Heterogeneity Of Autism Spectrum Disorder Research Articles

Obstáculos para o diagnóstico e tratamento do transtorno do espectro autista

Objective: To understand the obstacles that reduce accessibility to the diagnosis and treatment of autism spectrum disorder (ASD). Literature review: ASD is a disorder characterized by difficulties in social interaction, impairments in communication and restricted patterns of behavior. Its diagnosis is clinical, based on behavioral observations and the patient's history and on criteria defined in the Diagnostic and Statistical Manual of Mental Disorders (DSM – 5). Treatment includes pharmacological and behavioral approaches. However, the difficulties and challenges found in the literature include the symptomatological heterogeneity of ASD, the need for training health professionals and a more accurate assessment through a multidisciplinary team. Late diagnosis or its absence can lead to unfavorable outcomes in adult life, such as: anxiety, depression, and difficulty in interpersonal relationships. Recent studies include the use of specific biomarkers and innovative therapies that are not yet accessible. Final considerations: Early diagnosis and appropriate treatment are ideal for improving the quality of life of individuals with ASD. The importance of the doctor and new studies to improve therapeutic choice in ASD is also highlighted.

Beyond the Spectrum: Subtype-Specific Molecular Insights into Autism Spectrum Disorder Via Multimodal Data Integration.

Some toddlers with autism spectrum disorder (ASD) have mild social symptoms and developmental improvement in skills, but for others, symptoms and abilities are moderately or even severely affected. Those with profound autism have the most severe social, language, and cognitive symptoms and are at the greatest risk of having a poor developmental outcome. The little that is known about the underlying biology of this important profound autism subtype, points clearly to embryonic dysregulation of proliferation, differentiation and neurogenesis. Because it is essential to gain foundational knowledge of the molecular biology associated with profound, moderate, and mild autism clinical subtypes, we used well-validated, data-driven patient subtyping methods to integrate clinical and molecular data at 1 to 3 years of age in a cohort of 363 ASD and controls representative of the general pediatric population in San Diego County. Clinical data were diagnostic, language, cognitive and adaptive ability scores. Molecular measures were 50 MSigDB Hallmark gene pathway activity scores derived from RNAseq gene expression. Subtyping identified four ASD, typical and mixed diagnostic clusters. 93% of subjects in one cluster were profound autism and 93% in a different cluster were control toddlers; a third cluster was 76% moderate ability ASD; and the last cluster was a mix of mild ASD and control toddlers. Among the four clusters, the profound autism subtype had the most severe social symptoms, language, cognitive, adaptive, social attention eye tracking, social fMRI activation, and age-related decline in abilities, while mild autism toddlers mixed within typical and delayed clusters had mild social symptoms, and neurotypical language, cognitive and adaptive scores that improved with age compared with profound and moderate autism toddlers in other clusters. In profound autism, 7 subtype-specific dysregulated gene pathways were found; they control embryonic proliferation, differentiation, neurogenesis, and DNA repair. To find subtype-common dysregulated pathways, we compared all ASD vs TD and found 17 ASD subtype-common dysregulated pathways. These common pathways showed a severity gradient with the greatest dysregulation in profound and least in mild. Collectively, results raise the new hypothesis that the continuum of ASD heterogeneity is moderated by subtype-common pathways and the distinctive nature of profound autism is driven by the differentially added profound subtype-specific embryonic pathways.

Factors of Bullying Victimization Among Students on the Autism Spectrum: A Systematic Review

AbstractStudies have increasingly suggested that autistic students face a heightened risk of bullying. Understanding the underlying factors for high rates of bullying victimization among autistic students is crucial for intervention, but the complexity of bullying and the heterogeneity of ASD (autism spectrum disorder) traits have made it challenging to explain these factors. Hence, this study systematically reviewed and summarized findings in this area, providing recommendations for intervention. It synthesized 34 studies investigating the predictive variable of bullying victimization among autistic students. Our review observed the role of schools, parents, and peers and of individual variables with respect to autistic traits and behavioral difficulties. We then proposed prevention and intervention strategies against bullying victimization toward autistic students.

Understanding Depression in Autism: The Role of Subjective Perception and Anterior Cingulate Cortex Volume.

