Relevant bibliographies by topics / Autism spectrum disorders – Classification

Academic literature on the topic 'Autism spectrum disorders – Classification'

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Published: 10 December 2022
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Journal articles on the topic "Autism spectrum disorders – Classification"

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Baker, Emma K., Amanda L. Richdale, and Agnes Hazi. "Employment status is related to sleep problems in adults with autism spectrum disorder and no comorbid intellectual impairment." Autism 23, no. 2 (February 18, 2018): 531–36. http://dx.doi.org/10.1177/1362361317745857.

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Both sleep problems and unemployment are common in adults with autism spectrum disorder; however, little research has explored this relationship in this population. This study aimed to explore factors that may be associated with the presence of an International Classification of Sleep Disorders–Third Edition defined sleep disorder in adults with autism spectrum disorder (IQ > 80). A total of 36 adults with autism spectrum disorder and 36 controls were included in the study. Participants completed a 14-day actigraphy assessment and questionnaire battery. Overall, 20 adults with autism spectrum disorder met the International Classification of Sleep Disorders–Third Edition criteria for insomnia and/or a circadian rhythm sleep-wake disorder, while only 4 controls met criteria for these disorders. Adults with autism spectrum disorder and an International Classification of Sleep Disorders–Third Edition sleep disorder had higher scores on the Pittsburgh Sleep Quality Index and were more likely to be unemployed compared to adults with autism spectrum disorder and no sleep disorder. The findings demonstrate, for the first time, that sleep problems are associated with unemployment in adults with autism spectrum disorder. Further research exploring the direction of this effect is required; sleep problems that have developed during adolescence make attainment of employment for those with autism spectrum disorder difficult, or unemployment results in less restrictions required for optimal and appropriate sleep timing.
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Baird, Gillian, and Courtenay Frazier Norbury. "Social (pragmatic) communication disorders and autism spectrum disorder." Archives of Disease in Childhood 101, no. 8 (December 23, 2015): 745–51. http://dx.doi.org/10.1136/archdischild-2014-306944.

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Changes have been made to the diagnostic criteria for autism spectrum disorder (ASD) in the recent revision of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), and similar changes are likely in the WHO International Classification of Diseases (ICD-11) due in 2017. In light of these changes, a new clinical disorder, social (pragmatic) communication disorder (SPCD), was added to the neurodevelopmental disorders section of DSM-5. This article describes the key features of ASD, SPCD and the draft ICD-11 approach to pragmatic language impairment, highlighting points of overlap between the disorders and criteria for differential diagnosis.
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Kyriakopoulos, M. "Psychosis and Autism Spectrum Disorders." European Psychiatry 41, S1 (April 2017): S45. http://dx.doi.org/10.1016/j.eurpsy.2017.01.198.

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Autism spectrum disorders (ASD) and schizophrenia were separated into different diagnostic categories in the late 1970's (DSM-III) having previously been considered as related diagnostic entities. Since then, several lines of evidence have indicated that these disorders show clinical and cognitive overlaps as well as some common neurobiological characteristics. Furthermore, there is a group of patients presenting with ASD and psychotic experiences who pose particular diagnostic and management challenges and may represent a subgroup of ASD more closely linked to psychosis. Evidence from a study of the first empirically derived classification of children with ASD in relation to psychosis based on three underlying symptom dimensions, anxiety, social deficits and thought disorder, will be presented. Further phenomenological, genetic and neuroimaging research on the clinical boundaries and overlapping pathophysiology of ASD and psychosis may help better define their relationship and lead to more effective interventions. Understanding this relationship will also provide a framework of working with patients with mixed clinical presentations.Disclosure of interestThe author declares that he has no competing interest.
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Woodbury-Smith, Marc. "Changing diagnostic practices: autism spectrum disorder." Advances in Psychiatric Treatment 20, no. 1 (January 2014): 23–26. http://dx.doi.org/10.1192/apt.bp.112.010801.

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SummaryIn medical practice it is crucial that symptom descriptions are as precise and objective as possible, which psychiatry attempts to achieve through its psychopathological lexicon. The term ‘autism spectrum disorder’ has now entered psychiatric nosology, but the symptom definitions on which it is based are not robust, potentially making reliable and valid diagnoses a problem. This is further compounded by the spectral nature of the disorder and its lack of clear diagnostic boundaries. To overcome this, there is a need for a psychopathological lexicon of 'social cognition’ and a classification system that splits rather than lumps disorders with core difficulties in social interaction.
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Popovic-Deusic, Smiljka, Milica Pejovic-Milovancevic, Saveta Draganic-Gajic, Olivera Aleksic-Hil, and Dusica Lecic-Tosevski. "Psychotic spectrum disorders in childhood." Srpski arhiv za celokupno lekarstvo 136, no. 9-10 (2008): 555–58. http://dx.doi.org/10.2298/sarh0810555p.

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For a long time, there was a strong belief of existing continuity between childhood-onset psychoses and adult psychoses. Important moment in understanding psychotic presentations during infancy and childhood is Kanner's description of early infantile autism. Later studies of Rutter and Kolvin, as well as new classification systems, have delineated pervasive developmental disorders from all other psychotic disorders in childhood. But clinical experience is showing that in spite of existence of the group of pervasive developmental disorders with subgroups within it and necessary diagnostic criteria there are children with pervasive symptoms, who are not fulfilling all necessary diagnostic criteria for pervasive developmental disorder. Therefore, in this paper we are discussing and pointing at psychotic spectrum presentations in children, which have not the right place in any existing classification system (ICD-10, DSM-IV).
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Stankovic, Miodrag, Aneta Lakic, and Neda Ilic. "Autism and autistic spectrum disorders in the context of new DSM-V classification, and clinical and epidemiological data." Srpski arhiv za celokupno lekarstvo 140, no. 3-4 (2012): 236–43. http://dx.doi.org/10.2298/sarh1204236s.

