Modeling and analysis of longitudinal multimodal magnetic resonance imaging: application to early brain development

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Publication Type dissertation
School or College College of Engineering
Department Bioengineering
Author Sadeghi, Neda
Title Modeling and analysis of longitudinal multimodal magnetic resonance imaging: application to early brain development
Date 2013-12
Description Many mental illnesses are thought to have their origins in early stages of development, encouraging increased research efforts related to early neurodevelopment. Magnetic resonance imaging (MRI) has provided us with an unprecedented view of the brain in vivo. More recently, diffusion tensor imaging (DTI/DT-MRI), a magnetic resonance imaging technique, has enabled the characterization of the microstrucutral organization of tissue in vivo. As the brain develops, the water content in the brain decreases while protein and fat content increases due to processes such as myelination and axonal organization. Changes of signal intensity in structural MRI and diffusion parameters of DTI reflect these underlying biological changes. Longitudinal neuroimaging studies provide a unique opportunity for understanding brain maturation by taking repeated scans over a time course within individuals. Despite the availability of detailed images of the brain, there has been little progress in accurate modeling of brain development or creating predictive models of structure that could help identify early signs of illness. We have developed methodologies for the nonlinear parametric modeling of longitudinal structural MRI and DTI changes over the neurodevelopmental period to address this gap. This research provides a normative model of early brain growth trajectory as is represented in structural MRI and DTI data, which will be crucial to understanding the timing and potential mechanisms of atypical development. Growth trajectories are described via intuitive parameters related to delay, rate of growth, and expected asymptotic values, all descriptive measures that can answer clinical questions related to quantitative analysis of growth patterns. We demonstrate the potential of the framework on two clinical studies: healthy controls (singletons and twins) and children at risk of autism. Our framework is designed not only to provide qualitative comparisons, but also to give researchers and clinicians quantitative parameters and a statistical testing scheme. Moreover, the method includes modeling of growth trajectories of individuals, resulting in personalized profiles. The statistical framework also allows for prediction and prediction intervals for subject-specific growth trajectories, which will be crucial for efforts to improve diagnosis for individuals and personalized treatment.
Type Text
Publisher University of Utah
Dissertation Name Doctor of Philosophy
Language eng
Rights Management (c) Neda Sadeghi
Format Medium application/pdf
Format Extent 5,461,028 bytes
Identifier etd3/id/2647
ARK ark:/87278/s6rz2m8s
Setname ir_etd
Date Created 2014-01-14
Date Modified 2021-05-06
ID 196222
Reference URL
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