Increased number of excitatory synapsis and decreased number of inhibitory synapsis in the prefrontal cortex in autism.

Previous studies in autism spectrum disorder demonstrated an increased number of excitatory pyramidal cells and a decreased number of inhibitory parvalbumin+ chandelier interneurons in the prefrontal cortex of postmortem brains. How these changes in cellular composition affect the overall abundance of excitatory and inhibitory synapses in the cortex is not known. Herein, we quantified the number of excitatory and inhibitory synapses in the prefrontal cortex of 10 postmortem autism spectrum disorder brains and 10 control cases. To identify excitatory synapses, we used VGlut1 as a marker of the presynaptic component and postsynaptic density protein-95 as marker of the postsynaptic component. To identify inhibitory synapses, we used the vesicular gamma-aminobutyric acid transporter as a marker of the presynaptic component and gephyrin as a marker of the postsynaptic component. We used Puncta Analyzer to quantify the number of co-localized pre- and postsynaptic synaptic components in each area of interest. We found an increase in the number of excitatory synapses in upper cortical layers and a decrease in inhibitory synapses in all cortical layers in autism spectrum disorder brains compared with control cases. The alteration in the number of excitatory and inhibitory synapses could lead to neuronal dysfunction and disturbed network connectivity in the prefrontal cortex in autism spectrum disorder.

Neuroanatomy of autism: what is the role of the cerebellum?

Autism (or autism spectrum disorder) was initially defined as a psychiatric disorder, with the likely cause maternal behavior (the very destructive "refrigerator mother" theory). It took several decades for research into brain mechanisms to become established. Both neuropathological and imaging studies found differences in the cerebellum in autism spectrum disorder, the most widely documented being a decreased density of Purkinje cells in the cerebellar cortex. The popular interpretation of these results is that cerebellar neuropathology is a critical cause of autism spectrum disorder. We challenge that view by arguing that if fewer Purkinje cells are critical for autism spectrum disorder, then any condition that causes the loss of Purkinje cells should also cause autism spectrum disorder. We will review data on damage to the cerebellum from cerebellar lesions, tumors, and several syndromes (Joubert syndrome, Fragile X, and tuberous sclerosis). Collectively, these studies raise the question of whether the cerebellum really has a role in autism spectrum disorder. Autism spectrum disorder is now recognized as a genetically caused developmental disorder. A better understanding of the genes that underlie the differences in brain development that result in autism spectrum disorder is likely to show that these genes affect the development of the cerebellum in parallel with the development of the structures that do underlie autism spectrum disorder.

Cortical dysmorphology and reduced cortico-collicular projections in an animal model of autism spectrum disorder.

Autism spectrum disorder is a neurodevelopmental disability that includes sensory disturbances. Hearing is frequently affected and ranges from deafness to hypersensitivity. In utero exposure to the antiepileptic valproic acid is associated with increased risk of autism spectrum disorder in humans and timed valproic acid exposure is a biologically relevant and validated animal model of autism spectrum disorder. Valproic acid-exposed rats have fewer neurons in their auditory brainstem and thalamus, fewer calbindin-positive neurons, reduced ascending projections to the midbrain and thalamus, elevated thresholds, and delayed auditory brainstem responses. Additionally, in the auditory cortex, valproic acid exposure results in abnormal responses, decreased phase-locking, elevated thresholds, and abnormal tonotopic maps. We therefore hypothesized that in utero, valproic acid exposure would result in fewer neurons in auditory cortex, neuronal dysmorphology, fewer calbindin-positive neurons, and reduced connectivity. We approached this hypothesis using morphometric analyses, immunohistochemistry, and retrograde tract tracing. We found thinner cortical layers but no changes in the density of neurons, smaller pyramidal and non-pyramidal neurons in several regions, fewer neurons immunoreactive for calbindin-positive, and fewer cortical neurons projecting to the inferior colliculus. These results support the widespread impact of the auditory system in autism spectrum disorder and valproic acid-exposed animals and emphasize the utility of simple, noninvasive auditory screening for autism spectrum disorder.

Expansion of the Genotypic and Phenotypic Spectrum of ASH1L-Related Syndromic Neurodevelopmental Disorder.

Pathogenic ASH1L variants have been reported in probands with broad phenotypic presentations, including intellectual disability, autism spectrum disorder, attention deficit hyperactivity disorder, seizures, congenital anomalies, and other skeletal, muscular, and sleep differences. Here, we review previously published individuals with pathogenic ASH1L variants and report three further probands with novel ASH1L variants and previously unreported phenotypic features, including mixed receptive language disorder and gait disturbances. These novel data from the Brain Gene Registry, an accessible repository of clinically derived genotypic and phenotypic data, have allowed for the expansion of the phenotypic and genotypic spectrum of this condition.

