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Although the electroencephalogram (EEG) activity of Autism Spectrum Disorder (ASD) has extremely complicated dynamics, the basic research on its dynamic modeling remains unexplored. This paper aims to investigate the mechanisms underlying EEG abnormalities in ASD from the perspective of dynamic modeling, and delve into the therapeutic principles of neuromodulation. First, by adjusting the average inhibitory synaptic gain and the average excitatory synaptic connection strength in the Wendling model, we reproduce clinical EEG characteristics of ASD, including Interictal Epileptiform Discharges (IEDs), increased relative power of the $\delta +𝜃$ band, decreased relative power of the $\alpha$ band, and reduced EEG complexity. This indicates that Excitation–Inhibition (EI) imbalance is potentially the trigger of abnormal EEG in ASD. Then, we demonstrate that both monophasic and biphasic repetitive Transcranial Magnetic Stimulation (rTMS) effectively control pathological discharges in ASD, with biphasic rTMS showing a reduced risk of over-control at the same frequency and intensity, consistent with clinical findings. Additionally, we show that Deep Brain Stimulation (DBS) plays a similar role to rTMS in the treatment of ASD, which proves the low-frequency DBS to be more effective in enhancing the complexity of brain activity, suggesting that DBS may hold promise as an innovative neuromodulation strategy for ASD. Our work helps to promote the development of dynamic modeling for ASD and to inspire therapeutic approaches for regulating the EI balance.

Mirror neurons fire action potentials both when the agent performs a certain behavior and watches someone performing a similar action. Here, we present an original mirror neuron model based on the spike-timing-dependent plasticity (STDP) between two morpho-electrical models of neocortical pyramidal neurons. Both neurons fired spontaneously with basal firing rate that follows a Poisson distribution, and the STDP between them was modeled by the triplet algorithm. Our simulation results demonstrated that STDP is sufficient for the rise of mirror neuron function between the pairs of neocortical neurons. This is a proof of concept that pairs of neocortical neurons associating sensory inputs to motor outputs could operate like mirror neurons. In addition, we used the mirror neuron model to investigate whether channelopathies associated with autism spectrum disorder could impair the modeled mirror function. Our simulation results showed that impaired hyperpolarization-activated cationic currents (Ih) affected the mirror function between the pairs of neocortical neurons coupled by STDP.

Identifying brain abnormalities in autism spectrum disorder (ASD) is critical for early diagnosis and intervention. To explore brain differences in ASD and typical development (TD) individuals by detecting structural features using T1-weighted magnetic resonance imaging (MRI), we developed a deep learning-based approach, three-dimensional (3D)-ResNet with inception (I-ResNet), to identify participants with ASD and TD and propose a gradient-based backtracking method to pinpoint image areas that I-ResNet uses more heavily for classification. The proposed method was implemented in a preschool dataset with 110 participants and a public autism brain imaging data exchange (ABIDE) dataset with 1099 participants. An extra epilepsy dataset with 200 participants with clear degeneration in the parahippocampal area was applied as a verification and an extension. Among the datasets, we detected nine brain areas that differed significantly between ASD and TD. From the ROC in PASD and ABIDE, the sensitivity was 0.88 and 0.86, specificity was 0.75 and 0.62, and area under the curve was 0.787 and 0.856. In a word, I-ResNet with gradient-based backtracking could identify brain differences between ASD and TD. This study provides an alternative computer-aided technique for helping physicians to diagnose and screen children with an potential risk of ASD with deep learning model.

