Resolving autism spectrum disorder (ASD) through brain topologies using fMRI dataset with multi-layer perceptron (MLP)

Autism is a neurodevelopmental disorder that manifests in individuals during childhood and has enduring consequences for their social interactions and communication. The prediction of Autism Spectrum Disorder (ASD) in individuals based on the differences in brain networks and activities have been studied extensively in the recent past, however, with lower accuracies. Therefore in this research, identification at the early stage through computer-aided algorithms to differentiate between ASD and TD patients is proposed. In order to identify features, a Multi-Layer Perceptron (MLP) model is developed which utilizes logistic regression on characteristics extracted from connectivity matrices of subjects derived from fMRI images. The features that significantly contribute to the classification of individuals as having Autism Spectrum Disorder (ASD) or typically developing (TD) are identified by the logistic regression model. To enhance emphasis on essential attributes, an AND operation is integrated. This involves selecting features demonstrating statistical significance across diverse logistic regression analyses conducted on various random distributions. The iterative approach contributes to a comprehensive understanding of relevant features for accurate classification. By implementing this methodology, the estimation of feature importance became more dependable, and the potential for overfitting is moderated through the evaluation of model performance on various subsets of data. It is observed from the experimentation that the highly correlated Left Lateral Occipital Cortex and Right Lateral Occipital Cortex ROIs are only found in ASD. Also, it is noticed that the highly correlated Left Cerebellum Tonsil and Right Cerebellum Tonsil are only found in TD participants. Among the MLP classifier, a recall of 82.61 % is achieved followed by Logistic Regression with an accuracy of 72.46 %. MLP also stands out with a commendable accuracy of 83.57 % and AUC of 0.978.

New advances in the diagnosis and treatment of autism spectrum disorders

Autism spectrum disorders (ASD) are a group of neurodevelopmental disorders that affect individuals' social interactions, communication skills, and behavioral patterns, with significant individual differences and complex etiology. This article reviews the definition and characteristics of ASD, epidemiological profile, early research and diagnostic history, etiological studies, advances in diagnostic methods, therapeutic approaches and intervention strategies, social and educational integration, and future research directions. The highly heritable nature of ASD, the role of environmental factors, genetic-environmental interactions, and the need for individualized, integrated, and technology-driven treatment strategies are emphasized. Also discussed is the interaction of social policy with ASD research and the outlook for future research and treatment, including the promise of precision medicine and emerging biotechnology applications. The paper points out that despite the remarkable progress that has been made, there are still many challenges to the comprehensive understanding and effective treatment of ASD, and interdisciplinary and cross-cultural research and global collaboration are needed to further deepen the understanding of ASD and improve the quality of life of patients.

Multimodal Kernel-based discriminant correlation analysis data-fusion approach: an automated autism spectrum disorder diagnostic system

Autism spectrum disorder (ASD) diagnostic systems, based on association of multimodal tools such as combination of Electroencephalogram (EEG) and eye-tracking, have emerged as an analytical to provide objective biomarkers. However, the existing feature-redundancy-based systems have lacked in providing knowledge of fusion approaches and robust feature-set. The present paper aims to reduce disorder homogeneity by proposing a multimodal diagnostic system which can incorporate multimodal data. The paper has collected simultaneous-data from three modalities (laptop-performance tool, EEG machine, and Eye-tracker) fused the recorded computational, neural and visual data. The multimodal features are analyzed via proposed multimodal Kernel-based discriminant correlation analysis (MKDCA) fusion approach and classified using state-of-the-art machine-learning classifiers. The proposed framework has considered the distinct cardinality of the feature vectors and fused the group structure among multiple samples after ranking them in increasing order. As per the results, the proposed multimodal system provided fused feature set of 11 influential features out of total 39 features. The SVM classifier has diagnosed ASD with 92% testing accuracy and 0.988 AUC(ROC). The proposed automated fusion-based system has the potential to classify disorder by reducing the disorder heterogeneity and stratifying ASD individuals into homogeneous sub-groups. In future, the correlation of reduced feature set with ASD clinical symptoms accounted by screening scales can provide clinical relevance of proposed model.

Struggle for recognition, rights, and redistribution: Understanding the identity of parents of children with autism spectrum disorder in China

Introduction

The number of children diagnosed with autism spectrum disorders (ASD) worldwide has increased rapidly in the past decade and China is no exception. Yet the identity development of Chinese parents of children with ASD is little understood. This study employed an ethics of care perspective to explore the identity of parents of children with ASD as shaped in their social-cultural context in mainland China.

