Autism Spectrum Disorder Group Research Articles

Alterations in resting-state gamma-activity is adults with autism spectrum disorder: A High-Density EEG study

Autism spectrum disorder (ASD) is a neurodevelopmental disorder with a wide range of symptoms that include deficits in social cognition and difficulties with social interactions. Neural oscillations in the EEG gamma band have been proposed as an important candidate neurobiological marker of higher order cognitive processes and social interactions. We investigated resting-state gamma-activity of patients with ASD (n=23) in order to delineate alterations as compared to typically developing (TD) subjects (n=24). EEG absolute power was examined in the gamma (30-100Hz) frequency band. We found significantly reduced spectral power across the entire gamma range in the ASD group. The decrease was most pronounced over the inferior-frontal and temporo-parietal junction areas. We also found a significant decrease in gamma-activity over the dorsolateral prefrontal cortex, especially in the left side. Since these brain areas have been associated with social functioning, the reduced gamma-activity in ASD may represent a cortical dysfunction that could underlie a diminished capacity to interpret socially important information, thereby interfering with social functioning. The alterations we found may lend support for an improved diagnosis. Furthermore, they can lead to focused therapies, by targeting the dysfunctional brain activity to improve social cognitive and interaction abilities that are compromised in ASD.

Functional connectivity of the posterior cingulate cortex in autism spectrum disorder

The purpose of this study was to assess the functional connectivity of the posterior cingulate cortex in autism spectrum disorder (ASD). We used resting-state functional magnetic resonance imaging (rsfMRI) brain scans of adolescents diagnosed with ASD and a neurotypical control group. The Autism Brain Imaging Data Exchange (ABIDE) consortium was utilized to acquire data from the University of Michigan (145 subjects) and data from the New York University (183 subjects). The posterior cingulate cortex showed reduced connectivity with the anterior cingulate cortex for the ASD group compared to the control group. These two brain regions have previously both been linked to ASD symptomology. Specifically, the posterior cingulate cortex has been associated with behavioral control and executive functions, which appear to be responsible for the repetitive and restricted behaviors (RRB) in ASD. Our findings support previous data indicating a neurobiological basis of the disorder, and the specific functional connectivity changes involving the posterior cingulate cortex and anterior cingulate cortex may be a potential neurobiological biomarker for the observed RRBs in ASD.

Temporal resolution relates to sensory hyperreactivity independently of stimulus detection sensitivity in individuals with autism spectrum disorder.

Researchers have been focusing on perceptual characteristics of autism spectrum disorder (ASD) in terms of sensory hyperreactivity. Previously, we demonstrated that temporal resolution, which is the accuracy to differentiate the order of two successive vibrotactile stimuli, is associated with the severity of sensory hyperreactivity. We currently examined whether an increase in the perceptual intensity of a tactile stimulus, despite its short duration, is derived from high temporal resolution and high frequency of sensory temporal summation. Twenty ASD and 22 typically developing (TD) participants conducted two psychophysical experimental tasks to evaluate detectable duration of vibrotactile stimulus with same amplitude and to evaluate temporal resolution. The sensory hyperreactivity was estimated using self-reported questionnaire. There was no relationship between the temporal resolution and the duration of detectable stimuli in both groups. However, the ASD group showed severe sensory hyperreactivity in daily life than TD group, and the ASD participants with severe sensory hyperreactivity tended to have high temporal resolution, not high sensitivity of detectable duration. Contrary to the hypothesis, there might be different processing between temporal resolution and sensitivity for stimulus detection. We suggested that the atypical temporal processing would affect to sensory reactivity in ASD.

Sleep patterns of children with Cri du Chat syndrome before and during the COVID-19 pandemic: An observational study

BackgroundSleep patterns in children with Cri du Chat syndrome (CDC) remain understudied. The pandemic and isolation disrupted routines, potentially affecting their sleep. MethodsWe compared the sleep characteristics of 53 children with CDC before and during the COVID-19-related social isolation periods with that of 106 children with autism spectrum disorder (ASD) and 160 without either condition (comparison group), using the adapted Brief Infant Sleep Questionnaire (BISQ). The variables wake after sleep onset (WASO), awakenings, environment, strategies, and sleep latency were dichotomized based on relevant outcomes. Binomial logistic models were used for binomial outcomes and the lowest Akaike Information Criteria determined total sleep time distribution. Generalized estimating equations examined pandemic effects and intergroup differences. Multinomial regressions assessed pandemic and disorder impact on child and caregiver sleep quality. ResultsChildren with CDC had higher WASO, awakenings, and poorer sleep compared with the ASD and comparison groups in the pre-pandemic assessment DiscussionThese findings emphasize the importance of addressing sleep impairment in children with CDC to improve their overall well-being, both during exceptional circumstances like the pandemic and in their daily routines. The study provides valuable insights into sleep patterns and highlights the impact of the pandemic on sleep quality in different neurodevelopmental populations.

