Parental Language Input Predicts Neuroscillatory Patterns Associated with Language Development in Toddlers at Risk of Autism

In this study we investigated the impact of parental language input on language development and associated neuroscillatory patterns in toddlers at risk of Autism Spectrum Disorder (ASD). Forty-six mother-toddler dyads at either high (n = 22) or low (n = 24) familial risk of ASD completed a longitudinal, prospective study including free-play, resting electroencephalography, and standardized language assessments. Input quantity/quality at 18 months positively predicted expressive language at 24 months, and relationships were stronger for high-risk toddlers. Moderated mediations revealed that input-language relationships were explained by 24-month frontal and temporal gamma power (30-50 Hz) for high-risk toddlers who would later develop ASD. Results suggest that high-risk toddlers may be cognitively and neurally more sensitive to their language environments, which has implications for early intervention.

Keywords: Autism; EEG; Early experience; Language development; Language input.

Autism Spectrum Disorder (ASD) is diagnosed three to four times more frequently in males than in females. Genetic studies of rare variants support a female protective effect (FPE) against ASD. However, sex differences in common inherited genetic risk for ASD are less studied, particularly within families. Leveraging the Danish iPSYCH resource, we found siblings of female ASD cases (n = 1,707) had higher rates of ASD than siblings of male ASD cases (n = 6,270; p < 1.0 × 10−10). In the Simons Simplex and SPARK collections, mothers of ASD cases (n = 7,436) carried more polygenic risk for ASD than fathers of ASD cases (n = 5,926; 0.08 polygenic risk score [PRS] SD; p = 7.0 × 10−7). Further, male unaffected siblings under-inherited polygenic risk (n = 1,519; p = 0.03). Using both epidemiologic and genetic approaches, our findings strongly support an FPE against ASD’s common inherited influences.

This week’s podcast explores the question about whether or not it is beneficial or just confusing to teach your child with autism multiple languages, or suppress the use of more than one language at home. Turns out, being bilingual helps with executive functioning (or those with preserved executive functioning can be bilingual), language, and provides benefits in verbal IQ depending on SES. In other words, it’s not harmful, it can be helpful, and those who choose to speak two languages at home should continue to do so if they feel that it is enhancing their child’s learning. Listen to the podcast here and find more information in the links below:

https://pubmed.ncbi.nlm.nih.gov/35102760/

https://pubmed.ncbi.nlm.nih.gov/34813032/

https://pubmed.ncbi.nlm.nih.gov/36222993/

New neurons can be generated in a dish (amazing in itself), and then these neurons studied to examine how they grow, expand, divide and connect. Using this technology, researchers are finding differences in different cell functions in different forms of autism. These differences are in proliferation, which is an increase in the number of cells, as well as the ability of those cells to signal to each other once they are formed. Some autism brains have too many cells, others do not proliferate as quickly as typical developing cells. These things are somewhat dependent on the genetic background which controls head size.

While these different forms of autism all have differences in proliferation, sometimes in different directions, they are all altered, regardless of the genes involved. So, is this one basic biological features that may help identify autism from the earliest points in development? Since they can be studied at any time in life, is this a new biomarker? Much needs to be studied but please listen to this week’s ASF podcast with Dr. Robert Connacher to learn more about the studies going on at Rutgers University to examine this issue.

https://www.sciencedirect.com/science/article/pii/S2213671122002089

We’ve heard a lot about social robots – do they help? One or two studies are not going to answer this, but a systematic review and meta analysis will! It turns out when you combined all the data, they do help in social abilities, but not other areas. This is how technology can help those with autism, especially technology which can be adapted to address the heterogeneity across the spectrum. And what about more subtle changes in the environment like light, sound, the built environment in classrooms and the home? Are there things that can be done that should be taken into account when these things are being built or modified? Again, a review article can help decipher all of the little studies that have been published over the year. Listen here for specific recommendations for builders, architects, and even you as you make your home more autism friendly.

https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0269800

https://journals.sagepub.com/doi/10.1177/13623613221102753?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%20%200pubmed

https://lukerosen212.medium.com/the-supreme-courts-decision-impact-on-the-rare-genetic-disease-community-f9ac22bd1411

The amygdala has been shown to be differently sized in autistic people – at first it is too big then it becomes smaller than typically developing people. But how early are these differences seen and does it relate to a diagnosis? The Infant Brain Imaging Study tackled this question in a recent study which compared those who were likely to develop autism at 6 months to those with Fragile X to see if there were differences and if it was specific to autism. Their findings will surprise you and have implications for targeted supports and interventions. Listen to the podcast here.

https://pubmed.ncbi.nlm.nih.gov/35331012/

At this year’s International Society of Autism Research meeting in Austin, TX, there was a variety of themes explored. From early development and milestones, to intervention and supports, to different features like sensory issues, treatment, and how to solve the problem of heterogeneity. It comes down to this: Autism means different things to different people. This is just a small subset of everything that was presented at INSAR 2022 and I hope that if you want to see more, you advocate to have the presentations posted online or even have the program book made available publicly. In the meantime, enjoy the 30 minute summary here.

www.autism-insar.org

In February, the CDC worked with the American Academy of Pediatrics to update the developmental milestones that parents should use when referencing how their child is developing. These milestones describe what should be accomplished by times as young as 2 months and as old as 5 months. These are helpful to all parents who wonder “shouldn’t my child be walking by now” and “how many words should they be saying”? Pediatricians ask parents about these and parents are expected to know them, so prepare yourselves now. What are milestones? Why change them? What are the changes? Learn more on this week’s #ASFpodcast here.

