The candidate is an Assistant Professor at Washington University with her research focus on the development of novel statistical methods for handling missing data and high-dimensional data, with an emphasis on developing prediction tools in dementia, neuroscience, and genetics. Her long-term career goal is to become an independent translational biostatistician with additional expertise in statistical methods for analysis of functional magnetic resonance imaging (fMRI) data in dementia studies. An overall objective of this grant is to broaden the candidate's knowledge of statistical techniques for dementia and fMRI, applying her high- dimensional data, multivariate data, and inferential expertise to further develop statistical methods that account for spatial and temporal properties of fMRI data. While functional connectivity magnetic resonance imaging (fcMRI) is currently a research tool and its clinical utility in dementia is yet to be established, functional connectivity may provide insight to further understand how abnormalities in brain networks develop in Alzheimer's disease (AD). Although some progress has been made with modeling temporal properties and spatial properties, these methods are not conventionally utilized and insufficient emphasis has been placed on group differences in functional connectivity. The first research goal of this grant is to develop inferential statistical methods of functional connectivity that incorporate spatial and temporal properties of fcMRI data and can estimate group effects. These methods include marginal model generalized estimating equations and marginalized transition models, and generalized latent variable modeling and vector autoregressive models to further account for correlated regional data. This research will determine how the inter-regional associations and brain networks differ between AD and healthy cohorts. We will also further develop statistical methods from the first goal for the analysis of fcMRI data that model the extra variability of longitudinal data by developing covariance structures and applying single-index methods. These statistical techniques for longitudinal studies will be intended to improve the assessment of the progression of AD. This research will determine the practical utility of fcMRI as a clinical tool for AD through the application of novel statistical methods.
This research is linked to studies in the area of AD and fMRI that will be of great value to evaluate brain networks that will aid in: 1) evaluating and detecting early-stage AD;and, 2) assessing the progression of AD. fcMRI also has the potential to investigate the pathobiology of AD through the analysis of brain networks and their changes over time. The candidate will provide recommendations for optimal statistical techniques in these areas.
|Disabato, Brianne M; Morris, Carrie; Hranilovich, Jennifer et al. (2014) Comparison of brain structural variables, neuropsychological factors, and treatment outcome in early-onset versus late-onset late-life depression. Am J Geriatr Psychiatry 22:1039-46|
|D'Angelo, Gina M; Weissfeld, Lisa A (2013) Application of copulas to improve covariance estimation for partial least squares. Stat Med 32:685-96|
|Barch, Deanna M; D?Angelo, Gina; Pieper, Carl et al. (2012) Cognitive improvement following treatment in late-life depression: relationship to vascular risk and age of onset. Am J Geriatr Psychiatry 20:682-90|
|D'Angelo, Gina M; Luo, Jingqin; Xiong, Chengjie (2012) Missing Data Methods for Partial Correlations. J Biom Biostat 3:|
|D'Angelo, Gina M; Lazar, Nicole A; Zhou, Gongfu et al. (2012) Bootstrapping GEE models for fMRI regional connectivity. Neuroimage 63:1890-900|
|D'Angelo, Gina M; Lazar, Nicole A; Eddy, William F et al. (2011) A generalized estimating equations approach for resting-state functional MRI group analysis. Conf Proc IEEE Eng Med Biol Soc 2011:5064-7|