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.

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.

The role of glia in the dysregulation of neuronal spinogenesis in Ube3a-dependent ASD.

Overexpression of the Ube3a gene and the resulting increase in Ube3a protein are linked to autism spectrum disorder (ASD). However, the cellular and molecular processes underlying Ube3a-dependent ASD remain unclear. Using both male and female mice, we find that neurons in the somatosensory cortex of the Ube3a 2× Tg ASD mouse model display reduced dendritic spine density and increased immature filopodia density. Importantly, the increased gene dosage of Ube3a in astrocytes alone is sufficient to confer alterations in neurons as immature dendritic protrusions, as observed in primary hippocampal neuron cultures. We show that Ube3a overexpression in astrocytes leads to a loss of astrocyte-derived spinogenic protein, thrombospondin-2 (TSP2), due to a suppression of TSP2 gene transcription. By neonatal intraventricular injection of astrocyte-specific virus, we demonstrate that Ube3a overexpression in astrocytes in vivo results in a reduction in dendritic spine maturation in prelimbic cortical neurons, accompanied with autistic-like behaviors in mice. These findings reveal an astrocytic dominance in initiating ASD pathobiology at the neuronal and behavior levels. SIGNIFICANCE STATEMENT: Increased gene dosage of Ube3a is tied to autism spectrum disorders (ASDs), yet cellular and molecular alterations underlying autistic phenotypes remain unclear. We show that Ube3a overexpression leads to impaired dendritic spine maturation, resulting in reduced spine density and increased filopodia density. We find that dysregulation of spine development is not neuron autonomous, rather, it is mediated by an astrocytic mechanism. Increased gene dosage of Ube3a in astrocytes leads to reduced production of the spinogenic glycoprotein thrombospondin-2 (TSP2), leading to abnormalities in spines. Astrocyte-specific Ube3a overexpression in the brain in vivo confers dysregulated spine maturation concomitant with autistic-like behaviors in mice. These findings indicate the importance of astrocytes in aberrant neurodevelopment and brain function in Ube3a-depdendent ASD.

Volumetric alterations in the basal ganglia in autism Spectrum disorder: A systematic review.

INTRODUCTION: Recent research indicates that some brain structures show alterations in conditions such as Autism Spectrum Disorder (ASD). Among them, are the basal ganglia that are involved in motor, cognitive and behavioral neural circuits. OBJECTIVE: Review the literature that describes possible volumetric alterations in the basal ganglia of individuals with ASD and the impacts that these changes have on the severity of the condition. METHODOLOGY: This systematic review was registered in the design and reported according to the PRISMA Items and registered in PROSPERO (CRD42023394787). The study analyzed data from published clinical, case-contemplate, and cohort trials. The following databases were consulted: PubMed, Embase, Scopus, and Cochrane Central Register of Controlled Trials, using the Medical Subject Titles (MeSH) "Autism Spectrum Disorder" and "Basal Ganglia". The last search was carried out on February 28, 2023. RESULTS: Thirty-five eligible articles were collected, analyzed, and grouped according to the levels of alterations. CONCLUSION: The present study showed important volumetric alterations in the basal ganglia in ASD. However, the examined studies have methodological weaknesses that do not allow generalization and correlation with ASD manifestations.

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.

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.

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.

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.

De novo variants in SP9 cause a novel form of interneuronopathy characterized by intellectual disability, autism spectrum disorder, and epilepsy with variable expressivity.

PURPOSE: Interneuronopathies are a group of neurodevelopmental disorders characterized by deficient migration and differentiation of gamma-aminobutyric acidergic interneurons resulting in a broad clinical spectrum, including autism spectrum disorders, early-onset epileptic encephalopathy, intellectual disability, and schizophrenic disorders. SP9 is a transcription factor belonging to the Krüppel-like factor and specificity protein family, the members of which harbor highly conserved DNA-binding domains. SP9 plays a central role in interneuron development and tangential migration, but it has not yet been implicated in a human neurodevelopmental disorder. METHODS: Cases with SP9 variants were collected through international data-sharing networks. To address the specific impact of SP9 variants, in silico and in vitro assays were carried out. RESULTS: De novo heterozygous variants in SP9 cause a novel form of interneuronopathy. SP9 missense variants affecting the glutamate 378 amino acid result in severe epileptic encephalopathy because of hypomorphic and neomorphic DNA-binding effects, whereas SP9 loss-of-function variants result in a milder phenotype with epilepsy, developmental delay, and autism spectrum disorder. CONCLUSION: De novo heterozygous SP9 variants are responsible for a neurodevelopmental disease. Interestingly, variants located in conserved DNA-binding domains of KLF/SP family transcription factors may lead to neomorphic DNA-binding functions resulting in a combination of loss- and gain-of-function effects.

