Abnormalities of EEG Functional Connectivity and Effective Connectivity in Children with Autism Spectrum Disorder

An EEG-based marker of functional connectivity: detection of major depressive disorder

Major depressive disorder (MDD) is a prevalent psychiatric disorder globally. There are many assays for MDD, but rapid and reliable detection remains a pressing challenge. In this study, we present a fusion feature called P-MSWC, as a novel marker to construct brain functional connectivity matrices and utilize the convolutional neural network (CNN) to identify MDD based on electroencephalogram (EEG) signal. Firstly, we combine synchrosqueezed wavelet transform and coherence theory to get synchrosqueezed wavelet coherence. Then, we obtain the fusion feature by incorporating synchrosqueezed wavelet coherence value and phase-locking value, which outperforms conventional functional connectivity markers by comprehensively capturing the original EEG signal's information and demonstrating notable noise-resistance capabilities. Finally, we propose a lightweight CNN model that effectively utilizes the high-dimensional connectivity matrix of the brain, constructed using our novel marker, to enable more accurate and efficient detection of MDD. The proposed method achieves 99.92% accuracy on a single dataset and 97.86% accuracy on a combined dataset. Moreover, comparison experiments have shown that the performance of the proposed method is superior to traditional machine learning methods. Furthermore, visualization experiments reveal differences in the distribution of brain connectivity between MDD patients and healthy subjects, including decreased connectivity in the T7, O1, F8, and C3 channels of the gamma band. The results of the experiments indicate that the fusion feature can be utilized as a new marker for constructing functional brain connectivity, and the combination of deep learning and functional connectivity matrices can provide more help for the detection of MDD.

Neuroplasticity of children in autism spectrum disorder

Autism spectrum disorder (ASD) is a neurodevelopmental disorder that encompasses a range of symptoms including difficulties in verbal communication, social interaction, limited interests, and repetitive behaviors. Neuroplasticity refers to the structural and functional changes that occur in the nervous system to adapt and respond to changes in the external environment. In simpler terms, it is the brain's ability to learn and adapt to new environments. However, individuals with ASD exhibit abnormal neuroplasticity, which impacts information processing, sensory processing, and social cognition, leading to the manifestation of corresponding symptoms. This paper aims to review the current research progress on ASD neuroplasticity, focusing on genetics, environment, neural pathways, neuroinflammation, and immunity. The findings will provide a theoretical foundation and insights for intervention and treatment in pediatric fields related to ASD.

Development of EEG connectivity from preschool to school-age children

Introduction

Many studies have collected normative developmental EEG data to better understand brain function in early life and associated changes during both aging and pathology. Higher cognitive functions of the brain do not normally stem from the workings of a single brain region that works but, rather, on the interaction between different brain regions. In this regard studying the connectivity between brain regions is of great importance towards understanding higher cognitive functions and its underlying mechanisms.

Methods

In this study, EEG data of children (N = 253; 3-10 years old; 113 females, 140 males) from pre-school to schoolage was collected, and the weighted phase delay index and directed transfer function method was used to find the electrophysiological indicators of both functional connectivity and effective connectivity. A general linear model was built between the indicators and age, and the change trend of electrophysiological indicators analyzed for age.

Results

The results showed an age trend for the functional and effective connectivity of the brain of children.

Discussion

The results are of importance in understanding normative brain development and in defining those conditions that deviate from typical growth trajectories.

Enhancing autism spectrum disorder classification in children through the integration of traditional statistics and classical machine learning techniques in EEG analysis

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder hallmarked by challenges in social communication, limited interests, and repetitive, stereotyped movements and behaviors. Numerous research efforts have indicated that individuals with ASD exhibit distinct brain connectivity patterns compared to control groups. However, these investigations, often constrained by small sample sizes, have led to inconsistent results, suggesting both heightened and diminished long-range connectivity within ASD populations. To bolster our analysis and enhance their reliability, we conducted a retrospective study using two different connectivity metrics and employed both traditional statistical methods and machine learning techniques. The concurrent use of statistical analysis and classical machine learning techniques advanced our understanding of model predictions derived from the spectral or connectivity attributes of a subject's EEG signal, while also verifying these predictions. Significantly, the utilization of machine learning methodologies empowered us to identify a unique subgroup of correctly classified children with ASD, defined by the analyzed EEG features. This improved approach is expected to contribute significantly to the existing body of knowledge on ASD and potentially guide personalized treatment strategies.

Identification of diagnostic markers for ASD: a restrictive interest analysis based on EEG combined with eye tracking

Electroencephalography (EEG) functional connectivity (EFC) and eye tracking (ET) have been explored as objective screening methods for autism spectrum disorder (ASD), but no study has yet evaluated restricted and repetitive behavior (RRBs) simultaneously to infer early ASD diagnosis. Typically developing (TD) children (n = 27) and ASD (n = 32), age- and sex-matched, were evaluated with EFC and ET simultaneously, using the restricted interest stimulus paradigm. Network-based machine learning prediction (NBS-predict) was used to identify ASD. Correlations between EFC, ET, and Autism Diagnostic Observation Schedule-Second Edition (ADOS-2) were performed. The Area Under the Curve (AUC) of receiver-operating characteristics (ROC) was measured to evaluate the predictive performance. Under high restrictive interest stimuli (HRIS), ASD children have significantly higher α band connectivity and significantly more total fixation time (TFT)/pupil enlargement of ET relative to TD children (p = 0.04299). These biomarkers were not only significantly positively correlated with each other (R = 0.716, p = 8.26e-4), but also with ADOS total scores (R = 0.749, p = 34e-4) and RRBs sub-score (R = 0.770, p = 1.87e-4) for EFC (R = 0.641, p = 0.0148) for TFT. The accuracy of NBS-predict in identifying ASD was 63.4%. ROC curve demonstrated TFT with 91 and 90% sensitivity, and 78.7% and 77.4% specificity for ADOS total and RRB sub-scores, respectively. Simultaneous EFC and ET evaluation in ASD is highly correlated with RRB symptoms measured by ADOS-2. NBS-predict of EFC offered a direct prediction of ASD. The use of both EFC and ET improve early ASD diagnosis.

EEG Connectivity Diversity Differences between Children with Autism and Typically Developing Children: A Comparative Study

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by deficits in social interaction and communication, and repetitive or stereotyped behaviors. Previous studies have reported altered brain connectivity in ASD children compared to typically developing children. In this study, we investigated the diversity of connectivity patterns between children with ASD and typically developing children using phase lag entropy (PLE), a measure of the variability of phase differences between two time series. We also developed a novel wavelet-based PLE method for the calculation of PLE at specific scales. Our findings indicated that the diversity of connectivity in ASD children was higher than that in typically developing children at Delta and Alpha frequency bands, both within brain regions and across hemispheric brain regions. These findings provide insight into the underlying neural mechanisms of ASD and suggest that PLE may be a useful tool for investigating brain connectivity in ASD.