Subcortical Brain Morphometry Differences between Adults with Autism Spectrum Disorder and Schizophrenia

Subcortical-cortical white matter connectivity in adults with autism spectrum disorder and schizophrenia patients

Autism spectrum disorder (ASD) and schizophrenia (SZ) are neuropsychiatric disorders that overlap in symptoms associated with social-cognitive impairment. Alterations of the cingulate cortex, subcortical, medial-temporal, and orbitofrontal structures are frequently reported in both disorders. In this study, we examined white-matter connectivity between these structures in adults with ASD and SZ patients compared with their respective neurotypical controls and indirectly with each other, using probabilistic and local DTI tractography. This exploratory study utilized publicly available neuroimaging databases, of adults with ASD (ABIDE II; n = 28) and SZ (COBRE; n = 38), age-gender matched neurotypicals (NT) and associated phenotypic data. Tractography was performed using Freesurfer and MRtrix software, and diffusion metrics of white-matter tracts between cingulate-, orbitofrontal- cortices, subcortical structures, parahippocampal, entorhinal cortex were assessed. In ASD, atypical diffusivity parameters were found in the isthmus cingulate and parahippocampal connectivity to subcortical and rostral-anterior cingulate, which were also associated with IQ and social skills (SRS). In contrast, atypical diffusivity parameters were observed between the medial-orbitofrontal cortex and subcortical structures in SZ, and were associated with executive function (i.e., IQ, processing speed) and emotional regulation. Overall, the results suggest that defects in the isthmus cingulate, medial-orbitofrontal, and striato-limbic white matter connectivity may help unravel the neural underpinnings of executive and social-emotional dysfunction at the core of neuropsychiatric disorders.

Whole-brain gray matter maturation trajectories associated with autistic traits from adolescence to early adulthood

A growing number of evidence supports a continued distribution of autistic traits in the general population. However, brain maturation trajectories of autistic traits as well as the influence of sex on these trajectories remain largely unknown. We investigated the association of autistic traits in the general population, with longitudinal gray matter (GM) maturation trajectories during the critical period of adolescence. We assessed 709 community-based adolescents (54.7% women) at age 14 and 22. After testing the effect of sex, we used whole-brain voxel-based morphometry to measure longitudinal GM volumes changes associated with autistic traits measured by the Social Responsiveness Scale (SRS) total and sub-scores. In women, we observed that the SRS was associated with slower GM volume decrease globally and in the left parahippocampus and middle temporal gyrus. The social communication sub-score correlated with slower GM volume decrease in the left parahippocampal, superior temporal gyrus, and pallidum; and the social cognition sub-score correlated with slower GM volume decrease in the left middle temporal gyrus, the right ventromedial prefrontal and orbitofrontal cortex. No longitudinal association was found in men. Autistic traits in young women were found to be associated with specific brain trajectories in regions of the social brain and the reward circuit known to be involved in Autism Spectrum Disorder. These findings support both the hypothesis of an earlier GM maturation associated with autistic traits in adolescence and of protective mechanisms in women. They advocate for further studies on brain trajectories associated with autistic traits in women.

24S-Hydroxycholesterol in Neuropsychiatric Diseases: Schizophrenia, Autism Spectrum Disorder, and Bipolar Disorder

Neuropsychiatric diseases (NPDs) are severe, debilitating psychiatric conditions that affect the nervous system. These are among the most challenging disorders in medicine. Some examples include Alzheimer's, anxiety disorders, autism spectrum disorder, bipolar disorder, and schizophrenia. NPDs represent an ever-increasing burden on public health and are prevalent throughout the world. For most of these diseases, the particular etiopathogeneses are still enigmatic. NPDs are also associated with structural and functional changes in the brain, along with altered neurotransmitter and neuroendocrine systems.Approximately 25% of the total human body cholesterol is located in the brain. Its involvement in neuronal functions starts in the early growth stages and remains important throughout adulthood. It is also an integral part of the neuronal membrane, ensuring membrane lipid organization and regulating membrane fluidity. The main mechanism for removing cholesterol from the brain is cholesterol 24-hydroxylation by cytochrome P450 46A1 (CYP46A1), an enzyme specifically found in the central nervous system. Although research on 24S-OHC and its role in neuropsychiatric diseases is still in its early stages, this oxidized cholesterol metabolite is thought to play a crucial role in the etiology of NPDs. 24S-OHC can affect neurons, astrocytes, oligodendrocytes, and vascular cells. In addition to regulating the homeostasis of cholesterol in the brain, this oxysterol is involved in neurotransmission, oxidative stress, and inflammation. The role of 24S-OHC in NPDs has been found to be controversial in terms of the findings so far. There are several intriguing discrepancies in the data gathered so far regarding 24S-OHC and NPDs. In fact, 24S-OHC levels were reported to have decreased in a number of NPDs and increased in others.Hence, in this chapter, we first summarize the available data regarding 24S-OHC as a biomarker in NPDs, including schizophrenia, autism spectrum disorder, and bipolar disorder. Then, we present a brief synopsis of the pharmacological targeting of 24S-OHC levels through the modulation of CYP46A1 activity.

A personalized classification of behavioral severity of autism spectrum disorder using a comprehensive machine learning framework

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.