Autism - research Flashcards
Wolff (2004)
Autism remains a fascinating condition, perhaps the most prolifically researched of all child psychiatric disorders. Its history yields many lessons: early accounts of possible autism are, with one exception, unclear; the greatest contributions to our understanding have come from individual clinicians and researchers; the concept and definition of the disorder have changed greatly over the years; some ideas once held with conviction, were later proved to be unfounded; and socio-political shifts as well as research findings have radically altered our understanding of the syndrome as well as the care and treatment offered to people with autism
Nazeer et al. (2019)
Autism is a fascinating topic that has attracted the interest of researchers and the lay public alike. The history of autism is filled with accomplishments of people who were astute observers of childhood psychopathology. Autism spectrum disorder affects a diverse group of children who share certain commonalities. There are ongoing attempts to understand its etiology and to refine diagnostic criteria for improved and timely recognition. In recent years, rapidly evolving research has highlighted the limits of our understanding of a disorder that at one time was considered rare, but now presents itself in 1 in every 59 children in the United States.
John et al. (2017)
Individuals with autism are often stigmatised and isolated by their typically developing peers according to parental, teacher and self-reports. While quantitative studies often report negative attitudes towards individuals with autism, it is still unclear how understandings of autism influence attitudes. In this exploratory study, misconceptions or myths about autism, that is, the cognitive component of attitudes, were examined using focus groups. Purposive sampling was used to recruit undergraduate and postgraduate students, and adults with and without experience of autism, to one of the five focus groups (n = 37). Content analysis was used to identify emergent themes. The data identified seven commonly held beliefs about individuals with autism. The first four were related to social interaction, such as that people with autism do not like to be touched. The fifth reflected the view that all individuals with autism have a special talent, and the final two concerned beliefs that people with autism are dangerous. The findings from this study demonstrate that people with varying experience or knowledge of autism often hold inaccurate beliefs about autism. These findings improve our understandings of lay beliefs about autism and will aid the development and implementation of interventions designed to improve lay knowledge of autism.
Kroncke et al. (2016)
Increasing rates of autism have changed the face of child psychology, education, and family life. Clinicians and educators, in general education and special education alike, are challenged like never before to identify and treat children with autism. Autism assessment, school psychology, and forensic psychology fields are rapidly expanding to address critical issues in the ASD population. As children on the Autism Spectrum mature to adulthood, the community college and university system, as well as employment programs and adult service providers, encounter a new level of need for this expanding population. Although assessment and treatment technologies have advanced substantially over the past decade, there are a myriad of unanswered questions about the potential for people with ASD to function in school and the workplace, have families, and live fulfilling lives. Psychologists, scientists, and doctors feel a deep sense of urgency to find answers to these provoking questions that plague our time. This passion is ever increased through the continued deepening understanding of individuals with ASD who are often endearing, talented, intriguing and may see the world in a new way; offering us a window into the brain and to the breadth of human experience. In this chapter, the reader is invited to explore the meaning of the term “autism,” the history since its early foundations as “Kanner’s autism,” and the currently increasing prevalence estimates.
Wiggins et al. (2019)
Methods
Children between 2 and 5 years of age were enrolled in the Study to Explore Early Development-Phase 2 (SEED2) and received a comprehensive developmental evaluation. The clinician(s) who evaluated the child completed two diagnostic checklists that indicated the presence and severity of DSM-IV-TR and DSM-5 criteria. Definitions for DSM-5 ASD, DSM-IV-TR autistic disorder, and DSM-IV-TR Pervasive Developmental Disorder Not Otherwise Specified (PDD-NOS) were created from the diagnostic checklists.
Results
773 children met SEED2 criteria for ASD and 288 met criteria for another developmental disorder (DD). Agreement between DSM-5 and DSM-IV-TR definitions of ASD were good for autistic disorder (0.78) and moderate for PDD-NOS (0.57 and 0.59). Children who met DSM-IV-TR autistic disorder but not DSM-5 ASD (n = 71) were more likely to have mild ASD symptoms, or symptoms accounted for by another disorder. Children who met PDD-NOS but not DSM-5 ASD (n = 66), or vice versa (n = 120) were less likely to have intellectual disability and more likely to be female. Sensitivity and specificity were best balanced with DSM-5 ASD criteria (0.95 and 0.78, respectively).
Conclusions
The DSM-5 definition of ASD maximizes diagnostic sensitivity and specificity in the SEED2 sample. These findings support the DSM-5 conceptualization of ASD in preschool children.