Methods

Qualitatively driven mixed-method design was adopted. Qualitative data about their experiences were obtained from in-depth interviews with 20 parents from 17 families of children with ASD in Beijing and participant observation of 9 participants' daily parenting experience.

Results

A complex and dynamic parenting identity was revealed. With limited recognition within and external to the family, parents experienced constant challenges toward their sense of self. The parents used strategies to assert their rights as carers and develop positive self-perceptions. Yet because of the unjust distribution of care work within families and with the state, the parents retained a sense of insecurity throughout the process of parenting. The parents' sense of inferiority due to devaluing children with disabilities was accentuated by traditional Chinese cultural values about good parenting. They were intensely worried about the lack of policy for support as they and their children grew older.

Discussion

The findings reinforce the need for recognition of parents' dignity, capacity, and efforts in caring.

Relationship between different types of complement syntax and false belief in Mandarin-speaking children with autism spectrum disorder and typically developing children

Previous studies have shown that complement syntax is closely associated with false belief (FB) in children with autism spectrum disorder (ASD). However, the relationship between different types of complement syntax and FB remains unclear. This study examined the relationship between different types of complement syntax and FB in both ASD and typically developing (TD) children. Thirty Mandarin-speaking ASD and TD children, each matched for language ability, were included. Children completed different types of complement syntax tasks, verbal and nonverbal FB. For the ASD children, results demonstrated that sentential complement syntax independently predicted verbal and nonverbal FB, while phrasal complement syntax only predicted nonverbal FB. For the TD children group, sentential complement syntax only predicted verbal FB. This indicates that as the language demands of the FB task decrease, ASD children can use both types of complement syntax for its prediction. Moreover, the characteristics of ASD children differ from TD children in terms of the relationship between different types of complement syntax and FB. The results of this study support de Villiers’ point of view from the Mandarin perspective and provide evidence for the social-cognitive component of the theory of mind.

Linear discriminant analysis of phenotypic data for classifying autism spectrum disorder by diagnosis and sex

Autism Spectrum Disorder (ASD) is a developmental condition characterized by social and communication differences. Recent research suggests ASD affects 1-in-44 children in the United States. ASD is diagnosed more commonly in males, though it is unclear whether this diagnostic disparity is a result of a biological predisposition or limitations in diagnostic tools, or both. One hypothesis centers on the ‘female protective effect,’ which is the theory that females are biologically more resistant to the autism phenotype than males. In this examination, phenotypic data were acquired and combined from four leading research institutions and subjected to multivariate linear discriminant analysis. A linear discriminant model was trained on the training set and then deployed on the test set to predict group membership. Multivariate analyses of variance were performed to confirm the significance of the overall analysis, and individual analyses of variance were performed to confirm the significance of each of the resulting linear discriminant axes. Two discriminant dimensions were identified between the groups: a dimension separating groups by the diagnosis of ASD (LD1: 87% of variance explained); and a dimension reflective of a diagnosis-by-sex interaction (LD2: 11% of variance explained). The strongest discriminant coefficients for the first discriminant axis divided the sample in domains with known differences between ASD and comparison groups, such as social difficulties and restricted repetitive behavior. The discriminant coefficients for the second discriminant axis reveal a more nuanced disparity between boys with ASD and girls with ASD, including executive functioning and high-order behavioral domains as the dominant discriminators. These results indicate that phenotypic differences between males and females with and without ASD are identifiable using parent report measures, which could be utilized to provide additional specificity to the diagnosis of ASD in female patients, potentially leading to more targeted clinical strategies and therapeutic interventions. The study helps to isolate a phenotypic basis for future empirical work on the female protective effect using neuroimaging, EEG, and genomic methodologies.

Machine learning for autism spectrum disorder diagnosis using structural magnetic resonance imaging: Promising but challenging

