Search Results (2,031)

Background: Recent studies indicate that physical activity (PA) may improve executive functions (EFs) in children with Autism Spectrum Disorder (ASD). The Enriched Motor Program (EMP), which combines aerobic and cognitive exercises, shows potential for enhancing EFs in these children. The EMP was originally created for typically developing preschoolers and includes locomotor and fine motor activities enriched by cognitive stimuli to help the development of EFs in children with ASD. The current study aims to adapt a shorter version of EMP for these children’s needs. Methods: The research will use a cross-sectional, quasi-experimental design with a forecasted sample of 40 children, with the age ranging from six to eight, with a diagnosis of ASD. The children’s working memory and inhibitory control will be measured before and after the intervention. Results: According to the literature, the experimental group should obtain higher scores, especially in working memory tasks. Discussion: This is the first implementation of EMP, which merges physical activities with cognitive stimuli to enhance EFs in children with ASD. It could be used by specialized centers and clinicians to support EFs through engaging activities, and it could be potentially recommended as a best practice for EF treatments in children with ASD. Full article

Background/Objectives: The co-occurrence of Attention-deficit/hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD) is very common and worsens adaptive functioning. This systematic review evaluates both pharmacological and non-pharmacological interventions in this underserved population. Methods: Registered on PROSPERO (CRD42024526157), a systematic search was conducted on PubMed, Embase, and Web of Science until 5 April 2025. The review includes (a) pilot studies and RCTs, (b) participants aged <18 years, (c) diagnoses of ASD and ADHD based on DSM-IV/V or ICD-9/10, (d) at least one group receiving any intervention, and (e) publications in English, Italian, Spanish, or German. Newcastle Ottawa Scale tools for non-randomized studies and the Cochrane Risk of Bias Tools for randomized controlled trials were used to assess studies’ quality. Results: A total of 32 studies were included: 87.5% concerning pharmacological treatments. Specifically, methylphenidate (MPH, n = 11), atomoxetine (ATX, n = 11), guanfacina (n = 4), clonidine (n = 1), or atypical antipsychotics (n = 1) were examined. MPH and ATX were most frequently studied, with both showing positive effects in reducing ADHD core symptoms compared to placebo. ATX also reduces stereotyped behaviors and social withdrawal, although more withdrawals due to adverse events (AEs) were reported for ATX than MPH. Four studies (12.5%) examined non-pharmacological interventions, including treatment with virtual reality tools, digital platforms, educational animations, and biomedical protocols; improvements in emotion recognition, behavioral regulation, attention, and social functioning were found. Conclusions: While limited data prevent definitive conclusions, MPH and ATX appear to be relatively safe and effective on hyperactivity-impulsivity symptoms, even in individuals with ASD. Evidence on non-pharmacological treatments is limited, and further studies are needed to better establish their therapeutic potential. Full article

Background: Disorders of Gut–Brain Interaction (DGBIs) are present in 23.0% of the paediatric population, according to Rome IV. Latin American (LA) prevalence of DGBIs in children with Autism Spectrum Disorder (ASD) is unknown. The aim of this study was to determine the prevalence of DGBIs and possible associations in schoolchildren and adolescents with ASD from LA. Methods: An observational analytical study was conducted in LA cities. Caregivers of children with ASD completed the Rome IV Questionnaire for Pediatric Gastrointestinal Symptoms to identify DGBIs. Sociodemographic, clinical, and family variables were included. Statistical analysis involved central tendency measures, univariate and bivariate analysis, calculation odds ratios (ORs), and 95% confidence intervals (95%CIs), with p < 0.05 significance. Results: The study included 353 children with ASD. Predominantly male (78.8%), white (56.1%), attending private schools (79.3%), altered nutritional status (43.9%), born by c-section (57.5%), firstborn (54.7%), level of autism not classified at the time of the study (49.0%). A total of 58.9% presented DGBI. Functional constipation (FC) was the most frequent (27.2%). Those from Central America (CA) had a higher likelihood of presenting a DGBI (OR = 1.98, 95% CI = 1.25–3.12, p = 0.0018). Conclusions: Over half of LA schoolchildren and adolescents with ASD presented DGBI, FC being the most common, and higher likelihood of DGBI in CA. Full article

