A Comparative Study of Brain Structural and Functional Connectivity: Graph Topology, Individual Fingerprinting, and Predictive Modeling

Abstract

Brain connectivity analyses using neuroimaging data provide insights into the structural and functional organization of the human brain. Several approaches have thus been proposed for modeling structural and functional connectivity, each with its own strengths and limitations. This thesis compares functional connectivity (FC) estimates derived based on correlation and partial correlation, evaluating their graph topology and performance in individual identification and prediction tasks, while also contrasting them with structural connectivity (SC) networks. We begin by estimating FC networks based on marginal and partial correlation and further explore low-order partial correlation graphs as an intermediate approach. Motivated by studies suggesting that brain structural hubs are closely related to functional activity, we also propose an alternative FC construction method by regressing out the temporal activity of SC hubs. Our analysis shows that SC emphasizes within-hemisphere connections and exhibits small-world properties, while FC consistently reveals strong interhemispheric connections, regardless of the methodology. However, other network properties vary depending on the estimation method used. Finally, we assess these networks’ abilities to capture individual-specific features through subject identification and behavioral prediction tasks. We observe that partial correlation-based FC network performs well in subject identification when the sample size is large, but its performance deteriorates sharply with smaller samples. Additionally, regardless of estimation method, FC networks consistently outperform SC in predicting behavioral variables, and combining both typically improves predictive accuracy, except for partial correlation-based FC. Sample size and number of scan sessions used in FC estimation also have a non-trivial impact on predictive performance. Our study highlights the methodological implications of FC estimation strategies for brain network analysis.