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Activity Number: 254 - Contributed Poster Presentations: Section on Statistical Learning and Data Science
Type: Contributed
Date/Time: Monday, July 30, 2018 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #329458
Title: Variation of Functional Connectome Topology and Its Implications for Attention
Author(s): Kelson Zawack*
Companies: Yale University
Keywords: fMRI; Latent Class Modeling; Networks

The human brain is a network of billions of interconnected neurons. One important statistical question about this network is how it's topology varies across individuals. One hypothesis is that everyone's brain has a unique topology. Another is that, because there are far fewer genes than neurons, neurons are organized into larger scale variable modules. A powerful tool for interrogating functional connections between brain regions is functional magnetic resonance imaging (fMRI), which measures blood flow in the brain as an indicator of brain activity. To investigate the variation in topologies, resting state fMRI measurements were obtained for 113 individuals with a broad range of attention scores. Connections between regions of interest were inferred using pairwise correlations of the regions activation time series. Individual/connection subtypes were then inferred using a latent class model. This analysis indicates that these individual's topologies can be decomposed into a set of subtypes, and that some of these subtypes contain only individuals with normal attention. Interestingly, there are no subtypes exclusive to individuals with Attention Deficit Disorder.

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

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