Modern observational and experimental biological data has undergone a revolution. Driven by new biotechnology and computing advances, high dimensional, high density, functional multilevel and longitudinal biological signals are becoming commonplace in medical and public health research. These types of signals historically occurred in small clinical or experimental settings, often referred to as the "small n, large p" problem. We view the extension of these biological signals to cohort studies with longitudinal or hierarchical structure as a next generation of biostatistical problems. We've taken to calling this the "hierarchical large n, large p" problem. The goal of this grant is to introduce general methods for analyzing this form of biostatistical data. We propose three major aims for the analysis of multilevel or longitudinally collected biosignals. The first extends multilevel functional principal components, the investigators'generalization of functional principal components, to longitudinal and high dimensional settings. The second considers the investigators bi-directional filtering and extends it in high-dimensional and longitudinal settings. The third considers model-based independent component blind source separation and extends it to longitudinal settings. To solve this aim, we will also consider the fundamental problem of running MCMC samplers for high dimensional parameter spaces. Specifically, no current work exists for convergence control when the number of parameters is larger than the number of iterations. We propose a method of convergence control using finite population sampling. Our methods will be applied to unique data sets involving imaging (MRI, fMRI, DTI), electrophysiology (EEG, ECOG), sleep measurement (polysomnography) and novel measurements of aging (accelerometer). In the preliminary results, we demonstrate our capacity for working with such data with novel findings in the analysis of EEG, MRI and fMRI data sets. Methods such as unsupervised clustering, blind source separation and dimension reduction are generally recognized first steps in analyzing high dimensional data, and have been applied success- fully in an amazingly diverse collection of settings. Our proposal generalizes these basic approaches to high dimensional data while considering hierarchical and longitudinal directions of variation. Hence, our approaches will form a basic foundation for next generation biomedical functional data.

Public Health Relevance

Modern observational data is often longitudinal or multilevel functional biological signals. We propose a foundational approach for the analysis of such data, including scalable com- puting to next-generation data sets.

Agency
National Institute of Health (NIH)
Type
Research Project (R01)
Project #
5R01EB012547-05
Application #
8728008
Study Section
Biostatistical Methods and Research Design Study Section (BMRD)
Program Officer
Luo, James
Project Start
Project End
Budget Start
Budget End
Support Year
5
Fiscal Year
2014
Total Cost
Indirect Cost
Name
Johns Hopkins University
Department
Biostatistics & Other Math Sci
Type
Schools of Public Health
DUNS #
City
Baltimore
State
MD
Country
United States
Zip Code
21218
Lee, Kuo-Jung; Jones, Galin L; Caffo, Brian S et al. (2014) Spatial Bayesian Variable Selection Models on Functional Magnetic Resonance Imaging Time-Series Data. Bayesian Anal 9:699-732
Coughlin, Jennifer M; Wang, Yuchuan; Ma, Shuangchao et al. (2014) Regional brain distribution of translocator protein using [(11)C]DPA-713 PET in individuals infected with HIV. J Neurovirol 20:219-32
Risk, Benjamin B; Matteson, David S; Ruppert, David et al. (2014) An evaluation of independent component analyses with an application to resting-state fMRI. Biometrics 70:224-36
Lindquist, Martin A; Xu, Yuting; Nebel, Mary Beth et al. (2014) Evaluating dynamic bivariate correlations in resting-state fMRI: a comparison study and a new approach. Neuroimage 101:531-46
Shou, Haochang; Eloyan, Ani; Nebel, Mary Beth et al. (2014) Shrinkage prediction of seed-voxel brain connectivity using resting state fMRI. Neuroimage 102 Pt 2:938-44
Eloyan, Ani; Shou, Haochang; Shinohara, Russell T et al. (2014) Health effects of lesion localization in multiple sclerosis: spatial registration and confounding adjustment. PLoS One 9:e107263
Nebel, Mary Beth; Joel, Suresh E; Muschelli, John et al. (2014) Disruption of functional organization within the primary motor cortex in children with autism. Hum Brain Mapp 35:567-80
Bobb, Jennifer F; Schwartz, Brian S; Davatzikos, Christos et al. (2014) Cross-sectional and longitudinal association of body mass index and brain volume. Hum Brain Mapp 35:75-88
Swihart, Bruce J; Caffo, Brian S; Crainiceanu, Ciprian M (2014) A unifying framework for marginalized random intercept models of correlated binary outcomes. Int Stat Rev 82:275-295
Xiao, Luo; Thurston, Sally W; Ruppert, David et al. (2014) Bayesian Models for Multiple Outcomes in Domains with Application to the Seychelles Child Development Study. J Am Stat Assoc 109:1-10

Showing the most recent 10 out of 26 publications