Background: Around 1 in 59 individuals is diagnosed with Autism Spectrum Disorder (ASD), according to CDS statistics. Conventionally, ASD has been diagnosed using functional brain regions, regions of interest, or multi-tissue-based training in artificial intelligence models. The objective of the exhibit study is to develop an efficient deep learning network for identifying ASD using structural magnetic resonance imaging (MRI)-based brain scans. Methods: In this work, we developed a VGG-based deep learning network capable of diagnosing autism using whole brain gray matter (GM) tissues. We trained our deep network with 132 MRI T1 images from normal controls and 140 MRI T1 images from ASD patients sourced from the Autism Brain Imaging Data Exchange (ABIDE) dataset. Results: The number of participants in both ASD and normal control (CN) subject groups was not statistically different (p = 0.23). The mean age of the CN subject group was 14.62 years (standard deviation: 4.34), and the ASD group had mean age of 14.89 years (standard deviation: 4.29). Our deep learning model accomplished a training accuracy of 97% and a validation accuracy of 96% over 50 epochs without overfitting. Conclusions: To the best of our knowledge, this is the first study to use GM tissue alone for diagnosing ASD using VGG-Net. Full article

Autism spectrum disorder (ASD) is a multifactorial neurodevelopmental disorder, with lead (Pb) exposure increasingly linked to its risk. However, the molecular mechanisms linking Pb to ASD remain poorly understood. This study established a postnatal Pb-exposed mouse model and employed the three-chamber social test and the marble-burying test to assess ASD-like behavioral phenotypes. The Pb levels in both blood and the hippocampus were quantified, and hippocampal neurons were assessed for morphological alterations. Moreover, a Tandem Mass Tag (TMT)-based quantitative proteomics approach was applied to elucidate the underlying mechanisms. Neurobehavioral experiments revealed Pb-exposed C57BL/6 offspring exhibited reduced social interaction and novelty preference along with increased repetitive marble-burying behavior. The Pb levels in both the blood and hippocampus of Pb-treated mice were significantly elevated compared with those of control animals. Postnatal Pb exposure resulted in a reduction in the neuronal numbers and disorganized neuronal arrangement in the hippocampus. A total of 66 proteins were identified as being differentially expressed after postnatal Pb exposure. Among them, 34 differentially expressed proteins were common in both Pb exposure groups, with 33 downregulated and 1 upregulated. Bioinformatic analysis revealed multi-pathway regulation involved in Pb-induced neurodevelopmental disorders, including dysregulation of synaptic signaling, abnormal activation of neuron apoptosis, and neuroinflammation. Notably, the SYT10/IGF-1 signaling pathway may play a potential key role. These findings enhance understanding of Pb-induced autism-like behaviors, providing novel proteomic insights into the etiology of ASD. Full article

The bilateral interaction between the brain and the gut has recently been on the spectrum of researchers’ interests, including complex neural, endocrinological, and immunological signaling pathways. The first case reports and clinical studies have already reported that delivering microbes through fecal microbial transplantation (FMT) may alleviate symptoms of psychiatric disorders. Therefore, modifying the gut microbiota through FMT holds promise as a potential treatment for psychiatric diseases. This scoping review assessed studies from PubMed related to FMT in autism spectrum disorder and attention deficit hyperactivity disorder. The evaluation included nine clinical studies and case reports. The beneficial and persistent effect on the autism spectrum disorder (ASD) symptoms has been reported. Also, an increased microflora diversity and altered levels of neurometabolites in serum were identified, albeit with a tendency to return to baseline over time. The microbiome–gut–brain axis could provide new targets for preventing and treating psychiatric disorders. However, a recent large randomized clinical trial has shed light on the previously collected data and suggested a possible contribution of the placebo effect. This highlights the necessity of large randomized double-blind studies to reliably assess the effect of FMT in ASD. Full article