Autism spectrum disorder is a neurodevelopmental disorder typically characterized by abnormalities in social interaction and stereotyped and repetitive behaviors. Diagnosis of autism is mainly based on behavioral tests and interviews. In recent years, studies involving the diagnosis of autism based on analysis of EEG signals have increased. In this paper, recorded signals from people suffering from autism and healthy individuals are divided to without overlap windows considered as images and these images are classified using a two-dimensional Deep Convolution Neural Network (2D-DCNN). Deep learning models require a lot of data to extract the appropriate features and automate data classification. But, in most neurological studies, preparing a large number of measurements is difficult (a few 1000s as compared to million natural images), due to the cost, time, and difficulty of recording these signals. Therefore, to make the appropriate number of data, in our proposed method, some of the data augmentation methods are used. These data augmentation methods are mainly introduced for image databases and should be generalized for EEG-as-an-image database. In this paper, one of the nonlinear image mixing methods is used that mixes the rows of two images. According to the fact that any row in our image is one channel of EEG signal, this method is named channel combination. The result is that in the best case, i.e., augmentation according to channel combination, the average accuracy of 88.29% is achieved in the classification of short signals of healthy people and ASD ones and 100% for ASD and epilepsy ones, using 2D-DCNN. After the decision on joined windows related to each subject, we could achieve 100% accuracy in detecting ASD subjects, according to long EEG signals.

Autism Spectrum Disorder (ASD) is a complex and heterogeneous neurodevelopmental disorder which affects a significant proportion of the population, with estimates suggesting that about 1 in 100 children worldwide are affected by ASD. This study introduces a new Deep Neural Network for identifying ASD in children through gait analysis, using features extracted from frames composing video recordings of their walking patterns. The innovative method presented herein is based on imagery and combines gait analysis and deep learning, offering a noninvasive and objective assessment of neurodevelopmental disorders while delivering high accuracy in ASD detection. Our model proposes a bimodal approach based on the concatenation of two distinct Convolutional Neural Networks processing two feature sets extracted from the same videos. The features obtained from the convolutions of both networks are subsequently flattened and merged into a single vector, serving as input for the fully connected layers in the binary classification process. This approach demonstrates the potential for effective ASD detection in children through the combination of gait analysis and deep learning techniques.

Autism Spectrum Disorder (ASD) is a disorder of brain growth with great variability whose clinical presentation initially shows up during early stages or youth, and ASD follows a repetitive pattern of behavior in most cases. Accurate diagnosis of ASD has been difficult in clinical practice as there is currently no valid indicator of ASD. Since ASD is regarded as a neurodevelopmental disorder, brain signals specially electroencephalography (EEG) are an effective method for detecting ASD. Therefore, this research aims at developing a method of extracting features from EEG signal for discriminating between ASD and control subjects. This study applies six prominent connectivity features, namely Cross Correlation (XCOR), Phase Locking Value (PLV), Pearson’s Correlation Coefficient (PCC), Mutual Information (MI), Normalized Mutual Information (NMI) and Transfer Entropy (TE), for feature extraction. The Connectivity Feature Maps (CFMs) are constructed and used for classification through Convolutional Neural Network (CNN). As CFMs contain spatial information, they are able to distinguish ASD and control subjects better than other features. Rigorous experimentation has been performed on the EEG datasets collected from Italy and Saudi Arabia according to different criteria. MI feature shows the best result for categorizing ASD and control participants with increased sample size and segmentation.

A medical Qigong protocol was applied to a group of eight autistic children under the age of six. The children received medical Qigong massage twice weekly from the physician and daily Qigong massage from the parents for a five-week period, followed by daily parent massage for an additional four weeks. Standardized tests showed a decrease in autistic behaviors and increase in language development in all the children, as well as improvement in motor skills, sensory function and general health.

Autism spectrum disorder (ASD) is a mental developmental disorder associated with social and communicational defects and Stereotypical Motor Movements (SMM). SMM is a set of repetitive motor activities associated with several mental developmental disorders like Autism. SMM has several forms like arm flapping, head banging, ear covering, and spinning with various degrees of severity that might lead to self-injury in severe cases.

