% of the total patient population (n = 136) had “normal” range cognitive performance when using traditional population-based norms, but also had significant deviation from expected cognitive ability. The magnitude of this deviation was associated with more severe neurophysiologic abnormalities, longer illness duration, higher levels of negative symptoms, and worse psychosocial functioning. Assessment of cognitive deviation is thus a complementary metric for characterizing the severity of illness-related cognitive declines in patients, while also reflecting the expression and severity of key endophenotypes of schizophrenia.

Non-heme iron is a critical metabolic cofactor essential for healthy development. Iron deficiency is the most common nutritional disorder in the world, with greater prevalence of non-heme iron deficiency among individuals of lower socioeconomic status (SES). However, it remains unknown how brain iron accumulation during development may impact cognition. Brain iron can be measured in vivo using R2* weighted magnetic resonance imaging (MRI). Here, we used R2* MRI to quantify iron in the basal ganglia (BG) in a large sample of youth ages 8-23 imaged as part of the Philadelphia Neurodevelopmental Cohort (N=1,147, 54% female). Mean R2* signal within each bilateral BG region was modeled using generalized additive models with penalized splines to capture both linear and nonlinear developmental effects. As expected from prior reports, R2* increased in the BG throughout youth (all regions pfdr

BackgroundOur understanding of the complex relationship between schizophrenia symptomatology and etiological factors can be improved by studying brain-based correlates of schizophrenia. Research showed that impairments in value processing and executive functioning, which have been associated with prefrontal brain areas [particularly the medial orbitofrontal cortex (MOFC)], are linked to negative symptoms. Here we tested the hypothesis that MOFC thickness is associated with negative symptom severity.MethodsThis study included 1985 individuals with schizophrenia from 17 research groups around the world contributing to the ENIGMA Schizophrenia Working Group. Cortical thickness values were obtained from T1-weighted structural brain scans using FreeSurfer. A meta-analysis across sites was conducted over effect sizes from a model predicting cortical thickness by negative symptom score (harmonized Scale for the Assessment of Negative Symptoms or Positive and Negative Syndrome Scale scores).ResultsMeta-analytical results showed that left, but not right, MOFC thickness was significantly associated with negative symptom severity (βstd = −0.075; p = 0.019) after accounting for age, gender, and site. This effect remained significant (p = 0.036) in a model including overall illness severity. Covarying for duration of illness, age of onset, antipsychotic medication or handedness weakened the association of negative symptoms with left MOFC thickness. As part of a secondary analysis including 10 other prefrontal regions further associations in the left lateral orbitofrontal gyrus and pars opercularis emerged.ConclusionsUsing an unusually large cohort and a meta-analytical approach, our findings point towards a link between prefrontal thinning and negative symptom severity in schizophrenia. This finding provides further insight into the relationship between structural brain abnormalities and negative symptoms in schizophrenia.

Studying developmental changes in white matter connectivity is critical for understanding neurobiological substrates of cognition, learning, and neuropsychiatric disorders. This becomes especially important during adolescence when a rapid expansion of the behavioral repertoire occurs. Several factors such as brain geometry, genetic expression profiles, and higher level architectural specifications such as the presence of segregated modules have been associated with the observed organization of white matter connections. However, we lack understanding of the extent to which such factors jointly describe the brain network organization, nor have insights into how their contribution changes developmentally. We constructed a multifactorial model of white matter connectivity using Bayesian network analysis and tested it with diffusion imaging data from a large community sample. We investigated contributions of multiple factors in explaining observed connectivity, including architectural specifications, which promote a modular yet integrative organization, and brain’s geometric and genetic features. Our results demonstrated that the initially dominant geometric and genetic factors become less influential with age, whereas the effect of architectural specifications increases. The identified structural modules are associated with well-known functional systems, and the level of association increases with age. This integrative analysis provides a computational characterization of the normative evolution of structural connectivity during adolescence.