Autism Spectrum Disorder (ASD) poses significant challenges in diagnosis due to its diverse symptomatology and the complexity of early detection. Atypical gait and gesture patterns, prominent behavioural markers of ASD, hold immense potential for facilitating early intervention and optimising treatment outcomes. These patterns can be efficiently and non-intrusively captured using modern computational techniques, making them valuable for ASD recognition. Various types of research have been conducted to detect ASD through deep learning, including facial feature analysis, eye gaze analysis, and movement and gesture analysis. In this study, we optimise a dual-stream architecture that combines image classification and skeleton recognition models to analyse video data for body motion analysis. The first stream processes Skepxels—spatial representations derived from skeleton data—using ConvNeXt-Base, a robust image recognition model that efficiently captures aggregated spatial embeddings. The second stream encodes angular features, embedding relative joint angles into the skeleton sequence and extracting spatiotemporal dynamics using Multi-Scale Graph 3D Convolutional Network(MSG3D), a combination of Graph Convolutional Networks (GCNs) and Temporal Convolutional Networks (TCNs). We replace the ViT model from the original architecture with ConvNeXt-Base to evaluate the efficacy of CNN-based models in capturing gesture-related features for ASD detection. Additionally, we experimented with a Stack Transformer in the second stream instead of MSG3D but found it to result in lower performance accuracy, thus highlighting the importance of GCN-based models for motion analysis. The integration of these two streams ensures comprehensive feature extraction, capturing both global and detailed motion patterns. A pairwise Euclidean distance loss is employed during training to enhance the consistency and robustness of feature representations. The results from our experiments demonstrate that the two-stream approach, combining ConvNeXt-Base and MSG3D, offers a promising method for effective autism detection. This approach not only enhances accuracy but also contributes valuable insights into optimising deep learning models for gesture-based recognition. By integrating image classification and skeleton recognition, we can better capture both global and detailed motion patterns, which are crucial for improving early ASD diagnosis and intervention strategies. Full article

A significant proportion of children with Autism spectrum disorder (ASD) experience sleep issues, such as insomnia and other disorders, as assessed by the Sleep Disturbance Scale for Children. Our study investigated the link between six single nucleotide polymorphisms (SNPs) in the melatonin receptor genes MT1 and MT2 and ASD susceptibility, clinical severity and associated sleep problems. A total of 139 ASD children, 82 siblings, and 53 unrelated healthy controls, all of Sardinian ancestry, were studied; among them, 38 children with co-occurring sleep issues were assessed for the outcomes of a rehabilitative program, including behavioral therapy and sleep hygiene. The MT2 rs10830963 G allele is more prevalent in ASD children and their siblings compared to the healthy controls, while rs2119882 (MT1) and rs1562444 (MT2) are associated with DIMS, DA, and SHY. ASD Children carrying the rs2119882 T allele have higher scores for DIMS and DA compared to C allele carriers, and those carrying rs1562444 A allele have higher scores for SHY than G allele carriers. After rehabilitative treatment, homozygous TT carriers of rs2119882 showed less improvement in DIMS symptoms compared to CT and CC carriers. A similar result was observed for AA carriers of SNP rs1562444 about SHY. We may suggest that the MT1 and MT2 variants may serve as useful predictive genetic markers for the severity of sleep disorders in children with ASD, potentially informing the design of more targeted rehabilitative treatments. Full article

Background: Autism spectrum disorder (ASD) is a complex neurodevelopmental condition with a rising prevalence, driven by multifactorial genetic and environmental factors. Among the genetic contributors identified, SCN2A, a critical gene encoding the Nav1.2 sodium channel, has been implicated in ASD and other related neurological conditions. This systematic review aims to explore the relationship between SCN2A mutations and ASD phenotypes. Methods: This review systematically analyzed data from studies reporting SCN2A mutations in individuals diagnosed with ASD. The primary focus was on the characterization of mutation types, associated clinical features, and phenotypic variability. Results: The mutations identified were predominantly de novo missense mutations and were associated with a spectrum of neurological and developmental challenges, including seizures, intellectual disability, movement disorders, and repetitive behaviors. A notable finding was the significant phenotypic variability observed across individuals. Gender differences emerged, suggesting a potentially greater impact on females compared to trends typically seen in ASD genetic studies. Specific mutations, such as c.2919+4delT, and mosaicism were identified as novel contributors to the observed heterogeneity. Conclusions: The review highlights the clinical significance of SCN2A mutations in ASD and highlights their relevance in genetic counseling and the development of targeted therapies. Understanding the diverse genotype–phenotype correlations associated with SCN2A can drive progress in personalized medicine, paving the way for precision therapies tailored to individuals with SCN2A-related ASD. Full article

