Diagnosis Of Autism Spectrum Disorder Research Articles

228 Improving access to timely assessment and diagnosis of Autism Spectrum Disorders (ASD): a quality improvement study at a subspecialty developmental center in Arkansas

228 Improving access to timely assessment and diagnosis of Autism Spectrum Disorders (ASD): a quality improvement study at a subspecialty developmental center in Arkansas

AI-enabled clinical decision support tools for mental healthcare: A product review.

The review seeks to promote transparency in the availability of regulated AI-enabled Clinical Decision Support Systems (AI-CDSS) for mental healthcare. From 84 potential products, seven fulfilled the inclusion criteria. The products can be categorized into three major areas: diagnosis of autism spectrum disorder (ASD) based on clinical history, behavioral, and eye-tracking data; diagnosis of multiple disorders based on conversational data; and medication selection based on clinical history and genetic data. We found five scientific articles evaluating the devices' performance and external validity. The average completeness of reporting, indicated by 52% adherence to the Consolidated Standards of Reporting Trials Artificial Intelligence (CONSORT-AI) checklist, was modest, signaling room for improvement in reporting quality. Our findings stress the importance of obtaining regulatory approval, adhering to scientific standards, and staying up-to-date with the latest changes in the regulatory landscape. Refining regulatory guidelines and implementing effective tracking systems for AI-CDSS could enhance transparency and oversight in the field.

The value of hippocampal sub-region imaging features for the diagnosis and severity grading of ASD in children

BackgroundHippocampal structural changes in Autism Spectrum Disorder (ASD) are inconsistent. This study investigates hippocampal subregion changes in ASD patients to reveal intrinsic hippocampal anomalies. MethodsA retrospective study from Hainan Children’s Hospital database (2020–2023) included ASD patients and matched controls. We classified ASD participants based on severity, dividing all subjects into four groups: normal, mild, moderate, and severe. High-resolution T1-weighted MRI images were analyzed for hippocampal subregion segmentation and volume calculations using Freesurfer. Texture features were extracted via the Gray-Level Co-occurrence Matrix. The Receiver Operating Characteristic curve was used to evaluate seven random forest predictive models constructed from volume, subregion, and texture features, as well as their combinations following feature selection. ResultsThe study included 114 ASD patients (98 boys, 2–8 years; 16 girls, 2–6 years; 17 mild, 57 moderate, 40 severe) and 111 healthy controls (HCs). No significant differences in volumes were found between ASD patients and HCs (adjusted P-value >0.05). The seven random forest models showed that single volume and texture features performed poorly for ASD classification; however, integrating various feature types improved AUC values. Further selection of texture, subregion, and volume features enhanced AUC performance across normal and varying severity categories, demonstrating the potential value of specific subregions and integrated features in ASD diagnosis. ConclusionRandom forest models revealed that hippocampal volume, texture features, and subregion characteristics are crucial for diagnosing and assessing the severity of ASD. Integrating selected texture and subregion features optimized diagnostic efficacy, while combining texture, subregion, and volume features further improved severity grading effectiveness.

The challenge of a late diagnosis of Autism Spectrum Disorder: co-occurring trajectories and camouflage tendencies. a case report of a young Autistic female with Avoidant Restrictive Food Intake Disorder

IntroductionAutistic individuals may show several psychiatric co-occurrences, including Feeding and Eating Disorders (FEDs). Avoidant and Restrictive Food Intake Disorder (ARFID) consists of avoidance or restriction in food intake, leading to significant weight loss, nutritional deficiencies, and marked interference with psychosocial functioning. Both Autism Spectrum Disorder (ASD) and ARFID are characterized by the two main features of cognitive rigidity and sensory sensitivity, which may complicate differential diagnosis. There is a notable lack of information on the manifestation of ASD-ARFID co-occurrence, as well as tailored assessment tools and practice, and therapeutic approaches.Case descriptionThis report provides a detailed description of L., a young girl with a late diagnosis of ASD who also developed unspecific depressive mood disorder and ARFID in co-occurrence. After the diagnosis of ASD, L. underwent multiple evaluations to address emerging psychiatric co-occurrences and symptom exacerbation, and in order to develop the most effective integrated treatment.ConclusionThe case of L. expands the knowledge on the phenotype of Autistic females and exemplifies how delayed diagnosis may exacerbate functioning differences and increase the camouflage phenomenon. Additionally, it underscores the importance of improving tailored evaluation, combined treatment plans, with both cognitive-behavioral therapy and drugs, and monitoring the evolving patterns of Autistic manifestations and associated psychiatric co-occurrences.

