Publication Date

2018-05-03

Availability

Open access

Embargo Period

2018-05-03

Degree Type

Dissertation

Degree Name

Doctor of Philosophy (PHD)

Department

Psychology (Arts and Sciences)

Date of Defense

2018-04-16

First Committee Member

Lucina Q. Uddin

Second Committee Member

Maria M. Llabre

Third Committee Member

Aaron S. Heller

Fourth Committee Member

Roger C. McIntosh

Fifth Committee Member

Michael A. Anderson

Abstract

The growing literature of task-based functional magnetic resonance imaging (task-fMRI) has increased calls for an adequate organizing ontology, or taxonomy, of task fMRI experiments. Researchers differ over what should be the dominant features of such an ontology: should it be concrete/observable dimensions, such as task paradigm (e.g. n-back vs. flanker task), or latent/theoretical dimensions, such as cognitive domains (e.g. working-memory vs. bottom-up attention)? This dissertation attempts to address what is important for a task-fMRI ontology in a quantifiable manner. We use a simple quantitative criterion for categories/dimensions of a task-fMRI ontology: the ability to explain observed variations in task-fMRI activation patterns Using meta-analysis tools and multivariate statistical methods, we identify those dimensions and categories of the task-fMRI environment that explain observed variations in task-fMRI activation patterns. In study one, we observed that a preliminary ontology of four or seven latent cognitive categories provides a simplified description of the observed differences in whole-brain blood-oxygen-level dependent (BOLD) activation patterns. In study two, we observed that while these categories may provide an adequate description of BOLD activation patterns at the population level, inter-subject variability restrains inferences from these population level distinctions to the subject level. In conclusion, results from both studies suggest that a data-driven task-fMRI ontology is a viable project for cognitive neuroscience.

Keywords

task-fMRI; ontology; exploratory; data-driven

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