Altered neurological and neurobehavioral phenotypes in a mouse model of the recurrent KCNB1-p.R306C voltage-sensor variant.

Pathogenic variants in KCNB1 are associated with a neurodevelopmental disorder spectrum that includes global developmental delays, cognitive impairment, abnormal electroencephalogram (EEG) patterns, and epilepsy with variable age of onset and severity. Additionally, there are prominent behavioral disturbances, including hyperactivity, aggression, and features of autism spectrum disorder. The most frequently identified recurrent variant is KCNB1-p.R306C, a missense variant located within the S4 voltage-sensing transmembrane domain. Individuals with the R306C variant exhibit mild to severe developmental delays, behavioral disorders, and a diverse spectrum of seizures. Previous in vitro characterization of R306C described altered sensitivity and cooperativity of the voltage sensor and impaired capacity for repetitive firing of neurons. Existing Kcnb1 mouse models include dominant negative missense variants, as well as knockout and frameshifts alleles. While all models recapitulate key features of KCNB1 encephalopathy, mice with dominant negative alleles were more severely affected. In contrast to existing loss-of-function and dominant-negative variants, KCNB1-p.R306C does not affect channel expression, but rather affects voltage-sensing. Thus, modeling R306C in mice provides a novel opportunity to explore impacts of a voltage-sensing mutation in Kcnb1. Using CRISPR/Cas9 genome editing, we generated the Kcnb1R306C mouse model and characterized the molecular and phenotypic effects. Consistent with the in vitro studies, neurons from Kcnb1R306C mice showed altered excitability. Heterozygous and homozygous R306C mice exhibited hyperactivity, altered susceptibility to chemoconvulsant-induced seizures, and frequent, long runs of slow spike wave discharges on EEG, reminiscent of the slow spike and wave activity characteristic of Lennox Gastaut syndrome. This novel model of channel dysfunction in Kcnb1 provides an additional, valuable tool to study KCNB1 encephalopathies. Furthermore, this allelic series of Kcnb1 mouse models will provide a unique platform to evaluate targeted therapies.

GAN-MAT: Generative adversarial network-based microstructural profile covariance analysis toolbox.

Multimodal magnetic resonance imaging (MRI) provides complementary information for investigating brain structure and function; for example, an in vivo microstructure-sensitive proxy can be estimated using the ratio between T1- and T2-weighted structural MRI. However, acquiring multiple imaging modalities is challenging in patients with inattentive disorders. In this study, we proposed a comprehensive framework to provide multiple imaging features related to the brain microstructure using only T1-weighted MRI. Our toolbox consists of (i) synthesizing T2-weighted MRI from T1-weighted MRI using a conditional generative adversarial network; (ii) estimating microstructural features, including intracortical covariance and moment features of cortical layer-wise microstructural profiles; and (iii) generating a microstructural gradient, which is a low-dimensional representation of the intracortical microstructure profile. We trained and tested our toolbox using T1- and T2-weighted MRI scans of 1,104 healthy young adults obtained from the Human Connectome Project database. We found that the synthesized T2-weighted MRI was very similar to the actual image and that the synthesized data successfully reproduced the microstructural features. The toolbox was validated using an independent dataset containing healthy controls and patients with episodic migraine as well as the atypical developmental condition of autism spectrum disorder. Our toolbox may provide a new paradigm for analyzing multimodal structural MRI in the neuroscience community and is openly accessible at https://github.com/CAMIN-neuro/GAN-MAT.

Whole-brain structural connectome asymmetry in autism.

Autism spectrum disorder is a common neurodevelopmental condition that manifests as a disruption in sensory and social skills. Although it has been shown that the brain morphology of individuals with autism is asymmetric, how this differentially affects the structural connectome organization of each hemisphere remains under-investigated. We studied whole-brain structural connectivity-based brain asymmetry in individuals with autism using diffusion magnetic resonance imaging obtained from the Autism Brain Imaging Data Exchange initiative. By leveraging dimensionality reduction techniques, we constructed low-dimensional representations of structural connectivity and calculated their asymmetry index. Comparing the asymmetry index between individuals with autism and neurotypical controls, we found atypical structural connectome asymmetry in the sensory and default-mode regions, particularly showing weaker asymmetry towards the right hemisphere in autism. Network communication provided topological underpinnings by demonstrating that the inferior temporal cortex and limbic and frontoparietal regions showed reduced global network communication efficiency and decreased send-receive network navigation in the inferior temporal and lateral visual cortices in individuals with autism. Finally, supervised machine learning revealed that structural connectome asymmetry could be used as a measure for predicting communication-related autistic symptoms and nonverbal intelligence. Our findings provide insights into macroscale structural connectome alterations in autism and their topological underpinnings.

