Risk Factors For Autism Spectrum Disorder Research Articles

Inborn errors of immunity present with neuropsychiatric symptoms overlapping with autistic behavioral symptoms

Autism spectrum disorder (ASD) is a behaviorally defined syndrome affected by multiple genetic and environmental factors. A wide variety of risk factors for ASD have been identified and many of these affect immune functions. This may not be surprising, since the immune system and the nervous system share common signaling mechanisms and affect each other as a part of the neuroimmune network. The ever-expanding scope of inborn errors of immunity (IEIs) has revealed multiple pathogenic gene variants that manifest overlapping clinical features of common neuropsychiatric diseases, including ASD. These IEIs often cause dysregulated immune activation and resultant chronic inflammation affecting multiple organs. Some IEIs also cause changes in morphogenesis and plasticity of the central nervous system. Such patients often present with a puzzling array of clinical features and some of them may be diagnosed with ASD or other neuropsychiatric conditions. The progress of our understanding of disease mechanisms for IEIs at the molecular levels has led to gene-specific treatment measures in some diseases. In addition, some ASD patients are found to have laboratory findings of neuroinflammation that resemble those seen in IEI patients. This may pave the way for applying specific treatment measures used for IEI patients in such ASD patients. This review focuses on describing IEIs that have overlapping features of ASD. Emphasis is also on IEIs that can be treated by targeting identified disease mechanisms. Such information may be helpful for clinicians who are considering genetic/metabolic workup in ASD patients.

Prenatal caffeine exposure induces autism-like behaviors in offspring under a high-fat diet via the gut microbiota-IL-17A-brain axis

Prenatal caffeine exposure (PCE) is a significant contributor to intrauterine growth retardation (IUGR) in offspring, which has been linked to an increased susceptibility to autism spectrum disorder (ASD) later in life. Additionally, a high-fat diet (HFD) has been shown to exacerbate ASD-like behaviors, but the underlying mechanisms remain unclear. In this study, we first noted in the rat model of IUGR induced by PCE that male PCE offspring exhibited typical ASD-like behaviors post-birth, in contrast to their female counterparts. The female PCE offspring demonstrated only reduced abilities in free exploration and spatial memory. Importantly, both male and female PCE offspring displayed ASD-like behaviors when exposed to HFD. We further observed that PCE + HFD offspring exhibited damaged intestinal mucus barriers and disturbed gut microbiota, resulting in an increased abundance of Escherichia coli (E. coli). The induced differentiation of colonic Th17 cells by E. coli led to an increased secretion of IL-17A, which entered the hippocampus through peripheral circulation and caused synaptic damage in hippocampal neurons, ultimately resulting in ASD development. Our strain transplantation experiment suggested that E. coli-mediated increase of IL-17A may be the core mechanism of ASD with a fetal origin. In conclusion, PCE and HFD are potential risk factors for ASD, and E. coli-mediated IL-17A may play a crucial role in fetal-originated ASD through the gut-brain axis.

Study of Machine Learning Models to Detect Autism Spectrum Disorder

ASD is a spectrum disorder, some people with ASD may have mild symptoms and can live independently, while others may need more support and assistance in their daily lives. Some of the risk factors for ASD include having an older parent, being born prematurely, having a sibling with ASD, or being exposed to certain infections or toxins during pregnancy some of the common types of treatments include behavioural therapy, occupational therapy, physical therapy, and passionate about their interests. Such as the Modified Checklist for Autism in Toddlers, observational tools, such as the Autism Diagnostic Observation Schedule, and developmental assessments, such as the Mullen Scales of Early Learning. This study refers to some Machine Learning (ML) based applications that are able to detect Autism among individuals. This comprehensive review explores the innovative integration of machine learning (ML) models in the detection and diagnosis of Autism Spectrum Disorder (ASD). It begins by highlighting the complexities and diagnostic challenges of ASD, noting the limitations of traditional assessment methods. The review then delves into the realm of artificial intelligence (AI), discussing how AI, particularly ML and deep learning techniques, are revolutionizing the approach to ASD detection. It covers various ML models, including supervised, unsupervised, and reinforcement learning, and their application using behavioural, genetic, and neuroimaging data. A significant focus is given to the use of Logistic Regression and Hybrid Autism Screening Models in predicting ASD. The review also examines the efficacy and performance of supervised and deep learning models in ASD detection, evaluating their accuracy and precision. By providing a detailed analysis of the current state of AI in healthcare, specifically for ASD, this review underscores the potential of ML models in offering more accurate, accessible, and efficient diagnosis methods for ASD

