Robert M. Bilder - US grants

Specific Aim 1. Develop methods for extracting and visualizing differences in brain shape using information obtained from volume-based nonlinear warping algorithms such as AIR. Specific Aim 2. Develop and validate statistical methods to allow brain shape descriptors derived from nonlinear warping algorithms to be compared across groups of subjects. Specific Aim 3. Develop and validate methods for using shape descriptors derived from nonlinear warping algorithms to characterize developmental morphometric changes over time. Specific Aim 4. Develop and validate a statistical framework for comparing the temporal dynamics of morphometric changes across different groups of subjects. Specific Aim 5. Cross-validate and integrate the methods developed here with methods based on Surface Parameterization (Project 1) and other comparable or compatible approaches.

DESCRIPTION: The Cognitive Neuroscience Unit (CNU) has been conceived and designed based on the premise that cognitive neuroscience methods are now central to interventions research in schizophrenia. The Principal Investigator argues persuasively that because neurocognitive deficits are a core feature of schizophrenia and are important predictors of outcome, neurocognitive deficits represent a critical intervention target. Cognitive neuroscience methods provide measures of brain structure and function that can significantly augment standard symptomatology ratings. Thus, neurocognitive indices offer the potential to classify more specific intervention-relevant patient subgroups, identify the stable enduring features of the illness that are most likely to predict distinctive outcomes, and identify individuals who are at increased risk for schizophrenia and may benefit from early intervention strategies. The proposed CNU is designed to provide an infrastructure ensuring uniform, state of the art neurocognitive methodology across all intervention studies. The CNU has 3 main scientific goals: 1) To specific cognitive targets for interventions and measure the effects of interventions, 2) To develop and implement new neurocognitive methods with greater sensitivity to pharmacologic outcomes, and 3) To identify neurocognitive indices with potential to predict morbidity in high risk samples, differential treatment response and outcome, and longitudinal course characteristics. The 3 service goals of the CNU are to provide: 1) uniform data acquisition and analysis methods across studies by providing a core neurocognitive battery, standardized procedures for structural and functional neuroimaging, electrophysiological (EP) recording, and analysis, and 3) a unique education and training environment.

[unreadable] DESCRIPTION (provided by applicant): The exploratory Center for Cognitive Phenomics (CCP) aims to accelerate identification and efficient measurement of cognitive phenotypes across syndromes and across species to advance interdisciplinary research on neuropsychiatric therapeutics. Cognitive abnormalities have been identified in all major neuropsychiatric disorders, offer quantitative phenotypes for genomic studies and clinical trials, and provide strong bridging relations to neural systems models. The CCP will iteratively refine cognitive phenotypes in interdisciplinary research using neurobehavioral, neuroimaging, and neuropsychopharmacological approaches to provide translational validation of physiological endophenotypes. The CCP will coordinate activities of a large group of experts at UCLA and elsewhere to: (1) generate cross-disorder and cross-species catalogs of phenotypes; (2) develop a phenotype selection algorithm to identify the most promising candidates for research; (3) design a phenomics database for empirical data representation, data mining, and hypothesis testing; and (4) support proof of- concept pilot projects. To advance these aims the CCP will initiate core services for: High Throughput Cognitive Phenotyping; Neuroimaging; and Translational Neuropsychopharmacology. The CCP will initially leverage UCLA campus-wide resources to provide bridging infrastructure and expertise in: Genomics, Pharmacogenomics and Statistical Genetics; Biostatistics and Psychometrics; Bioinformatics; Clinical Trial Design and Regulatory Affairs; and Bioethics. The CCP aims to overcome bottlenecks in the discovery of treatments for neuropsychiatric syndromes that are caused by the use of traditional behavioral "symptom" phenotypes, which are heterogeneous and overlapping, and difficult to translate to basic research. The long-term goal is establishment of a mature CCP that will provide the international research community with efficient, well validated phenotype assays; a cognitive phenomics data repository linked to genomics, proteomics, and other biological knowledge-bases; and novel strategies for interdisciplinary research on neuropsychiatric therapeutics. [unreadable] [unreadable]

NIH Roadmap Initiative tag

NIH Roadmap Initiative tag; medical outreach /case finding; statistical service /center