Interpersonal Motor Coordination in Children with Autism and the Establishment of Machine Learning Models to Objectively Classify Children with Autism and Typical Development

BackgroundThe global prevalence of autism spectrum disorder (ASD) is around 1%. Yet the current diagnosis of ASD mainly depends on clinicians' experience and caregivers' report, which are subjective, time consuming, and labor demanding. An objective and efficient way to diagnose ASD is urgently needed. The objective of this study was to quantify an omnipresent yet least studied behavioral characteristic in children with ASD – interpersonal motor coordination (IMC), and to investigate the feasibility of using IMC related features to identify ASD by implementing machine learning algorithms. MethodsTwenty children with ASD and twenty-three children with typical development (TD) were filmed in a conversation with an interviewer. Motion energy analysis was implemented to obtain the movement time series, and cross wavelet analysis (CWA) quantified the level of IMC at different movement frequencies. Machine learning algorithms were utilized to examine whether these two groups of children could be accurately classified using features of IMC. ResultsStatistical analysis revealed reduced IMC in the ASD group at relatively high movement frequencies. The establishment of machine learning (ML) models showed that the maximum classification accuracy was 85.37% (specificity = 95.24%, sensitivity = 75.00%) using five original coherence values computed with CWA. In addition, the classification accuracy could be improved to 92.68% (specificity = 95.24%, sensitivity = 90.00%) with three novel features created by taking the sum of statistically significant features. ConclusionsChildren with ASD demonstrated an atypical profile of IMC, and IMC could be used to objectively classify children with ASD and TD. In addition, our analyses showed that creating novel features based on statistically significant features could help improve classification performance. It is proposed that such economic, contactless, and calibration-free approach to data collection might well serve both ASD research and practice, particularly early objective identification. However, this study could be improved with respect to larger sample size with balanced gender ratio and different severity.

The Neural Bases of Language Processing During Social and Non-Social Contexts: A fNIRS Study of Autistic and Neurotypical Preschool-Aged Children.

Little is known about how the brains of autistic children process language during real-world "social contexts," despite the fact that challenges with language, communication, and social interaction are core features of Autism Spectrum Disorder (ASD). We investigated the neural bases of language processing during social and non-social contexts in a sample of N=20 autistic and N=20 neurotypical (NT) preschool-aged children, 3 to 6 years old. Functional near-infrared spectroscopy (fNIRS) was used to measure children's brain response to "live language" spoken by a live experimenter during an in-person social context (i.e., book reading), and "recorded language" played via an audio recording during a non-social context (i.e., screen time). We examined within-group and between-group differences in the strength and localization of brain response to live language and recorded language, as well as correlations between children's brain response and language skills measured by the Preschool Language Scales. In the NT group, brain response to live language was greater than brain response to recorded language in the right temporal parietal junction (TPJ). In the ASD group, the strength of brain response did not differ between conditions. The ASD group showed greater brain response to recorded language than the NT group in the right inferior and middle frontal gyrus (IMFG). Across groups, children's language skills were negatively associated with brain response to recorded language in the right IMFG, suggesting that processing recorded language required more cognitive effort for children with lower language skills. Children's language skills were also positively associated with the difference in brain response between conditions in the right TPJ, demonstrating that children who showed a greater difference in brain response to live language versus recorded language had higher language skills. Findings should be considered preliminary until they are replicated in a larger sample. Findings suggest that the brains of NT children, but not autistic children, process language differently during social and non-social contexts. Individual differences in how the brain processes language during social and non-social contexts may help to explain why language skills are so variable across children with and without autism.

Posterior default mode network is associated with the social performance in male children with autism spectrum disorder: A dynamic causal modeling analysis based on triple-network model.