Maternal immune activation during pregnancy is associated with more difficulties in socio-adaptive behaviors in autism spectrum disorder

Autism spectrum disorder (ASD) are neurodevelopmental conditions characterised by deficits in social communication and interaction and repetitive behaviours. Maternal immune activation (MIA) during the mid-pregnancy is a known risk factor for ASD. Although reported in 15% of affected individuals, little is known about the specificity of their clinical profiles. Adaptive skills represent a holistic approach to a person's competencies and reflect specifically in ASD, their strengths and difficulties. In this study, we hypothesised that ASD individual with a history of MIA (MIA+) could be more severely socio-adaptively impaired than those without MIA during pregnancy (MIA-). To answer this question, we considered two independent cohorts of individuals with ASD (PARIS study and FACE ASD) screened for pregnancy history, and used supervised and unsupervised machine learning algorithms. We included 295 mother–child dyads with 14% of them with MIA+. We found that ASD-MIA+ individuals displayed more severe maladaptive behaviors, specifically in their socialization abilities. MIA+ directly influenced individual's socio-adaptive skills, independent of other covariates, including ASD severity. Interestingly, MIA+ affect persistently the socio-adaptive behavioral trajectories of individuals with ASD. The current study has a retrospective design with possible recall bias regarding the MIA event and, even if pooled from two cohorts, has a relatively small population. In addition, we were limited by the number of covariables available potentially impacted socio-adaptive behaviors. Larger prospective study with additional dimensions related to ASD is needed to confirm our results. Specific pathophysiological pathways may explain these clinical peculiarities of ASD- MIA+ individuals, and may open the way to new perspectives in deciphering the phenotypic complexity of ASD and for the development of specific immunomodulatory strategies.

B - 10 The Effect of Gestational Age on Development of Autism Spectrum Disorder and Attention-Deficit/Hyperactivity Disorder.

Studies show individuals with comorbid neurodevelopmental disorders of autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) have poorer emotional, behavioral, and cognitive outcomes. While preterm birth (< 37weeks) is an identified risk factor for ASD and ADHD, it is important to consider gestational age (ga) as it relates to the development of comorbid ASD/ADHD (ASD/ADHD). The current study aims to understand the relationship between level of prematurity (LoP) and neurodevelopmental disorders. Participants included 10,711 children/adolescents (52.5% male) from the Environmental Influences on Child Health Outcomes (ECHO) Program. LoP was categorized as Extremely Preterm (36weeks). Chi-square analysis was conducted to examine the relationship between LoP and neurodevelopmental disorder diagnoses as indicated in medical records. Logistic regression was also conducted to analyze the relationship between ga (weeks) and neurodevelopmental disorder diagnosis. 1521 individuals were diagnosed with ASD (n = 537), ADHD (n = 778), or ASD/ADHD (n = 206); ga ranged from 22 to 43weeks. Results indicated the relationship between LoP and diagnoses was significant, ꭓ2(9, 10,771) = 148.31, p < 0.001. Extremely Preterm individuals were more likely than Full-term individuals to have ASD, ADHD, or ASD/ADHD. It was found that the odds of having a neurodevelopmental disorder decreased by 5.8% per 1-week ga increase (95% CI[-0.069, -0.048]). Results indicate gestational age is associated with greater risk of neurodevelopmental disorders, including comorbid ASD/ADHD. Improved understanding of the association between level of prematurity and comorbid ASD/ADHD diagnoses may help inform early intervention services enhancing children's outcomes throughout the lifespan.

23 Association between Autism Spectrum Disorder and Parental Immigration among a Cohort of Preschool Children in Manitoba

