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Background: Attention-deficit/hyperactivity disorder (ADHD) and posttraumatic stress disorder (PTSD) are associated, but it is unclear if this is a causal relationship or confounding. We used genetic analyses and sibling comparisons to clarify the direction of this relationship. Methods: Linkage disequilibrium score regression and 2-sample Mendelian randomization were used to test for genetic correlation (r g) and bidirectional causal effects using European ancestry genome-wide association studies of ADHD (20,183 cases and 35,191 controls) and 6 PTSD definitions (up to 320,369 individuals). Several additional variables were included in the analysis to verify the independence of the ADHD-PTSD relationship. In a population-based sibling comparison (N = 2,082,118 individuals), Cox regression models were fitted to account for time at risk, a range of sociodemographic factors, and unmeasured familial confounders (via sibling comparisons). Results: ADHD and PTSD had consistent r g (r g range, 0.43–0.52; p < .001). ADHD genetic liability was causally linked with increased risk for PTSD (β = 0.367; 95% CI, 0.186–0.552; p = 7.68 × 10 −5). This result was not affected by heterogeneity, horizontal pleiotropy (Mendelian randomization Egger intercept = 4.34 × 10 −4, p = .961), or other phenotypes and was consistent across PTSD datasets. However, we found no consistent associations between PTSD genetic liability and ADHD risk. Individuals diagnosed with ADHD were at a higher risk for developing PTSD than their undiagnosed sibling (hazard ratio = 2.37; 95% CI, 1.98–3.53). Conclusions: Our findings add novel evidence supporting the need for early and effective treatment of ADHD, as patients with this diagnosis are at significantly higher risk to develop PTSD later in life.

First-degree relatives of patients diagnosed with schizophrenia (SZ-FDRs) show similar patterns of brain abnormalities and cognitive alterations to patients, albeit with smaller effect sizes. First-degree relatives of patients diagnosed with bipolar disorder (BD-FDRs) show divergent patterns; on average, intracranial volume is larger compared to controls, and findings on cognitive alterations in BD-FDRs are inconsistent. Here, we performed a meta-analysis of global and regional brain measures (cortical and subcortical), current IQ, and educational attainment in 5,795 individuals (1,103 SZ-FDRs, 867 BD-FDRs, 2,190 controls, 942 schizophrenia patients, 693 bipolar patients) from 36 schizophrenia and/or bipolar disorder family cohorts, with standardized methods. Compared to controls, SZ-FDRs showed a pattern of widespread thinner cortex, while BD-FDRs had widespread larger cortical surface area. IQ was lower in SZ-FDRs (d= -0.42,p= 3 x 10(-5)), with weak evidence of IQ reductions among BD-FDRs (d= -0.23,p= .045). Both relative groups had similar educational attainment compared to controls. When adjusting for IQ or educational attainment, the group-effects on brain measures changed, albeit modestly. Changes were in the expected direction, with less pronounced brain abnormalities in SZ-FDRs and more pronounced effects in BD-FDRs. To conclude, SZ-FDRs and BD-FDRs show a differential pattern of structural brain abnormalities. in contrast, both had lower IQ scores and similar school achievements compared to controls. Given that brain differences between SZ-FDRs and BD-FDRs remain after adjusting for IQ or educational attainment, we suggest that differential brain developmental processes underlying predisposition for schizophrenia or bipolar disorder are likely independent of general cognitive impairment., Australian National Health and Medical Research CouncilNational Health and Medical Research Council of Australia [1037196, 1063960, 1066177, 510135, 1176716]; Canadian Institutes of Health ResearchCanadian Institutes of Health Research (CIHR) [103703, 106469, 142255]; Departament de Salut de la Generalitat de CatalunyaGeneralitat de Catalunya [SLT002/16/00331]; Deutsche ForschungsgemeinschaftGerman Research Foundation (DFG) [1617]; Development Service Merit Review Award [I01CX000227]; Dokuz Eylul University Department of Scientific Research [2012.KB.SAG.062]; e:Med program [O1ZX1314B, O1ZX1314G]; Ege University School of Medicine Research Foundation [2009-D-00017]; Fundacio Marato TV3 [091630]; Netherlands Organisation for Health Research and DevelopmentNetherlands Organization for Health Research and Development [10-000-1002]; Generalitat de CatalunyaGeneralitat de Catalunya [2017SGR01271]; German Federal Ministry for Education and ResearchFederal Ministry of Education & Research (BMBF); Medical Research CouncilMedical Research Council UK (MRC) [G0901310]; Ministerstvo Zdravotnictvi Ceske Republiky [NR8786, NT13891]; National Alliance for Research on Schizophrenia and DepressionNARSAD [17319, 20244, 26731]; Swiss National Centre of Competence in Research Robotics [51NF40-185897]; National Institute of Mental HealthUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of Mental Health (NIMH) [1S10OD017974-01, P30 NS076408, R01 MH052857, R01 MH080912, R01 MH113619, U01 MH108150, R01 MH085667]; National Institute on AgingUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute on Aging (NIA) [T32AG058507]; National Institutes of HealthUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA [P41 EB015922, R01 MH111671, R01 MH116147, R01 MH117601, R01MH121246, R03 MH105808, U54EB020403]; Research Council of NorwayResearch Council of Norway [223273]; Spanish Ministry of Economy and Competitiveness/Instituto de Salud Carlos III [CPII19/00009, PI070066, PI1100683, PI1500467, PI18/00976]; Stanley Medical Research Institute; Swedish Research CouncilSwedish Research Council [K2007-62X-15077-04-1, K2008-62P-20597-01-3, K2010-62X-15078-07-2, K2012-61X-15078-09-3]; Swiss National Science FoundationSwiss National Science Foundation (SNSF) [32003B_156914]; VIDINetherlands Organization for Scientific Research (NWO) [452-11-014, 917-46-370]; Wellcome TrustWellcome Trust [085475/B/08/Z, 085475/Z/08/Z, 064971]; ZonMwNetherlands Organization for Health Research and Development [908-02-123], Australian National Health and Medical Research Council Grants, Grant/Award Numbers: 1037196, 1063960, 1066177, 510135, 1176716; Canadian Institutes of Health Research, Grant/Award Numbers: 103703, 106469, 142255; Departament de Salut de la Generalitat de Catalunya, Grant/Award Number: SLT002/16/00331; Deutsche Forschungsgemeinschaft, Grant/Award Number: 1617; Development Service Merit Review Award, Grant/Award Number: I01CX000227; Dokuz Eylul University Department of Scientific Research Projects Funding, Grant/Award Number: 2012.KB.SAG.062; e:Med program, Grant/Award Numbers: O1ZX1314B, O1ZX1314G; Ege University School of Medicine Research Foundation, Grant/Award Number: 2009-D-00017; Fundacio Marato TV3, Grant/Award Number: 091630; Geestkracht program of the Netherlands Organisation for Health Research and Development, Grant/Award Number: 10-000-1002; Generalitat de Catalunya, Grant/Award Number: 2017SGR01271; German Federal Ministry for Education and Research; Medical Research Council, Grant/Award Number: G0901310; Ministerstvo Zdravotnictvi Ceske Republiky, Grant/Award Numbers: NR8786, NT13891; National Alliance for Research on Schizophrenia and Depression, Grant/Award Numbers: 17319, 20244, 26731; Swiss National Centre of Competence in Research Robotics, Grant/Award Number: 51NF40-185897; National Institute of Mental Health, Grant/Award Numbers: 1S10OD017974-01, P30 NS076408, R01 MH052857, R01 MH080912, R01 MH113619, U01 MH108150, R01 MH085667; National Institute on Aging, Grant/Award Number: T32AG058507; National Institutes of Health, Grant/Award Numbers: P41 EB015922, R01 MH111671, R01 MH116147, R01 MH117601, R01MH121246, R03 MH105808, U54EB020403; Research Council of Norway, Grant/Award Number: 223273; Spanish Ministry of Economy and Competitiveness/Instituto de Salud Carlos III, Grant/Award Numbers: CPII19/00009, PI070066, PI1100683, PI1500467, PI18/00976; Stanley Medical Research Institute; Swedish Research Council, Grant/Award Numbers: K2007-62X-15077-04-1, K2008-62P-20597-01-3, K2010-62X-15078-07-2, K2012-61X-15078-09-3; Swiss National Science Foundation, Grant/Award Number: 32003B_156914; VIDI, Grant/Award Numbers: 452-11-014, 917-46-370; Wellcome Trust, Grant/Award Numbers: 085475/B/08/Z, 085475/Z/08/Z; Wellcome Trust Research Training Fellowship, Grant/Award Number: 064971; ZonMw, Grant/Award Number: 908-02-123

