Autism spectrum disorder in India: a scoping review

Unveiling the role of phytochemicals in autism spectrum disorder by employing network pharmacology and molecular dynamics simulation

Autism Spectrum Disorder (ASD) comprises a myriad of disorders with vast pathologies, aetiologies, and involvement of genetic and environmental risk factors. Given the polygenic aspect of ASD, targeting several genes/proteins responsible for pathogenesis at once might prove advantageous in its remediation. Various phytochemicals have been proven to possess neuroprotective, anti-inflammatory, and antioxidant properties by alleviating symptoms and targeting a complex network of genes/proteins related to disease pathology. However, the effects of many of these phytochemicals on ASD are enigmatic, and their molecular targets and molecular mechanisms are still elusive. Here, we provide a comprehensive comparative study on the therapeutic potential of 6 phytochemicals viz. Cannabidiol, Crocetin, Epigallocatechin-3-gallate, Fisetin, Quercetin, and Resveratrol based on their neuroprotective properties in managing ASD. We aimed to identify and target a network of core proteins in the pathology of ASD via phytochemicals using network pharmacology, molecular docking, and simulation studies. The methodology includes screening genes/proteins implicated in ASD as targets of each phytochemical, followed by network construction using Protein-Protein Interactions, Gene Ontology, and enrichment analysis. The constructed network was further narrowed down to the hub genes in the network, followed by their spatio-temporal analysis, molecular docking, and molecular dynamics simulation. 6 core genes were obtained for ASD, 3 of which are directly involved in disease pathogenesis. The study provides a set of novel genes that phytochemicals can target to ameliorate and regulate ASD pathogenesis. Cannabidiol can inhibit ABCG2, MAOB, and PDE4B, Resveratrol can target ABCB1, and Quercetin can regulate AKR1C4 and XDH. This study demonstrated the potential of phytochemicals to target and regulate ABCG2, ABCB1, AKR1C4, MAOB, PDE4B, and XDH, which in turn modulate the dysfunctional network present in the ASD pathology and provide therapeutic potential in the management of ASD.

Identifying autism using EEG: unleashing the power of feature selection and machine learning

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition that is characterized by communication barriers, societal disengagement, and monotonous actions. Currently, the diagnosis of ASD is made by experts through a subjective and time-consuming qualitative behavioural examination using internationally recognized descriptive standards. In this paper, we present an EEG-based three-phase novel approach comprising 29 autistic subjects and 30 neurotypical people. In the first phase, preprocessing of data is performed from which we derived one continuous dataset and four condition-based datasets to determine the role of each dataset in the identification of autism from neurotypical people. In the second phase, time-domain and morphological features were extracted and four different feature selection techniques were applied. In the last phase, five-fold cross-validation is used to evaluate six different machine learning models based on the performance metrics and computational efficiency. The neural network outperformed when trained with maximum relevance and minimum redundancy (MRMR) algorithm on the continuous dataset with 98.10% validation accuracy and 0.9994 area under the curve (AUC) value for model validation, and 98.43% testing accuracy and AUC test value of 0.9998. The decision tree overall performed the second best in terms of computational efficiency and performance accuracy. The results indicate that EEG-based machine learning models have the potential for ASD identification from neurotypical people with a more objective and reliable method.

When a Sibling Has Autism: Narrative Review of Interventions for Typically Developing Siblings

Being a typically developing sibling to a child with autism spectrum disorder (ASD-Sib) can be associated with several behavioral and mental health problems. With this understanding, researchers are beginning to focus on sibling-oriented interventions. This review seeks to evaluate the current literature on such interventions. We searched digital databases and identified eight relevant studies. The sample (n = 247) had an age range of 4-16 years. The outcomes were generally positive, with improvements in different facets of ASD-Sibs. Results differed due to varied intervention styles, outcome variables, methods, and samples. Our findings highlight the potential for improvement in ASD-Sib's knowledge of autism and their psychological functioning and hence call for further research with robust methods.

