Online Program Home
  My Program

All Times EDT

Abstract Details

Activity Number: 532 - Making Big and Complex Imaging Data Count with New Statistical Tools
Type: Invited
Date/Time: Thursday, August 6, 2020 : 1:00 PM to 2:50 PM
Sponsor: SSC (Statistical Society of Canada)
Abstract #309260
Title: Adaptive Regularization in Complex Settings: Multimodal Brain Imaging
Author(s): Jaroslaw Harezlak* and Damian Brzyski and Kewin Paczek and Joaquin Goni and Timothy Randolph and Beau Ances
Companies: Indiana University and Wroclaw University of Science and Technology, Poland and Jagiellonian University, Krakow, Poland and Purdue University and Fred Hutchinson Cancer Research Center and Washington University School of Medicine
Keywords: Regularization; Brain imaging; Structural Connectivity; Functional Connectivity; Longitudinal Data
Abstract:

A problem frequently occurring in brain imaging research is a principled incorporation of information from different imaging modalities in association studies. Oftentimes, data from each modality are studied separately resulting in a loss of information. We propose a novel regularization method incorporating information from structural imaging, structural connectivity and functional connectivity in the longitudinal setting. In our work, the penalty term is defined from the structural and functional connectivity modularity information. We address both theoretical and computational issues and show that our method adapts to the incomplete or mis-specified brain connectivity information. Our regularization method is evaluated via extensive simulation studies and it is applied in a study of HIV+ individuals’ longitudinal neurodegeneration.


Authors who are presenting talks have a * after their name.

Back to the full JSM 2020 program