Training people interested in implementing Applied Behavior Analysis (ABA) interventions to children with autism spectrum disorder (ASD) is important to promote skill gains. A recommended training package is called behavioral skills training (BST), which involves four components (didactic instruction, modeling, role-play, and performance feedback). Background/Objectives: The purpose was to assess the effects of BST on the accurate teaching of multiple skills via DTT by six psychology university students to a confederate and six children diagnosed with ASD. Generalization and maintenance assessments were conducted. Results: Through the research conditions, all university participants were able to teach ten different skills (sitting still, motor imitation, making requests, vocal imitation, receptive identification of non-verbal stimuli, making eye contact, following instructions, intraverbal, labeling, receptive identification of non-verbal stimuli by function, feature and class) with a high integrity level to the children. In addition, across four months after training, all participants maintained high teaching integrity levels while teaching skills to the children related to their individualized curriculum goals. Each child accumulated over 1000 correct responses across several sessions. The university participants rated their training with the highest possible score in a social validity assessment. Conclusions: BST successfully trained psychology university students to accurately teach multiple skills via DTT to children with ASD and involved long lasting effects. Limitations and new avenues for research were discussed. Full article

With positron emission tomography followed by functional magnetic resonance imaging (fMRI), we demonstrated that rapid eye movements (REMs) in sleep are saccades that scan dream imagery. The brain “sees” essentially the same way while awake and while dreaming in REM sleep. As expected, an event-related fMRI study (events = REMs) showed activation time-locked to REMs in sleep (“REM-locked” activation) in the oculomotor circuit that controls saccadic eye movements and visual attention. More crucially, the fMRI study provided a series of unexpected findings, including REM-locked multisensory integration. REMs in sleep index the processing of endogenous visual information and the hierarchical generation of dream imagery through multisensory integration. The neural processes concurrent with REMs overlap extensively with those reported to be atypical in autism spectrum disorder (ASD). Studies on ASD have shown atypical visual processing and multisensory integration, emerging early in infancy and subsequently developing into autistic symptoms. MRI studies of infants at high risk for ASD are typically conducted during natural sleep. Simply timing REMs may improve the accuracy of early detection and identify markers for stratification in heterogeneous ASD patients. REMs serve as a task-free probe useful for studying both infants and animals, who cannot comply with conventional visual activation tasks. Note that REM-probe studies would be easier to implement in early infancy because REM sleep, which is markedly preponderant in the last trimester of pregnancy, is still pronounced in early infancy. The brain may practice seeing the world during REM sleep in utero before birth. The REM-probe controls the level of attention across both the lifespan and typical-atypical neurodevelopment. Longitudinal REM-probe studies may elucidate how the brain develops the ability to “see” and how this goes awry in autism. REMs in sleep may allow a straightforward comparison of animal and human data. REM-probe studies of animal models of autism have great potential. This narrative review puts forth every reason to believe that employing REMs as a probe into the development of the visual brain will have far-reaching implications. Full article

(1) Background: Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by social communication deficits and repetitive behaviors. Functional MRI (fMRI) has been widely applied to investigate brain functional abnormalities associated with ASD, yet challenges remain due to complex data characteristics and limited spatiotemporal information capture. This study aims to improve the ability to capture spatiotemporal dynamics of brain activity by proposing an advanced framework. (2) Methods: This study proposes an ASD recognition method that combines 3D Convolutional Neural Networks (3D-CNNs) and segmented temporal decision networks. The method first uses the 3D-CNN to automatically extract high-dimensional spatial features directly from the raw 4D fMRI data. It then captures temporal dynamic properties through a designed segmented Long Short-Term Memory (LSTM) network. The concatenated spatiotemporal features are classified using Gradient Boosting Decision Trees (GBDTs), and finally, a voting mechanism is applied to determine whether the subject belongs to the ASD group based on the prediction results. This approach not only enhances the efficiency of spatiotemporal feature extraction but also improves the model’s ability to learn complex brain activity patterns. (3) Results: The proposed method was evaluated on the ABIDE dataset, which includes 1035 subjects from 17 different brain imaging centers. The experimental results demonstrate that our method outperforms existing state-of-the-art approaches, achieving an average accuracy of 0.85. (4) Conclusions: Our method provides a new solution for ASD classification by leveraging the spatiotemporal information of 4D fMRI data, achieving a significant improvement in classification performance. These results not only offer a new computational tool for ASD diagnosis but also provide important insights into understanding its neurobiological mechanisms. Full article

