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• Published: 13 June 2023

Detection of autism spectrum disorder (ASD) in children and adults using machine learning

• Muhammad Shoaib Farooq 1 ,
• Rabia Tehseen 2 ,
• Maidah Sabir 1 &
• Zabihullah Atal 3  

Scientific Reports volume  13 , Article number:  9605 ( 2023 ) Cite this article

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• Neurological disorders

Autism spectrum disorder (ASD) presents a neurological and developmental disorder that has an impact on the social and cognitive skills of children causing repetitive behaviours, restricted interests, communication problems and difficulty in social interaction. Early diagnosis of ASD can prevent from its severity and prolonged effects. Federated learning (FL) is one of the most recent techniques that can be applied for accurate ASD diagnoses in early stages or prevention of its long-term effects. In this article, FL technique has been uniquely applied for autism detection by training two different ML classifiers including logistic regression and support vector machine locally for classification of ASD factors and detection of ASD in children and adults. Due to FL, results obtained from these classifiers have been transmitted to central server where meta classifier is trained to determine which approach is most accurate in the detection of ASD in children and adults. Four different ASD patient datasets, each containing more than 600 records of effected children and adults have been obtained from different repository for features extraction. The proposed model predicted ASD with 98% accuracy (in children) and 81% accuracy (in adults).

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Introduction

Autism is categorized as neuro-developmental disorder which has severe effects on social growth and development in children and adults. Although its complete cure seems not possible but early diagnosis is preferable as it helps in more effective treatment compared to conventional behavioural investigations that take much time in detecting and diagnosing ASD by analysing children behaviour in clinics 1 . ASD has been mostly diagnosed in 2 years old child but it can be diagnosed in children later depending on complexity of symptoms and severity of the disorder 2 . It has generally occurred due to environmental factors or any genetic linkage which not only effects the nervous system but also has an overall impact on social and cognitive skills of the children and adults. The extent and the intensity of its symptoms are quite variable. Common signs of the condition include difficulty in communication particularly in social situations, obsessional interests, and repeated mannerisms 3 . A complete examination is needed to detect ASD comprising thorough evaluation and series of assessments performed by child healthcare professionals and psychologists. Early treatment and diagnosis of ASD are crucial since they help to somewhat lessen symptoms, which enhances the person's overall quality of life 4 . However, a lot of critical time can be lost in diagnosing ASD because it cannot be properly detected by depicting only behaviours of children or adults in clinic. Autism can be identified as early as possible using a range of clinical approaches, but actually these are time-consuming diagnostic procedures infrequently carried out unless the predictive risk of ASD development is high 5 . Machine learning (ML) gives an opportunity to train ASD models in less time and more accuracy 6 . ML techniques are crucial for quick and accurate assessment of ASD risk and streamlining the entire diagnostic process which assist families in getting to the critical therapies more quickly 7 . Various classification models of ML can be used for early prediction of autism to prevent its prolonged effects in adults as well as children 8 .

Many other computational techniques have also been proposed in literature 9 such as Hosseinzadeh et al. 10 proposed IoT based solution for ASD detection and Eslami and Saeed 11 presented deep learning based model for healthcare of ASD effected patents. However, obtaining huge amount of data for model training in centralized or distributed environment remained a challenge. Hospitals hesitate to share their data as data are the most valuable asset and regional data protection legislations also prohibit data sharing 12 . Data owner organizations have many serious concerns about data privacy, data security and data protection. Moreover, transmission of big dataset over the network for training machine learning model introduces further barriers of network latency, communication delay and data theft 13 . Therefore, it is the immense need of time that a model should be proposed in which data remain safe with owner organization.

Federated Learning (FL) technique is the most advanced approach of ML in which data remains secure with owner organization and small sized local ML based classifier is trained onsite without moving data over the network 14 . FL is very beneficent in ensuring data security as data are not being shared over the network therefore data privacy, data protection and data security issues are automatically resolved 15 . Moreover, network issues will not be raised as only small sized local data model is travelling over the network towards central server instead of huge data 16 . Many researchers have applied FL for detection of multiple neurological disorders 17 . Ali et al. 18 have applied FL for the detection of colon cancer using pixel level segmentation dataset. Ghosh et al. 19 have applied FL for medical image segmentation. Nigmatullina et al. 17 proposed a digital platform to monitor and support children with ASD using FL. Novelty of our work is the application of FL technique for detection of ASD in both children and adults. Two different ML models including SVM and LR have been trained locally using four different ASD datasets of features containing records about children and adults obtained from free sources and data providing agencies listed in Table 1 for autism detection. We have also compared the results of proposed model with already proposed ASD detection methods and comparable accuracy has been obtained. Major contribution of this work is the combination of different local ML based models for training central FL based meta classifier on features dataset of children and adults to detect ASD risk factors with reasonable accuracy.

Our article is organized in multiple sections. In “ Introduction ” section presents introduction of the autism detection approaches. Most recent studies conducted on autism detection have been summarized in “ Related work ” section. Research methodology, experimentation, analysis and results have been presented in “ Material and method ” section. Results have been discussed in “ Discussion ” section. Conclusion and future directions have been illustrated in “ Conclusion ” section.

Related work

Autism spectrum disorder (ASD) is a neuro-developmental disorder that results various impairments in social interaction, communication, and the existence of unvaried patterns of behaviour in children and adults 20 . Alfalasi 21 reported that in United States 1 out of 54 children is affected by autism. Detecting autism earlier in one life can make a big difference than treating it later 22 . According to World Health Organization (WHO) every year one among 160 children is diagnosed with ASD traits all over the world 23 . Treating ASD earlier is always the best option for toddlers as they are still developing 24 .

Different symptoms identified in ASD patients have been considered as features that can be used for ASD detection. Lawan et al. 25 and Cantin-Garside et al. 26 observed behavioural disorder, Beary et al. 27 and Derbali et al. 28 recorded facial expression disorder and Devika et al. 29 observed structural disorder in ASD effected persons. Emotional disorder in ASD affected persons has been studied by Makhnytkina et al. 30 and mental disorder has been analysed in Liu et al. 31 and Lord et al. 32 . Many researchers explored medical imageries for ASD detection including Bilic et al. 33 , Husna et al. 34 , Liu et al. 35 , Nogay and Adeli 36 . Images of brain have been used by Subah et al. 37 , Xu et al. 38 , Yin et al. 39 , Shenouda et al. 40 to detect ASD in patients. Single and cross order strategy for ASD detection has been proposed in Wawer et al. 41 .

Researchers have used wearable devices containing sensors for detection of ASD 42 , 43 . Application of intelligent approaches present advanced ways to economically detect ASD effected children and adults 44 . Models have been proposed in the literature describing application of different methods and approaches for ASD detection like structural MRI 45 , neural networks 46 , machine learning 47 , 48 , 49 , deep learning 50 , transfer learning 51 , 52 and IoT 53 . All these techniques have been applied to detect ASD with reasonable accuracy in children and adults but faced limitations of data acquisition as hospitals hesitate or refuse to share patient records due to organizational policies and regional data protection legislations. Data security, data privacy and data availability are the huge challenges in developing effective intelligent models. Even if access to data is granted, transferring huge dataset over the network is again challenging, rising a lot of network issues regarding network congestion, latency and data theft.

Federated learning (FL) provides a generous solution to address all above mentioned problems. FL is an advanced ML based approach that never transmits data over the network 54 . Data is kept with its generating organization 55 whereas only a small sized local data model is trained from onsite data and transmitted over the network towards central server where all local models are combined to train meta classifier for determining which ML model is most effective in autism detection 56 . Objective of proposed model is to detect ASD symptoms at different stages of age with minimum time, controlled expense and maximum accuracy. Novelty of our work is the application of federated learning technique for autism detection in children and adults by processing four different datasets by training SVM and LR classifiers locally. Major contribution of this work is the detection of ASD by the application of most advanced Federated Learning technique by training ML classifiers locally on features dataset of children and adults to find the predictive risk factors of Autism with reasonable accuracy.

Material and method

ASD indicates a disability in human development due to variations of neurons present in human brain 57 . Practitioners believe that there are multivariate sources that work jointly to cause ASD 58 . Diagnosis of ASD is also very challenging task as no medical test like blood test exists to detect ASD. Doctors usually apply psychological and observational strategies to sense ASD in a patient by analysing multiple aspects of their daily routine as mentioned in Fig.  1 .

Aspects observed while diagnosing ASD.

In this article, a unique federated learning based model has been proposed in which four different datasets of adults and children have been analysed using LR and SVM locally to train local data models. These local models have been transmitted towards central server for training of meta classifier in global model to predict autism in children and adults. Proposed model architecture presented in Fig.  2 comprises of five components including dataset acquisition, data pre-processing, ML models training for ASD detection and performance comparison of different ML models to determine the most effective model that can accurately diagnose autism. The first step was acquisition of data in which publicly available four datasets of children and adults from data sources listed in Table 1 have been obtained. In second step, data pre-processing and normalization was performed for data compression and data cleaning and removal of noisy data. After normalization, in third step, four datasets have been locally processed by SVM and LR classifiers for autism detection. Results of training ML classifiers have been transmitted to central server where meta classifier has been trained to compare results and identify the best model to detect autism. In last step, results of meta classifier were validated by calculating accuracy, precision and F1 score to detect autism disorder with more accuracy as shown in Fig.  2 .

Proposed model architecture.

Step1: Dataset

Four datasets have been obtained covering two dimensions: children and adults. Source and specifications of each dataset is listed in Table 1 .

Step 2: Pre-processing

According to Q-Chart-10, ten different features have been unanimously identified for processing of adults and children datasets at same scale for segregation of autism effected patients from normal ones as shown in Table 2 .

The Quantitative Checklist for Autism in Children (Q-CHART-10) screening approach approved by Transforming autism project, UK, served as the foundation for the conduction of this research 3 . Thirty questions have been asked to record responses (R1–R10) for features mentioned in Table 2 . The value of these responses is assigned to classes as per following criteria for assigning weightage (score) to every response.

If score of class is more than 3, it indicates that ASD feature exits, its weight is incremented by 1 and “Yes” will be stored in response set otherwise value of flag will remain 0 that shows absence of any ASD features and “No” will be stored in the response set. Each class variable corresponds to more than one questions confirming the presence of feature extracted from Q-CHART-10 checklist. Information stored in class response set is in the binary format indicating Yes (stored as 1) and No (stored as 0). Local ML models have been trained on these responses presented in Table 2 .

The response dataset contained some noisy and missing records therefore data transformations were needed to carry out prior to train ML classifier for model training and analysis. Category variables are handled using label encoding. To make labels machine-readable, label encoding transforms them into numeric form. Repeated labels receive the same value as those that were previously allocated. The binary label encoding of classes with ten features have been chosen.

Step 3: Federated Learning process

In the proposed architecture, Federated learning process starts from step three in which pre-processed and normalized datasets have been processed for training of SVM and LR classifiers. Workflow of FL process is presented in Fig.  3 . Results of these classifiers in terms of accuracy, precision and F1 score have been calculated and transmitted to central server for training of meta classifier at server. Meta classifier will determine which model is more appropriate in detecting autism and will train the global model accordingly. Global model will be disseminated in all clients as a single tool for autism detection.

Proposed model workflow.

The children and adult datasets (A, C respectively) presented in Table 1 have been divided into training and test datasets. Training datasets contained 80% records and testing datasets which will be used to test the proposed model contained 20% of total records.

Experimental setup

Experiment has been performed in two different dimensions. In first dimension, SVM and LR has been applied on dataset of adults presented in Table 1 . In second dimension, SVM and LR has been applied on dataset of children as presented in Fig.  4 .

Experimental setup.