Comprehensive exploration of multi-modal and multi-branch imaging markers for autism diagnosis and interpretation: insights from an advanced deep learning model.

Autism spectrum disorder is a complex neurodevelopmental condition with diverse genetic and brain involvement. Despite magnetic resonance imaging advances, autism spectrum disorder diagnosis and understanding its neurogenetic factors remain challenging. We propose a dual-branch graph neural network that effectively extracts and fuses features from bimodalities, achieving 73.9% diagnostic accuracy. To explain the mechanism distinguishing autism spectrum disorder from healthy controls, we establish a perturbation model for brain imaging markers and perform a neuro-transcriptomic joint analysis using partial least squares regression and enrichment to identify potential genetic biomarkers. The perturbation model identifies brain imaging markers related to structural magnetic resonance imaging in the frontal, temporal, parietal, and occipital lobes, while functional magnetic resonance imaging markers primarily reside in the frontal, temporal, occipital lobes, and cerebellum. The neuro-transcriptomic joint analysis highlights genes associated with biological processes, such as "presynapse," "behavior," and "modulation of chemical synaptic transmission" in autism spectrum disorder's brain development. Different magnetic resonance imaging modalities offer complementary information for autism spectrum disorder diagnosis. Our dual-branch graph neural network achieves high accuracy and identifies abnormal brain regions and the neuro-transcriptomic analysis uncovers important genetic biomarkers. Overall, our study presents an effective approach for assisting in autism spectrum disorder diagnosis and identifying genetic biomarkers, showing potential for enhancing the diagnosis and treatment of this condition.

Diagnosing autism severity associated with physical fitness and gray matter volume in children with autism spectrum disorder: Explainable machine learning method.

PURPOSE: This study aimed to investigate the relationship between physical fitness, gray matter volume (GMV), and autism severity in children with autism spectrum disorder (ASD). Besides, we sought to diagnose autism severity associated with physical fitness and GMV using machine learning methods. METHODS: Ninety children diagnosed with ASD underwent physical fitness tests, magnetic resonance imaging scans, and autism severity assessments. Diagnosis models were established using extreme gradient boosting (XGB), random forest (RF), support vector machine (SVM), and decision tree (DT) algorithms. Hyperparameters were optimized through the grid search cross-validation method. The shapley additive explanation (SHAP) method was employed to explain the diagnosis results. RESULTS: Our study revealed associations between muscular strength in physical fitness and GMV in specific brain regions (left paracentral lobule, bilateral thalamus, left inferior temporal gyrus, and cerebellar vermis I-II) with autism severity in children with ASD. The accuracy (95 % confidence interval) of the XGB, RF, SVM, and DT models were 77.9 % (77.3, 78.6 %), 72.4 % (71.7, 73.2 %), 71.9 % (71.1, 72.6 %), and 66.9 % (66.2, 67.7 %), respectively. SHAP analysis revealed that muscular strength and thalamic GMV significantly influenced the decision-making process of the XGB model. CONCLUSION: Machine learning methods can effectively diagnose autism severity associated with physical fitness and GMV in children with ASD. In this respect, the XGB model demonstrated excellent performance across various indicators, suggesting its potential for diagnosing autism severity.

EGR1 Regulates SHANK3 Transcription at Different Stages of Brain Development.

