2026-09-19T13:47:14Zhttps://wakespace.lib.wfu.edu/server/oai/request

oai:null:10339/923892026-09-02T15:36:09Zcom_10339_14934col_10339_38132

Mokhtari, Fatemeh 2018-08-23T08:35:41Z 2019-08-22T08:30:13Z 2018 https://wakespace.lib.wfu.edu/handle/10339/92389 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. en Wake Forest University Data science Functional connectivity networks Machine learning Statistics Tensor decomposition DYNAMIC FUNCTIONAL CONNECTIVITY NETWORKS: NEW ANALYSIS AND INTERPRETATION STRATEGIES Dissertation Laurienti, Paul J Laurienti, Paul J Rejeski, W Jack Simpson, Sean L Gage, H Donald Jung, Youngkyoo Wu, Guorong Biomedical Engineering 2019-08-22 open.access