The triple-network model has been widely applied in neuropsychiatric disorders including autism spectrum disorder (ASD). However, the mechanism of causal regulations within the triple-network and their relations with symptoms of ASD remains unclear. 81 male ASD and 80 well matched typically developing control (TDC) were included in this study, recruited from Autism Brain Image Data Exchange-I datasets. Spatial reference-based independent component analysis was used to identify the anterior and posterior part of default-mode network (aDMN and pDMN), salience network (SN), and bilateral executive-control network (ECN) from resting-state functional magnetic resonance imaging data. Spectral dynamic causal model and parametric empirical Bayes with Bayesian model reduction/average were adopted to explore the effective connectivity (EC) within triple-network and the relationship between EC and autism diagnostic observation schedule (ADOS) scores. After adjusting for age and site effect, ASD and TDC groups both showed inhibition patterns. Compared with TDC, ASD group showed weaker self-inhibition in aDMN and pDMN, stronger inhibition in pDMN→aDMN, weaker inhibition in aDMN→LECN, pDMN→SN, LECN→SN, and LECN→RECN. Furthermore, negative relationships between ADOS scores and pDMN self-inhibition strength, as well as with the EC of pDMN→aDMN were observed in ASD group. The present study reveals imbalanced effective connections within triple-networks in ASD children. More attentions should be focused at the pDMN, which modulates the core symptoms of ASD and may serve as an important region for ASD diagnosis and the target region for ASD treatments.

Abnormal Development of the Corpus Callosum in Autism Spectrum Disorder: An MRI Study.

Altered size in the corpus callosum (CC) has been reported in individuals with autism spectrum disorder (ASD), but few studies have investigated younger children. Moreover, knowledge about the age-related changes in CC size in individuals with ASD is limited. Our objective was to investigate the age-related size of the CC and compare them with age-matched healthy controls between the ages of 2 and 18 years. Structural-weighted images were acquired in 97 male patients diagnosed with ASD; published data were used for the control group. The CC was segmented into 7 distinct subregions (rostrum, genu, rostral body, anterior midbody, posterior midbody, isthmus, and splenium) as per Witelson's technique using ITK-SNAP software. We calculated both the total length and volume of the CC as well as the length and height of its 7 subregions. The length of the CC measures was studied as both continuous and categorical forms. For the continuous form, Pearson's correlation was used, while categorical forms were based on age ranges reflecting brain expansion during early postnatal years. Differences in CC measures between adjacent age groups in individuals with ASD were assessed using a Student t-test. Mean and standard deviation scores were compared between ASD and control groups using the Welch t-test. Age showed a moderate positive association with the total length of the CC (r = 0.43; Padj = 0.003) among individuals with ASD. Among the subregions, a positive association was observed only in the anterior midbody of the CC (r = 0.41; Padj = 0.01). No association was found between the age and the height of individual subregions or with the total volume of the CC. In comparison with healthy controls, individuals with ASD exhibited shorter lengths and heights of the genu and splenium of the CC across wide age ranges. Overall, our results highlight a distinct abnormal developmental trajectory of CC in ASD, particularly in the genu and splenium structures, potentially reflecting underlying pathophysiological mechanisms that warrant further investigation.

EEG findings and clinical severity and quality of life in non-epileptic patients with autism spectrum disorders

ABSTRACT Electroencephalogram (EEG) abnormalities could be seen in up to 60% of non-epileptic children with autism spectrum disorder (ASD). They have been used as biomarkers of ASD severity. The objective of our study is to identify EEG abnormalities in children with different degrees of ASD severity based on the Autism Treatment Evaluation Checklist (ATEC). We also want to assess the quality of life for children with ASD. All of the children underwent at least one hour of sleep-deprived EEG. Forty-five children were enrolled, of whom 42 were male. EEG abnormalities were found in 10 (22.2%) children, predominantly in the bilateral frontal areas. There were no differences in EEG findings among the mild, moderate, and severe ASD groups. The severity of ASD was associated with female sex (p-value = 0.013), ASD with attention deficit hyperactivity disorder (ADHD) (p-value = 0.032), ASD children taking medications (p-value = 0.048), and a lower Pediatric Quality of Life Inventory (PedsQL) (p-value <0.001). Social and emotional domains were the most problematic for health-related quality of life in ASD children, according to parent reports of PedsQL. Further studies with a larger sample size will help to clarify the potential associations between EEG abnormalities and the severity of ASD, as well as the impact on quality of life.