The expression levels of SHANK3 are associated with autism spectrum disorder (ASD). The dynamic changes in SHANK3 expression during different stages of brain development may impact the progression of ASD. However, no studies or detailed analyses exploring the upstream mechanisms that regulate SHANK3 expression have been reported. In this study, we employed immunofluorescence to examine the expression of SHANK3 in brain organoids at various stages. Our results revealed elevated levels of SHANK3 expression in brain-like organoids at Day 60. Additionally, we utilized bioinformatics software to predict and analyze the SHANK3 gene's transcription start site. Through the dual luciferase reporter gene technique, we identified core transcription elements within the SHANK3 promoter. Site-directed mutations were used to identify specific transcription sites of SHANK3. To determine the physical binding of potential transcription factors to the SHANK3 promoter, we employed electrophoretic mobility shift assay (EMSA) and chromatin immunoprecipitation (ChIP). Our findings demonstrated that the transcription factor EGR1 regulates SHANK3 expression by binding to the transcription site of the SHANK3 promoter. Although this study did not investigate the pathological phenotypes of human brain organoids or animal model brains with EGR1 deficiency, which could potentially substantiate the findings observed for SHANK3 mutants, our findings provide valuable insights into the relationship between the transcription factor, EGR1, and SHANK3. This study contributes to the molecular understanding of ASD and offers potential foundations for precise targeted therapy.

Structural and biochemical alterations in dendritic spines as key mechanisms for severe mental illnesses.

Severe mental illnesses (SMI) collectively affect approximately 20% of the global population, as estimated by the World Health Organization (WHO). Despite having diverse etiologies, clinical symptoms, and pharmacotherapies, these diseases share a common pathophysiological characteristic: the misconnection of brain areas involved in reality perception, executive control, and cognition, including the corticolimbic system. Dendritic spines play a crucial role in excitatory neurotransmission within the central nervous system. These small structures exhibit remarkable plasticity, regulated by factors such as neurotransmitter tone, neurotrophic factors, and innate immunity-related molecules, and other mechanisms - all of which are associated with the pathophysiology of SMI. However, studying dendritic spine mechanisms in both healthy and pathological conditions in patients is fraught with technical limitations. This is where animal models related to these diseases become indispensable. They have played a pivotal role in elucidating the significance of dendritic spines in SMI. In this review, the information regarding the potential role of dendritic spines in SMI was summarized, drawing from clinical and animal model reports. Also, the implications of targeting dendritic spine-related molecules for SMI treatment were explored. Specifically, our focus is on major depressive disorder and the neurodevelopmental disorders schizophrenia and autism spectrum disorder. Abundant clinical and basic research has studied the functional and structural plasticity of dendritic spines in these diseases, along with potential pharmacological targets that modulate the dynamics of these structures. These targets may be associated with the clinical efficacy of the pharmacotherapy.

Quantitative Relaxometry Assessment of Brain Microstructural Abnormality of Preschool Children With Autism Spectrum Disorder With Synthetic Magnetic Resonance Imaging.

OBJECTIVE: This study aimed to perform an assessment of brain microstructure in children with autism aged 2 to 5 years using relaxation times acquired by synthetic magnetic resonance imaging. MATERIALS AND METHODS: Thirty-four children with autism spectrum disorder (ASD) (ASD group) and 17 children with global developmental delay (GDD) (GDD group) were enrolled, and synthetic magnetic resonance imaging was performed to obtain T1 and T2 relaxation times. The differences in brain relaxation times between the 2 groups of children were compared, and the correlation between significantly changed T1/T2 and clinical neuropsychological scores in the ASD group was analyzed. RESULTS: Compared with the GDD group, shortened T1 relaxation times in the ASD group were distributed in the genu of corpus callosum (GCC) ( P = 0.003), splenium of corpus callosum ( P = 0.002), and right thalamus (TH) ( P = 0.014), whereas shortened T2 relaxation times in the ASD group were distributed in GCC ( P = 0.011), left parietal white matter ( P = 0.035), and bilateral TH (right, P = 0.014; left, P = 0.016). In the ASD group, the T2 of the left parietal white matter is positively correlated with gross motor (developmental quotient [DQ] 2) and personal-social behavior (DQ5), respectively ( r = 0.377, P = 0.028; r = 0.392, P = 0.022); the T2 of the GCC was positively correlated with DQ5 ( r = 0.404, P = 0.018); and the T2 of the left TH is positively correlated with DQ2 and DQ5, respectively ( r = 0.433, P = 0.009; r = 0.377, P = 0.028). All significantly changed relaxation values were not significantly correlated with Childhood Autism Rating Scale scores. CONCLUSIONS: The shortened relaxometry times in the brain of children with ASD may be associated with the increased myelin content and decreased water content in the brain of children with ASD in comparison with GDD, contributing the understanding of the pathophysiology of ASD. Therefore, the T1 and T2 relaxometry may be used as promising imaging markers for ASD diagnosis.