Developing a computer-vision-based technology to detect noticeable SMM behaviors can help in the early diagnosis of autism. In this paper, a computer vision-based neural network model was proposed to detect and recognize repetitive motor behaviors. The proposed model went through three main stages: First, data preparation. Second, human body features extraction using deep learning pose estimation and the skeleton representation model, and finally, multiclass classification to distinguish between several classes of headbanging. The proposed solution was evaluated using the Self Stimulatory Behavior Dataset (SSBD) which is a public dataset of three classes of repetitive motor behaviors associated with autism. We also collected a set of 40 videos of autistic children exhibiting headbanging from public domains like YouTube. In addition to that, we captured 25 videos of typically developing subjects mimicking headbanging. The collected and the videoed videos were used to evaluate the proposed model. This work proves the applicability of diagnosing mental developmental syndrome symptoms using vision-based techniques in cooperation with neural networks. The produced results prove that the used techniques can operate well in real-world challenging applications. The proposed model achieved 85.5% accuracy on SSBD and 93% on the collected and recorded videos.

National Science and Technology Prizes.

Virologist Hou Yunde won the State Preeminent Science and Technology Award.

Highlights from the State Natural Science Award.

Highlights from the State Technological Invention Award.

Breakthrough: Chinese researchers cloned monkeys using Dolly’s cloning method.

Chinese GM rice approved by U.S. FDA.

Chinese Government supports TCM innovations.

First bio-safety level four lab put into operation in Wuhan.

New way to develop flu vaccines.

New genome research project in China.

Chinese scientists enhance e-skin sensory capability.

Gene technology start-up offers genetic testing to trace ancestry.

Genetic basis for biological motion perception and linkage to autistic traits.

Novel drug efflux pump in gram-positive bacteria.

China FDA approves new once-weekly Bydureon to improve glycemic control in patients with Type-2 Diabetes.

Illumina and KingMed Diagnostics partner to develop next-generation sequencing technology for Chinese FDA approval.

Varian and Ping An sign MoU to expand access to high quality cancer care in China.

Eisai completes construction of oral solid dose production facility at new Suzhou plant in China.

Ping An Technology sets world records in international medical imaging evaluation.

Marken opens new kit building facility in Shanghai.

ASLAN Pharmaceuticals announces shortened timeline to commercialisation for varlitinib in China.

ASLAN Pharmaceuticals appoints Stephen Doyle as head of China.

HKUST Scientists reveal how human brains keep balance.

Machine learning (ML) is a branch of computer science that is rapidly gaining popularity within the healthcare arena due to its ability to explore large datasets to discover useful patterns that can be interepreted for decision-making and prediction. ML techniques are used for the analysis of clinical parameters and their combinations for prognosis, therapy planning and support and patient management and wellbeing. In this research, we investigate a crucial problem associated with medical applications such as autism spectrum disorder (ASD) data imbalances in which cases are far more than just controls in the dataset. In autism diagnosis data, the number of possible instances is linked with one class, i.e. the no ASD is larger than the ASD, and this may cause performance issues such as models favouring the majority class and undermining the minority class. This research experimentally measures the impact of class imbalance issue on the performance of different classifiers on real autism datasets when various data imbalance approaches are utilised in the pre-processing phase. We employ oversampling techniques, such as Synthetic Minority Oversampling (SMOTE), and undersampling with different classifiers including Naive Bayes, RIPPER, C4.5 and Random Forest to measure the impact of these on the performance of the models derived in terms of area under curve and other metrics. Results pinpoint that oversampling techniques are superior to undersampling techniques, at least for the toddlers’ autism dataset that we consider, and suggest that further work should look at incorporating sampling techniques with feature selection to generate models that do not overfit the dataset.

Data imbalance with respect to the class labels has been recognised as a challenging problem for machine learning techniques as it has a direct impact on the classification model’s performance. In an imbalanced dataset, most of the instances belong to one class, while far fewer instances are associated with the remaining classes. Most of the machine learning algorithms tend to favour the majority class and ignore the minority classes leading to classification models being generated that cannot be generalised. This paper investigates the problem of class imbalance for a medical application related to autism spectrum disorder (ASD) screening to identify the ideal data resampling method that can stabilise classification performance. To achieve the aim, experimental analyses to measure the performance of different oversampling and under-sampling techniques have been conducted on a real imbalanced ASD dataset related to adults. The results produced by multiple classifiers on the considered datasets showed superiority in terms of specificity, sensitivity, and precision, among others, when adopting oversampling techniques in the pre-processing phase.