Abstract Background Recent data indicate that rates of autism spectrum disorder (ASD) are increasing and therefore, studies exploring risk factors for ASD that can further support early diagnosis and intervention are needed. A link between immigration and autism has been made by several international studies, but despite high rates of immigration, a Canadian study examining this association has not been conducted. Objectives To determine the proportion of children diagnosed with ASD at Manitoba’s primary autism referral site (the only publicly funded site for ASD evaluation of children &amp;lt;6 years of age) that were born to immigrant parents, and compare it with the known proportion of immigrants in Manitoba. Also, to compare demographic and clinical characteristics of children whose parents are immigrants with those whose parents were born in Canada. Design/Methods We conducted a retrospective chart review of &amp;gt;2000 electronic records that identified all children &amp;lt;6 years diagnosed with ASD at the referral site between May 2016 and September 2021. Data on parental immigration, demographics, diagnostic evaluation, and medical history was collected. Descriptive statistics were used to compare the relative proportion of children with ASD from immigrant parents with the proportion of immigrants in Manitoba based on 2016 census data. Results A total of 1865 children were diagnosed with ASD during the study period. The mean age at referral and diagnosis was 2.81 (SD 1.26) years and 3.88 (SD 2.90) years respectively; 78% were male and 13.5% were born preterm. The proportion of children with ASD from immigrant parents (36%) was greater than the expected proportion (18.3%) of immigrants based on Manitoba census data (p &amp;lt; 0.001). The most common countries of origin of immigration were the Philippines (30.7%), India (11.6%), Nigeria (8.6%), and Ethiopia (6.4%). Overall, children of immigrants were younger at referral (2.71 years, SD = 0.98 versus 2.91 years, SD = 1.43; p &amp;lt;0.001), less likely to have a family history of ASD (17.1% versus 35.6%; p = &amp;lt;0.001) and less likely to have comorbidities (46.8% versus 53.9%; p &amp;lt;0.001) including global developmental delay (18% versus 27.6%; p &amp;lt;0.001). Conclusion At Manitoba’s primary autism referral site, there was a greater proportion of children diagnosed with ASD born to immigrant parents than expected when compared to the Manitoba population. Overall, children of immigrants were younger, less likely to have comorbidities, developmental delay, and a family history of ASD. Future prospective studies are required to better understand the complex relationship between immigration and ASD diagnosis.

Bidirectional genetic overlap between autism spectrum disorder and cognitive traits

Autism spectrum disorder (ASD) is a highly heritable condition with a large variation in cognitive function. Here we investigated the shared genetic architecture between cognitive traits (intelligence (INT) and educational attainment (EDU)), and risk loci jointly associated with ASD and the cognitive traits. We analyzed data from genome-wide association studies (GWAS) of INT (n = 269,867), EDU (n = 766,345) and ASD (cases n = 18,381, controls n = 27,969). We used the bivariate causal mixture model (MiXeR) to estimate the total number of shared genetic variants, local analysis of co-variant annotation (LAVA) to estimate local genetic correlations, conditional false discovery rate (cond/conjFDR) to identify specific overlapping loci. The MiXeR analyses showed that 12.7k genetic variants are associated with ASD, of which 12.0k variants are shared with EDU, and 11.1k are shared with INT with both positive and negative relationships within overlapping variants. The majority (59–68%) of estimated shared loci have concordant effect directions, with a positive, albeit modest, genetic correlation between ASD and EDU (rg = 0.21, p = 2e−13) and INT (rg = 0.22, p = 4e−12). We discovered 43 loci jointly associated with ASD and cognitive traits (conjFDR<0.05), of which 27 were novel for ASD. Functional analysis revealed significant differential expression of candidate genes in the cerebellum and frontal cortex. To conclude, we quantified the genetic architecture shared between ASD and cognitive traits, demonstrated mixed effect directions, and identified the associated genetic loci and molecular pathways. The findings suggest that common genetic risk factors for ASD can underlie both better and worse cognitive functioning across the ASD spectrum, with different underlying biology.

Prenatal and postnatal exposure to heavy metals in PM2.5 and autism spectrum disorder

Autism spectrum disorder (ASD) is a group of neurodevelopmental disorders, and its incidence is increasing over time. Although several environmental factors have been suspected to be risk factors for ASD, studies on the effects of airborne heavy metals on newly developed ASD are still limited. We conducted a large birth cohort study of 168,062 live term births in Taichung during 2004–2011 to assess the association of heavy metals in particulate matter with an aerodynamic diameter less than 2.5 μm (PM2.5) with ASD, and identify sensitive time windows during prenatal and postnatal periods. Heavy metals, including arsenic (As), cadmium (Cd), mercury (Hg), and lead (Pb) in PM2.5, were estimated using the Weather Research and Forecasting/Chem (WRF/Chem), inserted from the top 75 emission sources for the module. The association between childhood ASD and 4 metals were analyzed from pregnancy to 9 months after birth. The Cox proportional hazard model with a distributed lag nonlinear model (DLNM) was used to estimate the association between heavy metals in PM2.5 and ASD. We identified 666 incident ASD cases in 168,062 participants. A positive association between Hg and ASD was found at 9 months after birth (Hazard Ratio: 1.63; 95% CI: 1.13–2.36). According to the DLNM, there was an increased risk of exposure to Hg during 10–25 weeks after birth, and decreased risk of exposure to Hg during gestational weeks 4–6. Exposure to As and Hg on the risk of ASD were significantly stronger in low birth weight infants (<2500 g) than in those of birth weight ≥2500 g during postnatal period. Postnatal exposure to Hg in PM2.5 may associate with increased ASD incidence. Infants with low birth weight and exposure to As and Hg in PM2.5 are more likely to develop ASD.

