Podcast: Are Naturalistic Developmental Behavioral Interventions controversial?

While NDBIs are generally considered beneficial, they still face controversies – do they actually work and does that translate to an improved quality of life for the family? This week’s #ASF podcast interviews Molly Reilly and Jinwei Song of @UConn to dive into these issues, as well as the role of the caregiver in the intervention and how their influence affects the outcome. References below.

https://pubmed.ncbi.nlm.nih.gov/38719439

https://journals.sagepub.com/doi/epub/10.1177/13623613241227516

https://link.springer.com/article/10.1007/s10803-023-06198-x

Thank you to Dennis Wall from Stanford University for explaining what Machine Learning is, how it’s related to Artificial Intelligence (today’s four buzz words) and how these new technologies are helping families get a diagnosis. In this week’s podcast episode, he talks about the overall goals of these techniques, highlighting Cognoa’s CanvasDx to provide remote diagnoses to potentially reduce the waiting lists for families.

Did you miss the ASF 2024 Day of Learning and can’t wait for the videos to be posted? This is a 17 minute brief summary of what was discussed, but unfortunately, with no visuals. Don’t just listen to the podcast, watch the videos when they are posted. Also included in this podcast is a shoutout to the Profound Autism Summit which brought together hundreds of advocates around those who need 24/7 care for their lives. The link to their advocacy page is here: https://www.votervoice.net/ProfoundAutism/campaigns/112917/respond

This podcast episode provides updates on studies that help with prediction of an autism diagnosis – which is important for preparing for the future and for intervening early. First, a study that uses environmental factors to create an equation for the probability of a diagnosis following a combination of of non-genetic factors only which does a fairly good, but not perfect, job at predicting a diagnosis. Second, a study that looks at the accuracy of a machine that predicts autism from eye gaze as early as 9 months of age and with only a 2 minute test. This one wasn’t as accurate as the one that takes longer and tests older kids, but it’s a first step. No ONE thing does a perfect job at predicting a diagnosis – it’s going to be a combination of things, tested over time and multiple times that will be most helpful at predicting a diagnosis. Both studies are open access!

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10904522/pdf/fpsyt-15-1291356.pdf

https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/38429348/

Vision problems, including far- and near-sightedness, affect up to 44% of children with autism. These deficits may lead to sensory deprivation and impair skills related to autism, including attention and communication. Refractive errors are usually corrected by the use of eyeglasses or contact lenses, but most individuals with profound autism cannot tolerate wearing them and may benefit from vision-correcting, refractive surgery. In this study, pediatric ophthalmologist Dr. Margaret Reynolds will examine social reciprocity, social interaction, and adaptive behavior in children with profound autism who have had this vision-correcting surgery. The methods used to track these outcomes do not rely on the child’s speech or language ability, so those who are non- or minimally-verbal can participate. While at present only a few doctors perform this surgery in children, this relatively simple medical procedure could lead to improved quality of life and function.

Sleep problems are highly prevalent in individuals with profound autism and exacerbate emotional disturbances, cognitive deficits, and challenging behaviors. Existing studies of sleep in autism have mostly excluded children with profound autism. This omission has been blamed on the added burden, expense, and difficulty of studying sleep in children with profound autism in a lab setting. This grant will expand a sleep study currently in progress to add a cohort of children with profound autism. The goal of the study is to validate the use of a minimally invasive headband device that measures sleep quality at home and provides data on specific brainwave patterns during different phases of sleep in people with autism vs. people without autism

Genetic testing is recommended for all children with autism. However, many children receive test results that reveal mutations in genes that have not yet been associated with autism. Unfortunately, these variants of uncertain significance can cause confusion and problems for parents seeking clinical diagnoses and support. This study will utilize machine learning to integrate genetic findings with the child’s attainment of key developmental milestones, because often milestone delays are associated with rare genetic disorders. Eventually, this research could lead to a brief, low-cost clinical prediction tool that increases the diagnostic certainty of genetic testing in autism.

