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Autism Spectrum Disorder (ASD) is characterized as a neurodevelopmental disorder with a heterogeneous nature, influenced by genetics and exhibiting diverse clinical presentations. In this study, we dissect Autism Spectrum Disorder (ASD) into its behavioral components, mirroring the diagnostic process used in clinical settings. Morphological features are extracted from magnetic resonance imaging (MRI) scans, found in the publicly available dataset ABIDE II, identifying the most discriminative features that differentiate ASD within various behavioral domains. Then, each subject is categorized as having severe, moderate, or mild ASD, or typical neurodevelopment (TD), based on the behavioral domains of the Social Responsiveness Scale (SRS). Through this study, multiple artificial intelligence (AI) models are utilized for feature selection and classifying each ASD severity and behavioural group. A multivariate feature selection algorithm, investigating four different classifiers with linear and non-linear hypotheses, is applied iteratively while shuffling the training-validation subjects to find the set of cortical regions with statistically significant association with ASD. A set of six classifiers are optimized and trained on the selected set of features using 5-fold cross-validation for the purpose of severity classification for each behavioural group. Our AI-based model achieved an average accuracy of 96%, computed as the mean accuracy across the top-performing AI models for feature selection and severity classification across the different behavioral groups. The proposed AI model has the ability to accurately differentiate between the functionalities of specific brain regions, such as the left and right caudal middle frontal regions. We propose an AI-based model that dissects ASD into behavioral components. For each behavioral component, the AI-based model is capable of identifying the brain regions which are associated with ASD as well as utilizing those regions for diagnosis. The proposed system can increase the speed and accuracy of the diagnostic process and result in improved outcomes for individuals with ASD, highlighting the potential of AI in this area.

 

In addition to the standard observational assessment for autism spectrum disorder (ASD), recent advancements in neuroimaging and machine learning (ML) suggest a rapid and objective alternative using brain imaging. This work presents a pipelined framework, using functional magnetic resonance imaging (fMRI) that allows not only an accurate ASD diagnosis but also the identification of the brain regions contributing to the diagnosis decision. The proposed framework includes several processing stages: preprocessing, brain parcellation, feature representation, feature selection, and ML classification. For feature representation, the proposed framework uses both a conventional feature representation and a novel dynamic connectivity representation to assist in the accurate classification of an autistic individual. Based on a large publicly available dataset, this extensive research highlights different decisions along the proposed pipeline and their impact on diagnostic accuracy. A large publicly available dataset of 884 subjects from the Autism Brain Imaging Data Exchange I (ABIDE-I) initiative is used to validate our proposed framework, achieving a global balanced accuracy of 98.8% with five-fold cross-validation and proving the potential of the proposed feature representation. As a result of this comprehensive study, we achieve state-of-the-art accuracy, confirming the benefits of the proposed feature representation and feature engineering in extracting useful information as well as the potential benefits of utilizing ML and neuroimaging in the diagnosis and understanding of autism. 

In Annual Arts and Sciences Undergraduate Research Poster Session, Louisville, Kentucky, April 22, 2022. 

Autism Spectrum Disorder (ASD) is a neurodevelopmental disease originating from combined genetic and environmental factors. Post-mortem human studies and some animal ASD models have shown brain neuroinflammation, oxidative stress, and changes in blood–brain barrier (BBB) integrity. However, the signaling pathways leading to these inflammatory findings and vascular alterations are currently unclear. The BBB plays a critical role in controlling brain homeostasis and immune response. Its dysfunction can result from developmental genetic abnormalities or neuroinflammatory processes. In this review, we explore the role of the Sonic Hedgehog/Wingless-related integration site (Shh/Wnt) pathways in neurodevelopment, neuroinflammation, and BBB development. The balance between Wnt-β-catenin and Shh pathways controls angiogenesis, barriergenesis, neurodevelopment, central nervous system (CNS) morphogenesis, and neuronal guidance. These interactions are critical to maintain BBB function in the mature CNS to prevent the influx of pathogens and inflammatory cells. Genetic mutations of key components of these pathways have been identified in ASD patients and animal models, which correlate with the severity of ASD symptoms. Disruption of the Shh/Wnt crosstalk may therefore compromise BBB development and function. In turn, impaired Shh signaling and glial activation may cause neuroinflammation that could disrupt the BBB. Elucidating how ASD-related mutations of Shh/Wnt signaling could cause BBB leaks and neuroinflammation will contribute to our understanding of the role of their interactions in ASD pathophysiology. These observations may provide novel targeted therapeutic strategies to prevent or alleviate ASD symptoms while preserving normal developmental processes.