T1w/T2w ratio maps identify children with autism spectrum disorder and the relationships between myelin-related changes and symptoms

BackgroundModern neuroimaging methods have revealed that autistic symptoms are associated with abnormalities in brain morphology, connectivity, and activity patterns. However, the changes in brain microstructure underlying the neurobiological and behavioral deficits of autism remain largely unknown. Methodswe characterized the associated abnormalities in intracortical myelination pattern by constructing cortical T1-weighted/T2-weighted ratio maps. Voxel-wise comparisons of cortical myelination were conducted between 150 children with autism spectrum disorder (ASD) and 139 typically developing (TD) children. Group differences in cortical T1-weighted/T2-weighted ratio and gray matter volume were then examined for associations with autistic symptoms. A convolutional neural network (CNN) model was also constructed to examine the utility of these regional abnormalities in cortical myelination for ASD diagnosis. ResultsCompared to TD children, the ASD group exhibited widespread reductions in cortical myelination within regions related to default mode, salience, and executive control networks such as the inferior frontal gyrus, bilateral insula, left fusiform gyrus, bilateral hippocampus, right calcarine sulcus, bilateral precentral, and left posterior cingulate gyrus. Moreover, greater myelination deficits in most of these regions were associated with more severe autistic symptoms. In addition, children with ASD exhibited reduced myelination in regions with greater gray matter volume, including left insula, left cerebellum_4_5, left posterior cingulate gyrus, and right calcarine sulcus. Notably, the CNN model based on brain regions with abnormal myelination demonstrated high diagnostic efficacy for ASD. ConclusionsOur findings suggest that microstructural abnormalities in myelination contribute to autistic symptoms and so are potentially promising therapeutic targets as well as biomarkers for ASD diagnosis.

Comparison of Serum Neurofilament Light Chain and Tau Protein Levels in Cases with Autism Spectrum Disorder and Their Healthy Siblings and Healthy Controls.

: There is a growing interest among clinicians and researchers in identifying potential biomarkers associated with autism. Neurofilament light chain (NfL) and Tau protein, which are proteins associated with neurodegeneration and neuroaxonal degeneration, are particularly promising potential biomarker candidates in this field. : In this study, we compared serum NfL (sNfL) and serum Tau (sTau) levels in Autism spectrum disorder (ASD) patients, their healthy siblings (HS), and healthy controls (HC), aimed to investigate their relationship with ASD severity. Our study included 43 ASD-diagnosed participants, 43 HS participants and 42 HC participants. Clinical characteristics of the participants were assesed by Kiddie Schedule for Affective Disorders and Schizophrenia, Childhood Autism Rating Scale, Aberrant Behavior Checklist, and Strengths and Difficulties Questionnaire. Serum samples were subjected to analysis via enzyme-linked immunosorbent assay to quantitatively measure the levels of NfL and Tau protein. : sNfL levels in the ASD group were significantly higher than both of the control groups. Regarding sTau levels, no significant difference was found between study and control groups. In addition, NfL and Tau levels were not significantly correlated with ASD symptom severity. : Our findings may indicate that the sNfl levels associated with neuroaxonal damage may constitue a potential clinical biomarker rather than being an endophenotype phenomena.

Serum BAFF, APRIL, TWEAK, TNFSF18, TNFR2, and TNFRS12A levels in preschool children with autism spectrum disorder

Objectives A gradually increasing number of studies have reported that dysregulation in the immune system may be a contributing factor in the etiopathogenesis of autism spectrum disorder (ASD). However, only limited data has existed about the role of tumor necrosis factor (TNF) superfamily members in ASD. This study aimed to investigate whether serum levels of BAFF, APRIL, TWEAK, TNFSF18 TNFR2, and TNFRS12A are associated with ASD in preschool children. Methods A total of 65 (52 boys/13 girls) preschool children with ASD and 40 (28 boys/12 girls) healthy controls were included in the study. The severity of the ASD symptoms was assessed by the Autism Behavior Checklist and the Childhood Autism Rating Scale. Serum levels of immunologic parameters were measured by enzyme-linked immunosorbent assay. Results Analysis indicated that serum BAFF, TNFSF18, TNFR2, and TNFRS12A levels were significantly lower and serum APRIL and TWEAK levels were significantly higher in the ASD group than in the control group. However, with logistic regression only low BAFF levels and high APRIL levels had relationships with the presence of ASD. Conclusions These findings suggest that TNF superfamily members, especially the BAFF/APRIL system may play a role in the pathophysiology of ASD.

