Region-Wise Brain Response Classification of ASD Children Using EEG and BiLSTM RNN

Autism Spectrum ◽

Neural Dynamics ◽

Sensory Modulation ◽

Distance Metric ◽

Brain Response ◽

Fixed Amount ◽

Quantification Analysis

Abstract Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by impairment in sensory modulation. These sensory modulation deficits would ultimately lead them to difficulties in adaptive behavior and intellectual functioning. The purpose of this study was to observe changes in the nervous system with responses to auditory/visual and only audio stimuli in children with autism and typically developing (TD) through electroencephalography (EEG). In this study, 20 children with ASD and 20 children with TD were considered to investigate the difference in the neural dynamics. The neural dynamics could be understood by non-linear analysis of the EEG signal. In this research to reveal the underlying nonlinear EEG dynamics, recurrence quantification analysis (RQA) is applied. RQA measures were analyzed using various parameter changes in RQA computations. In this research, the cosine distance metric was considered due to its capability of information retrieval and the other distance metrics parameters are compared for identifying the best biomarker. Each computational combination of the RQA measure and the responding channel was analyzed and discussed. To classify ASD and TD, the resulting features from RQA were fed to the designed BiLSTM (bi-long short-term memory) network. The classification accuracy was tested channel-wise for each combination. T3 and T5 channels with neighborhood selection as FAN (fixed amount of nearest neighbors) and distance metric as cosine is considered as the best-suited combination to discriminate between ASD and TD with the classification accuracy of 91.86%, respectively.

Investigating electroencephalography signals of autism spectrum disorder (ASD) using Higuchi Fractal Dimension

Biomedical Engineering / Biomedizinische Technik ◽

10.1515/bmt-2019-0313 ◽

2020 ◽

Vol 0 (0) ◽

Author(s):

Menaka Radhakrishnan ◽

Daehan Won ◽

Thanga Aarthy Manoharan ◽

Varsha Venkatachalam ◽

Renuka Mahadev Chavan ◽

...

Keyword(s):

Fractal Dimension ◽

Autism Spectrum ◽

Spectrum Disorder ◽

Brain Dynamics ◽

Time Interval ◽

Typically Developing ◽

Brain Response ◽

Higuchi Fractal Dimension

AbstractAutism spectrum disorder (ASD) is a neurodevelopmental disorder with a deficit of social relationships, interaction, sense of imagination, and constrained interests. Early diagnosis of ASD will aid in devising appropriate training procedures and placing those children in the normal stream. The objective of this research is to analyze the brain response for auditory/visual stimuli in Typically Developing (TD) and children with autism through electroencephalography (EEG). Brain dynamics in the EEG signal can be analyzed well with the help of nonlinear feature primitives. Recent research reveals that, application of fractal-based techniques proves to be effective to estimate of degree of nonlinearity in a signal. This research attempts to analyze the effect of brain dynamics with Higuchi Fractal Dimension (HFD). Also, the performance of the fractal based techniques depends on the selection of proper hyper-parameters involved in it. One of the key parameters involved in computation of HFD is the time interval parameter ‘k’. Most of the researches arbitrarily fixes the value of ‘k’ in the range of all channels. This research proposes an algorithm to estimate the optimal value of the time parameter for each channel. Sub-band analysis was also carried out for the responding channels. Statistical analysis on the experimental reveals that a difference of 30% was observed between autistic and Typically Developing children.

