Autistic Disorder Research Articles

The 32nd Ion Channels Meeting, 17th-20th September 2023, Sète, France.

The 32nd Ion Channel Meetings were organized by the Ion Channels Association from September 17 to 20, 2023 in the Occitanie region (Sète). Researchers, post-docs and students from France, Europe and non-European countries came together to present and discuss their work on various themes covering the field of neuroscience, stem cells, hypoxia and pathophysiology cardiac. Through the plenary conference given by Professor Emilio Carbone and the 5 conferences organized by the scientific committee, attention was paid this year to autism, neuromotor and cardiac disorders and tumor aggressive processes. The scientific exchanges were enriched by two general conferences on the biometric analysis of publications related to ion channels and a retrospective presentation of proven cases of scientific fraud. These presentations are summarized in this meeting report.

Coping strategies in mothers of children with autism and attention deficit hyperactivity disorder: latent profile analysis

Objectives Mothers of children with autism and children with attention deficit hyperactivity disorder employ distinct coping strategies when confronted with stressful situations related to the parenting process. The current study aimed to identify distinct coping profiles among mothers, based on four indicators: problem-focused coping, active avoidance coping, positive coping, and religious/denial coping. The second objective was to examine the differences between the identified profiles in terms of the mother and children’s outcomes. Methods Participants were 90 mothers of children with autism and attention deficit hyperactivity disorder. Latent profile analysis revealed three coping profiles based on different coping strategies: ‘Adaptative coping’ (42.22%), ‘Avoidance coping’ (40%), and ‘Low coping’ (17.77%). Results The ‘Low coping’ profile showed more dysfunctionality than the others and it evidenced significant differences with the other two profiles on parenting stress, social support and children social, behavioral and sleep problems. Conclusions The findings emphasize the importance of exploring coping skills employed by mothers of children with these neurodevelopmental disorders and developing interventions for those who adopt ineffective coping strategies.

The Risk Genes for Neuropsychiatric Disorders negr1 and opcml Are Expressed throughout Zebrafish Brain Development.

The immunoglobulin LAMP/OBCAM/NTM (IgLON) family of cell adhesion molecules comprises five members known for their involvement in establishing neural circuit connectivity, fine-tuning, and maintenance. Mutations in IgLON genes result in alterations in these processes and can lead to neuropsychiatric disorders. The two IgLON family members NEGR1 and OPCML share common links with several of them, such as schizophrenia, autism, and major depressive disorder. However, the onset and the underlying molecular mechanisms have remained largely unresolved, hampering progress in developing therapies. NEGR1 and OPCML are evolutionarily conserved in teleosts like the zebrafish (Danio rerio), which is excellently suited for disease modelling and large-scale screening for disease-ameliorating compounds. To explore the potential applicability of zebrafish for extending our knowledge on NEGR1- and OPCML-linked disorders and to develop new therapeutic strategies, we investigated the spatio-temporal expression of the two genes during early stages of development. negr1 and opcml are expressed maternally and subsequently in partially distinct domains of conserved brain regions. Other areas of expression in zebrafish have not been reported in mammals to date. Our results indicate that NEGR1 and OPCML may play roles in neural circuit development and function at stages earlier than previously anticipated. A detailed functional analysis of the two genes based on our findings could contribute to understanding the mechanistic basis of related psychiatric disorders.