Background: Posttraumatic stress disorder (PTSD) is accompanied by disrupted cortical neuroanatomy. We investigated alteration in covariance of structural networks associated with PTSD in regions that demonstrate the case-control differences in cortical thickness (CT) and surface area (SA). Methods: Neuroimaging and clinical data were aggregated from 29 research sites in >1300 PTSD cases and >2000 trauma-exposed control subjects (ages 6.2–85.2 years) by the ENIGMA-PGC (Enhancing Neuro Imaging Genetics through Meta Analysis–Psychiatric Genomics Consortium) PTSD working group. Cortical regions in the network were rank ordered by the effect size of PTSD-related cortical differences in CT and SA. The top-n (n = 2–148) regions with the largest effect size for PTSD > non-PTSD formed hypertrophic networks, the largest effect size for PTSD < non-PTSD formed atrophic networks, and the smallest effect size of between-group differences formed stable networks. The mean structural covariance (SC) of a given n-region network was the average of all positive pairwise correlations and was compared with the mean SC of 5000 randomly generated n-region networks. Results: Patients with PTSD, relative to non-PTSD control subjects, exhibited lower mean SC in CT-based and SA-based atrophic networks. Comorbid depression, sex, and age modulated covariance differences of PTSD-related structural networks. Conclusions: Covariance of structural networks based on CT and cortical SA are affected by PTSD and further modulated by comorbid depression, sex, and age. The SC networks that are perturbed in PTSD comport with converging evidence from resting-state functional connectivity networks and networks affected by inflammatory processes and stress hormones in PTSD.