School Attendance Prediction among Students with Autism: A Deep Learning-based Framework

10.21203/rs.3.rs-691760/v1 ◽

2021 ◽

Author(s):

Mohammed Jarbou ◽

Daehan Won ◽

Jennifer Gillis ◽

Raymond Romanczyk

Keyword(s):

Deep Learning ◽

Short Term Memory ◽

School Attendance ◽

The United States ◽

Autism Spectrum ◽

Short Term ◽

Children With Asd ◽

Wide Range

Abstract Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that affects the areas of social communication and behavior. The term “spectrum” refers to the wide range of symptoms observed across individuals with ASD. Many children with ASD experience difficulty with daily functioning at school andhome. ASD prevalenceincreases in the United States, with the most recent prevalence of 1.9%. Given the wide range of social and learning, difficulties experienced by children with ASD, it is paramount that they are able to attend school to receive the appropriate range of interventions. School absenteeism (SA) is a significant concern given its association with many negativeconsequences such as school drop-out.Early prediction of SA would help school districtto implement effective interventions to ameliorate this issue. Due to its heterogeneity, students with ASD show within-group differences concerning their SA. This research introduces a deep learning-based framework for predicting short-and long-term SA of students with ASD. The Long Short-Term Memory (LSTM) algorithm is used to predict short-term SA. Similarly, Multilayer Perceptron(MLP) and Random Forest (RF) algorithms are used to predict long-term SA. The proposed framework achieves a high accuracy of 89% and 90% to predict short-term and long-term SA, respectively.

Behavioural and EEG atypicalities during rest, visual perception, and cognitive control in autistic adults

10.31234/osf.io/42zrq ◽

2020 ◽

Author(s):

Amandine Lassalle ◽

Michael X Cohen ◽

Laura Dekkers ◽

Elizabeth Milne ◽

Rasa Gulbinaite ◽

...

Keyword(s):

Visual Perception ◽

Cognitive Control ◽

Visual Processing ◽

Autism Spectrum ◽

Neural Dynamics ◽

Simple Explanation ◽

Control Task ◽

Neural Structure ◽

Eeg Power ◽

Autistic Adults

Background: People with an Autism Spectrum Condition diagnosis (ASD) are hypothesized to show atypical neural dynamics, reflecting differences in neural structure and function. However, previous results regarding neural dynamics in autistic individuals have not converged on a single pattern of differences. It is possible that the differences are cognitive-set-specific, and we therefore measured EEG in autistic individuals and matched controls during three different cognitive states: resting, visual perception, and cognitive control.Methods: Young adults with and without an ASD (N=17 in each group) matched on age (range 20 to 30 years), sex, and estimated Intelligence Quotient (IQ) were recruited. We measured their behavior and their EEG during rest, a task requiring low-level visual perception of gratings of varying spatial frequency, and the “Simon task” to elicit activity in the executive control network. We computed EEG power and Inter-Site Phase Clustering (ISPC; a measure of connectivity) in various frequency bands.Results: During rest, there were no ASD vs. controls differences in EEG power, suggesting typical oscillation power at baseline. During visual processing, without pre-baseline normalization, we found decreased broadband EEG power in ASD vs. controls, but this was not the case during the cognitive control task. Furthermore, the behavioral results of the cognitive control task suggest that autistic adults were better able to ignore irrelevant stimuli.Conclusions: Together, our results defy a simple explanation of overall differences between ASD and controls, and instead suggest a more nuanced pattern of altered neural dynamics that depend on which neural networks are engaged.

Specialized Diet Therapies: Exploration for Improving Behavior in Autism Spectrum Disorder (ASD)

Current Medicinal Chemistry ◽

10.2174/0929867327666200217101908 ◽

2020 ◽

Vol 27 (40) ◽

pp. 6771-6786

Author(s):

Geir Bjørklund ◽

Nagwa Abdel Meguid ◽

Maryam Dadar ◽

Lyudmila Pivina ◽

Joanna Kałużna-Czaplińska ◽

...