The existing procedures for autism spectrum disorder (ASD) diagnosis are often time consuming and tiresome both for highly-trained human evaluators and children, which may be alleviated by using humanoid robots in the diagnostic process. Hence, this paper proposes a framework for robot-assisted ASD evaluation based on partially observable Markov decision process (POMDP) modeling, specifically POMDPs with mixed observability (MOMDPs). POMDP is broadly used for modeling optimal sequential decision making tasks under uncertainty. Spurred by the widely accepted autism diagnostic observation schedule (ADOS), we emulate ADOS through four tasks, whose models incorporate observations of multiple social cues such as eye contact, gestures and utterances. Relying only on those observations, the robot provides an assessment of the child’s ASD-relevant functioning level (which is partially observable) within a particular task and provides human evaluators with readable information by partitioning its belief space. Finally, we evaluate the proposed MOMDP task models and demonstrate that chaining the tasks provides fine-grained outcome quantification, which could also increase the appeal of robot-assisted diagnostic protocols in the future.

Recent studies suggest that robot-based interventions are potentially effective in diagnosis and therapy of autism spectrum disorder (ASD), demonstrating that robots can improve the engagement abilities and attention in autistic children. While methodological approaches vary significantly in these studies and are not unified yet, researchers often develop similar solutions based on similar conceptual and practical premises. We systematically review the latest robot-intervention techniques in ASD research (18 research papers), comparing multiple dimensions of technological and experimental implementation. In particular, we focus on sensor-based assessment systems for automated and unbiased quantitative assessments of children’s engagement and attention fluctuations during interaction with robots. We examine related technologies, experimental and methodological setups, and the empirical investigations they support. We aim to assess the strengths and limitations of such approaches in a diagnostic context and to evaluate their potential in increasing our knowledge of autism and in supporting the development of social skills and attentional dispositions in ASD children. Using our acquired results from the overview, we propose a set of social cues and interaction techniques that can be thought to be most beneficial in robot-related autism intervention.

Previous studies reported that children with autism spectrum disorder (ASD) show a certain interest in social robots. This makes social robots potential to be a model to teach social skills. This exploratory study aims to investigate whether three types of joint attention skills (i.e., eye-contact, pointing, gaze-following) could be improved for five preschoolers with ASD using an evidence-based robot-modeling intervention with a humanoid social robot NAO. Our observation shows that these children were motivated when interacting with NAO by following and responding correctly to NAO’s joint attention behaviors. Although some improvements were found, no pattern or systematic effect could be revealed. In the future, more evidence-based studies are needed to investigate the benefits of robot-assisted therapy more deeply.

In this paper, a system supporting behavioral therapy for autistic children is presented. The system consists of sensors network, base station and a brooch indicating person's emotional states. The system can be used to measure values of physiological parameters that are associated with changes in the emotional state. In the future, it can be useful to inform the autistic child and the therapist about the emotional state of the interlocutor objectively, on the basis of performed measurements. The selected physiological parameters were chosen during the experiment which was designed and conducted by authors. In this experiment, a group of volunteers under controlled conditions was exposed to a stressful situation caused by the picture or sound. For each of the volunteers, a set of physiological parameters, was recorded, including: skin conductance, heart rate, peripheral temperature, respiration rate and electromyography. The bio-statistical analysis allowed us to discern the proper physiological parameters that are most associated to changes due to emotional state of a patient, such as: skin conductance, temperatures and respiration rate. This allowed us to design electronic sensors network for supporting behavioral therapy for children with autism.