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.

Single-cell brain organoid screening identifies developmental defects in autism.

The development of the human brain involves unique processes (not observed in many other species) that can contribute to neurodevelopmental disorders1-4. Cerebral organoids enable the study of neurodevelopmental disorders in a human context. We have developed the CRISPR-human organoids-single-cell RNA sequencing (CHOOSE) system, which uses verified pairs of guide RNAs, inducible CRISPR-Cas9-based genetic disruption and single-cell transcriptomics for pooled loss-of-function screening in mosaic organoids. Here we show that perturbation of 36 high-risk autism spectrum disorder genes related to transcriptional regulation uncovers their effects on cell fate determination. We find that dorsal intermediate progenitors, ventral progenitors and upper-layer excitatory neurons are among the most vulnerable cell types. We construct a developmental gene regulatory network of cerebral organoids from single-cell transcriptomes and chromatin modalities and identify autism spectrum disorder-associated and perturbation-enriched regulatory modules. Perturbing members of the BRG1/BRM-associated factor (BAF) chromatin remodelling complex leads to enrichment of ventral telencephalon progenitors. Specifically, mutating the BAF subunit ARID1B affects the fate transition of progenitors to oligodendrocyte and interneuron precursor cells, a phenotype that we confirmed in patient-specific induced pluripotent stem cell-derived organoids. Our study paves the way for high-throughput phenotypic characterization of disease susceptibility genes in organoid models with cell state, molecular pathway and gene regulatory network readouts.

Modeling idiopathic autism in forebrain organoids reveals an imbalance of excitatory cortical neuron subtypes during early neurogenesis.

Idiopathic autism spectrum disorder (ASD) is highly heterogeneous, and it remains unclear how convergent biological processes in affected individuals may give rise to symptoms. Here, using cortical organoids and single-cell transcriptomics, we modeled alterations in the forebrain development between boys with idiopathic ASD and their unaffected fathers in 13 families. Transcriptomic changes suggest that ASD pathogenesis in macrocephalic and normocephalic probands involves an opposite disruption of the balance between excitatory neurons of the dorsal cortical plate and other lineages such as early-generated neurons from the putative preplate. The imbalance stemmed from divergent expression of transcription factors driving cell fate during early cortical development. While we did not find genomic variants in probands that explained the observed transcriptomic alterations, a significant overlap between altered transcripts and reported ASD risk genes affected by rare variants suggests a degree of gene convergence between rare forms of ASD and the developmental transcriptome in idiopathic ASD.

The anterior insular cortex processes social recognition memory.

Impaired social abilities are characteristics of a variety of psychiatric disorders such as schizophrenia, autism spectrum disorder, and bipolar disorder. Studies consistently implicated the relationship between the anterior insular cortex (aIC) and social ability, however, how the aIC involves in processing specific subtypes of social ability was uninvestigated. We, therefore, investigated whether the absence or presence of the aIC affects the social behaviors of mice. We found that electrolytic lesions of the aIC specifically impaired mice's ability to recognize a novel stranger mouse, while the sociability of the aIC-lesioned mice was intact. Interestingly, the aIC-lesioned mice were still distinguished between a mouse that had been housed together before the aIC lesion and a novel mouse, supporting that retrieval of social recognition memory may not involve the aIC. Additional behavioral tests revealed that this specific social ability impairment induced by the aIC lesion was not due to impairment in olfaction, learning and memory, locomotion, or anxiety levels. Together our data suggest that the aIC is specifically involved in processing social recognition memory, but not necessarily involved in retrieving it.

Pleiotropy of autism-associated chromatin regulators.

