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Autistic individuals often experience a wide range of barriers and challenges with employment across their lifetime. Despite their strengths and abilities to contribute to the workforce, many individuals experience unemployment, underemployment and malemployment. However, current supports and services are often inadequate to meet their needs. To allow autistic people to achieve vocational success, we explore four contributors to employment and expand upon the issues and potential solutions to each. These positions include the importance of family support and its consideration in the application of vocational support interventions, addressing transitional needs for autistic youth, building employer capacity, and conducting research that advises the development of meaningful programs and policies. By advocating for these positions, we aim to foster greater inclusivity and support for individuals with ASD in the workplace. [ABSTRACT FROM AUTHOR]

The number of autism spectrum disorder individuals is dramatically increasing. For them, it is difficult to get an early diagnosis or to intervene for preventing challenging behaviors, which may be the cause of social isolation and economic loss for all their family. This SLR aims at understanding and summarizing the current research work on this topic and analyze the limitations and open challenges to address future work. We consider papers published between 2015 and the beginning of 2021. The initial selection included about 2140 papers. 11 of them respected our selection criteria. The papers have been analyzed by mainly considering: (1) the kind of action taken on the autistic individual, (2) the considered wearables, (3) the machine learning approaches, and (4) the evaluation strategies. Results revealed that the topic is very relevant, but there are many limitations in the considered studies, such as reduced number of participants, absence of datasets and experimentation in real contexts, need for considering privacy issues, and the adoption of appropriate validation approaches. The issues highlighted in this analysis may be useful for improving machine learning techniques and highlighting areas of interest in which experimenting with the use of different noninvasive sensors. [ABSTRACT FROM AUTHOR]

Autism spectrum disorder (ASD) is a neurodevelopmental condition that significantly impacts the lives of many children due to its hidden symptoms. Early detection of ASD is challenging because of its complex and heterogeneous nature. Magnetic resonance imaging (MRI) has emerged as a crucial tool for early detection, offering non-invasive imaging with detailed soft tissue information. However, existing approaches face limitations such as overfitting, underfitting, class imbalance, control, domain shift, and behavioral issues. To address these challenges, this paper proposes a novel ASD detection and classification model called the Autism Spectrum Disorder-based Attention Graph Neural Network and Crossover Boosted Meerkat Optimization (ASD-AttGCBMO) algorithm. The proposed method utilizes structural Magnetic Resonance Imaging (sMRI) data from the ABIDE 1 dataset. The data undergoes preprocessing to remove artifacts and noise, ensuring high image quality and consistency. Node feature extraction employs voxel-based morphometry (VBM) and surface-based analysis, which extract relevant features such as surface area, cortical thickness, shape descriptors, and brain volumes. The ASD-AttGCBMO model is trained using preprocessed sMRI images, employing the Adam and Stochastic Gradient Descent (SGD) optimizers to prevent overfitting, reduce classification loss, and improve convergence. The model is designed to enhance the learning process and capture complex patterns for accurate feature classification between ASD and control subjects. To optimize the hyperparameters in the attention-based neural network model, the CBMO algorithm is employed. Experimental validation is conducted using essential performance evaluation measures. The proposed method achieves impressive results, with accuracy, precision, recall, specificity, F1-score, Area under Receiver Operating curve (AUC/ROC), and computational time values of 98.8%, 99%, 98.5%, 98.6%, 98.2%, 0.989, and 3.05 s, respectively. Comparative analysis demonstrates that the proposed method outperforms other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

This article comprehensively reviews motor impairments in children with autism spectrum disorder (ASD) to: (1) determine the prevalence of motor problems in children with ASD; (2) understand the nature of motor difficulties in ASD and whether they are consistent with developmental coordination disorder (DCD); and (3) determine if the term DCD was used as a co-occurring diagnosis in children with ASD after publication of the DSM-5 in 2013. The following databases were systematically searched: MEDLINE, EMBASE, CINAHL, and PsycINFO from 2010 to December 2021. Articles were included if they: (1) were peer-reviewed and published in a scientific journal; (2) included children with ASD who were between 5 and 12 years; (3) used motor or function measures to assess motor abilities in children with ASD. Studies that included children with intellectual disabilities were excluded. Two independent reviewers reviewed titles, abstracts, and full-text articles for inclusion. Twenty-seven studies met the inclusion criteria and were assessed for quality by two independent reviewers using the Appraisal tool for Cross-Sectional Studies. The majority of articles (92.5%) indicated that 50–88% of children with ASD had significant motor impairments on standardized motor assessments and/or functional questionnaires. The nature of motor and function problems in ASD were consistent with DCD; however, only three out of 20 papers (15%) that were published from 2014 described the motor problems as DCD. One study reported that 15.1% of children with ASD with motor impairments had a co-occurring diagnosis of DCD, suggesting that DCD is under-recognized in this clinical population. [ABSTRACT FROM AUTHOR]

This paper examines the role of dietary peptides gluten and casein in modulating brain function in individuals with autism spectrum disorder (ASD) from a biochemical perspective. Neurotransmitter systems and neural networks are crucial for brain function, and alterations at the biochemical level can contribute to the characteristic symptoms and behaviors of ASD. The paper explores how dietary peptides influence neurotransmitter systems and neural networks, highlighting their potential as interventions to improve brain function in ASD. The evidence suggests that dietary peptides can impact neurotransmitter synthesis, release, and receptor interactions, disrupting the balance of neurotransmitter systems and affecting neural network function. The findings underscore the potential of dietary interventions in modulating brain function in ASD and call for further research to elucidate the underlying mechanisms and optimize clinical practice. Considering individual dietary sensitivities and preferences, personalized dietary approaches may be necessary for optimal outcomes. Dietary interventions' timing, duration, and integration with other evidence-based treatments are crucial considerations. Safety considerations and regular monitoring are important to ensure the implementation of dietary interventions safely and effectively. [ABSTRACT FROM AUTHOR]

Autism spectrum disorder (ASD) is a complex, severe disorder related to brain development. It impairs patient language communication and social behaviors. In recent years, ASD researches have focused on a single-modal neuroimaging data, neglecting the complementarity between multi-modal data. This omission may lead to poor classification. Therefore, it is important to study multi-modal data of ASD for revealing its pathogenesis. Furthermore, recurrent neural network (RNN) and gated recurrent unit (GRU) are effective for sequence data processing. In this paper, we introduce a novel framework for a Multi-Kernel Learning Fusion algorithm based on RNN and GRU (MKLF-RAG). The framework utilizes RNN and GRU to provide feature selection for data of different modalities. Then these features are fused by MKLF algorithm to detect the pathological mechanisms of ASD and extract the most relevant the Regions of Interest (ROIs) for the disease. The MKLF-RAG proposed in this paper has been tested in a variety of experiments with the Autism Brain Imaging Data Exchange (ABIDE) database. Experimental findings indicate that our framework notably enhances the classification accuracy for ASD. Compared with other methods, MKLF-RAG demonstrates superior efficacy across multiple evaluation metrics and could provide valuable insights into the early diagnosis of ASD. [ABSTRACT FROM AUTHOR]

An overview of Applied Behavior Analysis (ABA) and Autism Spectrum Disorder (ASD) in the Middle East was published by Kelly and colleagues in 2016. The focus of the review was to explore clinical services, educational opportunities, and published research in the six countries of the Gulf Cooperation Council, namely the Kingdom of Bahrain, the State of Kuwait, the Sultanate of Oman, the State of Qatar, the Kingdom of Saudi Arabia, and the United Arab Emirates. The objective of the current paper was to provide an update on the current status of ABA and ASD in the Gulf region, with a focus on successes, challenges, and recommendations for future directions. [ABSTRACT FROM AUTHOR]

