In honor of the last week of Autism Awareness/Acceptance Month, we review in this podcast episode two new scientific findings that call for more awareness and action, and less acceptance of the status quo. First: sex differences in autism are not well understood, and as it turns out, the influences on a diagnosis are different. Males have a higher rate of heritability compared to females. Second, those with rare genetic disorders have very few options for treatment, but a new study promises hope for more personalized approaches. The researchers use Timothy Syndrome as an example of how cells can start to function properly through a targeted approach which focuses on a small part of a gene. This is potentially life saving for individuals with this disorder.
Podcast: Research for the end of Autism Action Month
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
Large gaps exist in healthcare for Black autistic children, yet the lived experiences of these families are rarely investigated or considered when designing research studies. This student will collect data from families, including information about their diagnostic experience and the factors that matter most to them. The results will help researchers and healthcare providers develop culturally competent interventions for Black families across the world.
Adults with profound autism have unique healthcare needs that are often overlooked by providers. This student will expand an existing project to add a cohort of middle and older-aged autistic adults in a residential facility to measure overall health, co-occurring conditions, healthcare quality & satisfaction, and quality of life. Determining how co-morbid health conditions change as autistic adults age will enable services to be delivered that better meet people’s needs.
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
Females are less likely to receive an autism diagnosis than males and several studies are examining the biological, psychological, and developmental reasons for this disparity. One theory is that language abilities and patterns in females are superior to males, possibly reflecting better social ability, which may contribute to lower diagnostic rates. This study will look at a measure of language called prosody, or the rhythm, tone and pattern used during spoken language. Studies around prosody in autistic females are lacking, mostly because there are fewer girls with an autism diagnosis who can participate in research on prosody. This fellow will examine prosody in males and females with and without autism, and compare prosody to assessments of social function and interest. These results will inform caregivers, educators, and clinicians when considering a possible autism diagnosis for girls.
Given the historically higher prevalence of white males in autism research studies, many autism diagnostic and outcome instruments have not been specifically validated in people of color or in females. This study will recruit women and individuals from racially and ethnically diverse communities to understand how a measure of treatment outcome, called the BOSCC (Brief Observation of Social Communication Change), can be used more effectively in these communities.
Early intervention is vital for children on the autism spectrum but is often only available after a formal diagnosis. Because of the COVID- 19 pandemic, many assessments are now conducted online. This change has sometimes occurred without studying whether modifications made to support online assessments affect the outcomes of the assessments. Researchers at the University of Massachusetts Boston, the University of Washington, Rush University Medical Center, and Michigan State University recently adapted an assessment protocol (the Communication Play Protocol; CPP), to be conducted as an online assessment of ASD (RISE-CPP). ASF’s funding will allow researchers to determine if clinicians can diagnose ASD online using the RISE-CPP protocol as accurately as they can using traditional in-person assessments. An online version has the advantages of possibly reaching a more diverse community and improving opportunities for early intervention.
Quality of Life (QoL) outcome measures have traditionally excluded autistic individuals with minimal verbal ability or cognitive disability. The Patient-Reported Outcomes Measurement Information System (PROMIS®) Autism Battery – Lifespan (PAB-L) is a
recently developed instrument to measure autistic QoL across the lifespan. Although PAB-L has been shown to be an acceptable QoL measure in autism, nonverbal people with cognitive disability were underrepresented among participants in the original validation studies. This grant will expand the research on the PAB-L to examine whether it is appropriate in those with profound autism, and also determine what changes, if any, should be made to effectively measure quality of life in this underserved population.
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.