Abstract
dc:description.abstractThe default mode network (DMN) is a highly cited neural network whose functional roles are not well understood. Until recently, event related fMRI experiments used to study the DMN could only be conducted in an open-loop format. The purpose of this study was to demonstrate the potential statistical advantages of real-time fMRI studies to conduct closed-loop experiments to directly test putative DMN functions. Using both fMRI simulations and large archival datasets, we demonstrate that open-loop designs are less statistically powerful than closed-loop experiments that can trigger stimuli at controlled levels of brain activity. When simulating event scheduling on resting state data, DMN levels were normally distributed, but the event timing proved to be ineffective in capturing the highest and lowest DMN values on average across subjects. Statistical differences in DMN levels collected by the Human Connectome Project-Aging (HCP-A) during a Go/NoGo task were also reported, along with the network's distributional effects across subjects. When examining DMN levels in 136 subjects more prone to commission errors the mean DMN levels were reported to be higher during and prior to incorrect NoGo responses. Exploring DMN levels in these same individuals reacting to a Go task also revealed differing measurement patterns when compared to all 711 subjects in the study. Additionally, the distribution of total DMN levels across all participants, as well as during a Go or NoGo trial, showed a shift in the mean towards deactivation. Furthermore, the peak at this location was greater and revealed that increased sampling occurred at the mean and under sampling at the tails. Overall, the cumulative findings in this study were successful in providing statistical arguments to support propositions for more powerful closed-loop experimentation in fMRI.
Degree
thesis:*- Name thesis:degree_name
- Master of Science
- Level thesis:degree_level
- masters
- Discipline thesis:degree_discipline
- Biomedical Engineering
- Department dc:contributor.department
- Department of Biomedical Engineering and Mechanics
- Grantor dc:publisher
- Virginia Tech
- Year dc:date.issued
- 2023
Author and committee
dc:creator, dc:contributor.*- Author dc:creator
-
- Norfleet, David George
- Chair dc:contributor.committeechair
-
- LaConte, Stephen M.
- Committee members dc:contributor.committeemember
-
- Arena, Christopher B.
- Wang, Vincent M.
Subjects
dc:subject × 5Rights
dc:rights- Statement dc:rights
-
- In Copyright
- Licence dc:rights.uri
- Language dc:language.iso
- en
Identifiers
dc:identifier.*- Dc Identifier Other
- vt_gsexam:37851
- OAI identifier oai:identifier
- oai:vtechworks.lib.vt.edu:10919/115591