Funding Information: This work was supported by the National Institute of Mental Health / U.S. Army Medical Research and Development Command (Grant No. R01MH106595 [to CMN, IL, MBS, KJRe, and KCK], National Institutes of Health (Grant No. 5U01MH109539 to the Psychiatric Genomics Consortium ), and Brain & Behavior Research Foundation (Young Investigator Grant [to KWC]). Genotyping of samples was provided in part through the Stanley Center for Psychiatric Genetics at the Broad Institute supported by Cohen Veterans Bioscience . Statistical analyses were carried out on the LISA/Genetic Cluster Computer ( https://userinfo.surfsara.nl/systems/lisa ) hosted by SURFsara. This research has been conducted using the UK Biobank resource (Application No. 41209). This work would have not been possible without the financial support provided by Cohen Veterans Bioscience, the Stanley Center for Psychiatric Genetics at the Broad Institute, and One Mind. Funding Information: MBS has in the past 3 years received consulting income from Actelion, Acadia Pharmaceuticals, Aptinyx, Bionomics, BioXcel Therapeutics, Clexio, EmpowerPharm, GW Pharmaceuticals, Janssen, Jazz Pharmaceuticals, and Roche/Genentech and has stock options in Oxeia Biopharmaceuticals and Epivario. In the past 3 years, NPD has held a part-time paid position at Cohen Veterans Bioscience, has been a consultant for Sunovion Pharmaceuticals, and is on the scientific advisory board for Sentio Solutions for unrelated work. In the past 3 years, KJRe has been a consultant for Datastat, Inc., RallyPoint Networks, Inc., Sage Pharmaceuticals, and Takeda. JLM-K has received funding and a speaking fee from COMPASS Pathways. MU has been a consultant for System Analytic. HRK is a member of the Dicerna scientific advisory board and a member of the American Society of Clinical Psychopharmacology Alcohol Clinical Trials Initiative, which during the past 3 years was supported by Alkermes, Amygdala Neurosciences, Arbor Pharmaceuticals, Dicerna, Ethypharm, Indivior, Lundbeck, Mitsubishi, and Otsuka. HRK and JG are named as inventors on Patent Cooperative Treaty patent application number 15/878,640, entitled “Genotype-guided dosing of opioid agonists,” filed January 24, 2018. RP and JG are paid for their editorial work on the journal Complex Psychiatry. OAA is a consultant to HealthLytix. All other authors report no biomedical financial interests or potential conflicts of interest. Funding Information: This work was supported by the National Institute of Mental Health/ U.S. Army Medical Research and Development Command (Grant No. R01MH106595 [to CMN, IL, MBS, KJRe, and KCK], National Institutes of Health (Grant No. 5U01MH109539 to the Psychiatric Genomics Consortium), and Brain & Behavior Research Foundation (Young Investigator Grant [to KWC]). Genotyping of samples was provided in part through the Stanley Center for Psychiatric Genetics at the Broad Institute supported by Cohen Veterans Bioscience. Statistical analyses were carried out on the LISA/Genetic Cluster Computer (https://userinfo.surfsara.nl/systems/lisa) hosted by SURFsara. This research has been conducted using the UK Biobank resource (Application No. 41209). This work would have not been possible without the financial support provided by Cohen Veterans Bioscience, the Stanley Center for Psychiatric Genetics at the Broad Institute, and One Mind. This material has been reviewed by the Walter Reed Army Institute of Research. There is no objection to its presentation and/or publication. The opinions or assertions contained herein are the private views of the authors and are not to be construed as official or as reflecting true views of the U.S. Department of the Army or the Department of Defense. We thank the investigators who comprise the PGC-PTSD working group and especially the more than 206,000 research participants worldwide who shared their life experiences and biological samples with PGC-PTSD investigators. We thank Mark Zervas for his critical input. Full acknowledgments are in Supplement 1. MBS has in the past 3 years received consulting income from Actelion, Acadia Pharmaceuticals, Aptinyx, Bionomics, BioXcel Therapeutics, Clexio, EmpowerPharm, GW Pharmaceuticals, Janssen, Jazz Pharmaceuticals, and Roche/Genentech and has stock options in Oxeia Biopharmaceuticals and Epivario. In the past 3 years, NPD has held a part-time paid position at Cohen Veterans Bioscience, has been a consultant for Sunovion Pharmaceuticals, and is on the scientific advisory board for Sentio Solutions for unrelated work. In the past 3 years, KJRe has been a consultant for Datastat, Inc. RallyPoint Networks, Inc. Sage Pharmaceuticals, and Takeda. JLM-K has received funding and a speaking fee from COMPASS Pathways. MU has been a consultant for System Analytic. HRK is a member of the Dicerna scientific advisory board and a member of the American Society of Clinical Psychopharmacology Alcohol Clinical Trials Initiative, which during the past 3 years was supported by Alkermes, Amygdala Neurosciences, Arbor Pharmaceuticals, Dicerna, Ethypharm, Indivior, Lundbeck, Mitsubishi, and Otsuka. HRK and JG are named as inventors on Patent Cooperative Treaty patent application number 15/878,640, entitled ?Genotype-guided dosing of opioid agonists,? filed January 24, 2018. RP and JG are paid for their editorial work on the journal Complex Psychiatry. OAA is a consultant to HealthLytix. All other authors report no biomedical financial interests or potential conflicts of interest. Publisher Copyright: © 2021 Society of Biological Psychiatry Background: Posttraumatic stress disorder (PTSD) is heritable and a potential consequence of exposure to traumatic stress. Evidence suggests that a quantitative approach to PTSD phenotype measurement and incorporation of lifetime trauma exposure (LTE) information could enhance the discovery power of PTSD genome-wide association studies (GWASs). Methods: A GWAS on PTSD symptoms was performed in 51 cohorts followed by a fixed-effects meta-analysis (N = 182,199 European ancestry participants). A GWAS of LTE burden was performed in the UK Biobank cohort (N = 132,988). Genetic correlations were evaluated with linkage disequilibrium score regression. Multivariate analysis was performed using Multi-Trait Analysis of GWAS. Functional mapping and annotation of leading loci was performed with FUMA. Replication was evaluated using the Million Veteran Program GWAS of PTSD total symptoms. Results: GWASs of PTSD symptoms and LTE burden identified 5 and 6 independent genome-wide significant loci, respectively. There was a 72% genetic correlation between PTSD and LTE. PTSD and LTE showed largely similar patterns of genetic correlation with other traits, albeit with some distinctions. Adjusting PTSD for LTE reduced PTSD heritability by 31%. Multivariate analysis of PTSD and LTE increased the effective sample size of the PTSD GWAS by 20% and identified 4 additional loci. Four of these 9 PTSD loci were independently replicated in the Million Veteran Program. Conclusions: Through using a quantitative trait measure of PTSD, we identified novel risk loci not previously identified using prior case-control analyses. PTSD and LTE have a high genetic overlap that can be leveraged to increase discovery power through multivariate methods. publishersversion published

Investigations within the Human Connectome Project have expanded to include studies focusing on brain disorders. This paper describes one of the investigations focused on psychotic psychopathology: The psychosis Human Connectome Project (P-HCP). The data collected as part of this project were multimodal and derived from clinical assessments of psychopathology, cognitive assessments, instrument-based motor assessments, blood specimens, and magnetic resonance imaging (MRI) data. The dataset will be made publicly available through the NIMH Data Archive. In this report we provide specific information on how the sample of participants was obtained and characterized and describe the experimental tasks and procedures used to probe neural functions involved in psychotic disorders that may also mark genetic liability for psychotic psychopathology. Our goal in this paper is to outline the data acquisition process so that researchers intending to use these publicly available data can plan their analyses. MRI data described in this paper are limited to data acquired at 3 Tesla. A companion paper describes the study's 7 Tesla image acquisition protocol in detail, which is focused on visual perceptual functions in psychotic psychopathology.

The brain at rest generates cycles of electrical activity that have been shown to be abnormal in people with schizophrenia. The alpha rhythm (~ 10 Hz) is the dominant resting state electrical cycle and each person has a propensity toward a particular frequency of oscillation for this rhythm. This individual alpha peak frequency (IAPF) is hypothesized to be central to visual perceptual processes and may have downstream influences on cognitive functions such as attention, working memory, or problem solving. In the current study we sought to determine whether IAPF was slower in schizophrenia, and whether lower IAPF predicted deficits in visual perception and cognition that are often observed in schizophrenia. Eyes-closed resting state EEG activity, visual attention, and global cognitive functioning were assessed in individuals with schizophrenia (N = 104) and a group of healthy controls (N = 101). Compared to controls, the schizophrenia group showed slower IAPF and was associated with poorer discrimination of visual targets and nontargets on a computerized attention task, as well as impaired global cognition measured using neuropsychological tests across groups. Notably, disruptions in visual attention fully mediated the relationship between IAPF and global cognition across groups. The current findings demonstrate that slower alpha oscillatory cycling accounts for global cognitive deficits in schizophrenia by way of impairments in perceptual discrimination measured during a visual attention task.