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Autism is one of disorders from the autism spectrum, besides Asperger syndrome, atypical autism and pervasive developmental disorder not otherwise specified. They are classified as mental disorders as being manifested by a wide range of cognitive, emotional and neurobehavioural abnormalities. Key categorical characteristics of the disorder are clear impairments of the development of the child?s socialisation, understanding and production of verbal and non-verbal communication and restricted and repetitive patterns of behaviour. Demarcation boundaries are not clear, neither within the very group of the disorders from the autistic spectrum, nor with respect to the autistic behavioural features in the general population. For this reason, the term spectrum points out the significance of the dimensional assessment of autistic disorders, which will most likely be the basis of the new diagnostic classification of the disorders belonging to the current group of pervasive developmental disorders in the new DSM-V classification. The understanding, as well as the prevalence of the autistic spectrum disorders has changed drastically in the last four decades. From the previous 4 per 10,000 people, today?s prevalence estimates range from 0.6 to around 1%, and the increase of prevalence cannot be explained solely by better recognition on the part of experts and parents or by wider diagnostic criteria. The general conclusion is that the autistic spectrum disorders are no longer rare conditions and that the approach aimed at acknowledging the warning that this is an urgent public health problem is completely justified.
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Kuznetsova, L., M. Brychuk, L. Pogasiy, and K. Zhizhkun. "Features of the influence of playing activities on preschool children with a spectrum of autistic disorders in the process of adaptive physical education." Scientific Journal of National Pedagogical Dragomanov University. Series 15. Scientific and pedagogical problems of physical culture (physical culture and sports), no. 1(121) (January 29, 2020): 53–59. http://dx.doi.org/10.31392/npu-nc.series15.2019.1(121)20.10.

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The article deals with the peculiarities of the mental development of children with autism spectrum disorders, their psychophysical abilities, the formation of cognitive functions, the means of communication, the development of the emotional-volitional sphere, behavior in society. Features of correctional and pedagogical work with autistic children at the present stage are considered. A detailed definition of the definition of "autism", a modern classification of autism, the main features of autistic disorders in all its clinical variants are presented. Statistics on the incidence of autism in the world are provided. The characteristics and peculiarities of psychomotor development in preschool children with autism spectrum disorders and the logic of psychomotor development, the features of psychomotor development, the offered educational and correction tasks are presented. Importance and place of mobile games as the main means of adaptive physical education of preschool children with this nosology have been determined. Mobility games are distributed in the focus on the development of motor skills of preschool children with autism spectrum disorders. A modified classification of mobile games, entertainment, and entertainment that can be used in adaptive physical education and extracurricular forms of preschool-age children with autism spectrum disorders is presented.
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Vohra, Rini, Suresh Madhavan, and Usha Sambamoorthi. "Comorbidity prevalence, healthcare utilization, and expenditures of Medicaid enrolled adults with autism spectrum disorders." Autism 21, no. 8 (October 20, 2016): 995–1009. http://dx.doi.org/10.1177/1362361316665222.

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A retrospective data analysis using 2000–2008 three state Medicaid Analytic eXtract was conducted to examine the prevalence and association of comorbidities (psychiatric and non-psychiatric) with healthcare utilization and expenditures of fee-for-service enrolled adults (22–64 years) with and without autism spectrum disorders (International Classification of Diseases, Ninth Revision–clinical modification code: 299.xx). Autism spectrum disorder cases were 1:3 matched to no autism spectrum disorder controls by age, gender, and race using propensity scores. Study outcomes were all-cause healthcare utilization (outpatient office visits, inpatient hospitalizations, emergency room, and prescription drug use) and associated healthcare expenditures. Bivariate analyses (chi-square tests and t-tests), multinomial logistic regressions (healthcare utilization), and generalized linear models with gamma distribution (expenditures) were used. Adults with autism spectrum disorders (n = 1772) had significantly higher rates of psychiatric comorbidity (81%), epilepsy (22%), infections (22%), skin disorders (21%), and hearing impairments (18%). Adults with autism spectrum disorders had higher mean annual outpatient office visits (32ASD vs 8noASD) and prescription drug use claims (51ASD vs 24noASD) as well as higher mean annual outpatient office visits (US$4375ASD vs US$824noASD), emergency room (US$15,929ASD vs US$2598noASD), prescription drug use (US$6067ASD vs US$3144noASD), and total expenditures (US$13,700ASD vs US$8560noASD). The presence of a psychiatric and a non-psychiatric comorbidity among adults with autism spectrum disorders increased the annual total expenditures by US$4952 and US$5084, respectively.
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Bachmann, Christian J., Bettina Gerste, and Falk Hoffmann. "Diagnoses of autism spectrum disorders in Germany: Time trends in administrative prevalence and diagnostic stability." Autism 22, no. 3 (December 20, 2016): 283–90. http://dx.doi.org/10.1177/1362361316673977.

