Modeling intra-individual inter-trial EEG response variability in autism.

Autism spectrum disorder (autism) is a prevalent neurodevelopmental condition characterized by early emerging impairments in social behavior and communication. EEG represents a powerful and non-invasive tool for examining functional brain differences in autism. Recent EEG evidence suggests that greater intra-individual trial-to-trial variability across EEG responses in stimulus-related tasks may characterize brain differences in autism. Traditional analysis of EEG data largely focuses on mean trends of the trial-averaged data, where trial-level analysis is rarely performed due to low neural signal to noise ratio. We propose to use nonlinear (shape-invariant) mixed effects (NLME) models to study intra-individual inter-trial EEG response variability using trial-level EEG data. By providing more precise metrics of response variability, this approach could enrich our understanding of neural disparities in autism and potentially aid the identification of objective markers. The proposed multilevel NLME models quantify variability in the signal's interpretable and widely recognized features (e.g., latency and amplitude) while also regularizing estimation based on noisy trial-level data. Even though NLME models have been studied for more than three decades, existing methods cannot scale up to large data sets. We propose computationally feasible estimation and inference methods via the use of a novel minorization-maximization (MM) algorithm. Extensive simulations are conducted to show the efficacy of the proposed procedures. Applications to data from a large national consortium find that children with autism have larger intra-individual inter-trial variability in P1 latency in a visual evoked potential (VEP) task, compared to their neurotypical peers.

Cognitive Disengagement Syndrome in Young Autistic Children, Children with ADHD, and Autistic Children with ADHD.

OBJECTIVE: Cognitive Disengagement Syndrome (CDS; previously called Sluggish Cognitive Tempo) refers to a constellation of cognitive and motor behaviors characterized by a predisposition toward mind wandering (cognitive subdomain) and slowed motor behavior (hypoactive). While there are a number of studies linking CDS traits to greater global impairment in children with attention-deficit/hyperactivity disorder (ADHD) and autistic children, there are few studies examining the prevalence and impact of CDS traits in autistic children with co-occurring ADHD (Autistic+ADHD). The current study explored CDS traits in autistic children with and without co-occurring ADHD, children with ADHD, and neurotypical children. METHODS: Participants were 196 children between 3- and 7-years-of-age comprising four groups: Neurotypical (N = 44), ADHD (N = 51), Autistic (N = 55), and Autistic+ADHD (N = 46). CDS traits, social and communication skills, repetitive behaviors, and sensory processing were all assessed via parent report. RESULTS: Children diagnosed with ADHD, autistic children, and Autistic+ADHD children exhibited similar levels of overall CDS traits. However, when explored separately, Autistic+ADHD children had higher cognitive CDS trait scores compared to children with ADHD alone. Both overall CDS traits and the cognitive subdomain were associated with greater social difficulties, particularly social withdrawal, higher levels of repetitive behaviors, and more sensory sensitivities, regardless of diagnosis. CONCLUSIONS: Findings suggest that CDS traits may be an additional factor directly impact functional outcomes in both autistic and ADHD children. As such, clinicians should be assessing CDS traits in addition to other clinical domains associated with ADHD and autism when developing intervention plans for young neurodiverse children.

Complexity analysis of head movements in autistic toddlers.

BACKGROUND: Early differences in sensorimotor functioning have been documented in young autistic children and infants who are later diagnosed with autism. Previous research has demonstrated that autistic toddlers exhibit more frequent head movement when viewing dynamic audiovisual stimuli, compared to neurotypical toddlers. To further explore this behavioral characteristic, in this study, computer vision (CV) analysis was used to measure several aspects of head movement dynamics of autistic and neurotypical toddlers while they watched a set of brief movies with social and nonsocial content presented on a tablet. METHODS: Data were collected from 457 toddlers, 17-36 months old, during their well-child visit to four pediatric primary care clinics. Forty-one toddlers were subsequently diagnosed with autism. An application (app) displayed several brief movies on a tablet, and the toddlers watched these movies while sitting on their caregiver's lap. The front-facing camera in the tablet recorded the toddlers' behavioral responses. CV was used to measure the participants' head movement rate, movement acceleration, and complexity using multiscale entropy. RESULTS: Autistic toddlers exhibited significantly higher rate, acceleration, and complexity in their head movements while watching the movies compared to neurotypical toddlers, regardless of the type of movie content (social vs. nonsocial). The combined features of head movement acceleration and complexity reliably distinguished the autistic and neurotypical toddlers. CONCLUSIONS: Autistic toddlers exhibit differences in their head movement dynamics when viewing audiovisual stimuli. Higher complexity of their head movements suggests that their movements were less predictable and less stable compared to neurotypical toddlers. CV offers a scalable means of detecting subtle differences in head movement dynamics, which may be helpful in identifying early behaviors associated with autism and providing insight into the nature of sensorimotor differences associated with autism.