Adults with psychotic disorders have dysconnectivity in critical brain networks, including the default mode (DM) and the cingulo-opercular (CO) networks. However, it is unknown whether such deficits are present in youth with less severe symptoms. We conducted a multivariate connectome-wide association study examining dysconnectivity with resting state functional magnetic resonance imaging in a population-based cohort of 188 youths aged 8-22 years with psychosis-spectrum (PS) symptoms and 204 typically developing (TD) comparators. We found evidence for multi-focal dysconnectivity in PS youths, implicating the bilateral anterior cingulate, frontal pole, medial temporal lobe, opercular cortex and right orbitofrontal cortex. Follow-up seed-based and network-level analyses demonstrated that these results were driven by hyper-connectivity among DM regions and diminished connectivity among CO regions, as well as diminished coupling between frontal and DM regions. Collectively, these results provide novel evidence for functional dysconnectivity in PS youths, which show marked correspondence to abnormalities reported in adults with established psychotic disorders.

Multiple studies have shown that data quality is a critical confound in the construction of brain networks derived from functional MRI. This problem is particularly relevant for studies of human brain development where important variables (such as participant age) are correlated with data quality. Nevertheless, the impact of head motion on estimates of structural connectivity derived from diffusion tractography methods remains poorly characterized. Here, we evaluated the impact of in-scanner head motion on structural connectivity using a sample of 949 participants (ages 8-23 years old) who passed a rigorous quality assessment protocol for diffusion tensor imaging (DTI) acquired as part of the Philadelphia Neurodevelopmental Cohort. Structural brain networks were constructed for each participant using both deterministic and probabilistic tractography. We hypothesized that subtle variation in head motion would systematically bias estimates of structural connectivity and confound developmental inference, as observed in previous studies of functional connectivity. Even following quality assurance and retrospective correction for head motion, eddy currents, and field distortions, in-scanner head motion significantly impacted the strength of structural connectivity in a consistency-and length-dependent manner. Specifically, increased head motion was associated with reduced estimates of structural connectivity for high-consistency network edges, which included both short-and long-range connections. In contrast, motion inflated estimates of structural connectivity for low-consistency network edges that were primarily shorter-range. Finally, we demonstrate that age-related differences in head motion can both inflate and obscure developmental inferences on structural connectivity. Taken together, these data delineate the systematic impact of head motion on structural connectivity, and provide a critical context for identifying motion-related confounds in studies of structural brain network development.

Individuals with 22q11.2 deletion syndrome (22q11DS) are at markedly elevated risk for schizophrenia-related disorders. Stability, emergence, remission and persistence of psychosis-spectrum symptoms were investigated longitudinally. Demographic, clinical and cognitive predictors of psychosis were assessed. Prospective follow-up over 2.8 years was undertaken in 75 individuals with 22q11DS aged 8-35 years. Mood, anxiety, attention-deficit hyperactivity disorders and psychosis-spectrum symptoms were assessed with the Kiddie-Schedule for Affective Disorders and Schizophrenia and Scale of Prodromal Symptoms (SOPS). Four domains of cognition were evaluated with the Penn Computerized Neurocognitive Battery (executive functioning, memory, complex cognition and social cognition). Psychotic disorder or clinically significant SOPS-positive ratings were consistently absent in 35%, emergent in 13%, remitted in 22% and persistent in 31% of participants. Negative symptoms and functional impairment were found to be predictive of the emergence of positive psychosis-spectrum symptoms and to reflect ongoing deficits after remission of positive symptoms. Dysphoric mood and anxiety were predictive of emergent and persistent-positive psychosis-spectrum symptoms. Lower baseline global cognition and greater global cognitive decline were predictive of psychosis-spectrum outcomes but no particular cognitive domain stood out as being significantly more discriminating than others. Our findings suggest that negative symptoms, functioning and dysphoric mood are important predictors of psychosis risk in this population.

