DYNAMIC FUNCTIONAL CONNECTIVITY NETWORKS: NEW ANALYSIS AND INTERPRETATION STRATEGIES
Electronic Theses and Dissertations
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Item Details
- abstract
- Traditional brain network studies have implicitly assumed that functional connectivity between distinct brain regions is static over the recording period of resting state scans. Brain network organization must dynamically recognize, integrate and respond to internal and external stimuli across multiple time scales. Thus, investigating dynamic brain network connectivity may provide greater insight into understanding fundamental properties of brain networks. This project takes three approaches to studying dynamic brain connectivity.
- subject
- Data science
- Functional connectivity networks
- Machine learning
- Statistics
- Tensor decomposition
- contributor
- Laurienti, Paul J (committee chair)
- Laurienti, Paul J (committee member)
- Rejeski, W Jack (committee member)
- Simpson, Sean L (committee member)
- Gage, H Donald (committee member)
- Jung, Youngkyoo (committee member)
- Wu, Guorong (committee member)
- date
- 2018-08-23T08:35:41Z (accessioned)
- 2019-08-22T08:30:13Z (available)
- 2018 (issued)
- degree
- Biomedical Engineering (discipline)
- embargo
- 2019-08-22 (terms)
- identifier
- http://hdl.handle.net/10339/92389 (uri)
- language
- en (iso)
- publisher
- Wake Forest University
- title
- DYNAMIC FUNCTIONAL CONNECTIVITY NETWORKS: NEW ANALYSIS AND INTERPRETATION STRATEGIES
- type
- Dissertation