A growing number of studies have examined alterations in white matter organization in people with posttraumatic stress disorder (PTSD) using diffusion MRI (dMRI), but the results have been mixed, which may be partially due to relatively small sample sizes among studies. Altered structural connectivity may be both a neurobiological vulnerability for, and a result of, PTSD. In an effort to find reliable effects, we present a multi-cohort analysis of dMRI metrics across 3,047 individuals from 28 cohorts currently participating in the PGC-ENIGMA PTSD working group (a joint partnership between the Psychiatric Genomics Consortium and the Enhancing NeuroImaging Genetics through Meta-Analysis consortium). Comparing regional white matter metrics across the full brain in 1,426 individuals with PTSD and 1,621 controls (2174 males/873 females) between ages 18–83, 92% of whom were trauma-exposed, we report associations between PTSD and disrupted white matter organization measured by lower fractional anisotropy (FA) in the tapetum region of the corpus callosum (Cohen’s d=−0.11, p=0.0055). The tapetum connects the left and right hippocampus, structures for which structure and function have been consistently implicated in PTSD. Results remained significant/similar after accounting for the effects of multiple potentially confounding variables: childhood trauma exposure, comorbid depression, history of traumatic brain injury, current alcohol abuse or dependence, and current use of psychotropic medications. Our results show that PTSD may be associated with alterations in the broader hippocampal network.

Posttraumatic stress disorder (PTSD) is often complicated by the after-effects of mild traumatic brain injury (mTBI). The mixture of brain conditions results in abnormal affective and cognitive functioning, as well as maladaptive behavior. To better understand how brain activity explains cognitive and emotional processes in these conditions, we used an emotional N-back task and functional magnetic resonance imaging (fMRI) to study neural responses in US military veterans after deployments to Iraq and Afghanistan. Additionally, we sought to examine whether hierarchical dimensional models of maladaptive personality could account for the relationship between combat-related brain conditions and fMRI responses under cognitive and affective challenge. FMRI data, measures of PTSD symptomatology (PTSS), blast-induced mTBI (bmTBI) severity, and maladaptive personality (MMPI-2-RF) were gathered from 93 veterans. Brain regions central to emotion regulation were selected for analysis, and consisted of bilateral amygdala, bilateral dorsolateral prefrontal (dlPFC), and ventromedial prefrontal/subgenual anterior cingulate (vmPFC-sgACC). Cognitive load increased activity in dlPFC and reduced activity in emotional responding brain regions. However, individuals with greater PTSS showed blunted deactivations in bilateral amygdala and vmPFC-sgACC, and weaker responses in right dlPFC. Additionally, we found that elevated emotional/internalizing dysfunction (EID), specifically low positive emotionality (RC2), accounted for PTSS-related changes in bilateral amygdala under increased cognitive load. Findings suggest that PTSS might result in amygdala and vmPFC-sgACC activity resistant to moderation by cognitive demands, reflecting emotion dysregulation despite a need to marshal cognitive resources. Anhedonia may be an important target for interventions that improve the affective and cognitive functioning of individuals with PTSD.

Sensory discrimination thresholds (i.e., the briefest stimulus that can be accurately perceived) can be measured using tablet-based auditory and visual sweep paradigms. These basic sensory functions have been found to be diminished in patients with psychosis. However, the extent to which worse sensory discrimination characterizes genetic liability for psychosis, and whether it is related to clinical symptomatology and community functioning remains unknown. In the current study we compared patients with psychosis (PSY; N=76), their first-degree biological relatives (REL; N=44), and groups of healthy controls (CON; N=13 auditory and visual/N=275 auditory/N=267 visual) on measures of auditory and visual sensory discrimination, and examined relationships with a battery of symptom, cognitive, and functioning measures. Sound sweep thresholds differed among the PSY, REL, and CON groups, driven by higher thresholds in the PSY compared to CON group, with the REL group showing intermediate thresholds. Visual thresholds also differed among the three groups, driven by higher thresholds in the REL versus CON group, and no significant differences between the REL and PSY groups. Across groups and among patients, higher thresholds (poorer discrimination) for both sound and visual sweeps strongly correlated with lower global cognitive scores. We conclude that low-level auditory and visual sensory discrimination deficits in psychosis may reflect genetic liability for psychotic illness. Critically, these deficits relate to global cognitive disruptions that are a hallmark of psychotic illnesses such as schizophrenia.

BackgroundPTSD and depression commonly co-occur and have been associated with smaller hippocampal volumes compared to healthy and trauma-exposed controls. However, the hippocampus is heterogeneous, with subregions that may be uniquely affected in individuals with PTSD and depression.MethodsWe used random effects regressions and a harmonized neuroimaging protocol based on FreeSurfer (v6.0) to identify sub-structural hippocampal markers of current PTSD (C-PTSD), depression, and the interaction of these conditions across 31 cohorts worldwide (N=3,115;Mage=38.9±13.9 years). Secondary analyses tested these associations by sex and after modeling the simultaneous effects of remitted PTSD, childhood trauma, mild traumatic brain injury, and alcohol use disorder.ResultsA significant negative main effect of depression (n=800, vs. no depression, n=1456) was observed in the hippocampal tail (ß=−0.13) and CA1 (ß=−0.09) after adjusting for covariates and multiple testing (adjusted p’s (q)=0.028). A main effect of C-PTSD (n=1042 vs. control, n=1359) was not significant, but an interaction between C-PTSD and depression was significant in the CA1 (ß=−0.24, q=0.044). Pairwise comparisons revealed significantly smaller CA1 volumes in individuals with C-PTSD+Depression than controls (ß=−0.12, q=0.012), C-PTSD-only (ß=−0.17, q=0.001), and Depression-only (ß=−0.18, q=0.023). Follow-up analyses revealed sex effects in the hippocampal tail of depressed females, and an interaction effect of C-PTSD and depression in the fimbria of males.ConclusionsCollectively our results suggest that depression is a stronger predictor of hippocampal volumetry than PTSD, particularly in the CA1, and provide compelling evidence of more pronounced hippocampal phenotypes in comorbid PTSD and depression compared to either condition alone.