CLASSIFICATION OF COGNITIVE STRESS AS A PSYCHOLOGICAL INDICATOR THROUGH MACHINE LEARNING

In this study, we explored the potential of Support Vector Machine (SVM) method for classifying levels of cognitive stress using EEG (Electroencephalogram) signals. The goal is to develop accurate models that would enable the prediction and understanding of not only the current mental state of the subjects, but also potential real-time interventions. In medical fields, the application can be seen in the treatment of attention, focus, hyperactivity, autism, and depression disorders. Additionally, there is an extremely high potential for application in areas such as psychology, sociology, education, economics, neuromarketing, security, and in enhancing workplace stress management, anxiety treatment, digital marketing, economicfinancial forensics, as well as improving user experience in virtual environments and video games. The results have shown that it is possible to differentiate high and low levels of cognitive stress with satisfactory accuracy, opening the way for the application of these findings in various fields. Cognitive stress represents one of the fundamental cognitive processes that causes individuals to behave and think differently in certain situations than in their usual state of consciousness. Predicting, analyzing, and understanding the level of cognitive stress from EEG signals is of great importance in various fields, including neuroscience, psychology, education, professional sports, human-computer interaction, and many other areas. Machine learning represents a subgroup of artificial intelligence that uses statistical models, and functions to ‘learn’ and ‘train’ data resulting in corresponding output values. The brain-computer interface, through which data on cognitive stress, among other parameters and psychological categories, is collected, is based on the functioning of EEG devices. The prediction of cognitive stress represents the application of machine learning, recording and using brain EEG signals or extracted characteristics from EEG signals as input values, in order to predict the level of output values of cognitive stress, of high or low degree, reflecting the mental state of the subjects in real time

Shifting Paradigms

Shifting Paradigms

Cultivating Neurodiverse Connections Through Competent Leadership: Integrative Literature Review

This study examined the relationship between neurodiversity and leadership competency to create inclusive and encouraging settings. The concept of neurodiversity in the workplace stresses the acceptance and celebration of neurological variations, including autism, ADHD, and other neurodevelopmental disorders. In order to promote diversity, inclusion, and shape corporate culture, competent leadership is essential. In order to investigate how leaders might develop neurodiverse connections, this review synthesises the literature and looks at how they may embrace inclusive practices, recognise the special advantages and difficulties faced by neurodivergent people, and create a positive work environment. An extensive review of the present state of knowledge in this burgeoning topic is provided by the analysis, which includes studies from a variety of disciplines such as management, organisational behaviour, and psychology. The results underscore the significance of leadership competencies, including empathy, adaptability, and communication, in fostering settings that enable neurodivergent persons, hence augmenting creativity and efficiency. Furthermore, this study investigated the complex relationship between neurodiversity and leadership ability, with a particular emphasis on creating inclusive and supportive workplace settings. The findings highlight the importance of competent leadership in encouraging diversity, inclusiveness, and moulding corporate culture, particularly in terms of embracing neurodiverse relationships. As a result, this assessment suggests that leaders actively engage in inclusive practices, recognise the particular strengths and challenges that neurodivergent people confront, and help to create a pleasant work environment. Moving forward, it is believed that applying these recommendations will result in a more inclusive workplace culture, increasing acceptance and appreciation of neurological variances such as autism, ADHD, and other neurodevelopmental diseases. Organisations may empower neurodivergent individuals by building leadership abilities such as empathy, adaptability, and effective communication, resulting in increased creativity and efficiency. This study, which draws on a thorough evaluation of current knowledge from a variety of fields, is an invaluable resource for organisations looking to promote neurodiversity and capitalise on its potential benefits in the pursuit of a more inclusive and innovative workplace

Exploring AI Robots-based Visual Strategy in Training Children with Autism Disorder

Children with autism spectrum disorder (ASD) exhibit deficiencies in the socio-communicative domain and commonly struggle with emotion perception and expression. Robots are becoming increasingly prevalent in our lives, notably in the medical field. Some therapists at therapeutic centers are beginning to experiment with techniques such as computer games, Online exchanges that are available and robot-assisted therapy. Robot-assisted therapy has been widely proven to provide a reliable and effective intervention for enhancing communication and social skills in children having ASD. The humanoid robot may grab the attention of young children and later draw the interest of researchers. This study is accomplished through the use of a revolutionary technique based on deep learning algorithms that comprises essential data and understanding about patients, diagnostic procedures, and medicines. A robot therapist can transmit the results responsibly using this paradigm. Here parameter tunned CNN based model are used and the model is achieved with an accuracy rate of 96% in ASD detection.

