Ruben C. Gur, PhD - US grants

schizophrenia; neuropsychology; neurophysiology; brain circulation; behavior; brain mapping; biomedical facility; method development; functional ability; positron emission tomography; longitudinal human study; nuclear magnetic resonance spectroscopy; aging; brain metabolism; glucose metabolism; magnetic resonance imaging; emotions; gender difference; bioimaging /biomedical imaging; human subject; behavioral /social science research tag; clinical research;

schizophrenia; neuropsychological tests; neural information processing; biomedical facility; neuropsychology; psychometrics; learning; cognition; longitudinal human study; language; brain mapping; attention; emotions; memory; performance; clinical research; human subject; psychological models;

DESCRIPTION: (Adapted from applicant's abstract) The goal of the proposed research is to examine emotion processing in schizophrenia through convergence of neurobehavioral, structural and functional neuroimaging methods. Affective dysfunction has been recognized as a major deficit in schizophrenia. Advances in neuroscience provide unprecedented tools to probe the neurobiology of emotional processing in healthy people and determine with increased precision the mechanism responsible for dysfunction in people with schizophrenia. This has implications for understanding the presentation, course and treatment response of individuals with schizophrenia. The proposed line of investigation will assess emotion processing applying 3-D stimuli of facial expressions of emotions. This neurobehavioral domain will be related to neurocognitive processes and to clinical measures that assess symptoms and function, with special emphasis on affective state. Brain behavior relations will be established by concurrent neuroanatomic evaluations with magnetic resonance imaging (MRI) and with more specific neurobehavioral probes using functional MRI (fMRI). The anatomic studies permit volumetric measures of fronto-temporal structures implicated in the regulation of emotion. The neurobehavioral tasks developed to dissect emotion and cognitive processing will be applied as neurobehavioral probes during online measurements of brain activity with fMRI. This will afford an evaluation of brain systems that are recruited in healthy people during task performance, relative to people with schizophrenia who manifest deficit on such tasks. The application of a genetic strategy by studying family members of affected probands with the neurobehavioral paradigms will permit the integration of two powerful research strategies in schizophrenia, genetics and behavioral neuroscience, needed to assess genetic vulnerability. The longitudinal design will allow assessment of the stability of the neurobehavioral measures and their relation, at intake, to course of illness.

DESCRIPTION (provided by applicant): Affective dysfunction has been recognized as a major deficit in schizophrenia, and flat affect is a prominent negative symptom, presenting a challenge for treatment. Our efforts in the current project have advanced the understanding of emotion processing deficits in schizophrenia through convergence of clinical, neurobehavioral and structural and functional neuroimaging methods. Our results underscored the importance of flat affect in adversely impacting course and outcome. They suggested that the interaction between limbic and prefrontal regions could underlie both emotion processing and some cognitive deficits. Our current study has also revealed substantial deficits in emotion processing in unaffected first-degree relatives of patients, and the renewal application proposes to study a group of unaffected siblings. Advances in neuroscience provide unprecedented tools to probe the neurobiology of emotion processing in healthy people and reveal with increased precision mechanisms that could be responsible for dysfunction in people with schizophrenia. The goal of the proposed competing renewal application is to build on current findings and focus on functional MRI (fMRI) paradigms to examine emotion processing in people with schizophrenia and their unaffected siblings. In the proposed study we will perform two experiments, both using a sparse event-related design that can isolate neural systems engaged in top-down emotion categorization tasks from those responding to threat related facial affect. In the first experiment we will test the hypothesis that abnormal bottom-up amygdale response to facial affect is especially impaired for fearful expressions, and contributes to misinterpretation and possibly failure to process affectively valenced stimuli. We will confirm our findings that this abnormal amygdala response is strongly associated with severity of flat affect and adversely impacts course and outcome, and examine the effects of gaze direction on limbic activation. The sample size for this experiment is powered to examine potentially modulating effects of anxiety, basal cognitive and emotion processing abilities, sex differences, and ethnicity effects. In the second experiment we will examine variables that could modulate the abnormal amygdala response to threat related facial expressions. We will examine the contribution of stimulus parameters by manipulating information conveyed in facial features. Specifically, we will determine the effects of changes in upper or lower face, and familiarity by using photographs of facial affect displays obtained from people familiar to the participants. In both experiments, incorporation of a recognition task will permit linking limbic response to the likelihood of correct face encoding. The application of a genetic strategy, by studying family members with the neurobehavioral and fMRI paradigms, will permit the integration of two powerful research strategies in schizophrenia - genetics and behavioral neuroscience - needed to assess vulnerability. We expect the results to shed light on mechanisms for affective dysfunction in schizophrenia that can lead to improved identification of vulnerability and treatment.

