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This book re-examines the case of Nadia, discovered as a child aged six, who had been drawing with phenomenal skill and visual realism from the age of three, despite having autism and severe learning difficulties. The original research was published in 1977 and caused great international interest. Nadia Revisited updates her story and reconsiders the theories that endeavour to explain her extraordinary talent. As well as summarising the central issues from the original case study and presenting her remarkable drawings, the book explains Nadia's subsequent development and present situation in light of the recent research on autistic spectrum disorders and representational drawing in children. The book also considers the phenomenon of savant syndrome, the condition in which those with autism or other learning disabilities have areas of unusual talent that contrast dramatically with their general functioning. Lorna Selfe uses this single case study to discuss theories of developmental psychology and considers the possible links between prodigious talent and underlying neurological dysfunction. The book is especially valuable for students and teachers of developmental psychology and neuropsychology, education and special education, as well as art and art education. Parents of autistic children or those with related disorders, learning difficulties or special needs will also be interested in the discussions presented in this book.

Background To investigate whether later diagnosis of psychiatric disorder can be predicted from analysis of mother-infant joint attention (JA) behaviours in social-communicative interaction at 12 months. Method Using data from a large contemporary birth cohort, we examined 159 videos of a mother-infant interaction for joint attention behaviour when children were aged one year, sampled from within the Avon Longitudinal Study of Parents and Children (ALSPAC) cohort. Fifty-three of the videos involved infants who were later considered to have a psychiatric disorder at seven years and 106 were same aged controls. Psychopathologies included in the case group were disruptive behaviour disorders, oppositional-conduct disorder, attention-deficit/hyperactivity disorder, pervasive development disorder, anxiety and depressive disorders. Psychiatric diagnoses were obtained using the Development and Wellbeing Assessment when the children were seven years old. Results None of the three JA behaviours (shared look rate, shared attention rate and shared attention intensity) showed a significant association with the primary outcome of case–control status. Only shared look rate predicted any of the exploratory sub-diagnosis outcomes and was found to be positively associated with later oppositional-conduct disorders (OR [95% CI]: 1.5 [1.0, 2.3]; p = 0.041). Conclusions JA behaviours did not, in general, predict later psychopathology. However, shared look was positively associated with later oppositional-conduct disorders. This suggests that some features of JA may be early markers of later psychopathology. Further investigation will be required to determine whether any JA behaviours can be used to screen for families in need of intervention.

Surface area expansion from 6–12 months precedes brain overgrowth in high risk infants diagnosed with autism at 24 months and cortical features in the first year predict individual diagnostic outcomes. Early brain overgrowth predicts autism spectrum disorder Autism spectrum disorder (ASD) is associated with brain overgrowth, but it has been unclear how this relates to behavioural symptoms. In a longitudinal neuroimaging study of young children at high familial risk of autism, Heather Hazlett and colleagues now show that high-risk children who receive a diagnosis of ASD at 24 months of age had an increased cortical growth rate at 6–12 months. Early overgrowth in high-risk children is associated with social impairments at 24 months, and imaging data obtained at 6 and 12 months can predict an ASD diagnosis at 24 months in high-risk children. These findings indicate that differences in the developmental trajectory towards ASD emerge as early as the first year of life. Brain enlargement has been observed in children with autism spectrum disorder (ASD), but the timing of this phenomenon, and the relationship between ASD and the appearance of behavioural symptoms, are unknown. Retrospective head circumference and longitudinal brain volume studies of two-year olds followed up at four years of age have provided evidence that increased brain volume may emerge early in development 1 , 2 . Studies of infants at high familial risk of autism can provide insight into the early development of autism and have shown that characteristic social deficits in ASD emerge during the latter part of the first and in the second year of life 3 , 4 . These observations suggest that prospective brain-imaging studies of infants at high familial risk of ASD might identify early postnatal changes in brain volume that occur before an ASD diagnosis. In this prospective neuroimaging study of 106 infants at high familial risk of ASD and 42 low-risk infants, we show that hyperexpansion of the cortical surface area between 6 and 12 months of age precedes brain volume overgrowth observed between 12 and 24 months in 15 high-risk infants who were diagnosed with autism at 24 months. Brain volume overgrowth was linked to the emergence and severity of autistic social deficits. A deep-learning algorithm that primarily uses surface area information from magnetic resonance imaging of the brain of 6–12-month-old individuals predicted the diagnosis of autism in individual high-risk children at 24 months (with a positive predictive value of 81% and a sensitivity of 88%). These findings demonstrate that early brain changes occur during the period in which autistic behaviours are first emerging.