Efficacy and Safety of Altibrain® as an Adjunctive Therapy for Autism Spectrum Disorder: An Open Label Trial Targeting Core Symptoms.

This study aimed to evaluate the effectiveness and safety of Altibrain® in combination with standard Autism Spectrum Disorder (ASD) treatment compared to standard ASD treatment alone in individuals diagnosed with ASD. A randomized, open-label trial was conducted involving 120 participants aged 3 to 17 years, randomly assigned to either the Standard ASD Treatment group or the Altibrain® + Standard ASD Treatment group. Sixty patients were randomly allocated to each Standard ASD Treatment group or the Altibrain® + Standard ASD Treatment group. participant allocation was done by computer-generated randomization. Participants had confirmed ASD diagnoses based on DSM-IV or ICD-11 criteria and demonstrated moderate to severe core ASD symptoms. Informed consent was obtained from caregivers. A total number of 120 subjects were included, consisting of 71 male and 49 female patients. Participants received either standard ASD treatment alone or Altibrain® in addition to standard ASD treatment orally once daily for 24 weeks. A total of 7 study visits/24 weeks to analyze the intervention efficacy of the Standard ASD Treatment group or the Altibrain ® + Standard ASD Treatment group. Primary outcomes included changes in core ASD symptoms measured by the Autism Diagnostic Observation Schedule (ADOS) and safety assessments. Secondary outcomes included alterations in social communication skills, reduction in repetitive behaviors, overall functional improvements, and safety and tolerability of Altibrain®. Altibrain® significantly improved qualitative deficits in social interaction and repetitive behaviors compared to standard ASD treatment alone (p < 0.0001). The Altibrain® + Standard ASD Treatment group demonstrated significant improvements in social functioning, social awareness, cognition, communication, and motivation compared to the Standard ASD Treatment group (p < 0.0001). Additionally, Altibrain® showed superior efficacy in reducing hyperactivity/noncompliance, inappropriate speech, irritability, lethargy/ social withdrawal, stereotypic behavior, and aberrant behavior compared to standard treatment alone (p < 0.0001). Additionally, Altibrain® exhibited a favorable safety profile as per the 4-week post-treatment safety follow-up. Further research is warranted to confirm and expand upon these results, including longer-term studies with larger cohorts and investigations into underlying mechanisms. Overall, Altibrain® holds promise as a valuable therapeutic option for individuals with ASD and their families. Limitations of the study include neuroimaging and biomarkers analysis.

Assessment of glymphatic function and white matter integrity in children with autism using multi-parametric MRI and machine learning.

To assess glymphatic function and white matter integrity in children with autism spectrum disorder (ASD) using multi-parametric MRI, combined with machine learning to evaluate ASD detection performance. This retrospective study collected data from 110 children with ASD (80 exploratory, 43 validation) and 68 typically developing children (50 exploratory, 18 validation) from two centers. The automated diffusion tensor imaging along the perivascular space (aDTI-ALPS), fractional anisotropy (FA), cerebrospinal fluid volume, and perivascular space (PVS) volume indices were extracted from DTI, three-dimensional T1-weighted, and T2-weighted images. Intergroup comparisons were conducted using t-tests, Mann-Whitney U-test, and tract-based spatial statistics. Correlation analysis assessed the relationship between glymphatic function, white matter integrity, and clinical scales. Machine learning models based on MRI indices were developed using the AutoGluon framework. The PVS volume (p < 0.001) was larger, and aDTI-ALPS index (p < 0.001) was lower in children with ASD compared to typically developing children. FA values were reduced in the ASD group and positively correlated with aDTI-ALPS index. The aDTI-ALPS index correlated with ASD severity (r = -0.27, p = 0.02) and developmental delays (r = 0.63, p < 0.001). Mediation analysis indicated the aDTI-ALPS index partially mediated the relationship between white matter integrity and developmental delay. The MRI-based model achieved an area under the curve of 0.84 for ASD diagnosis. Analyzing glymphatic function and white matter integrity enhances understanding of ASD's neurobiological underpinnings. The multi-parametric MRI, combined with machine learning, can facilitate the early detection of ASD. Question How can multi-parametric MRI based on the glymphatic system improve early diagnosis of autism spectrum disorder (ASD) beyond the limitations of current behavioral assessments? Findings Glymphatic dysfunction and disruptions in white matter integrity were associated with clinical symptoms of ASD. Multi-parametric MRI with machine learning can improve early ASD detection. Clinical relevance Multi-parametric MRI, focusing on glymphatic function and white matter integrity, enhances the diagnostic accuracy of ASD by serving as an objective complement to clinical scales.

