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.

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.

Autism Caregiver Coaching in Africa (ACACIA): Protocol for a type 1-hybrid effectiveness-implementation trial.

BACKGROUND: While early autism intervention can significantly improve outcomes, gaps in implementation exist globally. These gaps are clearest in Africa, where forty percent of the world's children will live by 2050. Task-sharing early intervention to non-specialists is a key implementation strategy, given the lack of specialists in Africa. Naturalistic Developmental Behavioral Interventions (NDBI) are a class of early autism intervention that can be delivered by caregivers. As a foundational step to address the early autism intervention gap, we adapted a non-specialist delivered caregiver coaching NDBI for the South African context, and pre-piloted this cascaded task-sharing approach in an existing system of care. OBJECTIVES: First, we will test the effectiveness of the caregiver coaching NDBI compared to usual care. Second, we will describe coaching implementation factors within the Western Cape Department of Education in South Africa. METHODS: This is a type 1 effectiveness-implementation hybrid design; assessor-blinded, group randomized controlled trial. Participants include 150 autistic children (18-72 months) and their caregivers who live in Cape Town, South Africa, and those involved in intervention implementation. Early Childhood Development practitioners, employed by the Department of Education, will deliver 12, one hour, coaching sessions to the intervention group. The control group will receive usual care. Distal co-primary outcomes include the Communication Domain Standard Score (Vineland Adaptive Behavior Scales, Third Edition) and the Language and Communication Developmental Quotient (Griffiths Scales of Child Development, Third Edition). Proximal secondary outcome include caregiver strategies measured by the sum of five items from the Joint Engagement Rating Inventory. We will describe key implementation determinants. RESULTS: Participant enrolment started in April 2023. Estimated primary completion date is March 2027. CONCLUSION: The ACACIA trial will determine whether a cascaded task-sharing intervention delivered in an educational setting leads to meaningful improvements in communication abilities of autistic children, and identify implementation barriers and facilitators. TRIAL REGISTRATION: NCT05551728 in Clinical Trial Registry (https://clinicaltrials.gov).

Proximity analysis of native proteomes reveals phenotypic modifiers in a mouse model of autism and related neurodevelopmental conditions.

One of the main drivers of autism spectrum disorder is risk alleles within hundreds of genes, which may interact within shared but unknown protein complexes. Here we develop a scalable genome-editing-mediated approach to target 14 high-confidence autism risk genes within the mouse brain for proximity-based endogenous proteomics, achieving the identification of high-specificity spatial proteomes. The resulting native proximity proteomes are enriched for human genes dysregulated in the brain of autistic individuals, and reveal proximity interactions between proteins from high-confidence risk genes with those of lower-confidence that may provide new avenues to prioritize genetic risk. Importantly, the datasets are enriched for shared cellular functions and genetic interactions that may underlie the condition. We test this notion by spatial proteomics and CRISPR-based regulation of expression in two autism models, demonstrating functional interactions that modulate mechanisms of their dysregulation. Together, these results reveal native proteome networks in vivo relevant to autism, providing new inroads for understanding and manipulating the cellular drivers underpinning its etiology.

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.