A Study of Resting State FMRI Dynamic Functional Network Analysis of MTBI

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dc.contributor.advisorJaJa, Josephen_US
dc.contributor.authorHou, Wenshuaien_US
dc.contributor.departmentElectrical Engineeringen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2015-06-25T05:48:32Z
dc.date.available2015-06-25T05:48:32Z
dc.date.issued2015en_US
dc.description.abstractMild Traumatic Brain Injury (MTBI) is one of the most common neurological disorders. A subset of patients develop persistent cognitive deficits. A number of the brain studies have been conducted to discover the abnormalities and disruptions in the brain functional networks using similar methods to those employed in more severe brain disorders such as Alzheimer's or Schizophrenia. Static functional network analysis using resting state brain fMRI images has shown some promising results in identifying characteristics of MTBI. However, recent development in the dynamics of functional networks have been able to reveal insightful information about anomalies in brain activities that have not been observed when using traditional static analysis. Our work focuses on both static and dynamic functional analysis of the brain. Our overall analysis pipeline is data-driven using a dataset of 47 MTBI subjects and a demographically matching healthy control group size of 30. The data-driven approach proactively removes noise, focuses on the entire brain functional networks and performs advanced independent component analysis, followed by statistical tests to characterize the functional networks of MTBI patients. A key distinction of our research is the finer labeling of MTBI subject according to their long term 6 months recovery status. Our results suggest that those MTBI subject who suffer prolonged recovery exhibit disturbed functional networks, and slowed dynamism in functional connectivity than those of the healthy control or those MTBI participants who recovered quickly. A number of useful network measurements have been found to capture the states and changes of the brain functional networks for healthy and different types of MTBI subjects in their resting state. We believe that our findings can shed more light into the impact of MTBI on the effectiveness of several functional networks and can contribute to helping clinicians make more informed decisions to aid in the recovery of MTBI patients.en_US
dc.identifierhttps://doi.org/10.13016/M28W4M
dc.identifier.urihttp://hdl.handle.net/1903/16501
dc.language.isoenen_US
dc.subject.pqcontrolledNeurosciencesen_US
dc.subject.pqcontrolledComputer scienceen_US
dc.subject.pqcontrolledElectrical engineeringen_US
dc.subject.pquncontrolledbrain parcellationen_US
dc.subject.pquncontrolledConcussionen_US
dc.subject.pquncontrolledfunctional analysisen_US
dc.subject.pquncontrolledgraphen_US
dc.subject.pquncontrolledMTBIen_US
dc.subject.pquncontrolledrecoveryen_US
dc.titleA Study of Resting State FMRI Dynamic Functional Network Analysis of MTBIen_US
dc.typeThesisen_US

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