Date of Award

2021

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Neuroscience

First Advisor

Patricia A. Prelock

Second Advisor

Donna M. Rizzo

Abstract

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder that af- fects nearly 1 in 54 children. Children with ASD struggle with social, communication, and behavioral challenges due to deficits in theory of mind (ToM). In addition, diag- nosis of ASD is complicated and there is an urgent need to identify ASD-associated biomarkers and features to help automate diagnostics and develop predictive ASD models. In this study, we conducted two experiments collecting behavioral and neu- roimaging data from 9 children with ASD and 19 neurotypical children (NT) between the age of 7 and 14 years.

The first experiment examined specific elements of emotion recognition to bet- ter understand those skills needed for meaningful social interaction among children with ASD. Two previously tested measures of ToM, the Theory of Mind Inventory-2 (ToMI-2) and the Theory of Mind Task Battery (ToMTB), were used to evaluate early developing, basic, and advanced theory of mind skills impacting children’s so- cial skills. We also created and implemented two novel fMRI paradigms to probe the neural mechanisms underlying ToM related desire-based emotion and more complex emotions (i.e., surprise and embarrassment), as well as two early-developing emotions (i.e., happy and sad). Results suggested impaired abilities in multiple ToM metrics and brain deficits associated with ToM-related emotion recognition and processing among children with ASD. Findings from this study established connections between behavior and brain activities surrounding ToM in ASD, which may assist the devel- opment of neuroanatomical diagnostic criteria and may provide new pathways for measuring intervention outcomes in special populations such as those with ASD.

The second experiment adopted a novel evolutionary algorithm, the conjunctive clause evolutionary algorithm (CCEA), to select features most significant for distin- guishing individuals with and without ASD, accommodating datasets having a small number of samples with a large number of feature measurements. Potential biomarker candidates identified included brain volume, area, cortical thickness, and mean cur- vature in specific regions around the cingulate cortex, the frontal cortex, and the temporal-parietal junction, as well as behavioral features associated with theory of mind. A separate machine learning classifier (i.e., k-nearest neighbors algorithm) was used to validate the CCEA feature selection and then used for ASD prediction. Study findings demonstrated how machine learning tools might help to facilitate diagnostic and predictive models of ASD.

Language

en

Number of Pages

122 p.

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