Classification Of Autism Research Articles

Toward understanding autism heterogeneity: Identifying clinical subgroups and neuroanatomical deviations.

Autism spectrum disorder ("autism") is a neurodevelopmental condition characterized by substantial behavioral and neuroanatomical heterogeneity. This poses significant challenges to understanding its neurobiological mechanisms and developing effective interventions. Identifying phenotypically more homogeneous subgroups and shifting the focus from average group differences to individuals is a promising approach to addressing heterogeneity. In the present study, we aimed to parse clinical and neuroanatomical heterogeneity in autism by combining clustering of clinical features with normative modeling based on neuroanatomical measures (cortical thickness [CT] and subcortical volume) within the Autism Brain Imaging Data Exchange data sets (N autism = 861, N nonautistic individuals [N NAI] = 886, age range = 5-64). First, model-based clustering was applied to autistic symptoms as measured by the Autism Diagnostic Observation Schedule to identify clinical subgroups of autism (N autism = 499). Next, we ran normative modeling on CT and subcortical parcellations (N autism = 690, N NAI = 744) and examined whether clinical subgrouping resulted in increased neurobiological homogeneity within the subgroups compared to the entire autism group by comparing their spatial overlap of neuroanatomical deviations. We further investigated whether the identified subgroups improved the accuracy of autism classification based on the neuroanatomical deviations using supervised machine learning with cross-validation. Results yielded two clinical subgroups primarily differing in restrictive and repetitive behaviors (RRBs). Both subgroups showed increased homogeneity in localized deviations with the high-RRB subgroup showing increased volume deviations in the cerebellum and the low-RRB subgroup showing decreased CT deviations predominantly in the postcentral gyrus and fusiform cortex. Nevertheless, substantial within-group heterogeneity remained highlighted by the lack of improvement of the classifier's performance when distinguishing between the subgroups and NAI. Future research should aim to further reduce heterogeneity incorporating additional neuroanatomical clustering in even larger samples. This will eventually pave the way for more tailored behavioral interventions and improving clinical outcomes. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

WS-BiTM: Integrating White Shark Optimization with Bi-LSTM for Enhanced Autism Spectrum Disorder Diagnosis

Autism Spectrum Disorder (ASD) is a multifaceted neurodevelopmental condition marked by challenges in social communication, sensory processing, and behavioral regulation. The delayed diagnosis of ASD significantly impedes timely interventions, which can exacerbate symptom severity. With approximately 62 million individuals affected worldwide, the demand for efficient diagnostic tools is critical. This study introduces a novel framework that combines a White Shark Optimization (WSO)-based feature selection method with a Bidirectional Long Short-Term Memory (Bi-LSTM) classifier for enhanced autism classification. Utilizing the WSO technique, we identify key features from autism screening datasets, which markedly improves the model's predictive capabilities. The optimized feature set is then processed by the Bi-LSTM classifier, enhancing its efficiency in handling sequential data. We comprehensively address methodological challenges, including overfitting, generalization, interpretability, and computational efficiency. Furthermore, we conduct a comparative analysis against baseline algorithms such as Neural Networks, Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) networks, while also employing Particle Swarm Optimization (PSO) for feature selection validation. We evaluate performance metrics, including accuracy, F1-score, specificity, precision, and sensitivity across three ASD datasets: Toddlers, Adults, and Children. Our results demonstrate that the WS-BiTM model significantly outperforms baseline methods, achieving accuracies of 97.6%, 96.2%, and 96.4% on the respective datasets. Additionally, we implemented leave-one-dataset cross-validation and confirmed the statistical significance of our findings through a paired t-test, supplemented by an ablation study to detail the contributions of individual model components. These findings highlight the potential of the WS-BiTM model as a robust tool for ASD classification.