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For Germany, no data on trends in autism spectrum disorder diagnoses are available. The primary aim of this study was to establish the time trends in the administrative prevalence of autism spectrum disorder diagnoses. The second aim was to assess the stability of autism spectrum disorder diagnoses over time. We analysed administrative outpatient data (2006–2012) from a nationwide health insurance fund and calculated the prevalence of autism spectrum disorder diagnoses for each year, stratified by age and sex. Additionally, we studied a cohort with a first-time diagnosis of autism spectrum disorder in 2007 through 2012, investigating the percentage of retained autism spectrum disorder diagnoses. From 2006 to 2012, the prevalence of autism spectrum disorder diagnoses in 0- to 24-year-olds increased from 0.22% to 0.38%. In insurees with a first-time autism spectrum disorder diagnosis in 2007, this diagnosis was carried on in all years through 2012 in 33.0% (The International Classification of Diseases, Tenth Revision diagnoses: F84.0/F84.1/F84.5) and 11.2% (F84.8/F84.9), respectively. In Germany, like in other countries, there has been an increase in the administrative prevalence of autism spectrum disorder diagnoses. Yet, prevalences are still lower than in some other Western countries. The marked percentage of autism spectrum disorder diagnoses which were not retained could indicate a significant portion of autism spectrum disorder misdiagnoses, which might contribute to rising autism spectrum disorder prevalences.
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Tkachuk, E. A. "Using the syndrome approach to the diagnosis of autism in children." Meditsinskiy sovet = Medical Council, no. 12 (July 12, 2022): 200–204. http://dx.doi.org/10.21518/2079-701x-2022-16-12-200-204.

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Autism spectrum disorders in children is a very urgent problem. Today, there is an increase in the number of children suffering from autism spectrum disorders around the world. The relevance of early diagnosis of autism spectrum disorders for timely treatment and correction is high. However, the diagnostic criteria for ASD require an assessment of certain psychomotor skills in a child, which mature much later than necessary for a timely diagnosis, so the diagnosis is made by 5-7 years. New clinical guidelines suggest that primary screening should be performed by a pediatrician. However, practice shows that in reality this does not happen. The reason for this is not only a contradiction in the diagnostic criteria, but also the current ICD-10 classification, which does not reflect the pathogenetic processes in the child's body. Despite this, new clinical guidelines recognize the role of genetic disorders and epigenetic factors in the development of autism spectrum disorders. In this regard, it is proposed to consider autism spectrum disorders as a syndrome that accompanies various genetic disorders, both chromosomal and monogenic anomalies. This approach enables early diagnosis of autism spectrum disorders, as well as the development of treatment and correction methods based on pathogenetic disorders. Currently, more than 100 genes associated with autism are known. In the above studies, it was noted that the polygenic nature of disorders in autism does not allow focusing on phenotypic features. Probably, this is the reason for the difficulties in diagnosing autism by external signs and the low efficiency of the currently known screening methods for diagnosing autism spectrum disorders. Therefore, autism spectrum disorders must be considered from the point of view of pathogenetic changes in the child's body, usually of a hereditary nature, which will allow us to offer effective methods of diagnosis, treatment and correction.
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Dissertations / Theses on the topic "Autism spectrum disorders – Classification"

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Urbach, Jonathan Aaron. "Autism or autisms? The clinical manifestations and classification of autism spectrum disorders." Thesis, Boston University, 2012. https://hdl.handle.net/2144/12660.

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Thesis (M.A.)--Boston University PLEASE NOTE: Boston University Libraries did not receive an Authorization To Manage form for this thesis or dissertation. It is therefore not openly accessible, though it may be available by request. If you are the author or principal advisor of this work and would like to request open access for it, please contact us at open-help@bu.edu. Thank you.
Individuals with Autism Spectrum Disorders (autistic disorder, Asperger's disorder, childhood disintegrative disorder, and pervasive developmental disorder - not otherwise specified) are a very heterogeneous group. The disorders on the spectrum are behaviorally defined (according to the American Psychiatric Association's Diagnostic and Statistical Manual-IV, Text Revision) with specific behaviors falling within categories. For autistic disorder, the categories reflect the core deficits of social interaction, communication, and restricted or repetitive behaviors or interests ("CDC- Autism Spectrum Disorder (ASDs)- NCBDDD," n.d.). The behaviors that fall within these categories have been carefully researched and described in order to allow for uniformity in diagnosis and the discussion of causality in research. The diagnosis of Autism Spectrum Disorders (ASD) relies on established thresholds within these categories, with the clinician responsible for characterizing and counting the number of behaviors that are present and in which category they fall. Other associated symptoms (low IQ, language impairments, epilepsy, and others) are often present, and while not diagnostic of ASD, can contribute much to the phenotypic heterogeneity. As a result, individuals who exhibit different behavioral symptoms might be diagnostically indistinguishable. This thesis is intended to be a critical review of the current state of autism research. In the different sections (Phenotype, Epidemiology, Genetics, Cellular/Molecular Mechanisms, Neural Circuits, and Therapeutics), the discussion is focused on what has been firmly established in the field. In many cases, what is known about autism leads to a better understanding of how to subdivide the population. Genetics, for instance, can divide autism into syndromic or idiopathic cases (those associated with a comorbid genetic condition such as Rett's Syndrome or Fragile X and those that have no apparent genetic etiology, respectively). Epidemiology research has shown that a host of chemical, social, and emotional exposures are correlated with varied risks of developing autism (leading to possible distinctions between autism caused by teratogens or autism caused by other mechanisms). Molecular research has revealed a subset of autistic individuals who have various causes of synaptic dysfunction, and within this group there have been certain proteins implicated, offering additional points of differentiation between individuals. The study of therapeutics, however, has largely left the population as a whole in research. As a result, the comparisons (based on mean differences between controls and ASD subjects) are not fine-grained enough to show benefits within certain subgroups of ASD individuals. What the research shows is that the autism spectrum can (and should) be subdivided. Establishing multiple well-defined "autisms" allows for much more targeted research. The first step is creating clear boundaries to the spectrum, and the proposed revisions to the Fifth Edition of the Diagnostic and Statistical Manual is intended to do just this (collapsing the spectrum disorders into one diagnosis with a streamlined set of common behavioral features). The answer to the "autism or autisms?" questions is both: once the spectrum is clearly distinguished from the non-spectrum, research will establish the points at which autism should be subdivided. Homogeneous subgroups (however they are defined) will allow for more robust study of the underlying pathophysiology and possible treatment options.
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Andrews, Derek Sayre. "Novel applications of surface based morphometry and pattern classification in autism spectrum disorders." Thesis, King's College London (University of London), 2018. https://kclpure.kcl.ac.uk/portal/en/theses/novel-applications-of-surface-based-morphometry-and-pattern-classification-in-autism-spectrum-disorders(e4ce8d68-12ee-42a6-928c-d6393c9489bd).html.