Results obtained after training local models have been transmitted to central server through 4G ethernet gateway where meta classifier is trained to predict which ML model is outperforming in prediction of ASD. Best model is selected for the training of global model that is transmitted back to the clients so that all clients use same efficient model for autism detection.

Analysis and results

Two-dimensional exploratory analysis has been performed on datasets by plotting several graphs to depict different perspectives of the ASD response set. In first dimension, variance between datasets has been analyzed using statistical method ANOVA. ANOVA being a powerful statistical tool compares the mean of datasets and determines that if there is a significant difference between them as summarized in Table 3 .

H o (Null hypothesis) = there is no significant difference between the means of datasets being compared.

H 1 (alternate hypothesis) = there is a significant difference between the means of datasets being compared.

Results of ANOVA have been listed in Table 4 . Total variability of data is calculated by sum of squares (SS). Degree of freedom represent the number of independent observations available to estimate every response. F-statistics and associated p-value are significant results obtained from ANOVA test. F-statistics determines the variability between the groups to the variability within the group. p value presents the probability to observe a difference as large as the one observed in response set.

The f -ratio value is 100.8232. The p value is < 0.00001. The result is significant at p  < 0.05. There is a significant difference between the means being compared. The p value is less than the commonly used significance level (0.05), it can be inferred that H o has been rejected and can be concluded that H 1 has been accepted indicating the significant difference between the means being compared.

Second dimension of analysis part focused on visualizing performance of global model trained on central server through meta classifier by drawn receiver operating characteristic (ROC) curve. Data characteristics of ROC curve are presented in Table 5 . Figure  5 compares the performance of global model on the basis of sensitivity [TP/(TP + FN)] and specificity [TP/(TP + FN)].

In response set, data points have been gathered into one of the following four classes to validate ASD diagnosis. Class1: true positive (TP) indicates that the person has autism, and we have correctly recorded autism positivity. Class 2: true negative (TN) means that a person does not has autism and wrongly recorded as negative in response dataset. Class 3: false positive (FP) depicts that response dataset incorrectly recorded that a person had ASD who does not have it. Class 4: false negative (FN) indicates that it was predicted mistakenly that the person does not have ASD, but they have ASD. The confusion matrix of ASD that facilitated in the validation process is given below in Table 6 .

Precision, recall and F1 score are the measures used to validate performance of LR and SVM classifiers. Precision demonstrates the cases that detected autism and we predicted them correctly. Whereas recall indicates the number of autism cases identified correctly are relevant out of total instances that had autism. Proposed model has been validated using dataset B, D given in Table 1 .

F1 score greater than 0.5 or above is considered Good. It can be observed from Table 7 that SVM is performing more accurately than LR although LR is also giving comparable results. Hence, it can be inferred from results that SVM and LR can detect autism more accurately in comparison of other ML models using features dataset and they can be used for early diagnosis of autism. Figures  6 and 7 present precision and recall curve of SVM and LR respectively. Precision and recall are the measures used to evaluate model’s performance. Precision demonstrates the cases that detected autism and we predicted them correctly. Whereas recall indicates how many autism cases model has identified correctly as relevant out of total instances that had autism.

Precision/Recall curve of SVM.

Precision/Recall curve of LR.

After performing detailed analysis, it has been observed that SVM and LR models can be best fit for diagnoses of autism disorder in people of various age groups ranging from children to adults. We have obtained 99% accuracy in prediction of ASD.

The performance of proposed model has also been compared with other models already proposed in the literature. We found three most relevant studies that have proposed models for ASD detection.

Ethical statement

Hereby, I Muhammad Shoaib Farooq consciously assure that for the manuscript “Detection of Autism Spectrum Disorder (ASD) in children and adults using Machine Learning” the following is fulfilled: (1) This material is the authors' own original work, which has not been previously published elsewhere. (2) The paper is not currently being considered for publication elsewhere. (3) The paper reflects the authors' own research and analysis in a truthful and complete manner. (4) The paper properly credits the meaningful contributions of co-authors. (5) The results are appropriately placed in the context of prior and existing research. (6) All sources used are properly disclosed (correct citation). Literally copying of text must be indicated as such by using quotation marks and giving proper reference. (7) All authors have been personally and actively involved in substantial work leading to the paper, and will take public responsibility for its content. The violation of the Ethical Statement rules may result in severe consequences. I agree with the above statements and declare that this submission follows the policies as outlined in the Guide for Authors and in the Ethical Statement.

Table 2 indicates the response set gathered by analysing multiple features extracted during pre-processing of datasets. Figures  8 and 9 have been drawn based upon response R1 that showed the region to which most of ASD patients belong and their ethnicity. It can be observed from the chart that United Kingdom (UK) is the most affected region. Similarly, graph in Fig.  9 presents that mostly White-Europeans have ASD.

ASD detection as per country-of-residence.

ASD detection as per ethnicity.

People infected with jaundice (response R8) are considered as on high risk of ASD. So, it is worthwhile to know that whether a person is born with or without jaundice. There is a high probability that they will screen positive for ASD if born with jaundice as shown in Fig.  10 .

ASD detection based on jaundice.

Application of ML in autism detection has significance due to its reliability, accuracy and quickness 1 . In the proposed model, datasets have been processed to train LR and SVM classifiers locally. Results of these classifiers are transmitted to central server where meta classifier is trained to generate global model for autism detection. The reason for selecting LR is to find a model that most accurately describes the relationship among binary response set and independent variables set 5 . SVMs has been applied in this study as datasets had multiple dimensions and are not linearly separable. SVM use hyperplane that separates ASD dataset into two classes namely ASD effected and Non-ASD to predict target and handle overfitting as well. SVM has separating hyper plane boundary to separate both classes 7 as presented in Fig.  11 .

SVM mechanism of ASD Classification.

Comparison with other studies

We have compared their work with our proposed model and summarized the strengths and limitations of existing model in relation to our proposed models in Table 8 . It has been noted that our proposed model is offering comparable accuracy and effectively applicable to diagnose ASD in patients belonging to different age groups ranging from children to adults.

Limitations of proposed model

FL is a ML technique that allows models to be trained on decentralized data sources without transferring the data to a central server. Proposed FL based model for ASD detection offers several advantages of data security and data privacy but it has some limitations too as listed below:

Limited model complexity In proposed architecture, FL models are trained on multiple devices with limited processing power and storage. This limitation can make it difficult to use the proposed model for more complex tasks that require deep neural networks or other advanced machine learning models.

Data heterogeneity The proposed model is designed to work with data that is distributed across different devices and locations. However, this can lead to data heterogeneity, where different devices have different types of data, making it challenging to develop models that perform well across all devices.

Communication overhead In the proposed architecture, models are trained on local devices, and the updated models need to be sent back to a central server for aggregation. This process can create significant communication overhead, especially when dealing with a large number of devices or when the models are updated frequently.

Lack of transparency The proposed model for ASD detection, makes it challenging to understand how models are trained or how they make predictions. This lack of transparency can make it difficult to identify and correct biases or errors in the models.

The assessment of ASD has been associated with multiple disorders recognized as features including, behavioural, emotional, structural and mental disorders that make it difficult to predict due to non-availability of medical tests for all features needed to detect ASD in a person. Practitioners diagnose ASD in patients by using psychological assessments and response observation. Detection process is time-consuming and complex as symptoms are not obvious. Presently, there is no screening method that has been optimized and thoroughly developed to specifically detect the ASD, nor is there a screening test that can accurately diagnose ASD. ML is the most recent development that can facilitate in predicting autism more accurately saving lots of time. ML can be helpful in early diagnosis of ASD in patients of all ages including children and adults. In this work, we have applied two different ML models (SVM, LR) on the dataset containing features of children and adults. It was observed that SVM showed 81% accuracy in detecting ASD in adults and LR gave 98% accuracy in determining ASD in children. In future, different transfer-learning models i.e. MobileNet, ResNet can also be used in ASD detection using images dataset of autistic children for early detection of ASD with improved accuracy. Moreover, severity of disorder can also be measured through deep learning methods in future.

Data availability

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M.S.F. and R.T. performed the measurements and analysis of the article. R.T., M.S.F. were involved in planning and supervised the research work. Z.A., R.T. and M.S.F. processed the experimental data, performed the analysis, drafted the manuscript and designed the figures. M.S.F. and M.S. obtained the dataset and characterized it. R.T. and M.S.F. performed the experimental work and worked on different analysis tools and article repositories. M.S.F., Z.A., and R.T. aided in interpreting the results and worked on drafting the manuscript. All authors discussed the results and commented on the whole manuscript.

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Farooq, M.S., Tehseen, R., Sabir, M. et al. Detection of autism spectrum disorder (ASD) in children and adults using machine learning. Sci Rep 13 , 9605 (2023). https://doi.org/10.1038/s41598-023-35910-1

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CONCEPTUAL ANALYSIS article

Research, clinical, and sociological aspects of autism.

• ESPA Research, Unit 133i Business Innovation Centre, The Robert Luff Laboratory, Education & Services for People With Autism Research, Sunderland, United Kingdom

The concept of autism continues to evolve. Not only have the central diagnostic criteria that define autism evolved but understanding of the label and how autism is viewed in research, clinical and sociological terms has also changed. Several key issues have emerged in relation to research, clinical and sociological aspects of autism. Shifts in research focus to encompass the massive heterogeneity covered under the label and appreciation that autism rarely exists in a diagnostic vacuum have brought about new questions and challenges. Diagnostic changes, increasing moves towards early diagnosis and intervention, and a greater appreciation of autism in girls and women and into adulthood and old age have similarly impacted on autism in the clinic. Discussions about autism in socio-political terms have also increased, as exemplified by the rise of ideas such as neurodiversity and an increasingly vocal dialogue with those diagnosed on the autism spectrum. Such changes are to be welcomed, but at the same time bring with them new challenges. Those changes also offer an insight into what might be further to come for the label of autism.

Introduction

Although there is still debate in some quarters about who first formally defined autism ( 1 ), most people accept that Kanner ( 2 ) should be credited as offering the first recognised description of the condition in the peer-reviewed scientific literature. The core diagnostic features covering issues in areas of social and communicative interaction alongside the presence of restricted and/or repetitive patterns of behaviour ( 3 ) described in his small caseload still remain central parts of the diagnosis today. The core issue of alterations in social cognition affecting emotion recognition and social attention ( 4 ) remain integral to the diagnosis of autism. The additional requirement for such behaviours to significantly impact on various areas of day-to-day functioning completes the diagnostic criteria.

From defining a relatively small group of people, the evolution of the diagnostic criteria for autism has gone hand-in-hand with a corresponding increase in the numbers of people being diagnosed. Prevalence figures that referred to 4.5 per 10,000 ( 5 ) in the 1960s have been replaced by newer estimates suggesting that 1 in 59 children (16 per 1,000) present with an autism spectrum disorder (ASD) in 2014 ( 6 ). The widening of the definition of autism has undoubtedly contributed to the significant increase in the numbers of people being diagnosed. It would be unacceptably speculative however, to define diagnostic changes as being the sole cause of the perceived prevalence increases.

Alongside the growth in numbers of people being diagnosed with autism so there have been changes in other areas related to autism; specifically those related to the research, clinical practice and sociological aspects of autism. Many of the changes have centred on key issues around the acceptance that autism is an extremely heterogeneous condition both in terms of presentation and also in relation to the genetic and biological complexity underlying its existence. That autism rarely exists in some sort of diagnostic vacuum is another part of the changes witnessed over the decades following the description of autism.

In this paper we highlight some of the more widely discussed changes in areas of research, clinical practice and sociological terms in relation to autism. We speculate on how such changes might also further develop the concept of autism in years to come.

