Background Functional Connectivity Reveals Neural Mechanisms of Top-Down Attentional Control
| dc.contributor.advisor | Hutchinson, Ben | |
| dc.contributor.author | Li, Yichen | |
| dc.date.accessioned | 2024-08-07T21:26:02Z | |
| dc.date.available | 2024-08-07T21:26:02Z | |
| dc.date.issued | 2024-08-07 | |
| dc.description.abstract | Top-down attentional control is essential for efficiently allocating our limited attentional resources to process complex natural environments, focusing on information relevant to our goals. The neural mechanism underlying this pervasive cognitive ability can be dichotomized into externally-oriented, which allocates attention to perceptual details, and internally-oriented, which direct attention to mnemonic episodes. Extensive research has investigated these neural mechanisms by focusing on the operations of attentional control, executed in response to a stimulus, by examining the evoked activity patterns in the brain. However, growing evidence indicates the importance of exploring these neural mechanisms supporting the states of attentional control that persist over time, by scrutinizing the intrinsic functional interaction patterns among brain regions. The present dissertation follows along the latter perspective to extend our current knowledge of the neural mechanism of top-down attentional control. In a series of two experiments, background functional connectivity (BGFC) analyses were applied to isolate intrinsic functional organizations of the brain from stimulus-evoked signals. Utilizing a whole-brain, data-driven approach combined with machine learning, important neural interaction circuits and pathways were revealed in response to switching between externally and internally oriented attentional control states (Chapter 2) and concurrently representing multiple states requiring either external or internal attention (Chapter 3). Moreover, evidence was provided suggesting the systematic distinctions between stimulus-related signals (captured by evoked activity) and state-related signals (captured by BGFC) in reflecting the process of top-down attentional control. Finally, in Chapter 4, a self-developed open-source Python library (BGFC-kit) was introduced for streamlining the preprocessing steps of BGFC analyses. Together, the works in this dissertation provide important insights and facilitate future investigations of the general neural mechanisms underlying top-down attentional control. | en_US |
| dc.identifier.uri | https://hdl.handle.net/1794/29731 | |
| dc.language.iso | en_US | |
| dc.publisher | University of Oregon | |
| dc.rights | All Rights Reserved. | |
| dc.subject | Attentional control | en_US |
| dc.subject | Functional connectivity | en_US |
| dc.subject | Human fMRI | en_US |
| dc.title | Background Functional Connectivity Reveals Neural Mechanisms of Top-Down Attentional Control | |
| dc.type | Electronic Thesis or Dissertation | |
| thesis.degree.discipline | Department of Psychology | |
| thesis.degree.grantor | University of Oregon | |
| thesis.degree.level | doctoral | |
| thesis.degree.name | Ph.D. |
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