Enhancing early autism prediction based on electronic records using clinical narratives.

Recent work has shown that predictive models can be applied to structured electronic health record (EHR) data to stratify autism likelihood from an early age (<1 year). Integrating clinical narratives (or notes) with structured data has been shown to improve prediction performance in other clinical applications, but the added predictive value of this information in early autism prediction has not yet been explored. In this study, we aimed to enhance the performance of early autism prediction by using both structured EHR data and clinical narratives. We built models based on structured data and clinical narratives separately, and then an ensemble model that integrated both sources of data. We assessed the predictive value of these models from Duke University Health System over a 14-year span to evaluate ensemble models predicting later autism diagnosis (by age 4 years) from data collected from ages 30 to 360 days. Our sample included 11,750 children above by age 3 years (385 meeting autism diagnostic criteria). The ensemble model for autism prediction showed superior performance and at age 30 days achieved 46.8% sensitivity (95% confidence interval, CI: 22.0%, 52.9%), 28.0% positive predictive value (PPV) at high (90%) specificity (CI: 2.0%, 33.1%), and AUC4 (with at least 4-year follow-up for controls) reaching 0.769 (CI: 0.715, 0.811). Prediction by 360 days achieved 44.5% sensitivity (CI: 23.6%, 62.9%), and 13.7% PPV at high (90%) specificity (CI: 9.6%, 18.9%), and AUC4 reaching 0.797 (CI: 0.746, 0.840). Results show that incorporating clinical narratives in early autism prediction achieved promising accuracy by age 30 days, outperforming models based on structured data only. Furthermore, findings suggest that additional features learned from clinician narratives might be hypothesis generating for understanding early development in autism.

Multilevel hybrid principal components analysis for region-referenced functional electroencephalography data.

Electroencephalography experiments produce region-referenced functional data representing brain signals in the time or the frequency domain collected across the scalp. The data typically also have a multilevel structure with high-dimensional observations collected across multiple experimental conditions or visits. Common analysis approaches reduce the data complexity by collapsing the functional and regional dimensions, where event-related potential (ERP) features or band power are targeted in a pre-specified scalp region. This practice can fail to portray more comprehensive differences in the entire ERP signal or the power spectral density (PSD) across the scalp. Building on the weak separability of the high-dimensional covariance process, the proposed multilevel hybrid principal components analysis (M-HPCA) utilizes dimension reduction tools from both vector and functional principal components analysis to decompose the total variation into between- and within-subject variance. The resulting model components are estimated in a mixed effects modeling framework via a computationally efficient minorization-maximization algorithm coupled with bootstrap. The diverse array of applications of M-HPCA is showcased with two studies of individuals with autism. While ERP responses to match vs mismatch conditions are compared in an audio odd-ball paradigm in the first study, short-term reliability of the PSD across visits is compared in the second. Finite sample properties of the proposed methodology are studied in extensive simulations.

A scalable computational approach to assessing response to name in toddlers with autism.