Proximity-based solutions for optimizing autism spectrum disorder treatment: integrating clinical and process data for personalized care

Autism Spectrum Disorder (ASD) affects millions of individuals worldwide, presenting challenges in social communication, repetitive behaviors, and sensory processing. Despite its prevalence, diagnosis can be lengthy, and access to appropriate treatment varies greatly. This project utilizes the power of Artificial Intelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL), to improve Autism Spectrum Disorder diagnosis and treatment. A central data hub, the Master Data Plan (MDP), will aggregate and analyze information from diverse sources, feeding AI algorithms that can identify risk factors for ASD, personalize treatment plans based on individual needs, and even predict potential relapses. Furthermore, the project incorporates a patient-facing chatbot to provide information and support. By integrating patient data, empowering individuals with ASD, and supporting healthcare professionals, this platform aims to transform care accessibility, personalize treatment approaches, and optimize the entire care journey. Rigorous data governance measures will ensure ethical and secure data management. This project will improve access to care, personalize treatments for better outcomes, shorten wait times, boost patient involvement, and raise ASD awareness, leading to better resource allocation. This project marks a transformative shift toward data-driven, patient-centred ASD care in Italy. This platform enhances treatment outcomes for individuals with ASD and provides a scalable model for integrating AI into mental health, establishing a new benchmark for personalized patient care. Through AI integration and collaborative efforts, it aims to redefine mental healthcare standards, enhancing the well-being for individuals with ASD.

Diagnostic Value of Serum miR-499a-5p in Chinese Children with Autism Spectrum Disorders.

The purpose of this study was to investigate the expression of miR-499a-5p in children with autism spectrum disorders (ASD) and its value in early diagnosis of ASD. This is a retrospective case-control study that included 40 children with ASD as a case group and 43 healthy children as a control group. Magnetic resonance imaging (MRI) was performed on all subjects, and the children were scored with childhood autism rating scale (CARS) and autism behavior checklist (ABC). The expression of miR-499a-5p in serum was detected by RT-qPCR, and the diagnostic value of miR-499a-5p in ASD was evaluated by ROC curve. Pearson correlation coefficient was used to evaluate the correlation between miR-499a-5p levels and scores. Compared with healthy children, the expression level of serum miR-499a-5p was significantly reduced in children with ASD. ROC curve showed that miR-499a-5p is of high diagnostic value for ASD. The results of MRI suggested that the volume of the amygdala in ASD children was significantly larger than that in healthy children, while the volume of the caudate nucleus was significantly reduced. Correlation results showed that the scores of CARS and ABC in the ASD group were significantly negatively correlated with the levels of miR-499a-5p. In the ASD group, the volume of the amygdala was negatively correlated with the level of miR-499a-5p, while the volume of the caudate nucleus was positively correlated with the level of miR-499a-5p. The decreased expression of miR-499a-5p in the serum of children with ASD was significantly related to the changes in brain volume of children with ASD, and the miRNA showed good diagnostic accuracy in children with ASD.