Association between maternal gestational diabetes mellitus and the risk of autism spectrum disorder in offspring

To explore the association between maternal gestational diabetes mellitus (GDM) exposure and the development of autism spectrum disorder (ASD) in offspring. A case-control study was conducted, recruiting 221 children with ASD and 400 healthy children as controls. Questionnaires and interviews were used to collect information on general characteristics of the children, socio-economic characteristics of the family, maternal pregnancy history, and maternal disease exposure during pregnancy. Multivariate logistic regression analysis was used to investigate the association between maternal GDM exposure and the development of ASD in offspring. The potential interaction between offspring gender and maternal GDM exposure on the development of ASD in offspring was explored. The proportion of maternal GDM was significantly higher in the ASD group compared to the control group (16.3% vs 9.4%, P=0.014). After adjusting for variables such as gender, gestational age, mode of delivery, parity, and maternal education level, maternal GDM exposure was a risk factor for ASD in offspring (OR=2.18, 95%CI: 1.04-4.54, P=0.038). On the basis of adjusting the above variables, after further adjusting the variables including prenatal intake of multivitamins, folic acid intake in the first three months of pregnancy, and assisted reproduction the result trend did not change, but no statistical significance was observed (OR=1.94, 95%CI: 0.74-5.11, P=0.183). There was an interaction between maternal GDM exposure and offspring gender on the development of ASD in offspring (P<0.001). Gender stratified analysis showed that only in male offspring of mothers with GDM, the risk of ASD was significantly increased (OR=3.67, 95%CI: 1.16-11.65, P=0.027). Maternal GDM exposure might increase the risk of ASD in offspring. There is an interaction between GDM exposure and offspring gender in the development of ASD in offspring.

Socioeconomic and demographic risk factors of autism spectrum disorder among children and adolescents in Bangladesh: Evidence from a cross-sectional study in 2022.

Autism spectrum disorder (ASD) is the assorted uneven conditions of the human brain that lead to developmental disabilities. This cross-sectional study aimed to identify the substantial risk factors of ASD among children in Bangladesh. The data were collected using convenience sampling through a questionnaire filled up by the trained interviewers. Mann-Whitney U and Kruskal-Wallis H tests were applied as bivariate analysis, and generalized beta regression was performed to determine the significant risk factors of autism spectrum disorder. The odds ratio (OR) along with 95% confidence interval (CI) were the measuring parameters of the risk factors of ASD. The result revealed that later birth order children have more risk of ASD (OR = 1.13, CI: 1.014-1.264, p = 0.027) compared to the children whose birth order is first. Premature birth of the child (OR: 0.87, CI: 0.76-1.00, p = 0.05) and father's age (OR: 0.86, CI: 0.76-0.97, p = 0.020) substantially affects ASD. The maternal history of specific illness (diabetes, thyroiditis, and hypertension) during pregnancy also significantly affect ASD (OR: 1.34, CI: 1.14-1.61, p = 0.002). The results of this study would assist policymakers in taking necessary steps to reduce the incidence of this disorder by targeting the potential risk factors.

