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Title page for ETD etd-11212018-085709

Type of Document Dissertation
Author Bailey, Stephen Kent
URN etd-11212018-085709
Title Attributes of functional network architecture supporting skilled reading
Degree PhD
Department Neuroscience
Advisory Committee
Advisor Name Title
Gavin Price Committee Chair
  • reading
  • brain networks
  • functional MRI
Date of Defense 2018-10-24
Availability unrestricted

It is now a well-established finding that the human brain has a ``small-world' architecture, which is characterized by densely connected modules and a highly-connected hub network. It has been hypothesized that this organization is central to the immense flexibility of human cognition, and inefficiencies have been implicated in psychiatric disorders.

Tasks induce a reorganization of this architecture, though, such that ``flexibility' of the network may be of primary importance to skill in multiple cognitive domains. However, how individual differences in this flexibility relate to individual differences in cognition remains to be explored.

Reading comprehension is a skill that requires the integration of multiple cognitive mechanisms, and therefore, requires passing information between many different neural networks. It is also a skill that has been well-characterized behaviorally, and so it represents an ideal model ability for evaluating the importance of network flexibility for fluent and skilled behavior.

In this dissertation, we present four studies that investigate network properties as they relate to reading comprehension and reading success. The common thread is that reading requires the integration of many different brain networks (even moreso than listening) and that better readers are more able to meet these demands from a young age.

Study 1 investigates individual differences in "intrinsic" network architecture and its relationship to reading skill using resting-state fMRI data from older children (ages 10 to 11). We establish a set of methods for analyzing variability in connectomes and use their attributes to predict individual differences in reading skill.

Study 2 describes how network architecture changes during reading. We examine changes within and between RSNs and attempt to localize these differences to specific RSNs, such as the visual, dorsal attention and default mode networks. We also elaborate on the results of Study 1, testing whether the relationship between network architecture and reading skill changes in the task-evoked connectome.

Study 3 moves beyond looking only at reading-evoked activity by comparing reading-evoked networks to listening-evoked ones, then between several other activities. The key questions this study addresses are whether greater variability between task-evoked networks is a beneficial attribute, and whether any particular RSNs (such as the fronto-parietal network) are more responsible for the reconfiguration of the whole-brain network.

Study 4 provides a preliminary investigation of network topology in various evoked architectures across development. In addition to comparing children, adolescents and adults, these analyses serve as a replication and extension of each of the previous studies: we test whether the relationship between reading and network architecture changes in more mature individuals and how task-evoked activity differs along the lifespan. While there is strong evidence that learning to decode creates persistent changes to the neural systems utilized in language, there have been no studies investigating the trajectory of brain modularity and its relationship to reading over time.

Overall, these studies will use reading-related brain activity and behavior as a model for understanding how individual differences in network architecture form a basis for individual differences in cognitive processing. We combine inferences from several different methodological approaches, including resting-state network analysis, task-based activation analyses, and the combination of the two. Through this systematic approach, we strive to make a meaningful contribution to our understanding of brain modularity and its relationship to cognition.

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