The prevalence of depression is elevated in individuals with autism spectrum disorder (ASD) compared to the general population, yet the reasons for this disparity remain unclear. While social deficits central to ASD may contribute to depression, it is uncertain whether social interaction behavior themselves or individuals' introspection about their social behaviors are more impactful. Although the anterior cingulate cortex (ACC) and amygdala are frequently implicated in ASD, depression, and social functioning, it is unknown if these regions explain differences between ASD adults with and without co-occurring depression. The present study contrasted observed vs. subjective perception of autism symptoms and social performances assessed with both standardized measures and a lab task, in 65 sex-balanced (52.24% male) autistic young adults. We also quantified ACC and amygdala volume with 7-Tesla structural neuroimaging to examine correlations with depression and social functioning. We found that ASD individuals with depression exhibited differences in subjective evaluations including heightened self-awareness of ASD symptoms, lower subjective satisfaction with social relations, and less perceived affiliation during the social interaction task, yet no differences in corresponding observed measures, compared to those without depression. Larger ACC volume was related to depression, greater self-awareness of ASD symptoms, and worse subjective satisfaction with social interactions. In contrast, amygdala volume, despite its association with clinician-rated ASD symptoms, was not related to depression. Due to the cross-sectional nature of our study, we cannot determine the directionality of the observed relationships. Additionally, we included only individuals with an IQ over 60 to ensure participants could complete the social task, which excluded many on the autism spectrum. We also utilized self-reported depression indices instead of clinically diagnosed depression, which may limit the comprehensiveness of the findings. Our approach highlights the unique role of subjective perception of autism symptoms and social interactions, beyond the observable manifestation of social interaction in ASD, in contributing to depression, with the ACC playing a crucial role. These findings imply possible heterogeneity of ASD concerning co-occurring depression. Using neuroimaging, we were able to demarcate depressive phenotypes co-occurring alongside autistic phenotypes.

Understanding heterogeneity within autism spectrum disorder: a scoping review

Purpose In this study, the research question posed was: What are the defining characteristics, limitations, and potential opportunities in the research on heterogeneity within ASD? Design/methodology/approach This scoping review used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses methodology to address the research question: “What are the defining characteristics, limitations, and potential opportunities in the research on heterogeneity within ASD?” A comprehensive literature search was conducted across databases including MEDLINE/PubMed, SciVerse Scopus and Springer Link, with keywords such as autism, autism spectrum disorder (ASD), heterogeneity and neurodevelopment. Inclusion criteria covered original research, reviews and protocols published since 1990, while irrelevant or out-of-date works were excluded. Thematic analysis was applied to collected data to identify common patterns, trends and key characteristics, leading to a narrative synthesis. Ethical review board approval was not required due to the nature of the review. Findings The scoping review underscored the multifaceted nature of ASD, emphasizing its clinical, methodological and investigational complexities. ASD’s diverse behavioral, social and biological characteristics challenged its classification as a uniform entity. To address this, the review examined strategies like stricter clinical criteria, categorization into functional subgroups, and larger, diverse sample sizes. Moreover, it highlighted the transformative role of Big Data and machine learning in advancing the comprehension of ASD’s manifold manifestations. This research contributed valuable insights and innovative approaches for addressing the intrinsic heterogeneity of ASD, reshaping the understanding of this complex condition. Research limitations/implications One limitation of this scoping review is that it primarily relied on existing literature and did not involve primary data collection. While the review synthesized and analyzed a substantial body of research, the absence of original data collection may limit the depth of insights into specific aspects of ASD heterogeneity. Future research could benefit from incorporating primary data collection methods, such as surveys or interviews with individuals with ASD and their families, to gain more nuanced perspectives on the condition’s heterogeneity. Practical implications The reliance on existing literature in this scoping review highlights the need for further empirical studies exploring ASD’s heterogeneity. Researchers should consider conducting primary data collection to capture real-world experiences and variations within the ASD population. This approach could provide more comprehensive and context-specific insights, ultimately informing the development of tailored interventions and support strategies for individuals with ASD and their families. Originality/value This paper offers a fresh perspective on understanding ASD by examining its clinical, methodological and investigational implications in light of its inherent heterogeneity. Rather than viewing ASD as a uniform condition, this study explores strategies such as stricter clinical criteria, subcategorization based on functionality and diverse sample sizes to address its complexity. In addition, this study highlights the innovative use of Big Data and machine learning to gain deeper insights into ASD’s diverse manifestations. This approach contributes new insights and promising directions for future research, challenging the conventional understanding of ASD as a singular entity.