Crane et al. (2015)
A sample of 1047 parents completed an online survey about their experiences and opinions regarding the process of attaining a diagnosis of autism spectrum disorder for their children. The results revealed that parents usually waited a year from when they first had concerns about their child’s development before they sought professional help. On average, there was a delay of around 3.5 years from the point at which parents first approached a health professional with their concerns to the confirmation of an autism spectrum disorder diagnosis. Just over half of the parents surveyed were dissatisfied with the diagnostic process as a whole. Several factors predicted parents’ overall levels of satisfaction with the diagnostic process, including the time taken to receive a diagnosis, satisfaction with the information provided at diagnosis, the manner of the diagnosing professional, the stress associated with the diagnostic process and satisfaction with post-diagnostic support. Post-diagnosis, the support (if any) that was provided to parents was deemed unsatisfactory, and this was highlighted as an area of particular concern among parents.
Campbell-Scherer (2019?)
In a cohort of 657 461 children born in 1999–2010, no association between the measles mumps and rubella (MMR) vaccination and autism was observed.
Despite this, it is unclear whether increasing medical evidence and facts about the lack of association between the MMR vaccination and autism will have beneficial impact in easing the minds of parents and preventing unnecessary deaths.
large sample
Baird et al. (2003) - what it is
Autism is a behaviourally defined disorder, which is the endpoint of several organic aetiologies
The number of children diagnosed as having autistic spectrum disorders is increasing for various reasons
A diagnosis of autism can be reliably made at between 2 and 3 years of age
Autism does not meet criteria for screening, but surveillance throughout the preschool years is recommended
Diagnosis is by history taking, focusing on the developmental story and systematically inquiring for core behaviours, and by observation in several settings
Baird et al. (2003) - identification
Features that may discriminate children with autism early in childhood
Lack of social smile, lack of appropriate facial expression, poor attention, impaired social interaction
Ignoring people, preference for aloneness, lack of eye contact, lack of appropriate gestures, lack of emotional expression, less looking at others, less pointing, less showing objects in the second year
Alerting signals of possible autistic spectrum disorder13
In the first year of life there are usually no clear discriminating features, but parental concerns should be elicited
Between 2 and 3 years of age, concerns in the following areas should prompt referralw1
Communication
Impairment in language development, especially
comprehension; unusual use of language; poor response to name; deficient non-verbal communication–for example, lack of pointing and difficulty following a point and failure to smile socially to share enjoyment and respond to the smiling of othersAbsolute indicators for referral
No babble, pointing, or other gesture by 12 months
No single words by 18 months
No two word spontaneous (non-echoed) phrases by 24 months
Any loss of any language or social skills at any agew2
Social impairments
Limitation in, or lack of imitation of, actions (for example, clapping); lack of showing with toys or other objects; lack of interest in other children or odd approaches to other children. Minimal recognition or responsiveness to other people’s happiness or distress; limited variety of imaginative play or pretence, especiallysocial imagination (that is, not joining with others in shared imaginary games),”in his or her own world;” failure to initiate simple play with others or participate in early social games;preference for solitary play activities; odd relationships with adults (too friendly or ignores)
Impairment of interests, activities, and other behaviours
Over-sensitivity to sound or touch; motor mannerisms; biting, hitting, or aggression to peers; oppositional to adults; over-liking for sameness or inability to cope with change, especially in unstructured setting; repetitive play with toys (for example, lining up objects); turning light switches on and off, regardless of scolding
Features that may discriminate children with autism in later childhood13
In school age children, the following features should alert teachers and others to the possibility of autistic spectrum disorder and trigger discussion with parents and possible implementation of the local referral pathway
Communication impairments
Abnormalities in language development, including muteness and odd or inappropriate prosody
Persistent echolalia
Reference to self as “you,” “she,” or “he” beyond 3 years
Unusual vocabulary for child’s age or social group
Limited use of language for communication or tendency to talk freely only about specific topics
Social impairments
Inability to join in with the play of other children or inappropriate attempts at joint play (may manifest asaggressive or disruptive behaviour)
Lack of awareness of classroom “norms”(criticising teachers; overt unwillingness to cooperate in classroom activities; inability to appreciate or follow current trends–for example, with regard to other children’s dress, style of speech, or interests)
Easily overwhelmed by social and other stimulation
Failure to relate normally to adults (too intense or no relationship)
Showing extreme reactions to invasion of personal space and extreme resistance to being “hurried”
Wing and Gould (1979)
The prevalence, in children aged under 15, of severe impairments of social interaction, language abnormalities, and repetitive stereotyped behaviors was investigated in an area of London. A “socially impaired” group (more than half of whom were severely retarded) and a comparison group of “sociable severely mentally retarded” children were identified. Mutism or echolalia, and repetitive stereotyped behaviors were found in almost all the socially impaired children, but to a less marked extent in a minority of the sociable severely retarded. Certain organic conditions were found more often in the socially impaired group. A subgroup with a history of Kanner’s early childhood autism could be identified reliably but shared many abnormalities with other socially impaired children. The relationships between mental retardation, typical autism, and other conditions involving social impairment were discussed, and a system of classification based on quality of social interaction was considered.