Exploring White Matter Abnormalities in Young Children with Autism Spectrum Disorder: Integrating Multi-shell Diffusion Data and Machine Learning Analysis.

RATIONALE AND OBJECTIVES: This study employed tract-based spatial statistics (TBSS) to investigate abnormalities in the white matter microstructure among children with autism spectrum disorder (ASD). Additionally, an eXtreme Gradient Boosting (XGBoost) model was developed to effectively classify individuals with ASD and typical developing children (TDC). METHODS AND MATERIALS: Multi-shell diffusion weighted images were acquired from 62 children with ASD and 44 TDC. Using the Pydesigner procedure, diffusion tensor (DT), diffusion kurtosis (DK), and white matter tract integrity (WMTI) metrics were computed. Subsequently, TBSS analysis was applied to discern differences in these diffusion parameters between ASD and TDC groups. The XGBoost model was then trained using metrics showing significant differences, and Shapley Additive explanations (SHAP) values were computed to assess the feature importance in the model's predictions. RESULTS: TBSS analysis revealed a significant reduction in axonal diffusivity (AD) in the left posterior corona radiata and the right superior corona radiata. Among the DK indicators, mean kurtosis, axial kurtosis, and kurtosis fractional anisotropy were notably increased in children with ASD, with no significant difference in radial kurtosis. WMTI metrics such as axonal water fraction, axonal diffusivity of the extra-axonal space (EAS_AD), tortuosity of the extra-axonal space (EAS_TORT), and diffusivity of intra-axonal space (IAS_Da) were significantly increased, primarily in the corpus callosum and fornix. Notably, there was no significant difference in radial diffusivity of the extra-axial space (EAS_RD). The XGBoost model demonstrated excellent classification ability, and the SHAP analysis identified EAS_TORT as the feature with the highest importance in the model's predictions. CONCLUSION: This study utilized TBSS analyses with multi-shell diffusion data to examine white matter abnormalities in pediatric autism. Additionally, the developed XGBoost model showed outstanding performance in classifying ASD and TDC. The ranking of SHAP values based on the XGBoost model underscored the significance of features in influencing model predictions.

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.

Cerebral cortical structural alteration patterns across four major psychiatric disorders in 5549 individuals.

According to the operational diagnostic criteria, psychiatric disorders such as schizophrenia (SZ), bipolar disorder (BD), major depressive disorder (MDD), and autism spectrum disorder (ASD) are classified based on symptoms. While its cluster of symptoms defines each of these psychiatric disorders, there is also an overlap in symptoms between the disorders. We hypothesized that there are also similarities and differences in cortical structural neuroimaging features among these psychiatric disorders. T1-weighted magnetic resonance imaging scans were performed for 5,549 subjects recruited from 14 sites. Effect sizes were determined using a linear regression model within each protocol, and these effect sizes were meta-analyzed. The similarity of the differences in cortical thickness and surface area of each disorder group was calculated using cosine similarity, which was calculated from the effect sizes of each cortical regions. The thinnest cortex was found in SZ, followed by BD and MDD. The cosine similarity values between disorders were 0.943 for SZ and BD, 0.959 for SZ and MDD, and 0.943 for BD and MDD, which indicated that a common pattern of cortical thickness alterations was found among SZ, BD, and MDD. Additionally, a generally smaller cortical surface area was found in SZ and MDD than in BD, and the effect was larger in SZ. The cosine similarity values between disorders were 0.945 for SZ and MDD, 0.867 for SZ and ASD, and 0.811 for MDD and ASD, which indicated a common pattern of cortical surface area alterations among SZ, MDD, and ASD. Patterns of alterations in cortical thickness and surface area were revealed in the four major psychiatric disorders. To our knowledge, this is the first report of a cross-disorder analysis conducted on four major psychiatric disorders. Cross-disorder brain imaging research can help to advance our understanding of the pathogenesis of psychiatric disorders and common symptoms.

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.