DESCRIPTION (provided by applicant): The application is a response to RFA-MH-08-090 requesting the adaptation of experimental tasks from cognitive science research for use in clinical assessment and intervention studies in schizophrenia. We propose a three-phase investigation. In the first phase we will examine available data from a computerized battery developed by the Penn Schizophrenia Research Center, which has already adapted neuroscience- based measures. The battery was applied in our center and in large-scale collaborative clinical and genetic studies, yielding a rich data set that can guide efforts to refine and optimize its application in schizophrenia. The data set will permit a rigorous investigation of the psychometric properties in healthy people and patients and determining how performance relates to clinical features of schizophrenia. Based on these analyses, we will prune the items with the aim of minimizing administration time and optimizing the yield. We will examine these data with traditional and novel approaches to guide the construction of efficient alternative forms. At this stage we will also explore more detailed theoretically driven parameters obtained from the test results to determine whether they improve diagnostic sensitivity or specificity or correlations with clinical features (Specific Aim 1). In the second phase we propose to take several new tasks through the process of adaptation and validation, and select those yielding the most promising results for inclusion in the final battery. Each test will be assessed for face validity, internal consistency, test-retest reliability, and construct validity (both convergent and divergent). It will also be administered to a preliminary sample of patients with schizophrenia and healthy controls to establish tolerance and basic psychometric properties in these populations. We will specifically amplify the existing set of tests with additional measures that tap earlier stages in the information-processing cascade, and expand the measures of social cognition to incorporate prosody (Specific Aim 2). In the third phase we will apply the alternate equivalent forms of the final battery (in counterbalanced order) to a new sample of patients with schizophrenia and demographically balanced healthy community controls. This will enable the assessment of its global and domain-specific sensitivity and specificity to diagnosis. We will establish effects of moderating variables such as sex differences, age, education and parental education. We will examine the clinical relevance of the neurobehavioral measures by correlating performance with clinical features of schizophrenia including symptom dimensions and functional outcome (Specific Aim 3). Data will be placed in the public domain and the battery will be available for downloading and implementation through a web interface. We expect the resulting battery to be user friendly and platform independent, require minimal training for administrators, include detailed implementation procedures, and have automated scoring and databasing features. Project Narrative Schizophrenia is a complex brain disorder with significant cognitive deficits that affect functional outcome. Integration of basic and clinical neuroscience is key to understanding the neural basis of the deficits and is required for developing targeted interventions that can ameliorate cognitive impairment. The goal of the proposed study is to adapt neurobehavioral tasks applied in functional neuroimaging research to use as tests in a battery that can be applied in large-scale treatment studies. We will evaluate a dataset from an existing battery to fine tune available measures and adapt new tasks to augment the battery.