An exploratory characterization of speech- and fine-motor coordination in verbal children with Autism spectrum disorder

Autism spectrum disorder (ASD) is a neurodevelopmental disorder often associated with difficulties in speech production and fine-motor tasks. Thus, there is a need to develop objective measures to assess and understand speech production and other fine-motor challenges in individuals with ASD. In addition, recent research suggests that difficulties with speech production and fine-motor tasks may contribute to language difficulties in ASD. In this paper, we explore the utility of an off-body recording platform, from which we administer a speech- and fine-motor protocol to verbal children with ASD and neurotypical controls. We utilize a correlation-based analysis technique to develop proxy measures of motor coordination from signals derived from recordings of speech- and fine-motor behaviors. Eigenvalues of the resulting correlation matrix are inputs to Gaussian Mixture Models to discriminate between highly-verbal children with ASD and neurotypical controls. These eigenvalues also characterize the complexity (underlying dimensionality) of representative signals of speech- and fine-motor movement dynamics, and form the feature basis to estimate scores on an expressive vocabulary measure. Based on a pilot dataset (15 ASD and 15 controls), features derived from an oral story reading task are used in discriminating between the two groups with AUCs > 0.80, and highlight lower complexity of coordination in children with ASD. Features derived from handwriting and maze tracing tasks led to AUCs of 0.86 and 0.91, however features derived from ocular tasks did not aid in discrimination between the ASD and neurotypical groups. In addition, features derived from free speech and sustained vowel tasks are strongly correlated with expressive vocabulary scores. These results indicate the promise of a correlation-based analysis in elucidating motor differences between individuals with ASD and neurotypical controls.

Specialization of anterior and posterior hippocampal functional connectivity differs in autism.

Structural and functional differences in the hippocampus have been related to the episodic memory and social impairments observed in autism spectrum disorder (ASD). In neurotypical individuals, hippocampal-cortical functional connectivity systematically varies between anterior and posterior hippocampus, with changes observed during typical development. It remains unknown whether this specialization of anterior-posterior hippocampal connectivity is disrupted in ASD, and whether age-related differences in this specialization exist in ASD. We examined connectivity of the anterior and posterior hippocampus in an ASD (N = 139) and non-autistic comparison group (N = 133) aged 5-21 using resting-state functional magnetic resonance imaging (MRI) data from the Healthy Brain Network (HBN). Consistent with previous results, we observed lower connectivity between the whole hippocampus and medial prefrontal cortex in ASD. Moreover, preferential connectivity of the posterior relative to the anterior hippocampus for memory-sensitive regions in posterior parietal cortex was reduced in ASD, demonstrating a weaker anterior-posterior specialization of hippocampal-cortical connectivity. Finally, connectivity between the posterior hippocampus and precuneus negatively correlated with age in the ASD group but remained stable in the comparison group, suggesting an altered developmental specialization. Together, these differences in hippocampal-cortical connectivity may help us understand the neurobiological basis of the memory and social impairments found in ASD.

Functional Gastrointestinal Symptoms in Children with Autism and ADHD: Profiles of Hair and Salivary Cortisol, Serum Leptin Concentrations and Externalizing/Internalizing Problems.

Functional Gastrointestinal Disorders (FGIDs) present a higher prevalence in individuals with Neurodevelopmental Disorders (NDDs). The Stress System and the Gut-Brain axis (GBA) may mediate these relations. We aimed to assess the prevalence and profile of FGIDs in a clinical sample of children with Autism Spectrum Disorder (ASD) and Attention Deficit/Hyperactivity Disorder (ADHD) compared to typically developing children (TD) as well as to investigate possible relations between stress-related biomarkers and internalizing/externalizing problems in children with NDDS. In total, 120 children, aged between 4 and 12 years old, formed three groups (N = 40, each): ADHD, ASD and TD. Salivary cortisol, hair cortisol and serum leptin were measured. The ASD group had more FGID problems than the TD group (p = 0.001). The ADHD and ASD groups had higher total internalizing/externalizing problems than the TD group (p < 0.0001, p < 0.0001, p = 0.005, respectively). Children with FGIDs showed more total, internalizing and externalizing problems compared to children without FGIDs (p < 0.0001, p < 0.0001, p = 0.041, respectively). The ADHD group showed lower AUCg values (p < 0.0001), while the hair cortisol was higher for the TD group (p < 0.0001). In conclusion, children with NDDs had more FGID symptoms and present higher internalizing and externalizing problems. Children with ADHD and FGIDs had more internalizing problems compared to those without FGIDs. No differences in stress-related biomarkers were shown to differentiate children with NDDs with and without FGIDs. Future prospective studies including a greater number of children may elucidate the biological pathways linking these comorbidities.