We recently read the interesting and informative paper entitled "Empathic accuracy and cognitive and affective empathy in young adults with and without autism spectrum disorder" (McKenzie et al. in Journal of Autism and Developmental Disorders 52: 1–15, 2021). This paper expands recent findings from our lab (Ben-David in Journal of Autism and Developmental Disorders 50: 741-756, 2020a; International Journal of Audiology 60: 319–321, 2020b) and a recent theoretical framework (Icht et al. in Autism Research 14: 1948–1964, 2021) that may suggest a new purview for McKenzie et al.'s results. Namely, these papers suggest that young adults with autism spectrum disorder without intellectual disability can successfully recruit their cognitive abilities to distinguish between different simple spoken emotions, but may still face difficulties processing complex, subtle emotions. McKenzie et al. (Journal of Autism and Developmental Disorders 52: 1–15, 2021) extended these findings to the processing of emotions in video clips, with both visual and auditory information. [ABSTRACT FROM AUTHOR]

Phygital interaction is a form of tangible interaction where digital and physical contents are combined in such a way that the locus of multimedia information is detached from the physical material(s) manipulated by the user. The use of phygital interaction is supported by several theoretical approaches that emphasize the development of cognitive skills dependent upon embodied interactions with the physical environment. Several studies demonstrate the potential of using phygital technologies for supporting people with intellectual disabilities (ID) in the development of cognitive, sensorimotor, social and behavioral skills. Our research aims at exploring the potential of phygital interaction for (young) adults with ID in a real setting, using a research platform called Reflex as a case study. For this purpose, we ran an empirical study involving 17 participants with ID and 8 specialists, and compared Reflex with approaches making use of only digital contents or paper-based materials. Our findings highlighted the potentials of phygital approaches to perform interventions with people with ID, enhancing their performances with an appreciated interaction method. In addition, the post-study interviews with specialists favoured the adoption of phygital technologies in a social care context. [ABSTRACT FROM AUTHOR]

Autism Spectrum Disorder (ASD) is neurodevelopment-based impact on interactive communication and social skills. Diagnosing ASD is one of serious issues that start manifesting at low ages, and is difficult to diagnose at early stages. Autism is characterized by both environmental and genetic factors. Lack of communication issues, social interaction, and limited interest behaviors are possible individuality of autism noticed in children, along other symptoms. This paper aims at ASD detection by Deep Quantum Neural Network (DQNN), wherein this network is trained by proposed Fractional Social Driving Training-Based Optimization (FSDTBO). The initial stage of this processing starts with acquisition of image from dataset, and further pre-processing is carried out using Gaussian filter, and this filtered image is suspended for Regions of Interest (ROI) extraction. Also, extraction of nub region is done by proposed Social Driving Training-Based Optimization (SDTBO), from which classification process is done by considering extracted features too. Here, proposed FSDTBO is integration process among Fractional Calculus (FC) and SDTBO, wherein SDTBO is collaboration between Social Optimization Algorithm (SOA) and Driving Training-Based Optimization (DTBO). Moreover, classification performance of ASD is found based on three metrics, like accuracy, specificity, and sensitivity with superior values of 0.90, 0.94, and 0.96. [ABSTRACT FROM AUTHOR]

Children with autism face challenges in areas like language and social skills, which hinder their ability to undergo regular fire training. Fire is one of the most common and dangerous disaster in real life, making it essential to provide children with appropriate prevention and response education. Virtual humans can offer diverse presentation forms and interact with children with autism, thus better stimulating their willingness to participate. To train the fire safety skills of children with autism, this paper proposes the application of highly realistic virtual humans in a fire education system, aiming to improve their fire safety skills. The results show that this approach effectively enhances the fire safety skills of children with autism. To enhance the realism of virtual humans in the fire education system, this paper improves the 3D pose estimation method and proposes a multi-physical factor pose estimation algorithm. By evaluating the Mean Penetration Error (MPE) and the Percentage Not Penetrated (PNP) it was shown that the pose estimation algorithm achieved higher accuracy with only 6.4% foot penetration. We counted the number of movements and the number of movements captured by the system for all participants in the fire training and showed that the system’s motion capture accuracy was over 90%. [ABSTRACT FROM AUTHOR]

Autism spectrum disorder (ASD) is a prevalent neurodevelopmental condition. Its early and accurate diagnosis is critical in enhancing the quality of life for affected individuals. Graph neural networks supply promising approaches for ASD diagnosis. However, existing works typically focus on brain-level or population-level classification methods, where the former usually disregards subjects' non-imaging information and inter-subject relationships, and the latter generally fails to adequately evaluate and detect disease-associated brain regions and biomarkers. Furthermore, relatively static graph structures and shallow network architectures hinder the abundant extraction of information, affecting the performance of ASD identification. Accordingly, this paper proposes a new spectral graph network with brain-population graph structure joint learning (BPGLNet) for ASD diagnosis. This new framework involves two main components. Firstly, a brain-level graph learning module is designed to acquire valuable features of brain regions and identify effective biomarkers for each subject. In particular, it constructs a brain-aware representation learning network by fusing an improved graph pooling strategy and spectral graph convolution to learn subgraph structures and features of brain regions. Subsequently, based on these generated features, a population-level graph learning module is developed to capture relationships between different subjects. It builds an adaptive edge generator network by integrating non-imaging and imaging data, forming a learnable population graph. Further, this module also devises a deep cascade spectral graph network to enrich high-level feature representation of data and complete ASD identification. Experiments on the benchmark dataset reveal the state-of-the-art performance of BPGLNet. [ABSTRACT FROM AUTHOR]

Background: Over the past years, researchers have been using head-mounted eye-tracking systems to study young children's gaze behaviors in everyday activities through which children learn about the world. This method has great potential to further our understanding of how millisecond-level gaze behaviors create multisensory experiences and fluctuate around social environments. While this line of work can yield insight into early perceptual experiences and potential learning mechanisms, the majority of the work is exclusively conducted with typically-developing children. Sensory sensitivities, social-communication difficulties, and challenging behaviors (e.g., disruption, elopement) are common among children with developmental disorders, and they may represent potential methodological challenges for collecting high-quality data. Results: In this paper, we describe our research practices of using head-mounted eye trackers with 41 autistic children and 17 children with increased likelihood of later autism diagnosis without auditory or visual impairments, including those who are minimally or nonspeaking and/or have intellectual disabilities. The success rate in gathering data among children with autism was 92.68%. 3 of 41 children failed to complete the play-session, resulting in an 86.36% success rate among 1–4-year-olds and a 100.00% success rate among 5–8-year-olds. 1 of 17 children with increased likelihood of later autism diagnosis failed to complete the play-session, resulting in a success rate of 94.11%. There were numerous "challenging" behaviors relevant to the method. The most common challenging behaviors included taking the eye-tracking device off, elopement, and becoming distressed. Overall, among children with autism, 88.8% of 1–4-year-olds and 29.4% of 5–8-year-olds exhibited at least one challenging behavior. Conclusions: Research capitalizing on this methodology has the potential to reveal early, socially-mediated gaze behaviors that are relevant for autism screening, diagnosis, and intervention purposes. We hope that our efforts in documenting our study methodology will help researchers and clinicians effectively study early naturally-occuring gaze behaviors of children during non-experimental contexts across the spectrum and other developmental disabilities using head-mounted eye-tracking. Ultimately, such applications may increase the generalizability of results, better reflect the diversity of individual characteristics, and offer new ways in which this method can contribute to the field. [ABSTRACT FROM AUTHOR]