DESCRIPTION (provided by applicant): Bariatric surgery is the most effective weight loss option for persons with extreme obesity (i.e., body mass index = 40 kg/m2). Roux-en-Y gastric bypass (RYGB) and laparoscopic adjustable gastric banding (LAGB) are the most common bariatric procedures, and they induce long-term reductions of ~25% and ~15% of initial weight, respectively. Anatomical differences resulting from the two procedures are associated with postoperative differences in endocrine functioning. In particular, the orexigenic hormone, ghrelin, is generally suppressed in RYGB and increased in LAGB patients. Furthermore, postprandial increases in the satiety factors, glucagon-like peptide 1 (GLP-1) and peptide YY (PYY3-36), are significantly increased after RYGB, compared with LAGB. Each of these appetite-regulating hormones has been found to act in brain regions related to both the homeostatic and hedonic control of food intake. A separate literature has examined neural activation in feeding centers, as measured with functional magnetic resonance imaging (fMRI) and positron emitted tomography (PET), in response to food cues. Most of these studies have compared responses to images of high-calorie vs. low-calorie foods or non-food items. Some have further compared responses in lean vs. obese individuals. Generally, high-calorie food images stimulate activation in the prefrontal cortex, mesolimbic dopamine system (e.g., ventral-tegmental area and nucleus accumbens), and other limbic areas (e.g., orbitofrontal cortex, amygdala, insula, and cingulate cortex). Furthermore, responses are greater in obese vs. lean individuals. Fewer studies have examined neural response to meal consumption; those investigations have found that many of the same regions are activated by nutrient ingestion. The proposed research is a prospective observational study that seeks to integrate two areas of inquiry: 1) endocrine effects of bariatric surgery; and 2) neural response to food cues and feeding. Patients who undergo RYGB or LAGB, and matched obese controls who do not seek weight loss, will complete assessment visits at 0, 6, and 18 months, which include: 1) a fMRI scan while viewing high- and low-calorie food images in the fasted state; 2) a perfusion MRI scan to measure cerebral blood flow in the fasted state; 3) fasting blood draw; 4) consumption of a liquid test meal; 5) serial perfusion MRI scans to assess the effects of the meal; and 5) serial blood draws to assess postprandial changes in ghrelin, GLP-1, and PYY3-36. Comparisons of changes among the three groups at 6-months and 18-months follow-up will comprise our primary analyses. The primary hypotheses are that following surgery: 1) fMRI response to high-calorie food images will be reduced in RYGB vs. LAGB patients and controls; 2) RYGB patients will show larger increases in postprandial GLP-1 and PYY3-36 (accompanied with a blunted postprandial ghrelin response) than will LAGB patients and controls; and 3) RYGB patients will demonstrate a greater postprandial increase in resting brain activity in homeostatic and hedonic feeding areas than will LAGB and control participants.

DESCRIPTION (provided by applicant): In this collaborative R01, Networks from multidimensional data for schizophrenia and related disorders submitted in response to RFA-MH-12-020, we propose to develop methods for integrating a broad range of genomic, imaging, and clinical data, hosting all data, methods, and results on a novel, flexible and extensible computing platform. Subsequently, these data and methods will be used to establish workflows available to the research community to integrate and mine the data for discovery. As proof-of-concept, multiple datasets for schizophrenia (SCZ) will be used and then extended to additional mental disorders. Specifically, in AIM 1 we will adapt the Synapse platform at Sage Bionetworks to host, QC, normalize, and transform data in an analysis ready format. Synapse will also enable computation, storage, sharing, and integration of SCZ specific data with pre-existing public data. The Sage platform will be hosted by the data center in the Institute of Genomics and Multiscale Biology at the Mount Sinai School of Medicine consisting of a data warehouse (organized file systems and databases), a web service tier and applications tier adapted to facilitate network reconstruction and more generally model building with SCZ data. In AIM 2, we will develop a pipeline of analytic methods that include new and existing tools for the primary processing of multiple types of data. Using direct experimental findings we will generate primary analysis datasets (e.g., expression QTLs, imaging QTLs, GWAS with SNP/CNV genotypes, RNASeq signatures, and DNA methylation and RNAseq associations), construct interaction networks with population-based expression and imaging datasets (e.g. gene expression, functional MRI and structural MRI), transform all data and results into analysis ready formats, and construct a standard set of queries to facilitate SCZ gene discovery. In AIM 3 following platform development, generation of primary analysis datasets, and basic network constructions, we will develop and apply methods to construct integrated, higher-order molecular networks and more generalized models to enhance our understanding of the genetic loci and gene networks underlying schizophrenia. Using a Bayesian framework, methods will be developed that identify network modules and the underlying genetic variance component (including epistatic interactions), incorporate prior disease information and extensive prior biological knowledge to construct more detailed probabilistic causal models, and identify causal regulators of networks associated with SCZ. In AIM 4, we will assess the extent to which the models validate in independent SCZ data and in bipolar disorder and autism. This proposal should have a major impact on the field as it proposes to create a solution, in the form of new platforms and analytic methods, for the bottleneck in gene discovery that results from our limited ability to fully analyze the data currently available on large samples of individuals suffering fro mental illness. This proposal will make possible the efficient use of this wealth of multi-dimensional data.

