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Activity Number: 88
Type: Invited
Date/Time: Sunday, July 31, 2016 : 6:00 PM to 8:00 PM
Sponsor: Section on Statistics in Imaging
Abstract #319093
Title: A Mixed-Effects Modeling Approach to Study the Impact of Pesticides on Farmworkers' Brain Networks Using RS-fMRI Data
Author(s): Mohsen Bahrami* and Paul Laurienti and Thomas Arcury and Sean Simpson
Companies: Virginia Tech and Wake Forest School of Medicine and Wake Forest School of Medicine and Wake Forest School of Medicine
Keywords: Mixed-Effects Modelling ; RS-fMRI ; Brain Network

Network analysis of the brain has emerged as a powerful method over the last decade to study brain function. We use RS-fMRI data to study the impact of pesticides on farmworkers' brain networks using a two-part mixed-effects modelling approach. We investigate the impact of pesticides on the probability and strength of interregional connections as well as on topological structure. The adjacency matrix for each participant's network was constructed by computing correlations (partial and full) between all pairs of the 116 regions of the brain according to the AAL template. These matrices served as the outcome variables in our approach. Model covariates included: 1) network features such as clustering coefficient and global efficiency, 2) covariate of interest (pesticide exposure group), 3) physiological parameters such as blood cotinine and cholinesterase, 4) confounders such as age and distance between regions, and 5) interactions between pesticide grouping and network features. The random effects and error term capture within and between network variability. Our approach allows comprehensively studying the brain network differences between those exposed and unexposed to pesticides.

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

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