While graph convolution-based methods have become the de-facto standard for graph representation learning, their applications to disease prediction tasks remain quite limited, particularly in the classification of neurodevelopmental and neurodegenerative brain disorders. In this paper, we introduce an aggregator normalization graph convolutional network by leveraging aggregation in graph sampling, as well as skip connections and identity mapping. The proposed model learns discriminative graph node representations by incorporating both imaging and non-imaging features into the graph nodes and edges, respectively, with the aim of augmenting predictive capabilities and providing a holistic perspective on the underlying mechanisms of brain disorders. Skip connections enable the direct flow of information from the input features to later layers of the network, while identity mapping helps maintain the structural information of the graph during feature learning. We benchmark our model against several recent baseline methods on two large datasets, Autism Brain Imaging Data Exchange (ABIDE) and Alzheimer's Disease Neuroimaging Initiative (ADNI), for the prediction of autism spectrum disorder and Alzheimer's disease, respectively. Experimental results demonstrate the competitive performance of our approach in comparison with recent baselines in terms of several evaluation metrics, achieving relative improvements of 50% and 13.56% in classification accuracy over graph convolutional networks (GCNs) on ABIDE and ADNI, respectively. Our study involved the development of a graph convolutional aggregation model, which aimed to predict the status of subjects in a population graph. We learned discriminative node representations by utilizing imaging and non-imaging features associated with the graph nodes and edges. Our model outperformed existing graph convolutional-based methods for disease prediction on two large benchmark datasets, as shown through extensive experiments. We achieved significant relative improvements in classification accuracy over GCN and other strong baselines. [ABSTRACT FROM AUTHOR]

Background: Phosphatase and tensin homologue (PTEN) is a cancer suppressor gene. Constitutional mutations affecting this gene are associated with several conditions, collectively termed PTEN hamartoma tumour syndromes (PHTS). In addition to hamartomas, PTEN aberrations have been associated with a range of non-tumoural phenotypes such as macrocephaly, and research indicates possibly increased rates of developmental delay and autism spectrum disorder (ASD) for people with germline mutations affecting PTEN. Method: A systematic review of literature reporting behavioural and psychological variables for people with constitutional PTEN mutations/PHTS was conducted using four databases. Following in-depth screening, 25 articles met the inclusion criteria and were used in the review. Fourteen papers reported the proportion of people with PTEN mutations/PTHS meeting criteria for or having characteristics of ASD and were thus used in a pooled prevalence meta-analysis. Results: Meta-analysis using a random effects model estimated pooled prevalence of ASD characteristics at 25% (95% CI 16–33%), although this should be interpreted cautiously due to possible biases in existing literature. Intellectual disability and developmental delay (global, motor and speech and language) were also reported frequently. Emotional difficulties and impaired cognitive functioning in specific domains were noted but assessed/reported less frequently. Methods of assessment of psychological/behavioural factors varied widely (with retrospective examination of medical records common). Conclusions: Existing research suggests approximately 25% of people with constitutional PTEN mutations may meet criteria for or have characteristics of ASD. Studies have also begun to establish a range of possible cognitive impairments in affected individuals, especially when ASD is also reported. However, further large-scale studies are needed to elucidate psychological/behavioural corollaries of this mutation, and how they may relate to physiological/physical characteristics. [ABSTRACT FROM AUTHOR]

This study tests a handwriting model for children and youth with Autism Spectrum Disorder (ASD) that displays the relationships between handwriting process and product characteristics, and the predictors of these characteristics. Structural Equation Modelling was used to test the model for children and youth with ASD (n = 50) and typically developing peers (n = 50), ages 10 to 15, for a copying and freestyle handwriting task. Findings suggest a generic handwriting model applying to both groups and both handwriting tasks. Unique effects for children and youth with ASD were identified for: (1) high interdependence of handwriting process characteristics, (2) longer pen stroke in air leading to better legibility, and (3) lower scores for executive functions leading to lower scores for legibility. [ABSTRACT FROM AUTHOR]

Numerous studies suggest that children with Autism Spectrum Disorder (ASD) encounter language problems related to syntax. In particular, these children face difficulties in comprehending and answering complex language structures in Arabic. This paper examines whether a prototype Arabic assistive application can have an impact on the ability of children with ASD to comprehend and answer content questions and on their communicative skills. Via two questionnaires targeting 57 caregivers and ASD specialists in Jordan and a focus-group discussion with three teachers working in three autism centers in Jordan, a prototype assistive application named Aseel was created. To test the effect of using this app on enhancing ASD children's ability to comprehend and answer Arabic questions, a sample consisting of two groups of children: 20 verbal and 5 nonverbal children with ASD enrolled in three autism centers in Amman, Jordan was recruited. A pre-test containing 55 content questions was designed and tested on the two groups. Then, a treatment for three weeks took place in which the teachers trained the children on answering these questions using the app. A post-test was conducted after a three-day break to test whether the app affected the ability of verbal and nonverbal children with ASD to comprehend and answer the questions accurately. The data analysis revealed that the differences between the answers of the two groups in the pre- and post-tests were statistically significant. This suggests that this technology has the potential to aid both verbal and nonverbal children with ASD in effectively learning complex Arabic content questions. This assistive application enhances collaboration between teacher and children with ASD, visual-spatial thinking and communication with others. Another advantage of this app is increasing ASD children's vocabulary repertoire. Preliminary results involving nonverbal children showed that the icons which visually and auditorily represent the most basic needs for a person are proving effective in helping this group communicate with their caregivers and teachers. [ABSTRACT FROM AUTHOR]

Autism spectrum disorder (ASD) is one of the major problems, which manifests at a young age and is hard to identify in the early stages. Typically, it affects the interaction skills and social behavior in children aged 6–17 years old. Autism is caused by both genetic and environmental factors. Autism can manifest itself in children as a lack of communication, social interaction, and limited interest behaviors, among other symptoms. ASD is a mild retardation, in which many people on ASD have incredible talents and abilities. Approximately 40% are academically above average and possess a special ability to view the world with joy from a unique viewpoint. This paper developed the ASD classification using deep Q learning network (DQN), and SpinalNet, wherein these network's hyperparameters are trained by the proposed driving training political optimizer (DTPO). The initial stage of this processing starts with the acquisition of an image from the dataset, and further preprocessing is carried out by an adaptive Wiener filter, and this filtered image is sent to regions of interest (ROI) extraction. After that, extraction of the nub region is done by the proposed DTPO, from which the classification process is done by considering extracted features. Moreover, the classification performance of ASD is performed, and the output of DQN and SpinalNet is subjected to a fusion process to provide the classification of ASD with the help of Czekanowski similarity. Furthermore, the classification is based on three metrics like accuracy, sensitivity, specificity, mean-squared error, root-mean-squared error, R, and mean absolute error, with superior values of 0.907, 0.958, 0.936, 0.536, 0.732, 0.488, and 0.409, respectively. [ABSTRACT FROM AUTHOR]

Autism spectrum disorder (ASD) is brain enabled disorder representing behaviors in a repetitive manner and social deficits. In this paper, ASD is diagnosed using brain magnetic resonance imaging (MRI) enabled deep learning with a hybrid optimization algorithm. Also, the hybrid optimization algorithm utilized is hybrid sewing training optimization (HSTO) which trains ZFNet for ASD detection. Pre-processing of the MRI image is done by Wiener filter and the filtered image is fed for region of interest extraction. Moreover, pivotal region extraction is carried out by the proposed HSTO, which is finally allowed for ASD detection by ZFNet. The proposed HSTO is formed by combining sewing training-based optimization and hybrid leader-based optimization. Furthermore, the performance of HSTO_ZFNet is found by five performance metrics of accuracy with 95.7%, true negative rate with 92.6%, true positive rate with 93.7%, false negative rate with 68.7%, and false positive rate with75.9%. [ABSTRACT FROM AUTHOR]

Background: Unusual responses to sensory stimuli are considered a diagnostic symptom of autism spectrum disorder with mounting research efforts put towards understanding, characterizing, and treating such symptoms. Methods: This paper examines self and caregiver report tools used to measure sensory features in ASD through a systematic review of the psychometric evidence for their use. A total of 31 empirical papers were reviewed across 20 assessment tools. Substantial differences were identified in the specific sensory features defined across assessment tools. Sensory assessment questionnaires were evaluated against quality psychometric evidence criteria to provide a use recommendation. Results: Five assessments were identified to be "appropriate with conditions," while no sensory assessment tools were identified to have sufficient quality psychometric evidence to provide a recommendation of "Appropriate" for measuring sensory features in ASD. Conclusion: Evidence from this review highlights potentially significant shortcomings among the current methods used to measure sensory features in ASD and suggests the need for more efforts in developing psychometrically sound sensory assessment tools for use in ASD populations. [ABSTRACT FROM AUTHOR]