Understanding Early Diagnosis of Autism Spectrum Disorder in South Indian Children: A Multicentric Real World Data

ABSTRACT Background: Global autism spectrum disorder (ASD) incidence is rising, emphasizing early diagnosis for improved outcomes. Most data are from developed nations, pointing to the need for information from developing nations like India. Objectives: The study aims to analyze the sociodemographic and clinical profiles of children with ASD, to assess ASD severity using Childhood Autism Rating Scale (CARS) and Indian Scale for Assessment of Autism (ISAA), to evaluate treatment outcomes with Autism Treatment Evaluation Checklist (ATEC), and to identify factors associated with early diagnosis. Subjects and Methods: A cohort study conducted in the outpatient departments of five centers in Tamil Nadu from May 1, 2022, to May 31, 2023, focused on children with ASD. SPSS Version 26 was used for analysis. Descriptive statistics included frequency, percentage, and mean ± standard deviation. Chi-squared or Fisher’s exact test (P &lt; 0.05) examined factors associated with age at diagnosis, whereas Spearman’s rank correlation assessed associations among ATEC, CARS, and ISAA scores. Results: The male-to-female ratio was 7:3, with a mean diagnosis age of 3.2 years. 67.9% were from urban residence, 68.9% belonged to upper socio-economic class, and 6.6% had a history of consanguinity. Neonatal convulsions occurred in 35%, 23% required NICU, and 4.7% had a family history of ASD. Poor eye contact and solitary play were reported in 87% &amp; 83%, respectively, Tantrums in 57%, and poor interaction in 64%. Conclusion: Urban residence, neonatal convulsions, and ASD severity were significantly associated with early diagnosis. There was no correlation of the ATEC scale with either the CARS or ISAA scale.

The association between preterm delivery and autism spectrum disorder in childhood: A retrospective cohort study.

Prematurity complications are a leading cause of mortality and morbidity in offspring, including adverse neurodevelopmental outcomes. The association between preterm birth (PTB) and autism spectrum disorder (ASD) remains debated. To investigate the association between PTB and ASD diagnosis during childhood. This cohort study analyzed data from community clinics and a tertiary hospital, encompassing deliveries from 2005 to 2017. ASD incidence was compared across gestational age categories: extremely preterm (<28 weeks), very preterm (28-32 weeks), moderate to late preterm (32-37 weeks), and term (≥37 weeks). Additional comparisons were made between all preterm (<37 weeks) and term deliveries (≥37 weeks). Cumulative ASD incidence was assessed using Kaplan-Meier survival curves and a Cox proportional hazards model adjusted for potential confounders. Among 114 975 pregnancies, 0.3% delivered at <28 weeks, 0.6% at 28-32 weeks, and 6% at 32-37 weeks, with 6.9% preterm deliveries overall. Univariable analysis revealed a significant association between PTB and ASD (1.6% for <28 weeks vs 0.3% for 28-32 weeks vs 0.8% for 32-37 weeks vs 0.7% for term, P = 0.036). Crude ASD incidence was 0.8% (odds ratio [OR] 1.21, 95% confidence interval [CI] 0.93-1.56, P = 0.15). However, adjusted results showed no significant association: adjusted hazard ratio = 0.74 (95% CI 0.24-2.34, P = 0.61) for <28 weeks, 0.99 (95% CI 0.24-3.99, P = 0.98) for 28-32 weeks, and 1.07 (95% CI 0.81-1.43, P = 0.63) for 32-37 weeks. Kaplan-Meier analysis showed similar cumulative ASD incidence across groups (P = 0.855). This retrospective cohort study found no significant association between PTB and childhood ASD diagnosis.

