Assessment of functional connectivity in the human brain : multivariate and graph signal processing methods
"Advances in neurophysiological recording have provided a noninvasive way of inferring cognitive processes. Recent studies have shown that cognition relies on the functional integration or connectivity of segregated specialized regions in the brain. Functional connectivity quantifies the statistical relationships among different regions in the brain. However, current functional connectivity measures have certain limitations in the quantification of global integration and characterization of network structure. These limitations include the bivariate nature of most functional connectivity measures, the computational complexity of multivariate measures, and graph theoretic measures that are not robust to network size and degree distribution. Therefore, there is a need of computationally efficient and novel measures that can quantify the functional integration across brain regions and characterize the structure of these networks. This thesis makes contributions in three different areas for the assessment of multivariate functional connectivity. First, we present a novel multivariate phase synchrony measure for quantifying the common functional connectivity within different brain regions. This measure overcomes the drawbacks of bivariate functional connectivity measures and provides insights into the mechanisms of cognitive control not accountable by bivariate measures. Following the assessment of functional connectivity from a graph theoretic perspective, we propose a graph to signal transformation for both binary and weighted networks. This provides the means for characterizing the network structure and quantifying information in the graph by overcoming some drawbacks of traditional graph based measures. Finally, we introduce a new approach to studying dynamic functional connectivity networks through signals defined over networks. In this area, we define a dynamic graph Fourier transform in which a common subspace is found from the networks over time based on the tensor decomposition of the graph Laplacian over time."--Pages ii-iii.
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- In Collections
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Electronic Theses & Dissertations
- Copyright Status
- In Copyright
- Material Type
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Theses
- Authors
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Villafañe-Delgado, Marisel
- Thesis Advisors
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Aviyente, Selin
- Committee Members
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Deller, John
Mukkamala, Ramakrishna
Radha, Hayder
Zhu, David C.
- Date
- 2017
- Subjects
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Signal processing
Neural networks (Neurobiology)
Neural circuitry
Multivariate analysis
Brain--Physiology
Graphic methods
- Program of Study
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Electrical Engineering - Doctor of Philosophy
- Degree Level
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Doctoral
- Language
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English
- Pages
- xii, 145 pages
- ISBN
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9780355216752
0355216752