Discovery of Structural Brain Imaging Markers of HIV-Associated Outcomes Using Connectivity-Informed Regularization Approach

• Activity Number: 150 - Recent Advances for Modeling Neuroimaging Data
• Type: Topic Contributed
• Date/Time: Monday, July 31, 2017 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistics in Imaging
• Author(s): Jaroslaw Harezlak* and Damian Brzyski and Marta Karas and Joaquin Goni and Beau Ances and Timothy Randolph
• Companies: Indiana University School of Public Health and Indiana University and Indiana University and Purdue University and Washington University School of Medicine and Fred Hutchinson Cancer Research Center
• Keywords: regularization ; multimodal brain imaging ; brain connectivity ; biomarkers ; HIV

Abstract

Study of multimodal brain imaging biomarkers of disease is frequently performed by analyzing each modality separately. In our work, we use a recently proposed regularization method, RidgePEER (ridge-enhanced partially empirical eigenvectors for regression), to discover early biomarkers of HIV-associated outcomes including CD4 count and HIV RNA plasma level. Specifically, we incorporate information arising from the functional and structural connectivity in the penalized generalized linear model framework to inform the associations between the brain cortical features and disease outcomes. Penalty terms are defined as a combination of Laplacian matrices arising from the functional and structural connectivity adjacency matrices. We study the advantages of employing different measures of connectivity as well as synergistic functional and structural information. Finally, we address the issue of using different cerebral cortex parcellations, from the common FreeSurfer parcellations (68 and 148 cortical areas) to a novel multi-modal parcellation into 360 cortical areas, in discovering global and local biomarkers.