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Structural connectivity and task-evoked dynamics of the default mode network
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Descriptive

TitleInfo
Title
Structural connectivity and task-evoked dynamics of the default mode network
Name (type = personal)
NamePart (type = family)
Mastrovito
NamePart (type = given)
Dana
NamePart (type = date)
1976-
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Dana Mastrovito
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author
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Denis
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Denis Pare
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Advisory Committee
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chair
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Rotstein
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Horacio
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Horacio Rotstein
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Advisory Committee
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internal member
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Cole
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Michael
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Michael Cole
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Advisory Committee
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internal member
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Hanson
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Stephen
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Stephen Hanson
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Advisory Committee
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internal member
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Hanson
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Catherine
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Catherine Hanson
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Advisory Committee
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internal member
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Biswal
NamePart (type = given)
Bharat
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Bharat Biswal
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Advisory Committee
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outside member
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Rutgers University
Role
RoleTerm (authority = RULIB)
degree grantor
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NamePart
Graduate School - Newark
Role
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school
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Text
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theses
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ETD doctoral
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DateCreated (qualifier = exact)
2018
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2018-10
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2018
Place
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xx
Language
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eng
Abstract (type = abstract)
The 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.
Subject (authority = RUETD)
Topic
Neuroscience
Subject (authority = ETD-LCSH)
Topic
Brain -- Localization of functions
RelatedItem (type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
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TitleInfo
Title
Graduate School - Newark Electronic Theses and Dissertations
Identifier (type = local)
rucore10002600001
Identifier
ETD_9252
Identifier (type = doi)
doi:10.7282/t3-20bc-bt51
PhysicalDescription
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electronic resource
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application/pdf
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text/xml
Extent
1 online resource (237 pages : illustrations)
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
Note (type = statement of responsibility)
by Dana Mastrovito
Location
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NjNbRU
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Rights

RightsDeclaration (ID = rulibRdec0006)
The author owns the copyright to this work.
RightsHolder (type = personal)
Name
FamilyName
Mastrovito
GivenName
Dana
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2018-09-26 13:45:24
AssociatedEntity
Name
Dana Mastrovito
Role
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Affiliation
Rutgers University. Graduate School - Newark
AssociatedObject
Type
License
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Author Agreement License
Detail
I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
RightsEvent
Type
Embargo
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2018-10-31
DateTime (encoding = w3cdtf); (qualifier = exact); (point = end)
2019-05-02
Detail
Access to this PDF has been restricted at the author's request. It will be publicly available after May 2nd, 2019.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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Technical

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ETD
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windows xp
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2019-01-15T13:25:07
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2018-10-01T14:44:57
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