Explaining deep learning-based representations of resting state functional connectivity data: focusing on interpreting nonlinear patterns in autism spectrum disorder.

Resting state Functional Magnetic Resonance Imaging fMRI (rs-fMRI) has been used extensively to study brain function in psychiatric disorders, yielding insights into brain organization. However, the high dimensionality of the rs-fMRI data presents significant challenges for data analysis. Variational autoencoders (VAEs), a type of neural network, have been instrumental in extracting low-dimensional latent representations of resting state functional connectivity (rsFC) patterns, thereby addressing the complex nonlinear structure of rs-fMRI data. Despite these advances, interpreting these latent representations remains a challenge. This paper aims to address this gap by developing explainable VAE models and testing their utility using rs-fMRI data in autism spectrum disorder (ASD). One-thousand one hundred and fifty participants (601 healthy controls [HC] and 549 patients with ASD) were included in the analysis. RsFC correlation matrices were extracted from the preprocessed rs-fMRI data using the Power atlas, which includes 264 regions of interest (ROIs). Then VAEs were trained in an unsupervised manner. Lastly, we introduce our latent contribution scores to explain the relationship between estimated representations and the original rs-fMRI brain measures. We quantified the latent contribution scores for both the ASD and HC groups at the network level. We found that both ASD and HC groups share the top network connectivitives contributing to all estimated latent components. For example, latent 0 was driven by rsFC within ventral attention network (VAN) in both the ASD and HC. However, we found significant differences in the latent contribution scores between the ASD and HC groups within the VAN for latent 0 and the sensory/somatomotor network for latent 2. This study introduced latent contribution scores to interpret nonlinear patterns identified by VAEs. These scores effectively capture changes in each observed rsFC feature as the estimated latent representation changes, enabling an explainable deep learning model that better understands the underlying neural mechanisms of ASD.

Comparison of urine glycosaminoglycan excretion between children with autism spectrum disorder and typically developed children

BackgroundAbnormalities pertaining to glycosaminoglycan metabolism have been demonstrated in children with autism spectrum disorder. The aim of the present study was to compare urine excretion of sulfated glycosaminoglycans in children with autism spectrum disorder, with neurotypical controls and explore its association with co-occurring symptoms. MethodRandom urine samples were collected from children with autism spectrum disorder (n = 61) between the ages of 2 and 6 years, and age- and sex-matched neurotypical controls. Urine glycosaminoglycan levels were quantified by the dimethylmethylene blue (DMMB) dye-binding assay. ResultsMean glycosaminoglycan/creatinine ratio of children with autism spectrum disorder was 22.279 ± 13.044 mg/mmol while that of neurotypical controls was 19.121 ± 7.319 mg/mmol. Eight patients with autism spectrum disorder (13.11%) exhibited abnormally high glycosaminoglycan excretion. Unstandardized urine glycosaminoglycan levels are significantly higher (p = 0.019) in the autism spectrum disorder group when covariates such as age, urinary creatinine, and height are taken into consideration by ANCOVA. However, the outcome showed a trend towards significance when glycosaminoglycan/creatinine ratio was used in ANCOVA (p = 0.058). In neurotypical subjects, the urine glycosaminoglycan levels appear to decline with age, height, and weight while this trend was not apparent in subjects with autism spectrum disorder. Glycosaminoglycan excretion did not correlate with the presence of co-occurring symptoms of autism spectrum disorder; frequent gastrointestinal symptoms, self-injurious behaviors, food aversions, or parent-reported sleep problems. ConclusionsA subset of children with autism spectrum disorder exhibits higher urine glycosaminoglycan excretion. Further research is needed to explore the molecular basis of this finding.