BACKGROUND: Visual illusions and hallucinations in psychosis likely reflect deviant functions within the hierarchy of visual cortical regions. Recent investigations point to aberrant sensory cortical processes as a source of visual perceptual errors in psychotic disorders like schizophrenia. METHODS: To better understand the basis of errant visual perception in the disorder, we conducted two experiments. First, we combined magneto-encephalography (MEG) and functionally localized magnetic resonance imaging (MRI) data to compute cortical source signals for people with schizophrenia (PSZ) and healthy controls (HCs). Imaging data were acquired while participants detected a simple contour made up of five visual elements that were surrounded by a dense field of similar elements. In our second experiment, we examined event-related potentials (ERPs) recorded during the same contour perception task in PSZ, HCs, as well as people with bipolar affective disorder (PBP) and first-degree biological relatives of PSZ (RelPSZ), which allowed us to examine the diagnostic specificity of perceptual abnormalities. RESULTS: Source signals revealed PSZ to exhibit diminished delta and theta frequency responses in visual cortex. HCs showed theta responses to the contour within visual areas V1 and V3 that were lateralized (contralateral to the visual field where the contour appeared), while PSZ failed to show such lateralization. HCs also had theta responses to the contour that were modulated by the perceptual context created by surrounding stimuli, while this theta modulation was absent in PSZ. Interestingly, PSZ who had stronger contextual modulation of theta in V1 tended to better discern the contours, and PSZ who more strongly modulated theta in V3 reported more unusual perceptual experiences in their daily lives. In the second experiment, contextual modulation of brain responses was absent in the early brain responses (P1 and N1 ERPs prior to 200 msec) for all groups. The P2 ERP response (240 msec), recorded over lateral occipital regions, was significantly modulated by perceptual context, but this modulation was weaker for both PSZ and RelPSZ. These similar aberrations suggest that genetic liability for schizophrenia is associated with diminished suppressive functions in visual cortex involved in visual context processing. CONCLUSIONS: In sum, multimodal imaging and electrophysiological data provide evidence that initial registration of visual stimuli is specifically aberrant in schizophrenia. Diminished effects of perceptual context during contour detection may reflect genetic liability for schizophrenia and is apparent in biological relatives of individuals with this disorder. Additionally, low-frequency oscillations within visual cortex may reflect the pathophysiology of abnormal visual perception in schizophrenia.

BACKGROUND: Individuals with schizophrenia are known to have deficits in social cognition, including theory of mind. Impaired theory of mind has also been reported in first degree biological relatives of individuals with schizophrenia. However, there have been only a few investigations into the neural correlates of theory of mind dysfunction in relatives of individuals with schizophrenia. The current study examined neural activity in individuals with psychosis and first-degree biological relatives during implicit theory of mind, a more automatic and less cognitively demanding component of this social cognitive process. METHODS: Fifty-eight individuals participated in this study; data were collected in the context of a Human Connectome Project study related to human disease focused on psychosis. Participants were 21 individuals with a psychotic disorder, 14 biological first-degree relatives, and 23 controls. Participants underwent fMRI while completing an implicit theory of mind task. In this task, 20 second animations were shown depicting two triangles, with the movement of the triangles corresponding to one of three conditions. In the Theory of Mind condition, the triangles appeared to be interacting with each other and taking each other’s thoughts and feelings into account. In the Physical Interaction condition, the movement of the triangles appeared related (i.e., mirror movement), but the shapes did not appear to be having a social interaction/taking each other’s thoughts and feelings into account. In the Random condition, the shapes’ movements were unrelated to each other. There were six animations presented for each condition across 3 runs, and after viewing each video clip participants selected which type of interaction they believed was going on. fMRI data were analyzed using a canonical preprocessing pipeline in FSL, and first-level GLM analyses modeled neural activity that corresponded to viewing the different types of animations. RESULTS: For the Theory of Mind minus Physical Interaction contrast, there was significantly greater activation in controls compared to both individuals with psychosis and relatives. Specifically, there was one significant cluster of greater activation in controls versus individuals with psychosis (k = 388), and one significant cluster of greater activation in controls versus relatives (k = 287) (corrected cluster significance threshold of p = 0.05). Both of these clusters included right temporoparietal junction (TPJ). There were no areas of activation that were significantly greater in either individuals with psychosis or relatives compared to controls, and there were no significant differences in activation between individuals with psychosis and relatives. DISCUSSION: Results revealed decreased TPJ activation in both individuals with psychosis and relatives compared to controls. TPJ is involved in theory of mind, and abnormalities in TPJ activity have been reported in individuals with schizophrenia during theory of mind. Therefore, these results are consistent with previous literature, and the use of an implicit theory of mind task reduces the concern of confounds related to cognitive deficits. Findings of decreased TPJ activation during implicit theory of mind in relatives suggests that abnormal neural activity during theory of mind may be associated with unexpressed genetic liability for psychosis.

BACKGROUND: Psychotic disorders, including schizophrenia and bipolar disorder with psychotic features, can be conceptualized in a dimensional rather than categorical way. Such disorders might share similarities in illness pathophysiology. Disruptions in certain neurotransmitter systems (aberrant dopamine; hypo-functioning glutamatergic NMDA receptors) are thought to be involved in the development of psychosis. Whether such neurotransmitters fluctuate in tandem with psychotic symptom severity remains unclear. We sought to examine dimensional symptom severity of psychosis in relation to neurochemical concentrations using magnetic resonance spectroscopy, which allows for the quantification of neurochemicals. METHODS: As part of the ongoing Psychosis Human Connectome Project, we acquired MR spectroscopy data at 7 Tesla, which has better spectral resolution in general than data acquired at lower field-strengths. Using a STEAM sequence, we measured both excitatory (glutamate) and inhibitory metabolites (Gamma-Aminobutyric Acid; GABA) in the prefrontal cortex among individuals with psychosis (n=18) and healthy control participants (n=12). In order to explore potential genetic liability, we also studied first-degree biological relatives of individuals with a psychotic disorder (n=8). RESULTS: Among individuals with psychosis, we found a significant inverse correlation between glutamate and disorientation as measured by the Brief Psychiatric Rating Scale (BPRS; r = -.61, p = .004). Among the full sample with good-quality imaging data (N=30), we found a significant positive correlation between GABA and cognitive-perceptual disturbances as measured by the Schizotypal Personality Questionnaire (SPQ; r = .40, p = .030). DISCUSSION: More severe psychotic-like symptoms corresponded with lower concentrations of the excitatory neurotransmitter glutamate and higher concentrations of inhibitory GABA. These findings suggest that an imbalance in excitatory and inhibitory neurotransmitters may underlie psychotic-like symptoms that are present in a range of clinical and non-clinical populations. As we continue to collect data, we will increase our sample size which will enable us to examine whether concentrations of these metabolites differ across individuals affected by psychotic illness, those carrying genetic liability for such disorders, and non-psychiatric control participants.