BACKGROUND: This study is part of a larger research program focused on developing objective, scalable tools for digital behavioral phenotyping. We evaluated whether a digital app delivered on a smartphone or tablet using computer vision analysis (CVA) can elicit and accurately measure one of the most common early autism symptoms, namely failure to respond to a name call. METHODS: During a pediatric primary care well-child visit, 910 toddlers, 17-37 months old, were administered an app on an iPhone or iPad consisting of brief movies during which the child's name was called three times by an examiner standing behind them. Thirty-seven toddlers were subsequently diagnosed with autism spectrum disorder (ASD). Name calls and children's behavior were recorded by the camera embedded in the device, and children's head turns were coded by both CVA and a human. RESULTS: CVA coding of response to name was found to be comparable to human coding. Based on CVA, children with ASD responded to their name significantly less frequently than children without ASD. CVA also revealed that children with ASD who did orient to their name exhibited a longer latency before turning their head. Combining information about both the frequency and the delay in response to name improved the ability to distinguish toddlers with and without ASD. CONCLUSIONS: A digital app delivered on an iPhone or iPad in real-world settings using computer vision analysis to quantify behavior can reliably detect a key early autism symptom-failure to respond to name. Moreover, the higher resolution offered by CVA identified a delay in head turn in toddlers with ASD who did respond to their name. Digital phenotyping is a promising methodology for early assessment of ASD symptoms.

A Phase II Randomized Clinical Trial of the Safety and Efficacy of Intravenous Umbilical Cord Blood Infusion for Treatment of Children with Autism Spectrum Disorder.

OBJECTIVE: To evaluate whether umbilical cord blood (CB) infusion is safe and associated with improved social and communication abilities in children with autism spectrum disorder (ASD). STUDY DESIGN: This prospective, randomized, placebo-controlled, double-blind study included 180 children with ASD, aged 2-7 years, who received a single intravenous autologous (n = 56) or allogeneic (n = 63) CB infusion vs placebo (n = 61) and were evaluated at 6 months postinfusion. RESULTS: CB infusion was safe and well tolerated. Analysis of the entire sample showed no evidence that CB was associated with improvements in the primary outcome, social communication (Vineland Adaptive Behavior Scales-3 [VABS-3] Socialization Domain), or the secondary outcomes, autism symptoms (Pervasive Developmental Disorder Behavior Inventory) and vocabulary (Expressive One-Word Picture Vocabulary Test). There was also no overall evidence of differential effects by type of CB infused. In a subanalysis of children without intellectual disability (ID), allogeneic, but not autologous, CB was associated with improvement in a larger percentage of children on the clinician-rated Clinical Global Impression-Improvement scale, but the OR for improvement was not significant. Children without ID treated with CB showed significant improvements in communication skills (VABS-3 Communication Domain), and exploratory measures including attention to toys and sustained attention (eye-tracking) and increased alpha and beta electroencephalographic power. CONCLUSIONS: Overall, a single infusion of CB was not associated with improved socialization skills or reduced autism symptoms. More research is warranted to determine whether CB infusion is an effective treatment for some children with ASD.

Synaptic, transcriptional and chromatin genes disrupted in autism.

The genetic architecture of autism spectrum disorder involves the interplay of common and rare variants and their impact on hundreds of genes. Using exome sequencing, here we show that analysis of rare coding variation in 3,871 autism cases and 9,937 ancestry-matched or parental controls implicates 22 autosomal genes at a false discovery rate (FDR) < 0.05, plus a set of 107 autosomal genes strongly enriched for those likely to affect risk (FDR < 0.30). These 107 genes, which show unusual evolutionary constraint against mutations, incur de novo loss-of-function mutations in over 5% of autistic subjects. Many of the genes implicated encode proteins for synaptic formation, transcriptional regulation and chromatin-remodelling pathways. These include voltage-gated ion channels regulating the propagation of action potentials, pacemaking and excitability-transcription coupling, as well as histone-modifying enzymes and chromatin remodellers-most prominently those that mediate post-translational lysine methylation/demethylation modifications of histones.

Social impairments in alternating hemiplegia of childhood.