Plaisted (2015)
In this chapter, I argue that more recent evidence is highly suggestive of a causal link between the perceptual and attentional abnormalities in individuals with autism and their deficits in social processing. Given this, we should perhaps readopt the parsimonious position that many aspects of autism stem from one underlying deficit. The question then becomes whether this deficit is indeed a
KC Plaisted-Grant 2015 weakening of central-coherence mechanisms, or whether weak central coherence “effects” may be better explained by alternative mechanisms. This
question is answered, in part, by reviewing some of the studies that have assessed the weak central coherence hypothesis at the level of perception and selective attention. On the whole, the evidence is not in its favour, although the same cannot be said of studies that assess the weak central coherence hypothesis at the level of conception. My argument is that there is an alternative
explanation of the perceptual and attentional abnormalities in autism to weak central coherence that pivots on the notion that individuals with autism are unable to draw pieces of information together because of an inability to
recognize the similarities between stimuli or situations. However, the challenge is to assess whether this alternative can also explain weak central coherence
effects at the conceptual level. I draw on some recent insights concerning perceptual and conceptual processes in developmentally normal individuals (Goldstone & Barsalou, 1998) to offer possible ways in which a reduced ability to process similarity at the perceptual and attentional level of the kind observed in autism can lead to abnormalities at the conceptual level.
Baird et al. (2006)
Methods
Within a total population cohort of 56 946 children aged 9–10 years, we screened all those with a current clinical diagnosis of ASD (n=255) or those judged to be at risk for being an undetected case (n=1515). A stratified subsample (n=255) received a comprehensive diagnostic assessment, including standardised clinical observation, and parent interview assessments of autistic symptoms, language, and intelligence quotient (IQ). Clinical consensus diagnoses of childhood autism and other ASDs were derived. We used a sample weighting procedure to estimate prevalence.
Findings
The prevalence of childhood autism was 38·9 per 10 000 (95% CI 29·9–47·8) and that of other ASDs was 77·2 per 10 000 (52·1–102·3), making the total prevalence of all ASDs 116·1 per 10 000 (90·4–141·8). A narrower definition of childhood autism, which combined clinical consensus with instrument criteria for past and current presentation, provided a prevalence of 24·8 per 10 000 (17·6–32·0). The rate of previous local identification was lowest for children of less educated parents.
Interpretation
Prevalence of autism and related ASDs is substantially greater than previously recognised. Whether the increase is due to better ascertainment, broadening diagnostic criteria, or increased incidence is unclear. Services in health, education, and social care will need to recognise the needs of children with some form of ASD, who constitute 1% of the child population
Ennis-Cole (2019) - ASD
This chapter explains the three core areas of impairment in Autism Spectrum Disorder: communication/language (expressive, receptive, and mixed), behavioral challenges (stereotypical motor behavior, restrictive, repetitive), and social interaction. In addition to these major challenges, sensory issues are also described. Many parents of children and adults on the autism spectrum experience additional parenting problems because of both sensory issues and the core areas of impairment in their child or children with ASD. Parents describe themselves as “uber-parents” who face difficult challenges with their kids: repetitive behaviors, aggression, non-compliance, perseveration, and anxiety. Many parents find themselves taking on multiple roles that tax their time and resources as they look for interventions to help their child with a variety of issues, track their child’s progress, pay for interventions, create a structured environment conducive to learning at home, and provide support based on their child’s level of understanding and functional ability. This chapter examines the challenges of parents and offers some applications of technology that can be used for practicing communication skills and making social connections.
Ennis-Cole (2019) - communication
In this chapter, advanced technologies are discussed; these provide value in autism research, intervention, instruction, communication, and support. Technology is discussed as a tool for providing education and social interaction. Advanced technologies like intelligent tutoring systems, video modeling, robotics, and simulated virtual environments are discussed as valuable tools to aid learning and understanding. Video modeling has been used to help learners with Autism develop initiation skills, take the perspective of others, and make requests. Robots have been used to foster social communication, collaboration, and joint attention. Simulated environments have been used to help learners on the autism spectrum develop social and academic competence.