Autism Spectrum Disorder (ASD) and Huntington’s Disease (HD) are distinct neurodevelopmental and neurodegenerative disorders, respectively, characterized by significant genetic and molecular alterations. ASD primarily affects early childhood and is associated with genetic mutations impacting brain development, while HD, an autosomal dominant disorder, leads to progressive neurodegeneration due to mutations in the HTT gene. Despite their differences, both disorders share common genetic pathways and molecular mechanisms. This study aims to explore the genetic and molecular connections between ASD and HD through a comprehensive analysis of differentially expressed genes (DEGs) and protein–protein interaction (PPI) networks to uncover shared pathways and potential overlapping mechanisms. Transcriptomic data were acquired from the NCBI-GEO database, specifically GSE180185 for ASD and GSE1751 for HD. DEGs were identified using thresholds of log2 fold change (FC)>1 and an adjusted $p$-value <0.05. Common DEGs between the two disorders were determined and analyzed using Cytoscape’s STRING app to construct a PPI network with a confidence level of 0.7. Functional enrichment was conducted through KEGG and Gene Ontology (GO) analyses. Key regulatory modules and hubs were identified using CytoNCA and MCODE plugins. The ASD dataset revealed 565 DEGs, with 206 upregulated and 347 downregulated, while the HD dataset had 1091 DEGs, with 743 upregulated and 202 downregulated. Twelve genes were common to both conditions, including 4 upregulated and 8 downregulated. The PPI network comprised 62 nodes and 215 edges, with significant pathways including ascorbate metabolism and steroid hormone biosynthesis. Notably, Module 3, containing 12 nodes, was linked to EGFR tyrosine kinase resistance and apoptosis. This study identifies shared genetic and molecular pathways between ASD and HD, highlighting common regulatory mechanisms and potential targets for further research. The use of transcriptomic data and PPI network analysis reveals significant overlaps in the molecular mechanisms underlying these disorders. Further experimental validation and expanded dataset analyses could elucidate specific interactions and enhance our understanding of the shared pathways. Investigating these common mechanisms may also provide insights into potential therapeutic approaches for both ASD and HD.

Poincar section is a tool used in analysis and even control of non-linear systems like chaotic and uncertain systems. Although it has been presented long ago, yet this approach is artistic and heuristic. Poincar Section is destitute of any definite methodologies and problems including indefinite structure and model parameters that can be generally attributed to this approach; machine learning based on Poincar section is impossible. In this article, first of all, signal modeling steps using Poincar is explained, then considering the occurred events, the concept of information and relativism applying Poincar section and information approach, we will diagnose the brain pattern variations in Autistic cases. The reason we have taken Autism into consideration is because we believe its origin is information, in other words the big problem in Autism disorder is software kind, which can lead to hardware kind over time.

In this research a new kind of representation, namely Extended Complementary Plot, in which the main characteristic is special attention to signal phase as embedded information in the signal and ineffectiveness of energy, is introduced. All the introduced state-of-art concepts on Electroencephalography are implemented on Autistic children. Recording the EEG signal in Autistic children has always been a challenge for the specialists. Implementations of the article have been carried out on over 120 cases including 60 Autistic children and 60 normal ones ranging from 3 to 10 years old, in three different states; asleep, open eyes and a new record based on brain dynamics which has been suggested from the authors and does not have the other records problems for Autistic kids.

Prodigious results accomplished, suggests the common dynamic presence in Autism disorder which is entirely different from normal dynamics, and this is only due to the potency of the applied information tool; Poincar section, and cybernetic modeling in this research. We hope that the empirical results of this research to be a strong and effective step towards quantification of Autism disorder and conversion of diagnosis process from Clinical to Para clinical, and even early Autism diagnosis.