Keyword(s):

Caloric Intake ◽

Autism Spectrum ◽

Spectrum Disorder ◽

Nutritional Deficiencies ◽

Childhood And Adolescence ◽

Restricted Interests ◽

Balanced Diet ◽

The Individual

As a major neurodevelopmental disorder, Autism Spectrum Disorder (ASD) encompasses deficits in communication and repetitive and restricted interests or behaviors in childhood and adolescence. Its etiology may come from either a genetic, epigenetic, neurological, hormonal, or an environmental cause, generating pathways that often altogether play a synergistic role in the development of ASD pathogenesis. Furthermore, the metabolic origin of ASD should be important as well. A balanced diet consisting of the essential and special nutrients, alongside the recommended caloric intake, is highly recommended to promote growth and development that withstand the physiologic and behavioral challenges experienced by ASD children. In this review paper, we evaluated many studies that show a relationship between ASD and diet to develop a better understanding of the specific effects of the overall diet and the individual nutrients required for this population. This review will add a comprehensive update of knowledge in the field and shed light on the possible nutritional deficiencies, metabolic impairments (particularly in the gut microbiome), and malnutrition in individuals with ASD, which should be recognized in order to maintain the improved socio-behavioral habit and physical health.

Improving Sentiment Analysis using Hybrid Deep Learning Model

Recent Advances in Computer Science and Communications ◽

10.2174/2213275912666190328200012 ◽

2020 ◽

Vol 13 (4) ◽

pp. 627-640 ◽

Cited By ~ 1

Author(s):

Avinash Chandra Pandey ◽

Dharmveer Singh Rajpoot

Keyword(s):

Neural Network ◽

Deep Learning ◽

Sentiment Analysis ◽

Classification Accuracy ◽

Short Term Memory ◽

Computational Cost ◽

Extraction Process ◽

Learning Model ◽

Sentiment Classification ◽

Deep Learning Model

Background: Sentiment analysis is a contextual mining of text which determines viewpoint of users with respect to some sentimental topics commonly present at social networking websites. Twitter is one of the social sites where people express their opinion about any topic in the form of tweets. These tweets can be examined using various sentiment classification methods to find the opinion of users. Traditional sentiment analysis methods use manually extracted features for opinion classification. The manual feature extraction process is a complicated task since it requires predefined sentiment lexicons. On the other hand, deep learning methods automatically extract relevant features from data hence; they provide better performance and richer representation competency than the traditional methods. Objective: The main aim of this paper is to enhance the sentiment classification accuracy and to reduce the computational cost. Method: To achieve the objective, a hybrid deep learning model, based on convolution neural network and bi-directional long-short term memory neural network has been introduced. Results: The proposed sentiment classification method achieves the highest accuracy for the most of the datasets. Further, from the statistical analysis efficacy of the proposed method has been validated. Conclusion: Sentiment classification accuracy can be improved by creating veracious hybrid models. Moreover, performance can also be enhanced by tuning the hyper parameters of deep leaning models.

FOXP1 syndrome: a review of the literature and practice parameters for medical assessment and monitoring

Journal of Neurodevelopmental Disorders ◽

10.1186/s11689-021-09358-1 ◽

2021 ◽

Vol 13 (1) ◽

Author(s):

Reymundo Lozano ◽

Catherine Gbekie ◽

Paige M. Siper ◽

Shubhika Srivastava ◽

Jeffrey M. Saland ◽

...

Keyword(s):

Autism Spectrum ◽

Medical Assessment ◽

Forkhead Box ◽

Organ Systems ◽

Practice Parameters ◽

Assessment And Monitoring ◽

Multidisciplinary Group ◽

The Brain

AbstractFOXP1 syndrome is a neurodevelopmental disorder caused by mutations or deletions that disrupt the forkhead box protein 1 (FOXP1) gene, which encodes a transcription factor important for the early development of many organ systems, including the brain. Numerous clinical studies have elucidated the role of FOXP1 in neurodevelopment and have characterized a phenotype. FOXP1 syndrome is associated with intellectual disability, language deficits, autism spectrum disorder, hypotonia, and congenital anomalies, including mild dysmorphic features, and brain, cardiac, and urogenital abnormalities. Here, we present a review of human studies summarizing the clinical features of individuals with FOXP1 syndrome and enlist a multidisciplinary group of clinicians (pediatrics, genetics, psychiatry, neurology, cardiology, endocrinology, nephrology, and psychology) to provide recommendations for the assessment of FOXP1 syndrome.