BACKGROUND: Abnormal perceptual experiences are common in people with psychosis and have been linked to greater disease severity and poorer outcomes. While the neural basis of psychosis remains unknown, it has been suggested that an imbalance between excitation and inhibition may lead to neural dysfunction in people with psychotic disorders. It is also unclear to what extent abnormal perceptual functioning occurs across the spectrum of psychotic disorders, or is specific to particular diagnoses (e.g., schizophrenia). METHODS: To examine the link between neural dysfunction and abnormal perception, we performed a series of experiments to measure: 1) visual contrast discrimination behavior, 2) 7 tesla fMRI responses in visual cortex during contrast discrimination, and 3) neurochemical concentrations in visual cortex using 7 tesla MR spectroscopy. Data were acquired from people with psychosis, their unaffected first-degree biological relatives, and healthy control subjects as part of the ongoing Psychosis Human Connectome Project. In our behavioral task performed outside the scanner, subjects were asked to discriminate between two visual grating stimuli, and report which was higher in contrast. Discrimination thresholds, the minimum difference in contrast that could be perceived with 80% accuracy, were measured using a psychophysical adaptive staircase method. Data from 1 subject with psychosis were excluded for poor catch trial performance (< 80% accuracy). RESULTS: Thresholds were significantly higher for subjects with psychosis vs. healthy controls, indicating a reduced sensitivity to visual contrast. Threshold data were fit with a standard model that describes the nonlinear contrast-response function (input vs. output). Preliminary results suggest that the difference in contrast discrimination performance between subjects with psychosis (n = 27) and controls (n = 18) could be described by the model in terms of a multiplicative reduction in visual response magnitude. Preliminary analyses of spectroscopy data revealed no significant differences between subjects with psychosis (n = 22) and controls (n = 25) in the concentrations of glutamate or GABA (scaled to total creatine) in occipital cortex. However, across both groups, higher occipital glutamate concentrations were associated with greater response magnitude values (obtained from modeling the contrast discrimination task data). DISCUSSION: Our results may suggest a role for glutamate levels in occipital cortex in determining individual differences in contrast discrimination performance, as well as in impaired contrast perception among individuals with psychosis.

A quarter of patients with schizophrenia endorse visual hallucinations while two-thirds report subtle perceptual anomalies of visual experience. Yet, little is known about the origin of these visual disturbances and their significance to the disorder. Does schizophrenia represent a unique class of perceptual anomalies or do common mechanisms affect other disorders? Understanding the nature of this defining feature of schizophrenia could help elucidate etiology and address whether visual perceptual phenomena should be considered diagnostic. This symposium will present new findings from four independent laboratories regarding the nature and diagnostic specificity of visual disturbances in schizophrenia. Brian Keane will present results demonstrating visual shape completion deficits in first-episode and chronic schizophrenia patients (d>.8; p

BACKGROUND: The dot pattern expectancy (DPX) task is previously validated for cognitive control deficits in psychotic disorders. Patients with schizophrenia commit more errors as well as show less activation in bilateral prefrontal cortex during proactive control in the DPX task. Recent neural recordings in rhesus macaques indicate that proactive and reactive control processes in the DPX task engaged the same neurons in the prefrontal cortex. However, it is not clear if the overlap between these two mechanisms can be observed in human blood-oxygen-level dependent signals. Further, the role of these dual mechanism regions in the cognitive control deficits of patients with psychotic disorders remains unclear. METHODS: 150 probands with a psychotic disorder, 100 first degree relatives, and 50 healthy controls in the Psychosis Human Connectome Project will complete the DPX task while undergoing 3T functional magnetic resonance imaging scan. Traditional general linear model, as well as a novel multiway method called parallel factor analysis, will be used to examine brain mechanisms involved in proactive as well as reactive control during the DPX task. RESULTS: In a preliminary analysis of 24 probands (38.3 ± 12.8 years, 9 females) and 27 healthy controls (36.2 ± 14.0 years, 11 females), probands showed behavioral deficits in proactive control as compared to healthy controls. Neuroimaging analysis in healthy controls with general linear model showed a cluster in the left inferior frontal gyrus that was involved in both proactive and reactive control. Additionally, overlap in proactive and reactive control was observed in bilateral supramarginal/angular gyrus and right inferior occipital gyrus, although these two mechanisms also involved distinct brain regions. DISCUSSION: Consistent with previous findings, probands showed a specific deficit in proactive control in the DPX task. Proactive and reactive control in healthy controls engaged the same brain regions in the prefrontal cortex, replicating findings in rhesus macaques. Further analyses will reveal the role of the prefrontal cortex as well as other brain regions in the cognitive control deficits in psychotic disorders.

Visual object recognition is a complex skill that relies on the interaction of many spatially distinct and specialized visual areas in the human brain. One tool that can help us better understand these specializations and interactions is a set of visual stimuli that do not differ along low-level dimensions (e.g., orientation, contrast) but do differ along high-level dimensions, such as whether a real-world object can be detected. The present work creates a set of line segment-based images that are matched for luminance, contrast, and orientation distribution (both for single elements and for pair-wise combinations) but result in a range of object and non-object percepts. Image generation started with images of isolated objects taken from publicly available databases and then progressed through 3-stages: a computer algorithm generating 718 candidate images, expert observers selecting 217 for further consideration, and naive observers performing final ratings. This process identified a set of 100 images that all have the same low-level properties but cover a range of recognizability (proportion of naive observers (N = 120) who indicated that the stimulus "contained a known object") and semantic stability (consistency across the categories of living, non-living/manipulable, and non-living/non-manipulable when the same observers named "known" objects). Stimuli are available at https://github.com/caolman/FAOT.git.