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder that affects approximately 1% of the population and causes significant burdens. ASD's pathogenesis remains elusive; hence, diagnosis is based on a constellation of behaviors. Structural magnetic resonance imaging (sMRI) studies have shown several abnormalities in volumetric and geometric features of the autistic brain. However, inconsistent findings prevented most contributions from being translated into clinical practice. Establishing reliable biomarkers for ASD using sMRI is crucial for the correct diagnosis and treatment. In recent years, machine learning (ML) and specifically deep learning (DL) have quickly extended to almost every sector, notably in disease diagnosis. Thus, this has led to a shift and improvement in ASD diagnostic methods, fulfilling most clinical diagnostic requirements. However, ASD discovery remains difficult. This review examines the ML-based ASD diagnosis literature over the past 5 years. A literature-based taxonomy of the research landscape has been mapped, and the major aspects of this topic have been covered. First, we provide an overview of ML's general classification pipeline and the features of sMRI. Next, representative studies are highlighted and discussed in detail with respect to methods, and biomarkers. Finally, we highlight many common challenges and make recommendations for future directions. In short, the limited sample size was the main obstacle; Thus, comprehensive data sets and rigorous methods are necessary to check the generalizability of the results. ML technologies are expected to advance significantly in the coming years, contributing to the diagnosis of ASD and helping clinicians soon.

Disentangling response initiation difficulties from response inhibition in autism spectrum disorder: A sentence-completion task

It has been proposed that individuals with autism spectrum disorder (ASD) struggle both with response initiation and with response inhibition, both of which are functions of the executive system. Experimental tasks are unlikely pure measures of a single cognitive domain, and in this study we aim at understanding the contributions of response initiation difficulties to possible deficits in inhibitory control in autism. A sample of adults diagnosed with ASD and a control sample participated in this study. To participants it was asked to perform a sentence-completion task with two different condition: Part A—targeting response initiation and Part B—engaging inhibitory processes. Importantly, we have analyzed the B-A latencies that have been proposed for the removal of the response initiation confound effect. Results show that no differences between the groups were found in accuracy measures, either in Part A (ASD: M = 0.78; Controls: M = 0.90) nor Part B (ASD: M = 0.03; Controls: M = 0.02). However, in both conditions autistic participants were significantly slower to respond than the group of participants with typical development (Part A—ASD: M = 2432.5 ms; Controls M = 1078.5 ms; Part B—ASD M = 6758.3 ms; Controls M = 3283.9 ms). Critically, we show that when subtracting the response times of Part A from Part B (B-A latencies) no group differences attributable to inhibitory processes remained (ASD: M = 4325.76; Controls: M = 2205.46). With this study we corroborate the existence of difficulties with response initiation in autism and we question the existence of troubles in inhibition per se.

Channels and Features Identification: A Review and a Machine-Learning Based Model With Large Scale Feature Extraction for Emotions and ASD Classification

Autism Spectrum Disorder (ASD) is characterized by impairments in social and cognitive skills, emotional disorders, anxiety, and depression. The prolonged conventional ASD diagnosis raises the sheer need for early meaningful intervention. Recently different works have proposed potential for ASD diagnosis and intervention through emotions prediction using deep neural networks (DNN) and machine learning algorithms. However, these systems lack an extensive large-scale feature extraction (LSFE) analysis through multiple benchmark data sets. LSFE analysis is required to identify and utilize the most relevant features and channels for emotion recognition and ASD prediction. Considering these challenges, for the first time, we have analyzed and evaluated an extensive feature set to select the optimal features using LSFE and feature selection algorithms (FSA). A set of up to eight most suitable channels was identified using different best-case FSA. The subject-wise importance of channels and features is also identified. The proposed method provides the best-case accuracies, precision, and recall of 95, 92, and 90%, respectively, for emotions prediction using a linear support vector machine (LSVM) classifier. It also provides the best-case accuracy, precision, and recall of 100% for ASD classification. This work utilized the largest number of benchmark data sets (5) and subjects (99) for validation reported till now in the literature. The LSVM classification algorithm proposed and utilized in this work has significantly lower complexity than the DNN, convolutional neural network (CNN), Naïve Bayes, and dynamic graph CNN used in recent ASD and emotion prediction systems.

Teachers' Knowledge and Use of Evidenced-Based Practices for Students With Autism Spectrum Disorder in Saudi Arabia

The evidenced-based practices (EBPs) movement in the field of special education began ~20 years ago. This study contributes to that literature. It investigates the teachers' knowledge and use of EBPs to teach students with autism spectrum disorder (ASD) in Saudi Arabia. The Teachers' Knowledge and Use of EBPs Survey was administered to 240 special education teachers. The participants generally reported a medium level of knowledge and use of EBPs for students with ASD. Female teachers' use of EBPs was greater than that of males, and teachers who attended more than five professional development programs reported greater use of EBPs than those that attended fewer programs. Knowledge and use of EBPs were related. Gender and professional development programs were predictors of teachers' use of EBPs for students with ASD. Teachers' knowledge of EBPs for students with ASD is a vital indicator of teachers' use of those practices, professional development programs can improve such knowledge and use, and teachers' use of EBPs for students with ASD could be improved by offering high-quality professional development programs.

