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Activity Number: 112 - Smoothing for Spatially and Temporally Indexed Data
Type: Topic Contributed
Date/Time: Monday, July 30, 2018 : 8:30 AM to 10:20 AM
Sponsor: Royal Statistical Society
Abstract #330287 Presentation
Title: Lagged Hierarchical Semiparametric Models for Task-Based Dynamic Functional Connectivity (DFC)
Author(s): Jaroslaw Harezlak* and Zikai Lin and Maria Kudela and Brandon Oberlin and Joaquin Goni and David A Kareken and Mario Dzemidzic
Companies: Indiana University Bloomington and Indiana University and Takeda Pharmaceuticals and Indiana University School of Medicine and Purdue University and Indiana University School of Medicine and Indiana University School of Medicine
Keywords: brain imaging; connectivity; lagged time series; functional data; hierarchical models

Functional magnetic resonance imaging (fMRI) studies are utilized to assess both brain activation and co-activation among brain regions. Data produced in an MRI scanner consist of hundreds of thousands of time series indicating changes in the blood oxygenation level. In our work, we propose a method to estimate co-activation of hundreds of brain regions at the task-, subject- and population-level at both concurrent time points and at the lagged time intervals. We assess our methodology via its application to the study of social and heavy alcohol drinkers' reaction to different gustatory cues, including beer, Gatorade and water.

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

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