DESCRIPTION (provided by applicant): Schizophrenia is a common and debilitating condition with high personal costs to affected individuals and their families as well as high societal costs. Relatively little is known about the pathophysiology of schizophrenia. Although there is strong evidence for a genetic component to risk of schizophrenia, few specific genes involved in its etiology have been identified. In this set of coordinated R01s, we propose to take an alternative approach to localizing genes influencing risk of schizophrenia, combining established intermediate risk factors for schizophrenia with identification of novel transcriptional endophenotypes and combining standard GWAS gene localization approaches with innovative methods utilizing joint analysis of association and linkage and joint analysis of genomic and transcriptomic evidence. We will utilize existing samples and data from three ongoing studies: the Consortium on the Genetics of Schizophrenia (COGS); the Multiplex Multigenerational Investigation of Schizophrenia (MGI); and the Project among African Americans to Explore Risks for Schizophrenia (PAARTNERS). These three family studies were designed to investigate genetic influences on schizophrenia using neurocognitive phenotypes associated with schizophrenia risk. We hypothesize that alterations in gene regulation are responsible for some portion of the genetic liability to schizophrenia. Thus, we will use RNA expression levels both as potential endophenotypes for schizophrenia and as an alternative method of genome scanning. Identification of transcriptional correlates of schizophrenia will be facilitated by use of a novel Endophenotype Ranking Value (ERV) that combines the strength of the genetic signal on a potential endophenotype with the strength of its correlation with the disease of interest (i.e. schizophrenia) in a single measure. We will conduct a conventional genome-wide association study (GWAS) for schizophrenia, for newly identified transcriptional endophenotypes, and for classical neurocognitive risk factors. We will also take advantage of the large families in these samples to conduct joint linkage and association. Finally we will combine genomic and transcriptomic lines of evidence in a joint test to identify genes influencing schizophrenia and associated neurocognitive risk factors. All data generated in the course of the project will be shared through dbGaP and the NIMH Genetics Repository.