A comprehensive review of neuroimaging-based AI models for autism classification

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition characterized by challenges in social interaction, communication, and behavior. Traditional diagnostic methods rely heavily on behavioral assessments, such as the Autism Diagnostic Observation Schedule (ADOS) and Autism Diagnostic Interview-Revised (ADI-R), which are often subjective and limited by the absence of definitive biomarkers. Recent advances in neuroimaging techniques, including structural MRI (sMRI), functional MRI (fMRI), and positron emission tomography (PET), have provided valuable insights into the structural and functional abnormalities associated with ASD. However, the analysis of neuroimaging data is complex, requiring sophisticated machine learning (ML) and artificial intelligence (AI) models. This paper reviews state-of-the-art machine learning approaches, such as Random Forests, Support Vector Machines (SVM), and deep learning models, aimed at improving the diagnostic accuracy of ASD. We particularly focus on multimodal deep learning models that integrate neuroimaging data with behavioral assessments. In this review, we critically evaluate the strengths and limitations of these approaches, and how emerging AI tools not only enhance classification accuracy but also hold potential for identifying biomarkers and tracking the disorder's progression. Our analysis underscores the need for advanced methods that exploit both spatial and temporal features of brain data to better understand ASD, paving the way for improved diagnostics and personalized treatments.

Facial-Based Autism Classification Using Support Vector Machine Method

Facial-Based Autism Classification Using Support Vector Machine Method

Functional Connectivity and Graph Embedding-Based Domain Adaptation for Autism Classification from Multi-site Data

Functional Connectivity and Graph Embedding-Based Domain Adaptation for Autism Classification from Multi-site Data

Using dynamic spatio-temporal graph pooling network for identifying autism spectrum disorders in spontaneous functional infrared spectral sequence signals

Background:Autism classification work on fNIRS data using dynamic graph networks. Explore the impact of the dynamic connection relationship between brain channels on ASD, and compare the brain channel connection diagrams of ASD and TD to explore potential factors that influence the development of autism. Method:Using dynamic graph construction to mine the dynamic relationships of fNIRS data, obtain spatio-temporal correlations through dynamic feature extraction, and improve the information extraction capabilities of the network through spatio-temporal graph pooling to achieve classification of ASD. Result:A classification effect with an accuracy of 97.2% was achieved using a short sequence of 1.75s. The results showed that the dynamic connections of channel 5 and 19, channel 12 and 25, and channel 7 and 34 have a greater impact on the classification of autism. Comparison with previously used method(s): Compared with previous deep learning models, our model achieves efficient classification using short-term fNIRS data of 1.75s, and analyzes the impact of dynamic connections on classification through dynamic graphs. Conclusion:Using Dynamic Spatio-Temporal Graph Pooled Neural Networks (DSTGPN), dynamic connectivity between brain channels was found to have an impact on the classification of autism. By modeling the brain channel relationship maps of ASD and TD, hyperlink clusters were found to exist on the brain channel connections of ASD.

Enhancing early autism diagnosis through machine learning: Exploring raw motion data for classification.

In recent years, research has been demonstrating that movement analysis, utilizing machine learning methods, can be a promising aid for clinicians in supporting autism diagnostic process. Within this field of research, we aim to explore new models and delve into the detailed observation of certain features that previous literature has identified as prominent in the classification process. Our study employs a game-based tablet application to collect motor data. We use artificial neural networks to analyze raw trajectories in a "drag and drop" task. We compare a two-features model (utilizing only raw coordinates) with a four-features model (including velocities and accelerations). The aim is to assess the effectiveness of raw data analysis and determine the impact of acceleration on autism classification. Our results revealed that both models demonstrate promising accuracy in classifying motor trajectories. The four-features model consistently outperforms the two-features model, as evidenced by accuracy values (0.90 vs. 0.76). However, our findings support the potential of raw data analysis in objectively assessing motor behaviors related to autism. While the four-features model excels, the two-features model still achieves reasonable accuracy. Addressing limitations related to sample size and noise is essential for future research. Our study emphasizes the importance of integrating intelligent solutions to enhance and assist autism traditional diagnostic process and intervention, paving the way for more effective tools in assessing motor skills.

The visualization of autism: Filming children at the Maudsley Hospital, London, 1957-8.

This article examines three films made during the 1950s by Elwyn James Anthony at the psychotic clinic for children at the Maudsley Hospital that marked an important transition in the purpose and practice of visual documentation in a clinical setting: film as a research tool was transitioning from the recording of external signs as indicators of internal subjective states, to the capture of the visual flow of communication between subjects. It is a shift that had a particular impact on the emergent classification of autism, a modality not yet properly separated from the broader term of psychosis, as a non-relational condition whose visual capture demonstrated a void of inter-human communicational exchange. Film was significant not only as a recording apparatus, but as a method of cutting and crafting sequences of movements into brief repetitive motifs. The filmed behaviour of children remained opaque to interpretation, a 'finding' that facilitated the modelling of an emergent autism as subjects who were isolated, alienated and automaton-like, inhabiting a separate temporality. The article situates this 'second', affectless autism, within a broader context of post-war research into gestures as a language of the body, developed largely through an intellectual network of German émigré psychoanalysts who had fled to the US and UK in the 1930s.

