Podcast: Biology of profound and non-profound autism

Scientists have spent a lot of time trying to understand the biology of autism, unfortunately in the past, scientific studies had everyone with autism lumped together in one group and there are so many differences between people with a diagnosis that any features of the diagnosis itself were hard to detect. In the past, researchers grouped those who are cognitively abled with those who have average or superior intellectual disability, those who are able to express themselves verbally with those who cannot, and those who need 24-hour care with those who can live independently. This week, researchers changed that pattern of lumping all the autisms together by using profound autism as a subgroup and as a way to determine differences across autism subgroups. Researchers at @UCSD examined the cell sizes and the brain sizes of individuals with profound autism and compared them to those with non-profound autism. They found the larger the brain cell, the larger the brain size in different areas, and the more profound the autism. There were differences between profound autism, non-profound autism and typically developing controls. This is just a first step in using different classifications of behavior to understand the neurobiology of ASD and link brain function to autism behaviors, leading to more specific support for those across the spectrum. Learn more on this week’s podcast episode.

https://molecularautism.biomedcentral.com/articles/10.1186/s13229-024-00602-8#Sec26

As health care and outcomes for very premature infants has improved, scientists are able to track their longer term behavioral development, and that includes risk of developmental disorders like autism. On this week’s #ASFpodcast, Dr. Jessica Bradshaw discusses her recent research examining biological predictors like body temperature and heart rate and how they are linked to early autism features like social communication deficits in toddlerhood. All parents of pre-meet need to be vigilant and lean into resources like @BabyNavigator to help track their infant’s development.

https://www.nature.com/articles/s41372-024-01942-2

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.

https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/38630491/

https://www.nature.com/articles/s41586-024-07310-6

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/

Individuals with profound autism have been historically underrepresented in research. Though profoundly autistic individuals make up roughly 27 percent of the ASD population, they represent only a small portion of research participants. Consequently, research findings in the field underrepresent profoundly autistic individuals. One of the most significant reasons for this underrepresentation is the need for research participants to follow spoken or written instructions and maintain engagement with a task. In this project researchers will test a novel interactive experimental delivery system that helps people participate in research without needing to understand complex instructions. The experiment uses computer vision systems that reward participants for sitting still and attending, rather than asking a participant to sit quietly and attend to a computer screen without incentive. Using this method, researchers will study two promising biomarkers, the balance of neural activity in the brain using electroencephalography (EEG) (which is associated with sensory sensitivity), and arousal using pupil diameter (which is associated with symptoms like disordered sleep and aggression). The goal is to develop a novel system for including profoundly autistic individuals in research.

Restricted and repetitive behaviors (RRBs) range from hand flapping to debilitating self-injury. This student will investigate the biological basis for the broad range of RRBs by examining the development of the circuits in an area of the brain called the striatum. Pictures of the brain will be collected and analyzed at multiple time points in individuals from 1-4 years of age and matched with the presence and type of RRBs and later outcomes, like real-world function or adaptive behavior. The results will help identify critical windows for brain development when intervention can be most beneficial.

The Autism Biomarkers Consortium for Clinical Trials (ABC-CT) is a multicenter research study based at Yale that also includes Duke University, Boston Children’s Hospital, the University of Washington/Seattle Children’s Research Institute and the University of California, Los Angeles. The aim of the consortium is to develop reliable and objective measurements of social function and communication in people with autism, based on underlying neurobiological signals rather than on behavior. To date, measuring several of these biological signals (by both the ABC-CT and other research groups) as objective markers, has only taken place in a laboratory environment by showing participants videos on computers.   

Because many autistic individuals cannot sit still in a clinical setting, and because people normally don’t encounter the world in front of a computer,  it is not known if these biomarkers are valid in real-life settings. The ASF accelerator grant will enable researchers to expand their study by going out into the community with mobile biomarker measuring devices that allow participants to move freely rather than be tethered to a computer.  Data from this portion of the project will provide information about whether specific biomarkers are present in real-world settings. It will also enable researchers to access a broader diversity of participants. 

Individuals with a mutation in ASH1L exhibit symptoms of profound autism, as well as several medical comorbidities. Building on this fellow’s expertise in pre-clinical models of ASH1L-related autism, the fellow will advance to a natural history study of human patients with this mutation, and their families. In addition, the fellow will collect EEG data from families and identify potential biomarkers of this gene mutation. These are critical steps that enable future drug development and seizure treatment. When the study is complete, the findings have potential to guide development of new drugs to treat symptoms of profound autism, including those with and without an ASH1L mutation.

Hypersensitivity to auditory stimuli, including even regular sounds and voices, is seen in a high percentage of people with autism. This project will expand on existing research at Vanderbilt looking at brain activity in autistic and non-autistic individuals with different levels of sound tolerance to understand the factors that play a role in the brain’s response to noise.

Even in cases of autism with a known genetic mutation, there can be differences in the presentation of symptoms, which is also known as “phenotypic heterogeneity.” One way to measure this variability across individuals with autism is by examining brainwave patterns. Earlier research in people with Fragile X Syndrome has shown that individuals have different patterns of brainwave activity, which may predict their response to treatments. Building on this research, the fellow will collect cells from individuals with Fragile X Syndrome and turn them into neurons. These cells will then be tested for their own electrical activity, validating the brainwave data collected earlier. This study will then take the research a step further by examining if and how different therapeutics affect these neurons in different ways, leading to more targeted therapeutics.

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.