scholarly journals Autism and its Obstetrical Causes

2018 ◽  
Vol 1 (1) ◽  
pp. 46-48
Author(s):  
Petrikovsky Boris
Keyword(s):  
Autism Spectrum Disorder ◽  
Social Interaction ◽  
Developmental Disorder ◽  
Repetitive Behavior ◽  
Autism Spectrum ◽  
Spectrum Disorder

Autism spectrum disorder (ASD) is a developmental disorder characterized by abnormal social interaction and communication and manifested by repetitive behavior. Parents notice signs of autism in the first two or three years of their child’s life.

scholarly journals Parent Training Program for Autism Spectrum Disorder During The Covid-19 Pandemic Period in Indonesia

2020 ◽  
Vol 9 (2) ◽  
pp. 60
Author(s):  
Ina Dewi Ardiyani ◽  
Nining Febriyana ◽  
Yunias Setiawati ◽  
Royke Tony Kalalo
Keyword(s):  
Autism Spectrum Disorder ◽  
Early Intervention ◽  
Social Interaction ◽  
Parent Training ◽  
Developmental Disorder ◽  
Primary Caregivers ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Treatment Service ◽  
Parent Training Program

Autism Spectrum Disorder (ASD) is a pervasive developmental disorder that shows difficulties in communication, social interaction, behavior, interests and activities that are limited and repetitive. The prevalence of ASD also continues to increase worldwide, followed by an increase in the need for early intervention in ASD children. The limited services available make early intervention a challenge in itself. The long Covid-19 pandemic has resulted in limited therapy, because therapy in treatment service facilities cannot be carried out as before. In this case it is important to involve parents as the primary caregivers for ASD children in interventions to anticipate the limited interventions due to a pandemic situation. Parent Training can be done as an effort to provide information, education, and skills to parents so that they can provide intensive, comprehensive, sustainable, and early intervention.

scholarly journals Recognition of Emotions for People with Autism: An Approach to Improve Skills

2022 ◽  
Vol 2022 ◽  
pp. 1-21
Author(s):  
Adilmar Coelho Dantas ◽  
Marcelo Zanchetta do Nascimento
Keyword(s):  
Autism Spectrum Disorder ◽  
Social Interaction ◽  
Interpersonal Communication ◽  
Facial Expressions ◽  
Repetitive Behavior ◽  
Mental States ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Basic Emotions ◽  
Emotional Skills

Autism spectrum disorder refers to a neurodevelopmental disorders characterized by repetitive behavior patterns, impaired social interaction, and impaired verbal and nonverbal communication. The ability to recognize mental states from facial expressions plays an important role in both social interaction and interpersonal communication. Thus, in recent years, several proposals have been presented, aiming to contribute to the improvement of emotional skills in order to improve social interaction. In this paper, a game is presented to support the development of emotional skills in people with autism spectrum disorder. The software used helps to develop the ability to recognize and express six basic emotions: joy, sadness, anger, disgust, surprise, and fear. Based on the theory of facial action coding systems and digital image processing techniques, it is possible to detect facial expressions and classify them into one of the six basic emotions. Experiments were performed using four public domain image databases (CK+, FER2013, RAF-DB, and MMI) and a group of children with autism spectrum disorder for evaluating the existing emotional skills. The results showed that the proposed software contributed to improvement of the skills of detection and recognition of the basic emotions in individuals with autism spectrum disorder.

scholarly journals Comparative Study of Machine Learning Techniques Used in Autism Spectrum Disorder

2020 ◽  
pp. 121-124
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
Comparative Analysis ◽  
Social Interaction ◽  
Developmental Disorder ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Patterns Of Behavior

Autism Spectrum Disorder is a neuro developmental disorder characterized by persistent deficits in social interaction and communication and restricted, repetitive patterns of behavior, interests or activities. The paper shows the detailed comparative analysis of various machine learning techniques used in the field of autism spectrum disorder.

scholarly journals A novel automated autism spectrum disorder detection system

2021 ◽  
Author(s):  
Shu Lih Oh ◽  
V. Jahmunah ◽  
N. Arunkumar ◽  
Enas W. Abdulhay ◽  
Raj Gururajan ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Developmental Disorder ◽  
Detection System ◽  
Brain Activity ◽  
Limited Range ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Nonlinear Features ◽  
Genetic And Environmental Factors ◽  
Future Work

