Characterisation of Functional Brain Networks underlying Cognitive Reasoning and Intelligence

Abstract

Intelligence is a general mental capacity that encompasses the ability to plan, problem-solve, reason, think abstractly and comprehend complex relations between concepts. Measured via complex, multistep reasoning tasks, individual differences in intelligence scores are highly predictive of a number of crucial life outcomes. Despite many decades of research, however, the neural bases of intelligence and reasoning abilities are not well understood. Early neuroimaging and lesion studies suggested that regions of the prefrontal cortex are exclusively responsible for intelligent behaviour and higher cognitive reasoning abilities. More recently, however, investigators have conceptualized intelligence as relying upon a widely distributed network of interconnected nodes. The aim of my thesis was to investigate human reasoning and intelligence within this newer, network-centric framework. I conducted a series of functional magnetic resonance imaging (fMRI) experiments to characterize relevant networks in the brain, and related these networks to reasoning task performance, individual differences in intelligence, and the breakdown of reasoning ability amongst individuals with developmental anomalies in brain wiring.The thesis is organized into seven chapters. In the first chapter, I introduce the research question in the context of previous literature that has investigated the brain-basis of reasoning and intelligence by measuring brain activity using fMRI. In doing so, I discuss the origins of intelligence, why task complexity matters, as well as modern in-vivo brain imaging techniques and their biological basis. I then present the concepts of brain network organization, connectivity and graph theory as a relatively untapped avenue to explain higher cognitive reasoning. Armed with this new framework for understanding the brain, I discuss a number of studies that have used this approach to inform the key questions investigated in this thesis.In Chapter 2 I employed an individual-differences approach to investigate the relationship between 'resting-state' baseline functional networks and commonly used measures of intelligence.Previous work has implicated a fronto-parietal network of brain regions that support higher cognitive reasoning, formalized in the parieto-frontal theory of intelligence. However, existing evidence for the fronto-parietal theory is mixed, at least with respect to findings from connectivity studies. To address this issue, I undertook a quantitative summary of previous literature and performed an empirical analysis of data from a large cohort of individuals (N = 317) sourced from the Human Connectome Project. I found that connectivity between the default-mode and fronto-parietal networks best explained individual differences in intelligence.In Chapter 3 I investigated the relationship between default-mode and cognitive control networks during a complex reasoning task. Participants completed a modified version of the classic Wason Selection Task, a measure of relational reasoning, in which the num...