Autism Research

As the definition of autism has subtly changed over the years, so ideas and trends in autism research have waxed and waned. The focus on psychology and behaviour as core descriptive features of autism has, in many respects, guided research and clinical views and opinions about the condition. Social cognition, including areas as diverse as social motivation, emotion recognition, social attention and social learning ( 4 ), remains a mainstay of research in this area. The rise of psychoanalysis and related ideas such as attachment theory in the early 20th century for example, played a huge role in the now discredited ideas that maternal bonding or cold parenting were a cause of autism. The seemingly implicit need for psychology to formulate theories has also no doubt played a role in perpetuating all-manner of different grand and unifying reasons on why autism comes about and the core nature of the condition.

As time moved on and science witnessed the rise of psychiatric genetics, where subtle changes to the genetic code were correlated with specific behavioural and psychiatric labels, so autism science also moved in the same direction. Scientific progress allowing the genetic code to be more easily and more cost-effectively read opened up a whole new scientific world in relation to autism and various other labels. It was within this area of genetic science that some particularly important discoveries were made: (a) for the vast majority of people, autism is not a single gene “disorder,” and (b) genetic polymorphisms whilst important, are not the only mechanism that can affect gene expression. Mirroring the role of genetics in other behavioural and psychiatric conditions ( 7 ), the picture that is emerging suggests that yes, there are genetic underpinnings to autism, but identifying such label-specific genetic issues is complicated and indeed, wide-ranging.

What such genetic studies also served to prove is that autism is heterogeneous. They complemented the wide-ranging behavioural profiles that are included under the diagnostic heading of autism. Profiles that ranged from those who are profoundly autistic and who require almost constant attention to meet their daily needs, to those who have jobs, families and are able to navigate the world [seemingly] with little or minimal support for much of the time.

It is this heterogeneity that is perhaps at the core of where autism is now from several different perspectives. A heterogeneity that not only relates to the presentation of the core traits of autism but also to how autism rarely manifests in a diagnostic vacuum ( 8 ). Several authors have talked about autism as part of a wider clinical picture ( 9 , 10 ) and how various behavioural/psychiatric/somatic issues seem to follow the diagnosis. Again, such a shift mirrors what is happening in other areas of science, such as the establishment of the Research Domain Criteria (RDoC) project ( 11 ). RDoC recognised that defining behavioural and psychiatric conditions on the basis of presented signs and symptoms does not necessarily “reflect” the relevant underlying processes and systems that might be important. It recognised that in order to deliver important clinical information about how and why a condition manifests, or the best strategies to intervene, research cannot just singularly start with the label. Science and clinical practice need more information rather than just a blanket descriptive label such as autism.

To talk about autism as a condition that also manifests various over-represented comorbid labels also asks a fundamental question: is the word “comorbidity” entirely accurate when referring to such labels? ( 12 ). Does such comorbidity instead represent something more fundamental to at least some presentations of autism or is it something that should be seen more transiently? Numerous conditions have been detailed to co-occur alongside autism. These include various behavioural and psychiatric diagnoses such as depression, anxiety and attention-deficit hyperactivity disorder (ADHD) ( 13 ). Other more somatic based conditions such as epilepsy ( 14 ), sleep ( 15 ) and various facets of gastrointestinal (GI) functioning ( 16 ) have also been discussed in the peer-reviewed science literature. Some of these co-occurring conditions have been described in the context of specific genetic conditions manifesting autism. Issues with the BCKDK (Branched Chain Ketoacid Dehydrogenase Kinase) gene for example, have been discussed in the context of autism, intellectual (learning) disability and epilepsy appearing together ( 17 ). Such a diagnostic combination is not unusual; autism often being described as the primary diagnosis with epilepsy and learning disability seen as “add-ons.” But should this be the case? Other evidence pointing to the possibility that epilepsy might under some circumstances beget autism ( 18 ) suggests that under some circumstances, such co-occurring conditions are so much more than just co-occurring or comorbid.

Other evidence for questioning the label “comorbid” comes from various animal models of autism. Accepting that one has to be particularly careful about extrapolating from animal models of autism to the more complex presentation of autism in humans ( 19 ), various models have suggested that autism may for some, fundamentally coexist with GI or bowel issues ( 20 , 21 ). Such observations have been noted across different animal models and cover important issues such as gut motility for example, that have been talked about in the context of autism ( 22 ).

Similarly, when one talks about the behavioural and psychiatric comorbidity in the context of autism, an analogous question arises about whether comorbidity is the right term. Anxiety and depression represent important research topics in the context of autism. Both issues have long been talked about in the context of autism ( 1 , 13 , 23 ) but only in recent years have their respective “links” to autism been more closely scrutinised.

Depression covers various different types of clinical presentations. Some research has suggested that in the context of autism, depressive illnesses such as bipolar disorder can present atypically ( 24 ). Combined with other study ( 25 ) suggesting that interventions targeting depressive symptoms might also impact on core autistic features, the possibility that autism and depression or depressive symptoms might be more closely linked than hitherto appreciated arises. Likewise with anxiety in mind, similar conclusions could be drawn from the existing research literature that anxiety may be a more central feature of autism. This on the basis of connections observed between traits of the two conditions ( 26 ) alongside shared features such as an intolerance of uncertainty ( 27 ) exerting an important effect.

A greater appreciation of the heterogeneity of autism and consideration of the myriad of other conditions that seem to be over-represented alongside autism pose serious problems to autism research. The use of “autism pure” where research participants are only included into studies on the basis of not having epilepsy or not possessing a diagnosis of ADHD or related condition pose a serious problem when it comes to the generalisation of research results to the wider population. Indeed, with the vast heterogeneity that encompasses autism, one has to question how, in the context of the current blanket diagnosis of autism or ASD, one could ever provide any universal answers about autism.

Autism in the Clinic

As mentioned previously, various subtle shifts in the criteria governing the diagnosis of autism have been witnessed down the years. Such changes have led to increased challenges for clinicians diagnosing autism from several different perspectives. One of the key challenges has come about as a function of the various expansions and contractions of what constitutes autism from a diagnostic point of view. This includes the adoption of autism as a spectrum disorder in more recent diagnostic texts.

The inclusion of Asperger syndrome in the DSM-IV and ICD-10 diagnostic schedules represented an expansion of the diagnostic criteria covering autism. Asperger syndrome defined by Hans Asperger ( 28 ) as autistic features without significant language impairment and with intelligence in the typical range, was included in the text for various different reasons. Allen Frances, one of the architects of the DSM-IV schedule, mentioned the importance of having a “ specific category to cover the substantial group of patients who failed to meet the stringent criteria for autistic disorder, but nonetheless had substantial distress or impairment from their stereotyped interests, eccentric behaviors, and interpersonal problems ” ( 29 ). It is now widely accepted that the inclusion of Asperger syndrome in diagnostic texts led to an increase in the number of autism diagnoses being given.

More recent revisions to the DSM criteria covering autism—DSM-5—included the removal of Asperger syndrome as a discrete diagnosis on the autism spectrum ( 30 ). Instead, a broader categorisation of autism spectrum disorder (ASD) was adopted. The reasons for the removal of Asperger syndrome from DSM-5 are complex. The removal has however generally been positively greeted as a function of on-going debates about whether there are/were important differences between autism and Asperger syndrome to require a distinction ( 31 ) alongside more recent revelations about the actions of Asperger during World War II ( 32 ). Studies comparing DSM-IV (and its smaller revisions) with DSM-5 have also hinted that the diagnostic differences between the schedules may well-impact on the numbers of people in receipt of a diagnosis ( 33 ).

Shifts in the diagnostic text covering autism represent only one challenge to autism in the clinical sense. Other important factors continue to complicate the practice of diagnosing autism. Another important issue is a greater realisation that although the presence of observable autistic features are a necessary requirement for a diagnosis of autism, such features are also apparent in various other clinical labels. Autistic features have been noted in a range of other conditions including schizophrenia ( 34 ), personality disorders ( 35 ) and eating disorders ( 36 ) for examples. Coupled with the increasingly important observation that autism rarely exists in a diagnostic vacuum, the clinical challenges to accurately diagnosing autism multiply as a result.

The additional suggestion of “behavioural profiles” within the autism spectrum adds to the complexity. Terms such as pathological demand avoidance (PDA) coined by Newson and colleagues ( 37 ) have started to enter some diagnostic processes, despite not yet being formally recognised in diagnostic texts. Including various autistic traits alongside features such as “resisting and avoiding the ordinary demands of life” and the “active use of various strategies to resist demands via social manipulation,” debate continues about the nature of PDA and its diagnostic value ( 38 ).

Early diagnosis and intervention for autism have also witnessed some important clinical changes over the years. Driven by an acceptance of the idea that earlier diagnosis means that early intervention can be put in place to “ameliorate” some of the more life-changing effects of autism, there has been a sharp focus on the ways and means of identifying autism early and/or highlighting those most at risk of a diagnosis. It's long been known that there is a heritable aspect to autism, whether in terms of traits or diagnosis ( 39 ). In this respect, preferential screening for autism in younger siblings when an older child has been diagnosed is not an uncommon clinical sentiment ( 40 ). Other work looking at possible “red flags” for autism, whether in behaviour ( 41 ) or in more physiological terms still continue to find popularity in both research and clinical terms.

But still however, autism continues to confound. As of yet, there are only limited reliable red flags to determine or preclude the future presence of autism ( 42 ). Early behavioural interventions for autism have not yet fulfilled the promise they are said to hold ( 43 ) and autism is not seemingly present in the earliest days of development for all ( 44 , 45 ). There is still a way to go.

Autism in a modern clinical sense is also witnessing change in several other quarters. The traditional focus of autism on children, particularly boys, is being replaced by a wider acceptance that (a) autism can and does manifest in girls and women, and (b) children with autism age and mature to become adults with autism. Even the psychological mainstay of autism—issues with social cognition—is undergoing discussion and revision.

On the issue of autism presentation in females, several important themes are becoming more evident. Discussions about whether there may be subtle differences in the presentation of autism in females compared to males are being voiced, pertinent to the idea that there may be one or more specific female phenotypes of autism ( 46 ). Further characterisation has hinted that sex differences in the core domain of repetitive stereotyped behaviours ( 47 ) for example, may be something important when it comes to assessing autism in females.

Allied to the idea of sex differences in autism presentation, is an increasing emphasis on the notion of camouflaging or masking ( 48 ). This masking assumes that there may active or adaptive processes on-going that allow females to hide some of their core autistic features and which potentially contributes to the under-identification of autism. Although some authors have talked about the potentially negative aspects of masking in terms of the use of cognitive resources to “maintain the mask,” one could also view such as adaptation in a more positive light relating to the learning of such a strategy as a coping mechanism. Both the themes of possible sex differences in presentation and masking add to the clinical complexity of reliably assessing for autism.

Insofar as the growing interest in the presentation of autism in adulthood, there are various other clinical considerations. Alongside the idea that the presentation of autism in childhood might not be the same as autism in adulthood ( 49 ), the increasing number of people receiving a diagnosis in adulthood is a worthy reminder that autism is very much a lifelong condition for many, but not necessarily all ( 50 ). The available research literature also highlights how autism in older adults carries some unique issues ( 51 ) some of which will require clinical attention.

Insofar as the issue of social cognition and autism, previous sweeping generalisations about a deficit in empathy for example, embodying all autism are also being questioned. Discussions are beginning debating issues such as how empathy is measured and whether such measurements in the context of autism are as accurate as once believed ( 52 ). Whether too, the concept of social cognition and all the aspects it encompasses is too generalised in its portrayal of autism, including the notion of the “double empathy problem” ( 53 ) where reciprocity and mutual understanding during interaction are not solely down to the person with autism. Rather, they come about because experiences and understanding differ from an autistic and non-autistic point of view. Such discussions are beginning to have a real impact on the way that autism is perceived.