There is accruing evidence of spontaneous dyskinesia in individuals with schizophrenia that is independent of medication exposure. Dyskinetic motor behavior is also present in individuals who are at high risk of schizophrenia and appears to have prognostic value for the development of psychosis. Nonetheless, it remains unclear whether dyskinesia is present in first-degree relatives of individuals with schizophrenia and thus associated with genetic liability for schizophrenia (i.e., an endophenotype), or whether the motor abnormality is a biomarker specific to the disease state spectrum. There is also limited information about links between dyskinesia and clinically relevant phenomena such as symptoms and cognition. Because dyskinesia marking genetic liability is likely to be subtle, we used sensitive instrument-based measurement of handwriting fluency to quantify dyskinesia in medicated individuals with schizophrenia or schizoaffective disorder, unaffected first-degree biological relatives of individuals with schizophrenia and schizoaffective disorder, and control participants. Results indicated that medicated individuals with schizophrenia or schizoaffective disorder exhibited more dyskinesia than both relatives and controls, with no difference between relatives and controls. Dyskinesia in individuals with schizophrenia or schizoaffective disorder was unrelated to current antipsychotic medication dosage, but associated with worse working memory function and greater positive formal thought disorder. These results provide evidence that dyskinesia is not associated with unexpressed genetic liability for schizophrenia.

The latent structure of schizotypy and psychosis-spectrum symptoms remains poorly understood. Furthermore, molecular genetic substrates are poorly defined, largely due to the substantial resources required to collect rich phenotypic data across diverse populations. Sample sizes of phenotypic studies are often insufficient for advanced structural equation modeling approaches. In the last 50 years, efforts in both psychiatry and psychological science have moved toward (1) a dimensional model of psychopathology (eg, the current Hierarchical Taxonomy of Psychopathology [HiTOP] initiative), (2) an integration of methods and measures across traits and units of analysis (eg, the RDoC initiative), and (3) powerful, impactful study designs maximizing sample size to detect subtle genomic variation relating to complex traits (the Psychiatric Genomics Consortium [PGC]). These movements are important to the future study of the psychosis spectrum, and to resolving heterogeneity with respect to instrument and population. The International Consortium of Schizotypy Research is composed of over 40 laboratories in 12 countries, and to date, members have compiled a body of schizotypy- and psychosis-related phenotype data from more than 30 000 individuals. It has become apparent that compiling data into a protected, relational database and crowdsourcing analytic and data science expertise will result in significant enhancement of current research on the structure and biological substrates of the psychosis spectrum. The authors present a data-sharing infrastructure similar to that of the PGC, and a resource-sharing infrastructure similar to that of HiTOP. This report details the rationale and benefits of the phenotypic data collective and presents an open invitation for participation.

Background: Schizophrenia has a large genetic component, and the pathways from genes to illness manifestation are beginning to be identified. The Genetics of Endophenotypes of Neurofunction to Understand Schizophrenia (GENUS) Consortium aims to clarify the role of genetic variation in brain abnormalities underlying schizophrenia. This article describes the GENUS Consortium sample collection. Methods: We identified existing samples collected for schizophrenia studies consisting of patients, controls, and/or individuals at familial high-risk (FHR) for schizophrenia. Samples had single nucleotide polymorphism (SNP) array data or genomic DNA, clinical and demographic data, and neuropsychological and/or brain magnetic resonance imaging (MRI) data. Data were subjected to quality control procedures at a central site. Results: Sixteen research groups contributed data from 5199 psychosis patients, 4877 controls, and 725 FHR individuals. All participants have relevant demographic data and all patients have relevant clinical data. The sex ratio is 56.5% male and 43.5% female. Significant differences exist between diagnostic groups for premorbid and current IQ (both p < 1 × 10− 10). Data from a diversity of neuropsychological tests are available for 92% of participants, and 30% have structural MRI scans (half also have diffusion-weighted MRI scans). SNP data are available for 76% of participants. The ancestry composition is 70% European, 20% East Asian, 7% African, and 3% other. Conclusions: The Consortium is investigating the genetic contribution to brain phenotypes in a schizophrenia sample collection of > 10,000 participants. The breadth of data across clinical, genetic, neuropsychological, and MRI modalities provides an important opportunity for elucidating the genetic basis of neural processes underlying schizophrenia. We are grateful for the support of all study staff and participants. Acknowledgements for each sample are provided in the Supplementary Materials. Data processing and analyses (of the legacy data) at the central site was supported by the National Institute of Mental Health (NIMH) of the National Institutes of Health (NIH) grant number R01MH092380 to T.L.P. supporting the Genetics of Endophenotypes of Neurofunction to Understand Schizophrenia (GENUS) Consortium, and NIMH grant R21MH109819 to E.D.R. Appendix A

Background: The efference copy (EC)/corollary discharge (CD) mechanism of predictive coding has been hypothesized to function as a neural indicator of self-generation of action. According to this hypothesis, performing an action generates a copy of the motor command (the EC), which results in a CD (representing the sensory consequences expected from performing that action). As a result, activity in the sensory cortex related to the sensory consequences of performing the action is suppressed. This mechanism has been shown to be dysfunctional in individuals with schizophrenia (SZ). There is some evidence that this mechanism is conserved even when the action and sensory consequence are not mechanically coupled (ie, an EC and CD are generated in response to pushing a button to hear a tone, respectively). This suggests that information related to self-initiation in addition to self-generation of action may be carried by the EC. To further explore this phenomenon, we investigated the strength of the CD in SZ and controls (CN) in response to cued action across conditions requiring different degrees of cognitive control. We hypothesized that increased cognitive control would recruit increased intentionality (ie, greater degree of self-initiation) of motor actions, possibly resulting in a more robust CD compared to a simple reaction time task, wherein intention to move is largely offloaded onto the appearance of the stimulus cuing action.

Background: Positive symptoms including hallucinations and delusions are hallmark symptoms of schizophrenia, but their precise neural mechanisms have not yet been clarified. Recent clinical findings suggested that positive symptoms might be related to abnormalities in the claustrum. The claustrum, a thin grey matter deep brain structure, has uniquely high structural connectivity with almost all cortical and limbic brain regions and contains both cell types found in cortical and subcortical brain. The claustrum has been hypothesized to be a neural locus of consciousness that enhances perceptual salience through its neural oscillatory synchronization with cortical regions. Deficient claustral–cortical neural synchrony might underlie abnormal cortical functional coordination associated with perceptual deficits and hallucinations in schizophrenia. A recent functional MRI study found that adolescents with psychotic symptoms had reduced claustral–cortical intrinsic functional connectivity during resting state. Therefore, abnormal claustral connectivity with key nodes of sensory cortical and intrinsic networks might play an important role in the development of psychotic symptoms.

© 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.