Autism spectrum disorder (A.S.D.) is considered a heterogeneous mental disorder, which is notoriously difficult to identify for a better diagnosis, especially among children. The current diagnosis methodology is purely based on the behavioural observation of symptoms prone to misdiagnosis. Several hybrid methods were explored, which also needs its improvisation in better prediction and diagnosis to move this field towards intelligent and accurate diagnosis. The main objective of this research paper was to develop the new diagnosis software which integrates the novel fuzzy hybrid deep convolutional neural networks and fusion of facial expressions and human gaits based on input video sequences. The algorithm has been trained and validated with the different video datasets such as Kaggle FER2013 and Karolinska Directed Emotional Faces (KDEF) datasets with real-time test scenarios, and various parameters such as accuracy, recall and F1-score were evaluated. Our proposed deep learning model outperforms another state-of-the-art method with an increase in prediction accuracy up to 30% with maximum accuracy of 95%. The model presented in this paper yields more advantages in terms of prediction time and accuracy also. However, the speech therapists, teachers, caretakers, and parents can use the software as a technological tool when working with children with A.S.D. [ABSTRACT FROM AUTHOR]

Economic evaluations can help decision makers identify what services for children with neurodevelopmental disorders provide best value-for-money. The aim of this paper is to review the best available economic evidence to support decision making for attention deficit-hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) in children and adolescents. We conducted a systematic review of economic evaluations of ADHD and ASD interventions including studies published 2010–2020, identified through Econlit, Medline, PsychINFO, and ERIC databases. Only full economic evaluations comparing two or more options, considering both costs and consequences were included. The quality of the studies was assessed using the Drummond checklist. We identified ten studies of moderate-to-good quality on the cost-effectiveness of treatments for ADHD and two studies of good quality of interventions for ASD. The majority of ADHD studies evaluated pharmacotherapy (n = 8), and two investigated the economic value of psychosocial/behavioral interventions. Both economic evaluations for ASD investigated early and communication interventions. Included studies support the cost-effectiveness of behavioral parenting interventions for younger children with ADHD. Among pharmacotherapies for ADHD, different combinations of stimulant/non-stimulant medications for children were cost-effective at willingness-to-pay thresholds reported in the original papers. Early intervention for children with suspected ASD was cost-effective, but communication-focused therapy for preschool children with ASD was not. Prioritizing more studies in this area would allow decision makers to promote cost-effective and clinically effective interventions for this target group. [ABSTRACT FROM AUTHOR]

Autism spectrum disorder (ASD) is a disorder in neurological growth, which includes cognitive and behavioral impairment and it starts from infancy. However, the reason for ASD is still vague and no effective medical ways are used for its discovery. Earlier discovery of ASD is extensively beneficial for children's health sustainability. The classical detection models rely on expertise analysis which tends to be expensive and inaccurate. Thus, this paper presents an effectual autism diagnostic model with electroencephalogram (EEG) signals that are produced through the electrical activities of the brain for detecting ASD. The Gaussian filter is employed to abandon the noise. Various statistical features signal and spectral-based features are extracted and provided to DRN for enhanced efficiency. The ASD detection is undergone using Chronological Sewing Training Optimization-Deep Residual Network (CSTO-DRN) wherein DRN is pre-trained using CSTO algorithm by tuning finest weights. The CSTO is built by incorporating the Chronological concept with Sewing Training-Based Optimization (STBO). The CSTO-DRN provided finest accuracy of 88.6%, Negative Predictive Value (NPV) of 87.8%, Positive Predictive Value (PPV) of 89.4%, True negative rate (TNR) of 85%, True positive rate (TPR) of 88.9%, and F-Measure of 87.5%. Its execution can enhance the efficiency of detection and minimize cost and human intervention. [ABSTRACT FROM AUTHOR]

Autism Spectrum Disorder (ASD) is a neuro-developmental disorder that limits social and cognitive abilities. ASD has no cure so early diagnosis is important for reducing its impact. The current behavioral observation-based subjective-diagnosis systems (e.g., DSM-5 or ICD-10) frequently misdiagnose subjects. Therefore, researchers are attempting to develop automated diagnosis systems with minimal human intervention, quicker screening time, and better outreach. This paper is a PRISMA-based systematic review examining the potential of automated autism detection system with Human Activity Analysis (HAA) to look for distinctive ASD characteristics such as repetitive behavior, abnormal gait and visual saliency. The literature from 2011 onward is qualitatively and quantitatively analyzed to investigate whether HAA can identify the features of ASD, the level of its classification accuracy, the degree of human intervention, and screening time. Based on these findings, we discuss the approaches, challenges, resources, and future directions in this area. According to our quantitative assessment of the dataset Zunino et al. (IEEE: 3421–3426, 2018 [1]), Inception v3 and LSTM Zunino et al. (IEEE: 3421–3426, 2018 [1]) give the highest accuracy (89%) for repetitive behavior. For abnormal gait-based approach, the multilayer perceptron gives 98% accuracy based on 18 features from dataset Abdulrahman et al. (COMPUSOFT: An International Journal of Advanced Computer Technology 9(8):3791–3797, 2020 [2]). For gaze pattern, a saliency-metric feature-based learning Rahman et al. (Int Conf Pattern Recognit, 2020 [3]) gives 99% accuracy on dataset Duan et al. (Proceedings of the 10th ACM Multimedia Systems Conference: 255–260, 2019 [4]), while an algorithm involving statistical features and Decision Trees yields an accuracy of 76% on dataset Yaneva et al. (Proceedings of the Internet of Accessible Things. W4A '18, Association for Computing Machinery, New York, NY, USA, 1–10, 2018 [5]). In terms of the state of the art, fully automated HAA systems for ASD diagnosis show promise but are still in developmental stages. However, this is an active research field, and HAA has good prospects for helping to diagnose ASD objectively in less time with better accuracy. [ABSTRACT FROM AUTHOR]

Theory of mind (ToM) represents a complex ability, while persons with autism spectrum disorder (ASD) encounter difficulties in the processing of ToM. The present ToM-focused studies on adults with ASD report inconsistent results, possibly owing to the differences between tasks. For instance, different ToM-related tasks involve different cognitive abilities, but the development of these cognitive abilities is different among adults with ASD, thereby leading to different behaviors by the same individual with ASD in different tasks. Therefore, it is of vital significance to explore the potential reasons for inconsistencies in the existing studies based on the task classification perspective. Hence, this study primarily reviews the existing ToM tasks used in studies on adults with ASD; afterward, based on the forms and characteristics of the task, the current ToM tasks are classified into four categories—reading comprehension, perceptual scene comprehension, comprehensive scene comprehension , and self–other processing. Subsequently, a meta-analysis is undertaken to determine the difference in each ToM task category between the ASD group and the typically developing (TD) group. As a result, 110 research papers (including 3,205 adults with ASD and 3,675 TD adults) that fulfilled the stated criteria are examined in this study. The study findings suggest that adults with ASD demonstrate worse performance in terms of all four ToM task categories as compared to TD adults. Furthermore, compared with tasks of self–other processing and perceptual scene comprehension, adults with ASD perform worse in reading comprehension and comprehensive scene comprehension. This shows that the differences between tasks may exert a potential influence on the study results. Future studies should focus on different abilities involved in ToM processing and the choice of ToM tasks, in order to elucidate the critical problems of ToM in adults with ASD. [ABSTRACT FROM AUTHOR]

This paper presents an multimodal feature fusion hybrid-CNN method to discriminate between typically developing (TD) and patients having autism spectrum disorder (ASD). Furthermore, it finds levels of those with ASD. The model, which uses the right and left pupil diameter variation of individuals, consists of six stages: augmentation, spectrogram, image fusion, feature extraction, selection, and classifier. ASD and TD discrimination and severity levels of patients having ASD are classified by SVM and k-NN algorithm, and 95.33% and 93.33% accuracy values are obtained respectively. The results are as follows: The proposed method is effective in determining the severity levels of TD patients and ASD patients. Pupil diameter, which is one of the gaze characteristics, can be used to detect ASD patients. [ABSTRACT FROM AUTHOR]