Personalized or precision medicine is predicated on the assumption that the average response to treatment is not necessarily representative of the response of each individual. A commitment to personalized medicine demands an effort to bring evidence-based medicine and personalized medicine closer together. The use of relatively homogeneous groups, defined using a priori criteria, may constitute a promising initial step for developing more accurate risk-prediction models with which to advance the development of personalized evidence-based medicine approaches to heterogeneous syndromes such as schizophrenia. However, this can lead to a paradoxical situation in the field of psychiatry. Since there has been a tendency to loosely define psychiatric disorders as ones without a known aetiology, the discovery of an aetiology for psychiatric syndromes (e.g. 22q11.2 deletion syndrome in some cases of schizophrenia), while offering a path toward more precise treatments, may also lead to their reclassification away from psychiatry. We contend that psychiatric disorders with a known aetiology should not be removed from the field of psychiatry. This knowledge should be used instead to guide treatment, inasmuch as psychotherapies, pharmacotherapies and other treatments can all be valid approaches to mental disorders. The translation of the personalized clinical approach inherent to psychiatry into evidence-based precision medicine can lead to the development of novel treatment options for mental disorders and improve outcomes.

Contains fulltext : 193179.pdf (Publisher’s version ) (Open Access) The regional distribution of white matter (WM) abnormalities in schizophrenia remains poorly understood, and reported disease effects on the brain vary widely between studies. In an effort to identify commonalities across studies, we perform what we believe is the first ever large-scale coordinated study of WM microstructural differences in schizophrenia. Our analysis consisted of 2359 healthy controls and 1963 schizophrenia patients from 29 independent international studies; we harmonized the processing and statistical analyses of diffusion tensor imaging (DTI) data across sites and meta-analyzed effects across studies. Significant reductions in fractional anisotropy (FA) in schizophrenia patients were widespread, and detected in 20 of 25 regions of interest within a WM skeleton representing all major WM fasciculi. Effect sizes varied by region, peaking at (d=0.42) for the entire WM skeleton, driven more by peripheral areas as opposed to the core WM where regions of interest were defined. The anterior corona radiata (d=0.40) and corpus callosum (d=0.39), specifically its body (d=0.39) and genu (d=0.37), showed greatest effects. Significant decreases, to lesser degrees, were observed in almost all regions analyzed. Larger effect sizes were observed for FA than diffusivity measures; significantly higher mean and radial diffusivity was observed for schizophrenia patients compared with controls. No significant effects of age at onset of schizophrenia or medication dosage were detected. As the largest coordinated analysis of WM differences in a psychiatric disorder to date, the present study provides a robust profile of widespread WM abnormalities in schizophrenia patients worldwide. Interactive three-dimensional visualization of the results is available at www.enigma-viewer.org.

BACKGROUND AND PURPOSE: The 22q11.2 deletion syndrome is a common genetic microdeletion syndrome that results in cognitive delays and an increased risk of several psychiatric disorders, particularly schizophrenia. The current study investigates the prevalence of incidental neuroradiologic findings within this population and their relationships with psychiatric conditions. MATERIALS AND METHODS: Brain MR imaging from 58 individuals with 22q11.2 deletion syndrome was reviewed by board-certified radiologists by using standard clinical procedures. Intracranial incidental findings were classified into 8 categories and compared with a large typically developing cohort. RESULTS: The rate of incidental findings was significantly higher (P .0001) in 22q11.2 deletion syndrome compared with typically developing individuals, driven by a high prevalence of cavum septum pellucidum (19.0%) and white matter abnormalities (10.3%). Both of these findings were associated with psychosis in 22q11.2 deletion syndrome. CONCLUSIONS: Cavum septum pellucidum and white matter hyperintensities are significantly more prevalent in patients with the 22q11.2 deletion syndrome and may represent biomarkers for psychosis. ABBREVIATIONS: CSP cavum septum pellucidum; PNC Philadelphia Neurodevelopmental Cohort; 22q11DS 22q11.2 deletion syndrome; TD typically developing

© 2016 Macmillan Publishers Limited. The profile of brain structural abnormalities in schizophrenia is still not fully understood, despite decades of research using brain scans. To validate a prospective meta-analysis approach to analyzing multicenter neuroimaging data, we analyzed brain MRI scans from 2028 schizophrenia patients and 2540 healthy controls, assessed with standardized methods at 15 centers worldwide. We identified subcortical brain volumes that differentiated patients from controls, and ranked them according to their effect sizes. Compared with healthy controls, patients with schizophrenia had smaller hippocampus (Cohen's d=-0.46), amygdala (d=-0.31), thalamus (d=-0.31), accumbens (d=-0.25) and intracranial volumes (d=-0.12), as well as larger pallidum (d=0.21) and lateral ventricle volumes (d=0.37). Putamen and pallidum volume augmentations were positively associated with duration of illness and hippocampal deficits scaled with the proportion of unmedicated patients. Worldwide cooperative analyses of brain imaging data support a profile of subcortical abnormalities in schizophrenia, which is consistent with that based on traditional meta-analytic approaches. This first ENIGMA Schizophrenia Working Group study validates that collaborative data analyses can readily be used across brain phenotypes and disorders and encourages analysis and data sharing efforts to further our understanding of severe mental illness.