Thermal Image Classification of Autistic Children Using Res-Net Architecture

The thermal Image Classification Method has been widely used for significant applications in many fields, including thermal images of the face. This study presents a method for thermal facial classification in children with autism spectrum disorder (ASD). Children with ASD have a neurological disorder that affects communication skills essential in daily life and often causes difficulties in social situations. As we know, the diagnosis of ASD currently still relies on human methods and does not yet have definite biological markers. Early diagnosis of ASD has a significant positive impact, especially in children. Deep learning techniques, especially in facial medical image analysis, have become a new research focus in ASD detection. Initial screening using a Convolutional Neural Network (CNN) model with a transfer learning approach offers great potential for early diagnosis of ASD. The use of thermal imaging as a passive method to analyze ASD-related physiological signals has been proposed. In previous research, a deep learning model was developed to classify the faces of autistic children using thermal images. Therefore, this study aims to create a new Thermal Image Classification model for Autistic Children Using Res-Net Architecture. The architectures applied are ResNet-18, ResNet-34, and ResNet-50. As a comparison system, several of the same parameter values are used: epoch 100, batch size 2, SGD, Cross-entropy, learning rate 0.001, and momentum 0.9. The study test results show that the results of ResNet-18 are 97.22%, ResNet-34 99.22%, and ResNet-50 99.41%. Based on these results, ResNet-50 has the highest value.

The Impact of a Music- and Movement-Based Intervention on Motor Competence, Social Engagement, and Behavior in Children with Autism Spectrum Disorder.

The main objective of this manuscript is to evaluate the effects of training, music, and movement intervention on motor functions, social engagement, and behaviors in autistic children. Twenty-one children with a diagnosis of mild autism spectrum disorder (ASD), with an age range of 5-to-13 years, were divided into two groups: the experimental group (n = 10) and the control group (n = 11). All participants were examined before (T0) and after the intervention (T1) to evaluate their motor functions (Bruininks-Oseretsky Motor Performance Test (BOT-2)), maladaptive behavior (RCS (Response to Challenge Scale)), and enjoyment and engagement (PACES (Physical Activity Enjoyment Scale)). Statistical analysis showed that music and movement intervention significantly improved motor functions such as balance and bilateral coordination (p < 0.0001), social engagement (p = 0.002), and adaptive behaviors (p = 0.005) in children with ASD. Our research supports the feasibility of music and movement intervention and documents the interest in participating in children with ASD. This study demonstrates the benefits of movement and music interventions and can be considered a useful way to manage autism spectrum disorders in the future.

3T dilated inception network for enhanced autism spectrum disorder diagnosis using resting-state fMRI data.

Autism spectrum disorder (ASD) is one of the complicated neurodevelopmental disorders that impacts the daily functioning and social interactions of individuals. It includes diverse symptoms and severity levels, making it challenging to diagnose and treat efficiently. Various deep learning (DL) based methods have been developed for diagnosing ASD, which rely heavily on behavioral assessment. However, existing techniques have suffered from poor diagnostic outcomes, higher computational complexity, and overfitting issues. To address these challenges, this research work introduces an innovative framework called 3T Dilated Inception Network (3T-DINet) for effective ASD diagnosis using resting-state functional Magnetic Resonance Imaging (rs-fMRI) images. The proposed 3T-DINet technique designs a 3T dilated inception module that incorporates dilated convolutions along with the inception module, allowing it to extract multi-scale features from brain connectivity patterns. The 3T dilated inception module uses three distinct dilation rates (low, medium, and high) in parallel to determine local, mid-level, and global features from the brain. In addition, the proposed approach implements Residual networks (ResNet) to avoid the vanishing gradient problem and enhance the feature extraction ability. The model is further optimized using a Crossover-based Black Widow Optimization (CBWO) algorithm that fine-tunes the hyperparameters thereby enhancing the overall performance of the model. Further, the performance of the 3T-DINet model is evaluated using the five ASD datasets with distinct evaluation parameters. The proposed 3T-DINet technique achieved superior diagnosis results compared to recent previous works. From this simulation validation, it's clear that the 3T-DINet provides an excellent contribution to early ASD diagnosis and enhances patient treatment outcomes.