An exploratory investigation of acoustic features underlying arousal and valence perception of vocalizations from non-speaking individuals

Emotion perception of vocalizations, especially from individuals with no or few spoken words, remains an underexplored topic in acoustical research. The aim of this exploratory study was to identify acoustic features within non-speech vocalizations correlated with perceived arousal and valence. 364 vocalizations were selected from the open-access ReCANVo dataset, comprising non-speech communicative sounds by non-speaking individuals with autism and neurodevelopmental disorders. 108 listeners independently rated each vocalization for arousal and valence on a 5-point Likert scale (78624 total ratings). We then used random forest and elastic net regression techniques to determine correlations between acoustic-based features and perceived affect. Using 4 published (e.g., GeMAPs) and 2 custom feature sets (dim 12, 28) encompassing duration, intensity, pitch, formant, and spectral centroid features, we evaluated each model using R-squared and mean-squared error. Our 12-feature custom elastic net model achieved the highest performance for predicting arousal (R-sq = 0.763, MSE = 0.075), with intensity features weighted highest, comparable to other sets. Our 28-feature random forest model, which included vocal quality features, significantly outperformed other models in valence prediction (R- sq = 0.491; MSE = 0.139, p < 0.001). While further validation on a held- out test set is essential, these exploratory results expand our understanding of affective acoustic features of non-speech sounds from individuals with neurodevelopmental disorders.

Model for autism disorder detection using deep learning

<span lang="EN-US">Autism spectrum disorder (ASD) is a neurodegenerative illness that impacts individuals' social abilities. The majority of available approaches rely on structural and resting-state functional magnetic resonance imaging (fMRI) to detect ASD with a small dataset, resulting in high accuracy but low generality. To detect ASD with a limited dataset, the bulk of known technologies involve Machine Learning, pattern recognition, and other techniques, leading to high accuracy but moderate generality. To address this constraint and improve the efficacy of the automated autism diagnosis model, an ASD detection model based on deep learning (DL) is provided in this work. The classification challenge is solved using a convolutional neural network classifier. The suggested model beats state-of-the-art methodologies in terms of accuracy, according to simulation findings. The proposed approach investigates how anatomical and functional connectivity indicators can be used to determine whether or not a person is autistic. The proposed method delivers state-of-the-art results, with the classification of Autistic patients achieving 93.41% accuracy and the localization of the classified data regressed to 0.29 mean absolute error (MAE).</span>

Developmental changes in prefrontal cortex activation in children with or without autism spectrum traits on near-infrared spectroscopy

BackgroundAutism spectrum disorder (ASD) ranges from mild to severe symptoms, with autistic traits possibly distributed throughout the population. However, the precise neurodevelopmental differences in children with autistic traits remain unknown. Subjects and methodsFifty-three healthy volunteers (32 male and 21 female, mean [standard deviation] age: 12.9 [2.5] years) having a normal intelligence quotient and without social impairment were divided into two groups according to scores of the Pervasive Developmental Disorders Autism Society Japan Rating Scale (PARS). Subjects with or without autistic traits were placed into the high-PARS (n = 14) or low-PARS (n = 39) group, respectively. Activation of the prefrontal cortex was estimated using change in hemoglobin oxygenation concentration (Δ[oxy-Hb]) on near-infrared spectroscopy (NIRS) during a verbal fluency test. Age-related changes in prefrontal cortex activation were first assessed for each group. Then, the effects of age (elementary school age or junior/senior high school age) and PARS score on Δ[oxy-Hb] in the task were analyzed by two-way analysis of variance. ResultsWe observed significant positive correlations between mean Δ[oxy-Hb] and age in the prefrontal cortex region in the low-PARS group. Mean Δ[oxy-Hb] in the low-PARS group was significantly higher than in the high-PARS group. Task performance results were comparable between the groups. ConclusionIn PARS-determined typically developed children, prefrontal cortex activation on NIRS correlated positively with age. In healthy volunteers without ASD but harboring autistic traits, prefrontal cortex activation was markedly lower than in normal counterparts. Our results provide biological evidence that ASD may be a pervasively distributed disorder.