Background/Objectives: Autism spectrum disorder (ASD) has a wide-ranging impact on individuals’ quality of life and development, and there is a critical need for greater awareness, early intervention, and comprehensive support strategies to effectively address the unique needs of those affected by ASD. Recent studies highlight the gut microbiome’s potential role in modulating ASD symptoms via the gut–brain axis, but specific microbial biomarkers remain unclear. This study aims to investigate differences in gut microbiota composition between ASD patients and neurotypical controls in a novel approach, specifically assessing ratios of Firmicutes/Bacteroidetes (F/B), Actinobacteria/Proteobacteria (A/P), and Prevotella/Bacteroides (P/B) as potential biomarkers. Methods: We analyzed gut microbiome samples from 302 Bulgarian children and adolescents diagnosed with ASD (aged 2–19 years). Microbial ratios (F/B, A/P, and P/B) were calculated and compared against previously reported reference meta-analytic means from European neurotypical populations. The statistical significance of deviations was assessed using parametric (t-tests), non-parametric (Wilcoxon signed-rank tests), and proportion-based (binomial tests) methods. Effect sizes were quantified using Cohen’s d. Significant differences between ASD cases and neurotypical reference values were observed across several age groups. Results: Notably, children with ASD demonstrated significantly lower F/B and A/P ratios, with the youngest cohort (0–4 years) exhibiting the greatest differences. Deviations in the P/B ratio varied across age groups, with a significant elevation in the oldest group (≥10 years). Collectively, ASD cases consistently exhibited microbiota profiles indicative of dysbiosis. Conclusions: Our findings support gut microbiome dysbiosis as a potential biomarker for ASD, highlighting significantly altered bacterial ratios compared to neurotypical controls. These microbiome shifts could reflect early-life disruptions influencing neurodevelopment. Future studies should adopt longitudinal and mechanistic approaches to elucidate causal relationships and evaluate therapeutic microbiome modulation strategies. Full article

The gut microbiota (GM) may play a crucial role in the development and progression of central nervous system (CNS) diseases. Microecologics and exercise can influence the composition and function of GM, thereby exerting positive effects on the CNS. Combined interventions of exercise and microecologics are expected to more comprehensively and effectively address CNS diseases through the microbiota–gut–brain axis (MGBA), potentially outperforming single interventions. However, there is currently a lack of relevant reviews on this topic. In this review, we examine the associations between changes in the microbiota and CNS diseases, including Alzheimer’s disease (AD), Parkinson’s disease (PD), multiple sclerosis (MS), and autism spectrum disorder (ASD). We also summarize studies on various types of microecologics (such as probiotics, prebiotics, synbiotics, and postbiotics) and exercise in improving CNS disease symptoms. Although current individual studies on microecologics and exercise have achieved certain results, the mechanisms underlying their synergistic effects remain unclear. This review aims to explore the theoretical basis, potential mechanisms, and clinical application prospects of combined interventions of microecologics and exercise in improving CNS diseases through the MGBA, providing a scientific basis for the development of more comprehensive and effective therapeutic interventions. Full article

Background/Objectives: Emerging evidence suggests that individuals with autism spectrum disorder (ASD) exhibit altered neural connectivity and disrupted brain network dynamics, which can be captured through EEG microstate analysis. Most research to date has focused on older children, adolescents, or adults with ASD, while studies focusing on preschool-aged children with ASD remain limited. Given that early brain development is critical for understanding the onset and progression of ASD, more research targeting this age group is essential. Methods: In this study, resting EEG data were collected from 59 preschool-aged children with ASD and 59 typically developing (TD) participants. Results: The results revealed a reduction in global explained variance and coverage of microstate in children with ASD, indicating poorer social performance that was independent of alpha power after the removal of the 1/f-like aperiodic signal. These findings reflect the social symptoms commonly observed in ASD. Additionally, alpha power was found to modulate the occurrence and duration of microstates in both groups. Conclusions: Our findings highlight that atypical microstates can serve as reliable biomarkers for ASD, offering valuable insights into the neurophysiological mechanisms underlying the disorder and paving the way for future research directions. Full article