Intranasal oxytocin administration ameliorates social behavioral deficits in a POGZWT/Q1038R mouse model of autism spectrum disorder

Molecular Brain ◽

10.1186/s13041-021-00769-8 ◽

2021 ◽

Vol 14 (1) ◽

Author(s):

Kohei Kitagawa ◽

Kensuke Matsumura ◽

Masayuki Baba ◽

Momoka Kondo ◽

Tomoya Takemoto ◽

...

Keyword(s):

Social Behavior ◽

Mouse Model ◽

Mouse Models ◽

De Novo ◽

Autism Spectrum ◽

Spectrum Disorder ◽

De Novo Mutation ◽

Behavioral Deficits

AbstractAutism spectrum disorder (ASD) is a highly prevalent neurodevelopmental disorder characterized by core symptoms of impaired social behavior and communication. Recent studies have suggested that the oxytocin system, which regulates social behavior in mammals, is potentially involved in ASD. Mouse models of ASD provide a useful system for understanding the associations between an impaired oxytocin system and social behavior deficits. However, limited studies have shown the involvement of the oxytocin system in the behavioral phenotypes in mouse models of ASD. We have previously demonstrated that a mouse model that carries the ASD patient-derived de novo mutation in the pogo transposable element derived with zinc finger domain (POGZWT/Q1038R mice), showed ASD-like social behavioral deficits. Here, we have explored whether oxytocin (OXT) administration improves impaired social behavior in POGZWT/Q1038R mice and found that intranasal oxytocin administration effectively restored the impaired social behavior in POGZWT/Q1038R mice. We also found that the expression level of the oxytocin receptor gene (OXTR) was low in POGZWT/Q1038R mice. However, we did not detect significant changes in the number of OXT-expressing neurons between the paraventricular nucleus of POGZWT/Q1038R mice and that of WT mice. A chromatin immunoprecipitation assay revealed that POGZ binds to the promoter region of OXTR and is involved in the transcriptional regulation of OXTR. In summary, our study demonstrate that the pathogenic mutation in the POGZ, a high-confidence ASD gene, impairs the oxytocin system and social behavior in mice, providing insights into the development of oxytocin-based therapeutics for ASD.

Application of Skeleton Data and Long Short-Term Memory in Action Recognition of Children with Autism Spectrum Disorder

Sensors ◽

10.3390/s21020411 ◽

2021 ◽

Vol 21 (2) ◽

pp. 411

Author(s):

Yunkai Zhang ◽

Yinghong Tian ◽

Pingyi Wu ◽

Dongfan Chen

Keyword(s):

Action Recognition ◽

Short Term Memory ◽

Autism Spectrum ◽

Spectrum Disorder ◽

Short Term ◽

Term Memory ◽

Clinical Observations ◽

The Neural Network ◽

Long Short Term Memory

The recognition of stereotyped action is one of the core diagnostic criteria of Autism Spectrum Disorder (ASD). However, it mainly relies on parent interviews and clinical observations, which lead to a long diagnosis cycle and prevents the ASD children from timely treatment. To speed up the recognition process of stereotyped actions, a method based on skeleton data and Long Short-Term Memory (LSTM) is proposed in this paper. In the first stage of our method, the OpenPose algorithm is used to obtain the initial skeleton data from the video of ASD children. Furthermore, four denoising methods are proposed to eliminate the noise of the initial skeleton data. In the second stage, we track multiple ASD children in the same scene by matching distance between current skeletons and previous skeletons. In the last stage, the neural network based on LSTM is proposed to classify the ASD children’s actions. The performed experiments show that our proposed method is effective for ASD children’s action recognition. Compared to the previous traditional schemes, our scheme has higher accuracy and is almost non-invasive for ASD children.