In this study the effectiveness of an equine-assisted therapy (EAT) in improving adaptive and executive functioning in children with autism spectrum disorder (ASD) was examined (children attending EAT, n = 15, control group n = 13; inclusion criteria: IQ > 70). Therapeutic sessions consisted in structured activities involving horses and included both work on the ground and riding. Results indicate an improvement in social functioning in the group attending EAT (compared to the control group) and a milder effect on motor abilities. Improved executive functioning was also observed (i.e. reduced planning time in a problem-solving task) at the end of the EAT program. Our findings provide further support for the use of animal-assisted intervention programs as complementary intervention strategies for children with ASD.

Autism is a diagnostic label based on behavior. While the diagnostic criteria attempt to maximize clinical consensus, it also masks a wide degree of heterogeneity between and within individuals at multiple levels of analysis. Understanding this multi-level heterogeneity is of high clinical and translational importance. Here we present organizing principles to frame research examining multi-level heterogeneity in autism. Theoretical concepts such as ‘spectrum’ or ‘autisms’ reflect non-mutually exclusive explanations regarding continuous/dimensional or categorical/qualitative variation between and within individuals. However, common practices of small sample size studies and case–control models are suboptimal for tackling heterogeneity. Big data are an important ingredient for furthering our understanding of heterogeneity in autism. In addition to being ‘feature-rich’, big data should be both ‘broad’ (i.e., large sample size) and ‘deep’ (i.e., multiple levels of data collected on the same individuals). These characteristics increase the likelihood that the study results are more generalizable and facilitate evaluation of the utility of different models of heterogeneity. A model’s utility can be measured by its ability to explain clinically or mechanistically important phenomena, and also by explaining how variability manifests across different levels of analysis. The directionality for explaining variability across levels can be bottom-up or top-down, and should include the importance of development for characterizing changes within individuals. While progress can be made with ‘supervised’ models built upon a priori or theoretically predicted distinctions or dimensions of importance, it will become increasingly important to complement such work with unsupervised data-driven discoveries that leverage unknown and multivariate distinctions within big data. A better understanding of how to model heterogeneity between autistic people will facilitate progress towards precision medicine for symptoms that cause suffering, and person-centered support.

There is a lack of measures that reflect the intervention priorities of parents of children with autism spectrum disorder (ASD) and that assess the impact of interventions on family experience and quality of life. The Autism Family Experience Questionnaire (AFEQ) was developed through focus groups and online consultation with parents, and reflected parental priorities. It was then administered to the parents of children enrolled in the Pre-school Autism Communication Trial and its 6-year follow-up study. The AFEQ showed good convergent validity with well-established measures of child adaptive functioning, parental mental health and parental wellbeing. It was sensitive to change in response to a parent-mediated intervention for young children with autism, showing treatment effect at treatment endpoint which increased at six-year follow-up.

Two community-based cohorts of children with autism spectrum disorder, examined using similar assessment protocols, were pooled (n = 301) and subdivided according to history of regression. Those with regression (n = 62), 20.5% of the combined cohort, were contrasted with those without regression (n = 241) at first assessment (age range 19–60 months) and at 2-year follow-up on a range of measures. The regression group was significantly more functionally impaired, with regard to intellectual function (p < .001), language development (p < .001), and to severity of autism (p < .01) at both T1 and T2. Only 14 (23.3%) had a clearly identified underlying etiology [24 (18.6%) in the non-regressive group]. There were no significant differences between those who had regressed ‘from normal’ and those who had regressed ‘from low’ functioning.

Autism spectrum disorder is thought to be a predominantly male diagnosis, however recent research suggests a smaller gender disparity in prevalence than previously assumed. Accounts of the female experience of autism are important to help reduce likely male-bias in current understanding and recognition of autism. Eighteen autistic females and four mothers of autistic females took part in discussions with a topic guide around diagnosis, impact and coping. A thematic analysis was conducted. Five themes were identified: fitting in the with the norm, potential obstacles for autistic women and girls, negative aspects of autism, the perspective of others, and positive aspects of having autism. We hope that greater understanding of the experiences of autistic females may lead to improved awareness, diagnosis and support for women and girls.

Little is known about ageing with autism spectrum disorder (ASD). We examined the characteristics of adults referred to a specialist diagnostic centre for assessment of possible ASD, 100 of whom received an ASD diagnosis and 46 did not. Few demographic differences were noted between the groups. Comorbid psychiatric disorders were high in individuals with ASD (58 %) and non-ASD (59 %). Individuals who received an ASD diagnosis had higher self-rated severity of ASD traits than non-ASD individuals. Within the ASD group, older age was associated with higher ratings of ASD traits and better cognitive performance. One interpretation is that general cognitive ability and the development of coping strategies across the lifespan, do not necessarily reduce ASD traits but may mitigate their effects.