UC Irvine

This work consists of four projects that explore human behavior from two perspectives: language use and neural patterns.

In my first and second projects, I focused on language, which can be used to categorize human behavior. In the first project, I used topic models to categorize the subjects and symptoms a patient discussed during psychotherapy treatment. The model functions by identifying topics that are representative of each subject or symptom. The model can predict the subjects and symptoms discussed in new therapy sessions with higher accuracy than discriminative techniques. Furthermore, the model can identify specific passages of text representative of a given subject or symptom.

My second project developed an automated system for routing citizen requests to federal agencies within the Mexican government. The automated system functions by linking pat- terns in language and the appropriate federal agency. The automated system routes requests more efficiently than the current routing system.

The third and fourth projects focused on neuroimaging, which is used to understand the underlying neural processes associated with human behavior. My neuroimaging work related blood-oxygen-level-dependent (BOLD) variability (BV) to experimental condition, behavior, and subject identity. The first phase of the neural work built on previous analyses showing that functional connectivity (FC) is predictive of the task a subject is performing and the identity of the subject performing a task. We extended these analyses to BV and compared its predictive accuracy with that of FC to assess whether some of the predictive power of FC is due to changes in BV. BV performed well compared to FC, suggesting that some of the predictive performance based on FC might be attributed to independent region-specific fluctuations.

Given the predictive relationship between BV and task/subject, the second phase of my neu- ral work developed the Variance Design General Linear Model (VDGLM), a novel framework to facilitate the detection of BV effects. The framework models the mean and variance in the BOLD time course as functions of experimental design. This allows the VDGLM to i) simul- taneously make inferences about a mean or variance effect while controlling for the other and ii) test for variance effects that could be associated with multiple conditions and/or noise regressors. We demonstrated the use of the VDGLM in a working memory application and showed that engagement in a working memory task is associated with whole-brain decreases in BOLD variance.