A Two-Part Mixed-Effects Modeling Framework for Analyzing Whole-Brain Network Data

• Activity Number: 590
• Type: Topic Contributed
• Date/Time: Thursday, August 7, 2014 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistics in Imaging
• Author(s): Sean Simpson*+
• Companies: Wake Forest School of Medicine

Abstract

Brain network analyses have moved to the forefront of neuroimaging research over the last decade. However, methods for statistically modeling and comparing groups of networks have lagged behind. Most current approaches either rely on a specific extracted summary metric, or on mass-univariate nodal or edge-based comparisons that ignore the inherent topological properties of the network while also yielding little power to determine significance. While some univariate approaches have proven useful, gleaning deeper insights into normal and abnormal changes in complex brain function demands methods that take advantage of the wealth of data present in an entire brain network. Fusing multivariate statistical approaches with network science presents the best path to develop these methods. Toward this end, we propose a two-part mixed-effects modeling framework that allows modeling both the probability of a connection and the strength of a connection if it exists. Models within this framework enable quantifying the relationship between an outcome and connectivity patterns in the brain while reducing spurious correlations through inclusion of confounding covariates.