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Autism spectrum disorder (ASD) is a lifelong, behaviorally defined neurodevelopmental condition that is characterized by deficits in social communication, interaction, and repetitive behaviors. These behavioral symptoms are associated with atypical brain structure, function, and connectivity. The studies that comprise this thesis employed structural magnetic resonance imaging (MRI) to address aims in three areas of ASD research. First, we examined a novel neuroimaging feature based on signal intensity contrast between grey and white matter to quantify atypical microstructure at the greywhite matter boundary in ASD. We found reduced tissue contrast at the grey-white matter boundary among adults with ASD when compared typically developing (TD) controls. This result indicates that measures of tissue contrast may serve as an in vivo proxy measure of atypical cortical microstructure that has previously been reported in histological studies. Second, we trained multivariate pattern recognition models to identify individuals with ASD based on measures of cortical morphometry, and examined the predictive value of these models in a representative clinical sample. We demonstrated that these models have modest ability to distinguish cases from controls in the research setting. Only one model that was based on measures of grey-white matter tissue contrast identified individuals with and without ASD diagnoses at high overall accuracy (81%) in the clinical setting. However, this model did not provide significant accuracies above chance in the research setting, and therefore these results should be considered as preliminary and suggestive only. Third, we established normative models of phenotypic diversity in brain structure associated with biological sex in a sample of TD males and females which was subsequently applied to males and females with ASD. Across different morphometric features, females with ASD displayed a significant shift towards a more male-typical presentation of the brain. Sample probabilities for ASD also increased with predicted probabilities for male-typical brain phenotypes across both sexes. These studies highlight advances in the field of structural neuroimaging research in areas of feature development, clinical translation, and efforts to understand the modulating role of biological sex on the prevalence of ASD. Taken together, the work presented within this thesis thus constitutes an important step toward establishing translational imaging tools for ASD that may one day be applied in the clinical setting.
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Fung, Kar-yan Cecilia, and 馮嘉欣. "Use of dysmorphology for subgroup classification on autism spectrum disorder in Chinese Children." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2010. http://hub.hku.hk/bib/B45160697.

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Snow, Anne V. "Specifying the Boundaries of Pervasive Developmental Disorder - Not Otherwise Specified: Comparisons to Autism and other Developmental Disabilities on Parent-Reported Autism Symptoms and Adaptive and Behavior Problems." Columbus, Ohio : Ohio State University, 2009. http://rave.ohiolink.edu/etdc/view.cgi?acc%5Fnum=osu1245250357.

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Wong, Tsz-yan Polly, and 黃芷欣. "Pilot study for subgroup classification for autism spectrum disorder based on dysmorphology and physical measurements in Chinese children." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2012. http://hub.hku.hk/bib/B4786932X.

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Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder affecting individuals along a continuum of severity in communication, social interaction and behaviour. The impact of ASD significantly varies amongst individuals, and the cause of ASD can originate broadly between genetic and environmental factors. Previous ASD researches indicate that early identification combined with a targeted treatment plan involving behavioural interventions and multidisciplinary therapies can provide substantial improvement for ASD patients. Currently there is no cure for ASD, and the clinical variability and uncertainty of the disorder still remains. Hence, the search to unravel heterogeneity within ASD by subgroup classification may provide clinicians with a better understanding of ASD and to work towards a more definitive course of action. In this study, a norm of physical measurements including height, weight, head circumference, ear length, outer and inner canthi, interpupillary distance, philtrum, hand and foot length was collected from 658 Typical Developing (TD) Chinese children aged 1 to 7 years (mean age of 4.19 years). The norm collected was compared against 80 ASD Chinese children aged 1 to 12 years (mean age of 4.36 years). We then further attempted to find subgroups within ASD based on identifying physical abnormalities; individuals were classified as (non)dysmorphic with the Autism Dysmorphology Measure (ADM) from physical examinations of 12 body regions. Our results show that there were significant differences between ASD and TD children for measurements in: head circumference (p=0.009), outer (p=0.021) and inner (p=0.021) canthus, philtrum length (p=0.003), right (p=0.023) and left (p=0.20) foot length. Within the 80 ASD patients, 37(46%) were classified as dysmorphic (p=0.00). This study attempts to identify subgroups within ASD based on physical measurements and dysmorphology examinations. The information from this study seeks to benefit ASD community by identifying possible subtypes of ASD in Chinese population; in seek for a more definitive diagnosis, referral and treatment plan.
published_or_final_version
Paediatrics and Adolescent Medicine
Master
Master of Philosophy
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Atchison, Abigail. "Classifying Challenging Behaviors in Autism Spectrum Disorder with Neural Document Embeddings." Chapman University Digital Commons, 2019. https://digitalcommons.chapman.edu/cads_theses/1.