Using mobile health technology to assess childhood autism in low-resource community settings in India: An innovation to address the detection gap

LAY ABSTRACT

Autism is diagnosed by highly trained professionals- but most autistic people live in parts of the world that harbour few or no such autism specialists and little autism awareness. So many autistic people go undiagnosed, misdiagnosed, and misunderstood. We designed an app (START) to identify autism and related conditions in such places, in an attempt to address this global gap in access to specialists. START uses computerised games and activities for children and a questionnaire for parents to measure social, sensory, and motor skills. To check whether START can flag undiagnosed children likely to have neurodevelopmental conditions, we tested START with children whose diagnoses already were known: Non-specialist health workers with just a high-school education took START to family homes in poor neighbourhoods of Delhi, India to work with 131 two-to-seven-year-olds. Differences between typically and atypically developing children were highlighted in all three types of skills that START assesses: children with neurodevelopmental conditions preferred looking at geometric patterns rather than social scenes, were fascinated by predictable, repetitive sensory stimuli, and had more trouble with precise hand movements. Parents' responses to surveys further distinguished autistic from non-autistic children. An artificial-intelligence technique combining all these measures demonstrated that START can fairly accurately flag atypically developing children. Health workers and families endorsed START as attractive to most children, understandable to health workers, and adaptable within sometimes chaotic home and family environments. This study provides a proof of principle for START in digital screening of autism and related conditions in community settings.

Adequate Management of Autism Spectrum Disorder in Children in India

Despite the significant burden of neurodevelopmental disorders such as autism spectrum disorder (ASD) in India, there are areas of unmet needs at every level of the health care system. This includes screening and recognition, reliable and valid tools to evaluate, and to adequately manage ASD. There are also gaps in education and training of medical professionals, paraprofessionals, special education teachers and the related services. Lack of public awareness and cultural factors contribute to delays in early recognition and interventions. A framework is suggested to address these unmet needs at various levels to improve the care of these children with ASD. These include a) a focus on education of medical professionals, paraprofessionals, and teachers; b) setting up infrastructure at community, regional, and statewide levels, with adequate funding; and c) use of audiovisual technology and collaboration with international expertise.

Caregiver Perceptions of Autism and Neurodevelopmental Disabilities in New Delhi, India

Evidence suggests that parenting an autistic child or a child with neurodevelopmental disabilities can be more challenging than parenting a child meeting their developmental milestones, especially when there is a dearth of support services, such as in low- and middle-income countries (LMICs). Despite the majority of the world's children residing in LMICs, there are limited studies examining the understanding of developmental disorders and autism in these regions. We therefore aim to investigate perceptions of autism and developmental disabilities in caregivers of children in an urban setting in New Delhi, India. Thirteen semi-structured interviews with parents/caregivers of children were conducted in three groups: (1) caregivers with a child with a diagnosis of autism spectrum disorder (ASD); (2) caregivers with a child with a diagnosis of intellectual disability (ID); (3) and caregivers with children meeting their developmental milestones. Transcripts were analysed using framework analysis. Three themes on the impact of cultural and contextual factors on the recognition, interpretation, and reporting of autistic symptoms are discussed, and additional themes focus on the impact of diagnosis and family support. Our findings highlighted a vital need for greater community awareness and recognition of autism in India, for example through community and healthcare training, which may help to reduce stigma and facilitate wider family support.

Review of Progress in Diagnostic Studies of Autism Spectrum Disorder Using Neuroimaging

This review article summarizes the recent advances in the diagnostic studies of autism spectrum disorders (ASDs) considering some of the most influential research articles from the last two decades. ASD is a heterogeneous neurodevelopmental disorder characterized by abnormalities in social interaction, communication, and behavioral patterns as well as some unique strengths and differences. The current diagnosis systems are based on autism diagnostic observation schedule (ADOS) or autism diagnostic interview-revised (ADI-R), but biological markers are also important for an effective diagnosis of ASDs. The amalgamation of neuroimaging techniques, such as structural and functional magnetic resonance imaging (sMRI and fMRI), with machine-learning and deep-learning approaches helps throw new light on typical biological markers of ASDs at the early stage of life. To assess the performance of a deep neural network, we develop a light-weighted CNN model for ASD classification. The overall accuracy, precision, and F1-score of the proposed model are 99.92%, 99.93% and 99.92%, respectively. All the neuroimaging studies we have reviewed can be divided into 3 categories, viz. thickness, volume and functional connectivity-based studies. We conclude with a discussion of the major findings of considered studies and promising directions for future research in this field.