Evolutionary computing algorithms are computational intelligent systems that are used in a wide range of research applications, primarily for optimization. In this paper, an artificial neural network (ANN), a machine learning technique, is used to classify the data. The weights associated with each neuron and the architecture of the neural network always bias the output of the network model. With prior knowledge or trial and error techniques, different metrics or objectives can be used to optimise these weights. The optimization of weights using multiple objectives refers to a "multi-objective optimization problem." In this paper, an evolutionary cultural algorithm is used to optimise weights in ANN, and the results are reported with improved accuracy. Three benchmark datasets for autism screening data are used, trained, and tested for model accuracy in the classification: toddlers (1054,19), children (292,21), and adults (704,21).With the support of the domain expert, real-time data were collected from parents and caregivers and totalled over 1000 records, with a moderate difference in attributes based on CARS-2 (Childhood Autism Rating Scale, 2nd Edition) for ASD screening. In this paper, the proposed model is compared using a curve-fitting mathematical technique. The proposed model is trained and tested, and the results showed that it outperformed other algorithms in terms of precision, accuracy, sensitivity, and specificity. [ABSTRACT FROM AUTHOR]

Being one of the most prominent neurological development disorder, autism spectrum disorder (ASD) has arisen as global issue. It emerges over the first few years of life and neurologist diagnose ASD by considering various factors. A biomarker, such as an electroencephalogram (EEG), may be employed to aid the diagnosis process as it can easily track abnormal changes in the patient's brain. Therefore, this paper intends to propose an ensemble empirical mode decomposition based novel approach with multiscale fluctuation dispersion entropy for automated diagnosis of ASD patients using multichannel EEG signals. The various boosting classifiers which are empowered with Bayesian optimization based parameter estimation technique are considered to evaluate performance of proposed approach. Finally, the overall analysis of results exhibits that light gradient boosting machine (LGBM) produces maximum accuracy, specificity, and sensitivity of 99.59%, 99.37%, and 99.18% respectively with 0.9998 value of area under curve and 0.9919 value of Kappa statistic with optimally small computational time and minimum error measures. [ABSTRACT FROM AUTHOR]

Autism spectrum disorder (ASD) is a neurodevelopmental illness characterized by impaired social interaction and restricted repetitive behaviors or interests. The rising prevalence of ASD diagnosis has triggered a surge in research into investigating the underlying neuropathological processes and finding new therapeutic approaches. ASD is characterized by neuroinflammation and dysregulation of neuro-immune cross-talk, which suggests that stem cell treatment might be a potential therapeutic approach. The beneficial and restorative effects of stem cells are mainly due to their paracrine activity, in which stem cells generate and release extracellular vesicles such as exosomes and distinct secreted non-vesicle soluble proteins, including, growth factors, chemokines, cytokines, and immunomodulatory molecules referred to as the Secretome. In this paper, we reviewed the existing research exploring the therapeutic potential of stem cell secretome focusing on their role in addressing ASD pathology. Furthermore, we proposed a comprehensive mechanism of action for stem cell secretions, encompassing the broader secretome as well as the specific contribution of exosomes, in alleviating ASD neuropathology. Across the reviewed studies, exosomes and secreted soluble factors of the transplanted stem cell demonstrate a potential efficacy in ameliorating autistic-like behaviors. The proposed mechanism of action involves the modulation of signaling pathways implicated in neuroinflammation, angiogenesis, cellular apoptosis, and immunomodulation. [ABSTRACT FROM AUTHOR]

Autism spectrum disorder (ASD) is a pervasive brain development disease. Recently, the incidence rate of ASD has increased year by year and posed a great threat to the lives and families of individuals with ASD. Therefore, the study of ASD has become very important. A suitable feature representation that preserves the data intrinsic information and also reduces data complexity is very vital to the performance of established models. Topological data analysis (TDA) is an emerging and powerful mathematical tool for characterizing shapes and describing intrinsic information in complex data. In TDA, persistence barcodes or diagrams are usually regarded as visual representations of topological features of data. In this paper, the Regional Homogeneity (ReHo) data of subjects obtained from Autism Brain Imaging Data Exchange (ABIDE) database were used to extract features by using TDA. The average accuracy of cross validation on ABIDE I database was 95.6% that was higher than any other existing methods (the highest accuracy among existing methods was 93.59%). The average accuracy for sampling with the same resolutions with the ABIDE I on the ABIDE II database was 96.5% that was also higher than any other existing methods (the highest accuracy among existing methods was 75.17%). [ABSTRACT FROM AUTHOR]

While Extended Reality (XR) autism research, ranging from Augmented to Virtual Reality, focuses on socio-emotional abilities and autistic children requiring low support, common interventions address the entire spectrum and focus on other abilities, including perceptual abilities. Based on these observations, this paper first addresses common practitioners' interventions, and then suggests XR use cases and guidelines to better support them. To do so, 34 interviews were conducted with stakeholders, mainly including practitioners, and then analyzed. Emerging XR use cases were compared with the findings from two former systematic literature reviews, and emerging design guidelines were compared with the findings from a literature survey that we conducted. Findings suggest that collaborative XR sensory-based and mediation approaches could benefit the entire spectrum. [ABSTRACT FROM AUTHOR]

Genetic and environmental factors increase autism spectrum disorder (ASD) incidence, and this has led to the generation of corresponding animal models, with some showing strong construct and face validity. This short review focuses on results we have recently obtained with environmental and genetic mouse models of ASD and that are the valproic acid, the poly I:C and the Shank 3 models. This has allowed us to provide a comparative description of these widely used animal models providing an interesting perspective as to the pros and cons of each one of them, in our experimental settings. In these papers, we focused on motor and gait disorders which are currently not included in the diagnosis criteria, but which may provide new insights to ASD pathophysiology potentially leading to innovative therapies for a disease that currently has none. In all these models, we reported behavioral, cellular and molecular alterations related to the cerebellum. Motor and gait deficits were observed to various degrees in animal models and, when strongly present, they were correlated to the severity of social deficits as well as to the number of cerebellar Purkinje cells. Additionally, we also reported that, like in humans, males are more severely affected than females in these ASD models. These findings, along with an increasing body of literature, open new hopes in the ASD field pointing to brain regions, such the cerebellum, that are at the crossroads between cognitive, social and motor deficits. Targeting these brain regions and their underlying pathways and synaptic connections may prove of significant benefits. [ABSTRACT FROM AUTHOR]

Autism spectrum disorder (ASD) is a neurodevelopment disorders, which is very difficult to diagnose at early stages of life, and is a complex spectrum disorder, involving many persistent challenges with lack of social interactions, eye contact and facial expressions. Facial features of children having ASD are different from typically developing (TD) children, which is a major clue for early detection of autism using demographics of facial features. In this paper, a novel framework for early detection of autism has been developed, using a semi-supervised approach that consists ConvNeXt-T model and clustering of image embedding vectors. State-of-the-art ConvNeXt-T model is trained for autism dataset, and after that, image embedding clusters are created based on cosine similarity threshold. By doing this, model's classification layer and embedding generation layers are trained for the subject data. The proposed framework provides embeddings and autism classifier from the trained model, where cluster embeddings can be used in semi-supervised classification. Hence, after training process, inference architecture has two modes: (i) classification using ConvNeXt-T and (ii) classification using embedding clusters. The classification accuracy of framework using ConvNeXt-T is 89.5%, where clustering accuracy using cosine similarity of image embedding is 93%. Most of the other techniques in this area focus only on classification using deep neural networks. The proposed framework is also providing classification based on embedding clusters and can work on unlabelled dataset. Moreover, the proposed framework is based on server–client architecture, which allows easy usability and integration without multi acceleration devices and extra resource consumption. A single image of child is required to detect autism using the proposed framework in real-life scenario. [ABSTRACT FROM AUTHOR]

