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DYNAMIC FUNCTIONAL CONNECTIVITY NETWORKS: NEW ANALYSIS AND INTERPRETATION STRATEGIES

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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
Mokhtari, Fatemeh (author)
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

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