Psychosis commonly develops in adolescence or early adulthood. Youths at clinical high risk (CHR) for psychosis exhibit similar, subtle symptoms to those with schizophrenia (SZ). Malfunctioning neurotransmitter systems, such as glutamate, are implicated in the disease progression of psychosis. Yet, in vivo imaging techniques for measuring glutamate across the cortex are limited. Here, we use a novel 7 Tesla MRI glutamate imaging technique (GluCEST) to estimate changes in glutamate levels across cortical and subcortical regions in young healthy individuals and ones on the psychosis spectrum. Individuals on the psychosis spectrum (PS; n=19) and healthy young individuals (HC; n=17) underwent MRI imaging at 3 and 7 T. At 7 T, a single slice GluCEST technique was used to estimate in vivo glutamate. GluCEST contrast was compared within and across the subcortex, frontal, parietal and occipital lobes. Subcortical (χ2 (1)=4.65, P=0.031) and lobular (χ2 (1)=5.17, P=0.023) GluCEST contrast levels were lower in PS compared with HC. Abnormal GluCEST contrast levels were evident in both CHR (n=14) and SZ (n=5) subjects, and correlated differentially, across regions, with clinical symptoms. Our findings describe a pattern of abnormal brain neurochemistry early in the course of psychosis. Specifically, CHR and young SZ exhibit diffuse abnormalities in GluCEST contrast attributable to a major contribution from glutamate. We suggest that neurochemical profiles of GluCEST contrast across cortex and subcortex may be considered markers of early psychosis. GluCEST methodology thus shows promise to further elucidate the progression of the psychosis disease state.

BackgroundThe contribution of ‘environment’ has been investigated across diverse and multiple domains related to health. However, in the context of large-scale genomic studies the focus has been on obtaining individual-level endophenotypes with environment left for future decomposition. Geo-social research has indicated that environment-level variables can be reduced, and these composites can then be used with other variables as intuitive, precise representations of environment in research.MethodUsing a large community sample (N = 9498) from the Philadelphia area, participant addresses were linked to 2010 census and crime data. These were then factor analyzed (exploratory factor analysis; EFA) to arrive at social and criminal dimensions of participants' environments. These were used to calculate environment-level scores, which were merged with individual-level variables. We estimated an exploratory multilevel structural equation model (MSEM) exploring associations among environment- and individual-level variables in diverse communities.ResultsThe EFAs revealed that census data was best represented by two factors, one socioeconomic status and one household/language. Crime data was best represented by a single crime factor. The MSEM variables had good fit (e.g. comparative fit index = 0.98), and revealed that environment had the largest association with neurocognitive performance (β = 0.41, p < 0.0005), followed by parent education (β = 0.23, p < 0.0005).ConclusionsEnvironment-level variables can be combined to create factor scores or composites for use in larger statistical models. Our results are consistent with literature indicating that individual-level socio-demographic characteristics (e.g. race and gender) and aspects of familial social capital (e.g. parental education) have statistical relationships with neurocognitive performance.

