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Dissertation/Thesis Abstract

Large-scale Network Representations during Episodic Mnemonic Processing in Humans
by Schedlbauer, Amber, Ph.D., University of California, Davis, 2017, 166; 10682669
Abstract (Summary)

Spatial and temporal details constitute critical components of our memories for recently experienced events, termed episodic memories. Both knowledge about where we were and approximately when things happened during our day are important cues for remembering what happened to us at an earlier event. A region called the hippocampus has been strongly implicated in the forming and retrieving of these episodic memories. However, the relative importance of cortical areas in conjunction with the hippocampus to episodic memory remains under debate. Specifically, a vast majority of the studies have focused only on characterizing the individual contributions of the hippocampus or specific cortical modules to episodic memory rather than determining the interactions among them.

With the studies conducted here, I have represented episodic mnemonic processes as large-scale networks derived from functional connectivity analyses applied to human neuroimaging and electrophysiology data. By applying quantitative measures from the field of graph theory, I aim to provide insight into the global and local organization of coordinated interactions between regions throughout the brain. I hypothesize that network analyses will demonstrate that different aspects of episodic memory, including encoding and retrieval, will produce specific network representations. Furthermore, the large-scale functional network organization will vary based upon the type of information being processed and during directed cognition as compared to rest.

First, I provide an overview of past research dedicated to understanding the neural basis of memory processes through networks. Next, I present a neuroimaging study evaluating the unique networks present during successful and unsuccessful retrieval and during spatiotemporal processing. Increases in overall network connectivity exemplified successful retrieval with density correlating with retrieval accuracy. Furthermore, I identified distinct but overlapping subnetworks for spatial and temporal retrieval with the hippocampus and additional neocortical regions as hubs of connectivity within the networks. In a second neuroimaging experiment, I again established separate spatiotemporal networks and applied a data-driven approach to characterize connectivity patterns involved in the encoding and retrieval of contextual information. A comparison of task-based with resting-state partitions showed that data-driven models capture variance in memory performance and supply a parsimonious view of network topology. Finally, in an electrophysiology study, we disrupted network connectivity (and possibly communication) by directly stimulating specific areas in order to provide evidence for the utility and accuracy of network representations. Specifically, we targeted the spatial retrieval network and produced a deficit in spatial but not temporal retrieval performance. Thus, network models are a new avenue of research into the brain-wide interactions thought to represent relevant mnemonic processes and can be leveraged to gain insight into brain-behavior relationships.

Indexing (document details)
Advisor: Ekstrom, Arne
Commitee: Ferrer, Emilio, Geng, Joy, Gurkoff, Gene, Ranganath, Charan
School: University of California, Davis
Department: Neuroscience
School Location: United States -- California
Source: DAI-B 79/08(E), Dissertation Abstracts International
Subjects: Neurosciences
Keywords: Episodic memory, Functional connectivity, Graph theory, Hippocampus, Network
Publication Number: 10682669
ISBN: 978-0-355-76437-6
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