AbstractAutism spectrum disorder (ASD) is a neurological and developmental disorder that begins early in childhood and lasts throughout a person’s life. Autism is influenced by both genetic and environmental factors. Lack of social interaction, communication problems, and a limited range of behaviors and interests are possible characteristics of autism in children, alongside other symptoms. Electroencephalograms provide useful information about changes in brain activity and hence are efficaciously used for diagnosis of neurological disease. Eighteen nonlinear features were extracted from EEG signals of 40 children with a diagnosis of autism spectrum disorder and 37 children with no diagnosis of neuro developmental disorder children. Feature selection was performed using Student’s t test, and Marginal Fisher Analysis was employed for data reduction. The features were ranked according to Student’s t test. The three most significant features were used to develop the autism index, while the ranked feature set was input to SVM polynomials 1, 2, and 3 for classification. The SVM polynomial 2 yielded the highest classification accuracy of 98.70% with 20 features. The developed classification system is likely to aid healthcare professionals as a diagnostic tool to detect autism. With more data, in our future work, we intend to employ deep learning models and to explore a cloud-based detection system for the detection of autism. Our study is novel, as we have analyzed all nonlinear features, and we are one of the first groups to have uniquely developed an autism (ASD) index using the extracted features.

scholarly journals The Gut-Brain Axis in Autism Spectrum Disorder: A Focus on the Metalloproteases ADAM10 and ADAM17

2020 ◽  
Vol 22 (1) ◽  
pp. 118
Author(s):  
Yuanpeng Zheng ◽  
Tessa A. Verhoeff ◽  
Paula Perez Pardo ◽  
Johan Garssen ◽  
Aletta D. Kraneveld
Keyword(s):  
Autism Spectrum Disorder ◽  
Intestinal Tract ◽  
Synapse Formation ◽  
Inflammatory Responses ◽  
Repetitive Behavior ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Protein Substrates ◽  
Specific Proteins ◽  
Membrane Bound

Autism Spectrum Disorder (ASD) is a spectrum of disorders that are characterized by problems in social interaction and repetitive behavior. The disease is thought to develop from changes in brain development at an early age, although the exact mechanisms are not known yet. In addition, a significant number of people with ASD develop problems in the intestinal tract. A Disintegrin And Metalloproteases (ADAMs) include a group of enzymes that are able to cleave membrane-bound proteins. ADAM10 and ADAM17 are two members of this family that are able to cleave protein substrates involved in ASD pathogenesis, such as specific proteins important for synapse formation, axon signaling and neuroinflammation. All these pathological mechanisms are involved in ASD. Besides the brain, ADAM10 and ADAM17 are also highly expressed in the intestines. ADAM10 and ADAM17 have implications in pathways that regulate gut permeability, homeostasis and inflammation. These metalloproteases might be involved in microbiota-gut–brain axis interactions in ASD through the regulation of immune and inflammatory responses in the intestinal tract. In this review, the potential roles of ADAM10 and ADAM17 in the pathology of ASD and as targets for new therapies will be discussed, with a focus on the gut–brain axis.

Assessment and Treatment of Selective Mutism in a Child With Autism Spectrum Disorder

2020 ◽  
pp. 153465012098345
Author(s):  
Mirela Cengher ◽  
Joy C. Clayborne ◽  
Adrianna E. Crouch ◽  
Julia T. O’Connor
Keyword(s):  
Autism Spectrum Disorder ◽  
Social Interaction ◽  
Environmental Variables ◽  
Behavioral Interventions ◽  
Selective Mutism ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Future Research ◽  
Assessment And Treatment

Over 60% of children diagnosed with selective mutism are also diagnosed with Autism Spectrum Disorder. Previous research established that behavioral interventions are effective at increasing speech in children with both diagnoses. However, few studies conducted assessments to determine environmental variables that inhibit speech, and such assessments are necessary for the development of effective and efficient treatments. This case study describes an assessment that evaluated the function(s) of selective mutism. The results confirmed that the participant did not talk to avoid social interaction and that mutism occurred primarily in the presence of multiple, unfamiliar people. Our first treatment focused on increasing tolerance for social interaction, demonstrated by an increase in speech production in the presence of unfamiliar people. Our second treatment focused on increasing qualitative aspects of the participant’s speech (i.e., both responses and initiations). Finally, we taught the participant’s parents to implement the treatment in naturalistic settings, and the participant demonstrated generalization of treatment effects across people and settings. Implications for clinical practice and future research are discussed.