Wijngaarden et al. (2015)
Autism is an extensively studied disorder in which the gender disparity in
prevalence has received much attention. In contrast, only a few studies
examine gender differences in symptomatology. This systematic review and
meta-analysis of 22 peer-reviewed original publications examines gender
differences in the core triad of impairments in autism. Gender differences were
transformed and concatenated using standardized mean differences, and
analyses were stratified in five age categories (toddlerhood, preschool children,
childhood, adolescence, young adulthood).
Boys showed more repetitive and stereotyped behavior as from the age of six,
but not below the age of six. Males and females did not differ in the domain of
social behavior and communication. There is an underrepresentation of
females with ASD an average to high intelligence. Females could present
another autistic phenotype than males. As ASD is now defined according to the
male phenotype this could imply that there is an ascertainment bias. More
research is needed into the female phenotype of ASD with development of
appropriate instruments to detect and ascertain them
Cashin et al. (2009)
CONCLUSIONS: Exceptional pioneering work in the late 1970s gave rise to the concept of the triad of impairments as the central plank of the construct of autism: impaired communication; impaired social skills; and a restricted and repetitive way of being‐in‐the‐world. This clear articulation of the structures of the phenomena allowed a new way for professionals and families to see and understand autism, and to relate to those with autism. Like the evolution of many concepts, this was a transitional idea. The original triad of impairments described the behavioral manifestation; the actual triad of impairments is at the level of cognitive processing. The actual triad of impairment is static and ubiquitous unlike the variable and fluctuating behavioral manifestation. The actual triad of impairment in autism is visual as opposed to linguistic processing, impaired abstraction, and lack of theory of mind. The actual triad is central to all diagnosis that together makes up the autism spectrum.
Mahendiran et al. (2019)
Background: Sex differences in the prevalence of neurodevelopmental disorders such as autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) are well documented, but studies examining sex differences in social and communication function remain limited and inconclusive. Objectives: The objective of this study is to conduct a meta-analysis of sex differences in social-communication function in children with ASD or ADHD and typically developing controls. Methods: Using PRISMA, a search was performed on Medline and PSYCHINFO on English-language journals (2000-2017) examining sex differences in social and communication function in ASD and ADHD compared to controls. Inclusion criteria: 1) peer reviewed journal articles, 2) diagnosis of ASD or ADHD and controls, 3) age 6-18 years, 4) measures of social-communication function, and 5) means, standard deviations, and sample sizes reported in order to calculate standardized mean differences (SMD). Results: Eleven original/empirical studies met inclusion criteria for ASD and six for ADHD. No significant sex differences were found between ASD and controls in social (SMD = -0.43; p = 0.5; CI: -1.58-0.72), or communication function (SMD = 0.86; p = 0.5 CI; -1.57–3.30) and between ADHD and controls in social function (SMD = -0.68: p = 0.7, CI: -4.17-2.81). No studies evaluated sex differences in communication in ADHD. Significant heterogeneity was noted in all analyses. Type of measure may have partially accounted for some variability between studies. Conclusions: The meta-analysis did not detect sex differences in social and communication function in children with ASD and ADHD; however, significant heterogeneity was noted. Future larger studies, controlling for measure and with adequate numbers of female participants are required to further understand sex differences in these domains.
Krupa et al. (2019)
Assessment of communication skills in children with autism spectrum disorder (ASD) is challenging in an unfamiliar clinical environment due to their limited verbal output and inadequate motivation to communicate. To analyze whether the communication sample recorded at clinic represents the child’s competence and performance, this study compared caregiver-child interaction in 24 to 48 months old children with ASD (n = 10, M = 38.2 months) at clinic and home. The 30-minute caregiver-child interaction at clinic and home was video recorded and analyzed for frequency of pragmatic acts (initiations and responses) and duration of joint engagement. Results indicated that children initiated and responded more at home than at clinic, whereas caregivers initiated and responded more at clinic. The study provides evidence that communication profile of children with ASD in multiple environments needs to be considered for obtaining representative and reliable communication sample for child-centered assessment and intervention.
Jones et al. (2008)
Main Outcome Measure Preferential attention was measured as percentage of visual fixation time to 4 regions of interest: eyes, mouth, body, and object. Level of social disability was assessed by the Autism Diagnostic Observation Schedule.
Results Looking at the eyes of others was significantly decreased in 2-year-old children with autism (P < .001), while looking at mouths was increased (P < .01) in comparison with both control groups. The 2 control groups were not distinguishable on the basis of fixation patterns. In addition, fixation on eyes by the children with autism correlated with their level of social disability; less fixation on eyes predicted greater social disability (r = − 0.669, P < .01).
