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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- Creators: Braden, B. Blair
Description
The rapid development in acquiring multimodal neuroimaging data provides opportunities to systematically characterize human brain structures and functions. For example, in the brain magnetic resonance imaging (MRI), a typical non-invasive imaging technique, different acquisition sequences (modalities) lead to the different descriptions of brain functional activities, or anatomical biomarkers. Nowadays, in addition to the traditional voxel-level analysis of images, there is a trend to process and investigate the cross-modality relationship in a high dimensional level of images, e.g. surfaces and networks.
In this study, I aim to achieve multimodal brain image fusion by referring to some intrinsic properties of data, e.g. geometry of embedding structures where the commonly used image features reside. Since the image features investigated in this study share an identical embedding space, i.e. either defined on a brain surface or brain atlas, where a graph structure is easy to define, it is straightforward to consider the mathematically meaningful properties of the shared structures from the geometry perspective.
I first introduce the background of multimodal fusion of brain image data and insights of geometric properties playing a potential role to link different modalities. Then, several proposed computational frameworks either using the solid and efficient geometric algorithms or current geometric deep learning models are be fully discussed. I show how these designed frameworks deal with distinct geometric properties respectively, and their applications in the real healthcare scenarios, e.g. to enhanced detections of fetal brain diseases or abnormal brain development.
In this study, I aim to achieve multimodal brain image fusion by referring to some intrinsic properties of data, e.g. geometry of embedding structures where the commonly used image features reside. Since the image features investigated in this study share an identical embedding space, i.e. either defined on a brain surface or brain atlas, where a graph structure is easy to define, it is straightforward to consider the mathematically meaningful properties of the shared structures from the geometry perspective.
I first introduce the background of multimodal fusion of brain image data and insights of geometric properties playing a potential role to link different modalities. Then, several proposed computational frameworks either using the solid and efficient geometric algorithms or current geometric deep learning models are be fully discussed. I show how these designed frameworks deal with distinct geometric properties respectively, and their applications in the real healthcare scenarios, e.g. to enhanced detections of fetal brain diseases or abnormal brain development.
ContributorsZhang, Wen (Author) / Wang, Yalin (Thesis advisor) / Liu, Huan (Committee member) / Li, Baoxin (Committee member) / Braden, B. Blair (Committee member) / Arizona State University (Publisher)
Created2020
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 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