LSD1 enzyme inhibitor TAK-418 unlocks aberrant epigenetic machinery and improves autism symptoms in neurodevelopmental disorder models

Science Advances ◽

10.1126/sciadv.aba1187 ◽

2021 ◽

Vol 7 (11) ◽

pp. eaba1187

Author(s):

Rina Baba ◽

Satoru Matsuda ◽

Yuuichi Arakawa ◽

Ryuji Yamada ◽

Noriko Suzuki ◽

...

Keyword(s):

Gene Expression ◽

Enzyme Activity ◽

Neurodevelopmental Disorders ◽

Specific Inhibitor ◽

Autism Spectrum ◽

Poly I:C ◽

Control Of Gene Expression ◽

Epigenetic Machinery ◽

The Brain

Persistent epigenetic dysregulation may underlie the pathophysiology of neurodevelopmental disorders, such as autism spectrum disorder (ASD). Here, we show that the inhibition of lysine-specific demethylase 1 (LSD1) enzyme activity normalizes aberrant epigenetic control of gene expression in neurodevelopmental disorders. Maternal exposure to valproate or poly I:C caused sustained dysregulation of gene expression in the brain and ASD-like social and cognitive deficits after birth in rodents. Unexpectedly, a specific inhibitor of LSD1 enzyme activity, 5-((1R,2R)-2-((cyclopropylmethyl)amino)cyclopropyl)-N-(tetrahydro-2H-pyran-4-yl)thiophene-3-carboxamide hydrochloride (TAK-418), almost completely normalized the dysregulated gene expression in the brain and ameliorated some ASD-like behaviors in these models. The genes modulated by TAK-418 were almost completely different across the models and their ages. These results suggest that LSD1 enzyme activity may stabilize the aberrant epigenetic machinery in neurodevelopmental disorders, and the inhibition of LSD1 enzyme activity may be the master key to recover gene expression homeostasis. TAK-418 may benefit patients with neurodevelopmental disorders.

UTMOST, a single and cross-tissue TWAS (Transcriptome Wide Association Study), reveals new ASD (Autism Spectrum Disorder) associated genes

Translational Psychiatry ◽

10.1038/s41398-021-01378-8 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Cristina Rodriguez-Fontenla ◽

Angel Carracedo

Keyword(s):

In Silico ◽

Association Studies ◽

Meta Analysis ◽

Gastrointestinal Symptoms ◽

Tissue Expression ◽

Autism Spectrum ◽

Biological Characterization ◽

Large Size ◽

Body Tissues

AbstractAutism spectrum disorders (ASD) is a complex neurodevelopmental disorder that may significantly impact on the affected individual’s life. Common variation (SNPs) could explain about 50% of ASD heritability. Despite this fact and the large size of the last GWAS meta-analysis, it is believed that hundreds of risk genes in ASD have yet to be discovered. New tools, such as TWAS (Transcriptome Wide Association Studies) which integrate tissue expression and genetic data, are a great approach to identify new ASD susceptibility genes. The main goal of this study is to use UTMOST with the publicly available summary statistics from the largest ASD GWAS meta-analysis as genetic input. In addition, an in silico biological characterization for the novel associated loci was performed. Our results have shown the association of 4 genes at the brain level (CIPC, PINX1, NKX2-2, and PTPRE) and have highlighted the association of NKX2-2, MANBA, ERI1, and MITF at the gastrointestinal level. The gastrointestinal associations are quite relevant given the well-established but unexplored relationship between ASD and gastrointestinal symptoms. Cross-tissue analysis has shown the association of NKX2-2 and BLK. UTMOST-associated genes together with their in silico biological characterization seems to point to different biological mechanisms underlying ASD etiology. Thus, it would not be restricted to brain tissue and it will involve the participation of other body tissues such as the gastrointestinal.