Stem-Skilled Parents and Autism Spectrum Disorder in Offspring: A Case-Control Study

Autism spectrum disorder (ASD) is a neurodevelopment disorder characterised by a range of deficits in two specific domains: social communication and social interaction and repetitive patterns of behaviour. Several studies have explored the link between ASD and STEM (science, technology, engineering and mathematics, or other mathematics-grounded disciplines), but results are still uncertain. Objective of the study was to estimate the potential role of systemising abilities in parents as a risk factor for ASD in the offspring, using the achievement of a degree in STEM disciplines as a proxy characteristic of the exposure. There were 1,316 participants overall. There were 658 incident consecutive cases of definite ASD, diagnosed in a Reference Centre for ASD in Italy, from 2001 to 2020. The main exposure variable was parental education level. The risk of ASD in the offspring associated with the main exposure variable and the exposure covariates (e.g. use of neurotropic drugs during the first trimester of the mother’s pregnancy, perinatal outcomes of participants and/or preterm birth) was studied by using conditional logistic regression analysis. In addition, we carried out a mediation analysis to investigate whether and the extent to which covariates significantly associated with ASD risk mediate the relationship between parental education level and ASD in offspring. A STEM degree in parents was significantly associated with risk of ASD in offspring (OR 1.43, 95% CI 1.03-2.54). Familiarity was weakly associated with the risk of ASD (OR 1.33, 95% CI 1.00-1.66) and is the stronger mediator (PME 28%). Sensitivity analysis did not show deviations related to gender or ASD level. Our study moves in the direction of confirming the risk of occurrence of ASD in the offspring of parents with elevated systemising abilities.

A Comparative Analysis of the Full and Short Versions of the Social Responsiveness Scale in Estimating an Established Autism Risk Factor Association in ECHO: Do we Get the Same Estimates?

Prior work developed a shortened 16-item version of the Social Responsiveness Scale (SRS), a quantitative measure of social communication and autism spectrum disorder (ASD)-related traits. However, its properties for use in risk factor estimation have not been fully tested compared to the full SRS. We compared the associations between gestational age (previously established risk factor for ASD) and the 65-item "full" and 16-item "short" versions of the SRS to test the shortened version's ability to capture associations in epidemiologic analyses of ASD risk factors. We used data from participants in the Environmental influences on Child Health Outcomes (ECHO) Program (n = 2,760). SRS scores were collected via maternal/caregiver report when children were aged 2.5-18 years. We compared estimates of associations between gestational age and preterm birth between the full and short SRS using multivariable linear regression, quantile regression, and prediction methods. Overall, associations based on full and short SRS scores were highly comparable. For example, we observed positive associations between preterm birth with both full ([Formula: see text]=2.8; 95% CI [1.7, 4.0]) and short ([Formula: see text]=2.9; 95% CI [1.6, 4.3]) SRS scores. Quantile regression analyses indicated similar direction and magnitude of associations across the distribution of SRS scores between gestational age with both short and full SRS scores. The comparability in estimates obtained for full and short SRS scores with an "established" ASD risk factor suggests ability of the shortened SRS in assessing associations with potential ASD-related risk factors and has implications for large-scale research studies seeking to reduce participant burden.

Autism Spectrum Disorder in Children with an Early History of Paediatric Acquired Brain Injury.

Autism spectrum disorder (ASD) is a neurodevelopmental condition that arises from a combination of both genetic and environmental risk factors. There is a lack of research investigating whether early acquired brain injury (ABI) may be a risk factor for ASD. The current study comprehensively reviewed all hospital records at The Brain Injury Service, Kids Rehab at the Children's Hospital at Westmead (Australia) from January 2000 to January 2020. Of the approximately 528 cases, 14 children with paediatric ABI were subsequently given an ASD diagnosis (2.7%). For this ASD sample, the mean age at the time of the ABI was 1.55 years, indicating a high prevalence of early ABI in this diagnostic group. The mean age of ASD diagnosis was, on average, 5 years later than the average ASD diagnosis in the general population. Furthermore, 100% of children had at least one medical comorbidity and 73% had three or more co-occurring DSM-5 diagnoses. Although based on a small data set, results highlight early paediatric ABI as a potential risk factor for ASD and the potential for a delayed ASD diagnosis following early ABI, with comorbidities possibly masking symptoms. This study was limited by its exploratory case series design and small sample size. Nonetheless, this study highlights the need for longitudinal investigation into the efficacy of early screening for ASD symptomatology in children who have sustained an early ABI to maximise potential intervention.

