UC Berkeley

Everyday human cognition and behavior is accomplished via the coordinated efforts of numerous complex processes. From retaining information in memory to maintaining long-term personal goals, human behavior is multifaceted. In light of this complex nature, there exists massive variability from human to human in their ability to perform and implement these behaviors. In this dissertation, I present three experiments that elucidate the neural processes underlying this variability. Each experiment involved the collection and analysis of functional magnetic resonance imaging (fMRI) data. In one of these experiments, subjects performed a cognitive task during fMRI, while the other two assessed the relationship between baseline neural activity and complex cognition.

The first chapter of this dissertation uses behavioral modeling and fMRI to assess individual differences in the ability to learn and subsequently implement a hierarchically-structured rule set. I show that humans are capable of learning and implementing the complex hierarchical rule and that neural activity across multiple networks supports this ability. First, I find that brain regions across frontal and parietal cortices support the initial discovery of the hierarchical rule. Next, activity across a cingulo-opercular network of brain regions supports the generalization of this knowledge to novel settings.

I present evidence in the second chapter that individual differences in cognition and behavior are not only predicted by the patterns of coordinated neural activity across the entire brain, but that novel temporally-varying analysis approaches provide additional predictive power unobtainable with previous approaches. Here, I analyzed fMRI data collected while subjects performed no explicit task, a procedure referred to as “resting-state” fMRI. Subjects also completed a set of cognitive computer tasks that measured complex cognitive abilities such as working memory and cognitive control. By applying both traditional time-invariant and novel time-varying graph theoretical analyses to the resting-state fMRI data, I was able to predict individual differences in the cognitive abilities measured outside the MRI scanner. Moreover, the novel time-varying analysis revealed relationships to behavior that better captured task-specific behavioral variability.

The final chapter examines the ability of resting-state graph theoretical approaches to predict cognitive abilities related to attention and inhibitory control. Moreover, neural measures were interrogated alongside measures of human physiological functioning. Here, I find that all attentional and inhibitory abilities are accurately predicted across all neural and physiological measures. Specifically, neural measures indicative of brain-wide activity patterns predicted attentional accuracy and global inhibition ability, while neural measures reflecting activity patterns of particular neural networks additionally predicted attentional reactivity. Further, measures of physiological functioning were able to predict individual aspects of inhibitory control.

Together, the three experiments presented here contribute to our knowledge of how neural activity patterns ultimately beget complex cognition and behavior. Using multiple complimentary analytical approaches, I find evidence for the role of multiple neural networks in explaining the differences in cognitive abilities across the healthy human population.