This paper describes the (Sin Exclusión) Transdisciplinary Autism Assessment and Resources ([S]TAAR) model and presents early metrics tracking efficiency and equity in access to high-quality comprehensive autism evaluations for young children. Retrospective chart reviews over one year (08/2018–08/2019) with n = 173 children were reviewed. Through care coordination with community providers, the model was developed to meet local needs by increasing throughput of children (< 4 years old) evaluated by a transdisciplinary team. Team-based processes included pre-visit triage, synchronous evaluation procedures, case conferencing, huddles, and care coordination. The model led to increased patient throughput, reductions in waitlist and time to diagnosis, and improved provider satisfaction. Improvements in access to care were equitable across patient race, ethnicity, language, and insurance type. [ABSTRACT FROM AUTHOR]

Health data collection of children with autism spectrum disorder (ASD) is challenging, time-consuming, and expensive; thus, working with small datasets is inevitable in this area. The diagnosis rate in ASD is low, leading to several challenges, including imbalance classes, potential overfitting, and sampling bias, making it difficult to show its potential in real-life situations. This paper presents a data analytics pilot-case study using a small dataset leveraging domain-specific knowledge to uncover differences between the gestural patterns of children with ASD and neurotypicals. We collected data from 59 children using an elastic display we developed during a sensing campaign and 9 children using the elastic display as part of a therapeutic program. We extracted strength-related features and selected the most relevant ones based on how the motor atypicality of children with ASD influences their interactions: children with ASD make smaller and narrower gestures and experience variations in the use of strength. The proposed machine learning models can correctly classify children with ASD with 97.3% precision and recall even if the classes are unbalanced. Increasing the size of the dataset via synthetic data improved the model precision to 99%. We finish discussing the importance of leveraging domain-specific knowledge in the learning process to successfully cope with some of the challenges faced when working with small datasets in a concrete, real-life scenario. [ABSTRACT FROM AUTHOR]

This paper describes a process to define a comprehensive list of exemplars for seven core Diagnostic and Statistical Manual (DSM) diagnostic criteria for autism spectrum disorder (ASD), and report on interrater reliability in applying these exemplars to determine ASD case classification. Clinicians completed an iterative process to map specific exemplars from the CDC Autism and Developmental Disabilities Monitoring (ADDM) Network criteria for ASD surveillance, DSM-5 text, and diagnostic assessments to each of the core DSM-5 ASD criteria. Clinicians applied the diagnostic exemplars to child behavioral descriptions in existing evaluation records to establish initial reliability standards and then for blinded clinician review in one site (phase 1) and for two ADDM Network surveillance years (phase 2). Interrater reliability for each of the DSM-5 diagnostic categories and overall ASD classification was high (defined as very good.60–.79 to excellent ≥.80 Kappa values) across sex, race/ethnicity, and cognitive levels for both phases. Classification of DSM-5 ASD by mapping specific exemplars from evaluation records by a diverse group of clinician raters is feasible and reliable. This framework provides confidence in the consistency of prevalence classifications of ASD and may be further applied to improve consistency of ASD diagnoses in clinical settings. [ABSTRACT FROM AUTHOR]

There is increasing recognition of the heterogeneity of origin of cases of autism spectrum disorder (ASD) with multiple forms of ASD having been identified over the decades. Among these, a genetic etiology can be identified in 20–40% of cases when a full genetic work-up is completed. The Fragile X premutation state (characterized by the presence of 55–200 CGG repeats in the FMR1 gene) is a relatively newly identified disease state that has since been associated with several disorders including fragile X-associated tremor ataxia syndrome (FXTAS), fragile X-associated primary ovarian insufficiency (FXPOI) and most recently, fragile X-associated neurodevelopmental disorders (FXAND) which commonly includes anxiety and depression. In addition to these associated disorders, extant literature and clinical observations have suggested an association between the premutation state and ASD. In this paper, we review the literature pertinent to this and discuss possible molecular mechanisms that may explain this association. This includes lowered levels of the FMR1 Protein (FMRP), GABA deficits, mitochondrial dysfunction and secondary genetic abnormalities that is seen in premutation carriers as well as their increased vulnerability to environmental stressors. Understanding these mechanisms can facilitate development of targeted treatment for specific sub-groups of ASD and premutation disorders in future. [ABSTRACT FROM AUTHOR]

High rates of video game use in Autism Spectrum Disorder (ASD) have often been reported. This might suggest more prevalent video gaming addiction in this population, but elevated video game use could also be explained by restricted interest in video games. We thus reviewed the literature on video game use in ASD to examine the role of vulnerability for video game addiction and the role of restricted interest in video gaming. We conducted a search following PRISMA guidelines. From 511 papers, 15 were included. This review demonstrates that the high rates of video game use in boys and young males with ASD without intellectual disabilities can be predominantly explained by gaming addiction. However, an effect of restricted interest in video games cannot be excluded. Moreover, addictive process and restricted interest may interact. This result raises several issues for differentiating between video game addiction and restricted interest in regard to assessment, overlapping characteristics and interventions. Based on the results, we make recommendations for caregivers and clinicians on the management of problematic video game use in ASD. [ABSTRACT FROM AUTHOR]

Use of face masks is one of the measures adopted by the general community to stop the transmission of disease during this ongoing COVID-19 pandemic. This wide use of face masks has indeed been shown to disrupt day-to-day face recognition. People with autism spectrum disorder (ASD) often have predisposed impairment in face recognition and are expected to be more vulnerable to this disruption in face recognition. Here, we recruited typically developing adult participants and those with ASD, and we measured their non-verbal intelligence, autism spectrum quotient, empathy quotient, and recognition performances of faces with and without a face mask covering the lower halves of the face. When faces were initially learned unobstructed, we showed that participants had a general reduced face recognition performance for masked faces. In contrast, when masked faces were first learned, typically developing adults benefit with an overall advantage in recognizing both masked and unmasked faces; while adults with ASD recognized unmasked faces with a significantly more reduced level of performance than masked faces—this face recognition discrepancy is predicted by a higher level of autistic traits. This paper also discusses how autistic traits influence processing of faces with and without face masks. [ABSTRACT FROM AUTHOR]

Among the main characteristics of an individual with autism spectrum disorder are repetitive behavioral patterns, deficiencies in social interaction and both verbal and nonverbal communication present since childhood. The ability to recognize mental states from facial expressions plays a vital role in social interaction and interpersonal communication. In recent years, several studies have been carried out with the aim of motivating individuals to use computer technologies to learn emotions in order to improve social interactions. In this paper, a game that can support the development of emotional and social skills is presented for people with autism spectrum disorder. Our game allows people to develop the ability to recognize and express basic emotions: joy, sadness, anger, disgust, surprise and fear. Experiments were performed on a public domain image database and with a group of individuals from an educational institution, in order to evaluate the performance of the proposed tool. The results showed that the use of our approach improved these capabilities in individuals with autism spectrum disorder. [ABSTRACT FROM AUTHOR]

Autism spectrum disorder (ASD) is theoretically characterized by alterations in functional connectivity between brain regions. Many works presented approaches to determine informative patterns that help to predict autism from typical development. However, most of the proposed pipelines are not specifically designed for the autism problem, i.e. they do not corroborate with autism theories about functional connectivity. In this paper, we propose a framework that takes into account the properties of local connectivity and long range under-connectivity in the autistic brain. The originality of the proposed approach is to adopt elimination as a technique in order to well emerge the autistic brain connectivity alterations, and show how they contribute to differentiate ASD from controls. Experimental results conducted on the large multi-site Autism Brain Imaging Data Exchange (ABIDE) show that our approach provides accurate prediction up to 70% and succeeds to prove the existence of deficits in the long-range connectivity in the ASD subjects brains. [ABSTRACT FROM AUTHOR]