Computer-aided diagnosis of autism spectrum disorder from EEG signals using deep learning with FAWT and multiscale permutation entropy features

Autism spectrum disorder (ASD), a neurodevelopment disorder, is characterized by significant difficulties in social interaction and emerges as a major threat to children. Its computer-aided diagnosis used by neurologists improves the detection process and has a favorable impact on patients' health. Currently, a biomarker termed electroencephalography (EEG) is considered as vital tool to detect abnormal electrical activity in the brain. In this context, the present paper brings forth a novel approach for automated diagnosis of ASD from multichannel EEG signals using flexible analytic wavelet transform (FAWT). Firstly, this approach processes the acquired EEG signals with filtering and segmentation into short-duration EEG segments in the range of 5-20 s. These segmented EEG signals are decomposed into five levels using FAWT technique to obtain various sub-bands. Further, multiscale permutation entropy values are extracted from decomposed sub-bands which are used as feature vectors in the present work. Afterwards, these feature vectors are evaluated by traditional machine learning algorithms viz., k-nearest neighbor, logistic regression, support vector machine, and random forest, as well as convolutional neural network (CNN) as deep learning algorithm with different segment durations. The analysis of results reveals that CNN provides maximum accuracy, sensitivity, specificity, and area under the curve of 99.19%, 99.34%, 99.21%, and 0.9997, respectively, for 10 s duration EEG segment to identify ASD patients among healthy individuals. Thus, the proposed CNN architecture would be extremely helpful during diagnostic process of autism disease for neurologists.

A case study on the effect of light and colors in the built environment on autistic children's behavior

Background

The importance of strategies and services by caregivers and family members substantially impact the psychological and emotional wellbeing of autistic children. The rapid research developments in clinical and non-clinical methods benefit the features of autistic children. Among various internal and external factors, the influence of the built environment also impacts the characteristics of autistic children. This study investigates primarily the psychological effect of light and colors on the mood and behavior of autistic children to identify the most favorable and preferred indoor lights and color shades.

Methods

A questionnaire survey was conducted at an autism center among autistic children and their parents. This study included autistic children aged between 6 and 16 (45 males, 42 females, mean age 8.7 years, standard deviation 2.3). Eighty-seven participants were involved in the survey to determine the sensory perceptions, intolerance, preferences, and sensitivities of children with an autism spectrum disorder toward colors and lighting. The margin of error at the statistical analysis's 95% confidence level is ± 0.481.

Results

As per this case report, the children have various color preferences and respond differently to different shades. Different hues have varying effects on autistic children, with many neutral tones and mellow shades proven to be autistic-friendly with their calming and soothing effect, while bright, bold, and intense colors are refreshing and stimulating. The stimulus of bright-lighting causes behavioral changes in autistic children prone to light sensitivity.

Conclusion

The insights gained from this interaction with parents and caretakers of autistic children could be helpful for designers to incorporate specific autistic-friendly design elements that make productive interior spaces. A complete understanding of the effect of factors like color and lighting on the learning ability and engagement of autistic children in an indoor environment is essential for designers and clinicians. The main findings of this study could be helpful for a designer and clinicians to address designing an autism-friendly built environment with a color palette and lighting scheme conducive to their wellbeing and to maximize their cognitive functioning.

Attitudes Towards Evidence-Based Practice of Professionals Working with Children and Adolescents with Autism Spectrum Disorder in Bangladesh

Like in many lower-middle-income countries (LMIC), progress in implementing evidence-based practices (EBPs) for children with autism spectrum disorder (ASD) has been slow in Bangladesh. This cross-sectional study examined professionals' attitudes towards evidence-based practice (EBP) for children and adolescents with ASD and explored how providers' demographic factors are related to attitudes to and adoption of EBPs in Bangladesh. The sample consisted of 150 mental health professionals and special teachers from the urban area of Dhaka. Attitudes were assessed by the Evidence-based Practice Attitude Scale-36. Findings indicated that professionals have favorable attitudes towards EBP. Their attitudes varied depending on service settings (public clinical, private clinical, and special school) and caseload per year. Professionals who work in private and special school settings claimed to be more willing to adopt an EBP when required and perceived a higher fit of EBPs and their work than those in public clinical settings. The number of different EBPs used also differed by service setting. Every type of intervention (except medication) was used by more professionals in special schools than in private and public clinical settings. Many professionals reported few barriers to the implementation of EBPs. These findings indicate conditions that are often conducive to the implementation of EBPs. However, these results do not reflect the situation in rural areas, in which poverty is more widespread and the number of specialized professionals is low.