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The understanding and treatment of challenging behaviors in individuals with Autism Spectrum Disorder is paramount to enabling the success of behavioral therapy; an essential step in this process being the labeling of challenging behaviors demonstrated in therapy sessions. These manifestations differ across individuals and within individuals over time and thus, the appropriate classification of a challenging behavior when considering purely qualitative factors can be unclear. In this thesis we seek to add quantitative depth to this otherwise qualitative task of challenging behavior classification. We do so through the application of natural language processing techniques to behavioral descriptions extracted from the CARD Skills dataset. Specifically, we construct 3 sets of 50-dimensional document embeddings to represent the 1,917 recorded instances of challenging behaviors demonstrated in Applied Behavior Analysis therapy. These embeddings are learned through three processes: a TF-IDF weighted sum of Word2Vec embeddings, Doc2Vec embeddings which use hierarchical softmax as an output layer, and Doc2Vec which optimizes the original Doc2Vec architecture through Negative Sampling. Once created, these embeddings are initially used as input to a Support Vector Machine classifier to demonstrate the success of binary classification within this problem set. This preliminary exploration achieves promising classification accuracies ranging from 78.2-100% and establishes the separability of challenging behaviors given their neural embeddings. We next construct a multi-class classification model via a Gaussian Process Classifier fitted with Laplace approximation. This classification model, trained on an 80/20 stratified split of the seven most frequently occurring behaviors in the dataset, produces an accuracy of 82.7%. Through this exploration we demonstrate that the semantic queues derived from the language of challenging behavior descriptions, modeled using natural language processing techniques, can be successfully leveraged in classification architectures. This study represents the first of its kind, providing a proof of concept for the application of machine learning to the observations of challenging behaviors demonstrated in ASD with the ultimate goal of improving the efficacy of the behavioral treatments which intrinsically rely on the accurate identification of these behaviors.
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Garside, Kristine Dianne Cantin. "Behavioral Monitoring to Identify Self-Injurious Behavior among Children with Autism Spectrum Disorder." Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/88533.

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Self-injurious behavior (SIB) is one of the most dangerous behavioral responses among individuals with autism spectrum disorder (ASD), often leading to injury and hospitalization. There is an ongoing need to measure the triggers of SIB to inform management and prevention. These triggers are determined traditionally through clinical observations of the child with SIB, often involving a functional assessment (FA), which is methodologically documenting responses to stimuli (e.g., environmental or social) and recording episodes of SIB. While FA has been a "gold standard" for many years, it is costly, tedious, and often artificial (e.g., in controlled environments). If performed in a naturalistic environment, such as the school or home, caregivers are responsible for tracking behaviors. FA in naturalistic environments relies on caregiver and patient compliance, such as responding to prompts or recalling past events. Recent technological developments paired with classification methods may help decrease the required tracking efforts and support management plans. However, the needs of caregivers and individuals with ASD and SIB should be considered before integrating technology into daily routines, particularly to encourage technology acceptance and adoption. To address this, the perspectives of SIB management and technology were first collected to support future technology design considerations (Chapter 2). Accelerometers were then selected as a specific technology, based on caregiver preferences and reported preferences of individuals with ASD, and were used to collect movement data for classification (Chapter 3). Machine learning algorithms with featureless data were explored, resulting in individual-level models that demonstrated high accuracy (up to 99%) in detecting and classifying SIB. Group-level classifiers could provide more generalizable models for efficient SIB monitoring, though the highly variable nature of both ASD and SIB can preclude accurate detection. A multi-level regression model (MLR) was implemented to consider such individual variability (Chapter 4). Both linear and nonlinear measures of motor variability were assessed as potential predictors in the model. Diverse classification methods were used (as in Chapter 3), and MLR outperformed other group level classifiers (accuracy ~75%). Findings from this research provide groundwork for a future smart SIB monitoring system. There are clear implications for such monitoring methods in prevention and treatment, though additional research is required to expand the developed models. Such models can contribute to the goal of alerting caregivers and children before SIB occurs, and teaching children to perform another behavior when alerted.
Doctor of Philosophy
Autism spectrum disorder (ASD) is a prevalent developmental disorder that adversely affects communication, social skills, and behavioral responses. Roughly half of individuals diagnosed with ASD show self-injurious behavior (SIB), including self-hitting or head banging), which can lead to injury and hospitalization. Clinicians or trained caregivers traditionally observe and record events before/after SIB to determine possible causes (“triggers”) of this behavior. Clinicians can then develop management plans to redirect, replace, or extinguish SIB at the first sign of a known trigger. Tracking SIB in this way, though, requires substantial experience, time, and effort from caregivers. Observations may suffer from subjectivity and inconsistency if tracked across caregivers, or may not generalize to different contexts if SIB is only tracked in the home or school. Recent technological innovations, though, could objectively and continuously monitor SIB to address the described limitations of traditional tracking methods. Yet, “smart” SIB tracking will not be adopted into management plans unless first accepted by potential users. Before a monitoring system is developed, caregiver needs related to SIB, management, and technology should be evaluated. Thus, as an initial step towards developing an accepted SIB monitoring system, caregiver perspectives of SIB management and technology were collected here to support future technology design considerations (Chapter 2). Sensors capable of collecting the acceleration of movement (accelerometers) were then selected as a specific technology, based on the reported preferences of caregivers and individuals with ASD, and were used to capture SIB movements from individuals with ASD (Chapter 3). These movements were automatically classified as “SIB” or “non-SIB” events using machine learning algorithms. When separately applying these methods to each individual, up to 99% accuracy in detecting and classifying SIB was achieved. Classifiers that predict SIB for diverse individuals could provide more generalizable and efficient methods for SIB monitoring. ASD and SIB presentations, however, range across individuals, which impose challenges for SIB detection. A multi-level regression model (MLR) was implemented to consider individual differences, such as those that may occur from diagnosis or behavior (Chapter 4). Model inputs included measures capturing changes of movement over time, and these were found to enhance SIB identification. Diverse classification models were also developed (as in Chapter 3), though MLR outperformed these (yielding accuracy of ~75%). Findings from this research provide groundwork for a smart SIB monitoring system. There are clear implications for monitoring methods in prevention, though additional research is required to expand the developed models. Such models can contribute to the goal of alerting caregivers and children before SIB occurs, and teaching children to perform another behavior when alerted.
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Chmielewski, W. X., N. Wolff, V. Roessner, M. Mückschel, and C. Beste. "Effects of multisensory integration processes on response inhibition in adolescent autism spectrum disorder." Cambridge University Press, 2016. https://tud.qucosa.de/id/qucosa%3A70681.