Analysis of simultaneous visual and complex neural dynamics during cognitive learning to diagnose ASD

The interactions between gaze processing and neural activities mediate cognition. The present paper aims to identify the involvement of visual and neural dynamics in shaping the cognitive behavior in Autism Spectrum Disorder (ASD). Electroencephalogram (EEG) and Eye-tracker signals of ASD and Typically Developing (TD) are recorded while performing two difficulty levels of a maze-based experimental task. During task, the performance metrics, complex neural measures extracted from EEG data using Visibility Graph (VG) algorithm and visual measures extracted from eye-tracker data are analyzed and compared. For both task levels, the cognition processing is examined via performance metrics (reaction-time and poor accuracy), gaze measures (saccade, fixation duration and blinkrate) and VG-based metrics (average weighted degree, clustering coefficient, path length, global efficiency, mutual information). An engagement in cognitive processing in ASD is revealed statistically by high reaction time, poor accuracy, increased fixation duration, raised saccadic amplitude, higher blink rate, reduced average weighted degree, global efficiency, mutual information as well as higher eigenvector centrality and path length. Over the course of repetitive trials, the cognitive improvement is although poor in ASD compared to TDs, the reconfigurations of visual and neural network dynamics revealed activation of Cognitive Learning (CL) in ASD. Furthermore, the correlation of gaze-EEG measures reveal that independent brain region functioning is not impaired but declined mutual interaction of brain regions causes cognitive deficit in ASD. And correlation of EEG-gaze measures with clinical severity measured by Autism Diagnostic Observation Schedule(ADOS) suggest that visual-neural activities reveals social behavior/cognition in ASD. Thus, visual and neural dynamics together support the revelation of the cognitive behavior in ASD.

Psychological Distress, Disorder Severity, and Perception of Positive Contributions in Couples Raising Individuals With Autism

Parents' perception of the positive contributions associated with raising children with autism is considered to be a protective factor in the process of psychological adaptation. Thus, it is essential to unveil what factors are related to this perception. We explore how parents' psychological distress (parental stress and anxiety) predicts the perception of positive contributions in fathers and mothers who raise individuals with different levels of autism severity. The sample comprises 135 couples (270 fathers and mothers) parenting individuals diagnosed with autism aged 3–38 years. Participants completed different self-report questionnaires, including measures of parental stress, anxiety, and positive contributions. To estimate the actor–partner interdependence model, data were analyzed using structural equation modeling (SEM) to explore transactional effects between fathers' and mothers' psychological distress and their perceptions of positive contributions associated with autism. Two separate multigroup models were tested, respectively, analyzing parental stress and anxiety. Each multigroup model considers two levels of disorder severity. The findings revealed that actor and partner effects of stress and anxiety were important predictors of the perception of positive contributions in both disorder severity groups. We conclude that it is necessary to develop family support programs that focus on controlling fathers' and mothers' stress and anxiety symptoms, as these mental states negatively impact the ability to perceive positive contributions.

Children ASD Evaluation Through Joint Analysis of EEG and Eye-Tracking Recordings With Graph Convolution Network

Recent advances in neuroscience indicate that analysis of bio-signals such as rest state electroencephalogram (EEG) and eye-tracking data can provide more reliable evaluation of children autism spectrum disorder (ASD) than traditional methods of behavior measurement relying on scales do. However, the effectiveness of the new approaches still lags behind the increasing requirement in clinical or educational practices as the “bio-marker” information carried by the bio-signal of a single-modality is likely insufficient or distorted. This study proposes an approach to joint analysis of EEG and eye-tracking for children ASD evaluation. The approach focuses on deep fusion of the features in two modalities as no explicit correlations between the original bio-signals are available, which also limits the performance of existing methods along this direction. First, the synchronization measures, information entropy, and time-frequency features of the multi-channel EEG are derived. Then a random forest applies to the eye-tracking recordings of the same subjects to single out the most significant features. A graph convolutional network (GCN) model then naturally fuses the two group of features to differentiate the children with ASD from the typically developed (TD) subjects. Experiments have been carried out on the two types of the bio-signals collected from 42 children (21 ASD and 21 TD subjects, 3–6 years old). The results indicate that (1) the proposed approach can achieve an accuracy of 95% in ASD detection, and (2) strong correlations exist between the two bio-signals collected even asynchronously, in particular the EEG synchronization against the face related/joint attentions in terms of covariance.