Analisis Kemampuan Komunikasi Siswa Autis Kelas VII di SLB Negeri Autis Sumatera Utara (Studi Kasus Siswa Autis di SLB Negeri Autis Sumatera Utara)

This research was conducted with the aim of knowing how the communication development of class VII autistic students at SLB Negeri Autism North Sumatra and to find out the forms of verbal and non-verbal communication. The method used in this study is a qualitative method with a case study approach. The primary subjects in this study were two students with autistic disorder in class VII autistic, and the secondary subjects were teachers and parents. Data were collected using interviews, observation, and documentation studies. The results showed that the development of communication in autistic students at the North Sumatra Autistic Public SLB was influenced by several things including the teacher's stimulus when teaching, therapy, children's hobbies, and parenting strategies. Forms of children's verbal communication is speaking, writing, reading, and listening. While the forms of non-verbal communication are touch, movement, and eye contact. Based on the research findings, it can be concluded that communication disorders in autistic children can be helped by providing stimulus by being invited to communicate more often and implementing appropriate parenting strategies.

Epigenetic regulation of autophagy in neuroinflammation and synaptic plasticity.

Autophagy is a conserved cellular mechanism that enables the degradation and recycling of cellular organelles and proteins via the lysosomal pathway. In neurodevelopment and maintenance of neuronal homeostasis, autophagy is required to regulate presynaptic functions, synapse remodeling, and synaptic plasticity. Deficiency of autophagy has been shown to underlie the synaptic and behavioral deficits of many neurological diseases such as autism, psychiatric diseases, and neurodegenerative disorders. Recent evidence reveals that dysregulated autophagy plays an important role in the initiation and progression of neuroinflammation, a common pathological feature in many neurological disorders leading to defective synaptic morphology and plasticity. In this review, we will discuss the regulation of autophagy and its effects on synapses and neuroinflammation, with emphasis on how autophagy is regulated by epigenetic mechanisms under healthy and diseased conditions.

The Assessment and Treatment of Post-traumatic Stress Disorder in Autistic People: A Systematic Review

AbstractAdverse life events and mental health conditions are unfortunately common amongst autistic adults and children; this may present a vulnerability to developing post-traumatic stress disorder (PTSD). This systematic review provides an update of Rumball’s (Review Journal of Autism and Developmental Disorders, 6, 294–324, 2019) systematic review of PTSD in autistic individuals and identifies 18 new studies published from 2017 to 2022, reflecting increased research interest in PTSD in autistic populations. Included literature suggests that autistic adults and children experience more severe PTSD symptoms compared to their non-autistic peers, with at least comparable rates of occurrence. We provide a comprehensive overview of this emerging field and identify the need for future research to validate PTSD symptom assessment tools and treatment strategies and investigate unique manifestations of trauma-related symptoms in autistic individuals.

Hybrid weight optimized deep learning for autism and visual sensory processing disorder detection: A heuristic strategy on medical domain

An Autism Spectrum Disorder (ASD) affected individual has several difficulties with social-emotional cues. The existing model is observed with emotional face processing in adolescents and ASD and Typical Development (TD) by utilizing various body parameters. Scanning facial expressions is the initial task, and recognizing the face’s sensitivity to different emotional expressions is the next complex task. To address this shortcoming, in this work, a new autism and visual Sensory Processing Disorder (SPD) detection model for supporting healthcare applications by processing facial expressions and sensory data of heart rate and body temperature. Here, initially, the individual data regarding facial emotions and other body parameters like heart rate and body temperature are collected from various subjects. Then, the selection of optimal features is executed by a hybrid algorithm named Density Factor-based Artificial Bee Honey Badger Optimization (DF-ABHBO), where the most essential features are attained and fed to the detection phase. The optimal feature selection is made by resolving the fitness function with constraints like correlation, data variance, and cosine similarity for inter and intra-class. Finally, the autism and visual SPD detection are performed through a Hybrid Weight Optimized Deep Neural Recurrent Network (HWODNRN), where the hyperparameter and weights of “Deep Neural Network (DNN) and Recurrent Neural Network (RNN)” are optimized with the developed DF-ABHBO technique. From the result analysis, the accuracy and F1-score rate of the offered DF-ABHBO-HWODNRN method have attained 96% and 93%. The findings obtained from the simulations of the designed system achieve better performance.

