Social cognition and functional brain network in autism spectrum disorder: Insights from EEG graph-theoretic measures

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.

Brain-region specific autism prediction from electroencephalogram signals using graph convolution neural network

BACKGROUND

Brain variations are responsible for developmental impairments, including autism spectrum disorder (ASD). EEG signals efficiently detect neurological conditions by revealing crucial information about brain function abnormalities.

OBJECTIVE

This study aims to utilize EEG data collected from both autistic and typically developing children to investigate the potential of a Graph Convolutional Neural Network (GCNN) in predicting ASD based on neurological abnormalities revealed through EEG signals.

METHODS

In this study, EEG data were gathered from eight autistic children and eight typically developing children diagnosed using the Childhood Autism Rating Scale at the Central Institute of Psychiatry, Ranchi. EEG recording was done using a HydroCel GSN with 257 channels, and 71 channels with 10-10 international equivalents were utilized. Electrodes were divided into 12 brain regions. A GCNN was introduced for ASD prediction, preceded by autoregressive and spectral feature extraction.

RESULTS

The anterior-frontal brain region, crucial for cognitive functions like emotion, memory, and social interaction, proved most predictive of ASD, achieving 87.07% accuracy. This underscores the suitability of the GCNN method for EEG-based ASD detection.

CONCLUSION

The detailed dataset collected enhances understanding of the neurological basis of ASD, benefiting healthcare practitioners involved in ASD diagnosis.

Disrupted cognitive and affective empathy network interactions in autistic children viewing social animation

Empathy can be divided into two core components, cognitive empathy (CE) and affective empathy (AE), mediated by distinct neural networks. Deficient empathy is a central feature of autism spectrum conditions (ASCs), but it is unclear if this deficit results from disruption solely within empathy networks or from disrupted functional integration between CE and AE networks. To address this issue, we measured functional connectivity (FC) patterns both within and between empathy networks in autistic children (4-8 years, n = 31) and matched typically developing (TD) children (n = 26) using near-infrared spectroscopy during the presentation of an animated story evoking CE and AE. Empathy and social communication ability were also assessed using the Empathy Quotient/Systemizing Quotient (EQ/SQ) and Social Responsiveness Scale, respectively. The results showed that the FC in the AE network of autistic children did not differ from the TD group across conditions; however, the ASC group showed weaker FC in the CE network under the CE condition and weaker FC between networks when processing AE information, the latter of which was negatively correlated with EQ scores in ASC. The empathy defect in ASC may involve abnormal integration of CE and AE network activities under AE conditions.

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.

Resting state brain subnetwork relates to prosociality and compassion in adolescents

Adolescence is a crucial time for social development, especially for helping (prosocial) and compassionate behaviors; yet brain networks involved in adolescent prosociality and compassion currently remain underexplored. Here, we sought to evaluate a recently proposed domain-general developmental (Do-GooD) network model of prosocial cognition by relating adolescent functional and structural brain networks with prosocial and compassionate disposition. We acquired resting state fMRI and diffusion MRI from 95 adolescents (ages 14–19  years; 46 males; 49 females) along with self-report questionnaires assessing prosociality and compassion. We then applied the Network-Based Statistic (NBS) to inductively investigate whether there is a significant subnetwork related to prosociality and compassion while controlling for age and sex. Based on the Do-GooD model, we expected that this subnetwork would involve connectivity to the ventromedial prefrontal cortex (VMPFC) from three domain-general networks, the default mode network (DMN), the salience network, and the control network, as well as from the DMN to the mirror neuron systems. NBS revealed a significant functional (but not structural) subnetwork related to prosociality and compassion connecting 31 regions (p = 0.02), showing DMN and DLPFC connectivity to the VMPFC; DMN connectivity to mirror neuron systems; and connectivity between the DMN and cerebellum. These findings largely support and extend the Do-GooD model of prosocial cognition in adolescents by further illuminating network-based relationships that have the potential to advance our understanding of brain mechanisms of prosociality.

A Multimodal Approach for Identifying Autism Spectrum Disorders in Children

Identification of autism spectrum disorder (ASD) in children is challenging due to the complexity and heterogeneity of ASD. Currently, most existing methods mainly rely on a single modality with limited information and often cannot achieve satisfactory performance. To address this issue, this paper investigates from internal neurophysiological and external behavior perspectives simultaneously and proposes a new multimodal diagnosis framework for identifying ASD in children with fusion of electroencephalogram (EEG) and eye-tracking (ET) data. Specifically, we designed a two-step multimodal feature learning and fusion model based on a typical deep learning algorithm, stacked denoising autoencoder (SDAE). In the first step, two SDAE models are designed for feature learning for EEG and ET modality, respectively. Then, a third SDAE model in the second step is designed to perform multimodal fusion with learned EEG and ET features in a concatenated way. Our designed multimodal identification model can automatically capture correlations and complementarity from behavior modality and neurophysiological modality in a latent feature space, and generate informative feature representations with better discriminability and generalization for enhanced identification performance. We collected a multimodal dataset containing 40 ASD children and 50 typically developing (TD) children to evaluate our proposed method. Experimental results showed that our proposed method achieved superior performance compared with two unimodal methods and a simple feature-level fusion method, which has promising potential to provide an objective and accurate diagnosis to assist clinicians.

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.

Automated ASD detection using hybrid deep lightweight features extracted from EEG signals

BACKGROUND

Autism spectrum disorder is a common group of conditions affecting about one in 54 children. Electroencephalogram (EEG) signals from children with autism have a common morphological pattern which makes them distinguishable from normal EEG. We have used this type of signal to design and implement an automated autism detection model.

MATERIALS AND METHOD

We propose a hybrid lightweight deep feature extractor to obtain high classification performance. The system was designed and tested with a big EEG dataset that contained signals from autism patients and normal controls. (i) A new signal to image conversion model is presented in this paper. In this work, features are extracted from EEG signal using one-dimensional local binary pattern (1D_LBP) and the generated features are utilized as input of the short time Fourier transform (STFT) to generate spectrogram images. (ii) The deep features of the generated spectrogram images are extracted using a combination of pre-trained MobileNetV2, ShuffleNet, and SqueezeNet models. This method is named hybrid deep lightweight feature generator. (iii) A two-layered ReliefF algorithm is used for feature ranking and feature selection. (iv) The most discriminative features are fed to various shallow classifiers, developed using a 10-fold cross-validation strategy for automated autism detection.

RESULTS

A support vector machine (SVM) classifier reached 96.44% accuracy based on features from the proposed model.

CONCLUSIONS

The results strongly indicate that the proposed hybrid deep lightweight feature extractor is suitable for autism detection using EEG signals. The model is ready to serve as part of an adjunct tool that aids neurologists during autism diagnosis in medical centers.