Gene ontology analyses of high-confidence autism spectrum disorder (ASD) risk genes highlight chromatin regulation and synaptic function as major contributors to pathobiology. Our recent functional work in vivo has additionally implicated tubulin biology and cellular proliferation. As many chromatin regulators, including the ASD risk genes ADNP and CHD3, are known to directly regulate both tubulins and histones, we studied the five chromatin regulators most strongly associated with ASD (ADNP, CHD8, CHD2, POGZ and KMT5B) specifically with respect to tubulin biology. We observe that all five localize to microtubules of the mitotic spindle in vitro in human cells and in vivo in Xenopus. Investigation of CHD2 provides evidence that mutations present in individuals with ASD cause a range of microtubule-related phenotypes, including disrupted localization of the protein at mitotic spindles, cell cycle stalling, DNA damage and cell death. Lastly, we observe that ASD genetic risk is significantly enriched among tubulin-associated proteins, suggesting broader relevance. Together, these results provide additional evidence that the role of tubulin biology and cellular proliferation in ASD warrants further investigation and highlight the pitfalls of relying solely on annotated gene functions in the search for pathological mechanisms.

Reduced grey matter volume of amygdala and hippocampus is associated with the severity of autistic symptoms and language abilities in school-aged children with Autism Spectrum Disorder: an exploratory study.

The core symptoms of Autism Spectrum Disorder (ASD) are impairments in social interaction/communication and the presence of stereotyped and repetitive behaviour. The amygdala and hippocampus are involved in core functions in the "social brain" and, thus, may be of particular interest in ASD. Previous studies demonstrated inconsistent results, revealing both increased and reduced volume of these brain structures in individuals with ASD. In this study, we investigated the grey and white matter volumes of amygdala and hippocampus in primary-school-aged children with and without ASD. Also, we assessed the relationships between the volume of brain structures and behavioural measures in children with ASD. A total of 36 children participated in the study: 18 children with ASD (13 boys, age range 8.01-14.01 years, mean age (Mage) = 10.02, standard deviation (SD) = 1.76) and 18 age- and sex-matched typically developing controls (13 boys, age range 7.06-12.03 years, Mage = 10.00, SD = 1.38). The whole-brain structural magnetic resonance imaging (MRI) was applied to acquire T1 images for each child. The results showed a bilateral reduction in grey matter volume of amygdala and hippocampus in children with ASD, but no difference was found in white matter volume. Importantly, pathological reduction in grey matter volume of amygdala was associated with lower language skills and more severe autistic traits; also, a reduced grey matter volume of the left hippocampus was related to lower language skills in the ASD group.

Parsing the heterogeneity of brain-symptom associations in autism spectrum disorder via random forest with homogeneous canonical correlation.

BACKGROUND: Autism spectrum disorder (ASD) is a highly heterogeneous developmental disorder, but the neuroimaging substrates of its heterogeneity remain unknown. The difficulty lies mainly on the significant individual variability in the brain-symptom association. METHODS: T1-weighted magnetic resonance imaging data from the Autism Brain Imaging Database Exchange (ABIDE) (NTDC = 1146) were used to generate a normative model to map brain structure deviations of cases (NASD = 571). Voxel-based morphometry (VBM) was used to compute gray matter volume (GMV). Singular Value Decomposition (SVD) was employed to perform dimensionality reduction. A tree-based algorithm was proposed to identify the ASD subtypes according to the pattern of brain-symptom association as assessed by a homogeneous canonical correlation. RESULTS: We identified 4 ASD subtypes with distinct association patterns between residual volumes and a social symptom score. More severe the social symptom was associated with greater GMVs in both the frontoparietal regions for the subtype1 (r = 0.29-0.44) and the ventral visual pathway for the subtype3 (r = 0.19-0.23), but lower GMVs in both the right anterior cingulate cortex for the subtype4 (r = -0.25) and a few subcortical regions for the subtype2 (r = -0.31 to -0.20). The subtyping significantly improved the classification accuracy between cases and controls from 0.64 to 0.75 (p < 0.05, permutation test), which was also better than the accuracy of 0.68 achieved by the k-means-based subtyping (p < 0.01). LIMITATIONS: Sample size limited the study due to the missing data. CONCLUSIONS: These findings suggest that the heterogeneity of ASD might reflect changes in different subsystems of the social brain, especially including social attention, motivation, perceiving and evaluation.