Environmental enrichment promotes adaptive responding during tests of behavioral regulation in male heterogeneous stock rats

Organisms must regulate their behavior flexibly in the face of environmental challenges. Failure can lead to a host of maladaptive behavioral traits associated with a range of neuropsychiatric disorders, including attention deficit hyperactivity disorder, autism, and substance use disorders. This maladaptive dysregulation of behavior is influenced by genetic and environmental factors. For example, environmental enrichment produces beneficial neurobehavioral effects in animal models of such disorders. The present study determined the effects of environmental enrichment on a range of measures related to behavioral regulation using a large cohort of male, outbred heterogeneous stock (HS) rats as subjects. Subjects were reared from late adolescence onwards either in pairs in standard housing with minimal enrichment (n = 200) or in groups of 16 in a highly enriched environment consisting of a large multi-level cage filled with toys, running wheels, and shelters (n = 64). Rats were subjected to a battery of tests, including: (i) locomotor response to novelty, (ii) light reinforcement, (iii) social reinforcement, (iv) reaction time, (v) a patch-depletion foraging test, (vi) Pavlovian conditioned approach, (vii) conditioned reinforcement, and (viii) cocaine conditioned cue preference. Results indicated that rats housed in the enriched environment were able to filter out irrelevant stimuli more effectively and thereby regulate their behavior more efficiently than standard-housing rats. The dramatic impact of environmental enrichment suggests that behavioral studies using standard housing conditions may not generalize to more complex environments that may be more ethologically relevant.

The Effects of a Gluten-Free/Casein-Free Diet on Behavioral Indices in Children with Autism Spectrum Disorder: A Systematic Review and Meta-analysis

Introduction: Autism is a neurological disorder that has seen an increase in prevalence in recent years. It is hypothesized that diet may influence mental health and improve the behavioral symptoms of autism. Objectives: The purpose of this study is to examine the effects of gluten-free and casein-free diets on children with autism. Data Sources: International databases such as Google Scholar, PubMed, Cochrane, and Science Direct were searched using keywords including Autism, Gluten-free, Casein, Diet, and Autistic Disorder, without any time restriction. Study Selection: Ultimately, 8 articles were selected for the final analysis. Data Extraction: The study revealed that gluten-free/casein-free (GF/CF) diets had a significantly positive effect on the behavior index of autistic children. In terms of the behavior index, the effect size was a standard mean difference (SMD) of -0.27 (P = 0.001) with a 95% confidence interval (CI) of -0.424 to -0.116. A meta-analysis comparing intervention and control groups indicated that the GF/CF diet had an effect size of SMD = -0.27 (P = 0.001) with a 95% CI of -0.429 to -0.112 on the behavior index. Results: The results of these studies were synthesized using the fixed effects model of meta-analysis with STATA version 14. Conclusions: Given the positive impact of the GF/CF diet on improving the behavior of autistic children, this dietary approach can be considered as a means to enhance behavioral symptoms in these children.