The assessment of autism spectrum disorder (ASD) is based on semi-structured procedures addressed to children and caregivers. Such methods rely on the evaluation of behavioural symptoms rather than on the objective evaluation of psychophysiological underpinnings. Advances in research provided evidence of modern procedures for the early assessment of ASD, involving both machine learning (ML) techniques and biomarkers, as eye movements (EM) towards social stimuli. This systematic review provides a comprehensive discussion of 11 papers regarding the early assessment of ASD based on ML techniques and children's social visual attention (SVA). Evidences suggest ML as a relevant technique for the early assessment of ASD, which might represent a valid biomarker-based procedure to objectively make diagnosis. Limitations and future directions are discussed. [ABSTRACT FROM AUTHOR]

Autistic individuals often struggle to successfully navigate emerging adulthood (EA). College is an increasingly common context in which individuals learn and hone the necessary skills for adulthood. The goal of this paper is to systematically review and assess the existing research on college as a context of EA development in autistic individuals, particularly in terms of understanding whether and how this context might be critically different for those who are typically developing or developing with other disabilities. Our findings indicate that ASD college students report feeling prepared academically, but exhibit weaknesses in daily living and social skills. Interventions largely focus on social skills, and rarely evaluate outcomes relevant to college success or longer-term emerging adulthood independence. We conclude with hypotheses and recommendations for future work that are essential for understanding and supporting ASD students as they navigate potentially unique challenges in college and their transition to independence during EA. [ABSTRACT FROM AUTHOR]

Purpose: With the increasing prevalence of autism spectrum disorders (ASD), the importance of early screening and diagnosis has been subject to considerable discussion. Given the subtle differences between ASD children and typically developing children during the early stages of development, it is imperative to investigate the utilization of automatic recognition methods powered by artificial intelligence. We aim to summarize the research work on this topic and sort out the markers that can be used for identification.We searched the papers published in the Web of Science, PubMed, Scopus, Medline, SpringerLink, Wiley Online Library, and EBSCO databases from 1st January 2013 to 13th November 2023, and 43 articles were included.These articles mainly divided recognition markers into five categories: gaze behaviors, facial expressions, motor movements, voice features, and task performance. Based on the above markers, the accuracy of artificial intelligence screening ranged from 62.13 to 100%, the sensitivity ranged from 69.67 to 100%, the specificity ranged from 54 to 100%.Therefore, artificial intelligence recognition holds promise as a tool for identifying children with ASD. However, it still needs to continually enhance the screening model and improve accuracy through multimodal screening, thereby facilitating timely intervention and treatment.Methods: With the increasing prevalence of autism spectrum disorders (ASD), the importance of early screening and diagnosis has been subject to considerable discussion. Given the subtle differences between ASD children and typically developing children during the early stages of development, it is imperative to investigate the utilization of automatic recognition methods powered by artificial intelligence. We aim to summarize the research work on this topic and sort out the markers that can be used for identification.We searched the papers published in the Web of Science, PubMed, Scopus, Medline, SpringerLink, Wiley Online Library, and EBSCO databases from 1st January 2013 to 13th November 2023, and 43 articles were included.These articles mainly divided recognition markers into five categories: gaze behaviors, facial expressions, motor movements, voice features, and task performance. Based on the above markers, the accuracy of artificial intelligence screening ranged from 62.13 to 100%, the sensitivity ranged from 69.67 to 100%, the specificity ranged from 54 to 100%.Therefore, artificial intelligence recognition holds promise as a tool for identifying children with ASD. However, it still needs to continually enhance the screening model and improve accuracy through multimodal screening, thereby facilitating timely intervention and treatment.Results: With the increasing prevalence of autism spectrum disorders (ASD), the importance of early screening and diagnosis has been subject to considerable discussion. Given the subtle differences between ASD children and typically developing children during the early stages of development, it is imperative to investigate the utilization of automatic recognition methods powered by artificial intelligence. We aim to summarize the research work on this topic and sort out the markers that can be used for identification.We searched the papers published in the Web of Science, PubMed, Scopus, Medline, SpringerLink, Wiley Online Library, and EBSCO databases from 1st January 2013 to 13th November 2023, and 43 articles were included.These articles mainly divided recognition markers into five categories: gaze behaviors, facial expressions, motor movements, voice features, and task performance. Based on the above markers, the accuracy of artificial intelligence screening ranged from 62.13 to 100%, the sensitivity ranged from 69.67 to 100%, the specificity ranged from 54 to 100%.Therefore, artificial intelligence recognition holds promise as a tool for identifying children with ASD. However, it still needs to continually enhance the screening model and improve accuracy through multimodal screening, thereby facilitating timely intervention and treatment.Conclusion: With the increasing prevalence of autism spectrum disorders (ASD), the importance of early screening and diagnosis has been subject to considerable discussion. Given the subtle differences between ASD children and typically developing children during the early stages of development, it is imperative to investigate the utilization of automatic recognition methods powered by artificial intelligence. We aim to summarize the research work on this topic and sort out the markers that can be used for identification.We searched the papers published in the Web of Science, PubMed, Scopus, Medline, SpringerLink, Wiley Online Library, and EBSCO databases from 1st January 2013 to 13th November 2023, and 43 articles were included.These articles mainly divided recognition markers into five categories: gaze behaviors, facial expressions, motor movements, voice features, and task performance. Based on the above markers, the accuracy of artificial intelligence screening ranged from 62.13 to 100%, the sensitivity ranged from 69.67 to 100%, the specificity ranged from 54 to 100%.Therefore, artificial intelligence recognition holds promise as a tool for identifying children with ASD. However, it still needs to continually enhance the screening model and improve accuracy through multimodal screening, thereby facilitating timely intervention and treatment. [ABSTRACT FROM AUTHOR]

Purpose: There is a paucity in research supporting procedures to teach skills needed during an individual’s menstrual cycle. The purpose of this study was two-fold. First, a literature review was conducted to find publications on the topic of menstrual care. Second, the studies found were evaluated against What Works Clearinghouse™ (WWC) standards and analyzed to determine the presence of clinical components relevant to teaching these skills.A literature review was conducted according to PRISMA guidelines. The review identified publications that taught menstrual care skills to individuals diagnosed with autism spectrum disorder (ASD) or other disabilities. The review focused specifically on studies that employed single-subject research methodology. Studies found were analyzed against the WWC’s criteria to assess the rigor of each studies’ methodology. Finally, studies were categorized across indicators that are clinically relevant to teaching menstrual care skills.The results highlighted a lack of empirical support for teaching menstrual care skills. 7 single-subject design studies were identified in the previous 40 years of research. One study met all criteria required to receive the WWC’s highest rating.The complexity and private nature of menstrual care skills can make intervention development daunting. This paper was intended to provide menstrual care researchers with guidance in implementing high-quality studies. Additionally, scientist-practitioners can find guidance regarding important considerations to support programming that is both effective and respectful.Methods: There is a paucity in research supporting procedures to teach skills needed during an individual’s menstrual cycle. The purpose of this study was two-fold. First, a literature review was conducted to find publications on the topic of menstrual care. Second, the studies found were evaluated against What Works Clearinghouse™ (WWC) standards and analyzed to determine the presence of clinical components relevant to teaching these skills.A literature review was conducted according to PRISMA guidelines. The review identified publications that taught menstrual care skills to individuals diagnosed with autism spectrum disorder (ASD) or other disabilities. The review focused specifically on studies that employed single-subject research methodology. Studies found were analyzed against the WWC’s criteria to assess the rigor of each studies’ methodology. Finally, studies were categorized across indicators that are clinically relevant to teaching menstrual care skills.The results highlighted a lack of empirical support for teaching menstrual care skills. 7 single-subject design studies were identified in the previous 40 years of research. One study met all criteria required to receive the WWC’s highest rating.The complexity and private nature of menstrual care skills can make intervention development daunting. This paper was intended to provide menstrual care researchers with guidance in implementing high-quality studies. Additionally, scientist-practitioners can find guidance regarding important considerations to support programming that is both effective and respectful.Results: There is a paucity in research supporting procedures to teach skills needed during an individual’s menstrual cycle. The purpose of this study was two-fold. First, a literature review was conducted to find publications on the topic of menstrual care. Second, the studies found were evaluated against What Works Clearinghouse™ (WWC) standards and analyzed to determine the presence of clinical components relevant to teaching these skills.A literature review was conducted according to PRISMA guidelines. The review identified publications that taught menstrual care skills to individuals diagnosed with autism spectrum disorder (ASD) or other disabilities. The review focused specifically on studies that employed single-subject research methodology. Studies found were analyzed against the WWC’s criteria to assess the rigor of each studies’ methodology. Finally, studies were categorized across indicators that are clinically relevant to teaching menstrual care skills.The results highlighted a lack of empirical support for teaching menstrual care skills. 7 single-subject design studies were identified in the previous 40 years of research. One study met all criteria required to receive the WWC’s highest rating.The complexity and private nature of menstrual care skills can make intervention development daunting. This paper was intended to provide menstrual care researchers with guidance in implementing high-quality studies. Additionally, scientist-practitioners can find guidance regarding important considerations to support programming that is both effective and respectful.Conclusion: There is a paucity in research supporting procedures to teach skills needed during an individual’s menstrual cycle. The purpose of this study was two-fold. First, a literature review was conducted to find publications on the topic of menstrual care. Second, the studies found were evaluated against What Works Clearinghouse™ (WWC) standards and analyzed to determine the presence of clinical components relevant to teaching these skills.A literature review was conducted according to PRISMA guidelines. The review identified publications that taught menstrual care skills to individuals diagnosed with autism spectrum disorder (ASD) or other disabilities. The review focused specifically on studies that employed single-subject research methodology. Studies found were analyzed against the WWC’s criteria to assess the rigor of each studies’ methodology. Finally, studies were categorized across indicators that are clinically relevant to teaching menstrual care skills.The results highlighted a lack of empirical support for teaching menstrual care skills. 7 single-subject design studies were identified in the previous 40 years of research. One study met all criteria required to receive the WWC’s highest rating.The complexity and private nature of menstrual care skills can make intervention development daunting. This paper was intended to provide menstrual care researchers with guidance in implementing high-quality studies. Additionally, scientist-practitioners can find guidance regarding important considerations to support programming that is both effective and respectful. [ABSTRACT FROM AUTHOR]

