Theses and dissertations

This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students. 

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Therapeutic neural mechanisms of mindfulness-based therapy in adults with autism

Description
Adults with autism spectrum disorder (ASD) face heightened risk of co-occurring psychiatric conditions, especially depression and anxiety disorders, which contribute to seven-fold higher suicide rates than the general population. Mindfulness-based stress reduction (MBSR) is an 8-week meditation intervention centered around training continuous redirection of attention toward present moment experience, and…

Adults with autism spectrum disorder (ASD) face heightened risk of co-occurring psychiatric conditions, especially depression and anxiety disorders, which contribute to seven-fold higher suicide rates than the general population. Mindfulness-based stress reduction (MBSR) is an 8-week meditation intervention centered around training continuous redirection of attention toward present moment experience, and has been shown to improve mental health in autistic adults. However, the underlying therapeutic neural mechanisms and whether behavioral and brain changes are mindfulness-specific have yet to be elucidated. In this randomized clinical trial, I utilized functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) to characterize fMRI functional activity (Study 1) and connectivity (Study 2) and EEG neurophysiological (Study 3) changes between MBSR and a social support/relaxation education (SE) active control group. Study 1 revealed an MBSR-specific increase in the midcingulate cortex fMRI blood oxygen level dependent signal which was associated with reduced depression. Study 2 identified nonspecific intervention improvements in depression, anxiety, and autistic, and MBSR-specific improvements in the mindfulness trait ‘nonjudgment toward experience’ and in the executive functioning domain of working memory. MBSR-specific decreases in insula-thalamus and frontal pole-posterior cingulate functional connectivity was associated with improvements in anxiety, mindfulness traits, and working memory abilities. Both MBSR and SE groups showed decreased amygdala-sensorimotor and frontal pole-insula connectivity which correlated with reduced depression. Study 3 consisted of an EEG spectral power analysis at high-frequency brainwaves associated with default mode network (DMN) activity. Results showed MBSR-specific and nonspecific decreases in beta- and gamma-band power, with effects being generally more robust in the MBSR group; additionally, MBSR-specific decreases in posterior gamma correlated with anxiolytic effects. Collectively, these studies suggest: 1) social support is sufficient for improvements in depression, anxiety, and autistic traits; 2) MBSR provides additional benefits related to mindfulness traits and working memory; and 3) distinct and shared neural mechanisms of mindfulness training in adults with ASD, implicating the salience and default mode networks and high-frequency neurophysiology. Findings bear relevance to the development of personalized medicine approaches for psychiatric co-morbidity in ASD, provide putative targets for neurostimulation research, and warrant replication and extension using advanced multimodal imaging approaches.
ContributorsPagni, Broc (Author) / Braden, B. Blair (Thesis advisor) / Newbern, Jason (Thesis advisor) / Davis, Mary (Committee member) / Brewer, Gene (Committee member) / Arizona State University (Publisher)
Created2022
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A Comparison of Hand and Computer Coding of Expressions of Emotion

Description
Hand-coding systems of measuring facial expressions were developed to study and analyze human emotions, but they are time-intensive and thus seldom used. As technology has advanced, new computer software programs, such as Affectiva, were developed to code facial expressions automatically using artificial intelligence and machine learning. Since this technology is…

Hand-coding systems of measuring facial expressions were developed to study and analyze human emotions, but they are time-intensive and thus seldom used. As technology has advanced, new computer software programs, such as Affectiva, were developed to code facial expressions automatically using artificial intelligence and machine learning. Since this technology is still new, Affectiva and its validity remain understudied, and no psychological research has been conducted to compare Affectiva computer coding and hand coding of children’s emotions. The purpose of this study was to compare hand and computer coding of children’s expressions of emotion during a videotaped parent-child interaction. The study answered the following questions: 1) Do hand and computer coding agree?; and 2) Are hand and computer coding in higher agreement for some emotions than others? The sample included 25 pairs of twins from the Arizona Twin Project. Facial expressions were coded from videotape by a trained and reliable human coder and using the software Affectiva. The results showed that hand and computer coded emotion were in agreement for positive, but not negative emotions. Changing the context of the interaction to elicit more negative emotion, and using the same indicators of each emotion in computer and hand coding are suggested to improve the comparison of computer and hand coding.
ContributorsKwok, Connie (Author) / Lemery-Chalfant, Kathryn (Thesis director) / Davis, Mary (Committee member) / Miadich, Samantha (Committee member) / Department of Psychology (Contributor) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05