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Background. In everyday life it is often required to integrate multisensory input to successfully conduct response inhibition (RI) and thus major executive control processes. Both RI and multisensory processes have been suggested to be altered in autism spectrum disorder (ASD). It is, however, unclear which neurophysiological processes relate to changes in RI in ASD and in how far these processes are affected by possible multisensory integration deficits in ASD. Method. Combining high-density EEG recordings with source localization analyses, we examined a group of adolescent ASD patients (n = 20) and healthy controls (n = 20) using a novel RI task. Results. Compared to controls, RI processes are generally compromised in adolescent ASD. This aggravation of RI processes is modulated by the content of multisensory information. The neurophysiological data suggest that deficits in ASD emerge in attentional selection and resource allocation processes related to occipito-parietal and middle frontal regions. Most importantly, conflict monitoring subprocesses during RI were specifically modulated by content of multisensory information in the superior frontal gyrus. Conclusions. RI processes are overstrained in adolescent ASD, especially when conflicting multisensory information has to be integrated to perform RI. It seems that the content of multisensory input is important to consider in ASD and its effects on cognitive control processes.
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Pinheiro, Tuany Dias. "Classificação de imagens faciais para o auxílio ao diagnóstico do transtorno do espectro autista." Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/100/100131/tde-23052018-140406/.

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O transtorno do espectro autista (TEA) é um transtorno de desenvolvimento que prejudica persistentemente a comunicação e a interação social e causa padrões restritos e repetitivos de comportamento, interesses e atividades. Esses sintomas estão presentes desde o início da infância e limitam ou prejudicam o cotidiano do indivíduo. Contudo, vários fatores impedem que seja possível diagnosticar antes dos três anos de idade, entre eles o fato de que o diagnóstico é essencialmente clínico e realizado com base nos critérios descritos no Manual diagnóstico e estatístico de transtornos mentais da sociedade americana de psiquiatria (DSM), entrevistas com os pais, observação do comportamento e aplicação de questionários e escalas padronizadas. Estas ferramentas e questionários para a realização do diagnóstico ainda carecem de validação e adaptação ao contexto brasileiro. O estudo das características antropométricas em indivíduos com TEA e indivíduos em desenvolvimento típico mostrou que podem existir diferenças como distâncias entre as pupilas, formato das orelhas, estrabismo e circunferência da cabeça. A hipótese é que seria possível classificar indivíduos com TEA e indivíduos em desenvolvimento típico com base nas medidas antropométricas faciais. Desta forma, este trabalho teve como objetivo a construção de um classificador que, dada uma imagem facial de uma criança, consiga discriminar entre os dois grupos, auxiliando assim o diagnóstico. A fim de testar a hipótese, foram coletadas imagens bidimensionais de crianças e adolescentes com TEA e em desenvolvimento tipico para a construção de uma base de dados. As imagens foram processadas por meio de um pipeline definido neste trabalho e foram testados e comparados diferentes métodos de redução de dimensionalidade e classificação e como resultado obteve-se acurácia de 80% na classificação com Random Forests
Autism Spectrum Disorder (ASD) is a developmental disorder that persistently impairs communication and social interaction and causes restricted and repetitive patterns of behavior, interests, and activities. These symptoms are present from the beginning of childhood and limit or impair the daily life of the individual. However, several factors prevent it from being possible to diagnose before the age of three, including the fact that the diagnosis is essentially clinical and performed based on the criteria described in the Diagnostic and Statistical Manual of Mental Disorders of the American Psychiatry Association (DSM) , interviews with parents, observation of behavior and application of questionnaires and standardized scales. These tools and questionnaires to carry out the diagnosis still lack validation and adaptation to the brazilian context. The study of anthropometric features in individuals with ASD and individuals in typical development showed that there may be differences such as distances between the pupils, ear format, strabismus and head circumference. The hypothesis is that it would be possible to classify individuals with ASD and individuals in typical development based on anthropometric facial measures. Therefore, this work aimed to construct a classifier that, given a childs facial image, can discriminate between the two groups, thus helping the diagnosis. In order to test the hypothesis, two-dimensional images of children and adolescents with ASD and in typical development were collected for the database construction. The images were processed in a pipeline defined in this work and different methods of dimensionality reduction and classification were tested and compared and as a result 80% accuracy was obtained in the classification with Random Forests algorithm
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Williams, Joanna Gwendolyn. "Screening for autism spectrum disorders." Thesis, University of Cambridge, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.615931.