AIM: To evaluate presence and severity of social impairments in alternating hemiplegia of childhood (AHC) and determine factors that are associated with social impairments. METHOD: This was a retrospective analysis of 34 consecutive patients with AHC (19 females, 15 males; mean age: 9y 7mo, SD 8y 2mo, range 2y 7mo-40y), evaluated with the Social Responsiveness Scale, Second Edition (SRS-2). RESULTS: SRS-2 scores, indicating level of social impairment, were higher than population means (75, SD 14 vs 50, SD 10, p<0.001). Of these, 27 out of 34 had high scores: 23 severe (>76), four moderate (66-76). All subscale domains, including social cognition, social communication, social awareness, social motivation, restricted interests, and repetitive behavior, had abnormal scores compared to population means (p<0.001). High SRS-2 scores were associated with the presence of autism spectrum disorder (ASD) and epilepsy (p=0.01, p=0.04), but not with other scales of AHC disease symptomatology. All nine patients who received formal evaluations for ASD, because they had high SRS-2 scores, were diagnosed with ASD. INTERPRETATION: Most patients with AHC have impaired social skills involving multiple domains. ASD is not uncommon. High SRS-2 scores in patients with AHC support referral to ASD evaluation. Our findings are consistent with current understandings of the pathophysiology of AHC and ASD, both thought to involve GABAergic dysfunction. WHAT THIS PAPER ADDS: Most patients with alternating hemiplegia of childhood (AHC) have impaired social skills involving multiple domains. These impairments are significant compared to population means. Most patients with AHC have high Social Responsiveness Scale, Second Edition (SRS-2) scores. Patients with AHC with high SRS-2 scores are likely to have autism spectrum disorder.

Use of a Digital Modified Checklist for Autism in Toddlers - Revised with Follow-up to Improve Quality of Screening for Autism.

OBJECTIVES: To assess changes in quality of care for children at risk for autism spectrum disorders (ASD) due to process improvement and implementation of a digital screening form. STUDY DESIGN: The process of screening for ASD was studied in an academic primary care pediatrics clinic before and after implementation of a digital version of the Modified Checklist for Autism in Toddlers - Revised with Follow-up with automated risk assessment. Quality metrics included accuracy of documentation of screening results and appropriate action for positive screens (secondary screening or referral). Participating physicians completed pre- and postintervention surveys to measure changes in attitudes toward feasibility and value of screening for ASD. Evidence of change was evaluated with statistical process control charts and χ2 tests. RESULTS: Accurate documentation in the electronic health record of screening results increased from 54% to 92% (38% increase, 95% CI 14%-64%) and appropriate action for children screening positive increased from 25% to 85% (60% increase, 95% CI 35%-85%). A total of 90% of participating physicians agreed that the transition to a digital screening form improved their clinical assessment of autism risk. CONCLUSIONS: Implementation of a tablet-based digital version of the Modified Checklist for Autism in Toddlers - Revised with Follow-up led to improved quality of care for children at risk for ASD and increased acceptability of screening for ASD. Continued efforts towards improving the process of screening for ASD could facilitate rapid, early diagnosis of ASD and advance the accuracy of studies of the impact of screening.

Detection of clinically relevant genetic variants in autism spectrum disorder by whole-genome sequencing.

Autism Spectrum Disorder (ASD) demonstrates high heritability and familial clustering, yet the genetic causes remain only partially understood as a result of extensive clinical and genomic heterogeneity. Whole-genome sequencing (WGS) shows promise as a tool for identifying ASD risk genes as well as unreported mutations in known loci, but an assessment of its full utility in an ASD group has not been performed. We used WGS to examine 32 families with ASD to detect de novo or rare inherited genetic variants predicted to be deleterious (loss-of-function and damaging missense mutations). Among ASD probands, we identified deleterious de novo mutations in six of 32 (19%) families and X-linked or autosomal inherited alterations in ten of 32 (31%) families (some had combinations of mutations). The proportion of families identified with such putative mutations was larger than has been previously reported; this yield was in part due to the comprehensive and uniform coverage afforded by WGS. Deleterious variants were found in four unrecognized, nine known, and eight candidate ASD risk genes. Examples include CAPRIN1 and AFF2 (both linked to FMR1, which is involved in fragile X syndrome), VIP (involved in social-cognitive deficits), and other genes such as SCN2A and KCNQ2 (linked to epilepsy), NRXN1, and CHD7, which causes ASD-associated CHARGE syndrome. Taken together, these results suggest that WGS and thorough bioinformatic analyses for de novo and rare inherited mutations will improve the detection of genetic variants likely to be associated with ASD or its accompanying clinical symptoms.

Functional impact of global rare copy number variation in autism spectrum disorders.

