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10631.pdf (1.76 MB)
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Identifying the Brain's most Locally Connected Regions
Author Info
Cao, Wenchao
Permalink:
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1406821683
Abstract Details
Year and Degree
2014, MS, University of Cincinnati, Engineering and Applied Science: Biomedical Engineering.
Abstract
Functional magnetic resonance imaging (fMRI) is a powerful technique in neuroscience studies. In recent years, the majority of fMRI researchers are focusing on the event-related studies, which is the feedback of the brain to a task or event. The goal of these studies is to identify the active brain networks under simulation. On the other hand, there is activation in the brain even without external stimulus. To study the brain in the resting state is equally important. In this work, we developed two innovative mathematical methods - Zipfian distribution method and sum statistics method - to test the local connectivity levels in of the brain regions in resting state. Multiple statistical approaches are used to test the function of these two models. Our findings could open the way to innovative seed-based analysis, and a new description of global connectivity.
Committee
Marepalli Rao, Ph.D. (Committee Chair)
Mekibib Altaye, Ph.D. (Committee Member)
Jing-Huei Lee, Ph.D. (Committee Member)
Pages
78 p.
Subject Headings
Biomedical Research
Keywords
fMRI
;
resting-state
;
Zipfian distribution
;
seed-based analysis
;
neuroscience
;
sum-statistics
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Citations
Cao, W. (2014).
Identifying the Brain's most Locally Connected Regions
[Master's thesis, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1406821683
APA Style (7th edition)
Cao, Wenchao.
Identifying the Brain's most Locally Connected Regions.
2014. University of Cincinnati, Master's thesis.
OhioLINK Electronic Theses and Dissertations Center
, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1406821683.
MLA Style (8th edition)
Cao, Wenchao. "Identifying the Brain's most Locally Connected Regions." Master's thesis, University of Cincinnati, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1406821683
Chicago Manual of Style (17th edition)
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Document number:
ucin1406821683
Download Count:
480
Copyright Info
© 2014, some rights reserved.
Identifying the Brain's most Locally Connected Regions by Wenchao Cao is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by University of Cincinnati and OhioLINK.