2016 — 2020 |
Anderson, Ariana |
K25Activity Code Description: Undocumented code - click on the grant title for more information. |
Hemodynamic Biomarkers of Healthy and Diseased Aging @ University of California Los Angeles
Project Summary This K25 proposal is for a five-year mentored training program to enable Dr. Ariana Anderson from UCLA to transition from a statistician to an independent investigator, with an active research plan in identifying functional biomarkers of cognitive decline and aging. Dr. Anderson's proposal includes a comprehensive training program involving formal training in calibrated fMRI, fMRI data collection, neuropsychological assessments, volunteering, directed readings, coursework, mentoring, conferences, and career development, which are designed to complement and complete the applicant's previous training in mathematics and statistics. The proposed research addresses an important issue that affects nearly all fMRI studies; the modeled blood-flow response to neuronal stimuli is assumed to be constant across ages, genotypes and diseases, even though we know that this assumption is categorically false. This lowers the statistical power of fMRI studies, increases necessary sample sizes, and introduces bias into fMRI studies of disease and aging. Moreover, a poor understanding of hemodynamic change with age in healthy patients makes identifying biomarkers of unhealthy aging difficult. We will use cerebral blood flow measurements (ASL), hypercapnic and hemodynamic changes, along with genetic risk factors, as biomarkers to predict future cognitive decline. The relationship between the hemodynamic response, cognitive decline, aging and disease will be unraveled through the following three aims. Aim 1.) Create age-corrected hemodynamic response functions, after adjusting for cardiac and respiratory artifacts recorded during scan-time, so that future age studies can use age-corrected models of blood flow. We will estimate this in subjects with and without genetic risk for Alzheimer's disease. Age-corrected hemodynamic response functions will reduce bias and increase statistical power (increase the sensitivity, and/or reduce the required sample sizes). Aim 2.) Evaluate whether age- abnormal hemodynamics predict abnormal cognitive ability. Modeling normal hemodynamics will allow us to identify abnormal hemodynamics, creating biomarkers for specific diseases such as vascular dementia. Aim 3.) Create a new HRF model to account for age-related CBF changes, using calibrated fMRI. Abnormal hemodynamics may better predict which patients are more likely to experience cognitive decline, leading to earlier treatment.
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2016 — 2017 |
Anderson, Ariana |
R03Activity Code Description: To provide research support specifically limited in time and amount for studies in categorical program areas. Small grants provide flexibility for initiating studies which are generally for preliminary short-term projects and are non-renewable. |
Modeling Rdoc Dimensions Across Levels of Analysis @ University of California Los Angeles
? DESCRIPTION (provided by applicant):The Research Domains Criteria (RDoC) initiative has proposed to overcome limitations in the existing diagnostic taxonomy by investigating new ways of classifying mental disorders based on dimensions of observable behavior and neurobiological measures. A fundamental challenge is determining the validity of the implied relations among these measures both within and across levels of analysis. Using an existing database, this project aims to implement novel data-analytic strategies to examine the validity of selected RDoC domains: working memory (maintenance, updating), and cognitive (effortful) control (response inhibition/suppression). We will accomplish this in two separate Aims: (1). Examine the cross-level relations of selected genetic variants, self-report, behavioral, and MRI measures using Bayesian network models. We propose related analytic approaches for each construct, first identifying measurement models at each available level, and then using exploratory methods (ESEM, MIMIC) to interrogate relations across dimensions. (2). Examine the cross- level relations of selected genetic variants, self-report, behavioral, and MRI measures using Bayesian network models. In the RDoC framework identified by the NIMH workgroups, there is an implied hierarchical structure among different levels of measurement. Using Bayesian network models, we will create cross-level models to investigate whether the hierarchical structure proposed by the RDoC working group is validated in the data, using both observed and latent measures within each level. The existing database includes extensive phenotyping of these RDoC dimensions at diverse levels of analysis including: self-reports, clinical rating scales, clinical diagnostic interview schedules, neuropsychological measures, experimental cognitive measures, and genome-wide genotyping assays. All these data types were acquired in 153 patients, including those with schizophrenia (SZ, n = 58), bipolar disorder (BP, n = 49) and ADHD (n = 46). Healthy volunteers (n =1,137) received all personality, neurocognitive measures and genotyping, along with the ASRS for ADHD screening and the SCID for diagnosis. Additional fMRI and MRI neuroimaging data were obtained in a subset of 128 healthy people and all 121 patients. Among the deliverables of this research will be objective determination about whether selected measures of neural circuit integrity (from structural and functional MRI methods) are truly intermediate phenotypes (i.e., do they mediate relations from genetic to behavioral measures) for both working memory and cognitive control. We will establish whether the dimensions of working memory and response inhibition are consistent across healthy controls and patient groups (ADHD, BP, SZ).
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