DESCRIPTION (provided by applicant): Neurobehavioral Family Study of Schizophrenia is a Multiplex Multigenerational Investigation (MGI) of three collaborative RO1s that combine genetic and neurobiologic paradigms to advance the understanding of pathogenesis and detection of genes that modulate susceptibility to schizophrenia (SCZ). Complex genetic mechanisms underlie the susceptibility to SCZ. Paralleling the progress in genetics, neurobiologic studies have identified neural systems that could provide pathophysiologic substrates for focused investigations. We have established a sample of multigenerational multiplex families that were ascertained, phenotypically characterized and genotyped for genome-wide linkage analyses. This sample was examined with a computerized neurocognitive battery that provides complementary quantitative phenotypes to diagnosis. We observed significant heritability for several neurocognitive domains as well as evidence for linkage. Our goal for the renewal application is to capitalize on this unique sample and obtain neuroimaging phenotypes of brain structure and function with Magnetic Resonance Imaging (MRI). We will examine brain structure using volume-based morphometry and connectivity with Diffusion Tensor Imaging (DTI). Functional MRI (fMRI) studies will examine brain circuitry activated in response to neurobehavioral probes. We will follow 300 individuals from the MGI sample for neuroimaging studies. We will also ascertain a new population-based sample of 300 community controls (Specific Aim 1). We will relate the heritability of neuroimaging phenotypes to symptom and neurobehavioral measures and perform multivariate quantitative genetic analyses to identify quantitative phenotypes influenced by the same genes (Specific Aim 2). To establish genetic mechanisms producing the neurobehavioral and neuroimaging phenotypes we will localize new quantitative trait loci through genome-wide association (GWA) analyses and follow-up significant linkage and GWA analysis signals as well as candidate genes identified through ongoing association studies (Specific Aim 3). Specimens will be sent to the NIMH repository for transformation and DNA extraction. Data collection and quality control will be maintained and verified data will be regularly uploaded to the NIMH repository (Specific Aim 4). The MGI augments other samples available with similar measures to confirm and extend present findings. In addition, the phenotypic characterization of participants with neurobehavioral and neuroimaging data will enable evaluation of the relation between genetic influences on neurobiological abnormalities and clinical manifestations. Finding additional potential quantitative markers for genetic vulnerability could improve our understanding of how genes related to brain development and regulation interact with environment in conferring SCZ susceptibility. Such efforts will enhance the integration of neurobiologic and genetic paradigms in human and animal research. In turn, this may pave the way for risk prediction and better treatment. PUBLIC HEALTH RELEVANCE: Schizophrenia is a complex brain disorder that commonly emerges in adolescence and early adulthood and has devastating effects on the individual and family. Understanding the genetic basis of the deficits in brain function is key to early detection and to advance treatments that may improve outcome. The goal of the Multiplex Multigenerational Investigation of the schizophrenia consortium is to integrate neurobehavioral and neuroimaging methods in high-risk families that will yield the data needed for progress in the field.

DESCRIPTION (provided by applicant): Social cognition deficits are debilitating features of psychosis associated with poor outcome. Such deficits are evident in young people at clinical high risk (CHR) for psychosis. Several treatment procedures have been implemented to remediate these deficits. A limitation of these approaches is that they are all presented as treatment of mental illness to a population characterized in part by limited insight into the exten to which one's thoughts and behaviors are maladaptive. Acting (Thespian) classes are a potential alternative more palatable to youths and yet honing skills that are at the core of social and emotional deficits in psychosis. Acting requires theory of mind, understanding other people, comprehending social dynamics, and expression and understanding of emotions. Acting Directors and programs have effective curricula to improve such skills through standard exercises. Because training requires teamwork toward a defined goal, it reduces the likelihood of dropout. By implementing Thespian Training Intervention for Promoting Social Skills (TIPS) we can offer this promising venue to CHR individuals. The proposed R34 capitalizes on convergence of expertise in social cognition and negative symptoms, the large community sample of CHR youths and the Aaron T. Beck Psychopathology Research Center at Penn. We collaborate in implementing CBT in patients with psychosis and met with considerable success. We have also worked with theatre directors to guide over 200 actors to generate emotional stimuli for social cognition studies. More recently, we have developed an 18-session curriculum that incorporates thespian principles into the CBT-guided treatment of social cognition deficits in CHR. We will apply the TIPS curriculum to a sample of 90 young CHR individuals and evaluate the effects on social cognition, symptoms and functioning in 3 successive cohorts of 30 participants stratified by age group. During this process, we will identify specific features most effective in producing socially adaptive behaviors. Strategies will be incorporated in the program to allow the results to generalize. We will obtain detailed documentation by clinical investigators theatre directors and staff. Results of this pilot will form the basis for implementing the TIPS approach in RCTs and the community. The Specific Aims are: 1. Implement the TIPS curriculum in CHR to gauge its feasibility and effects on clinical symptoms and functioning. We propose to enroll CHR individuals age 14-25 with social cognition impairment. Thespian training will be performed in two groups (14-18,18-25) over 18 sessions. Participants will be evaluated at the start, upon completion and at 6 months follow-up. 2. Design a revised curriculum based on the experience from Specific Aim 1 and implement it in a new group of CHR participants with similar characteristics using the same evaluation schedule. 3. Design the final curriculum and manual and disseminate it for feedback to selected mental health professionals, in schools and the community, and to theatre directors. 4. Apply TIPS to a new sample of CHR youths in a community setting after training the local professionals.