scholarly journals Changing diagnostic practices: autism spectrum disorder

2014 ◽  
Vol 20 (1) ◽  
pp. 23-26 ◽  
Author(s):  
Marc Woodbury-Smith
Keyword(s):  
Autism Spectrum Disorder ◽  
Social Cognition ◽  
Social Interaction ◽  
Medical Practice ◽  
Classification System ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Psychiatric Nosology ◽  
Diagnostic Practices

SummaryIn medical practice it is crucial that symptom descriptions are as precise and objective as possible, which psychiatry attempts to achieve through its psychopathological lexicon. The term ‘autism spectrum disorder’ has now entered psychiatric nosology, but the symptom definitions on which it is based are not robust, potentially making reliable and valid diagnoses a problem. This is further compounded by the spectral nature of the disorder and its lack of clear diagnostic boundaries. To overcome this, there is a need for a psychopathological lexicon of 'social cognition’ and a classification system that splits rather than lumps disorders with core difficulties in social interaction.

scholarly journals Prenatal Risk for Autism Spectrum Disorder (ASD): Fetal Cortisol Exposure Predicts Child ASD Symptoms

2018 ◽  
Vol 7 (2) ◽  
pp. 349-361 ◽  
Author(s):  
Sheena Ram ◽  
Mariann A. Howland ◽  
Curt A. Sandman ◽  
Elysia Poggi Davis ◽  
Laura M. Glynn
Keyword(s):  
Autism Spectrum Disorder ◽  
Developmental Disorder ◽  
Maternal Plasma ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Sexually Dimorphic ◽  
Fetal Exposure ◽  
Prospective Longitudinal Study ◽  
The Social ◽  
Prospective Longitudinal

The etiology of autism spectrum disorder (ASD) is multifactorial, complex, and likely involves interactions among genetic, epigenetic, and environmental factors. With respect to environmental influences, a growing literature implicates intrauterine experiences in the origin of this pervasive developmental disorder. In this prospective longitudinal study, we examined the hypothesis that fetal exposure to maternal cortisol may confer ASD risk. In addition, because ASD is four times more prevalent in males than in females, and because sexually dimorphic responses to intrauterine experiences are commonly observed, we examined whether or not any associations differ by fetal sex. Maternal plasma cortisol was measured at 15, 19, 25, 31, and 37 weeks’ gestation in a sample of 84 pregnant women. ASD symptoms were assessed in their 5-year-old children with the Social Communication Questionnaire (SCQ). Fetal exposure to lower levels of maternal cortisol was associated with higher levels of ASD symptoms only among boys. The observed hypocortisolemic profile exhibited by these mothers may indicate a risk factor that precedes the stress of caregiving for a child with ASD and may not be solely a consequence of the stress of caregiving, as previously thought. These findings confirm the value of examining prenatal hormone exposures as predictors of ASD risk and support the premise that altered prenatal steroid exposures may play a role in the etiology of ASD.

scholarly journals Fostering Emotion Recognition in Children with Autism Spectrum Disorder

2021 ◽  
Vol 5 (10) ◽  
pp. 57
Author(s):  
Vinícius Silva ◽  
Filomena Soares ◽  
João Sena Esteves ◽  
Cristina P. Santos ◽  
Ana Paula Pereira
Keyword(s):  
Autism Spectrum Disorder ◽  
Social Interaction ◽  
Emotion Recognition ◽  
Facial Expressions ◽  
Social Communication ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Support Vector ◽  
Robotic Platform ◽  
Children With Asd

Facial expressions are of utmost importance in social interactions, allowing communicative prompts for a speaking turn and feedback. Nevertheless, not all have the ability to express themselves socially and emotionally in verbal and non-verbal communication. In particular, individuals with Autism Spectrum Disorder (ASD) are characterized by impairments in social communication, repetitive patterns of behaviour, and restricted activities or interests. In the literature, the use of robotic tools is reported to promote social interaction with children with ASD. The main goal of this work is to develop a system capable of automatic detecting emotions through facial expressions and interfacing them with a robotic platform (Zeno R50 Robokind® robotic platform, named ZECA) in order to allow social interaction with children with ASD. ZECA was used as a mediator in social communication activities. The experimental setup and methodology for a real-time facial expression (happiness, sadness, anger, surprise, fear, and neutral) recognition system was based on the Intel® RealSense™ 3D sensor and on facial features extraction and multiclass Support Vector Machine classifier. The results obtained allowed to infer that the proposed system is adequate in support sessions with children with ASD, giving a strong indication that it may be used in fostering emotion recognition and imitation skills.

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