Conclusions Looking at the eyes of others is important in early social development and in social adaptation throughout one’s life span. Our results indicate that in 2-year-old children with autism, this behavior is already derailed, suggesting critical consequences for development but also offering a potential biomarker for quantifying syndrome manifestation at this early age.
Wang et al. (2019)
In this study, we leverage a new technology that combines eye tracking and automatic computer programs to help very young children with ASD look at social information in a more prototypical way. In a randomized controlled trial, we show that the use of this technology prevents the diminishing attention toward social information normally seen in children with ASD over the course of a single experimental session. This work represents development toward new social attention therapeutic systems that could augment current behavioral interventions.
Shic et al. (2019)
Methods
We investigated the impacts of speech (SP) and direct gaze (DG) on attention to faces in 22‐month‐old toddlers with ASD (n = 50) and typically developing controls (TD, n = 47) using the Selective Social Attention 2.0 (SSA 2.0) task. The task consisted of four conditions where the presence (+) and absence (−) of DG and SP were systematically manipulated. The severity of autism symptoms, and verbal and nonverbal skills were characterized concurrently with eye tracking at 22.4 (SD = 3.2) months and prospectively at 39.8 (SD = 4.3) months.
Results
Toddlers with ASD looked less than TD toddlers at face and mouth regions only when the actress was speaking (direct gaze absence with speech, DG−SP+: d = 0.99, p < .001 for face, d = 0.98, p < .001 for mouth regions; direct gaze present with speech, DG+SP+, d = 1.47, p < .001 for face, d = 1.01, p < .001 for mouth regions). Toddlers with ASD looked less at the eye region only when both gaze and speech cues were present (d = 0.46, p = .03). Salience of the combined DG and SP cues was associated concurrently and prospectively with the severity of autism symptoms, and the association remained significant after controlling for verbal and nonverbal levels.
Conclusions
The study links poor attention to faces with limited salience of audiovisual speech and provides no support for the face avoidance hypothesis in the early stages of ASD. These results are consequential for research on early discriminant and predictive biomarkers as well as identification of novel treatment targets.
Eycke and Muller (2018)
Little is known about the relation between cognitive processes and imagination and whether this relation differs between neurotypically developing children and children with autism. To address this issue, we administered a cognitive task battery and Karmiloff-Smith’s drawing task, which requires children to draw imaginative people and houses. For children with autism, executive function significantly predicted imaginative drawing. In neurotypically developing controls, executive function and cognitive-perceptual processing style predicted imaginative drawing, but these associations were moderated by mental age. In younger (neurotypically developing) children, better executive function and a local processing bias were associated with imagination; in older children, only a global bias was associated with imagination. These findings suggest that (a) with development there are changes in the type of cognitive processes involved in imagination and (b) children with autism employ a unique cognitive strategy in imaginative drawing.
Crespi et al. (2016)
Complex human social cognition has evolved in concert with risks for psychiatric disorders. Recently, autism and psychotic-affective conditions (mainly schizophrenia, bipolar disorder, and depression) have been posited as psychological ‘opposites’ with regard to social-cognitive phenotypes. Imagination, considered as ‘forming new ideas, mental images, or concepts’, represents a central facet of human social evolution and cognition. Previous studies have documented reduced imagination in autism, and increased imagination in association with psychotic-affective conditions, yet these sets of findings have yet to be considered together, or evaluated in the context of the diametric model. We first review studies of the components, manifestations, and neural correlates of imagination in autism and psychotic affective conditions. Next, we use data on dimensional autism in healthy populations to test the hypotheses that: (1) imagination represents the facet of autism that best accounts for its strongly male-biased sex ratio, and (2) higher genetic risk of schizophrenia is associated with higher imagination, in accordance with the predictions of the diametric model. The first hypothesis was supported by a systematic review and meta-analysis showing that Imagination exhibits the strongest male bias of all Autism Quotient (AQ) subscales, in non-clinical populations. The second hypothesis was supported, for males, by associations between schizophrenia genetic risk scores, derived from a set of single-nucleotide polymorphisms, and the AQ Imagination subscale. Considered together, these findings indicate that imagination, especially social imagination as embodied in the default mode human brain network, mediates risk and diametric dimensional phenotypes of autism and psychotic-affective conditions.
Skuse et al. (2005)
Method
A 12-item scale, the SCDC, was completed by three independent samples drawn from a twin register, a group with Turner syndrome and children with a diagnosis of autistic-spectrum disorder attending clinics. The data were used to establish the heritability reliability and validity of the checklist.
Results
Traits measured by the SCDC were highly heritable in both genders (0.74). Internal consistency was excellent (0.93) and test–retest reliability high (0.81). Discriminant validity between pervasive developmental disorder and other clinical groups was good, discrimination from non-clinical samples was better; sensitivity (0.90), specificity (0.69).