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Books on the topic "Autism spectrum disorders – Classification"

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Whiteley, Paul. Guidelines for the implementation of a gluten and/or casein free diet with people with autism or associated spectrum disorders. Sunderland: Autism Research Unit, 1997.

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Autism spectrum disorders. Minneapolis: Twenty-First Century Books, 2011.

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Sicile-Kira, Chantal. Autism Spectrum Disorders. New York: Penguin Group USA, Inc., 2008.

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Zager, Dianne. Autism Spectrum Disorders. Fourth editon. | New York : Routledge, 2017.: Routledge, 2016. http://dx.doi.org/10.4324/9781315794181.

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Autism spectrum disorders. New York: Oxford University Press, 2011.

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Zager, Dianne, David F. Cihak, and Angi Stone-MacDonald. Autism Spectrum Disorders. 5th ed. New York: Routledge, 2022. http://dx.doi.org/10.4324/9781003255147.

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1957-, Hollander Eric, ed. Autism spectrum disorders. New York: Dekker, 2003.

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Bowler, Dermot. Autism Spectrum Disorders. New York: John Wiley & Sons, Ltd., 2006.

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Autism spectrum disorder. St. Catharines, Ontario: Crabtree Publishing Company, 2014.

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Hollander, Eric. Textbook of autism spectrum disorders. Washington, DC: American Psychiatric Pub., 2011.

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Book chapters on the topic "Autism spectrum disorders – Classification"

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Maye, Melissa P., Ivy Giserman Kiss, and Alice S. Carter. "Definitions and Classification of Autism Spectrum Disorders." In Autism Spectrum Disorders, 3–26. 5th ed. New York: Routledge, 2022. http://dx.doi.org/10.4324/9781003255147-2.

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Volkmar, Fred R. "Diagnosis and Classification." In Encyclopedia of Autism Spectrum Disorders, 1–3. New York, NY: Springer New York, 2020. http://dx.doi.org/10.1007/978-1-4614-6435-8_1432-3.

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Stabel, Aaron, Kimberly Kroeger-Geoppinger, Jennifer McCullagh, Deborah Weiss, Jennifer McCullagh, Naomi Schneider, Diana B. Newman, et al. "Diagnosis and Classification." In Encyclopedia of Autism Spectrum Disorders, 917–19. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-1698-3_1432.

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Volkmar, Fred R. "Diagnosis and Classification." In Encyclopedia of Autism Spectrum Disorders, 1395–97. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-319-91280-6_1432.

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Stabel, Aaron, Kimberly Kroeger-Geoppinger, Jennifer McCullagh, Deborah Weiss, Jennifer McCullagh, Naomi Schneider, Diana B. Newman, et al. "Dimensional Versus Categorical Classification." In Encyclopedia of Autism Spectrum Disorders, 975–76. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-1698-3_875.

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Pickles, Andrew. "Dimensional Versus Categorical Classification." In Encyclopedia of Autism Spectrum Disorders, 1469–71. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-319-91280-6_875.

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Magboo, Vincent Peter C., and Ma Sheila A. Magboo. "Classification Models for Autism Spectrum Disorder." In Communications in Computer and Information Science, 452–64. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-21385-4_37.

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Zhang, Mingli, Xin Zhao, Wenbin Zhang, Ahmad Chaddad, Alan Evans, and Jean Baptiste Poline. "Deep Discriminative Learning for Autism Spectrum Disorder Classification." In Lecture Notes in Computer Science, 435–43. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59003-1_29.

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Preethi, S., A. Arun Prakash, R. Ramyea, S. Ramya, and D. Ishwarya. "Classification of Autism Spectrum Disorder Using Deep Learning." In Intelligent Systems, 247–55. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0901-6_24.

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Chen, Xiangjun, Zhaohui Wang, Faouzi Alaya Cheikh, and Mohib Ullah. "3D-Resnet Fused Attention for Autism Spectrum Disorder Classification." In Lecture Notes in Computer Science, 607–17. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87358-5_49.

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Conference papers on the topic "Autism spectrum disorders – Classification"

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Almeida, Javier, Nelson Velasco, and Eduardo Romero. "A multidimensional feature space for automatic classification of autism spectrum disorders (ASD)." In 12th International Symposium on Medical Information Processing and Analysis, edited by Eduardo Romero, Natasha Lepore, Jorge Brieva, and Ignacio Larrabide. SPIE, 2017. http://dx.doi.org/10.1117/12.2256952.

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Mohi-ud-Din, Qaysar, and A. K. Jayanthy. "Autism Spectrum Disorder classification using EEG and 1D-CNN." In 2021 10th International Conference on Internet of Everything, Microwave Engineering, Communication and Networks (IEMECON). IEEE, 2021. http://dx.doi.org/10.1109/iemecon53809.2021.9689100.

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Dewi, Erina S., and Elly M. Imah. "Comparison of Machine Learning Algorithms for Autism Spectrum Disorder Classification." In International Joint Conference on Science and Engineering (IJCSE 2020). Paris, France: Atlantis Press, 2020. http://dx.doi.org/10.2991/aer.k.201124.028.

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Lawi, Armin, and Firman Aziz. "Comparison of Classification Algorithms of the Autism Spectrum Disorder Diagnosis." In 2018 2nd East Indonesia Conference on Computer and Information Technology (EIConCIT). IEEE, 2018. http://dx.doi.org/10.1109/eiconcit.2018.8878593.

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Anirudh, Rushil, and Jayaraman J. Thiagarajan. "Bootstrapping Graph Convolutional Neural Networks for Autism Spectrum Disorder Classification." In ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2019. http://dx.doi.org/10.1109/icassp.2019.8683547.