? DESCRIPTION (provided by applicant): Current research typically examines single neuroimaging modalities to establish normative values, development related differences, and abnormalities in neuropsychiatric disorders. Little is known about how these complementary parameters of brain structure and function interrelate and how combined processes reflected in these parameters lead to a mature, healthy brain. Behavioral functioning, manifested in mental health and neurocognitive performance, shows marked developmental effects. While such measures have been related to specific neuroimaging modalities, there is limited knowledge on developmental effects of multimodal brain parameters related to psychopathology and neurocognition. The path from biological processes to behavior is through genomics, which can elucidate mechanistic neurobiological processes thereby offering hope for early identification, prevention and intervention in aberrant development. Finally, to understand how brain changes relate to behavioral changes it is essential to have longitudinal data. We propose to capitalize on our efforts to establish the Philadelphia Neurodevelopmental Cohort (PNC), which was designed to obtain data on neuropsychiatric features, neurocognitive performance, multimodal neuroimaging and genomics. In addition to analyzing the data on the initial assessment of the PNC sample that we share in dbGaP, we have been following a subsample of PNC participants that includes both typically developing and those at clinical high-risk (CHR) for psychosis. Therefore, we will be able to establish dimensionally and longitudinally which combination of clinical, neurocognitive, neuroimaging and genomic parameters best predicts progression to psychosis. PNC data analysis will identify biotypes based on development related differences in regional multimodal characterization of major brain structures and systems related to dimensions of psychopathology and neurocognitive domains. We will apply advanced anatomic parcellation and voxelwise connectome-wide association studies to delineate multi-modal development effects on structural and functional connectivity, and identify aberrations associated with psychopathology and neurocognitive deficits. Networks will be examined using hypergraphs and parameters such as segregation and modularity defined by multi- scale community detection methods. These efforts will establish candidate parameters for genomic analysis and will be used to examine the GWAS- findings from the PGC and associated polygene scores and their effects on patterns of development and emerging biotypes. We will test the ability of developmental biotypes derived from the current dataset to predict brain health and clinical status in a subsample of 500 participants with follow-up data at 24 and 36 months intervals after the PNC data were collected. Since the follow-up is on 200 typically developing, 200 psychosis prone and 100 individuals with other disorders, we will focus on the subgroup with psychosis risk while exploring associations with other clinical factor scores. The repeated- measures data will establish how changes in these parameters inform about developmental trajectories.