Deep learning autism classification and prediction

One of the most prevalent illnesses in children is autism spectrum disorder (ASD) (1 in 44). According to some estimates, 53% of kids with ASD engage in one or more challenging behaviors (CB; aggression, self-injury, property destruction, elopement, etc.), which is significantly higher than the prevalence among their peers who are typically developing or who have other developmental disorders. Numerous, significant negative effects of CB on the person exist, and they are linked to a worse long-term outlook. For caregivers of children with ASD, the presence of CB is a better indicator of stress than the severity of the child's core ASD symptoms. The validity of fixed features extracted from autistic children’s face photographs as a biomarker to demarcate them from healthy children is investigated in this study paper. The proposed paper aims to use deep learning models (CNN) to classify autism spectrum disorders based on facial expression images. By leveraging the power of deep convolutional neural networks, based on the Kaggle dataset. We used and prepared data input to CNN models where the split image in two parts horizontally and vertically as feature extractor’s model as a binary classifier to identify autism in children accurately. Our results reveal that the proposed model achieved an accuracy of 94%, Sensitivity of 93% and Specificity of 95% this indicator is considered important and can be built or relied on.

Autism Classification and Identification of Significant Brain Lobe Using Cepstral Coefficients

Autism Classification and Identification of Significant Brain Lobe Using Cepstral Coefficients

Grid-tuned ensemble models for 2D spectrogram-based autism classification

BackgroundAutism spectrum disorder (ASD) is a prevalent collection of developmental disorders that affects around one in every 36 children worldwide. Since autistic children exhibit aberrant electroencephalogram (EEG) signals in comparison to typically developing (TD) children, many authors have recently proposed several machine learning (ML) and deep learning (DL) models to classify ASD patients using EEG signals. However, these models lack extensive and large-scale feature extraction and apply multiple benchmark datasets. Furthermore, most of the proposed ML models need feature engineering to extract key features for classification. MethodThis study proposed a DL-based weighted ensemble model to avoid the need for feature engineering. The proposed model is based on finding the best suitable combination of weights using a grid search strategy, treating the highest weighted model as the most accurate, and giving more weightage. The strength of the suggested technique involves extracting features from EEG recordings by transforming raw EEG into multi-channel two-dimensional time–frequency maps (2D spectrograms) using the short-time Fourier transform (STFT). Furthermore, it applies two widely-researched public EEG datasets [i.e., the King Abdulaziz University (KAU) and the University of Sheffield (TUOS) databases] for performance comparison and better generalizability. Additionally, the performance of the proposed model has been compared with other well-known DL models, and our proposed straightforward, easily implemented models surpass these models in terms of accuracy and training time. ResultsThe suggested model yields an accuracy of 94.71% for ASD classification using the KAU dataset and 96.22% accuracy for utilizing the TUOS dataset. ConclusionsEnsembled models can efficiently enhance accuracy, stability, and reproducibility. The demonstrated finding solidifies the applicability of the weighted ensemble model to classify ASD patients utilizing EEG data. The suggested model might be used as a supplemental tool to assist neurologists in diagnosing ASD.

Análise do escore do Childhood Autism Rating Scale de crianças com Transtorno do Espectro do Autismo pré e pós-intervenção com Método de Desenvolvimento das Habilidades de Comunicação no Autismo

ABSTRACT Purpose: to analyze changes in autism classification after intervention with the Development of Communication Skills in Autism (DHACA method) in children assessed with the Childhood Autism Classification Scale. Methods: a quantitative analysis study applying the Childhood Autism Rating Scale before and after 20 speech-language-hearing sessions, using the DHACA method with 14 nonverbal or minimally verbal children, of both sexes, aged 2 to 6 years, presented with autism spectrum disorder. The Shapiro-Wilk test was used to verify the normality pattern, and the Wilcoxon test to compare measures of central tendency of the items assessed by the scale before and after the intervention, with a p-value < 0.05 (5%) considered significant. Results: the level of autism support needs in the sample decreased after the intervention. The largest sample group classified by the scale before the intervention was “Moderate Autism” (42.86%), followed by “Severe Autism” and “Mild Autism”, both with 28.57%. After the intervention, most groups had scores equivalent to “Mild Autism” (64.29%), followed by “No Autism” (21.43%), “Moderate Autism” and “Severe Autism”, both with 7.14%. Conclusion: implementing the DHACA method helped develop communication, cognitive, and behavioral skills and decreased the level of autism support needs in this sample of children.