Individual Deviation-Based Functional Hypergraph for Identifying Subtypes of Autism Spectrum Disorder.

Heterogeneity has been one of the main barriers to understanding and treatment of autism spectrum disorder (ASD). Previous studies have identified several subtypes of ASD through unsupervised clustering analysis. However, most of them primarily depicted the pairwise similarity between individuals through second-order relationships, relying solely on patient data for their calculation. This leads to an underestimation of the complexity inherent in inter-individual relationships and the diagnostic information provided by typical development (TD). To address this, we utilized an elastic net model to construct an individual deviation-based hypergraph (ID-Hypergraph) based on functional MRI data. We then conducted a novel community detection clustering algorithm to the ID-Hypergraph, with the aim of identifying subtypes of ASD. By applying this framework to the Autism Brain Imaging Data Exchange repository data (discovery: 147/125, ASD/TD; replication: 134/132, ASD/TD), we identified four reproducible ASD subtypes with roughly similar patterns of ALFF between the discovery and replication datasets. Moreover, these subtypes significantly varied in communication domains. In addition, we achieved over 80% accuracy for the classification between these subtypes. Taken together, our study demonstrated the effectiveness of identifying subtypes of ASD through the ID-hypergraph, highlighting its potential in elucidating the heterogeneity of ASD and diagnosing ASD subtypes.

Phenotyping variability in early socio-communicative skills in young children with autism and its influence on later development.

Children with autism spectrum disorder (ASD) often face challenges in early social communication skills, prompting the need for a detailed exploration of specific behaviors and their impact on cognitive and adaptive functioning. This study aims to address this gap by examining the developmental trajectories of early social communication skills in preschoolers with ASD aged 18-60 months, comparing them to age-matched typically developing (TD) children. Utilizing the early social communication scales (ESCS), the research employs a longitudinal design to capture changes over time. We apply a principal component analysis (PCA) to ESCS variables to identify underlying components, and cluster analysis to identify subgroups based on preverbal communication profiles. The results reveal consistent differences in early social communication skills between ASD and TD children, with ASD children exhibiting reduced skills. PCA identifies two components, distinguishing objects-directed behaviors and social interaction-directed behaviors. Cluster analysis identifies three subgroups of autistic children, each displaying specific communication profiles associated with distinct cognitive and adaptive functioning trajectories. In conclusion, this study provides a nuanced understanding of early social communication development in ASD, emphasizing the importance of low-level behaviors. The identification of subgroups and their unique trajectories contributes to a more comprehensive understanding of ASD heterogeneity. These findings underscore the significance of early diagnosis, focusing on specific behaviors predicting cognitive and adaptive functioning outcomes. The study encourages further research to explore the sequential development of these skills, offering valuable insights for interventions and support strategies.

Three autism subtypes based on single-subject gray matter network revealed by semi-supervised machine learning.

Autism spectrum disorder (ASD) is a heterogeneous, early-onset neurodevelopmental condition characterized by persistent impairments in social interaction and communication. This study aims to delineate ASD subtypes based on individual gray matter brain networks and provide new insights from a graph theory perspective. In this study, we extracted and normalized single-subject gray matter networks and calculated each network's topological properties. The heterogeneity through discriminative analysis (HYDRA) method was utilized to subtype all patients based on network properties. Next, we explored the differences among ASD subtypes in terms of network properties and clinical measures. Our investigation identified three distinct ASD subtypes. In the case-control study, these subtypes exhibited significant differences, particularly in the precentral gyrus, lingual gyrus, and middle frontal gyrus. In the case analysis, significant differences in global and nodal properties were observed between any two subtypes. Clinically, subtype 1 showed lower VIQ and PIQ compared to subtype 3, but exhibited higher scores in ADOS-Communication and ADOS-Total compared to subtype 2. The results highlight the distinct brain network properties and behaviors among different subtypes of male patients with ASD, providing valuable insights into the neural mechanisms underlying ASD heterogeneity.

Utilising Behavioural and Sensory Profiles and Associated Perinatal Factors to Identify Meaningful Subgroups in Autism Spectrum Disorder.