The autism spectrum disorders (ASDs) are a group of conditions characterized by impairments in reciprocal social interaction and communication, and the presence of restricted and repetitive behaviours. Individuals with an ASD vary greatly in cognitive development, which can range from above average to intellectual disability. Although ASDs are known to be highly heritable ( approximately 90%), the underlying genetic determinants are still largely unknown. Here we analysed the genome-wide characteristics of rare (<1% frequency) copy number variation in ASD using dense genotyping arrays. When comparing 996 ASD individuals of European ancestry to 1,287 matched controls, cases were found to carry a higher global burden of rare, genic copy number variants (CNVs) (1.19 fold, P = 0.012), especially so for loci previously implicated in either ASD and/or intellectual disability (1.69 fold, P = 3.4 x 10(-4)). Among the CNVs there were numerous de novo and inherited events, sometimes in combination in a given family, implicating many novel ASD genes such as SHANK2, SYNGAP1, DLGAP2 and the X-linked DDX53-PTCHD1 locus. We also discovered an enrichment of CNVs disrupting functional gene sets involved in cellular proliferation, projection and motility, and GTPase/Ras signalling. Our results reveal many new genetic and functional targets in ASD that may lead to final connected pathways.

Autism genome-wide copy number variation reveals ubiquitin and neuronal genes.

Autism spectrum disorders (ASDs) are childhood neurodevelopmental disorders with complex genetic origins. Previous studies focusing on candidate genes or genomic regions have identified several copy number variations (CNVs) that are associated with an increased risk of ASDs. Here we present the results from a whole-genome CNV study on a cohort of 859 ASD cases and 1,409 healthy children of European ancestry who were genotyped with approximately 550,000 single nucleotide polymorphism markers, in an attempt to comprehensively identify CNVs conferring susceptibility to ASDs. Positive findings were evaluated in an independent cohort of 1,336 ASD cases and 1,110 controls of European ancestry. Besides previously reported ASD candidate genes, such as NRXN1 (ref. 10) and CNTN4 (refs 11, 12), several new susceptibility genes encoding neuronal cell-adhesion molecules, including NLGN1 and ASTN2, were enriched with CNVs in ASD cases compared to controls (P = 9.5 x 10(-3)). Furthermore, CNVs within or surrounding genes involved in the ubiquitin pathways, including UBE3A, PARK2, RFWD2 and FBXO40, were affected by CNVs not observed in controls (P = 3.3 x 10(-3)). We also identified duplications 55 kilobases upstream of complementary DNA AK123120 (P = 3.6 x 10(-6)). Although these variants may be individually rare, they target genes involved in neuronal cell-adhesion or ubiquitin degradation, indicating that these two important gene networks expressed within the central nervous system may contribute to the genetic susceptibility of ASD.

Addressing the Gap in Research Training in Child Psychiatry and Neurodevelopment.

Psychiatric and neurodevelopmental conditions in children are common, often co-occur, and can be highly impairing. Moreover, psychiatric disorders that typically do not fully manifest until adulthood, such as schizophrenia, have their roots in early development, with atypical brain and behavioral patterns arising well before a clinical diagnosis is made. The relevance of brain development to improving outcomes of psychiatric and neurodevelopmental conditions underscores the need to cultivate a pipeline of investigators with the necessary training to conduct rigorous, developmentally focused research.

Prevalence of bias against neurodivergence-related terms in artificial intelligence language models.

Given the increasing role of artificial intelligence (AI) in many decision-making processes, we investigate the presence of AI bias towards terms related to a range of neurodivergent conditions, including autism, ADHD, schizophrenia, and obsessive-compulsive disorder (OCD). We use 11 different language model encoders to test the degree to which words related to neurodiversity are associated with groups of words related to danger, disease, badness, and other negative concepts. For each group of words tested, we report the mean strength of association (Word Embedding Association Test [WEAT] score) averaged over all encoders and find generally high levels of bias. Additionally, we show that bias occurs even when testing words associated with autistic or neurodivergent strengths. For example, embedders had a negative average association between words related to autism and words related to honesty, despite honesty being considered a common strength of autistic individuals. Finally, we introduce a sentence similarity ratio test and demonstrate that many sentences describing types of disabilities, for example, "I have autism" or "I have epilepsy," have even stronger negative associations than control sentences such as "I am a bank robber."

Autistic Individuals Do Not Alter Visual Processing Strategy During Encoding Versus Recognition of Faces: A Hidden Markov Modeling Approach.

