2010 — 2012 |
Sabb, Fred W. |
R01Activity Code Description: To support a discrete, specified, circumscribed project to be performed by the named investigator(s) in an area representing his or her specific interest and competencies. |
High-Throughput Cognitive Phenomics: a Novel Framework For Psychiatric Gwas @ University of California Los Angeles
DESCRIPTION (provided by applicant): Among the highest priorities in neuropsychiatry is relating the complex phenotypic expression to underlying genetic architecture. A major thrust of the NIH Blueprint and Roadmap initiatives is to prioritize underlying quantitative traits that are relevant across disorders and have better biological specificity than heterogeneous syndrome labels like schizophrenia, bipolar disorder, and attention-deficit/hyperactivity disorder (ADHD). Yet executing genetic studies of quantitative traits is currently prohibitively expensive in both time and money. Here we provide a novel approach using high-throughput cognitive phenomics for iterative examination of quantitative cognitive traits in pursuit of genome-wide association targets. This application targets memory and response inhibition phenotypes that are critical components of symptom (e.g. impulsivity), neural systems (e.g. prefrontal cortex), and signaling pathway (e.g. dopamine) deficits that are prevalent across neuropsychiatric syndromes. Focusing on quantitative cognitive traits may improve the biological specificity of phenotypes allowing us to identify important risk genes. The aim of this EUREKA application is to rigorously validate acquisition of a large Web-based cohort. This will provide: 1) a high-throughput framework for future GWAS applications, and 2) a sample of 7,000 with valid cognitive phenotypes and DNA able to demonstrate the utility of using quantitative cognitive traits for GWAS. Our preliminary data solidly demonstrates feasibility but rigorous validation of equivalency between measures across thousands of individuals is needed. The future of neuropsychiatric research must embrace high-throughput phenotyping to accumulate the necessary sample sizes needed for genetic study as well provide the flexibility to iteratively refine and rapidly improve the specificity of phenotypes. If successful, this approach would demonstrate the ability to rapidly collect well-powered studies of quantitative traits in a way not currently possible, providing the ability to iteratively refine and retest phenotypes quickly. This could have tremendous implications by drastically accelerating identification of novel risk genes for cognitive deficits that underlie many neuropsychiatric syndromes. PUBLIC HEALTH RELEVANCE: While we know that biological processes lead to increased risk for mental illnesses like attention deficit- hyperactivity disorder, bipolar disorder, and schizophrenia, their causes remain unknown, due in part to the complexity in the interaction between biological systems and behavior. While repeated study of behavioral processes in action is a good way to find these causes, carrying out these studies in enough people to identify genes currently requires many years of effort by both researchers and the public and is very costly. This research project validates a novel procedure capable of very quickly studying large numbers of healthy individuals using secure internet-based technology to pursue the genetic correlates of memory and impulse- control behaviors that go often awry in mental illness.
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1 |
2018 — 2019 |
Mcintyre, Laura Lee [⬀] Sabb, Fred W. |
R21Activity Code Description: To encourage the development of new research activities in categorical program areas. (Support generally is restricted in level of support and in time.) |
Functional Connectivity in Developmental Delay: Shared Etiology and Differential Outcomes
Project Summary/Abstract Developmental delay (DD) describes the heterogeneous symptoms associated with delay in one or more key aspects of development, including physical, cognitive, communication, social/emotional, and adaptive development. DD is associated with specific diagnoses, such as autism spectrum disorder (ASD), and with global delay or intellectual disability. In some cases the etiology of the delay is known, whereas in other cases, etiology is unknown. The heterogeneity of DD poses problems for identifying underlying mechanisms that may inform intervention and could set them on an optimal trajectory for success and recovery. Although there is significant focus on ASD specifically, many children are first served through early intervention under the broad diagnostic umbrella of DD. Unfortunately, little is known about brain mechanisms or clinical phenotypes associated with shared etiologies in DD, due mainly to the technical challenges of characterizing children at a young age, both behaviorally and with neuroimaging. However, technological advances in both acquisition and denoising of functional connectivity (FC) data may now provide an opportunity to explore these processes in situ. The overarching aim of the proposed study is to explore shared neurobiological etiology in DD and ASD by examining clinical and neurobehavioral indicators, including resting-state fMRI and child clinical outcomes. This proposed research will build on the strengths of our lab by studying two cohorts of children with disabilities, including DD and ASD, that we recruited in early childhood and are following longitudinally. These 255 children in our study cohorts are now in middle childhood. Middle childhood is a time of significant brain changes, including the initiation of large structural changes, such as myelination and synaptic pruning [17, 2], putatively critical in the pathophysiology of ASD [18?20]. Preliminary data from 34 5- and 6-year old children with DD strongly demonstrate the feasibility of our approach and our ability to successfully scan young children with DD and ASD. In the proposed research we will recruit 120 children from our existing cohorts (N = 80 DD; N = 40 ASD) and characterize their clinical and neuropsychological functioning and examine FC through resting-state fMRI. We will apply a recent denoising pipeline [25] from the Human Connectome Project (HCP) to minimize motion-related artifacts in resting-state scans with an admittedly challenging cohort in order to test our hypotheses of shared and unique neurobiological etiology of DD and ASD. Although there is little consensus about the best approach for minimizing micromovements in FC data, this exploratory project will quantify and test the effect of motion. Further, this project is specifically designed to provide extensive open data sharing of all the data that are acquired, so that other novel pipelines could replicate or extend this work readily. If successful, this work could provide novel intervention targets for treatment of DD and provide a roadmap for characterizing children during this critical time period.
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0.969 |