Verbal fluency deficits in schizophrenia are difficult to interpret because the tasks are multi-factorial and groups differ in total words generated. We manipulated retrieval and switching demands by requiring alternation between over-learned sequences in which retrieval is relatively automatic (OS) and semantic categories requiring increased retrieval effort (SC). Controlled processing was also manipulated by including switching and non-switching conditions, and formal thought disorder (FTD) was assessed with the communication disorders index (CDI). The OS/SC semantic fluency paradigm was administered during fMRI to 13 patients with schizophrenia and 14 matched controls. Images were acquired on a 3 Tesla Siemens scanner using compressed image acquisition to allow for cued overt word production. Subjects alternated between OS, SC, OS-switch, SC-switch, and baseline blocks. Images were pre-processed in SPM-2, and a two-stage random effects analysis tested within and between group contrasts. There were no group performance differences. fMRI analysis did not reveal any group differences during the OS non-switching condition. Both groups produced expected activation in bilateral prefrontal and inferior parietal regions. However, during the SC condition patients had greater activation than controls in left prefrontal, right anterior cingulate, right superior temporal, bilateral thalamus, and left parietal regions. There was also evidence of patient over-activation in prefrontal, superior temporal, superior parietal, and visual association areas when a switching component was added. FTD was negatively correlated with BOLD response in the right anterior cingulate, cuneus and superior frontal gyrus during increased retrieval demand, and positively correlated with fMRI activation in the left lingual gyrus, right fusiform gyrus and left superior parietal lobule during increased switching demand. These results indicate that patients are able to successfully perform effortful semantic fluency tasks during non-speeded conditions. When retrieval is relatively automatic there does not appear to be an effect of schizophrenia on fMRI response. However, when retrieval and controlled processing demands increase, patients have greater activation than controls despite unimpaired task performance. This inefficient BOLD response may explain why patients are slower and less accurate on standard self-paced fluency tasks.

The amygdala brain region has been implicated in the pathophysiology of schizophrenia through emotion processing. However, transcriptome messages in the amygdala of schizophrenia patients have not been well studied. We used RNA sequencing to investigate gene-expression profiling in the amygdala tissues, and identified 569 upregulated and 192 downregulated genes from 22 schizophrenia patients and 24 non-psychiatric controls. Gene functional enrichment analysis demonstrated that the downregulated genes were enriched in pathways such as 'synaptic transmission' and 'behavior', whereas the upregulated genes were significantly over-represented in gene ontology pathways such as 'immune response' and 'blood vessel development'. Co-expression-based gene network analysis identified seven modules including four modules significantly associated with 'synaptic transmission', 'blood vessel development' or 'immune responses'. Taken together, our study provides novel insights into the molecular mechanism of schizophrenia, suggesting that precision-tailored therapeutic approaches aimed at normalizing the expression/function of specific gene networks could be a promising option in schizophrenia.

BackgroundChromosome 22q11.2 deletion syndrome (22q11DS) is a common genetic disorder with high rates of psychosis and other psychopathologies, but few studies discuss treatment. Our aim was to characterize the prevalence and treatment of major psychiatric illnesses in a well-characterized sample of individuals with 22q11DS.MethodThis was a cross-sectional study of 112 individuals aged 8 to 45 years with a confirmed diagnosis of 22q11DS. Each participant was administered a modified Schedule for Affective Disorders and Schizophrenia for School-Age Children (K-SADS) and the Structured Interview for Prodromal Syndromes (SIPS). Phenotypes assessed were threshold and subthreshold psychosis, depression, mania, generalized and separation anxiety, obsessions/compulsions, inattention/hyperactivity and substance use. Histories of mental health care and current psychotropic treatment were obtained.ResultsPsychopathology was common, with 79% of individuals meeting diagnostic criteria for a disorder at the time of assessment. Diagnoses of psychosis were made in 11% of cases, attenuated positive symptom syndrome (APS) in 21%, and 47% experienced significant subthreshold symptoms. Peak occurrence of psychosis risk was during adolescence (62% of those aged 12–17 years). Criteria for a mood disorder were met by 14%, for anxiety disorder 34% and for attention deficit hyperactivity disorder (ADHD) 31%. Mental health care had been received by 63% of individuals in their lifetime, but only 40% continued therapy and 39% used psychotropics. Antipsychotics were used by 42% of participants with psychosis and none of the participants with APS. Half of those at risk for psychosis were receiving no mental health care.ConclusionsPsychopathology is common in 22q11DS but is not adequately treated or clinically followed. Particular attention should be paid to subthreshold psychotic symptoms, especially in adolescents.

