Podcast: The earliest differences across ASDs

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.


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.




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.


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.


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.


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.


Autism spectrum disorder (ASD) is a neurodevelopmental disorder diagnosed based on social impairment, restricted interests, and repetitive behaviors. Contemporary theories posit that cerebellar pathology contributes causally to ASD by disrupting error-based learning (EBL) during infancy. The present study represents the first test of this theory in a prospective infant sample, with potential implications for ASD detection.


Data from the Infant Brain Imaging Study (n=94, 68 male) were used to examine 6-month cerebellar functional connectivity (fcMRI) in relation to later (12/24-month) ASD-associated behaviors and outcomes. Hypothesis-driven univariate analyses and machine learning-based predictive tests examined cerebellar-frontoparietal (FPN; subserves error signaling in support of EBL) and cerebellar-default mode (DMN; broadly implicated in ASD) network connections. Cerebellar-FPN functional connectivity was used as a proxy for EBL, and cerebellar-DMN functional connectivity provided a comparative foil. Data-driven fcMRI enrichment examined brain-wide behavioral associations, with post-hoc tests of cerebellar connections.


Cerebellar-FPN and cerebellar-DMN connections did not demonstrate associations with ASD. fcMRI enrichment identified 6-month correlates of later ASD-associated behaviors in networks of a priori interest (FPN, DMN), as well as in cingulo-opercular (also implicated in error signaling) and medial visual networks. Post-hoc tests did not suggest a role for cerebellar connections.


We failed to identify cerebellar functional connectivity-based contributions to ASD. However, we observed prospective correlates of ASD-associated behaviors in networks that support EBL. Future studies may replicate and extend network-level positive results, and tests of the cerebellum may investigate brain-behavior associations at different developmental stages and/or using different neuroimaging modalities.


Autismfunctional connectivitycerebelluminfancyerror-based learningdevelopment

Although studies of PAF in individuals with autism spectrum disorder (ASD) report group differences and associations with non-verbal cognitive ability, it is not known how PAF relates to familial risk for ASD, and whether similar associations with cognition in are present in infancy. Using a large multi-site prospective longitudinal dataset of infants with low and high familial risk for ASD, metrics of PAF at 12 months were extracted and growth curves estimated for cognitive development between 12-36 months. Analyses tested whether PAF 1) differs between low and high risk infants, 2) is associated with concurrent non-verbal/verbal cognitive ability and 3) predicts developmental change in non-verbal/verbal ability. Moderation of associations between PAF and cognitive ability by familial risk status was also tested. No differences in 12-month PAF were found between low and high risk infants. PAF was associated with concurrent non-verbal cognitive ability, but did not predict change in non-verbal cognitive over development. No associations were found between PAF and verbal ability, along with no evidence of moderation. PAF is not related to familial risk for ASD, and is a neural marker of concurrent non-verbal cognitive ability, but not verbal ability, in young infants at low and high risk for ASD.

Keywords: Autism spectrum disorder; Cognitive development; EEG; Infant siblings; Peak alpha frequency.

Objective: To examine the emergence and trajectory of feeding difficulties in young children who are later diagnosed with autism spectrum disorder (ASD).

Methods: The Behavioral Pediatrics Feeding Assessment Scale (BPFAS) was administered to a sample of 93 toddlers with an older sibling with ASD-the high-risk group-and 62 toddlers with no known familial ASD-the low-risk group-as part of a larger infant sibling study. The BPFAS was completed by parents at 15, 18, 24, and 36 months of age. At 36 months, participants underwent a diagnostic assessment and were classified into 1 of the following 4 outcome groups: ASD, nontypical development, high-risk typically developing, and low-risk typically developing. The BPFAS was scored for total frequency of feeding difficulties and autism-specific factor scores previously described in the literature.

Results: The frequency of feeding difficulties increased significantly more rapidly in the ASD group between 15 and 36 months of age, and by 36 months, they exhibited a significantly higher total frequency score than all other groups. Analysis of the factor scores revealed a similar pattern for the food acceptance and mealtime behavior domains but no significant differences in the medical/oral motor domain.

Conclusion: Feeding difficulties develop significantly more rapidly in children with ASD, with longitudinal monitoring revealing the steeper trajectory earlier than can be detected with cross-sectional analysis. Children with ASD are at risk of health and social consequences of poor feeding behavior that may potentially be minimized if addressed early and appropriately.