M-CHAT-R: UTILIZING FOR SCREENING AUTISM SPECTRUM DISORDER

Background: Autism Spectrum Disorder (ASD) is a multifaceted condition marked by challenges in communication and social interactions, behavioral restrictions, and repetitive actions. Identifying children who may be at risk of ASD early on is crucial for prompt consultation, monitoring, and intervention. Objectives: Determine the rate of Autism Spectrum Disorder in children between the ages of 18 and 36 months by utilizing the M-CHAT-R scale at the clinic of Can Tho Children's Hospital. And identify the associated risk factors for that rate of Autism Spectrum Disorder. Materials and methods: A descriptive cross-sectional study conducted on 608 children aged 18-36 months who were examined at the clinic of Can Tho Children's Hospital. Results: The majority of children in the study, comprising 88.3%, fell within the age range of 24-36 months, with proportions of boys and girls at 57.6% and 42.4%, respectively. Among the children, 94.7% (576/608 children) were classified as low risk, while medium risk and high risk accounted for 4.28% (26/608 children) and 1.02% (6/608 children), respectively. Questions with notably high positive rates among at-risk children were item 2 (38.9%), item 5 (15.2%), item 12 (16.2%), item 11 (4.8%). Conversely, the least common symptom was indicated in question 9, where children showed a preference for moving activities, accounting for only 2.7%. Associations were observed between the risk of Autism Spectrum Disorder and factors such as gender (p=0.04), father’age (p=0.024), alcohol consumption (p=0.047), birth asphyxia (p=0.036), seizures due to high fever/unknown cause (p=0.035). Conclusion: In the study, the rate of positive M-CHAT-R was 5.3% (32 out of 608 children), predominantly observed in the low-risk group, accounting for 94.7%. The majority of positive cases fell within the 24-36 month age group, comprising 26 out of the 32 children. The risk of Autism Spectrum Disorder showed correlations with gender, father’s age older or equal to 35 years old, alcohol consumption, birth asphyxia and seizures triggered by high fever or unknown causes. However, there was no observed relationship between the risk of Autism Spectrum Disorder and age group factors.

A Systematic Review on Autism and Hyperserotonemia: State-of-the-Art, Limitations, and Future Directions.

Hyperserotonemia is one of the most studied endophenotypes in autism spectrum disorder (ASD), but there are still no unequivocal results about its causes or biological and behavioral outcomes. This systematic review summarizes the studies investigating the relationship between blood serotonin (5-HT) levels and ASD, comparing diagnostic tools, analytical methods, and clinical outcomes. A literature search on peripheral 5-HT levels and ASD was conducted. In total, 1104 publications were screened, of which 113 entered the present systematic review. Of these, 59 articles reported hyperserotonemia in subjects with ASD, and 26 presented correlations between 5-HT levels and ASD-core clinical outcomes. The 5-HT levels are increased in about half, and correlations between hyperserotonemia and clinical outcomes are detected in a quarter of the studies. The present research highlights a large amount of heterogeneity in this field, ranging from the characterization of ASD and control groups to diagnostic and clinical assessments, from blood sampling procedures to analytical methods, allowing us to delineate critical topics for future studies.

Panic-agoraphobic symptoms in adults with ASD: the role of ruminative thinking and inflexibility.

Scientific literature has highlighted the link between autism spectrum disorder (ASD) and anxiety disorders, but few studies have delved into the relationship between ASD and panic-agoraphobic disorders. The aim of this study is to investigate the relationship between autism spectrum and panic-agoraphobic symptoms, examining whether and which autistic domains are predictive of the presence of specific panic-agoraphobic symptoms. Forty-five adult subjects with ASD and 50 healthy controls (HCs) were evaluated through the Structured Clinical Interview for DSM-5, Research Version and assessed with the Adult Autism Subthreshold Spectrum (AdAS Spectrum) and the Panic-Agoraphobic - Short Version (PAS-SV) questionnaires. Statistical analyses included Mann-Whitney U test, chi-square test, and a set of linear and logistic regression analyses. The PAS-SV total and domain scores were significantly higher in the ASD group than in the HC group. A higher AdAS total score appeared to be predictive of a higher PAS-SV total score. The AdAS domain Restricted Interests and Rumination would increase the risk of obtaining higher PAS-SV total and domain scores. Conversely, the AdAS Spectrum domain Inflexibility and Adherence to Routine would predict lower total PAS-SV score. This study revealed a greater representation of panic-agoraphobic symptoms in adults with ASD, as well as an increased risk of showing such symptoms in the presence of significant autistic traits. Restricted interests and ruminative thinking emerged as predominant risk factors for panic-agoraphobic manifestations.