Autism in Sociological Terms

To talk about autism purely through a research or clinical practice lens does not do justice to the existing peer-reviewed literature in its entirety. Where once autism was the sole domain of medical or academic professionals, so now there is a growing appreciation of autism in socio-political terms too, with numerous voices from the autism spectrum being heard in the scientific literature and beyond.

There are various factors that have contributed to the increased visibility of those diagnosed with autism contributing to the narrative about autism. As mentioned, the fact that children with autism become autistic adults is starting to become more widely appreciated in various circles. The expansion of the diagnostic criteria has also played a strong role too, as the diagnostic boundaries of the autism spectrum were widened to include those with sometimes good vocal communicative abilities. The growth in social media and related communication forms likewise provided a platform for many people to voice their own opinions about what autism means to them and further influence discussions about autism. The idea that autistic people are experts on autism continues to grow ( 54 ).

For some people with autism, the existing narrative about autism based on a deficit model (deficits in socio-communicative abilities for example) is seemingly over-emphasised. The existing medical model of autism focusing such deficits as being centred on the person does not offer a completely satisfying explanation for autism and how its features can disable a person. Autism does not solely exist in a sociological as well as diagnostic vacuum. In this context, the rise and rise of the concept of neurodiversity offered an important alternative to the existing viewpoint.

Although still the topic of some discussion, neurodiversity applied to autism is based on several key tenets: (a) all minds are different, and (b) “ neurodiversity is the idea that neurological differences like autism and ADHD are the result of normal, natural variation in the human genome ” ( 55 ). The adoption of the social model of disability by neurodiversity proponents moves the emphasis on the person as the epicentre of disability to that where societal structures and functions tend to be “ physically, socially and emotionally inhospitable towards autistic people ” ( 56 ). The message is that subtle changes to the social environment could make quite a lot of difference to the disabling features of autism.

Although a popular idea in many quarters, the concept of neurodiversity is not without its critics both from a scientific and sociological point of view ( 57 ). Certain key terms often mentioned alongside neurodiversity (e.g., neurotypical) are not well-defined or are incompatible with the existing research literature ( 58 ). The idea that societal organisation is a primary cause of the disability experienced by those with the most profound types of autism is also problematic in the context of current scientific knowledge and understanding. Other issues such as the increasing use of self-diagnosis ( 59 ) and the seeming under-representation of those with the most profound forms of autism in relation to neurodiversity further complicate the movement and its aims.

The challenges that face the evolving concept of neurodiversity when applied to autism should not however detract from the important effects that it has had and continues to have. Moving away from the idea that autistic people are broken or somehow incomplete as a function of their disability is an important part of the evolution of autism. The idea that autism is something to be researched as stand-alone issue separate from the person is something else that is being slowly being eroded by such a theory.

The concept of autism continues to evolve in relation to research, clinical practice and sociological domains. Such changes offer clues as to the future directions that autism may take and the challenges that lie ahead.

The continuing focus on the huge heterogeneity and comorbidity clusters that define autism are ripe for the introduction of a new taxonomy for describing the condition. A more plural definition—the autisms—could represent one starting position ( 60 ) encompassing a greater appreciation that (a) there is variety in the presentation of the core features of autism, (b) there are seemingly several different genetic and biological pathways that bring someone to a diagnosis of autism, (c) different developmental trajectories are an important facet of the autism spectrum, and (d) the various “comorbidities” that variably present alongside autism may offer important clues about the classification of autism. Some authors have stressed that a multi-dimensional conceptualisation may be more appropriate than a categorical concept ( 61 ) but further investigations are required.

In relation to the proposed pluralisation of the label, several long held “beliefs” about autism are also ripe for further investigation. The idea that autism is innate and presents in the earliest days in all does not universally hold ( 45 ). The finding that some children experience a period of typical development and then regress into autism ( 62 ) is becoming more readily discussed in research and clinical circles, albeit not universally so. Similarly, the belief that autism is a lifelong condition for all is also not borne out by the peer-reviewed literature ( 63 ). Terms such as optimal outcome ( 64 ) might not be wholly appropriate, but do nonetheless, shed light on an important phenomenon noted in at least some cases of autism where diagnostic cut-off points are reached at one point but not another. These and other important areas provide initial support for the adoption of the idea of the plural autisms.

Allied to the notion of “the autisms” is the requirement to overhaul the terminology around the use of the “level of functioning” phrase ( 65 ). “High functioning” is typically used to describe those people on the spectrum who present with some degree of communicative language, possess typical or above-average intelligence and who can seemingly traverse the world with only minimal levels of support. “Low functioning”, conversely, is used to describe those with significant support needs who may also be non-communicative. Aside from the societal implications of labelling someone “low functioning” and the possible connotations stemming from such a label, such functioning categorisation do not seemingly offer as accurate a representation as many people might think. The high-functioning autistic child who for example, has been excluded from school on the basis of their behaviour, cannot be readily labelled “high-functioning” if the presentation of their autistic behaviours has led to such a serious outcome. This on the basis that part of the diagnostic decision to diagnose autism is taken by appreciation of whether or not presented behaviours significantly interfere with day-to-day living ( 3 ). What might replace functioning labels is still a matter for debate. The use of “levels of support requirement” utilised in current diagnostic criteria offer a template for further discussions. Such discussions may also need to recognise that the traits of autism are not static over a lifetime ( 51 ) and support levels may vary as a result.

Whatever terminology is put forward to replace functioning labels, there is a need to address some very apparent differences in the way that parts of the autism spectrum are viewed, represented and included in research. Described as the “understudied populations” by some authors ( 66 ) those with limited verbal communicative language and learning disability have long been disadvantaged in research terms and also in more general depictions of autism. In more recent times, there has been a subtle shift to acknowledge the bias that exists against those with a more profound presentation of autism ( 67 ). Further developments are however required to ensure that such groups are not excluded; not least also to guarantee the generalisability of autism research to the entire spectrum and not just one portion of it.

On the topic of generalisability to the entire autism spectrum, the moves to further involve those diagnosed with autism in research, clinical and sociological discussions presents opportunities and obstacles in equal measure. The application of the International Classification of Functioning, Disability and Health (ICF) to autism ( 68 ) to measure “health-related functioning” represented a key moment in autism participatory research. Taking on board various views and opinions about autism, the development of the ICF core autism sets has allowed those with autism and their significant others to voice their opinions about autism ( 69 ).

Such joint initiatives are to be welcomed on the basis of the multiple perspectives they offer including lived experience of autism. But with such participation, so questions are also raised about how representative such opinions are to the entire autism spectrum ( 70 ). Questions on whether those who are able to participate in such initiatives “can ever truly speak for the entire autism spectrum?” are bound to follow. Questions also about whether such first-hand reports are more important than parental or caregiver input when it comes to individuals on the autism spectrum are likewise important to ask. This bearing in mind that those with autism participating in such initiatives bring with them the same potential biases as researchers and clinicians carry with them about the nature of autism, albeit not necessarily in total agreement.

The translation of research findings into clinical practice represents another important issue that has yet to be suitably addressed. Although covering a sizeable area, several important stumbling blocks have prohibited the move from “bench to bedside” when it comes to autism research. The focus for example, on the overt behavioural presentation of autism, has in some senses continued to hinder the translational progress of more biological-based findings into autism practice. Nowhere is this seemingly more evident than when it comes to the over-representation of gastrointestinal (GI) issues in relation to autism and their management or treatment. Despite multiple findings of such issues being present ( 16 ), very little is seemingly offered despite autism-specific screening and management guidance being in place for nearly a decade at the time of writing ( 71 ). Other quite consistently reported research findings in relation to low functional levels of vitamin D ( 72 ) for example, have similarly not sparked massive shifts in clinical practices. Ignoring such potentially important clinical features contributes to a state of relative health inequality that is experienced by many on the autism spectrum.

Without trying to prioritise some areas over others, there are some important topics in relation to autism that are becoming important to autism research and clinical practice. Many of these topics are more “real life” focused; taking into account the impact of autism or autistic traits on daily living skills and functioning. These include issues such as the truly shocking early mortality statistics around autism ( 73 ) and the need for more detailed inquiry into the factors around such risks such as suicide ( 74 ) and self-injury ( 75 ) and wandering/elopement ( 76 ) alongside the considerable influence of conditions such as epilepsy.

Although already previously hinted at in this paper, the nature of the relationship between autism and various “comorbid” conditions observed to be over-represented alongside is starting to become more widely discussed in scientific circles. Whether for example, moves to intervene to mitigate issues such as depression in relation to autism might also have knock-on effects on the presentation of core autistic features is something being considered. Interest in other topics such as employment, ageing, parenting and the worrying issue of contact with law enforcement or criminal justice systems ( 77 ) are also in the ascendancy.

Conclusions

Autism as a diagnostic label continues to evolve in research, clinical practice and sociological terms. Although the core features described by Kanner and others have weathered such evolution, important shifts in knowledge, views and opinions have influenced many important issues around those core behaviours. As well as increasing understanding of autism, many of the changes, past and present, have brought about challenges too.

Author Contributions

All authors contributed equally to the writing and review of this manuscript.

This paper was fully funded by ESPA Research using part of a donation from the Robert Luff Foundation (charity number: 273810). The Foundation played no role in the content, formulation or conclusions reached in this manuscript.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Keywords: autism, research, clinical, sociological, knowledge, future

Citation: Whiteley P, Carr K and Shattock P (2021) Research, Clinical, and Sociological Aspects of Autism. Front. Psychiatry 12:481546. doi: 10.3389/fpsyt.2021.481546

Received: 28 June 2019; Accepted: 30 March 2021; Published: 29 April 2021.

Reviewed by:

Copyright © 2021 Whiteley, Carr and Shattock. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Paul Whiteley, paul.whiteley@espa-research.org.uk

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Autism Research Paper

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Get 10% off with 24start discount code, i. historical development of the concept of autism, ii. dsm-iv criteria, iii. behavioral and cognitive characteristics, a. behavioral characteristics in autism, 1. social behavior, 2. communication and play, 3. preoccupations, perseverations, and resistance to change, b. cognitive characteristics, 1. language, 2. social cognition, 3. attention, 5. executive functions, iv. developmental course and prognosis, v. epidemiology, vi. boundary conditions and comorbidity, vii. biological factors, a. associated biomedical conditions and genetic factors, b. neuroanatomical findings, c. neurophysiological findings, d. neurochemical findings, viii. clinical assessment, a. medical assessment, b. neuropsychological assessment, c. behavioral assessment, ix. treatment, a. pharmacological treatments, b. behavioral and educational treatments.

X. Bibliography

Autism was first described by Leo Kanner in 1943 and became known as Infantile Autism or Autistic Disorder. The concept has expanded since that date, and the term “Kanner autism” is sometimes used to refer to cases with symptoms similar to those of Kanner’s original sample; such cases are a subset of PDD. Kanner’s original description remains influential, and there is a tendency in the literature to assume that persons with “Kanner autism” represent the “nuclear” or “core” form of PDD, an assumption that may not be warranted. Kanner identified symptoms in three main groups: an autistic aloneness, a failure to use language communicatively, and an obsessive insistence on sameness in the environment; these are still the three areas of symptomatology used in current diagnostic systems. Although Kanner originally viewed the autistic aloneness as probably representing a constitutional defect, the two decades following his original work were marked by an unfortunate shift toward a psychodynamic/environmental view of the causation of autism. This served to derail any significant progress in understanding the disorder, as well as to cause a great deal of additional anguish to parents of autistic children.