Cross-disorder comparison of sensory reactivity, pain, gastro-intestinal symptoms and obsessive-compulsive symptoms in adolescents and young adults with autism and other neurodevelopmental disorders

There is considerable evidence of the relationship among sensory reactivity, abdominal pain, and gastrointestinal symptoms in individuals with Autism Spectrum Disorder (ASD). Furthermore, these events are linked to the gut-microbiota-brain axis via the enteric nervous system. However, few studies have compared autism with other neurodevelopmental disorders in terms of sensory reactivity, pain, or gastrointestinal symptoms. Consequently, this study aimed to analyze the differences in sensory reactivity, pain, and gastrointestinal symptoms between adolescents and young adults with ASD and other neurodevelopmental and neurotypical disorders. Differences in sensory reactivity, pain, gastrointestinal symptoms, and obsessive-compulsive symptoms between a group of individuals with ASD level I (n = 37), Attention Deficit Hyperactivity Disorder or ADHD (n = 15), Learning Difficulties or LD (n = 23), and a control or neurotypical group (n = 76) were analyzed. Higher levels of sensory hyperreactivity were found in individuals with ASD than in the other clinical (ADHD and LD) and neurotypical groups. Sensory hyporeactivity was greater in individuals with ASD as well as in individuals with ADHD than that in the neurotypical group. Higher levels of pain were found in the ADHD group than in the ASD or neurotypical group. Gastrointestinal symptoms of the abdominal type were more severe in the ASD group than in the ADHD, and LD groups, whereas dyspepsia was more severe in the ADHD group. The results indicate a sensory and gastrointestinal profile in individuals with ASD and ADHD. Future studies should increase the number of participants for each neurodevelopmental disorder and perform a more comprehensive analysis of the gut microbiota-brain axis.

Editorial: Advancements and challenges in autism and other neurodevelopmental disorders

Editorial: Advancements and challenges in autism and other neurodevelopmental disorders

Let's face it! The role of social anxiety and executive functions in recognizing others' emotions from faces: Evidence from autism and specific learning disorders.

Youth with different developmental disorders might experience challenges when dealing with facial emotion recognition (FER). By comparing FER and related emotional and cognitive factors across developmental disorders, researchers can gain a better understanding of challenges and strengths associated with each condition. The aim of the present study was to investigate how social anxiety and executive functioning might underlie FER in youth with and without autism spectrum disorders (ASD) and specific learning disorders (SLD). The study involved 263 children and adolescents between 8 and 16 years old divided into three groups matched for age, sex, and IQ: 60 (52 M) with ASD without intellectual disability, 63 (44 M) with SLD, and 140 (105 M) non-diagnosed. Participants completed an FER test, three executive functions' tasks (inhibition, updating, and set-shifting), and parents filled in a questionnaire reporting their children's social anxiety. Our results suggest that better FER was consistent with higher social anxiety and better updating skills in ASD, while with lower social anxiety in SLD. Clinical practice should focus on coping strategies in autistic youth who could feel anxiety when facing social cues, and on self-efficacy and social worries in SLD. Executive functioning should also be addressed to support social learning in autism.

Trans Species RNA Activity: Sperm RNA of the Father of an Autistic Child Programs Glial Cells and Behavioral Disorders in Mice.

Recently, we described the alteration of six miRNAs in the serum of autistic children, their fathers, mothers, siblings, and in the sperm of autistic mouse models. Studies in model organisms suggest that noncoding RNAs participate in transcriptional modulation pathways. Using mice, approaches to alter the amount of RNA in fertilized eggs enable in vivo intervention at an early stage of development. Noncoding RNAs are very numerous in spermatozoa. Our study addresses a fundamental question: can the transfer of RNA content from sperm to eggs result in changes in phenotypic traits, such as autism? To explore this, we used sperm RNA from a normal father but with autistic children to create mouse models for autism. Here, we induced, in a single step by microinjecting sperm RNA into fertilized mouse eggs, a transcriptional alteration with the transformation in adults of glial cells into cells affected by astrogliosis and microgliosis developing deficiency disorders of the 'autism-like' type in mice born following these manipulations. Human sperm RNA alters gene expression in mice, and validates the possibility of non-Mendelian inheritance in autism.