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Identifying the Brain's most Locally Connected Regions

Cao, Wenchao

Abstract Details

2014, MS, University of Cincinnati, Engineering and Applied Science: Biomedical Engineering.
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.
Marepalli Rao, Ph.D. (Committee Chair)
Mekibib Altaye, Ph.D. (Committee Member)
Jing-Huei Lee, Ph.D. (Committee Member)
78 p.

Recommended Citations

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)