Beginning with Rimland’s seminal work on autism in 1964, psychology and psychiatry began to explore seriously the biological foundations of autism, and theories of autism as resulting from disturbances in attention, language, sensory integration, perceptual constancy, and other neurological functions were promulgated and tested. Beginning in the early to mid-1980s, research attention also began to focus seriously on the social and affective aspects of autism, both to clarify the range of heterogeneity in autistic children’s functioning, and to posit new core deficits in these areas. Currently, researchers stressing both cognitive and social/affective deficits as primary are in agreement that the fundamental problem is a neurological, and not an environmental, one.

Differential diagnosis was a conceptual problem for early autism research. Some clinicians believed that autism was a variant of or precursor to schizophrenia. Only in the 1970s came an awareness that disorders beginning in infancy must be regarded as separate in kind from those with onset in later childhood, adolescence, or adulthood. Autistic-like disorders virtually always begin before age 3, while schizophrenic-like disorders virtually never begin before age 7. This realization revitalized interest in infantile autism as a distinct nosologic entity, leading to the development of more operationally precise diagnostic criteria and a reconceptualization of the syndrome as a pervasive developmental disorder, under which label it was incorporated by the American Psychiatric Association in the third edition of that body’s Diagnostic and Statistical Manual of Mental Disorders (DSM-III ); this conceptualization has been retained in DSM-III-R and DSM-IV.

Current diagnostic practice, as reflected in the DSM-IV, classifies Autistic Disorder as one of four specific entities within Pervasive Developmental Disorder. Autistic Disorder is marked by the presence of symptomatology in three areas: (1) qualitative impairment in social interaction, as manifested by such behaviors as abnormal or reduced eye contact with others, failure to develop peer relationships, lack of spontaneous sharing of interests with others (e.g., showing or pointing out objects of interest to the caregiver), (2) qualitative impairment in communication, as manifested by delayed or deviant language without attempts to compensate through nonverbal communication, poor conversational skills if speech is present, and repetitive and stereotyped language and play, and (3) a restricted and repetitive repertoire of behaviors and interests, including preoccupations and rituals, or severe resistance to environmental changes.

The specific behavioral manifestations of these traits differ by degree of accompanying retardation and age. A high-functioning, older autistic individual, for example, may attempt to be social, but violate implicit rules of social behavior and be insensitive to unspoken social signals, while a low-functioning or much younger autistic individual may react to other people as if they were little different from inanimate objects. Similarly, a high-functioning, older autistic individual may have perseverative interests in such topics as constellations, train schedules, or dinosaurs, and attempt to engage others in conversations on these subjects, while a lower functioning or younger autistic individual might engage in repetitive motor rituals.

The other specific syndromes classified as Pervasive Developmental Disorders include Rett’s Disorder, Asperger’s Disorder, and Childhood Disintegrative Disorder. Rett’s Disorder has marked behavioral commonalities with Autistic Disorder, including poor social engagement and mental retardation, but differs from Autistic Disorder in several ways: in Rett’s Disorder, the retardation is more invariant and more typically severe, the disorder seems to present only in girls, it is marked by a characteristic pattern of head growth deceleration and loss of purposeful hand movements, sometimes accompanied by hand wringing behavior, following a period of normal development. Many girls with Rett syndrome also have epilepsy and other neurologic abnormalities. Asperger’s Disorder is often considered a mild form of Autistic Disorder, and there is still controversy about how distinct it is from autism; diagnostically, it can be distinguished from autism by normal development of language. Childhood Disintegrative Disorder differs from Autistic Disorder in that the former is marked by a distinctive pattern of developmental regression following at least two years of normal development. This disorder is much rarer than autism, and controversy exists here, too, about the etiological and phenomenological distinctiveness between Childhood Disintegrative Disorder and Autistic Disorder.

Social behavior is considered by many as the hallmark of autism. As with all features of autism, social impairment is highly heterogeneous; it varies with age, with IQ, with setting, and with interactive partner, and is modifiable by treatment.

Social impairment is generally most severe in the preschool years; it is in early childhood that genuine aloofness is seen. Older children may initiate interaction to get their needs met, or may be responsive but noninitiating to others. Older or higher-functioning autistic individuals may approach others in an idiosyncratic, intrusive, and socially insensitive way. These three general styles (aloof, passive, active-but-odd) form the basis of a social typology described by Lorna Wing, and validated by several later studies.

Highly structured settings with enforced proximity to peers may elicit the best peer interactions. Relationships with other children are almost always more impaired than the corresponding behavior with adults; other children may be ignored when adults are not.

Behavioral deficits in social interaction are varied; among the most important, especially in early life, are (1) poor spontaneous imitation of others’ language and behavior, (2) gaze avoidance or other deficits in the use of eye-to-eye gaze to modulate or initiate interaction, and (3) deficits in various joint attention skills, including drawing an adult’s attention to an object of interest by showing or pointing, and following an adult’s attentional focus in order to share it. On the other hand, the “pervasive lack of responsiveness” described in DSM-III is actually uncommon; many young autistic children are selectively attached to their parents, derive comfort from their presence, and enjoy physical affection.

Older studies were composed primarily of clinical descriptions of language features such as pronoun reversal, echolalia, and metaphorical language. More systematic studies have appeared, based on modern understanding of the separable components of language. The more severely affected autistic child may remain nonverbal or minimally verbal and poorly intelligible. In those who develop more language, phonology, syntax, and (more arguably) semantics are relatively spared, although still often at a lower level than nonverbal skills. Verbal memory, prosody, and pragmatics, on the other hand, represent areas of particular difficulty for the average autistic child. In the domain of pragmatics, it is noted that communicative functions are generally more need-oriented and less affiliative, and that violations of language use rules are common, such as violations of implicit rules concerning interpersonal distance while speaking, and the rules of turn-taking in conversational exchange, as well as word selection which is overly formal or pedantic.

Symbolic play is also often observed to be lacking in young autistic children, who sometimes prefer nonsymbolic play activities such as puzzles or other manipulatives. When symbolic play develops, it can be unusually repetitive and inflexible in nature. Some recent evidence suggests that high-functioning autistic children may be not so much incapable as uninterested in engaging in frequent or complex symbolic play.

These constitute the third symptom group. These behaviors range from simple or complex motor stereotypies, to “self-stimulatory” sensory behaviors such as watching fans or water, to long-term perseverative interests. The resistance to change is manifested by tantrums or other extreme reactions to changes in environmental features or in routines. Despite the equal role assigned to perseverations/preoccupations and resistance to change in diagnostic criteria, some recent data suggest that resistance to change is a less common feature of autism than perseverations/preoccupations.

Overall cognitive level, or presence of mental retardation, is an important feature of the individual autistic child, and powerfully predicts the functional outcome that can be expected for the child. Recent work by several research groups, such as the group headed by Rapin (see Bibliography) suggests that high- and low-functioning autism may be significantly different in behavioral manifestations, history, and prognosis, that approximately half of the autistic population falls into each group, and that an IQ cutoff of about 65 makes the most appropriate division between highand low-functioning autism. Beyond studies of overall cognitive level, many investigators have examined typical cognitive profiles in autism, that is, areas of relative sparing and impairment. Some autistic children, both those with severe impairment and those who are higher functioning, display unusual gifts, especially in rote memory, calculations, and music. A majority of autistic children are known to have relative strengths in visuospatial abilities, while tasks requiring verbal reasoning, social cognition, or flexibility pose relative difficulty for the autistic child. Although this description suggests a typical cognitive profile, studies have shown that there is great heterogeneity in the autistic population, and that no single cognitive deficit is universal in autistic individuals.

The autistic child’s language profile is arguably the syndrome’s most distinctive cognitive feature, which has earned it a central position in some theories of the etiology of autism. Many aspects of verbal functioning are impaired in autistic children, as many as 40 to 50% of whom are mute, although this figure is declining with the advent of aggressive early intervention. Those with speech often display echolalia, difficulties with prepositions and pronouns, and inappropriate conversational behaviors. Verbal autistic children generally are able to acquire normal grammatical morphology and syntax, although onset and development are delayed. Some autistic children learn grapheme-phoneme correspondence, leading to early decoding of words; comprehension, however, lags far behind. Comprehension of oral language is significantly impaired relative to expression, and deficits in the semantic and pragmatic aspects of language are common. They are also deficient in interactive communication, including conversational behavior, nonverbal communication and speech prosody. In general, the more linguistic aspects of communication, including especially phonology and syntax, are spared relative to the pragmatic aspects; pragmatic deficits can be seen in the failure to use language functionally to share or request information, or perform other speech functions that serve social, rather than instrumental, functions.

A decade of research has documented substantial deficits in autistic children’s ability to understand the behavior, emotions, and cognitive states of other people. They have difficulty in matching pictures of emotional facial expressions to emotion words, to emotional situations, to similar expressions, and to vocal expressions of the same emotion.

Much recent interest has been stimulated by exploration of autistic performance on “theory of mind” tasks; in a typical theory of mind task, the subject is asked to predict behavior of a doll in a social scenario. The behavior can only be correctly predicted if the subject has a true theory of mind, that is, truly understands the concept of others’ minds, with their own representational capacities, and their own limits on available knowledge. Autistic subjects have been shown to be impaired on these tasks in several studies.

A cautionary note here, however, about all of these social cognitive tasks is that many higher functioning autistic individuals do well on them; the deficits are far from universal. Furthermore, verbal IQ explains much of the variance in performance. Therefore, it remains to be demonstrated that deficits in social cognition, including theory of mind, occupy a key causal role in the syndrome; they may be more a concomitant deficit related to the overall social impairment, although opinions differ widely on this.

Unusual attentional processes are characteristic of autistic children. Autistic children are generally able to sustain attention in tasks adequately when given potent reinforcement or when the task is of interest to them, and higher functioning individuals are able to perform well on standard neuropsychological tests of sustained attention. In contrast, many autistic children appear to have difficulty with tasks requiring the focusing and shifting of attention. They are found to be overselective in their attention to particular parts of stimuli, and studies indicate that they may have difficulties in shifting attention between stimuli, especially across sensory modalities, perhaps contributing to the perseveration so characteristic of their behavior.

Memory abilities in autism have not been as fully investigated as other cognitive functions. Anecdotally, amazing feats of memory have been reported, where autistic individuals recall distant episodes with great clarity and detail; hyperdeveloped memory for stimuli such as routes, spatial arrays, schedules and calendars, and music have also been frequently reported. Tested memory for visual material in high-functioning autistic individuals is often normal. In contrast, memory for linguistic and social material is usually impaired. Autistic individuals appear not to be able to use the intrinsic semantic structure of discourse or stories to aid recall, and in this regard, are more impaired than children with specific language disorders.

Executive functioning refers to the higher level cognitive processes of abstract conceptualization, planning, problem solving, and self-monitoring, self-correction and self-control. These processes are thought to be localized to prefrontal cortex, and are assessed with standardized neuropsychological tests developed for evaluation of frontal functions. Some autistic individuals have great difficulty with these tasks, especially in switching from incorrect strategies during tasks. Some researchers have noted similarities between certain symptoms of autism and those of patients with frontal lobe damage (e.g., perseveration, lack of inhibition), and have proposed that frontal executive system impairment causes distinct social cognitive deficits. Furthermore, some findings suggest that executive system adequacy may predict outcome for autistic adolescents better than measures of IQ.

Autism, as a developmental disorder, cannot be fully described at a single developmental point. The typical description of the autistic child is that he or she lacks interest in relating to others and lacks communicative language. These symptoms are most characteristic of autistic children in the preschool years. Even during this period, there are often signs of increasing social relatedness, especially to caretakers. On the other hand, stereotypies and especially resistance to change, may appear in the preschool years or somewhat later. During middle childhood, autistic children often master some daily living and academic skills and make behavioral adjustments to their parents and teachers. Their behavior may come to resemble that of hyperactive and/or retarded children, or they may develop into socially motivated children, who relate in an odd or idiosyncratic way, with deficits in emotional reciprocity.