Conclusions
The SCDC is a unique and efficient first-level screening questionnaire for autistic traits.
Colvert et al. (2015)
Main Outcomes and Measures Participants underwent screening using a population-based measure of autistic traits (CAST assessment), structured diagnostic assessments (DAWBA, ADI-R, and ADOS), and a best-estimate diagnosis.
Results On all ASD measures, correlations among monozygotic twins (range, 0.77-0.99) were significantly higher than those for dizygotic twins (range, 0.22-0.65), giving heritability estimates of 56% to 95%. The covariance of CAST and ASD diagnostic status (DAWBA, ADOS and best-estimate diagnosis) was largely explained by additive genetic factors (76%-95%). For the ADI-R only, shared environmental influences were significant (30% [95% CI, 8%-47%]) but smaller than genetic influences (56% [95% CI, 37%-82%]).
Conclusions and Relevance The liability to ASD and a more broadly defined high-level autism trait phenotype in this large population-based twin sample derives primarily from additive genetic and, to a lesser extent, nonshared environmental effects. The largely consistent results across different diagnostic tools suggest that the results are generalizable across multiple measures and assessment methods. Genetic factors underpinning individual differences in autismlike traits show considerable overlap with genetic influences on diagnosed ASD.
Mandy et al. (2018)
Methods
Participants were 9,744 males (n = 4,784) and females (n = 4,960) from ALSPAC, a UK birth cohort study. ASTs were assessed when participants were aged 7, 10, 13 and 16 years, using the parent‐report Social Communication Disorders Checklist. Data were modelled using latent growth curve analysis.
Results
Developmental trajectories of males and females were nonlinear, showing a decline from 7 to 10 years, followed by an increase between 10 and 16 years. At 7 years, males had higher levels of ASTs than females (mean raw score difference = 0.88, 95% CI [.72, 1.04]), and were more likely (odds ratio [OR] = 1.99; 95% CI, 1.82, 2.16) to score in the clinical range on the SCDC. By 16 years this gender difference had disappeared: males and females had, on average, similar levels of ASTs (mean difference = 0.00, 95% CI [−0.19, 0.19]) and were equally likely to score in the SCDC’s clinical range (OR = 0.91, 95% CI, 0.73, 1.10). This was the result of an increase in females’ ASTs between 10 and 16 years.
Conclusions
There are gender‐specific trajectories of autistic social impairment, with females more likely than males to experience an escalation of ASTs during early‐ and midadolescence. It remains to be discovered whether the observed female adolescent increase in ASTs represents the genuine late onset of social difficulties or earlier, subtle, pre‐existing difficulties becoming more obvious.
Baron-Cohen et al. (1996)
Method
Sixteen thousand children in the southeast of England were screened for autism by their health visitor or GP, during their routine 18-month-old developmental check-up, using the CHAT (Checklist for Autism in Toddlers). From a previous high-risk study we predicted that children at 18 months of age who failed three items (‘protodeclarative pointing‘, ‘gaze-monitoring‘, and ‘pretend play’) would be at risk for receiving a diagnosis of autism. From other evidence, we further predicted that those 18-month-olds who failed one or two of the key items (either pretend play, or protodeclarative pointing and pretend play) would be at risk for developmental delay without autism.
Results
Twelve children out of the total population of 16 000 consistently failed the three key items. Of these, 10 (83.3%) received a diagnosis of autism. Thus, the false positive rate was 16.6% (2 out of 12 cases), and even these 2 cases were not normal. When the 10 children with autism were reassessed at 3.5 years of age, their diagnosis remained the same. Thus the false positive rate among the cases diagnosed with autism was zero. In contrast, of 22 children who consistently failed either protodeclarative pointing and/or pretend play, none received a diagnosis of autism, but 15 (68.2%) received a diagnosis of language delay.
Conclusions
Consistent failure of the three key items from the CHAT at 18 months of age carries an 83.3% risk of autism; and this pattern of risk indicator is specific to autism when compared to other forms of developmental delay.
Spikol et al. (2019)
Methods Using retrospective parental report data from the Mental Health of Children and Young People in Great Britain survey (N = 7977), latent class analysis (LCA) and a quasi -latent transition analysis were used to (1) identify profiles of variation in parent reports of child ‘red flag’ traits before and after age 3 and (2) model transitions in risk from 3 years and below to ≥ 3 years, respectively, per the ‘optimal outcome’ model.