BackgroundHerpes virus infections can cause cognitive impairment during and after acute encephalitis. Although chronic, latent/persistent infection is considered to be relatively benign, some studies have documented cognitive impairment in exposed persons that is untraceable to encephalitis. These studies were conducted among schizophrenia (SZ) patients or older community dwellers, among whom it is difficult to control for the effects of co-morbid illness and medications. To determine whether the associations can be generalized to other groups, we examined a large sample of younger control individuals, SZ patients and their non-psychotic relatives (n=1852).MethodUsing multivariate models, cognitive performance was evaluated in relation to exposures to herpes simplex virus type 1 (HSV-1), herpes simplex virus type 2 (HSV-2) and cytomegalovirus (CMV), controlling for familial and diagnostic status and sociodemographic variables, including occupation and educational status. Composite cognitive measures were derived from nine cognitive domains using principal components of heritability (PCH). Exposure was indexed by antibodies to viral antigens.ResultsPCH1, the most heritable component of cognitive performance, declines with exposure to CMV or HSV-1 regardless of case/relative/control group status (p = 1.09 × 10−5 and 0.01 respectively), with stronger association with exposure to multiple herpes viruses (β = −0.25, p = 7.28 × 10−10). There were no significant interactions between exposure and group status.ConclusionsLatent/persistent herpes virus infections can be associated with cognitive impairments regardless of other health status.

Facial discrimination tasks were applied as activation probes during physiologic neuroimaging ("neurobehavioral probes"). The stimuli pictured professional actors and actresses, posing degrees of happy and sad emotion. Cortical cerebral blood flow (CBF) was determined using the 133Xenon inhalation method during resting baseline, two emotional (happy from neutral and sad from neutral faces) and one nonemotional discrimination task (age). The three tasks produced CBF increase over baseline, which was greater in the right hemisphere (Task x Hemisphere interaction, p = .0001). There were regionally specific effects (Task x Region x Hemisphere interaction, p = .022). Relative to the age discrimination task, both emotion discrimination tasks were associated with greater right parietal activation. In addition, the happy discrimination task induced greater left frontal activation relative to the sad discrimination task. While overall magnitude of CBF increase did not show regionally specific correlations with performance, laterality did show such specificity. Sad discrimination performance correlated with greater right parietal activation, while performance on the happy discrimination task correlated with left frontal activation. Age discrimination performance correlated with higher activated right temporal CBF. These results support the hypothesis of right hemispheric involvement in facial processing and further suggest regionally specific hemispheric participation in happy and sad emotional discrimination. The study underscores the utility of performance measures for understanding the behavioral significance of activation effects in physiologic neuroimaging studies.

Physiologic neuroimaging studies have shown lateralized regional increase in brain activity during cognitive tasks, but the hypothesis that such changes are correlated with task performance has not been tested directly. We examined cerebral blood flow (CBF) changes induced by cognitive tasks in relation to performance. CBF was measured with the 133Xenon clearance method in 34 normal right-handed young (age < 30) volunteers during resting baseline and during the performance of a verbal analogies and a spatial line orientation test. Performance measures included "speed" and "power" estimates of both activation tasks. Resting CBF was moderately correlated with performance. The correlations were slightly higher with activated CBF for verbal but not spatial performance. The degree of increase (task-baseline) did not correlate with performance for either task. The highest and topographically specific correlations were obtained between laterality of CBF and verbal performance. Higher left hemispheric activation was correlated with verbal performance, and this correlation was significantly higher in the angular gyrus region. For the spatial task the correlations were with relatively higher right hemispheric activation but without regional specificity. The results underscore the importance of integrating behavioral performance data with physiologic measures in neuroimaging activation studies.

A computerized system was developed to process standard spin-echo magnetic resonance (MR) imaging data for estimation of brain parenchyma and cerebrospinal fluid (CSF) volumes. In phantom experiments, the estimated volumes corresponded closely to the true volumes (r = .998), with a mean error less than 1.0 cm3 (for phantom volumes ranging from 5 to 35 cm3), with excellent intra- and interobserver reliability. In a clinical validation study with actual brain images of 10 human subjects, the average coefficient of variation between observers for the measurement of absolute brain and CSF volumes was 1.2% and 6.4%, respectively. The intraclass correlations for three expert operators is greater than .99 in the measurement of brain and ventricular volumes and greater than .94 for total CSF volume. Therefore, the authors believe that their technique to analyze MR images of the brain performed with acceptable levels of accuracy and reliability and that it can be used to measure brain and CSF volumes for clinical ...