Perinatal Risk Factors in Children Diagnosed with Autism Spectrum Disorder in Dubai: A Case-Control Study

Introduction: Autism spectrum disorder (ASD) is a heterogeneous disorder characterized by deficits in social-emotional reciprocity and associated behavioral symptoms. The goal of this study was to examine perinatal risk factors for ASD and their correlates in a tertiary care hospital in Dubai, United Arab Emirates (UAE). Methods: We conducted a case-control study of 87 children diagnosed with ASD using the DSM-5 criteria, who were born between 1999 and 2014, and diagnosed between 2014 and 2016, and 134 age- and gender-matched control subjects. Data were collected from hospital medical records. Associations between ASD and maternal, birth, and neonatal risk factors were examined using logistic regression analysis. Results: Maternal fever was associated with ASD (p = 0.018), with 0% in the control group and 5% in the cases. Multivariate logistic regression analysis revealed a trend for an association between high birthweight (>4,000 g) and ASD (95% CI: 0.76–59.93, p = 0.086). Conclusion: The data affirmed the established gender difference of approximately four males to one female and supported previous findings associating ASD with prematurity. Larger representative studies are further required to explore risk factors for ASD in the UAE.

The intersection of genome, epigenome and social experience in autism spectrum disorder: Exploring modifiable pathways for intervention

The number of children diagnosed with autism spectrum disorder (ASD) has increased substantially over the past two decades. Current research suggests that both genetic and environmental risk factors are involved in the etiology of ASD. The goal of this paper is to examine how one specific environmental factor, early social experience, may be correlated with DNA methylation (DNAm) changes in genes associated with ASD. We present an innovative model which proposes that polygenic risk and changes in DNAm due to social experience may both contribute to the symptoms of ASD. Previous research on genetic and environmental factors implicated in the etiology of ASD will be reviewed, with an emphasis on the oxytocin receptor gene, which may be epigenetically altered by early social experience, and which plays a crucial role in social and cognitive development. Identifying an environmental risk factor for ASD (e.g., social experience) that could be modified via early intervention and which results in epigenetic (DNAm) changes, could transform our understanding of this condition, facilitate earlier identification of ASD, and guide early intervention efforts.

Cholinergic neurons in the basal forebrain are involved in behavioral abnormalities associated with Cul3 deficiency: Role of prefrontal cortex projections in cognitive deficits

Loss-of-function mutations of the gene Cul3 have been identified as a risk factor for autism-spectrum disorder (ASD), but the pathogenic mechanisms are not well understood. Conditional Cul3 ablation in cholinergic neurons of mice (ChatCRECul3F/+) recapitulated ASD-like social and sensory gating phenotypes and caused significant cognitive impairments, with diminished activity of cholinergic neurons in the basal forebrain (BF). Chemogenetic inhibition of BF cholinergic neurons in healthy mice induced similar social and cognitive deficits. Conversely, chemogenetic stimulation of BF cholinergic neurons in ChatCRECul3F/+ mice reversed abnormalities in sensory gating and cognition. Cortical hypofunction was also found after ChAT-specific Cul3 ablation and stimulation of cholinergic projections from the BF to the prefrontal cortex (PFC) mitigated cognitive deficits. Overall, we demonstrate that cholinergic dysfunction due to Cul3 deficiency is involved in ASD-like behavioral abnormalities, and that BF cholinergic neurons are particularly critical for cognitive component through their projections to the PFC.

Pre- and Perinatal Risk Factors for Youth with Autism Spectrum Disorder Versus Youth with Other Mental Health Disorders.

Research has linked pre- and perinatal complications (PPCs) with increased risk for autism spectrum disorder (ASD). However, PPCs are also known risk factors for other mental health disorders. This study explored which PPCs are specific risk factors for ASD, as opposed to other forms of psychopathology, among a large sample of clinically-referred youth. Archival data were used from 1177 youth who were evaluated at a hospital-based autism clinic. Results from logistic regressions indicated that use of tobacco, alcohol, or drugs, or experiencing amniocentesis predicted inclusion in the non-ASD group, while physical difficulties with delivery predicted inclusion in the ASD group. Possible explanations and implications for these findings are discussed.