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Deng, Tingyan. "Classifying Autism Spectrum Disorder using Machine Learning Models." In 7th International Conference on Advances in Computer Science and Information Technology (ACSTY 2021). AIRCC Publishing Corporation, 2021. http://dx.doi.org/10.5121/csit.2021.110306.

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Autistic Spectrum Disorder (ASD) is a developmental disability, which can affect communication and behavior, causing significant social, communication, and behavior challenge. From a rare childhood disorder, ASD has evolved into a disorder that is found, according to the National Institute of Health, in 1% to 2% of the population in high income countries. A potential early and accurate diagnosis can not only help doctors to find the disease early, leading to a more on time treatment to the patient, but also can save significant healthcare costs for the patients. With the rapid growth of ASD cases, many open-source ASD related datasets were created for scientists and doctors to investigate this disease. Autistic Spectrum Disorder Screening Data for Adult is a well-known dataset, which contains 20 features to be utilized for further analysis on the potential cause and prediction of ASD. In this paper, we developed an Autism classification algorithm based on logistic regression model. Our model starts with featuring engineering to extract deep information from the dataset and then applied a modified logistic regression classifier to the data. The model can predict the ASD in an average F1 score of 0.97, which displays the superiority and feasibility of the proposed model. Besides, the data visualization technique was used to displays several feature distributions images for people to better understand the data and related feature engineering.
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Sunsirikul, Siriwan, and Tiranee Achalakul. "Associative classification mining in the behavior study of Autism Spectrum Disorder." In 2nd International Conference on Computer and Automation Engineering (ICCAE 2010). IEEE, 2010. http://dx.doi.org/10.1109/iccae.2010.5451851.

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Misman, Muhammad Faiz, Azurah A. Samah, Farah Aqilah Ezudin, Hairuddin Abu Majid, Zuraini Ali Shah, Haslina Hashim, and Muhamad Farhin Harun. "Classification of Adults with Autism Spectrum Disorder using Deep Neural Network." In 2019 1st International Conference on Artificial Intelligence and Data Sciences (AiDAS). IEEE, 2019. http://dx.doi.org/10.1109/aidas47888.2019.8970823.

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Prasad, Pindi Krishna Chandra, Yash Khare, Kamalaker Dadi, P. K. Vinod, and Bapi Raju Surampudi. "Deep Learning Approach for Classification and Interpretation of Autism Spectrum Disorder." In 2022 International Joint Conference on Neural Networks (IJCNN). IEEE, 2022. http://dx.doi.org/10.1109/ijcnn55064.2022.9892350.

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Haputhanthri, Dilantha, Gunavaran Brihadiswaran, Sahan Gunathilaka, Dulani Meedeniya, Yasith Jayawardena, Sampath Jayarathna, and Mark Jaime. "An EEG based Channel Optimized Classification Approach for Autism Spectrum Disorder." In 2019 Moratuwa Engineering Research Conference (MERCon). IEEE, 2019. http://dx.doi.org/10.1109/mercon.2019.8818814.

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Reports on the topic "Autism spectrum disorders – Classification"

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Wang, Xiaoxi. A Meta-Analysis of Acupuncture for Autism Spectrum Disorders. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, April 2020. http://dx.doi.org/10.37766/inplasy2020.4.0087.

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Mong, Jessica. Etiology of Sleep Disorders in ASD (Autism Spectrum Disorders): Role for Inflammatory Cytokines. Fort Belvoir, VA: Defense Technical Information Center, May 2011. http://dx.doi.org/10.21236/ada581407.

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Manoach, Dara. Neural Correlates of Restricted, Repetitive Behaviors in Autism Spectrum Disorders. Fort Belvoir, VA: Defense Technical Information Center, October 2013. http://dx.doi.org/10.21236/ada612865.

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Manoach, Dara. Neural Correlates of Restricted, Repetitive Behaviors in Autism Spectrum Disorders. Fort Belvoir, VA: Defense Technical Information Center, December 2014. http://dx.doi.org/10.21236/ada614050.

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Manoach, Dara, and Susan Santangelo. Neural Correlates of Restricted, Repetitive Behaviors in Autism Spectrum Disorders. Fort Belvoir, VA: Defense Technical Information Center, October 2012. http://dx.doi.org/10.21236/ada575709.

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Santangelo, Susan L., and Dara Manoach. Neural Correlates of Restricted, Repetitive Behaviors in Autism Spectrum Disorders. Fort Belvoir, VA: Defense Technical Information Center, October 2012. http://dx.doi.org/10.21236/ada583969.

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Sweeney, John A. Family Studies of Sensorimotor and Neurocognitive Heterogeneity in Autism Spectrum Disorders. Fort Belvoir, VA: Defense Technical Information Center, November 2014. http://dx.doi.org/10.21236/ada613859.

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Shin, Su-Jeong Hwang, Brianna Smith, and Kristi Gaines. Investigation of Therapy Clothing Products for Children with Autism Spectrum Disorders. Ames: Iowa State University, Digital Repository, November 2015. http://dx.doi.org/10.31274/itaa_proceedings-180814-1151.

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Platt, Michael L. Neural Basis of Empathy and Its Dysfunction in Autism Spectrum Disorders (ASD). Fort Belvoir, VA: Defense Technical Information Center, October 2014. http://dx.doi.org/10.21236/ada612863.

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Costa-Mattioli, Mauro. The Role of the New mTOR Complex, MTORC2, in Autism Spectrum Disorders. Fort Belvoir, VA: Defense Technical Information Center, October 2014. http://dx.doi.org/10.21236/ada613836.

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