Theses and Dissertations from UMD

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New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a give thesis/dissertation in DRUM

More information is available at Theses and Dissertations at University of Maryland Libraries.

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Author: search.filters.author.Hou, WenshuaiSubject: search.filters.subject.Electrical engineering

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    A Study of Resting State FMRI Dynamic Functional Network Analysis of MTBI
    (2015) Hou, Wenshuai; JaJa, Joseph; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Mild 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.