A Machine Learning Framework for Early-Stage Detection of Autism Spectrum Disorders

Autism Spectrum Disorder (ASD) is a type of neurodevelopmental disorder that affects the everyday life of affected patients. Though it is considered hard to completely eradicate this disease, disease severity can be mitigated by taking early interventions. In this paper, we propose an effective framework for the evaluation of various Machine Learning (ML) techniques for the early detection of ASD. The proposed framework employs four different Feature Scaling (FS) strategies i.e., Quantile Transformer (QT), Power Transformer (PT), Normalizer, and Max Abs Scaler (MAS). Then, the feature-scaled datasets are classified through eight simple but effective ML algorithms like Ada Boost (AB), Random Forest (RF), Decision Tree (DT), K-Nearest Neighbors (KNN), Gaussian Naïve Bayes (GNB), Logistic Regression (LR), Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA). Our experiments are performed on four standard ASD datasets (Toddlers, Adolescents, Children, and Adults). Comparing the classification outcomes using various statistical evaluation measures (Accuracy, Receiver Operating Characteristic: ROC curve, F1-score, Precision, Recall, Mathews Correlation Coefficient: MCC, Kappa score, and Log loss), the best-performing classification methods, and the best FS techniques for each ASD dataset are identified. After analyzing the experimental outcomes of different classifiers on feature-scaled ASD datasets, it is found that AB predicted ASD with the highest accuracy of 99.25%, and 97.95% for Toddlers and Children, respectively and LDA predicted ASD with the highest accuracy of 97.12% and 99.03% for Adolescents and Adults datasets, respectively. These highest accuracies are achieved while scaling Toddlers and Children with normalizer FS and Adolescents and Adults with the QT FS method. Afterward, the ASD risk factors are calculated, and the most important attributes are ranked according to their importance values using four different Feature Selection Techniques (FSTs) i.e., Info Gain Attribute Evaluator (IGAE), Gain Ratio Attribute Evaluator (GRAE), Relief F Attribute Evaluator (RFAE), and Correlation Attribute Evaluator (CAE). These detailed experimental evaluations indicate that proper finetuning of the ML methods can play an essential role in predicting ASD in people of different ages. We argue that the detailed feature importance analysis in this paper will guide the decision-making of healthcare practitioners while screening ASD cases. The proposed framework has achieved promising results compared to existing approaches for the early detection of ASD.

FMR1 deletion in rats induces hyperactivity with no changes in striatal dopamine transporter availability

Autism Spectrum Disorder (ASD) is a pervasive neurodevelopmental disorder emerging in early life characterized by impairments in social interaction, poor verbal and non-verbal communication, and repetitive patterns of behaviors. Among the best-known genetic risk factors for ASD, there are mutations causing the loss of the Fragile X Messenger Ribonucleoprotein 1 (FMRP) leading to Fragile X syndrome (FXS), a common form of inherited intellectual disability and the leading monogenic cause of ASD. Being a pivotal regulator of motor activity, motivation, attention, and reward processing, dopaminergic neurotransmission has a key role in several neuropsychiatric disorders, including ASD. Fmr1Δexon 8 rats have been validated as a genetic model of ASD based on FMR1 deletion, and they are also a rat model of FXS. Here, we performed behavioral, biochemical and in vivo SPECT neuroimaging experiments to investigate whether Fmr1Δexon 8 rats display ASD-like repetitive behaviors associated with changes in striatal dopamine transporter (DAT) availability assessed through in vivo SPECT neuroimaging. At the behavioral level, Fmr1Δexon 8 rats displayed hyperactivity in the open field test in the absence of repetitive behaviors in the hole board test. However, these behavioral alterations were not associated with changes in striatal DAT availability as assessed by non-invasive in vivo SPECT and Western blot analyses.

Metal Profiles in Autism Spectrum Disorders: A Crosstalk between Toxic and Essential Metals

Since hundreds of years ago, metals have been recognized as impacting our body's physiology. As a result, they have been studied as a potential cure for many ailments as well as a cause of acute or chronic poisoning. However, the link between aberrant metal levels and neuropsychiatric illnesses such as schizophrenia and neurodevelopmental disorders, such as autism spectrum disorders (ASDs), is a relatively new finding, despite some evident ASD-related consequences of shortage or excess of specific metals. In this review, we will summarize past and current results explaining the pathomechanisms of toxic metals at the cellular and molecular levels that are still not fully understood. While toxic metals may interfere with dozens of physiological processes concurrently, we will focus on ASD-relevant activity such as inflammation/immune activation, mitochondrial malfunction, increased oxidative stress, impairment of axonal myelination, and synapse formation and function. In particular, we will highlight the competition with essential metals that may explain why both the presence of certain toxic metals and the absence of certain essential metals have emerged as risk factors for ASD. Although often investigated separately, through the agonistic and antagonistic effects of metals, a common metal imbalance may result in relation to ASD.