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Structural connectivity and task-evoked dynamics of the default mode network
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Mastrovito, Dana. Structural connectivity and task-evoked dynamics of the default mode network. Retrieved from https://doi.org/doi:10.7282/t3-20bc-bt51

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TitleStructural connectivity and task-evoked dynamics of the default mode network
NameMastrovito, Dana (author); Pare, Denis (chair); Rotstein, Horacio (internal member); Cole, Michael (internal member); Hanson, Stephen (internal member); Hanson, Catherine (internal member); Biswal, Bharat (outside member); Rutgers University; Graduate School - Newark
Date Created2018
Other Date2018-10 (degree)
SubjectNeuroscience, Brain -- Localization of functions
Extent1 online resource (237 pages : illustrations)
DescriptionThe default mode network (DMN) is a structurally interconnected network of brain regions defined collectively by their high level of intrinsic activity at “rest” and relative decrease in activity while performing a range of cognitive tasks. The functional role of the network's intrinsic activity, as well as the significance of its task-evoked attenuation is unknown. However, aberrations in DMN activity are implicated in many disorders including Alzheimers, depression, autism, and schizophrenia, suggesting it may have a fundamental role in healthy brain function. Using diffusion imaging, I trace the large-scale anatomical connections of the network through the basal ganglia and thalamus, illustrating that the core regions of the network form a distributed cortico-striatal-thalamic circuit. Using Markov chain models of functional MRI, I explore the temporal dynamics of each region of the network during task execution and and present evidence suggesting the DMN may orchestrate switching between bottom up and top down processing in the brain through its connections to the basal ganglia. Subcomponents of the network in parietal cortex may support bottom-up processing while anterior portions in medial prefrontal cortex facilitate top-down. Finally, I classify resting-state data from patients with autism and schizophrenia and find that changes in DMN activity are potential biomarkers for distinguishing between the two disorders.
NotePh.D.
NoteIncludes bibliographical references
Noteby Dana Mastrovito
Genretheses, ETD doctoral
Persistent URLhttps://doi.org/doi:10.7282/t3-20bc-bt51
Languageeng
CollectionGraduate School - Newark Electronic Theses and Dissertations
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work.
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