All ETDs from UAB

Advisory Committee Chair

Rajesh K Kana

Advisory Committee Members

Fred J Biasini

Ingrid M Hopkins

David C Knight

Adrienne C Lahti

Document Type


Date of Award


Degree Name by School

Doctor of Philosophy (PhD) College of Arts and Sciences


Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder with widespread behavioral symptoms and a strong neurobiological origin. Neuroimaging studies of ASD have uncovered evidence for widespread abnormalities in brain anatomy and functioning. Abnormalities reported by structural brain imaging, diffusion tensor imaging (DTI), and proton magnetic resonance spectroscopy (1H-MRS) studies have aided in characterizing ASD as a complex, systems-wide disorder. The overarching goal of this project was to use multiple modalities of neuroimaging to investigate the underlying neural mechanisms of ASD. The following techniques were used to collect and analyze multimodal neuroimaging data from a relatively large number of participants with autism and matched typically developing (TD) peers: (1) Surface-based morphometry (SBM) examining cortical features (e.g., volume, cortical thickness, surface area, gyrification); (2) Diffusion tensor imaging (DTI) to study the integrity of major white matter tracts; (3) Proton magnetic resonance spectroscopy (1H-MRS) to assess the concentration of neurochemicals; and (4) Pattern classification modeling using measures from multimodal brain imaging to determine the diagnostic utility of neural markers found from these imaging methods. The current study is novel in that it applies relatively new techniques for analyzing DTI and anatomical data, reports more detailed brain measures from each modality, includes large sample sizes with children and adults, and reports on a classification analysis utilizing multimodal brain measures for predictors of diagnosis. We found significant differences in surface based features of brain structure, fractional anisotropy and radial diffusivity of major white matter tracts, as well as brain metabolite levels in children and adults with ASD, compared to TD participants. Specifically, we uncovered significant alterations in cortical volume and surface area in right posterior cingulate cortex and left temporoparietal junction, and a significant reduction in gyrification in right precentral gyrus in children and adults with ASD. In addition, we found significantly reduced fractional anisotropy (FA) and increased radial diffusivity (RD) along the left superior longitudinal fasciculus in children and adults with ASD. The 1H-MRS analysis uncovered significantly reduced levels of N-acetylaspartate in the dorsal anterior cingulate cortex in adults with ASD. Finally, an analysis utilizing brain measures as predictors for diagnosis revealed that measures of cortical thickness and white matter integrity (FA and RD) were the most effective at classifying participants with ASD, with some variables also predicting symptom severity. In summary, we observed significant alterations in brain structure, white matter connectivity, and neuronal health in participants with ASD. These measures were also useful in developing a classification model for identifying participants with ASD. Multimodal brain imaging evidence from this study provides a comprehensive characterization, which spans several levels and layers, of the neural architecture of autism.



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