Joint Estimation and Regularized Aggregation of Brain Network in fMRI Data

• Activity Number: 184 - Contributed Poster Presentations: Korean International Statistical Society
• Type: Contributed
• Date/Time: Monday, July 29, 2019 : 10:30 AM to 12:20 PM
• Sponsor: Korean International Statistical Society
• Author(s): Jongik Chung* and Cheolwoo Park and Jennifer McDowell
• Companies: and University of Georgia and University of Georgia
• Keywords: Aggregation; FMRI data; Graphical models; Precision matrix; Regularization

Abstract

In the Gaussian graphical model framework, precision matrices reveal conditional dependence structure among random variables. In functional magnetic resonance imaging (fMRI) data, estimating such precision matrices of multi-subjects and aggregating them to a group-level is an essential step for constructing a group brain network. In this article, we consider joint estimation of multiple precision matrices with regularized aggregation. A regularization approach induces sparsity which makes brain network estimation more realistic. Also, simply averaging multiple precision matrices may be affected by outliers and provide inconsistent outcomes between subject-level and group-level networks. In contrast, the proposed method yields a robust group graph which can identify and ease the effect of outliers. We demonstrate the effectiveness of the proposed method through simulated examples and analyses on saccade tasks fMRI data.