The heterogeneity of autism spectrum disorder (ASD) clinically and aetiologically hinders intervention matching and prediction of outcomes. This study investigated if the behavioural, sensory, and perinatal factor profiles of autistic children could be used to identify distinct subgroups. Participants on the autism spectrum aged 2 to 17 years and their families were sourced via the Australian Autism Biobank (AAB). Latent class analysis was used to identify subgroups within this cohort, utilising twenty-six latent variables representing child's behavioural and sensory features and perinatal factors. Four distinct subgroups within the sample (n = 1168) distinguished by sensory and behavioural autism traits and exposure to perinatal determinants were identified. Class 2 and Class 4, which displayed the greatest behavioural and sensory impairment respectively, were associated with the highest perinatal factor exposure. Class 1, labelled "Most behavioural concerns and moderate sensory and behavioural skills concerns" had mixed exposure to perinatal determinants while Class 3, named "Least sensory and behavioural skills concerns" had the least perinatal determinant exposure, indicating a directly proportional correlation between severity of clinical features and perinatal factor exposure. Additionally, association between specific exposures such as maternal mental illness in Class 1 and significant behavioural concerns was recognised. Identifying distinct subgroups among autistic children can lead to development of targeted interventions and supports. Close monitoring of children exposed to specific perinatal determinants for developmental differences could assist early intervention and supports.

Prevalence of autism spectrum disorder in mainland china over the past 6 years: a systematic review and meta-analysis

BackgroundCoupled with its rising prevalence, Autism spectrum disorder (ASD) has become a globally recognized public health concern. Nevertheless, large-scale, multicenter studies that analyze the epidemiology of ASD in China are relatively scarce.MethodsLiterature searches were conducted in PubMed/Medline, Embase, the Cochrane Library, Wanfang Data Knowledge Service Platform, China Biology Medicine database (CBM), China Science and Technology Journal Database (CSTJ), and China National Knowledge Infrastructure (CNKI) to retrieve studies published before April 8, 2023, related to ASD prevalence among children aged 0 to 14 years in mainland China. Meta-analysis was conducted using RevMan 5.2 and Stata 14.0.ResultsTwenty-one articles were included. The ASD prevalence among children in mainland China has been 0.7% (95% confidence interval(CI): 0.006–0.008) since 2017. The prevalence of ASD among boys was 1.0% (95% CI: 0.008–0.011), which was significantly higher than that among girls at 0.2% (95% CI: 0.002–0.003), with a statistically significant difference (OR = 3.198, 95% CI: 2.489–4.109, P = 0.000). Among the included studies, 18 reported an ASD prevalence of 0.8% (95% CI: 0.007–0.010), while 3 studies reported an autistic disorder (AD) prevalence of 0.7% (95% CI: 0.006–0.008). The prevalence of autism among urban children was 23.9% (95% CI: 0.149–0.328), and in rural areas, it was 0.7% (95% CI: 0.002–0.013), with no statistically significant difference (OR = 1.342, 95% CI: 0.258–6.975, P = 0.727). Regression analysis showed that factors such as region (P = 0.000), age (P = 0.000), study period (P = 0.000), sample size (P = 0.000), sampling method (P = 0.002), population source (P = 0.000), disease type (P = 0.000), quality score of the study (P = 0.000), and diagnostic criteria (P = 0.000) might have contributed to the heterogeneity in ASD prevalence.ConclusionThe prevalence of ASD in China from 2017 to 2023 was 7/1000, showing an upward trend compared to that before 2017 (26.50/10,000). The male-to-female prevalence ratio was 5:1.The overall prevalence remained significantly lower than that reported in foreign countries.

Dissecting the polygenic contribution of attention-deficit/hyperactivity disorder and autism spectrum disorder on school performance by their relationship with educational attainment

Attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorders (ASD) are strongly associated with educational attainment (EA), but little is known about their genetic relationship with school performance and whether these links are explained, in part, by the genetic liability of EA. Here, we aim to dissect the polygenic contribution of ADHD and ASD to school performance, early manifestation of psychopathology and other psychiatric disorders and related traits by their relationship with EA. To do so, we tested the association of polygenic scores for EA, ADHD and ASD with school performance, assessed whether the contribution of the genetic liability of ADHD and ASD to school performance is influenced by the genetic liability of EA, and evaluated the role of EA in the genetic overlap between ADHD and ASD with early manifestation of psychopathology and other psychiatric disorders and related traits in a sample of 4,278 school-age children. The genetic liability for ADHD and ASD dissected by their relationship with EA show differences in their association with school performance and early manifestation of psychopathology, partly mediated by ADHD and ASD symptoms. Genetic variation with concordant effects in ASD and EA contributes to better school performance, while the genetic variation with discordant effects in ADHD or ASD and EA is associated with poor school performance and higher rates of emotional and behavioral problems. Our results strongly support the usage of the genetic load for EA to dissect the genetic and phenotypic heterogeneity of ADHD and ASD, which could help to fill the gap of knowledge of mechanisms underlying educational outcomes.