Habitual walking and mental disorder in relation to autism spectrum disorder

Comorbid psychiatric conditions are highly prevalent in individuals with autism spectrum disorder (ASD). Habitual walking may have beneficial effects on mental health; however, it may increase social interactions, which are difficult for individuals with ASD. Hence, the beneficial influence of habitual walking on mental health may be limited to individuals without ASD. A cross-sectional study of 3,823 participants was conducted. Mental disorder was defined as a Kessler Psychological Distress Scale score ≥13. Participants with ASD diagnosis and an autism-spectrum quotient score ≥33 points were also regarded as having ASD. Among participants without ASD, in the reference group with no habitual walking, the fully adjusted odds ratios (95% confidence intervals) of mental disorders for walking at least 1 day/week but <3 days/week, walking at least 3 days/week but <7 days/week, and walking every day were 0.96 (0.70 and 1.33), 0.83 (0.64 and 1.08), and 0.68 (0.49 and 0.96), respectively. Among those with ASD, the corresponding values were 1.29 (0.62 and 2.72), 1.33 (0.72 and 2.45), and 1.55 (0.69 and 3.46), respectively. ASD status determines the association between habitual walking and mental disorders. For individuals with ASD, habitual walking may not be effective for preventing and treating mental disorders.

Diagnosis of autism spectrum disorder: a systematic review of clinical and artificial intelligence methods

Diagnosis of autism spectrum disorder: a systematic review of clinical and artificial intelligence methods

Analysis of Disparities in Diagnosis of Autism Spectrum Disorder in the Military Health System Pediatrics Population.

There have been disparities reported in prevalence of autism by gender, race, and socioeconomic status with older ages of diagnosis in non-White and in female children. Possible disparities in the ages of autism diagnosis are not well-established within the Military Health System (MHS) pediatric population, where we hypothesized less disparities given universal Tricare coverage for active-duty military families and theoretically equal access to the military treatment facility (MTF). We conducted retrospective cross-sectional analysis using deidentified database repository records from the MHS. We collected and analyzed demographic data on children covered by Tricare and newly diagnosed with autism within an MTF (N = 31,355) or outside of the MTF (5,579 respectively). Within the MTF, we identified younger ages of autism diagnosis in non-White children less than 18years old (p < 2.2e-16), without significant differences in ages of diagnosis by race in children less than 6years of age. There were no statistically significant differences in ages of diagnosis between males and females. Outside the MTF, we identified younger ages of autism diagnosis in males versus females with statistically significant difference in average ages of autism diagnosis between males and females less than the age of 18years (p = 4.4e-08). This difference was not seen in children less than 6years of age. Racial data was not available for diagnosis outside the MTF. The age of autism diagnosis in the military pediatric population within the MTF did not reflect historical disparities seen in non-White and in female children.

Comparison of children and adolescents diagnosed with attention deficit hyperactivity disorder with and without autism traits in terms of emotion regulation, clinical characteristics and functionality.

This study aims to demonstrate that children and adolescents diagnosed with Attention Deficit Hyperactivity Disorder (ADHD) who exhibit autism traits have a more severe clinical profile in terms of emotion regulation, clinical features related to ADHD, and functionality, compared to those diagnosed with ADHD without these traits. 50 patients with and 64 patients without autism traits between the ages of 8-16 were recruited for the study among the children and adolescents diagnosed with ADHD. The Schedule for Affective Disorders and Schizophrenia for School-Age Children-Present and Lifetime Version, DSM-5-2016-Turkish Adaptation (K-SADS-PL-DSM-5-T) was used to exclude the diagnosis of Autism Spectrum Disorder (ASD) and detect comorbid psychiatric diagnosis. The Social Reciprocity Scale (SRS) was completed by parents to determine groups based on autism traits. Children completed the Childhood Anxiety Sensitivity Index (CASI) and the Affective Reactivity Index (ARI). The Strengths and Difficulties Questionnaire (SDQ), Affective Reactivity Index-Parent Report (ARI-P) and Weiss Functional Impairment Rating Scale-Parent Report (WFIRS-P) were completed by the parents. We found that the group with autism traits had significantly more hyperactivity/inattention, conduct problems, emotional problems, and peer problems and significantly more irritability and frequent separation anxiety disorder. Although there was no significant impairment in functionality in either group, the level of impairment was significantly higher in the group with autism traits. Children diagnosed with Attention Deficit Hyperactivity Disorder who exhibit autism traits experience higher levels of irritability and separation anxiety disorder, as well as greater impairment in functionality, compared to those without these traits.

Early detection of autism spectrum disorder: gait deviations and machine learning.

