Modern neuroimaging technology has brought developmental neuroscience to the threshold of an era of unprecedented breakthroughs. With high-resolution brain scans acquired in increasingly large samples of children and adults, investigators are mapping both the normal development of the brain over the lifespan, and the developmental abnormalities that are associated with psychiatric disorders. These studies typically entail fitting models at tens of thousands of brain locations, and due in part to this hih computational load, investigators have tended to settle for suboptimal methods. A prominent example is fitting a polynomial model for the development of cortical thickness with age. Nonparametric smoothing offers well-known advantages over polynomial dependence when fitting a single model, but to date, smoothing methodology has not found application to settings in which many thousands of models are fitted concurrently. More broadly, there is a critical need for state-of-the-art statistical methods to tackle the massive neuroimaging data sets generated by studies of the developing brain. The objective of this proposal is to provide a comprehensive toolkit for statistical analyses of normal and abnormal brain development. The investigators have begun to develop a number of innovative techniques toward this end, and have forged a strong multi-institution collaboration ideally suited to meeting the many challenges that lie ahead. The first specific aim focuses on computationally feasible estimation of large numbers of curves representing the mean, or a given percentile, of the distribution of a quantity of interest, conditional on a predictor such as age.
The second aim encompasses several hypothesis testing methods that are particularly relevant to neuroimaging, including tests of polynomial null hypotheses against smooth alternatives, as well as tests for group differences in developmental trajectories and other complex outcomes.
The third aim, originally motivated by the need for succinct visual representations of spline fits at each point in a grid of brain locations, is to develop novel methods for clustering large amounts of functional data. The proposed methods will be applied to data acquired by multiple imaging modalities, including resting-state functional magnetic resonance imaging, diffusion tensor imaging, and cortical thickness measurement. Most of the methods proposed here are applicable to any imaging modality, and many can be applied outside the field of neuroimaging. Thus the proposed research will have a significant impact both on statistical methodology and on neuroscience, psychiatry, and other disciplines.

Public Health Relevance

Brain imaging has emerged as a critical tool for the studying how the human brain normally develops, and how some psychiatric disorders may reflect abnormalities of development. Increasingly massive quantities of data are being generated by brain imaging studies, and standard data analysis techniques are not equipped to extract scientifically relevant information from these data. The proposed work will develop new statistical methods for analyzing such data sets, which will advance scientific understanding of the brain and may ultimately lead to improved treatments for mental illness.

National Institute of Health (NIH)
National Institute of Mental Health (NIMH)
Research Project (R01)
Project #
Application #
Study Section
Biostatistical Methods and Research Design Study Section (BMRD)
Program Officer
Cavelier, German
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
New York University
Schools of Medicine
New York
United States
Zip Code
Chén, Oliver Y; Crainiceanu, Ciprian; Ogburn, Elizabeth L et al. (2018) High-dimensional multivariate mediation with application to neuroimaging data. Biostatistics 19:121-136
Urbanek, Jacek K; Zipunnikov, Vadim; Harris, Tamara et al. (2018) Prediction of sustained harmonic walking in the free-living environment using raw accelerometry data. Physiol Meas 39:02NT02
Reiss, Philip T (2018) Cross-sectional versus longitudinal designs for function estimation, with an application to cerebral cortex development. Stat Med 37:1895-1909
Mejia, Amanda F; Nebel, Mary Beth; Barber, Anita D et al. (2018) Improved estimation of subject-level functional connectivity using full and partial correlation with empirical Bayes shrinkage. Neuroimage 172:478-491
Reiss, Philip T; Huang, Lei; Wu, Pei-Shien et al. (2017) Pointwise influence matrices for functional-response regression. Biometrics 73:1092-1101
Reiss, Philip T; Goldsmith, Jeff (2017) Comment. J Am Stat Assoc 112:161-164
Urbanek, Jacek K; Harezlak, Jaroslaw; Glynn, Nancy W et al. (2017) Stride variability measures derived from wrist- and hip-worn accelerometers. Gait Posture 52:217-223
Muschelli, John; Sweeney, Elizabeth M; Ullman, Natalie L et al. (2017) PItcHPERFeCT: Primary Intracranial Hemorrhage Probability Estimation using Random Forests on CT. Neuroimage Clin 14:379-390
Huang, Lei; Reiss, Philip T; Xiao, Luo et al. (2017) Two-way principal component analysis for matrix-variate data, with an application to functional magnetic resonance imaging data. Biostatistics 18:214-229
Reiss, Philip T; Goldsmith, Jeff; Shang, Han Lin et al. (2017) Methods for scalar-on-function regression. Int Stat Rev 85:228-249

Showing the most recent 10 out of 41 publications