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One of the most universally accepted facts about autism is that it is heterogenous. Individuals diagnosed with autism spectrum disorder have a wide range of behavioral presentations and a variety of co-occurring medical and mental health conditions. The identification of more homogenous subgroups is likely to lead to a better understanding of etiologies as well as more targeted interventions and treatments. In 2006, we initiated the UC Davis MIND Institute Autism Phenome Project (APP) with the overarching goal of identifying clinically meaningful subtypes of autism. This ongoing longitudinal multidisciplinary study now includes over 400 children and involves comprehensive medical, behavioral, and neuroimaging assessments from early childhood through adolescence (2 to 19 years of age). We have employed several strategies to identify sub-populations within autistic individuals: subgrouping by neural, biological, behavioral or clinical characteristics as well as by developmental trajectories. In this Mini Review, we summarize findings to date from the APP cohort and describe progress made towards identifying meaningful subgroups of autism.

Background The core symptoms of autism spectrum disorder (ASD) are widely theorized to result from altered brain connectivity. Diffusion-weighted magnetic resonance imaging (DWI) has been a versatile method for investigating underlying microstructural properties of white matter (WM) in ASD. Despite phenotypic and etiological heterogeneity, DWI studies in majority male samples of older children, adolescents, and adults with ASD have largely reported findings of decreased fractional anisotropy (FA) across several commissural, projection, and association fiber tracts. However, studies in preschool-aged children (i.e., < 30–40 months) suggest individuals with ASD have increased measures of WM FA earlier in development. Methods We analyzed 127 individuals with ASD (85♂, 42♀) and 54 typically developing (TD) controls (42♂, 26♀), aged 25.1–49.6 months. Voxel-wise effects of ASD diagnosis, sex, age, and their interaction on DWI measures of FA, mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD) were investigated using tract-based spatial statistics (TBSS) while controlling mean absolute and relative motion. Results Compared to TD controls, males and females with ASD had significantly increased measures of FA in eight clusters (threshold-free cluster enhancement p < 0.05) that incorporated several WM tracts including regions of the genu, body, and splenium of the corpus callosum, inferior frontal-occipital fasciculi, inferior and superior longitudinal fasciculi, middle and superior cerebellar peduncles, and corticospinal tract. A diagnosis by sex interaction was observed in measures of AD across six significant clusters incorporating areas of the body, genu, and splenium of the corpus collosum. In these tracts, females with ASD showed increased AD compared to TD females, while males with ASD showed decreased AD compared to TD males. Conclusions The current findings support growing evidence that preschool-aged children with ASD have atypical measures of WM microstructure that appear to differ in directionality from alterations observed in older individuals with the condition. To our knowledge, this study represents the largest sample of preschool-aged females with ASD to be evaluated using DWI. Microstructural differences associated with ASD largely overlapped between sexes. However, differential relationships of AD measures indicate that sex likely modulates ASD neuroanatomical phenotypes. Further longitudinal study is needed to confirm and quantify the developmental relationship of WM structure in ASD.

Autism spectrum disorder (ASD) is a lifelong, behaviorally defined neurodevelopmental condition that is characterized by deficits in social communication, interaction, and repetitive behaviors. These behavioral symptoms are associated with atypical brain structure, function, and connectivity. The studies that comprise this thesis employed structural magnetic resonance imaging (MRI) to address aims in three areas of ASD research. First, we examined a novel neuroimaging feature based on signal intensity contrast between grey and white matter to quantify atypical microstructure at the greywhite matter boundary in ASD. We found reduced tissue contrast at the grey-white matter boundary among adults with ASD when compared typically developing (TD) controls. This result indicates that measures of tissue contrast may serve as an in vivo proxy measure of atypical cortical microstructure that has previously been reported in histological studies. Second, we trained multivariate pattern recognition models to identify individuals with ASD based on measures of cortical morphometry, and examined the predictive value of these models in a representative clinical sample. We demonstrated that these models have modest ability to distinguish cases from controls in the research setting. Only one model that was based on measures of grey-white matter tissue contrast identified individuals with and without ASD diagnoses at high overall accuracy (81%) in the clinical setting. However, this model did not provide significant accuracies above chance in the research setting, and therefore these results should be considered as preliminary and suggestive only. Third, we established normative models of phenotypic diversity in brain structure associated with biological sex in a sample of TD males and females which was subsequently applied to males and females with ASD. Across different morphometric features, females with ASD displayed a significant shift towards a more male-typical presentation of the brain. Sample probabilities for ASD also increased with predicted probabilities for male-typical brain phenotypes across both sexes. These studies highlight advances in the field of structural neuroimaging research in areas of feature development, clinical translation, and efforts to understand the modulating role of biological sex on the prevalence of ASD. Taken together, the work presented within this thesis thus constitutes an important step toward establishing translational imaging tools for ASD that may one day be applied in the clinical setting.