Children suffering from Autism Spectrum Disorder (ASD) have impaired social communication, interaction and restricted and repetitive behaviors. ASD is caused by abnormal brain developments which give rise to the behavioral characteristics associated with ASD. The clinical diagnosis of ASD is performed on the basis of behavioral assessment and it causes a time delay in early intervention, as there is a time gap between abnormal brain developments and associated behavioral characteristics. Electroencephalography (EEG) is a technique which measures the electrical activity produced by the brain and it has been used to detect several neurological disorders. Studies have shown that there is a variation in the EEG signals of a normal subject and EEG signals of ASD subjects. In this study, we obtained scalograms of EEG signals by using Continuous Wavelet Transform (CWT). Pre-trained deep Convolutional Neural Networks (CNNs) such as GoogLeNet, AlexNet, MobileNet and SqueezeNet were used for extracting the features from scalograms and classification of obtained scalograms from EEG signals of normal and ASD subjects. We also used Support Vector Machine (SVM) algorithm and Relevance Vector Machine (RVM) for classification of the features extracted by the deep CNNs. The GoogLeNet, AlexNet, MobileNet and SqueezeNet deep CNNs achieved a validation accuracy of 75%, 75.84%, 79.45% and 82.98% in classifying the scalograms generated from EEG signals. The SVM achieved an accuracy of 71.6%, 74.76%, 70.70% and 81.47% using GoogleNet, Mobilenet, AlexNet and SqueezeNet for scalogram feature extraction. The RVM achieved an accuracy of 65.5%, 69.9%, 65.3% and 72.59% when used for classification using the features generated from GoogLeNet, AlexNet, MobileNet and SqueezeNet.The SqueezeNet deep CNN performed better than GoogLeNet, AlexNet and MobileNet for classification of the EEG scalograms. The feature extraction using SqueezeNet also resulted in better classification accuracy obtained by SVM and RVM. The results indicate that pre-trained models can be used for classifying the ASD using scalograms of the EEG signals.

There have been reviews or meta-analysis showing that using manipulatives is an effective intervention for learning mathematics for students with disabilities, including autism spectrum disorders (ASD), without concentrating on the effects on generalization and maintenance. We conducted a meta-analysis to evaluate the effect of manipulatives on generalizing and/or maintaining mathematical skills for individuals with ASD and whether the effect varies with different participant characteristics, study design, intervention characteristics and mathematical content, focusing on the single-case studies. After application of the What Works Clearinghouse design standards, a total of 11 studies were included in the review: three studies collected data points during generalization phases, five studies collected data points during maintenance phases, the other three studies collected both generalization and maintenance data. Aggregate Tau-U and non-overlap of all pairs effect sizes (NAP) were calculated for each study and conducted moderator analyses. Overall, effect size scores ranged from small to significant effects across all comparisons. On average, most comparisons from the baseline to generalization and maintenance produced medium to large effects. Whereas, minor effects were found in most of the intervention of generalization and maintenance comparisons. Further moderator analysis regard to generalization and maintenance revealed that out of seven variables analyzed, only manipulatives types served as a moderator for maintenance. The findings suggest that manipulatives interventions were likely to result in mixed effects on mathematical skill generalization and maintenance within children with ASD, especially virtual manipulatives. Limitations and implications for future research and practice are discussed.

Despite mounting evidence for the strong role of genetics in the phenotypic manifestation of Autism Spectrum Disorder (ASD), the specific genes responsible for the variable forms of ASD remain undefined. ASD may be best explained by a combinatorial genetic model with varying epistatic interactions across many small effect mutations. Coalitional or cooperative game theory is a technique that studies the combined effects of groups of players, known as coalitions, seeking to identify players who tend to improve the performance--the relationship to a specific disease phenotype--of any coalition they join. This method has been previously shown to boost biologically informative signal in gene expression data but to-date has not been applied to the search for cooperative mutations among putative ASD genes. We describe our approach to highlight genes relevant to ASD using coalitional game theory on alteration data of 1,965 fully sequenced genomes from 756 multiplex families. Alterations were encoded into binary matrices for ASD (case) and unaffected (control) samples, indicating likely gene-disrupting, inherited mutations in altered genes. To determine individual gene contributions given an ASD phenotype, a “player” metric, referred to as the Shapley value, was calculated for each gene in the case and control cohorts. Sixty seven genes were found to have significantly elevated player scores and likely represent significant contributors to the genetic coordination underlying ASD. Using network and cross-study analysis, we found that these genes are involved in biological pathways known to be affected in the autism cases and that a subset directly interact with several genes known to have strong associations to autism. These findings suggest that coalitional game theory can be applied to large-scale genomic data to identify hidden yet influential players in complex polygenic disorders such as autism.