Current Search: Functional magnetic resonance imaging (x)
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Title
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A COMPARISON OF TASK RELEVANT NODE IDENTIFICATION TECHNIQUES AND THEIR IMPACT ON NETWORK INFERENCES: GROUP-AGGREGATED, SUBJECT-SPECIFIC, AND VOXEL WISE APPROACHES.
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Creator
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Falco, Dimitri, Bressler, Steven L., Florida Atlantic University, Center for Complex Systems and Brain Sciences, Charles E. Schmidt College of Science
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Abstract/Description
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The dissertation discusses various node identification techniques as well as their downstream effects on network characteristics using task-activated fMRI data from two working memory paradigms: a verbal n-back task and a visual n-back task. The three node identification techniques examined within this work include: a group-aggregated approach, a subject-specific approach, and a voxel wise approach. The first chapters highlight crucial differences between group-aggregated and subject-specific...
Show moreThe dissertation discusses various node identification techniques as well as their downstream effects on network characteristics using task-activated fMRI data from two working memory paradigms: a verbal n-back task and a visual n-back task. The three node identification techniques examined within this work include: a group-aggregated approach, a subject-specific approach, and a voxel wise approach. The first chapters highlight crucial differences between group-aggregated and subject-specific methods of isolating nodes prior to undirected functional connectivity analysis. Results show that the two techniques yield significantly different network interactions and local network characteristics, despite having their network nodes restricted to the same anatomical regions. Prior to the introduction of the third technique, a chapter is dedicated to explaining the differences between a priori approaches (like the previously introduced group-aggregated and subject-specific techniques) and no a priori approaches (like the voxel wise approach). The chapter also discusses two ways to aggregate signal for node representation within a network: using the signal from a single voxel or aggregating signal across a group of neighboring voxels. Subsequently, a chapter is dedicated to introducing a novel processing pipeline which uses a data driven voxel wise approach to identify network nodes. The novel pipeline defines nodes using spatial temporal features generated by a deep learning algorithm and is validated by an analysis showing that the isolated nodes are condition and subject specific. The dissertation concludes by summarizing the main takeaways from each of the three analyses as well as highlighting the advantages and disadvantages of each of the three node identification techniques.
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Date Issued
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2020
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PURL
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http://purl.flvc.org/fau/fd/FA00013553
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Subject Headings
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Functional magnetic resonance imaging, Brain mapping, Working memory, Neural networks (Neurobiology), Neuroimaging--methods
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Format
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Document (PDF)