Efforts to include behavioral measures in large-scale studies as envisioned by precision medicine are hampered by the time and expertise required. Paper-and-pencil tests currently dominating clinical assessment and neuropsychological testing are plainly unfeasible. The NIH Toolbox contains many computerized tests and clinical assessment tools varying in feasibility. Unique in the Toolbox is the Penn Computerized Neurocognitive Battery (CNB), which contains 14 tests that take one hour to administer. CNB has been validated with functional neuroimaging and in multiple normative and clinical populations across the lifespan worldwide, and is freely available for research. Clinical assessment tools are usually devoted to specific disorders, and scales vary in their concentration on symptoms that are disorder specific. We have developed a broad assessment tool (GOASSESS), which currently takes about one hour to administer. These instruments were constructed, optimized and validated with classical psychometric test theory (CTT), and are efficient as CTT allows. However, genomic studies require even more time-efficient tools that can be applied massively. Novel approaches, based on item response theory (IRT) can vastly enhance efficiency of testing and clinical assessment. IRT shifts the emphasis from the test to the items composing it by estimating item parameters such as ?difficulty? and ?discrimination? within ranges of general trait levels. IRT helps shorten the length of administration without compromising data quality, and for many domains leads to computer adaptive testing (CAT) that further optimizes tests to individual abilities. We propose to develop and validate adaptive versions of the CNB and GOASSESS, resulting in a neurocognitive and clinical screener that, using machine learning tools, will be continually optimized, becoming shorter and more precise as it is deployed. The tool will be in the Toolbox available in the public domain. We have item-level information to perform IRT analyses on existing data and use this information to develop CAT implementations and generate item pools for adaptive testing. Our Specific Aims are: 1. Use available itemwise data on the Penn CNB and the GOASSESS and add new tests and items to generate item pools for extending scope while abbreviating tests using IRT-CAT and other methods. The current item pool will be augmented to allow large selection of items during CAT administration and add clinical items to GOASSESS. New items will be calibrated through crowdsourcing. 2. Produce a modular CAT version of a neurocognitive and clinical assessment battery that covers major RDoC domains and a full range of psychiatric symptoms. We have implemented this procedure on some CNB tests and clinical scales and will apply similar procedures to remaining and new tests as appropriate. 3. Validate the CAT version in 100 individuals with psychosis spectrum disorders (PS), 100 with depression/anxiety disorders (DA), and 100 healthy controls (HC). We will use this dataset to implement and test data mining algorithms that optimize prediction of specific outcomes. All tests, algorithms and normative data will be in the toolbox.

PROJECT SUMMARY Efforts at early identification of individuals at risk for psychosis are propelled by the realization that psychosis is neurodevelopmental, with brain and behavioral abnormalities anteceding diagnosis of schizophrenia (SZ) by years. As longer duration of untreated psychosis portends poor outcome, early identification is important to bend the developmental trajectory in a favorable direction. Since most current studies of psychosis risk are based on help-seeking samples, there is a gap in knowledge on how psychosis unfolds in diverse community samples. While it is generally recognized that genomic and environmental factors (GxE) contribute to risk for psychosis, there is a paucity of complementary integrative studies that can chart causal pathways. Genomic ?case-control? GWAS studies of SZ identified multiple common alleles permitting calculation of a polygenic risk score (PRS). Recently, increased attention has been given to childhood adversity related to SZ. The goal of the proposed R01 is to build on our genotyped ~10,000 Philadelphia Neurodevelopmental Cohort (PNC) of 8 to 21 years old youths studied in 2009-2011, where we are following those who meet criteria or are at risk for psychosis (PS) and typically developing (TD) participants, whose current age range is 15-30 years. Available multi-level ?deep phenotyping? includes clinical, neurocognition and multi-modal neuroimaging on a subsample of ~1600. We have developed a preliminary environmental risk score (ERS) and will use it to dissect GxE. The proposed followup design will recruit PS and TD participants with the highest and lowest scorers (quartile) on the ERS, and within each of these four cells we will examine 120 individuals, 60 males and 60 females (total N=480). This sample will be examined clinically, neurocognitively and with multimodal neuroimaging. We will test the hypothesis that genomic vulnerabilities, based on PRS and family history, and environmental adversity, based on ERS, updated longitudinally, affect onset and course of PS by altering brain development in temporolimbic regions affecting fronto-limbic connectivity that underlies social functioning. We will augment current data with information on risk and resilience and multimodal brain-behavior parameters to establish developmental trajectories during this critical period of brain maturation when psychosis emerges. Our aims are: 1. Examine effects of ERS on PS clinical features and progression in relation to PRS. 2. Investigate brain- behavior parameters that bridge from genetic and environmental factors to clinical manifestations. 3. Establish developmental trajectories for PS features, associated brain parameters and neurocognitive deficits, and apply novel computational models to enable an adaptive ?risk and resilience calculator?. The proposed study will produce the data absent for a diverse US community sample but needed to move psychiatry into the precision medicine era. The project will inform on genomic and environmental risk and resilience indicators, offering an essential rung in the ladder toward individualized prediction, a part of implementation science. As with the PNC, data and associated algorithms will be a resource shared with the scientific community.