Early and middle adolescence can be particularly difficult. Besides the onset of seizures that sometimes occurs in early adolescence, a significant minority of PDD children regress behaviorally and even cognitively at this time. Some autistic adolescents show increasing interest in developing peer relationships during these years. Higher functioning individuals with PDD are prone to psychiatric problems, especially anxiety and depression, as they realize the extent of their difference from peers. On the positive side, both social and language skills often continue to improve during adolescence, and even those children who regress during early adolescence may recover and make developmental progress toward the middle or end of adolescence. Increasing interest in relating to other people can also set the stage for psychosocial interventions or behavioral skill training to be more effective.

Long-term follow-up studies indicate great variability in adult outcomes, but a generally guarded prognosis for good adjustment must be the rule. About half of all autistic adults require residential care; many of the remainder depend on relatives for daily assistance. Gainful employment and fully independent living may be achieved by about one in five. Even for the best-outcome group, social difficulties remain common, marriage or sexual relationships rare, and many social relationships revolve around work or structured activities and interests. It should be noted, however, that the generation of children who have received the benefit of modern special education and behavioral interventions have not yet reached adulthood, and their outcomes, it is hoped, will be significantly better.

Follow-up studies are consistent in demonstrating that higher IQ and communicative language by the age of 5 are strong predictors of better outcome, associated neurological signs and symptoms are predictors of poorer outcome.

Prevalence rates vary according to the definition of the syndrome. Earlier and more restrictive definitions of autism yielded prevalence estimates of 2-4/10,000. Broader definitions encompassing the full PDD spectrum suggested rates three or more times greater. Recent estimates have increased. This may be attributable to improved detection, more lenient diagnoses, or actual increases in prevalence. Most recent estimates are approximately 10/10,000 for PDD disorder, including autism, and another 10/10,000 with a more broadly defined triad of deficits in social relatedness, communication, and stereotyped behavior plus mental retardation. PDD spectrum disorders are more common in males than in females, with ratios found between 2:1 and 10:1, the higher ratios applying more to the Asperger-type clinical picture.

Specifying the boundary between autism and other PDD spectrum disorder (such as Rett’s and Asperger’s Disorder), mixed language disorder, or severe mental retardation can be problematic, and differential diagnoses among these conditions can be difficult. Although diagnostic definitions of autism and language disorder appear distinct, in practice, the differential diagnosis can be unclear, especially in preschool children. Studies from Rutter and colleagues in the 1970’s and from Rapin’s group indicate that the diagnostic groups can be distinguished not only by the presence of autism-related behaviors, but by differences in the language domain itself. The autistic children tend to have greater delays and deficits in language comprehension than the language-disordered children; in the expressive domain, delayed appearance of Wh-questions is highly discriminating. Regression of acquired language skills is also much more typical of autism, but also characterizes children with Landau-Kleffner syndrome. Landau-Kleffner syndrome, also referred to as acquired epileptic aphasia, refers to loss of language in a child in the context of clinical seizures or a frankly epileptiform EEG. There is disagreement as to whether the term should be reserved for children who have no serious associated behavior or cognitive disorders, or whether the term should be broadened so as to include those children who also develop autistic behaviors or become frankly autistic.

Autism may also have increased comorbidity with specific additional disorders. Although still controversial, some investigators present evidence that there is a greater than chance coincidence of autism and Tourette’s disorder. When tics occur in autism, they tend to occur in high-functioning autism.

The relationship between autism and schizophrenia also remains a matter of debate. At one time, the two disorders were believed to be related, but different ages of onset, patterns of symptomatology, and family histories have convinced many investigators that they are unrelated. Nevertheless, reports exist of schizophrenia developing in previously autistic individuals at a greater than chance rate, and a small number of researchers believe that autism is a particularly early and severe form of childhood schizophrenia.

Autism is also related to the presence of seizure disorders. About half of autistic individuals have clinical seizures and/or abnormal EEGs. Infancy and adolescence are high-risk periods for the appearance of seizures. All types of seizures occur; generalized tonicclonic are the most common.

Several specific medical conditions are associated with autism, including phenylketonuria, rubella embryopathy, herpes encephalitis, fragile X syndrome, and neurocutaneous disorders such as tuberous sclerosis. Some studies estimate that between one-eighth and one-fourth of autistic children have an associated medical condition, but it is not known whether these conditions play a causal role in the development of autistic symptoms. Of possible prenatal factors, maternal rubella is most commonly associated with autism, the prevalence for which is 100 times that for the general population. Other obstetrical factors are found more frequently in autistic children than in other populations, particularly midpregnancy maternal bleeding.

The fragile X genetic syndrome has been identified in an estimated 2 to 10% of the autistic population. Fragile X is a rare X-linked syndrome (most prevalent in boys) that involves intellectual impairment, attention deficits, and identifying physical features (prominent ears, long and narrow face, and macroorchidism). Within the fragile X population, it is estimated that 15 to 30% have autistic features, which are qualitatively distinct compared with those seen in the “typical” autistic child. Fragile X autistic children have been found to show perseverative speech as opposed to echolalia, and display active-but-odd social behaviors rather than aloofness. The specific route of pathology connecting fragile X to the expression of autistic symptoms is unknown.

A genetic basis for at least some forms of autism has been demonstrated by family studies. Approximately 3 % of families with an autistic child will produce another child with autism, a prevalence rate which equals 50 to 100 times that of the general population. In addition, the concordance rate for autism in monozygotic twins has been found to range from 40 to 96%. Further support for genetic involvement is found in studies of characteristics in families of autistic children. Siblings of autistic children may be more likely to show superiority in visuospatial over verbal abilities (analogous to the autistic profile), cognitive difficulties such as language disorder, and social disengagement. A few studies have found that some parents of autistic children may be more likely to show unusual social behaviors. The search for specific genetic markers for autism thus far has uncovered two prospects: a marker for a gene that regulates neuron development, and abnormalities of chromosome 15.

Taken together, studies suggest that at least a subset of autistic cases are attributable to genetic origin, either familial or mutational. The incidence of autistic symptoms in medical conditions that are not genetic, however, suggests that the PDD spectrum may represent a variety of etiologies ultimately affecting common brain systems.

Studies of neuroanatomical abnormalities in autistic patients have relied mainly upon postmortem neuropathology examinations and imaging techniques such as positron emission tomography (PET), computerized tomography (CT) and magnetic resonance imaging (MRI). They have generally focused on cortex, brainstem, limbic areas and cerebellum, and have found great variability in brain pathology. Gross cortical and ventricular abnormalities, for example, have been found in some cases and not others. Two structures of great interest are the amygdala and hippocampus, which are limbic structures involved in social/emotional behaviors and in memory. Abnormalities in limbic areas of the brain have been implicated in several studies, most notably in detailed postmortem examinations performed by Bauman and Kemper. These and other studies have also found abnormalities in the cerebellum, although the nature of these cerebellar abnormalities is not consistent across studies.

Findings from PET studies of regional cerebral blood flow have suggested diminished temporal lobe activity, and possible delayed frontal lobe maturation in autistic children. PET studies of regional glucose metabolism, which reflects brain energy utilization, have indicated abnormal patterns of regional activation. Several others have found global glucose hypermetabolism in autistic patients, which was thought to reflect inefficient processing. This feature, however, is not unique to autism.

Studies examining brain waves and oculomotor activity in REM sleep have suggested a developmental immaturity of brain mechanisms controlling sleep and an abnormally suppressed inhibition of sensory responding in autistic children. Brainstem dysfunction has been suggested for a subgroup of autistic individuals by findings of abnormalities in brainstem ERPs, although some studies have failed to support this. Many ERP studies offer support for abnormalities of attention and information processing in autism. High-functioning autistic subjects of varying ages usually show abnormally small amplitudes for a longer latency wave of the ERP thought to reflect the detection and classification of stimuli. Deficiencies in voluntary selective attention and orientation to novel stimuli also have been shown by diminished amplitudes in waves associated with these functions. Several other neurophysiological studies relying on cerebral electrical recording have indicated disruptions in normal hemispheric lateralization in autism.

Investigations of neurotransmitter function have produced inconsistent findings. The most replicated finding among autistic patients is that of elevated blood levels of the neurotransmitter serotonin, which occurs in an estimated one-third of this population, but also is observed in other patient populations. The reason for this elevation is not yet known. Treatments with the drug fenfluramine can greatly reduce levels of serotonin, and sometimes result in improvements in stereotypies and hyperactivity. Studies of the neurotransmitter dopamine are not in agreement, despite reported improvements in many symptoms after treatments with drugs that block dopamine. Overactivity of the opiate peptide beta-endorphin has been suggested by some studies, and supported by findings that opiate blockade improves autistic symptoms in some patients. The peptide oxytocin, shown to promote affiliation in animals, also may be reduced in autistic children. It has been suggested that excess opiates may render social contact unrewarding by producing a state of intrinsic contentment, and may also serve to dysregulate oxytocin.

If the diagnosis is made by a nonphysician and the child has not yet had a medical work-up relative to his/her autism, the following referrals should be considered. Assessment of hearing is important for successful language treatment; if behavior and cooperation are problematic, a brainstem evoked potential assessment should be done. Motor abnormalities are common; these should be assessed by a pediatric neurologist and a pediatric OT. Some physicians believe that a full medical work-up, including EEG, genetic and chromosomal testing, CT scan, and so on, is indicated; others feel that these investigations have a low yield unless there is a specific indication for their use.

Children and adolescents with autism or PDD should also have periodic neuropsychological evaluations. These will describe the child’s current level and profile of cognitive and language abilities, which will have implications for current education and for long-range goals. Periodic reevaluations to monitor the child’s progress will help to detect any deterioration that might signal negative medical or psychological events, and will document the success of treatment and education.

Thorough behavioral description is equally important. Included in the behavioral description should be a profile of the individual’s adaptive abilities and problem behaviors, including those central to the syndrome (such as social incapacity and resistance to change), those associated with the syndrome (such as self-injury and abnormal motor behaviors) and those sometimes found in association with it (such as hyperactivity, aggressiveness, and passivity). Analysis of antecedent conditions and consequences of the behaviors may clarify the role or function of the behavior for the particular autistic individual, and may dictate changes in stimulus conditions and reinforcements to ameliorate problem behaviors, as well as to foster positive behaviors.

Pharmacotherapy can be an effective tool in improving the behavior of some autistic children. Serotonergic agents are often used. Fenfluramine is sometimes prescribed, and has been found to reduce hyperactivity and stereotypies in some, but not all, studies. Clomipramine has been found to enhance social relatedness and decrease obsessional behavior and aggression. Fluoxetine and other serotonin reuptake inhibitors are also used with some autistic children.

Opiate antagonists may help to diminish selfinjury, and reduce social withdrawal and stereotypies. Self-injury and aggression have also reported to be improved by fluoxetine, clomipramine, buspirone and beta-blockers. Neuroleptics, such as haloperidol, and chlorpromazine, have also been found to reduce agitation, aggression and emotional lability, but most physicians are reluctant to use these agents in young children because they can produce movement disorders that may not regress even when the medication is stopped. Lithium is sometimes used to decrease aggressive, perseverative, and hyperactive behavior, and may be tried especially when a family history of bipolar disorder is present.

Other common pharmacological treatments are tricyclic antidepressants, which sometimes enhance language and social behavior. Stimulants have been administered for hyperactivity, but some autistic children experience a worsening of stereotypies or thought disorganization. Stimulants may work best in high-functioning autistic children with absent or mild stereotypies.

Natural treatments, such as dietary interventions or high-dose vitamin regimens have been advocated by some. Empirical support for the claims rests on a small number of studies, and mainstream physicians generally do not advocate their use.