Percepção de cuidadores de pré-escolares com TEA sobre seu comportamento e desempenho ocupacional durante a pandemia da COVID-19

Abstract Introduction Children with Autism Spectrum Disorder (ASD) face behavioral, sensory processing, cognitive, and language challenges that affect their ability to perform activities of daily living (ADL) and necessitate access to educational, therapeutic, and health services. The COVID-19 pandemic limited access to these essential services, exacerbating the challenges faced by this vulnerable population due to imposed social distancing measures. Objective To explore the perceptions of parent caregivers of preschoolers with ASD regarding their children’s behavior and performance in ADL during the COVID-19 pandemic. Method This cross-sectional, descriptive study utilized both quantitative and qualitative approaches, interviewing 60 caregivers of preschoolers with ASD. Participants were asked about any observed changes in their child’s behavior and ability to engage in ADL during the pandemic. Child characterization involved the Autism Classification System of Functioning: Social Communication (ACSF:SC), the Pediatric Evaluation of Disability Inventory - Computer Adaptive Test (PEDI-CAT), and the Sensory Profile 2 (SP-2). Interviews were documented, with 15 recorded and transcribed for thematic content analysis. Results Participants included 43 mothers, 15 couples, and two fathers, totaling 60 children, of whom 46 (76.7%) were boys aged between 3 and 5 years (mean age 53.6 ±10.18 months). Caregivers reported changes in behavior and performance in ADL, predominantly noting deterioration. Increases in stereotypic behaviors, rigidity, and hyperfocus were observed, along with decreased participation in ADL. While the presence of caregivers benefited some children, it also led to reported stress, particularly among mothers. Conclusion The study found a predominant trend of deteriorating behavior and performance in ADL among children with ASD during the pandemic, based on caregiver reports.

Exploring Sparse Gaussian Processes for Bayesian Optimization in Convolutional Neural Networks for Autism Classification

Exploring Sparse Gaussian Processes for Bayesian Optimization in Convolutional Neural Networks for Autism Classification

Autism classification and monitoring from predicted categorical and dimensional emotions of video features

Autism classification and monitoring from predicted categorical and dimensional emotions of video features

Interpretation of deep non-linear factorization for autism.

Autism, a neurodevelopmental disorder, presents significant challenges for diagnosis and classification. Despite the widespread use of neural networks in autism classification, the interpretability of their models remains a crucial issue. This study aims to address this concern by investigating the interpretability of neural networks in autism classification using the deep symbolic regression and brain network interpretative methods. Specifically, we analyze publicly available autism fMRI data using our previously developed Deep Factor Learning model on a Hibert Basis tensor (HB-DFL) method and extend the interpretative Deep Symbolic Regression method to identify dynamic features from factor matrices, construct brain networks from generated reference tensors, and facilitate the accurate diagnosis of abnormal brain network activity in autism patients by clinicians. Our experimental results show that our interpretative method effectively enhances the interpretability of neural networks and identifies crucial features for autism classification.

Individualized Education Programs and Transition Planning for Adolescents With Autism.

The study objectives were to examine the contents of individualized education programs (IEPs) of adolescents with autism spectrum disorder (ASD), including postsecondary transition goals, services, and changes in special education classification over time. This study involved a longitudinal population-based surveillance cohort from the Autism Developmental Disabilities Monitoring Network from 2002 to 2018 in 3 catchment areas in the United States. The sample included 322 adolescents who were born in 2002, identified with ASD, and had an IEP available for review at ages 15-16 years. We found that 297 (92%) adolescents with ASD had an IEP including a transition plan. Those without intellectual disability (ID) were more likely to have postsecondary education and employment goals and have those goals be to pursue higher education or competitive employment compared with those with ID. Forty-one percent of adolescents with ASD had a postsecondary living arrangement goal. Although 28% of adolescents with ASD received school-based mental health services, none of these adolescents were Black; additionally, 15% of those with ID received mental health services compared with 34% without ID. The percentage of adolescents with ASD served under an autism classification increased from 44% at age 8 years to 62% by age 16. We identified gaps and disparities in school-based postsecondary transition planning. Working with education partners, families, and adolescents will be important to identify what challenges contribute to these findings and what supports are needed to improve the equity and quality of the transition planning process for adolescents with ASD so they are prepared for adulthood.

