Early concerns in parents of infants at risk for autism

'Caregivers' awareness matters': Call for a child-centred approach to early detection of autism spectrum disorder through well-child visits in Japan

BACKGROUND

Early detection of autism spectrum disorder (ASD) is essential to provide children with timely treatment and support. Evidence-based screening measures make it possible to identify children with suspected ASD at an early stage. Although Japan has a universal healthcare system that covers well-child visits, detection rates of developmental disorders, including ASD, at 18 months vary widely between municipalities (0.2%-48.0%). The reasons for this high level of variation are poorly understood. The present study aims to describe the barriers and facilitators of incorporating ASD identification during well-child visits in Japan.

METHODS

This is a qualitative study that conducts semi-structured in-depth interviews in two municipalities of Yamanashi Prefecture. We recruited all public health nurses (n = 17) and paediatricians (n = 11) involved in the well-child visit in each municipality and caregivers of children who also participated in the visits during the study period (n = 21).

RESULTS

We identified four themes characterizing the process of ASD identification in the target municipalities: (1) Identification of children with ASD is driven by caregivers' sense of concern, acceptance and awareness. (2) Multidisciplinary cooperation and shared decision-making is limited. (3) Skills and training for developmental disabilities screening are underdeveloped. (4) Caregivers' expectations shape the interaction in important ways.

CONCLUSIONS

Non-standardization of screening methods, limited knowledge and skills on screening and child development among healthcare providers and poor coordination among healthcare providers and caregivers are the main barriers to effective early detection of ASD through well-child visits. The findings suggest the importance of promoting a child-centred care approach through the application of evidence-based screening measures and effective information sharing.

ECG Recordings as Predictors of Very Early Autism Likelihood: A Machine Learning Approach

In recent years, there has been a rise in the prevalence of autism spectrum disorder (ASD). The diagnosis of ASD requires behavioral observation and standardized testing completed by highly trained experts. Early intervention for ASD can begin as early as 1-2 years of age, but ASD diagnoses are not typically made until ages 2-5 years, thus delaying the start of intervention. There is an urgent need for non-invasive biomarkers to detect ASD in infancy. While previous research using physiological recordings has focused on brain-based biomarkers of ASD, this study investigated the potential of electrocardiogram (ECG) recordings as an ASD biomarker in 3-6-month-old infants. We recorded the heart activity of infants at typical and elevated familial likelihood for ASD during naturalistic interactions with objects and caregivers. After obtaining the ECG signals, features such as heart rate variability (HRV) and sympathetic and parasympathetic activities were extracted. Then we evaluated the effectiveness of multiple machine learning classifiers for classifying ASD likelihood. Our findings support our hypothesis that infant ECG signals contain important information about ASD familial likelihood. Amongthe various machine learning algorithms tested, KNN performed best according to sensitivity (0.70 ± 0.117), F1-score (0.689 ± 0.124), precision (0.717 ± 0.128), accuracy (0.70 ± 0.117, p-value = 0.02), and ROC (0.686 ± 0.122, p-value = 0.06). These results suggest that ECG signals contain relevant information about the likelihood of an infant developing ASD. Future studies should consider the potential of information contained in ECG, and other indices of autonomic control, for the development of biomarkers of ASD in infancy.

Emerging signs of autism spectrum disorder in infancy: Putative neural substrate

Autism spectrum disorder (ASD) is characterized by altered development of the social brain with prominent atypical features in the fronto-temporo-parietal cortex and cerebellum. Early signs of ASD emerge between 6 and 12 months: reduced social communication, slightly less advanced motor development, and repetitive behaviour. The fronto-temporo-parietal cortex and cerebellum play a prominent role in the development of social communication, whereas fronto-parietal-cerebellar networks are involved in the planning of movements, that is, movement selection. Atypical sensory responsivity, a core feature of ASD, may result in impaired development of social communication and motor skills and/or selection of atypical repetitive behaviour. In the first postnatal year, the brain areas involved are characterized by gradual dissolution of temporary structures: the fronto-temporo-parietal cortical subplate and cerebellar external granular layer. It is hypothesized that altered dissolution of the transient structures opens the window for the expression of early signs of ASD arising in the impaired developing permanent networks. WHAT THIS PAPER ADDS: The early social and motor signs of autism spectrum disorder emerge between the ages of 6 and 12 months. Altered dissolution of transient brain structures in the fronto-temporo-parietal cortex and cerebellum may underlie the emergence of these early signs.