The gut–brain axis refers to the bidirectional connection and communication between the gastrointestinal tract and the central nervous system. This paper explores two routes for this communication that have hitherto remained under-examined: epigenetics and redox signaling and their implications for autism spectrum disorder (ASD). The gut microbiota may induce epigenetic changes in the gut and potentially in the brain through their fermentation products. Instead of through other conceptualizations of them acting as neurotransmitters, gut microbial products may act as epigenetic agents, which are supported by the effects of gut bacterial-derived metabolites on gene regulation and expression. In addition to their epigenetic effects, gut bacterial-derived communicative agents can also influence host signaling by contributing to and even substituting host reactive oxygen species (ROS) production. These ROS can act as second messengers and exert oxidative activity on proteins to influence immune, inflammatory, and other signaling processes. ROS and epigenetic mechanisms may have interactive effects as well. ROS, in addition to their role in signaling pathways and cellular redox alterations, also influence redox-sensitive transcription factors, thereby having an effect on gene expression. Specifically, ROS are involved in the activation of transcription factors, chromatin remodeling, and histone/protein deacetylation. These two proposed mechanisms correspond with the recent findings related to ASD, where a cofactor that is shown to be lower in ASD has antioxidative properties, responds to epigenetic modulation, and increases via microbiota interventions. The current evidence reviewed here suggests the need to update models of the gut–brain communication to include these two mechanisms. Such a modeling can also contribute to understanding the unknowns of host metabolism and physiology in ASD and afford potential therapeutic avenues for this as well as other psychiatric and physiological conditions. [ABSTRACT FROM AUTHOR]

This paper reported the quality of life (QoL) of 260 Malaysian parents with autism spectrum disorder (ASD) and factors which influencing it. About half of these parents reported to having good QoL. The factors identified in the double ABCX model showed that perceived social support and sense of coherence (SOC) were mediated by coping strategies and had a positive direct and indirect effect on parents' QoL. These finding reiterate the importance of social support, coping skills, and strong SOC reflection skills in influencing the QoL of parents with ASD child. We therefore suggest parents to utilize the widely available social and organization supports to gain good QoL, which is also important for the wellbeing of their child. [ABSTRACT FROM AUTHOR]

Daily living skill deficits commonly co-occur in individuals with autism spectrum disorder (ASD). These deficits in adolescence are associated with poor outcomes, in both employment and independent living skills as adults. Currently, there are no interventions that directly target daily living skill acquisition in adolescents with ASD without an intellectual disability to facilitate a successful transition to adulthood. In this paper, we discuss the development, refinement, and initial efficacy studies of Surviving and Thriving in the Real World (STRW), a 14-session group treatment for both adolescents with ASD and their parent/caregiver that promotes attainment of critical daily living skills. We summarize initial feasibility studies that have been instrumental in the iterative development of STRW. The structure, core treatment elements, and content of STRW are described in detail. Lastly, we discuss the transition of the in-person STRW intervention to STRW-telehealth, which allows for adolescents with ASD to work on daily living skills in their own home with support from a therapist. [ABSTRACT FROM AUTHOR]

The Therapeutic Alliance (TA) between patient and health provider (therapist or clinician) is one of the most relevant factors for the success of a therapy. In the case of people suffering from Autism Spectrum Disorder (ASD), the alliance is extended to all the people involved in their care (i.e., teachers, therapists, clinicians, relatives). In this paper, we propose a multimedia application named Thea for empowering the TA of children with ASD by improving the communication among the TA members, sharing guidelines, multimedia contents, and strategies to comply with challenging behaviors and progress with particular attention towards end-users who are occasional smart-users. A detailed process for empowering the TA members by enhancing the informed interaction among all of them is proposed and implemented. A vocal assistant also supports patients/caregivers and therapists in documenting their activity with the person with ASD by recording videos in a free-hand modality. After a contextual analysis based on Thematic Analysis Template, Thea has been implemented using a user-centered development approach. We performed three iterations involving the end-users. A user study is performed at the third iteration. Results of the user study revealed a positive attitude towards the application. In particular, the perception of empowerment of participants increased after the tool had been used. We also highlighted the guidelines and tools that may be adopted for empowering different kinds of patients. The first results seem to suggest that the use of Thea may increase the belief of the caregivers of a person with ASD to be able to better take care of her, in a more controlled and informed way. [ABSTRACT FROM AUTHOR]

This paper examines the reliability and validity of parent target problems (PTPs) in a multi-site randomized controlled trial of parent training (PT) versus psychoeducation (PEP) in children (150 boys, 19 girls; mean age 4.7 ± 1.2 years) with autism spectrum disorder (ASD) and disruptive behavior. At baseline, treatment blind, independent evaluators asked parents to nominate the child's top two problems. Each problem was documented in a brief narrative. Narratives were reviewed and revised at follow-up visits during the six-month trial. When the trial was completed, five judges, blind to treatment condition, independently rated change from baseline on a 9-point scale (1 = normal; 2 = markedly improved; 3 = definitely improved; 4 = equivocally improved; 5 = no change; 6 = possibly worse; 7 = definitely worse; 8 = markedly worse; 9 = disastrously worse) at Weeks 8, 12, 16, and 24 (inter-rater intraclass correlation = 0.78). PTP scores for the two target problems were averaged across the five raters, yielding a mean score for each child at each time point. Mean PTP scores showed improvement in both treatment groups over the 24-week study. Compared to PEP, PTP ratings showed a steeper decline in PT based on significant interaction of group and time (t(df) = 2.14(155.9), p = 0.034; Week 24 effect size = 0.75). In categorical analysis, we compared cutoffs mean PTP scores of 3.0 (definitely improved), 3.25, and 3.5 with the positive response rate on the Clinical Global Impressions-Improvement scale from the original study. Sensitivities ranged from 52–78%. PTP narratives offer a systematic, reliable, and valid way to track child-specific outcomes in clinical trials and clinical practice. [ABSTRACT FROM AUTHOR]