Effectiveness of interventions for children and adolescents with autism spectrum disorder in high-income vs. lower middle-income countries: An overview of systematic reviews and research papers from LMIC

Background

There is a multitude of systematic reviews of interventions for children and adolescents with autism spectrum disorder (ASD). However, most reviews seem to be based on research conducted in High-Income Countries (HIC). Thus, summary findings may not directly apply to Lower Middle-Income Countries (LMIC). Therefore, we conducted a Meta-Review analyzing systematic reviews on the effectiveness of interventions for target outcomes in children and adolescents with ASD to find out whether there are differences in effectiveness between HIC and LMIC and which interventions can be considered evidence-based in LMIC.

Methods

Electronic databases (PsycINFO, PubMed, Cochrane database of systematic reviews) were searched for reviews on interventions for ASD in children and adolescents from January 2011 through December 2021, which included studies not coming from HIC. Systematic reviews with qualitative and quantitative syntheses of findings were included. Two investigators independently assessed studies against predetermined inclusion/exclusion criteria and extracted relevant data including quality and evidence assessments. Evidence for different types of interventions in HIC vs. LMIC was planned to be compared, but none of the reviews assessed potential differences. Therefore, a narrative review of the studies from LMIC was conducted including an assessment of quality and evidence.

Results

Thirty-five reviews fulfilled the inclusion criteria. Eleven considered findings from HIC and LMIC. Sixty-nine percent included studies with various research designs; 63% provided a qualitative synthesis of findings; 77% percent assessed the quality of studies; 43% systematically assessed the level of evidence across studies. No review compared evidence from HIC and LMIC. A review of the studies from LMIC found some promising results, but the evidence was not sufficient due to a small number of studies, sometimes poor quality, and small sample sizes.

Conclusion

Systematic reviews on interventions for children and adolescents with ASD did not look for potential differences in the effectiveness of interventions in HIC and LMIC. Overall, there is very little evidence from LMIC. None of the interventions can be considered evidence-based in LMIC. Hence, additional research and mutually agreed methodological standards are needed to provide a more secure basis for evidence-based treatments in LMIC trying to establish evidence-based practices.

Evaluation of Hyperandrogenism in Children with Autism Spectrum Disorder

This study evaluated 32 children (mean age: 8.5 y) with autism spectrum disorder (ASD) and 23 healthy controls (similar age, sex, and Tanner stage) for hyperandrogenism. These children underwent sexual maturity rating (Tanner staging), ASD severity assessment (Childhood Autism Rating Scale), and quantitative estimation for plasma testosterone, dehydroepiandrosterone sulfate (DHEAS), and androstenedione. There was no significant difference in androgen levels in the two groups. Elevated (> 95 centiles) testosterone, DHEAS, and androstenedione levels were seen in 12.3%, 6.2%, and 9% children with ASD, and 7/9 of these children (78%) with hyperandrogenism had severe ASD. However, there was no significant correlation between ASD severity and androgen levels.

A Feasible Methodological Approach to Estimate the Burden of Autism Spectrum Disorder: Results from the EPI-ASD Study in the Province of Lecce (Southern Italy)

Diagnoses of Autism Spectrum Disorder (ASD) have rapidly increased globally. However, the lack of comprehensive epidemiological surveys and surveillance systems, able to provide official data at a national or European level is one of the main issues in the monitoring of this condition. The present study aimed to estimate the prevalence of ASD in children and adolescents aged 3-18 years old living in the province of Lecce (Southern Italy) through official data provided by the Local Health Authority of Lecce (ASL/LE) up to 31 October 2020, and compare it with school-based data concerning the number of students needing support for ASD. Based on data provided by the ASL/LE, in 2020 there were 509 cases of ASD among children and adolescents aged 3-18 years old, corresponding to a prevalence of 0.46%. A total of 408 (80.2%) were boys and 101 (19.8%) were girls. In relation to their age, 155 ASD cases (0.90%) were diagnosed in the 3-5 age group, while 222 (0.55%) in the 6-11 age group and 132 (0.25%) in the 12-18 age group. Prevalence of ASD assessed by school-based dataset was underestimated in the 3-5 age group, while the 6-11 and 12-18 age groups were consistent with the official data provided by the ASL/LE.

Epidemiology of autism spectrum disorders: Global burden of disease 2019 and bibliometric analysis of risk factors

BACKGROUND

To explore the geographical pattern and temporal trend of autism spectrum disorders (ASD) epidemiology from 1990 to 2019, and perform a bibliometric analysis of risk factors for ASD.