Read the paper here.

https://www.cdc.gov/ncbddd/actearly/index.html

Awareness of autism has grown monumentally over the past 20 years. Yet, this increased awareness has not been accompanied by improvements in services to support autistic individuals and their families. Many fundamental questions remain about the care of people with autism—including which interventions are effective, for whom, when, and at what intensity. The Lancet Commission on the future of care and clinical research in autism aims to answer the question of what can be done in the next 5 years to address the current needs of autistic individuals and families worldwide

Background: Early identification of autism spectrum disorder (ASD) provides an opportunity for early intervention and improved developmental outcomes. The use of electroencephalography (EEG) in infancy has shown promise in predicting later ASD diagnoses and in identifying neural mechanisms underlying the disorder. Given the high co-morbidity with language impairment, we and others have speculated that infants who are later diagnosed with ASD have altered language learning, including phoneme discrimination. Phoneme learning occurs rapidly in infancy, so altered neural substrates during the first year of life may serve as early, accurate indicators of later autism diagnosis.

Methods: Using EEG data collected at two different ages during a passive phoneme task in infants with high familial risk for ASD, we compared the predictive accuracy of a combination of feature selection and machine learning models at 6 months (during native phoneme learning) and 12 months (after native phoneme learning), and we identified a single model with strong predictive accuracy (100%) for both ages. Samples at both ages were matched in size and diagnoses (n = 14 with later ASD; n = 40 without ASD). Features included a combination of power and nonlinear measures across the 10‑20 montage electrodes and 6 frequency bands. Predictive features at each age were compared both by feature characteristics and EEG scalp location. Additional prediction analyses were performed on all EEGs collected at 12 months; this larger sample included 67 HR infants (27 HR-ASD, 40 HR-noASD).

Results: Using a combination of Pearson correlation feature selection and support vector machine classifier, 100% predictive diagnostic accuracy was observed at both 6 and 12 months. Predictive features differed between the models trained on 6- versus 12-month data. At 6 months, predictive features were biased to measures from central electrodes, power measures, and frequencies in the alpha range. At 12 months, predictive features were more distributed between power and nonlinear measures, and biased toward frequencies in the beta range. However, diagnosis prediction accuracy substantially decreased in the larger, more behaviorally heterogeneous 12-month sample.

Conclusions: These results demonstrate that speech processing EEG measures can facilitate earlier identification of ASD but emphasize the need for age-specific predictive models with large sample sizes to develop clinically relevant classification algorithms.

Keywords: Autism; EEG; Infant; Language development; Machine learning; Sensitive period.

In this study we investigated the impact of parental language input on language development and associated neuroscillatory patterns in toddlers at risk of Autism Spectrum Disorder (ASD). Forty-six mother-toddler dyads at either high (n = 22) or low (n = 24) familial risk of ASD completed a longitudinal, prospective study including free-play, resting electroencephalography, and standardized language assessments. Input quantity/quality at 18 months positively predicted expressive language at 24 months, and relationships were stronger for high-risk toddlers. Moderated mediations revealed that input-language relationships were explained by 24-month frontal and temporal gamma power (30-50 Hz) for high-risk toddlers who would later develop ASD. Results suggest that high-risk toddlers may be cognitively and neurally more sensitive to their language environments, which has implications for early intervention.

Keywords: Autism; EEG; Early experience; Language development; Language input.

Infant vocalizations are early-emerging communicative markers shown to be atypical in autism spectrum disorder (ASD), but few longitudinal, prospective studies exist. In this study, 23,850 infant vocalizations from infants at low (LR)- and high (HR)-risk for ASD (HR-ASD = 23, female = 3; HR-Neg = 35, female = 13; LR = 32, female = 10; 80% White; collected from 2007 to 2017 near Philadelphia) were analyzed at 6, 12, and 24 months. At 12 months, HR-ASD infants produced fewer vocalizations than HR-Neg infants. From 6 to 24 months, HR-Neg infants demonstrated steeper vocalization growth compared to HR-ASD and LR infants. Finally, among HR infants, vocalizing at 12 months was associated with language, social phenotype, and diagnosis at age 2. Infant vocalizing is an objective behavioral marker that could facilitate earlier detection of ASD.