A mixed-modeling framework for analyzing multitask whole-brain network data

Abstract

The emerging area of brain network analysis considers the brain as a system, providing profound insight into links between system-level properties and health outcomes. Network science has facilitated these analyses and our understanding of how the brain is organized. While network science has catalyzed a paradigmatic shift in neuroscience, methods for statistically analyzing networks have lagged behind. To address this for cross-sectional network data, we developed a mixed-modeling framework that enables quantifying the relationship between phenotype and connectivity patterns, predicting connectivity structure based on phenotype, simulating networks to gain a better understanding of topological variability, and thresholding individual networks leveraging group information. Here we extend this comprehensive approach to enable studying system-level brain properties across multiple tasks. We focus on rest-to-task network changes, but this extension is equally applicable to the assessment of network changes for any repeated task paradigm. Our approach allows (a) assessing population network differences in changes between tasks, and how these changes relate to health outcomes; (b) assessing individual variability in network differences in changes between tasks, and how this variability relates to health outcomes; and (c) deriving more accurate and precise estimates of the relationships between phenotype and health outcomes within a given task.

Predicting Latent Links from Incomplete Network Data Using Exponential Random Graph Model with Outcome Misclassification

“…Network data has become increasingly important to study complex problems, for example, to understand the role of contact patterns in epidemics through social network analysis (Stattner and Vidot, 2011) and interactions between neuron populations in the human brain (Simpson et al, 2011, Simpson et al, 2019. A network can be represented by a graph, where a node denotes a study unit and an edge or link indicates interactions between a pair of nodes (Zhao et al, 2017).…”

“…Efficient computational algorithms have been also been developed using advanced algorithms (Miller et al, 2009, Al Hasan et al, 2006, O'Madadhain et al, 2005. The parametric network models have been widely used for social network analysis.Recently, network models have also been successfully applied to complex brain connectome data analysis (Simpson et al, 2019). However, these parametric network models are built on completely observed network data (i.e.…”

“…Recently, network models have also been successfully applied to complex brain connectome data analysis (Simpson et al, 2019). However, these parametric network models are built on completely observed network data (i.e.…”

Graph combinatorics based group-level network inference

“…Keywords: combinatorics, graph theory, graph topology, l 0 norm regularization, network statistics 1 Introduction There has been an increased interest in statistical literature to model group-level network data. For example, the population level brain connectome studies often aim to investigate whether brain functional and/or structural networks are related to behavioral and symptomatic phenotypes, and the microbiome network studies focus on whether microbial networks are influenced by the clinical status (Lukemire et al, 2017;Xia and Li, 2017;Cai et al, 2018;Simpson et al, 2019;Warnick et al, 2018;Shaddox et al, 2018). In these applications, the data structure of each subject can be represented by a graph notation, where a node represents a well-defined biological unit (e.g.…”

“…In addition, GLEN can also be a complement to the existing methods. For example, the locations and topological structures of phenotype-related subgraphs detected by GLEN can become prior information for existing network analysis models (Xia and Li, 2017;Simpson et al, 2019;Xia et al, 2019).…”