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.
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.
Background: Recent large-scale initiatives have led to systematically collected phenotypic data for several rare genetic conditions implicated in autism spectrum disorder (ASD). The onset of developmentally expected skills (e.g. walking, talking) serve as readily quantifiable aspects of the behavioral phenotype. This study’s aims were: (a) describe the distribution of ages of attainment of gross motor and expressive language milestones in several rare genetic conditions, and (b) characterize the likelihood of delays in these conditions compared with idiopathic ASD.
Methods: Participants aged 3 years and older were drawn from two Simons Foundation Autism Research Initiative registries that employed consistent phenotyping protocols. Inclusion criteria were a confirmed genetic diagnosis of one of 16 genetic conditions (Simons Searchlight) or absence of known pathogenic genetic findings in individuals with ASD (SPARK). Parent-reported age of acquisition of three gross motor and two expressive language milestones was described and categorized as on-time or delayed, relative to normative expectations.
Results: Developmental milestone profiles of probands with genetic conditions were marked by extensive delays (including nonattainment), with highest severity in single gene conditions and more delays than idiopathic ASD in motor skills. Compared with idiopathic ASD, the median odds of delay among the genetic groups were higher by 8.3 times (IQR 5.8-16.3) for sitting, 12.4 times (IQR 5.3-19.5) for crawling, 26.8 times (IQR 7.7-41.1) for walking, 2.7 times (IQR 1.7-5.5) for single words, and 5.7 times (IQR 2.7-18.3) for combined words.
Conclusions: Delays in developmental milestones, particularly in gross motor skills, are frequent and may be among the earliest indicators of differentially affected developmental processes in specific genetically defined conditions associated with ASD, as compared with those with clinical diagnoses of idiopathic ASD. The possibility of different developmental pathways leading to ASD-associated phenotypes should be considered when deciding how to employ specific genetic conditions as models for ASD.
Keywords: copy number variant; developmental phenotype; intellectual disability.
© 2021 Association for Child and Adolescent Mental Health. This article has been contributed to by US Government employees and their work is in the public domain in the USA.
The COVID-19 pandemic has posed unique challenges for families and caregivers, as well as for autism-focused clinicians, who are faced with providing a thorough and accurate evaluation of children’s specific needs and diagnoses in the absence of in-person assessment tools. The shift to telehealth assessments has challenged clinicians to reconsider approaches and assumptions that underlie the diagnostic assessment process, and to adopt new ways of individualizing standard assessments according to family and child needs. Mandates for physical distancing have uncovered deficiencies in diagnostic practices for suspected autism and have illuminated biases that have posed obstacles preventing children and families from receiving the services that they truly need. This Commentary outlines several considerations for improving diagnostic practices as we move forward from the current pandemic and continue to strive to build an adaptable, sustainable, equitable, and family-centered system of care. LAY SUMMARY: Physical distancing and the abrupt end to in-person services for many children on the autism spectrum has forced clinicians to examine the existing challenges with autism spectrum disorder (ASD) diagnostic assessment and consider things they want to keep and things that should be changed in the years ahead. New approaches such as telehealth both alleviated and exacerbated existing disparities, and brought into stark focus the importance of equitable and timely access to family-centered care. This commentary suggests ways of improving clinical practices related to ASD assessment to continue along this path.
Keywords: assessment; autism; challenges; children; diagnosis; disparities; pandemic.
Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by social deficits and repetitive behaviors that typically emerge by 24 months of age. To develop effective early interventions that can potentially ameliorate the defining deficits of ASD and improve long-term outcomes, early detection is essential. Using prospective neuroimaging of 59 6-month-old infants with a high familial risk for ASD, we show that functional connectivity magnetic resonance imaging correctly identified which individual children would receive a research clinical best-estimate diagnosis of ASD at 24 months of age. Functional brain connections were defined in 6-month-old infants that correlated with 24-month scores on measures of social behavior, language, motor development, and repetitive behavior, which are all features common to the diagnosis of ASD. A fully cross-validated machine learning algorithm applied at age 6 months had a positive predictive value of 100% [95% confidence interval (CI), 62.9 to 100], correctly predicting 9 of 11 infants who received a diagnosis of ASD at 24 months (sensitivity, 81.8%; 95% CI, 47.8 to 96.8). All 48 6-month-old infants who were not diagnosed with ASD were correctly classified [specificity, 100% (95% CI, 90.8 to 100); negative predictive value, 96.0% (95% CI, 85.1 to 99.3)]. These findings have clinical implications for early risk assessment and the feasibility of developing early preventative interventions for ASD.
Racial differences in parent report of concerns about their child’s development to healthcare providers may contribute to delayed autism spectrum disorder diagnoses in Black children. We tested the hypotheses that compared to White parents, Black parents of children with autism spectrum disorder would report fewer concerns about autism symptoms and would be more likely to report concerns about disruptive behaviors. A sample of 18- to 40-month-old toddlers ( N = 174) with autism spectrum disorder and their parent participated. After screening positive for autism spectrum disorder risk, but prior to a diagnostic evaluation, parents completed free-response questions soliciting concerns about their child’s development. Parent responses were coded for the presence or the absence of 10 possible concerns, which were grouped into autism concerns (e.g. social and restricted and repetitive behavior concerns) or non-autism concerns (e.g. general developmental and disruptive behavior concerns). Compared to White parents, Black parents reported significantly fewer autism concerns and fewer social and restricted and repetitive behavior concerns. However, Black parents did not report significantly fewer non-autism concerns. Race did not influence parent report of disruptive behavior concerns. Lower reporting of autism concerns by Black parents may impact providers’ abilities to identify children who need further screening or evaluation.
Autism spectrum disorder (ASD) is a complex and heterogeneous disorder, diagnosed on the basis of behavioral symptoms during the second year of life or later. Finding scalable biomarkers for early detection is challenging because of the variability in presentation of the disorder and the need for simple measurements that could be implemented routinely during well-baby checkups. EEG is a relatively easy-to-use, low cost brain measurement tool that is being increasingly explored as a potential clinical tool for monitoring atypical brain development. EEG measurements were collected from 99 infants with an older sibling diagnosed with ASD, and 89 low risk controls, beginning at 3 months of age and continuing until 36 months of age. Nonlinear features were computed from EEG signals and used as input to statistical learning methods. Prediction of the clinical diagnostic outcome of ASD or not ASD was highly accurate when using EEG measurements from as early as 3 months of age. Specificity, sensitivity and PPV were high, exceeding 95% at some ages. Prediction of ADOS calibrated severity scores for all infants in the study using only EEG data taken as early as 3 months of age was strongly correlated with the actual measured scores. This suggests that useful digital biomarkers might be extracted from EEG measurements.