Altered resting-state functional connectivity of the brain in children with autism spectrum disorder.

Autism spectrum disorder (ASD) is a group of neurodevelopmental disorders. Brain mapping has shown that functional brain connections are altered in autism. This study investigated the pattern of brain connection changes in autistic people compared to healthy people. This study aimed to analyze functional abnormalities within the brain due to ASD, using resting-state functional magnetic resonance imaging (fMRI). Resting-state functional magnetic resonance images of 26 individuals with ASD and 26 healthy controls were obtained from the Autism Brain Imaging Data Exchange (ABIDE) database. The DPARSF (data processing assistant for resting-state fMRI) toolbox was used for resting-state functional image processing, and features related to functional connections were extracted from these images. Then, the extracted features from both groups were compared using an Independent Two-Sample T Test, and the features with significant differences between the two groups were identified. Compared with healthy controls, individuals with ASD showed hyper-connectivity in the frontal lobe, anterior cingulum, parahippocampal, left precuneus, angular, caudate, superior temporal, and left pallidum, as well as hypo-connectivity in the precentral, left superior frontal, left middle orbitofrontal, right amygdala, and left posterior cingulum. Our findings show that abnormal functional connectivity exists in patients with ASD. This study makes an important advancement in our understanding of the abnormal neurocircuits causing autism.

Aluminium in the Human Brain: Routes of Penetration, Toxicity, and Resulting Complications.

Aluminium (Al) is the most ubiquitous metal in the Earth's crust. Even though its toxicity is well-documented, the role of Al in the pathogenesis of several neurological diseases remains debatable. To establish the basic framework for future studies, we review literature reports on Al toxicokinetics and its role in Alzheimer's disease (AD), autism spectrum disorder (ASD), alcohol use disorder (AUD), multiple sclerosis (MS), Parkinson's disease (PD), and dialysis encephalopathy (DE) from 1976 to 2022. Despite poor absorption via mucosa, the biggest amount of Al comes with food, drinking water, and inhalation. Vaccines introduce negligible amounts of Al, while the data on skin absorption (which might be linked with carcinogenesis) is limited and requires further investigation. In the above-mentioned diseases, the literature shows excessive Al accumulation in the central nervous system (AD, AUD, MS, PD, DE) and epidemiological links between greater Al exposition and their increased prevalence (AD, PD, DE). Moreover, the literature suggests that Al has the potential as a marker of disease (AD, PD) and beneficial results of Al chelator use (such as cognitive improvement in AD, AUD, MS, and DE cases).

Assessment of Neuroanatomical Endophenotypes of Autism Spectrum Disorder and Association With Characteristics of Individuals With Schizophrenia and the General Population.