GABA and Executive functions in ASD

Gamma-aminobutyric acid (GABA) is a neurotransmitter critically involved in various psychological and behavioral processes. This review highlights the impact of GABAergic dysfunction within specific brain regions on a range of mental disorders, executive processes, self-control, and behavioral regulation.Reductions in GABAergic neurotransmission within distinct brain regions have been consistently associated with several mental health conditions. Within the context of anxiety disorders, depression, and post-traumatic stress disorder (PTSD), alterations in GABAergic function contribute to symptomatology. GABA plays a pivotal role in anxiety and mood regulation, with its impairment linked to symptoms of depression and mood disorders. Dysfunctional GABAergic transmission also contributes to fear dysregulation and features of PTSD. In terms of executive functions, GABAergic neurotransmission affects working memory, attentional control, and inhibitory regulation. Changes in GABA levels have been linked to decision-making abilities, impulsivity, and attention deficits, especially in conditions like attention deficit hyperactivity disorder (ADHD). GABA further plays a crucial role in self-control mechanisms by regulating impulsivity and prefrontal cortex functionality. Substance use disorders, often accompanied by impaired self-control, are significantly influenced by GABAergic system changes. Additionally, GABA's involvement in anxiety and emotional management affects the control of emotional responses. Behavioral control is modulated by GABAergic action in motor circuitry, where GABA provides inhibitory control for motor actions. Spontaneity, aggression, and stress are influenced by GABAergic dysfunction, impacting behavioral control. In the realm of attentional control, GABAergic neurotransmission influences selective attention and sensory salience, maintaining a balance between neural stimulation and inhibition. The study also explores executive function deficits in individuals with Autism Spectrum Disorder (ASD) in relation to GABA levels within specific brain regions. Moreover, GABA and its network connectivity contribute to individual variations in sensory responsiveness, emphasizing the dynamic role of GABA in the phenotypic heterogeneity of ASD. In summary, this research underscores the critical role of GABAergic neurotransmission within specific brain regions in various psychological disorders, executive functions, self-control, behavioral regulation, and attentional processes. Understanding the directional influence of GABAergic changes on behavior and mental health conditions can pave the way for more targeted interventions in neuropsychiatric disorders.

Unraveling the immunogenetic landscape of autism spectrum disorder: a comprehensive bioinformatics approach.

Autism spectrum disorder (ASD) is a disease characterized by social disorder. Recently, the population affected by ASD has gradually increased around the world. There are great difficulties in diagnosis and treatment at present. The ASD datasets were obtained from the Gene Expression Omnibus database and the immune-relevant genes were downloaded from a previously published compilation. Subsequently, we used WGCNA to screen the modules related to the ASD and immune. We also choose the best combination and screen out the core genes from Consensus Machine Learning Driven Signatures (CMLS). Subsequently, we evaluated the genetic correlation between immune cells and ASD used GNOVA. And pleiotropic regions identified by PLACO and CPASSOC between ASD and immune cells. FUMA was used to identify pleiotropic regions, and expression trait loci (EQTL) analysis was used to determine their expression in different tissues and cells. Finally, we use qPCR to detect the gene expression level of the core gene. We found a close relationship between neutrophils and ASD, and subsequently, CMLS identified a total of 47 potential candidate genes. Secondly, GNOVA showed a significant genetic correlation between neutrophils and ASD, and PLACO and CPASSOC identified a total of 14 pleiotropic regions. We annotated the 14 regions mentioned above and identified a total of 6 potential candidate genes. Through EQTL, we found that the CFLAR gene has a specific expression pattern in neutrophils, suggesting that it may serve as a potential biomarker for ASD and is closely related to its pathogenesis. In conclusion, our study yields unprecedented insights into the molecular and genetic heterogeneity of ASD through a comprehensive bioinformatics analysis. These valuable findings hold significant implications for tailoring personalized ASD therapies.