While many studies have sought a window into the genetics of schizophrenia, few have focused on African-American families. An exception is the Project among African-Americans to Explore Risks for Schizophrenia (PAARTNERS), which seeks to identify novel and known risk variation for schizophrenia by genetic analyses of African-American families. We report a linkage study of diagnostic status in 217 African-American families using the Illumina Linkage Panel. Due to assumed incomplete and time-dependent penetrance, we performed linkage analysis using two different treatments of diagnosis: (1) treating both affected and unaffected individuals as informative for linkage (using the program SIBPAL) and (2) treating only affected individuals as informative (using the program MERLIN). We also explore three definitions of affected status: narrowly defined schizophrenia; one broadened to include schizoaffective disorder; and another including all diagnoses indicating psychosis. Several regions show a decrease in the evidence for linkage as the definition broadens 8q22.1 (rs911, 99.26 cM; SIBPAL p-value [p] goes from 0.006 to 0.02), 16q24.3 (rs1006547, 130.48 cM; p from 0.00095 to 0.0085), and 20q13.2 (rs1022689, 81.73 cM; p from 0.00015 to 0.032). One region shows a substantial increase in evidence for linkage, 11p15.2 (rs722317, 24.27 cM; p from 0.0022 to 0.0000003); MERLIN results support the significance of the SIBPAL results (p=0.00001). Our linkage results overlap two broad, previously-reported linkage regions: 8p23.3-p12 found in studies sampling largely families of European ancestry; and 11p11.2-q22.3 reported by a study of African-American families. These results should prove quite useful for uncovering loci affecting risk for schizophrenia.

Background Neurologic examination abnormalities (NEA) are more prevalent among patients with schizophrenia as well as their unaffected relatives when compared with healthy controls, suggesting that NEA may be endophenotypes for schizophrenia. We estimated the heritability of NEA in moderately sized pedigrees. We also evaluated correlations between NEA and cognitive performance in order to examine their construct validity. Methods Members of eight extended families, each consisting of two first degree relatives with schizophrenia/schizoaffective disorders, as well as available first- to fifth-degree relatives were examined ( n = 96 participants). A modification of the Neurological Evaluation Scale (NES) was employed, augmented with localizing signs. Where feasible, we used untransformed data such as error counts and completion time, rather than ordinal measures. Heritability was estimated using the variance component method, implemented in SOLAR. Results Statistically significant heritability ( h 2 ) estimates were obtained for several measures ( p h 2 ± standard error: rapid alternating movements, right-sided completion time, 0.99 ± 0.19; alternating fist–palm test, completion time, 0.77 ± 0.19 s, errors, 0.70 ± 0.32; fist–ring test, right-sided completion time, 0.53 ± 0.23 s, left-sided completion time, 0.70 ± 0.21 s; go–no go task, correct responses, 0.93 ± 0.33; audio–visual integration, correct responses, 0.79 ± 0.54). For most items, heritability analysis was hampered by insufficient data variability (infrequent errors). Correlational analyses show some degree of divergence among types of NEA, repetitive motor tasks being associated with most domains of cognitive functioning other than executive functioning, and cognitive–perceptual tasks being associated with memory and executive functioning. Conclusions Significant familial influences on certain aspects of neurologic performance were detected. These heritable measures were also correlated with heritable neurocognitive measures.

Quantitation of regional brain function as a means for the characterization of brain disorders is discussed. Such quantitation can be accomplished by three-dimensional volume analysis based on magnetic resonance images, even though simple thresholding cannot be used for the segmentation of such images. As a first step toward such analysis, techniques for identifying and isolating the contents of the skull and, in particular, cerebrospinal fluid are described. The techniques of gradient-based boundary tracking, partitioning in a two-dimensional feature space, and shape-based interpolation appear to be effective for achieving this limited aim. >

