Attentive brain states in infants with and without later autism

Autism Spectrum Disorder (ASD) affects a significant portion of the population, yet the underlying mechanisms remain largely unknown. ASD is characterized by difficulties in social communication and interaction, restrictive behaviors, and sensory sensitivities. Given its high heritability and late diagnosis (around age 4), longitudinal studies examining brain development from infancy offer crucial insights into ASD's emergence. This study focuses on the engagement of attentive brain states in response to social stimuli as a potential contributor to ASD traits. Previous research suggests that attention to social cues, particularly direct gaze, might be altered in infants who later develop ASD. Studies have shown behavioral differences in social attention as early as 6 months, with infants later diagnosed with ASD exhibiting declining attention to eyes and reduced attention to faces, particularly during conversations. While previous research has highlighted altered neural attention responses in toddlers with ASD and linked stronger brain responses to faces with improved social symptoms after intervention, it remains unclear whether atypical neural responses to people are evident between 6 and 12 months when brain regions become increasingly tuned to social cues. Prior studies using event-related potentials (ERPs), focusing on the Nc component, have shown smaller amplitudes and shorter latencies in infants who later developed ASD when attending to faces. However, traditional ERP analyses focus on pre-selected regions of interest rather than the broader distributed network involved in attention. This study aims to address these limitations by combining traditional topographic analyses with data-driven approaches to examine infants with and without later ASD, focusing on the Nc component and fine-grained analysis of brain states (microstates) to understand the relationship between attentive brain states and ASD.

The literature review summarizes existing research on early social attention deficits in infants later diagnosed with ASD. Studies using eye-tracking have revealed differences in attention to faces and gaze direction in high-risk infants compared to low-risk infants. Electrophysiological studies using ERPs have focused on the Nc component, finding differences in amplitude and latency in high-risk infants. The review highlights the need for more sophisticated analytical techniques to examine attention as a whole-brain state rather than focusing on specific regions of interest. The existing literature provides a foundation for investigating the relationship between early neural activity and later ASD diagnosis and social functioning. The researchers point to a gap in understanding whether differences in neural attention engagement to faces vary depending on gaze direction and how these differences relate to both ASD symptomatology in the child and the family history of ASD.

This study used a prospective longitudinal design, recruiting participants from the British Autism Study of Infant Siblings (BASIS). The sample included 170 infants with a family history of ASD (FH) and 77 infants without a family history (noFH). All participants underwent assessments using the ADOS-G, MSEL, and ADI-R. At 8 months of age, 247 infants participated in an EEG study, where they viewed faces with direct or averted gaze and a non-social control stimulus. EEG data was recorded using a 128-channel Hydrocel Sensor Net at a 500 Hz sampling rate. Preprocessing steps included artifact rejection and filtering. Analyses included classic regression analyses to examine group differences in Nc component (latency and amplitude) and data-driven multiscale analysis using machine learning to identify microstate features predictive of later ASD diagnosis and social skills (measured using VABS Socialization scores). The study examined the relationship between early brain measures and both categorical ASD outcome (noFH-noASD vs FH-ASD) and dimensional variation in social skills. The VABS Socialization standard scores at 3 years of age were selected as a dimensional measure of social adaptive skills. The selection of VABS Socialization scores was justified by the authors to its minimal skew and association with genetic variation, as compared to other measures with skewed distributions like the ADOS or the SRS.

The study's key findings demonstrate a relationship between early brain activity patterns and later ASD diagnosis and social adaptive skills. The FH-ASD group exhibited shorter Nc latency compared to other groups, suggesting faster initial attentional engagement, but possibly less sustained attention. Furthermore, the duration of attentive microstate responses to faces proved informative in predicting categorical ASD outcome. Reduced Nc amplitude difference between faces without gaze and non-social stimuli, as well as the strength of the attentive microstate to faces, contributed to the prediction of dimensional variation in social skills. These results suggest that both the timing and strength of brain states related to social attention are relevant in understanding the development of ASD, impacting both diagnostic categories and dimensional aspects of social functioning. Statistical analyses like one-way ANOVA and Kruskal-Wallis test were employed. The study found significant differences in various behavioral measures (MSEL, VABS, ADOS-2 CSS) between the three groups (noFH-noASD, FH-noASD, FH-ASD) at both 8 months and 3 years of age. These differences highlight the predictive power of early brain activity patterns in identifying infants at risk for ASD and understanding their trajectory of social development. The use of machine learning allowed identification of specific microstate features predictive of ASD and social skills, suggesting potential biomarkers for early intervention strategies.

The findings support the hypothesis that atypical cortical activation during infancy precedes difficulties in socialization associated with ASD. The observed differences in Nc latency and microstate characteristics highlight the importance of considering both the timing and intensity of neural responses in understanding the neurodevelopmental trajectory of ASD. The study's use of a combined top-down (hypothesis-driven) and bottom-up (data-driven) approach strengthens the conclusions by validating theory-informed selection of neural correlates with a data-driven approach. The integration of categorical and dimensional perspectives on ASD expands understanding of the disorder beyond simple diagnostic classifications and provides a more nuanced perspective on the spectrum. The identification of predictive microstate features opens new avenues for early detection and potential intervention strategies targeting specific aspects of brain function.

This study provides compelling evidence that atypical cortical activation during social attention in infancy is associated with later ASD diagnosis and social functioning. The combination of traditional ERP analyses and data-driven microstate analysis offers a novel approach to understanding the neurodevelopmental mechanisms underlying ASD. Future research could focus on replicating these findings in larger, more diverse samples and exploring the effectiveness of interventions targeting identified neural biomarkers. Further investigations could also elucidate specific environmental factors that might modulate these early neural patterns and contribute to individual variation in ASD expression.

The study's limitations include the relatively small sample size, particularly within the FH-ASD group, which could limit the generalizability of the findings. The reliance on a specific face processing paradigm might not capture the full range of social attentional processes relevant to ASD. Furthermore, the cross-sectional nature of the EEG data limits causal inferences about the relationship between early brain activity and later behavior. Cultural variations in social interaction styles could also influence the findings, though the sample was predominantly from the UK, reducing this risk.