In the last version of the Diagnostic and Statistical Manual, the different subtypes of autism were folded into one label: autism spectrum disorder. A similar revision is being made around the International Classification of Diseases, the system the WHO uses across the world to describe autism and provide appropriate reimbursements for services and supports. In this version, the ICD-11, a combination of 300 different presentations of autism are described. A diagnosis can be made if 1 feature of social-communication and 1 feature of repetitive behaviors are documented, with an onset of any time in life. This is causing a lot of confusion in the community, because since the presentations are not specific to autism, it is difficult to provide an accurate diagnosis using the ICD-11. On this week’s podcast episode we talk to German psychiatrist Inge Kamp-Becker, MD, who outlines what the changes are, and how misdiagnosis can be made and what those consequences might be. Her summary is linked below.

https://www.nature.com/articles/s41380-023-02354-y

Those who are minimally verbal or non speaking represent about 25% of those with an autism diagnosis, yet there is really a lack of effective interventions for this group of autistic individuals. It used to be that everyone who was non-speaking was thought to have minimal ability to understand language, since understanding and speaking are so linked in development. However, group at Boston University studied the largest group of non-speaking autistic individuals so far and discovered that about 25% of them understand more language than they can speak, although this ability is still far lower than those who are neurotypical. The other 75% understand about as much as they can communicate verbally. This indicates that in some cases, the ability to understand words and their meaning exceeds the ability to communicate those ideas verbally. Surprise surprise, just like everything autism – there are differences across the spectrum. Thanks to Yanru Chen at Boston University for explaining the study to us in this week’s podcast episode.

https://onlinelibrary.wiley.com/doi/10.1002/aur.3079

On the first podcast of 2024, we describe a new paper in the Journal of the American Medical Association or JAMA which uses physiological measurements like heart rate and skin conductance to predict severe and dangerous behaviors, specifically aggression. If aggression can be predicted, it might be able to be prevented. It turns out aggression can be predicted up to 3 minutes before an episode occurs, in the future these measures can be used to possibly redirect aggression. In a separate study, the issue of stigma is addressed. There is an intense debate over “person first” vs. “identity first” language in autism, promoting recommendations of using one over the other because fear that person first language promotes stigma against autism. A new study shows that there is no added prejudice or fear using either person first or identity first language, but the stigma associated with schizophrenia is worse than it is for autism. What contributes to stigma? There is a wide range of experiences and perceptions of autism that need to be addressed. It’s not as simple as the language used.

https://pubmed.ncbi.nlm.nih.gov/38127348/

https://pubmed.ncbi.nlm.nih.gov/37965364/

Abstract

Emerging evidence suggests that the higher prevalence of autism in individuals who are assigned male than assigned female at birth results from both biological factors and identification biases. Autistic individuals who are assigned female at birth (AFAB) and those who are gender diverse experience health disparities and clinical inequity, including late or missed diagnosis and inadequate support. In this Viewpoint, an international panel of clinicians, scientists, and community members with lived experiences of autism reviewed the challenges in identifying autism in individuals who are AFAB and proposed clinical and research directions to promote the health, development, and wellbeing of autistic AFAB individuals. The recognition challenges stem from the interplay between cognitive differences and nuanced or different presentations of autism in some AFAB individuals; expectancy, gender-related, and autism-related biases held by clinicians; and social determinants. We recommend that professional development for clinicians be supported by health-care systems, professional societies, and governing bodies to improve equitable access to assessment and earlier identification of autism in AFAB individuals. Autistic AFAB individuals should receive tailored support in education, identity development, health care, and social and professional sense of belonging

Abstract

Objective: The coronavirus pandemic drastically increased social isolation. Autistic youth already experience elevated social isolation and loneliness, making them highly vulnerable to the impact of the pandemic. We examined trajectories of social disruption and loneliness in autistic and non-autistic youth during a six-month period of the pandemic (June 2020 until November 2020).

Method: Participants were 76 youth, ages 8 through 17, (Mage = 12.82, Nautistic = 51) with an IQ ≥ 70. Youth completed a biweekly measure of loneliness (Revised UCLA Loneliness Scale) and their parent completed a measure of pandemic-related family social disruption (Epidemic Pandemic Impacts Inventory).

Results: There were no time trends in loneliness across all youth, however, social disruption displayed linear, quadratic, and cubic trends. Non-autistic youth reported relatively greater declines in social disruption compared to autistic youth. Additionally, autistic youth reported relatively greater declines in loneliness relative to non-autistic youth. Greater social disruption was associated with higher loneliness, however, autistic youth demonstrated a relatively stronger relationship between social disruption and loneliness compared to non-autistic youth.

Conclusions: The current study was one of the first to investigate social disruption and loneliness in autistic youth during the COVID-19 pandemic. Results indicated that autistic youth experienced relative decreases in loneliness during this time, perhaps due to reductions in social demands. Nonetheless, when autistic youth did experience social disruption, they reported relatively higher levels of loneliness. This work contributes to our understanding of risk factors for loneliness and highlights the need to understand the benefits, as well as the challenges, to remote schooling and social interactions.