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Functional magnetic resonance imaging data clustering [electronic resource] / by Jinae Lee.

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  • Additional Information
    • Abstract:
      Summary: In this dissertation, we aim to evaluate brain activation using Functional Magnetic Resonance Imaging (fMRI) data and activation changes across time associated with practice-related cognitive control during eye movement tasks. FMR images are acquired from participants engaged in antisaccade (generating a glance away from a cue) performance at two time points: 1) at a pre-test before any exposure to the task, and 2) at a post-test, after one week of daily practice on antisaccades, prosaccades (glancing towards a target), or fixation (maintaining gaze on a target). The three practice groups are compared across the two time points. Since there are many problems inherent in fMRI data such as the huge data size, various sources of noise, ill-balanced groups, and temporal correlations, it is challenging to detect the activated regions of the brain. We propose a model-free clustering technique based on wavelet analysis to overcome the problems inherent in fMRI data. The proposed clustering technique is composed of several steps: detrending, data aggregation, wavelet transform and thresholding, the adaptive pivotal thresholding test, principal component analysis, and K-means clustering. The main clustering algorithm is built in the wavelet domain to account for temporal correlation. We apply the adaptive pivotal thresholding test based on wavelets to significantly reduce the high dimension of the data. We cluster the thresholded wavelet coefficients of the remaining voxels (the units of the images in the three dimensional space) using principal component analysis K-means clustering. Over the series of analyses, we find that the antisaccade practice group is the only group to show decreased activation from pre- to post-test in saccadic circuitry. In order to examine the proposed wavelet-based clustering approach, we perform a simulation study by adding artificial fMRI signals to the real resting-state data, and the results demonstrate its effectiveness in fMRI clustering analysis. We also conduct Regions of interest (ROI) analysis to locate the regions in which attenuations occur. We apply bootstrap resampling and the mixed model with feature extraction approach to the eleven bilateral neural ROIs. We observe decreasing activation in the supplementary eye field, frontal eye field, superior parietal lobe, and cuneus for the antisaccade practice group.
    • Notes:
      Directed by Cheolwoo Park.
      Includes articles published in Human brain mapping and Quantitative bio-science.
      Thesis (Ph. D.)--University of Georgia, 2013.
      Includes bibliographical references (leaves 118-125).
      Electronic reproduction. [Athens, Ga. : University of Georgia Libraries, 2013]. Mode of access: World Wide Web. System requirements: Adobe Acrobat reader. s2013 guan s
    • Accession Number:
      ocn882068793
    • Accession Number:
      d.uga.4244152
  • Citations
    • ABNT:
      LEE, J. Functional magnetic resonance imaging data clustering. [electronic resource]. [S. l.: s. n.]. Disponível em: http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=cat02060a&AN=d.uga.4244152. Acesso em: 27 set. 2020.
    • AMA:
      Lee J. Functional Magnetic Resonance Imaging Data Clustering. [Electronic Resource].; 2013. Accessed September 27, 2020. http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=cat02060a&AN=d.uga.4244152
    • APA:
      Lee, J. (2013). Functional magnetic resonance imaging data clustering. [electronic resource].
    • Chicago/Turabian: Author-Date:
      Lee, Jinae. 2013. Functional Magnetic Resonance Imaging Data Clustering. [Electronic Resource]. http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=cat02060a&AN=d.uga.4244152.
    • Harvard:
      Lee, J. (2013) Functional magnetic resonance imaging data clustering. [electronic resource]. Available at: http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=cat02060a&AN=d.uga.4244152 (Accessed: 27 September 2020).
    • Harvard: Australian:
      Lee, J 2013, Functional magnetic resonance imaging data clustering. [electronic resource], viewed 27 September 2020, .
    • MLA:
      Lee, Jinae. Functional Magnetic Resonance Imaging Data Clustering. [Electronic Resource]. 2013. EBSCOhost, search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=cat02060a&AN=d.uga.4244152.
    • Chicago/Turabian: Humanities:
      Lee, Jinae. Functional Magnetic Resonance Imaging Data Clustering. [Electronic Resource], 2013. http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=cat02060a&AN=d.uga.4244152.
    • Vancouver/ICMJE:
      Lee J. Functional magnetic resonance imaging data clustering. [electronic resource] [Internet]. 2013 [cited 2020 Sep 27]. Available from: http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=cat02060a&AN=d.uga.4244152