METHODS

In this study, ASD epidemiology was estimated with prevalence, incidence, and disability-adjusted life-years (DALYs) of 204 countries and territories by sex, location, and sociodemographic index (SDI). Age-standardized rate (ASR) and estimated annual percentage change (EAPC) were used to quantify ASD temporal trends. Besides, the study performed a bibliometric analysis of ASD risk factors since 1990. Publications published were downloaded from the Web of Science Core Collection database, and were analyzed using CiteSpace.

RESULTS

Globally, there were estimated 28.3 million ASD prevalent cases (ASR, 369.4 per 100,000 populations), 603,790 incident cases (ASR, 9.3 per 100,000 populations) and 4.3 million DALYs (ASR, 56.3 per 100,000 populations) in 2019. Increases of autism spectrum disorders were noted in prevalent cases (39.3%), incidence (0.1%), and DALYs (38.7%) from 1990 to 2019. Age-standardized rates and EAPC showed stable trend worldwide over time. A total of 3,991 articles were retrieved from Web of Science, of which 3,590 were obtained for analysis after removing duplicate literatures. "Rehabilitation", "Genetics & Heredity", "Nanoscience & Nanotechnology", "Biochemistry & Molecular biology", "Psychology", "Neurosciences", and "Environmental Sciences" were the hotspots and frontier disciplines of ASD risk factors.

CONCLUSIONS

Disease burden and risk factors of autism spectrum disorders remain global public health challenge since 1990 according to the GBD epidemiological estimates and bibliometric analysis. The findings help policy makers formulate public health policies concerning prevention targeted for risk factors, early diagnosis and life-long healthcare service of ASD. Increasing knowledge concerning the public awareness of risk factors is also warranted to address global ASD problem.

Annual Research Review: Achieving universal health coverage for young children with autism spectrum disorder in low- and middle-income countries: a review of reviews

BACKGROUND

Autism presents with similar prevalence and core impairments in diverse populations. We conducted a scoping review of reviews to determine key barriers and innovative strategies which can contribute to attaining universal health coverage (UHC), from early detection to effective interventions for autism in low- and middle-income countries (LAMIC).

METHODS

A systematic literature search of review articles was conducted. Reviews relevant to the study research question were included if they incorporated papers from LAMIC and focused on children (<eight years old) with autism or their caregivers. The database search was supplemented with bibliographic search of included articles and key informant suggestions. Data were extracted and mapped onto a Theory of Change model toward achieving UHC for autism in LAMIC.

RESULTS

We identified 31 articles which reviewed data from over fifty countries across Africa, Latin America, Middle East, and Asia and addressed barriers across one or more of four inter-related domains: (a) the social context and family experience for a child with autism; (b) barriers to detection and diagnosis; (c) access to appropriate evidence-based intervention; and (d) social policy and legislation. Key barriers identified included: lack of appropriate tools for detection and diagnosis; low awareness and experienced stigma impacting demand for autism care; and the prevalence of specialist models for diagnosis and treatment which are not scalable in LAMIC.

CONCLUSIONS

We present a Theory of Change model which describe the strategies and resources needed to realize UHC for children with autism in LAMIC. We highlight the importance of harnessing existing evidence to best effect, using task sharing and adapted intervention strategies, community participation, and technology innovation. Scaling up these innovations will require open access to appropriate detection and intervention tools, systematic approaches to building and sustaining skills in frontline providers to support detection and deliver interventions embedded within a stepped care architecture, and community awareness of child development milestones.

The state of psychiatric research in the Asia Pacific region

INTRODUCTION

This study aims to review recent scientific publications and research output in the field of psychiatry, from a series of countries in the Asia-Pacific region (Australia, India, Indonesia, Iran, Lebanon, Malaysia, and Nepal), with a view to identify themes and similarities across regions, as well as to examine the barriers and challenges in mental health research faced by countries in the region.

METHODS

Seven psychiatrists from seven countries reviewed recent published and ongoing research in psychiatry in their respective nations, with respect to themes, as well as any barriers or challenges faced by mental health researchers.

RESULTS

While the seven nations included in this review vary in terms of research capabilities and economic development level, they share many similarities both in terms of research direction, and with regards to challenges faced. Limitations in the form of sociocultural differences from the West, and a lack of funding were some of the barriers identified.

DISCUSSION

Mental health research in the region has been progressing well. However, more varied research in the form of qualitative or economic studies are lacking, as are multi-center studies. The similar issues that nations face with regards to research could perhaps benefit from collaborative efforts and initiatives for the furtherance of research in the region.