The Consortium for Neuropsychiatric Phenomics (CNP) advances the NIH Roadmap Initiative by assembling an interdisciplinary research team to address major unsolved challenges in research on behavioral disorders. The CNP leverages the newdiscipline of phenomics - the systematic study of human phenotypes on a genome-wide level - by integrating basic, clinical and information sciences. Neuropsychiatric disorders have enormous public health significance, and there is currently a broad chasm between the basic and clinical research strategies used to study these disorders. The CNP breaks down artificial boundaries between psychiatric syndromes by studying important brain-relevant phenotypes across different diagnoses, and by combining basic and clinical sciences within the same projects. The CNP further focuses on developing novel analytic tools to study complex multivariate phenotypes on a genome-wide scale, and informatics approaches for the graphical visualization, representation and testing of multi-level interdisciplinary hypotheses spanning concepts from genome to syndrome. The CNP comprises 8 components led by a team of 52 investigators representing diverse disciplines. The CNP team will execute five interlocking R01 projects supported by two P30 cores and this U54 Coordinating Center. The U54 comprises: (1) an Administrative Group;(2) an Executive Operations Team that will collaboratively complete day-to-day consortium-wide planning, budget management, and evaluation activities to assure optimal integration among the research projects and cores;(3) the Steering Committee for twice-yearly reviews of progress and binding votes on critical strategic directions and resources allocations;and (4) an External Advisory Board to provide annual evaluative review, guiding performance through recommendations to the Steering Committee and Executive Operations Team. Interaction and innovation will be further promoted by annual retreats with invited external experts;a series of other knowledge-sharing activities;and a proof-of- concept pilot project program. The U54 team will also manage data sharing and dissemination activities consortium-wide, ensuring that the conceptual and data products of the CNP are freely available as a resource to the international research community.

The Human Translational Applications Core (P30-HTAC) supports the Consortium for Neuropsychiatric Phenomics (CNP) by providing services of three integrated units: (1) Human Recruitment and Phenotyping Unit; (2) Neuroimaging Unit; and (3) Statistics and Database Management Unit. The Human Recruitment and Phenotyping Unit serves the CNP by recruiting all human subjects and conducting all diagnostic, behavioral, personality, and cognitive data needed by Components 3 and 4. The human samples include a group of 2000 healthy people (referred to as the "LA2K"), and 100 individuals in each of three diagnostic groups (comprising people with the diagnoses of schizophrenia, bipolar disorder, and attention deficit/hyperactivity disorder), including people of both European and Hispanic/Latino ancestries. The Neuroimaging Unit will be responsible for sthe conduct of structural and functional neuroimaging aspects (including structural magnetic resonance imaging, functional magnetic resonance imaging, and diffusion tensor imaging) required for Components 3 and 4, acquiring all relevant data and providing analysis expertise to investigators within these components. The Statistics and Database Management Unit provides a range of statistical resource coordination, database management, and project management functions to all other CNP components. Each of the units within the P30-HTAC maintains standards to assure quality of CNP data. Collectively the three units of the P30-HTAC provide high quality services to the CNP with efficiency difficult for any individual project to achieve. The P30-HTAC further integrates delivery of these services to sustain the synergy and interactions across components within the CNP. The P30-HTAC also has scientific aims beyond its service roles: its units actively seeks to develop new methods in human behavioral and cognitive assessment, neuroimaging, and statistical methods for optimal phenotype definition and identification of relations between phenotypes and genotypes.

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. Primary support for the subproject and the subproject's principal investigator may have been provided by other sources, including other NIH sources. The Total Cost listed for the subproject likely represents the estimated amount of Center infrastructure utilized by the subproject, not direct funding provided by the NCRR grant to the subproject or subproject staff. The exploratory Center for Cognitive Phenomics (CCP) aims to accelerate identification and efficient measurement of cognitive phenotypes across syndromes and across species to advance interdisciplinary research on neuropsychiatric therapeutics. Cognitive abnormalities have been identified in all major neuropsychiatric disorders, offer quantitative phenotypes for genomic studies and clinical trials, and provide strong bridging relations to neural systems models. The CCP will iteratively refine cognitive phenotypes in interdisciplinary research using neurobehavioral, neuroimaging, and neuropsychopharmacological approaches to provide translational validation of physiological endophenotypes. The CCP will coordinate activities of a large group of experts at UCLA and elsewhere to: (1) generate cross-disorder and cross-species catalogs of phenotypes;(2) develop a phenotype selection algorithm to identify the most promising candidates for research;(3) design a phenomics database for empirical data representation, data mining, and hypothesis testing;and (4) support proof of- concept pilot projects. To advance these aims the CCP will initiate core services for: High Throughput Cognitive Phenotyping;Neuroimaging;and Translational Neuropsychopharmacology. The CCP will initially leverage UCLA campus-wide resources to provide bridging infrastructure and expertise in: Genomics, Pharmacogenomics and Statistical Genetics;Biostatistics and Psychometrics;Bioinformatics;Clinical Trial Design and Regulatory Affairs;and Bioethics. The CCP aims to overcome bottlenecks in the discovery of treatments for neuropsychiatric syndromes that are caused by the use of traditional behavioral "symptom" phenotypes, which are heterogeneous and overlapping, and difficult to translate to basic research. The long-term goal is establishment of a mature CCP that will provide the international research community with efficient, well validated phenotype assays;a cognitive phenomics data repository linked to genomics, proteomics, and other biological knowledge-bases;and novel strategies for interdisciplinary research on neuropsychiatric therapeutics.