PURPOSE: Visual face recognition-the ability to encode, discriminate, and recognize the faces of others-is fundamentally supported by eye movements and is a common source of difficulty for autistic individuals. We aimed to evaluate how visual processing strategies (i.e., eye movement patterns) directly support encoding and recognition of faces in autistic and neurotypical (NT) individuals. METHODS: We used a hidden Markov modeling approach to evaluate the spatiotemporal dynamics of eye movements in autistic (n = 15) and neurotypical (NT) adolescents (n = 17) during a face identity recognition task. RESULTS: We discovered distinct eye movement patterns among all participants, which included a focused and exploratory strategy. When evaluating change in visual processing strategy across encoding and recognition phases, autistic individuals did not shift their eye movement patterns like their NT peers, who shifted to a more exploratory visual processing strategy during recognition. CONCLUSION: These findings suggest that autistic individuals do not modulate their visual processing strategy across encoding and recognition of faces, which may be an indicator of less efficient face processing.

Brief Report: Relationships Between Caregiver-Reported Externalizing and Internalizing Behaviors and the Modified Checklist for Autism in Toddlers.

PURPOSE: Early detection and intervention are associated with improved outcomes for autistic children. Thus, it is important to understand factors influencing early screening tools designed to detect autism. This study examined the relationship between caregiver-reported emotional and behavioral symptoms and children's scores on a commonly used autism screening questionnaire, the Modified Checklist for Autism in Toddlers-Revised with Follow-Up (M-CHAT-R/F). METHODS: Toddlers were recruited from four primary care clinics between 2018 and 2021. Their caregivers completed the M-CHAT-R/F as well as the Child Behavior Checklist (CBCL), a well-validated, normed measure of emotional and behavioral functioning. Correlational and group analyses were evaluated to examine relationships between CBCL scales and M-CHAT-R/F scores. RESULTS: 1765 toddlers were recruited for the study. CBCL scores for the internalizing, externalizing, autism, ADHD, and anxiety scales were all modestly positively correlated with M-CHAT-R/F scores. Compared to toddlers with elevated autism scale scores only, toddlers with elevations in both autism and ADHD/externalizing scales had higher M-CHAT-R/F scores. In contrast, no significant difference in scores were found between toddlers with elevated autism scale scores only compared to those with elevated scores on both autism and internalizing scales. CONCLUSION: Findings suggest that, for children with elevated autism behaviors, the presence of externalizing symptoms, including ADHD-related concerns, is associated with elevated scores on the M-CHAT-R/F. In contrast, internalizing symptoms did not show an association with elevated M-CHAT-R/F scores among toddlers with elevated autism-related behaviors. Interpretation of the M-CHAT-R/F should include consideration of co-occurring psychiatric conditions, especially externalizing conditions such as ADHD.

Parsing evoked and induced gamma response differences in Autism: A visual evoked potential study.

OBJECTIVE: Electroencephalography (EEG) measures of visual evoked potentials (VEPs) provide a targeted approach for investigating neural circuit dynamics. This study separately analyses phase-locked (evoked) and non-phase-locked (induced) gamma responses within the VEP to comprehensively investigate circuit differences in autism. METHODS: We analyzed VEP data from 237 autistic and 114 typically developing (TD) children aged 6-11, collected through the Autism Biomarkers Consortium for Clinical Trials (ABC-CT). Evoked and induced gamma (30-90 Hz) responses were separately quantified using a wavelet-based time-frequency analysis, and group differences were evaluated using a permutation-based clustering procedure. RESULTS: Autistic children exhibited reduced evoked gamma power but increased induced gamma power compared to TD peers. Group differences in induced responses showed the most prominent effect size and remained statistically significant after excluding outliers. CONCLUSIONS: Our study corroborates recent research indicating diminished evoked gamma responses in children with autism. Additionally, we observed a pronounced increase in induced power. Building upon existing ABC-CT findings, these results highlight the potential to detect variations in gamma-related neural activity, despite the absence of significant group differences in time-domain VEP components. SIGNIFICANCE: The contrasting patterns of decreased evoked and increased induced gamma activity in autistic children suggest that a combination of different EEG metrics may provide a clearer characterization of autism-related circuitry than individual markers alone.