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Quantitative Trends and Topology in Developing Functional Brain Networks

Gozdas, Elveda
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1535381148527108

Abstract Details

2018, PhD, University of Cincinnati, Arts and Sciences: Physics.
With the advances in MRI, it has become possible to noninvasively observe function and structure of the developing brain in vivo. Functional magnetic resonance imaging (fMRI) of the brain is a non-invasive way to assess brain function using MRI signal changes associated with neuronal activity. The most widely used method is based on BOLD (Blood Oxygenation Level Dependent) signal changes caused by hemodynamic and metabolic neuronal responses. Functional connectivity has been defined as inter-regional temporal correlations among spontaneous BOLD fluctuations in different regions of the brain during a task as well as when the brain is idle. By identifying brain regions that exhibit highly correlated BOLD signal fluctuations, we can infer that the regions are functionally connected and co-activation during a particular task or at rest (fcMRI) suggests that these regions work together as part of a functional brain network. This method is now being used widely to study brain networks but has seen limited use in studies of the developing brain, particularly in infants. Functionally connected brain regions can be specified as components of integrated networks that enable specific sensory or cognitive brain functions. These brain networks demonstrate the basic connectivity pattern between brain regions, which can be represented mathematically using graph-theoretical approaches. Graph theory provides a convenient quantitative and visual format to sketch the topological organization of brain connectivity representing complex brain networks. Graph theory analysis also naturally provides quantitative descriptors of both global and regional topological properties of brain graphs. While this approach is now widely used with functional MRI data as a means of studying the topology of functional brain networks, it has not been applied to study the development of brain networks from birth, nor in the premature infant brain. The main goal of this dissertation is to use novel functional connectivity acquisition and analysis methods designed for infants and children to examine and better understand the effects of brain injury, prematurity and normal aging on functional brain networks during development. The work presented here makes significant methodological and scientific contributions to our understanding of developing brain networks with the following discoveries: 1) Infants with brain injury sustained in the perinatal period show evidence of decreased brain activity and functional connectivity during visual stimulation compared to healthy, full-term neonates; 2) Resting-state networks are already established early in development in very preterm infants but functional connectivity and network characteristics in preterm infants differ from those of full-term infants by term-equivalent age; 3) Developmental trajectories of the functional brain connectome in normal healthy children exhibit significant sex-related differences, with different rates of maturation of functional brain networks in boys and girls; 4) Differences in cognitive ability during childhood are supported by differences in regional network topology during brain development; continuing into late adolescence.
Scott Holland, Ph.D. (Committee Chair)
L. C. R. Wijewardhana, Ph.D. (Committee Chair)
Howard Jackson, Ph.D. (Committee Member)
Stephanie Merhar (Committee Member)
Jean Tkach, Ph.D. (Committee Member)
Jason Woods, Ph.D. (Committee Member)
134 p.
Radiology
fMRI; Brain; Preterm; Neonate; Connectivity; Functional Network

Recommended Citations

Citations

  • Gozdas, E. (2018). Quantitative Trends and Topology in Developing Functional Brain Networks [Doctoral dissertation, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1535381148527108

    APA Style (7th edition)

  • Gozdas, Elveda. Quantitative Trends and Topology in Developing Functional Brain Networks. 2018. University of Cincinnati, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1535381148527108.

    MLA Style (8th edition)

  • Gozdas, Elveda. "Quantitative Trends and Topology in Developing Functional Brain Networks." Doctoral dissertation, University of Cincinnati, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1535381148527108

    Chicago Manual of Style (17th edition)

ucin1535381148527108
341
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This open access ETD is published by University of Cincinnati and OhioLINK.