Project Summary/Abstract Multi-Level Assays of Working Memory and Psychopathology The NIMH Research Domains Criteria (RDoC) initiative aims to advance understanding of mental health through critical re-evaluation of the traditional diagnostic system and development of alternate methods for measurement of biologically valid dimensions and categories. The proposed project focuses on the RDoC Working Memory construct using a bottom-up strategy that first determines the validity of three neural circuits (Local Cortical, Fronto-Posterior, and Cortico-Hippocampal) using convergent measures from electroencephalography (EEG) and magnetic resonance imaging (MRI). Next the project determines the relations of these circuits to four working memory component dimensions that were specified by RDoC workshops (Storage Capacity, Goal Maintenance, Interference Control, and Long Term Memory). Finally, the project will determine how the circuit and cognitive dimensions compare to conventionally assessed symptoms and diagnostic categories in explaining real-world functional disability. The project innovates by recruiting participants in two broad groups - Care Seeking and Non-Care Seeking - to overcome the extreme-group bias affecting most previous research that has used conventional diagnostic categories as inclusion-exclusion criteria. By systematically testing the validity of associations across circuits, cognitive functions, symptoms, syndromes, and real-world disability, the project will determine how well Working Memory serves the RDoC mission, and accomplish this free of biases associated with diagnostic categories that have limited prior research.

Project Summary The Research Domains Criteria (RDoC) initiative was developed by the NIMH to address growing concern about the validity of the Diagnostic and Statistical Manual of Mental Disorders (DSM) and its potential to limit research on mental illness. The overarching aim of RDoC to identify dimensions of brain structure and function that will possess greater biological validity than our current taxonomy holds great promise for rational diagnosis and treatment of mental illness. The proposed study aims to examine one of the core components of RDoC that is focused on reward circuits and behavior, namely the Positive Valence Systems domain (PVS). The PVS has been linked to diverse psychiatric disorders including schizophrenia, bipolar disorder and major depressive disorder. There is consensus that schizophrenia and bipolar disorder share substantial genetic risk and multiple overlapping phenotypic variations at the levels of corticostriatal brain circuits, cognitive functions, and behavior. Yet it remains unknown precisely which dimensions are shared, which may diverge between syndromes, and how the associated phenotypes relate to shared and non-shared genetic risk. The proposed research will examine the RDoC PVS domains in two large genetically informative cohorts comprising more than 5,000 individuals already ascertained in the Netherlands. Participants include 1,000 schizophrenia patients, 1,750 subjects with bipolar disorder, more than 1,000 first-degree relatives and almost 1,500 healthy comparison subjects. Extensive clinical and neurocognitive data is already available, which will contribute significantly to our understanding of basic brain circuitry that controls behavior in health and disease. Available genome-wide genotype and whole genome sequencing data provide a unique opportunity to examine the heritability of the PVS measures and its relationship with the etiology of psychiatric disorder. We will collect multi-level data pertaining to each of the four primary PVS component processes identified by the RDoC Workshop focused on Reward Seeking and Consummatory Behavior (Approach Motivation) through online assessments of these cognitive constructs. These will be further validated by in-laboratory measures using functional MRI in a subset of participants (n=500). Analyses will focus on the ventral striatal (VS) and ventromedial prefrontal cortex (vmPFC) responses to anticipation and receipt of reward. We will derive response profiles from the four PVS cognitive tests that possess greatest concurrent validity with respect to individual differences in neural circuit function. Results of this study will provide important new data about the relations of the PVS component processes proposed in RDoC to the primary neural circuits, and enable the results in our genetically informative samples to be evaluated in the context of research on a broad range of mental disorders and healthy individuals.

Project Summary This proposal aims to develop a National Neuropsychology Network (NNN), starting with four clinical research sites in California, Florida, Georgia, and Wisconsin. The NNN aims to gather clinical diagnostic information following a shared protocol, collect item-level data on representative neuropsychological (NP) instruments, and deposit these data in the NIMH Data Archive (NDA; https://data-archive.nimh.nih.gov/), more specifically in the Research Domains Criteria database (RDoCdb). The infrastructure established in the network focuses on point- of-testing data acquisition, using iPads, leveraging existing technology developed by a leading test vendor (Q- interactive, from Pearson), and developing additional software to collect specific additional measures that are widely used in clinical neuropsychology laboratories and clinics but which are not available elsewhere. The NNN will collect data on more than 10,000 cases over 4 years, representing a broad range of neuropsychiatric disorders, reflecting populations seen nationwide, and then deposit all data in RDoCdb. Data analyses will specify the latent constructs underlying each test, the factors represented by larger batteries, and create proposals for new individual tests and batteries. Novel tests (short forms and adaptive tests) will be suggested based on item-response theory modeling of each test, with desired precision of measurement for evidence- based clinical decision-making. Novel battery proposals will be informed by examining the positive and negative predictive power of each test to contribute to key differential diagnostic questions that arise in NP assessment. Both battery and individual test proposals will focus on efficiency, and are expected to yield at least a doubling of efficiency. The NNN aims to serve as a nucleus and template for additional network nodes, that will in its next generation offer a national platform for co-norming novel tests, expanding to other languages, and ultimately designing new procedures that are validated with respect to both brain function and real world adaptive capacities.