Background.The salience of a visual stimulus is often reduced by nearby stimuli, an effect known as surround suppression of perceived contrast, which may help in locating the borders of an object. Weaker surround suppression has been observed in schizophrenia but it is unclear whether this abnormality is present in other mental disorders with similar symptomatology, or is evident in people with genetic liability for schizophrenia.Method.By examining surround suppression among subjects with schizophrenia or bipolar affective disorder, their unaffected biological relatives and healthy controls we sought to determine whether diminished surround suppression was specific to schizophrenia, and if subjects with a genetic risk for either disorder would show similar deficits. Measuring perceived contrast in different surround conditions also allowed us to investigate how this suppression depends on the similarity of target and surrounding stimuli.Results.Surround suppression was weaker among schizophrenia patients regardless of surround configuration. Subjects with bipolar affective disorder showed an intermediate deficit, with stronger suppression than in schizophrenia but weaker than control subjects. Surround suppression was normal in relatives of both patient groups. Findings support a deficit in broadly tuned (rather than sharply orientation- or direction-selective) suppression mechanisms.Conclusions.Weak broadly tuned suppression during visual perception is evident in schizophrenia and bipolar affective disorder, consistent with impaired gain control related to the clinical expression of these conditions.

Multiple genetic approaches have identified microRNAs as key effectors in psychiatric disorders as they post-transcriptionally regulate expression of thousands of target genes. However, their role in specific psychiatric diseases remains poorly understood. In addition, epigenetic mechanisms such as DNA methylation, which affect the expression of both microRNAs and coding genes, are critical for our understanding of molecular mechanisms in schizophrenia. Using clinical, imaging, genetic, and epigenetic data of 103 patients with schizophrenia and 111 healthy controls of the Mind Clinical Imaging Consortium (MCIC) study of schizophrenia, we conducted gene set enrichment analysis to identify markers for schizophrenia-associated intermediate phenotypes. Genes were ranked based on the correlation between DNA methylation patterns and each phenotype, and then searched for enrichment in 221 predicted microRNA target gene sets. We found the predicted hsa-miR-219a-5p target gene set to be significantly enriched for genes (EPHA4, PKNOX1, ESR1, among others) whose methylation status is correlated with hippocampal volume independent of disease status. Our results were strengthened by significant associations between hsa-miR-219a-5p target gene methylation patterns and hippocampus-related neuropsychological variables. IPA pathway analysis of the respective predicted hsa-miR-219a-5p target genes revealed associated network functions in behavior and developmental disorders. Altered methylation patterns of predicted hsa-miR-219a-5p target genes are associated with a structural aberration of the brain that has been proposed as a possible biomarker for schizophrenia. The (dys)regulation of microRNA target genes by epigenetic mechanisms may confer additional risk for developing psychiatric symptoms. Further study is needed to understand possible interactions between microRNAs and epigenetic changes and their impact on risk for brain-based disorders such as schizophrenia.

Background Cognitive deficits are prominent in schizophrenia and represent promising endophenotypes for genetic research. Methods The current study investigated the importance of two conceptually distinct genetic aggregates, one based on copy number variations (uncommon deletion burden), and one based on single nucleotide polymorphisms identified in recent risk studies (genetic risk score). The impact of these genetic factors, and their interaction, was examined on cognitive endophenotypes defined by principal component analysis (PCA) in a multi-center sample of 50 patients with schizophrenia and 86 controls. PCA was used to identify three different types of executive function (EF: planning, fluency, and inhibition), and in separate analyses, a measure general cognitive ability (GCA). Results Cognitive deficits were prominent among individuals with schizophrenia, but no group differences were evident for either genetic factor. Among patients the deletion burden measures predicted cognitive deficits across the three EF components and GCA. Further, an interaction was noted between the two genetic factors for both EF and GCA and the observed patterns of interaction suggested antagonistic epistasis. In general, the set of genetic interactions examined predicted a substantial portion of variance in these cognitive endophenotypes. Limitations Though adequately powered, our sample size is small for a genetic study. Conclusions These results draw attention to genetic interactions and the possibility that genetic influences on cognition differ in patients and controls.

Considering the diverse clinical presentation and likely polygenic etiology of schizophrenia, this investigation examined the effect of polygenic risk on a well-established intermediate phenotype for schizophrenia. We hypothesized that a measure of cumulative genetic risk based on additive effects of many genetic susceptibility loci for schizophrenia would predict prefrontal cortical inefficiency during working memory, a brain-based biomarker for the disorder. The present study combined imaging, genetic and behavioral data obtained by the Mind Clinical Imaging Consortium study of schizophrenia (n = 255). For each participant, we derived a polygenic risk score (PGRS), which was based on over 600 nominally significant single nucleotide polymorphisms, associated with schizophrenia in a separate discovery sample comprising 3322 schizophrenia patients and 3587 control participants. Increased polygenic risk for schizophrenia was associated with neural inefficiency in the left dorsolateral prefrontal cortex after covarying for the effects of acquisition site, diagnosis, and population stratification. We also provide additional supporting evidence for our original findings using scores based on results from the Psychiatric Genomics Consortium study. Gene ontology analysis of the PGRS highlighted genetic loci involved in brain development and several other processes possibly contributing to disease etiology. Our study permits new insights into the additive effect of hundreds of genetic susceptibility loci on a brain-based intermediate phenotype for schizophrenia. The combined impact of many common genetic variants of small effect are likely to better reveal etiologic mechanisms of the disorder than the study of single common genetic variants.

Recent years have witnessed tremendous growth in the scope and sophistication of statistical methods available to explore the latent structure of psychopathology, involving continuous, discrete, and hybrid latent variables. The availability of such methods has fostered optimism that they can facilitate movement from classification primarily crafted through expert consensus to classification derived from empirically based models of psychopathological variation. The explication of diagnostic constructs with empirically supported structures can then facilitate the development of assessment tools that appropriately characterize these constructs. Our goal in this article is to illustrate how new statistical methods can inform conceptualization of personality psychopathology and therefore its assessment. We use magical thinking as an example, because both theory and earlier empirical work suggested the possibility of discrete aspects to the latent structure of personality psychopathology, particularly forms of psychopathology involving distortions of reality testing, yet other data suggest that personality psychopathology is generally continuous in nature. We directly compared the fit of a variety of latent variable models to magical thinking data from a sample enriched with clinically significant variation in psychotic symptomatology for explanatory purposes. Findings generally suggested a continuous latent variable model best represented magical thinking, but results varied somewhat depending on different indexes of model fit. We discuss the implications of the findings for classification and applied personality assessment. We also highlight some limitations of this type of approach that are illustrated by these data, including the importance of substantive interpretation, in addition to use of model fit indexes, when evaluating competing structural models.