64th ISI World Statistics Congress - Ottawa, Canada

64th ISI World Statistics Congress - Ottawa, Canada

Applications of Network Representation Learning to Identify Associations of Brain Function with Political Ideology

Author

JW
James Wilson

Co-author

Conference

64th ISI World Statistics Congress - Ottawa, Canada

Format: IPS Abstract

Keywords: graph representation learning;, network, network-modelling

Session: IPS 315 - Recent advances in modeling and analysis of large high-dimensional networks

Monday 17 July 2 p.m. - 3:40 p.m. (Canada/Eastern)

Abstract

Emerging research examining functional connectivity (i.e, synchrony or correlation of activity between multiple brain regions) has begun investigating the neural underpinnings that drive political ideology, political attitudes, and political actions. This session will explore network-driven large sample, whole-brain analyses of functional connectivity across common fMRI tasks to identify associations with political ideology in a large sample from The Ohio State University Wellbeing project. I will discuss how to use network representation learning (NRL) and other classic data science techniques (graph-theoretic convolutional neural networks + principal component analysis + penalized regression techniques) to explore functional connectivity associations with political ideology. With NRL, I find that functional connectivity data can be used to accurately differentiate conservatives from liberals and that functional connectivity augments traditional survey-based predictions of political ideology. This case study motivates the use of NRL for downstream learning tasks like regression and classification.