ABSTRACT

It was only in the last decade that Magnetic Resonance Imaging (MRI) technologies have achieved high- quality data for individual human brain structure and functions. Such key innovation has opened a unique, revolutionized avenue to investigate human brain mechanisms. One of the most important concepts put forth by Thomas Yeo and colleagues in 2011 (Yeo et al. 2011) was the intrinsic functional connectivity MRI networks (fcMRI networks or functional networks (FNs) for short). Functional networks are subcircuits that are observed consistently across different individual brains. This dissertation aims to unravel different characteristics of human brain functional networks, separately through network morphospace and collectively through stochastic block models.

The quantification of human brain functional (re-)configurations across varying cognitive demands remains an unresolved topic. Functional configurations may be categorized into three different types: i) Network Configural Breadth, ii) Task-to-Task transitional reconfiguration, and iii) Within-Task reconfiguration. Such functional reconfigurations are rather subtle at the whole-brain level. Hence, we propose a mesoscopic framework focused on functional networks (FNs) or communities to quantify functional (re-)configurations. To do so, a 2D network morphospace was introduced. The 2D morphospace relies on two novel mesoscopic metrics, Trapping Efficiency (TE) and Exit Entropy (EE), which capture topology and integration of information within and between a reference set of FNs. This framework was used to quantify the Network Configural Breadth across different tasks. Network configural breadth can be shown to significantly predict behavioral measures, such as episodic memory, verbal episodic memory, fluid intelligence and general intelligence.

One of the most challenging tasks in brain connectomics is to properly estimate and assess whole-brain functional connectomes (FCs). One of the critical steps in constructing large-scale brain networks concerns the thresholding of statistically spurious edge(s) in FCs. State-of-the-art thresholding methods are largely ad hoc. Meanwhile, a dominant proportion of brain connectomic research relies heavily on using a priori set of highly reproducible human brain FNs without assessing their information- theoretically relevance with respect to a given FC. Leveraging recent theoretical developments in Stochastic block model (SBM), we first formally defined and subsequently quantified the level of information-theoretical prominence of a priori set of FNs across different subjects, fMRI task conditions for any given input FC. As an extension to the first aim, the main contribution of this work is to provide an automated thresholding method of FCs based on prior knowledge of human brain FNs. In this study, FCs are constructed according to the Schaefer Atlas scheme, which has multiple levels of granularities for a given subject, fMRI task or resting condition.