Importance: Autism spectrum disorder (ASD) is associated with significant clinical, neuroanatomical, and genetic heterogeneity that limits precision diagnostics and treatment. Objective: To assess distinct neuroanatomical dimensions of ASD using novel semisupervised machine learning methods and to test whether the dimensions can serve as endophenotypes also in non-ASD populations. Design, Setting, and Participants: This cross-sectional study used imaging data from the publicly available Autism Brain Imaging Data Exchange (ABIDE) repositories as the discovery cohort. The ABIDE sample included individuals diagnosed with ASD aged between 16 and 64 years and age- and sex-match typically developing individuals. Validation cohorts included individuals with schizophrenia from the Psychosis Heterogeneity Evaluated via Dimensional Neuroimaging (PHENOM) consortium and individuals from the UK Biobank to represent the general population. The multisite discovery cohort included 16 internationally distributed imaging sites. Analyses were performed between March 2021 and March 2022. Main Outcomes and Measures: The trained semisupervised heterogeneity through discriminative analysis models were tested for reproducibility using extensive cross-validations. It was then applied to individuals from the PHENOM and the UK Biobank. It was hypothesized that neuroanatomical dimensions of ASD would display distinct clinical and genetic profiles and would be prominent also in non-ASD populations. Results: Heterogeneity through discriminative analysis models trained on T1-weighted brain magnetic resonance images of 307 individuals with ASD (mean [SD] age, 25.4 [9.8] years; 273 [88.9%] male) and 362 typically developing control individuals (mean [SD] age, 25.8 [8.9] years; 309 [85.4%] male) revealed that a 3-dimensional scheme was optimal to capture the ASD neuroanatomy. The first dimension (A1: aginglike) was associated with smaller brain volume, lower cognitive function, and aging-related genetic variants (FOXO3; Z = 4.65; P = 1.62 × 10-6). The second dimension (A2: schizophrenialike) was characterized by enlarged subcortical volumes, antipsychotic medication use (Cohen d = 0.65; false discovery rate-adjusted P = .048), partially overlapping genetic, neuroanatomical characteristics to schizophrenia (n = 307), and significant genetic heritability estimates in the general population (n = 14 786; mean [SD] h2, 0.71 [0.04]; P < 1 × 10-4). The third dimension (A3: typical ASD) was distinguished by enlarged cortical volumes, high nonverbal cognitive performance, and biological pathways implicating brain development and abnormal apoptosis (mean [SD] ß, 0.83 [0.02]; P = 4.22 × 10-6). Conclusions and Relevance: This cross-sectional study discovered 3-dimensional endophenotypic representation that may elucidate the heterogeneous neurobiological underpinnings of ASD to support precision diagnostics. The significant correspondence between A2 and schizophrenia indicates a possibility of identifying common biological mechanisms across the 2 mental health diagnoses.

Magnetic Resonance Imaging in Autism Spectrum Disorders: clinical and neuroradiological phenotypes.

BACKGROUND AND AIM: Autism Spectrum Disorders (ASDs) are a group of neurodevelopmental disorders that can severely compromise social and cognitive functions in childhood. Magnetic Resonance Imaging (MRI) currently represents the gold standard as an in vivo and non-invasive study of the human brain morphology. This work aims to search for possible links between clinical phenotypes and radiological anomalies that may be relevant and pathognomonic in the subsequent diagnosis of ASDs. METHODS: This is a retrospective study in which 132 patients (112 males and 20 females) with neurodevelopment disorders, including ASDs, were enrolled. The population study was divided into three groups considering their own pathological diagnosis. All patients included in this population underwent genetic screening and one or multiple 1.5T MRI scans were performed to evaluate potential anomalies of the corpus callosum, periventricular white matter, ventricular space, cerebellum, subarachnoid space and thalamus. RESULTS: Univariate analysis showed that the presence of MRI brain abnormalities was a significant variable in predicting the presence of ASDs.  Increased ventricular volume was one of the most replicated findings in ASDs patients since it was reported to be statistically significant both in uni- and multivariate analysis, resulting even as a potentially predictive factor of diagnosis. CONCLUSIONS: This study can represent a starting point for the research of new radiological evidence that might be important to early diagnose ASDs and for making a differential diagnosis with all those conditions that mimic autistic traits, but which are not clinically connected to the spectrum disorder itself.

Neuroinflammation and Oxidative Stress in the Pathogenesis of Autism Spectrum Disorder.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder (NDD) characterized by impairments in social communication, repetitive behaviors, restricted interests, and hyperesthesia/hypesthesia caused by genetic and/or environmental factors. In recent years, inflammation and oxidative stress have been implicated in the pathogenesis of ASD. In this review, we discuss the inflammation and oxidative stress in the pathophysiology of ASD, particularly focusing on maternal immune activation (MIA). MIA is a one of the common environmental risk factors for the onset of ASD during pregnancy. It induces an immune reaction in the pregnant mother's body, resulting in further inflammation and oxidative stress in the placenta and fetal brain. These negative factors cause neurodevelopmental impairments in the developing fetal brain and subsequently cause behavioral symptoms in the offspring. In addition, we also discuss the effects of anti-inflammatory drugs and antioxidants in basic studies on animals and clinical studies of ASD. Our review provides the latest findings and new insights into the involvements of inflammation and oxidative stress in the pathogenesis of ASD.