Special education services and behavioral treatments are crucial in producing an optimal outcome. Recent work indicates that aggressive early intervention (as early as 15 to 18 months)can produce the best outcome. The leading proponent of intensive (ca. 40 hours/week) behavioral “drills” (O. I. Lovaas) has reported highly successful outcomes~almost half of the children being successfully included without support in a typical grade-school class. Others using his methods report results that do not replicate his degree of success, but that are nonetheless highly effective. These behavioral programs can be carried out in an educational setting or, especially for preschoolers, in the home. Other preschool programs emphasize a child-centered, developmentally oriented approach, which attempts to stimulate the child to move along a typical developmental trajectory. Any successful program must address each of the behavioral, social, language, and cognitive needs of the children specifically. To be effective, programs should be highly structured and should teach parents behavior management techniques that can be used in the home.

Individual differences in the children partly predict outcome: higher IQ, and the presence of communicative language by the age of 5 are positive prognostic signs.

The recent trend in special education has been strongly in favor of various forms of “mainstreaming, . . . . integration,” and “inclusion,” in which the child attends a class that is composed of a mixed group of special needs and typical peers, or of mainly typical peers, for part or all of the school day, sometimes with a one-on-one aide to facilitate participation. Although research has shown that exposure to normal peers can promote social behavior, the degree to which a severely autistic child can benefit from inclusion in a regular classroom remains to be demonstrated. It is clinically obvious that at least some autistic children need more intensive one-on-one teaching than is available in a mainstream setting before they can benefit from the teaching and social opportunities in a regular class.

In addition to special education or behavioral treatment, the autistic child often needs additional speech and language therapy, occupational therapy, and adapted physical education.

Clinicians must also help families to obtain other necessary services, such as respite care, extended day programs, and summer programs to prevent the behavioral and cognitive regression that can occur. They may also be able to suggest appropriate leisure activities, such as gymnastics, swimming, or play or social groups, that can provide constructive ways to spend after-school hours and opportunities for social interaction with typical children, and can promote self-esteem.

Prescription of therapies and services for the autistic individual must always include sensitivity to the often devastating effect of the disability on the family. Social support from other affected families, and keeping abreast of the latest developments in treatment and other research can help families manage their affected children and their own emotional reactions. The Autism Society of America publishes a regular newsletter with much information useful to parents; another good source of information for parents and professionals on recent developments in autism is Rimland’s newsletter Autism Research Review International.

Bibliography:

• Bauman, M. L., & Kemper, T. L. (Eds.). (1994). The neurobiology of autism. Baltimore: The Johns Hopkins University Press.
• Dawson, G. (Ed.). (1989). Autism: Nature, diagnosis and treatment. New York: Guilford Press.
• Gillberg, C., & Coleman, M. (1992). The biology of the autistic syndromes (2nd ed.). Clinics in Developmental Medicine, 126. London: Mac Keith Press.
• Rapin, I. (Ed.). (1996). Preschool children with inadequate communication: Developmental language disorder, autism, low IQ. Clinics in Developmental Medicine, 139. London: Mac Keith Press.
• Schopler, E., & Mesibov, G. (Eds.). (1995). Learning and cognition in autism. New York: Plenum Press.
• Schopler, E., Van-Bourgondien, M. E., & Bristol, M. (Eds.). (1993). Preschool issues in autism. New York: Plenum Press.

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What are the Environmental Factors that Cause Autism

This essay about environmental factors linked to autism spectrum disorder (ASD) discusses how various external elements may influence its development. It highlights the potential roles of air pollution, chemical exposures during pregnancy, nutritional deficiencies, and maternal health, particularly focusing on how these conditions could affect fetal brain development. The piece explores the complex interplay between genetics and environment, suggesting that ASD likely results from multiple contributing factors rather than a single cause. The summary of research supports the need for continued study into how environmental exposures combined with genetic predispositions can increase ASD risk, emphasizing the importance of understanding these relationships for prevention and support.

How it works

The ongoing debate about the origins of autism spectrum disorder (ASD) has expanded beyond genetic factors to include various environmental influences. Scientific research is increasingly focused on understanding how these environmental factors might contribute to the development of ASD, a condition characterized by challenges with social skills, repetitive behaviors, and speech and nonverbal communication. This post examines some of the most discussed environmental triggers that research suggests may play a role in the development of autism.

One of the primary environmental concerns is prenatal exposure to pollutants.

Studies have suggested that air pollutants like particulate matter, nitrogen dioxide, and other urban emissions can affect fetal brain development. The precise mechanisms are not fully understood, but it’s theorized that these pollutants may induce inflammation or alter the expression of genes during critical periods of neural development. For instance, research indicates that mothers living in high-pollution areas may be at a slightly higher risk of having a child with ASD, especially if exposure occurs during the third trimester of pregnancy.

Another significant environmental factor is the exposure to certain chemicals during pregnancy, including pesticides and phthalates. Pesticides, often used in agricultural settings, have been observed in several studies to correlate with an increased risk of autism, particularly when exposure occurs during key developmental windows. Phthalates, which are used to soften plastic and as solvents in cosmetics and other consumer products, have also been implicated. These chemicals are known endocrine disruptors, and while direct connections to autism are still being researched, their impact on developmental processes is of concern.

Nutritional factors during pregnancy also play a crucial role in fetal brain development. For instance, the availability of certain nutrients, such as folic acid, has been linked with reduced risk of neural development disorders, including ASD. Conversely, nutritional deficiencies, particularly in the early stages of pregnancy, could potentially increase ASD risk.

Additionally, the role of maternal health during pregnancy, including viral infections, has been scrutinized. Some research suggests that viral infections in the mother can disrupt the immune environment, potentially affecting the developing nervous system of the fetus. The evidence linking viral infections to autism is still emerging, but it underscores the importance of maternal health in prenatal development.

It’s essential to acknowledge that no single environmental factor explains all cases of autism, and the interplay between genetics and environment is complex. Most scientists agree that autism likely results from multiple factors rather than a single cause. This holistic view supports the necessity for continued research that considers genetic predispositions, environmental factors, and their interactions.

In conclusion, the environmental factors contributing to the development of autism spectrum disorders encompass a broad range of elements from air and chemical exposure to maternal health and nutrition. While the debate is far from settled, the evidence increasingly supports the notion that these environmental aspects, particularly when combined with genetic susceptibility, play a significant role in the development of ASD. Understanding these relationships is crucial for developing preventive measures and supporting the needs of affected families.

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Examining the Causes of Autism

Editor’s note:.

Autism is a broad, complex, and increasingly important brain disorder. New data from the Center for Disease Control and Prevention indicate that one in sixty-eight children is born with some degree of autism. Autism is also more common in males by a four to one ratio. Making it especially difficult to discuss in finite, conclusive terms is the fact that there is no biological test for autism; diagnosis is based on behavior, and the only verified treatment is intensive behavior therapy. Our author, one of the nation’s foremost researchers on autism, examines the prenatal factors that contribute to the disorder .

As an autism researcher, I often try to put myself in the shoes of parents who have just been told that their child has autism. More and more families in the United States and around the world are facing this difficult news. The families that I’ve seen go through this often respond emotionally at first. Some go through denial; others are sad or furious. But emotions soon give way to questions. What caused my child’s autism? Was I to blame? Which treatments will help? And what does the future hold?

Autism research has made tremendous progress over the last 20 years, but yet we still can’t provide definitive answers to most of these questions. I find the autism community to be proactive, combative, and opinionated. The complexity and ambiguity of autism has spawned myriad speculations about causes—many of which have little supportive evidence. It seems clear at this point, however, that when all is said and done, we will find that autism has multiple causes that occur in diverse combinations.

To begin with, many people struggle to understand the nature of a condition so wide ranging in its severity. Autism Spectrum Disorder (ASD) or autism is a behaviorally defined neurodevelopmental disorder characterized by 1) persistent deficits in social communication and interaction across multiple contexts, and 2) restricted, repetitive patterns of behavior, interests, or activities. Few would dispute that the causes of ASD include both genetic and environmental factors. Indeed, more than 100 genes are known to confer risk 1 , 2 and 1,000 or more may ultimately be identified. 3 A wide range of potential environmental challenges have also been associated with autism, although studies in this area lag behind genomics research. A short overview of data supports genetic and environmental contributions to ASD etiology. A focus on prenatal events will hopefully clarify that the cause of autism, in the vast majority of cases, occurs prenatally, even if behavioral signs first appear several years after birth.

Twin Studies

Strong evidence against the unfounded view that autism results from neglectful parenting came in 1977 from Folstein and Rutter and the first systematic, detailed study of twin pairs containing at least one child with autism. 4 In this study, 11 of the twin pairs were monozygotic (nearly identical genetics) and 10 were dizygotic (shared approximately half of their genome with each other). The major finding was that four of the monozygotic twin pairs were concordant (both had autism), whereas none of the dyzygotic twins were. Beyond autism, nine of the eleven monozygotic pairs were concordant for some form of cognitive impairment, compared to one of ten of the dyzygotic pairs.

The researchers concluded that autism and other neurodevelopmental disorders have a strong genetic component. But environmental factors must also contribute to autism etiology, they pointed out. For the 17 twin pairs that were discordant for autism—one child had a diagnosis and the other did not—the authors speculated that direct damage to the brain might have affected the diagnosed twin. They identified five features known to be associated with brain damage, such as severe hemolytic disease, a delay in breathing of at least five minutes after birth, and neonatal convulsions. In six of the pairs, one twin—always the autistic one—experienced one or more of these insults. Looking further, they found that one of an expanded list of “biological hazards” (e.g., discrepancies in birth weight, a pathologically narrow umbilical cord) occurred in the autistic twin in 6 of the 11 remaining discordant pairs and never in the non-autistic twin. The authors concluded that “some form of biological impairment, usually in the perinatal period, strongly predisposed to the development of autism.”

Since the Folstein and Rutter paper cited above, there have been a total of 13 twin studies focused on autism. All find genetic and environmental contributions to autism, although conclusions about the proportions of the two factors and interpretations have varied substantially. One research team, 5 for example, concluded that a large proportion of the variance in liability (55 percent for strictly defined autism and 58 percent under a broader definition) can be explained by shared environmental factors, whereas genetic heritability accounts for 37 percent. This somewhat surprising finding—that environmental factors contribute more substantially than genetics—has been challenged by a more recent, large-scale twin study, 6 which found that the largest contribution to autism liability comes from additive genetic effects. And, a recent meta-analysis 7 concludes that the causes of autism are due to strong genetic effects, and that shared environmental influences are seen only if autism is very narrowly defined. A brief synopsis of the history of autism twin studies 8 finds that concordance for monozygotic twins is roughly 45 percent, versus 16 percent for dizygotic twins.

The reason for this short review of autism twin studies is to emphasize that even the best evidence for both genetic and environmental etiologies of autism leads to inconsistent conclusions about their proportional contributions. Moreover, twin studies do not typically consider that the cause of autism may involve genetic and environmental factors working together (the so-called gene by environment effect); i.e., certain environmental exposures only cause autism in individuals with a particular genetic composition. The second point is that if autism had a completely genetic etiology, we would expect a much higher concordance rate in monozygotic twins; the actual rate may reflect, in part, that even monozygotic twins do not share an identical environment prenatally. 9 , 10 Therefore, one must seriously search for environmental factors that either alone, or in combination with genetic predisposition, can increase autism risk. What are these factors?

