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Activity Number: 445
Type: Contributed
Date/Time: Tuesday, August 2, 2016 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #320196
Title: Group Discrimination Using Sparse Network Modeling of Resting-State fMRI
Author(s): Maria Puhl* and William Coberly and Alejandro Hernandez and Kyle Simmons
Companies: University of Tulsa and University of Tulsa and University of Tulsa and Laureate Institute for Brain Research
Keywords: Sparse Modeling ; Networks ; Neuroinformatics ; LASSO ; regularization ; Brain Modeling

Resting state fMRI data generates dense functional connectivity networks that can be used to discriminate between neurologically differing groups of subjects. Functional connectivity is a measure of connectivity between brain regions sharing functional properties. This is measured by the correlation between regions of the brain, which results in a dense connectivity network. In attempting to find differences between groups, we treat each correlation as a variable in a regression model. This results in a difficult to analyze regression problem due to the fact that there are significantly more variables than the number of subjects. Therefore we investigate the use of sparse modeling algorithms such as LASSO, Sparse Discriminant Analysis, and others to reduce the number of variables in the model, while also focusing on the classification accuracy of distinguishing between the different subject groups.

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

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