The relative usefulness of the identification and analysis of biomarkers for the diagnosis of autism spectrum disorders in early childhood and the implementation of personalized precision medicine

Despite progress towards understanding the etiological mechanisms of autism spectrum disorder (ASD), efficient treatment strategy for the primary clinically presented autistic features remain elusive. Up to the heterogeneity and complex nature of ASD, clinical presentation differ in severity and usually accompanied with multiple comorbidities, including gastrointestinal (GI) problems, sleep disturbances, epilepsy, and attention deficit hyperactivity disorder (ADHD). Discovery of biomarkers of ASD is essential not only for clarifying the clinical features of this disorder but also as an primary diagnostic implement that could help to tailor early interventions. Therefore, this review describes the core areas of ASD biomarker research, including GABAergic/glutamatergic imbalance, mitochondrial dysfunction, hyperserotonemia, and impaired gut microbiota, all of which have demonstrated success in diagnosing ASD and could serve as targets for implementing personalized precision medicine. Additionally, this review includes accomplishment that focus on the importance of precision medicine and the current trials that make use of ASD biomarkers detection.

Pathogenic gating pore current conducted by autism-related mutations in the NaV1.2 brain sodium channel

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by social and communication deficits and repetitive behaviors. The genetic heterogeneity of ASD presents a challenge to the development of an effective treatment targeting the underlying molecular defects. ASD gating charge mutations in the KCNQ/KV7 potassium channel cause gating pore currents (Igp) and impair action potential (AP) firing of dopaminergic neurons in brain slices. Here, we investigated ASD gating charge mutations of the voltage-gated SCN2A/NaV1.2 brain sodium channel, which ranked high among the ion channel genes with mutations in individuals with ASD. Our results show that ASD mutations in the gating charges R2 in Domain-II (R853Q), and R1 (R1626Q) and R2 (R1629H) in Domain-IV of NaV1.2 caused Igp in the resting state of ~0.1% of the amplitude of central pore current. The R1626Q mutant also caused significant changes in the voltage dependence of fast inactivation, and the R1629H mutant conducted proton-selective Igp. These potentially pathogenic Igp were exacerbated by the absence of the extracellular Mg2+ and Ca2+. In silico simulation of the effects of these mutations in a conductance-based single-compartment cortical neuron model suggests that the inward Igp reduces the time to peak for the first AP in a train, increases AP rates during a train of stimuli, and reduces the interstimulus interval between consecutive APs, consistent with increased neural excitability and altered input/output relationships. Understanding this common pathophysiological mechanism among different voltage-gated ion channels at the circuit level will give insights into the underlying mechanisms of ASD.

Autism spectrum disorder: pathogenesis, biomarker, and intervention therapy.

Autism spectrum disorder (ASD) has become a common neurodevelopmental disorder. The heterogeneity of ASD poses great challenges for its research and clinical translation. On the basis of reviewing the heterogeneity of ASD, this review systematically summarized the current status and progress of pathogenesis, diagnostic markers, and interventions for ASD. We provided an overview of the ASD molecular mechanisms identified by multi-omics studies and convergent mechanism in different genetic backgrounds. The comorbidities, mechanisms associated with important physiological and metabolic abnormalities (i.e., inflammation, immunity, oxidative stress, and mitochondrial dysfunction), and gut microbial disorder in ASD were reviewed. The non-targeted omics and targeting studies of diagnostic markers for ASD were also reviewed. Moreover, we summarized the progress and methods of behavioral and educational interventions, intervention methods related to technological devices, and research on medical interventions and potential drug targets. This review highlighted the application of high-throughput omics methods in ASD research and emphasized the importance of seeking homogeneity from heterogeneity and exploring the convergence of disease mechanisms, biomarkers, and intervention approaches, and proposes that taking into account individuality and commonality may be the key to achieve accurate diagnosis and treatment of ASD.

From synaptic dysfunction to atypical emotional processing in autism.