Maternal Infection

If twin studies provide the best evidence for a genetic basis of autism, then naturally occurring pathogen exposures offer the strongest evidence of environmental etiology. The best example is maternal rubella (German measles) infection during pregnancy. Before development and widespread dissemination of effective vaccines, major pandemics occurred every 10 to 30 years. 11 The last of these was from 1963 to 1965 and infected an estimated 10 percent of pregnant women, resulting in more than 13,000 fetal or early infant deaths; 20,000 infants born with major birth defects and 10,000 to 30,000 infants born with moderate to severe neurodevelopmental disorders. Stella Chess, a child psychiatrist at New York University, studied 243 children exposed to rubella during pregnancy 12 , 13 and found that the largest category of neurodevelopmental disorder was intellectual disability, which affected 37 percent of the sample. Nine of these children were also diagnosed with autism; another, without intellectual disability, had a possible diagnosis; and eight a partial syndrome of autism. These numbers would translate to an autism prevalence of 741 per 10,000 rubella-exposed children, just over seven percent. This is striking in comparison to published prevalence rates, at the time of the study, of two to three per 10,000 in the general population. Fortunately, rubella epidemics have ended due to widespread dissemination of the measles, mumps and rubella vaccines and the association of autism with other viral or bacterial infections is weaker than with rubella. 14

Collier et al 15 have pointed out that nearly 64 percent of women surveyed in the US have experienced an infection during their pregnancies. This obviously does not lead to autism or any other neurodevelopmental disorder in most cases.

Examining prenatal environmental factors is best conducted in very large cohorts of subjects that have excellent health care records. This can be done in Scandinavian countries with their nationalized health care systems, and in large health care providers in the US.

One such study, conducted in Denmark, found no association between maternal bacterial or viral infection during pregnancy and diagnosis of ASD in the offspring, 16 although viral infection during the first trimester, or admission to the hospital due to infection during the second trimester were associated with the diagnosis. In a more recent study 17 Atladottir and colleagues found little evidence, overall, that common infectious diseases or fevers (lasting more than seven days) during pregnancy increased the risk of autism—noting, however, that influenza increased the risk of having an autistic child twofold. Use of antibiotics also increased risk. The link between influenza exposure during fetal life and increased risk for autism is in line with a series of animal studies 18 , 19 suggesting that the influenza virus activates the maternal immune system, which may be harmful to fetal brain development. But the Danish researchers seem to downplay even their statistically significant findings, suggesting that their results do not indicate that either mild infection or the use of antibiotics represent strong risk factors for autism.

A parallel set of studies has been carried out by Zerbo and colleagues in California. The first, 20 based on 1,122 children, found no association between maternal influenza and ASD but (in contrast to Atladottir et al), the occurrence of maternal fever did increase risk. A second study 21 of 2,482 children (407 with ASD) found that mothers of children with ASD were diagnosed with viral infections during pregnancy no more often than mothers of non-autistic children. Maternal bacterial infections during the second trimester and the third trimester, however, were associated with a twofold increase in ASD risk, and two or more infections diagnosed in the third trimester with even higher risk, again suggesting a link with more severe infection during pregnancy. The most recent study, 22 based on a large cohort of children (196,929) born between 2000 and 2010, found that neither maternal influenza infection during pregnancy nor influenza vaccination were associated with increased risk for ASD.

In conclusion: Some infections during pregnancy, such as German measles, clearly increase the risk of ASD. However, there seems relatively little evidence that today’s widely experienced infectious illnesses, such as influenza, during pregnancy substantially increase the risk of ASD. Perhaps the signal is weak because of gene by environment effects [as seems to be the case for different strains of mice 23 , 24 ]. If so, evidence will need to come from studies that combine large scale epidemiology with sophisticated genomic analyses.

Maternal Antibodies

Autoimmune diseases (in which immune cells erroneously identify cells in the body as foreign and attack them) mediated by circulating antibodies currently affect as much as nine percent of the world’s population, 25 and the notion that autoimmunity may be associated with neurological and psychiatric disorders goes back to the 1930s. Reviewing this contentious area of research, Goldsmith and Rogers 26 conclude that the literature, though conflicting, “contains a large amount of circumstantial, but not conclusive, evidence for immune dysfunction in patients with schizophrenia.” Interestingly, an auto-immune disorder with antibodies directed at the NMDA receptor causes an encephalopathy, which in its early stages can be indistinguishable from schizophrenia. 27

Precedents for antibody-related CNS disorders include Rasmussen encephalitis, stiff-person syndrome, neuromyelitis optica, post streptococcal movement disorders (Sydenham’s chorea and PANDAS), and systemic lupus erythematosus. 28 Judy Van de Water, of UC Davis, the main proponent of the idea that circulating antibodies may cause some forms of autism, first reported in 2008 that 12 percent of mothers of children with ASD have unusual antibodies directed at fetal brain proteins. 29 Based on more specific assays for these antibodies, she has since proposed that Maternal Antibody-Related (MAR) causes may be associated with as many as 22 percent of autism cases, suggesting that this may be a preventable form of ASD. 30 This area of research is exciting because it suggests potential therapeutic targets. Although many questions remain (e.g., how antibodies would enter the fetal brain, what neurodevelopmental processes they may alter), it is entirely possible that circulating antibodies represent prenatal environmental risk factors for ASD.

Efforts to understand the increased prevalence of autism spectrum disorder have led some to wonder whether the use of various drugs during pregnancy might be partly responsible. Historically, a strong case could be made for an association between autism and thalidomide, a potent sedative that was used (for several years around 1960) during pregnancy for the relief of nausea. A study of 100 adult Swedish patients whose mothers had taken thalidomide while pregnant 31 found that at least four had clear autistic characteristics. This was the first evidence that a drug ingested during pregnancy could substantially increase autism risk. More recently, concerns have been raised about valproic acid and serotonin reuptake inhibitors.

Valproic acid, an approved drug since the early 1960s, is primarily prescribed for epilepsy and seizure control, but also used for ailments ranging from migraine headaches to bipolar disorder. Both animal and human epidemiological studies have raised concerns that valproic acid is a teratogen. The largest epidemiological study to date 32 tracked 415 children, 201 of whom were born to mothers who took antiepileptic medication during their pregnancies. Nearly 7.5 percent of the children of the treated women had a neurodevelopmental disorder, primarily some form of autism, versus 1.9 percent in the non-epileptic women.

A recent concern has been the use of serotonin reuptake inhibitors (SSRIs) for the treatment of depression during pregnancy. Serotonin is an important brain neurotransmitter that plays a significant role in functions ranging from sleep to mood to appetite, and whose dysregulation during early fetal life can have serious negative consequences for brain development. 33 As the name implies, SSRIs, which have been in use since the late 1980s, delay the reuptake of serotonin from the synaptic cleft into the presynaptic terminal and thus enhances its effect on the postsynaptic receptors. A recent review and meta-analysis of six case-control studies and four cohort studies concluded that SSRI use during pregnancy 34 was significantly associated with increased risk of ASD in offspring.

The effect was most prominent with use of the drugs during the first and second trimesters of pregnancy. Interestingly, the researchers found that preconceptual exposure to SSRIs was also associated with increased ASD risk—as was the use of non-SSRI antidepressants. They note that a large cohort study found that, while ASD rates in the SSRI-exposed group were significantly higher than in the unexposed group, the rates in the SSRI-exposed group did not significantly differ from those among mothers with unmedicated psychiatric disorder and those who had discontinued SSRIs. It currently appears impossible to disentangle the deleterious effect of SSRIs from the fact of a maternal condition that necessitates the drug. Many authors also comment on the potentially worse effect on pregnancies of untreated maternal depression.

In sum, a brief review of the literature indicates that ingesting some drugs during pregnancy increases the risk of ASD, suggesting the need for more careful evaluation of drug safety during fetal development prior to widespread medical use.

Environmental Toxicants

Beyond viral and bacterial pathogens and medically prescribed drugs, researchers have begun investigating environmental toxicants. These range from automobile-produced air pollution to cigarette smoke to heavy metals and pesticides. 35 , 36 Small increases in autism risk have been reported if, for example, a family lives closer to a freeway or to an agricultural area during pregnancy. The field of autism environmental epidemiology is still in its infancy and techniques to comprehensively establish a prenatal “exposome” (i.e., all environmental factors affecting a fetus during pregnancy) are still under development. That said, given the unlikelihood that all autism will be explained by genetic factors, the determination of environmental causes, some of which might be avoided or minimized, may have far greater translational impact than the much better funded genetic studies. Strategies for exploring gene-by-environment interactions need to be enhanced with haste.

Postnatal Factors

Since autism is a neurological disorder that undoubtedly reflects altered brain function, it is possible that the insult to the brain occurs after birth. There is currently very little evidence for this. One historical concern was that vaccines, such as the measles, mumps, and rubella (MMR) vaccine, administered initially when the child is about one-year old, might transform a healthy child into one with autism. This fear was fueled by regressive onset in some cases—a child seems fine for the first year or so, then loses social and language function and regresses into a classical autistic syndrome. But we have found that even in children who demonstrate this regressive form of autism, brain changes begin by four to six months, long before behavior changes. 37 Moreover, many large-scale epidemiologic studies have unequivocally demonstrated no link between MMR administration and the risk of ASD (summarized in 38), the same conclusion that the US National Academy of Sciences reached in a thorough review carried out in 2011. 39

The only other postnatal experience that has been linked to the onset of ASD is profound social isolation in institution-reared children, such as those in the Romanian orphanage system. 40 Rutter and colleagues 41 found that nearly 10 percent of children raised in Romanian orphanages and adopted by British families showed some features of autism. These children were very poorly treated in the orphanage (most were underweight and had intellectual disability and various medical problems). While fully qualifying for an autism diagnosis at age 4, they showed substantial improvement and less severe autism symptoms by age 6. Is this truly autism? The authors conclude: “The characteristics of these children with autistic features, although phenomenologically similar in some respects to those found in “ordinary” autism, differed sharply in the marked improvement evident between 4 and 6 years of age and in the degree of social interest... The quasi-autistic pattern seemed to be associated with a prolonged experience of perceptual and experiential privation, with a lack of opportunity to develop attachment relationships, and with cognitive impairment.”

This sad epoch demonstrates both the potential for severely abnormal rearing practices to influence brain regions that are affected by typical causes of autism, and the resilience of the brain in compensating and restoring once the individual is placed in a more normal environment. But it does not provide evidence for the postnatal genesis of autism.

The research picture regarding the causes for Autism Spectrum Disorder remains complex, although there is certainly a very strong genetic component. While there are some genes, such as CHD8, the mutation of which almost always cause autism in a very low percentage of cases 42 most mutations seem to confer small increases in risk. Similarly, while some environmental factors, such as rubella infection or fetal exposure to valproic acid, have been highly associated with autism risk, the increase in risk associated with others, such as living close to a highway, is small. It is very likely that the answer to what causes autism will not reside solely in genetics or in environment but in a combination of the two. Whatever factors go into the mix, they most likely have their effect during fetal life: a person with autism is born with autism.

David G. Amaral , Ph.D., is a Distinguished Professor in the Department of Psychiatry and Behavioral Sciences at UC Davis. He is also the Beneto Foundation Chair and Research Director of the MIND Institute, which is dedicated to studying autism and other neurodevelopmental disorders. As research director, he coordinates a multidisciplinary analysis of children with autism called the Autism Phenome Project to define clinically significant subtypes of autism. More recently, Amaral has become Director of Autism BrainNet, a collaborative effort to solicit postmortem brain tissue to facilitate autism research. In April of 2015, Amaral became editor-in-chief of Autism Research , the journal of the International Society for Autism Research. In 2016, he was appointed to the Interagency Autism Coordinating Committee by the Secretary of Health and Human Services. Amaral received a joint Ph.D. in neuroscience and psychology from the University of Rochester and conducted postdoctoral research at the Department of Anatomy and Neurobiology at Washington University. He also conducted research at the Salk Institute for Biological Studies and served as an adjunct professor in the Department of Psychiatry at UC San Diego.