2026-09-18T17:31:35Zhttps://wakespace.lib.wfu.edu/server/oai/request

oai:null:10339/907362026-04-21T20:52:38Zcom_10339_14934col_10339_38132

Reynolds, Olivia 2018-05-24T08:36:13Z 2020-05-23T08:30:17Z 2018 https://wakespace.lib.wfu.edu/handle/10339/90736 Recently, Finn et al. (2015), and later Rosenberg et al. (2016), introduced a new methodology that could predict aspects of cognition such as fluid intelligence and attention, and produce potentially informative networks associated with the cognitive variable. Using resting-state fMRI data from the Human Connectome Project, we extended the Rosenberg et al. methodology to episodic memory and evaluated its ability to produce significantly predictive models and provide insights into episodic memory networks. We used two episodic measures, the Picture Sequence Memory Test (PSMT) and the Penn Word Memory Test (PWMT), in order to evaluate the methodology, as well as explore episodic memory by comparing the similarities and differences between the episodic networks produced. In the current study, we were able to significantly predict PSMT and PWMT scores of novel participants and produce episodic networks that provided potential insights into episodic memory, such as the involvement of the subcortical-cerebellum network in episodic memory. Additionally, the comparison among the episodic networks provided confirmatory evidence regarding the Medial Temporal Lobes' involvement in episodic memory. Overall, we successfully extended Rosenberg et al. methodology to episodic memory and gain insights about this construct by doing so. en Wake Forest University Predicting Individual Differences on Two Episodic Memory Measures Using Resting-State Functional Connectivity Data Thesis Dagenbach, Dale Blumenthal, Terry Jennings, Janine Laurienti, Paul Psychology 2020-05-23 open.access