Purpose: In this study, the contribution of the transaxial, coronal, and sagittal planes in evaluations of regional cerebral glucose metabolism was investigated in healthy volunteers as determined by fluorine-18-labeled 2-deoxy-2-fluoro-D-glucose (FDG) and high-resolution positron emission tomography (PET). Methods: One hundred twenty-seven healthy right-handed volunteers were injected with 4.2 MBq/kg (0.11 mCi) body weight FDG and imaged in a PENN PET H 240 scanner. Images were corrected for scatter and random coincidences and reconstructed in all three planes into 6- to 8-mm-thick slices. The reconstructed images were corrected for attenuation using the Chang algorithm. The transverse, coronal, and sagittal images were read independently of each other using a qualitative scale in which 1 = equal to, 2 = mildly, 3 = moderately, and 4 = markedly less than the area with the highest glucose metabolism in the respective plane. Results: The areas with the highest glucose metabolisms were the posterior cingulate gyri with mean scores of 1.1 to 1.2, thalami (1.2 to 1.3), basal ganglia (1.5 to 1.9), and visual cortex (1.6). The lowest values were found in the occipital cortex (2.7 to 2.8) and the cerebellum (2.3 to 2.4). Whereas reliable analysis of the mesial temporal aspects was not feasible in the sagittal plane, the anterior poles of the temporal and frontal lobes could not be evaluated in the coronal or the inferior temporal areas in the transaxial slices. In all three planes, regional glucose metabolism was less in the lateral temporal areas on the left than on the right (P < 0.001). The consistency of readings as measured in terms of coefficients of variation was greastest in the coronal plane for the caudates and posterior cingulate gyri, in the transaxial plane for the lateral temporal regions, and in the sagittal plane for the visual cortex. Age-dependent decreases in regional glucose metabolism in the inferior and lateral frontal regions and the parietal lobes were found in all three planes. Conclusions: All three projection planes must be used for a comprehensive qualitative evaluation of the regional glucose metabolism of the brain.

Attention Deficit Disorder (ADD) has been increasingly recognized as extending into adulthood, affecting occupational, interpersonal, and psychological functioning. The Minnesota Multiphasic Personality Inventory (MMPI) and its revision (MMPI-2) have been widely used in the assessment of patients with psychiatric disorders, but few studies have attempted to characterize the personality profiles of individuals with ADD and none have used the MMPI-2. Thirty-three patients with ADD and 33 schizophrenia patients were compared to 46 healthy control subjects on the MMPI-2 validity, clinical and Harris-Lingoes scales. With the exception of significantly lower scores for general affective distress (F), thought disorder (Sc), and paranoia (Pa), ADD subjects demonstrated remarkably similar profiles to those seen in the schizophrenia group. Significant differences between the three groups were found on a majority of the clinical scales, with ADD subjects showing similar profile elevations as schizophrenic subjects on both clinical- and sub-scales. These results were consistent with previous research using the original MMPI in adults with ADD, and confirm that examination of MMPI-2 profiles may be a useful diagnostic aid for this disorder.

While the P50 component (50-60-ms latency) of the auditory evoked potential has been reported as abnormal in schizophrenia, few studies have examined the relationship between this abnormality and clinical or neuropsychological measures. To examine these possible relationships, mid-latency auditory evoked potentials were recorded at the CZ recording site of 47 patients with schizophrenia in response to binaural clicks presented at three stimulus rates: 1, 5 and 10/sec. A sub-sample of patients were then divided into high- (n = 15) and low-P50 abnormality (n = 16) groups based on a median split of the P50 amplitude at a rate of 10/sec (a greater amplitude at this rate suggests a greater abnormality in recovery) of the entire sample. Only those patients with complete neuropsychological and clinical data and who were reasonably matched on demographic dimensions were included. A multivariate analysis of variance of 11 neuropsychological function profile scores showed a significant group x global score interaction (Hotelling t = 3.97, p < 0.005). The high-abnormality group had relatively greater deficits for attention profile scores than for the remaining neuropsychological measures. An analysis of global subscores for SAPS and SANS clinical measures revealed a significant difference only for the SANS attention subscale (p < 0.05). The high-abnormality group was rated as more severe on the attention measure. These convergent findings across both phenomenological and neuropsychological measures suggest that abnormalities in P50 recovery may be linked to deficits in attention processes in schizophrenia.