Results Three distinct classes, each characterised by variation in parent ‘red flag’ trait reporting were identified for the ‘≤ 3 years of age’ and the ‘≥ 3 years of age’ data. Both LCA class profiles comprised groups of children characterised by low, medium and high ASD risk. Dose–response effects for a number of recognised ASD correlates across the low, moderate and high risk ‘≥ 3 years of age’ classes seemed to validate older classes in terms of ASD relevance. Over 54% of children characterised by the highest levels of ASD ‘red flag’ trait probability at 3 years and below (2% of sample), also populated the high-risk class evidenced in the ‘≥ 3 years of age’ LCA.
Conclusions
Retrospective parental reports of child ASD ‘red flag’ traits ≤ 3 years of age were reliable indicators of ASD risk in later childhood.
Pijl et al. (2017)
The importance of early detection of autism spectrum disorder followed by early intervention is increasingly recognized. This quasi-experimental study evaluated the long-term effects of a program for the early detection of autism spectrum disorder (consisting of training of professionals and use of a referral protocol and screening instrument), to determine whether the positive effects on the age at referral were sustained after the program ended while controlling for overall changes in the number of referrals. Before, during, and after the program, the proportion of children referred before 3 years (versus 3–6 years) of age was calculated for children subsequently diagnosed with autism spectrum disorder (N = 513) or another, non-autism spectrum disorder, condition (N = 722). The odds of being referred before 3 years of age was higher in children with autism spectrum disorder than in children with another condition during the program than before (3.1, 95% confidence interval: 1.2–7.6) or after (1.7, 95% confidence interval: 1.0–3.0) the program but was not different before versus after the program. Thus, although the program led to earlier referral of children with autism spectrum disorder, after correction for other referrals, the effect was not sustained after the program ended. This study highlights the importance of continued investment in the early detection of autism spectrum disorder.
Osterling et al. (2002)
Previous work based on observations of home videotapes indicates that differences can be detected between infants with autism spectrum disorder and infants with typical development at 1 year of age. The present study addresses the question of whether autism can be distinguished from mental retardation by 1 year of age. Home videotapes of first birthday parties from 20 infants later diagnosed with autism spectrum disorder, 14 infants later diagnosed with mental retardation (without autism), and 20 typically developing infants were coded by blind raters with respect to the frequencies of specific social and communicative behaviors and repetitive motor actions. Results indicated that 1-year-olds with autism spectrum disorder can be distinguished from 1-year-olds with typical development and those with mental retardation. The infants with autism spectrum disorder looked at others and oriented to their names less frequently than infants with mental retardation. The infants with autism spectrum disorder and those with mental retardation used gestures and looked to objects held by others less frequently and engaged in repetitive motor actions more frequently than typically developing infants. These results indicate that autism can be distinguished from mental retardation and typical development by 1 year of age.
Egger et al. (2018)
Current tools for objectively measuring young children’s observed behaviors are expensive, time-consuming, and require extensive training and professional administration. The lack of scalable, reliable, and validated tools impacts access to evidence-based knowledge and limits our capacity to collect population-level data in non-clinical settings. To address this gap, we developed mobile technology to collect videos of young children while they watched movies designed to elicit autism-related behaviors and then used automatic behavioral coding of these videos to quantify children’s emotions and behaviors. We present results from our iPhone study Autism & Beyond, built on ResearchKit’s open-source platform. The entire study—from an e-Consent process to stimuli presentation and data collection—was conducted within an iPhone-based app available in the Apple Store. Over 1 year, 1756 families with children aged 12–72 months old participated in the study, completing 5618 caregiver-reported surveys and uploading 4441 videos recorded in the child’s natural settings. Usable data were collected on 87.6% of the uploaded videos. Automatic coding identified significant differences in emotion and attention by age, sex, and autism risk status. This study demonstrates the acceptability of an app-based tool to caregivers, their willingness to upload videos of their children, the feasibility of caregiver-collected data in the home, and the application of automatic behavioral encoding to quantify emotions and attention variables that are clinically meaningful and may be refined to screen children for autism and developmental disorders outside of clinical settings. This technology has the potential to transform how we screen and monitor children’s development.
Haglund et al. (2016)
Method: The OSA consists of 12 observations and takes less than 10 minutes to use. The performance of the test was investigated by assessing 37 children previously diagnosed ASD, 23 with Down Syndrome (DS) and 26 typically developing children (TD).
Results: Children diagnosed with ASD showed statistically significant higher scores in all 12 items compared to TD children, and significantly higher in 10 items compared to the children with DS. Most of the observations in OSA seemed to cover specific symptoms of ASD, but two of the observations were more related to developmental level. The nine most discriminative items for ASD were identified, and among those, a cut-off limit was chosen (≥3 items). Among children with ASD, 34/37 reached the proposed cut off, compared to 0/26 and 4/23 among children in the TD and DS groups, respectively.