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition mainly characterized by social impairments and repetitive behaviors. Among these core symptoms, a notable aspect of ASD is the presence of emotional complexities, including high rates of anxiety disorders. The inherent heterogeneity of ASD poses a unique challenge in understanding its etiological origins, yet the utilization of diverse animal models replicating ASD traits has enabled researchers to dissect the intricate relationship between autism and atypical emotional processing. In this review, we delve into the general findings about the neural circuits underpinning one of the most extensively researched and evolutionarily conserved emotional states: fear and anxiety. Additionally, we explore how distinct ASD animal models exhibit various anxiety phenotypes, making them a crucial tool for dissecting ASD's multifaceted nature. Overall, to a proper display of fear response, it is crucial to properly process and integrate sensorial and visceral cues to the fear-induced stimuli. ASD individuals exhibit altered sensory processing, possibly contributing to the emergence of atypical phobias, a prevailing anxiety disorder manifested in this population. Moreover, these individuals display distinctive alterations in a pivotal fear and anxiety processing hub, the amygdala. By examining the neurobiological mechanisms underlying fear and anxiety regulation, we can gain insights into the factors contributing to the distinctive emotional profile observed in individuals with ASD. Such insights hold the potential to pave the way for more targeted interventions and therapies that address the emotional challenges faced by individuals within the autism spectrum.

Symptom dimensions of resting-state electroencephalographic functional connectivity in autism.

Autism spectrum disorder (ASD) is a common neurodevelopmental disorder characterized by social and communication deficits (SCDs), restricted and repetitive behaviors (RRBs) and fixated interests. Despite its prevalence, development of effective therapy for ASD is hindered by its symptomatic and neurophysiological heterogeneities. To comprehensively explore these heterogeneities, we developed a new analytical framework combining contrastive learning and sparse canonical correlation analysis that identifies symptom-linked resting-state electroencephalographic connectivity dimensions within 392 ASD samples. We present two dimensions with multivariate connectivity basis exhibiting significant correlations with SCD and RRB, confirm their robustness through cross-validation and demonstrate their conceptual generalizability using an independent dataset (n = 222). Specifically, the right inferior parietal lobe is the core region for RRB, while connectivity between the left angular gyrus and the right middle temporal gyrus show key contribution to SCD. These findings provide a promising avenue to parse ASD heterogeneity with high clinical translatability, paving the way for ASD treatment development and precision medicine.

Specificity in the commonalities of inhibition control: using meta-analysis and regression analysis to identify the key brain regions in psychiatric disorders.

The differential diagnosis of psychiatric disorders is relatively challenging for several reasons. In this context, we believe that task-based magnetic resonance imaging (MRI) can serve as a tool for differential diagnosis. The aim of this study was to explore the commonalities in brain activities among individuals with psychiatric disorders and to identify the key brain regions that can distinguish between these disorders. The PubMed, MEDLINE, EMBASE, Web of Science, Scopus, PsycINFO, and Google Scholar databases were searched for whole-brain functional MRI studies that compared psychiatric patients and normal controls. The psychiatric disorders included schizophrenia (SCZ), bipolar disorder (BD), major depressive disorder (MDD), obsessive-compulsive disorder, attention-deficit/hyperactivity disorder (ADHD), and autism spectrum disorder (ASD). Studies using go-nogo paradigms were selected, we then conducted activation likelihood estimation (ALE) meta-analysis, factor analysis, and regression analysis on these studies subsequently. A total of 152 studies (108 with patients) were selected and a consistent pattern was found, that is, decreased activities in the same brain regions across six disorders. Factor analysis clustered six disorders into three pairs: SCZ and ASD, MDD and BD, and ADHD and BD. Furthermore, the heterogeneity of SCZ and ASD was located in the left and right thalamus; and the heterogeneity of MDD and BD was located in the thalamus, insula, and superior frontal gyrus. The results can lead to a new classification method for psychiatric disorders, benefit the differential diagnosis at an early stage, and help to understand the biobasis of psychiatric disorders.

Examination of AI Algorithms for Image and MRI-based Autism Detection

Precise identification of autism spectrum disorder (ASD) is a challenging task due to the heterogeneity of ASD. Early diagnosis and interventions have positive effects on treatment and later skills development. Hence, it is necessary to provide families and communities with the resources, training, and tools required to diagnose and help patients. Recent work has shown that artificial intelligence-based methods are suitable for the identification of ASD. AI-based tools can be good resources for parents for early detection of ASD in their kids. Even AI-based advanced tools are helpful for health workers and physicians to detect ASD. Facial images and MRI are the best sources to understand ASD symptoms, hence are input required in AI-based model training. The trained models are used for the classification of ASD patients and normal kids. The deep learning models are found to be very accurate in ASD detection. In this paper, we present a comprehensive study of AI techniques like machine learning, image processing, and deep learning, and their accuracy when these techniques are used on facial and MRI images of ASD and normally developed kids.