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Statistics Seminar: Brain Connectivity-Informed Adaptive Regularization for Generalized Outcomes

Start Date: March 5, 2019
End Date: March 5, 2019

By Prof. Jaroslaw Harezlak 
Department of Epidemiology and Biostatistics at the Indiana University 
School of Public Health in Bloomington

A challenging problem in the brain imaging research is a principled incorporation of information from different imaging modalities in regression models. Frequently, data from each modality is analyzed separately using, for instance, dimensionality reduction techniques, which result in a loss of information. We propose a novel regularization method, griPEER (generalized ridgified Partially Empirical Eigenvectors for Regression) to estimate the association between the brain structure features and a scalar outcome within the generalized linear regression framework. griPEER provides a principled approach to use external information from the structural brain connectivity to improve the regression coefficient estimation. Our proposal incorporates a penalty term, derived from the structural connectivity Laplacian matrix, in the penalized generalized linear regression. We address both theoretical and computational issues and show that our method is robust to the incomplete structural brain connectivity information. griPEER is evaluated via extensive simulation studies and it is applied in classification of the HIV+ and HIV- individuals.
Bio: Jaroslaw Harezlak is a Professor in the Department of Epidemiology and Biostatistics at the Indiana University School of Public Health in Bloomington. He has a Ph.D. in Biostatistics from the Harvard University. After a 2-year post-doctoral training at the Harvard School of Public Health, he joined Indiana University as an Assistant Professor. His interests span a number of medical areas: including NeuroHIV, physical activity, and mild traumatic brain injury (mTBI), as well as statistical areas: including semiparametric regression, regularization, functional data analysis and structured high-dimensional data. In his applied research, he uses data arising in structural and functional brain imaging, accelerometry, and intensively collected longitudinal studies. He has published over 80 peer-reviewed articles in the statistical, medical and epidemiological journals as well as a book on “Semiparametric Regression with R”.

More Information:

For more info contact: Prof. Hernando Ombao: email:
Date: Tuesday 5th Mar 2019
Time:04:00 PM - 05:00 PM
Location: Building 1, Level 4, room# 4102
light refreshments will be served around 3:45 PM.