Conclusion: The results suggest that the OSA discriminates children with ASD from TD children and children with DS. Using the suggested cut off, OSA provides high sensitivity for ASD (92%) with a very low false positive rate.
Aghdam et al. (2019)
Statistics show that the risk of autism spectrum disorder (ASD) is increasing in the world. Early diagnosis is most important factor in treatment of ASD. Thus far, the childhood diagnosis of ASD has been done based on clinical interviews and behavioral observations. There is a significant need to reduce the use of traditional diagnostic techniques and to diagnose this disorder in the right time and before the manifestation of behavioral symptoms. The purpose of this study is to present the intelligent model to diagnose ASD in young children based on resting-state functional magnetic resonance imaging (rs-fMRI) data using convolutional neural networks (CNNs). CNNs, which are by far one of the most powerful deep learning algorithms, are mainly trained using datasets with large numbers of samples. However, obtaining comprehensive datasets such as ImageNet and achieving acceptable results in medical imaging domain have become challenges. In order to overcome these two challenges, the two methods of “combining classifiers,” both dynamic (mixture of experts) and static (simple Bayes) approaches, and “transfer learning” were used in this analysis. In addition, since diagnosis of ASD will be much more effective at an early age, samples ranging in age from 5 to 10 years from global Autism Brain Imaging Data Exchange I and II (ABIDE I and ABIDE II) datasets were used in this research. The accuracy, sensitivity, and specificity of presented model outperform the results of previous studies conducted on ABIDE I dataset (the best results obtained from Adamax optimization technique: accuracy = 0.7273, sensitivity = 0.712, specificity = 0.7348). Furthermore, acceptable classification results were obtained from ABIDE II dataset (the best results obtained from Adamax optimization technique: accuracy = 0.7, sensitivity = 0.582, specificity = 0.804) and the combination of ABIDE I and ABIDE II datasets (the best results obtained from Adam optimization technique: accuracy = 0.7045, sensitivity = 0.679, specificity = 0.7421). We can conclude that the proposed architecture can be considered as an efficient tool for diagnosis of ASD in young children. From another perspective, this proposed method can be applied to analyzing rs-fMRI data related to brain dysfunctions.
Sun et al. (2019)
Methods
The study included a three-step process: (1) screening; (2) clinical assessment of ‘screen positives’ plus controls; and (3) research diagnostic assessment of those meeting clinical threshold for concerns at step 2. Prevalence estimates per 10,000 children aged 6–10 years old were weighted for study design using diagnostic criteria applied at the research assessment stage.
Results
In Jilin City, 77 cases of autism were identified from a total population of 7258, equating to a prevalence of 108 per 10,000 (95% confidence interval (CI) 89, 130). In Shenzhen City: 21,420 children were screened and 35 cases of autism were identified, resulting in a mainstream prevalence of 42 per 10,000 (95% CI 20–89). In Jiamusi City, 16,358 children were screened, with 10 autism cases being identified, with a mainstream prevalence of 19 per 10,000 (95% CI 10–38).
Conclusions
Results from Jilin City, where both mainstream and special school data were available, revealed a similar prevalence of autism in China to the West, at around 1%. Results from Shenzhen and Jiamusi cities, where only mainstream data were available, prevalence is also in line with Western estimates. In all three cities, new cases of autism were identified by the study in mainstream schools, reflecting current under-diagnosis. Non-significant variation across different cities is seen indicating the need to explore potential variation of autism across diverse Chinese regions with large sample sizes to achieve a fully robust national picture
Fombonne (2018)
The first autism surveys were simple head counts of children already diagnosed with a severe autism phenotype and residing in small, circumscribed geographical areas. Prevalence was low, ranging from 0.4 to 2/1,000 in the 1960’s and 1970’s. Today, the methodology of surveys has become more complex; studies include large populations, multiple sites, stratified samples and rely on intricate sets of screening activities followed by some form of diagnostic confirmation procedures. Yet, and as surprising as it may be, there is no standardization of autism survey methodology. Each survey has unique design features that reflect the local educational and health services infrastructure and current social policies for children with disabilities, they include or not parents, teachers and subjects with Autism Spectrum Disorder (ASD), and rely on variable screening and diagnostic instruments and methods. As such, prevalence differences between studies are hazardous to evaluate and whether observed discrepancies are due to method factors or true differences in population parameters, cannot be determined
Atladottir et al. (2012)
self-reported infections
didn’t find that infections during pregnancy were risk factors for autism
may be due to multiple testing - results could be due to chance