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder diagnosed by clinicians and experts through questionnaires, observations, and interviews. Current diagnostic practices focus on social and communication impairments, which often emerge later in life. This delay in detection results in missed opportunities for early intervention. Gait, a motor behavior, has been previously shown to be aberrant in children with ASD and may be a biomarker for early detection and diagnosis of ASD. The current study assessed gait in children with ASD using a single RGB camera-based pose estimation method by MediaPipe (MP). Data from 32 children with ASD and 29 typically developing (TD) children were collected. The ASD group exhibited significantly reduced step length and right elbow° and increased right shoulder° relative to TD children. Four machine learning (ML) algorithms were employed to classify the ASD and TD children based on the statistically significant gait parameters. The binomial logistic regression (Logit) performed the best, with an accuracy of 0.82, in classifying the ASD and TD children. The present study demonstrates the use of gait analysis and ML techniques for the early detection of ASD.

Early Recognition and Intervention in SIBlingS at High Risk for Neurodevelopment Disorders (ERI-SIBS): a controlled trial of an innovative and ecological intervention for siblings of children with autism spectrum disorder.

It has been widely demonstrated that siblings of children with autism spectrum disorder (ASD) have an increased risk of abnormal developmental trajectories. In response to this, early recognition protocols have been developed worldwide, aiming to promote early interventions that can positively impact the neurodevelopment of this population. This paper presents the protocol of a controlled trial: ERI-SIBS (Early Recognition and Intervention in SIBlingS at High Risk for Neurodevelopment Disorders) is an innovative and ecological early recognition and intervention program designed specifically for siblings of children with ASD. We aim to recruit siblings at low risk and high risk of neurodevelopmental disorders. Based on clinical evaluation at T0, we will allocate the infants into three groups: Group 1, infants at low risk without any signs of neurodevelopmental disorders; Group 2, infants at high risk without any signs of neurodevelopmental disorders; Group 3: infants at low or high risk with signs suggestive of neurodevelopmental disorders. Children of Group 2 will undergo Active Monitoring (one 90 min session once a month for 6 months), while children of Group 3 will undergo Early Intervention (one 90 min session once a week for 6 months). In both cases, the ERI-SIBS contents are based on a multidimensional and naturalistic approach and always involve caregivers. All recruited children will be evaluated at three different time points (T0 within the 8 months of life of the child, T1 after 6 months and T2 after 12 months) using behavioural, technological, and biological techniques to assess infants' neurodevelopmental functions, parent-infant interaction, and early ASD markers. The ERI-SIBS study will expand knowledge regarding the impact of early intervention on families of infants at risk of neurodevelopmental disorders for the presence of a child with a diagnosis of ASD. The study will have the potential to significantly contribute to future research and the scientific and clinical debate on the best way to implement early intervention in at-risk populations. Clinicaltrials.gov identifier (NCT06512649).

Characteristics for the diagnosis of Autism Spectrum Disorder

Introduction: autism Spectrum Disorder (ASD) is indeed a neurodevelopmental disorder whose exact cause is not yet fully understood. Symptoms can vary significantly among affected individuals, but generally include difficulties in communication and social interaction, as well as repetitive and restricted behaviors. Identification of early indicators and early diagnosis, as well as effective management of ASD, may lead to earlier and improved intervention in affected patients.Methods: a search for information on diagnostic indicators of ASD was conducted in Pubmed, Cochrane and clinicaltrials.org databases. Systematic reviews and meta-analyses were identified without considering publication status, reported results, last five years of publication and with language restriction to English, Spanish and Portuguese.Results: a total of 12 studies published between 2013 - 2023 were selected, and 7 systematic reviews and meta-analyses published between 2013 - 2021 that met the established inclusion criteria were included.Conclusion: diagnostic indicators of Autism Spectrum Disorder (ASD) in children are crucial for the family as a whole to lead a more age-appropriate life. ASD is increasingly common in society, but many are still unaware of the signs in their children, which delays detection and creates difficulties for everyone. Early intervention and appropriate support can significantly improve the prognosis of the disorder and help find the best treatment for the child