Classification of autism based on short-term spontaneous hemodynamic fluctuations using an adaptive graph neural network

Background:Short-term spontaneous hemodynamic fluctuations were collected by the functional near-infrared spectroscopy (fNIRS) system to classify children with autism spectrum disorder (ASD) and typical development (TD), and to explore abnormalities in the left inferior frontal gyrus in ASD. Methods:Using the fNIRS data of 25 children with ASD and 22 children with TD, a graph neural network combined with the temporal convolution module and the graph convolution module was used, to extract the spatio-temporal features of the data and achieve accurate classification of ASD. Results:The graph neural network was used to obtain a good classification result in the left inferior frontal gyrus, with an accuracy of 97.1%, precision of 95.1%, and specificity of 93.4%. It was found that the 5th channel (which is located in BA 10) and the 8th channel (which is located in BA 47) in the left inferior frontal gyrus were closely correlated with ASD. Comparison with previously used method(s):Compared with the previous deep learning model using the same input, the accuracy of our model has increased by up to 13%, and the correlation between channels in the left inferior frontal gyrus area with the best classification effect was explored through the graph neural network. Conclusion:The adaptive graph neural network (AGNN) model may be able to mine more valuable information to distinguish ASD from TD and in addition, the left inferior frontal gyrus may have greater investigative value.

Classification of low-functioning and high-functioning autism using task-based EEG signals

Autism is a neurodevelopmental disorder marked by a lack of interpersonal, social, and communication skills, and repetitive and limited behavioral patterns. Autism exhibits a different severity and level of functioning, ranging from high functioning (HF) to low functioning (LF), as per their intellectual/developmental abilities. Identifying the level of the functionality remains crucial in treating and training Autistic children. Brain functioning widely varies during task performance, and the consequent neural representations can act as potential biomarkers. This work aims to classify LF, and HF autistic children based on electroencephalography (EEG), a promising neural underpinning technique acquired while performing cognitive tasks. But task-based EEG data acquisition from autistic children remains challenging and hinders effective classification procedures due to inadequate sample sizes and difficulty acquiring artifact-free signals. Acquired EEG signals were pre-processed for artifact removal to overcome this limitation, and a Gaussian bootstrapping data augmentation technique was employed to strengthen the dataset. Frequency band-specific absolute power, relative power, and derived parameters such as theta-to-alpha ratio and theta-to-beta ratio were extracted. The statistically relevant features were considered for classifying high and low-functioning autism using a kernel-based support vector machine (SVM). Results show that the fine gaussian kernel outperformed the other kernel methods with an average accuracy of 99.9% in the classification process. Thus, this work seems to effectively employ task-based EEG parameters to distinguish the LF and HF groups rather than depending on behavioral measures to categorize autism.

An Improved AlexNet Model and Cepstral Coefficient-Based Classification of Autism Using EEG.

Autism is a neurodevelopmental disorder that cannot be completely cured, but early intervention during childhood can improve outcomes. Identifying autism spectrum disorder (ASD) has relied on subjective detection methods that involve questionnaires, medical professionals, and therapists and are subject to observer variability. The need for early diagnosis and the limitations of subjective detection methods has led researchers to explore machine learning-based approaches, such as Random Forests, K-Nearest Neighbors, Naive Bayes, and Support Vector Machines, to predict ASD meltdowns. In recent years, deep learning techniques have gained traction for early ASD detection. This study evaluates the performance of various deep learning networks, including AlexNet, VGG16, and ResNet50, using 5 cepstral coefficient features for ASD detection. The main contributions of this study are the utilization of Cepstral Coefficients in the processing stage to construct spectrograms and the modification of the AlexNet architecture for precise classification. Experimental observations indicate that the AlexNet with Linear Frequency Cepstral Coefficients (LFCC) yields the highest accuracy of 85.1%, while a customized AlexNet with LFCC achieves 90% accuracy.