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Updated: Nov 11, 2025

Eye Tracking Young Children with Autism
Published on: March 27, 2012
Attentive brain states in infants with and without later autism
Anna Gui1, Giorgia Bussu2, Charlotte Tye3
1Centre for Brain and Cognitive Development, Birkbeck College, University of London, Malet Street, London, WC1E 7HX, UK. agui01@mail.bbk.ac.uk.
Insights
Infants with a family history of autism spectrum disorder (ASD) show differences in attention engagement, measured by brain activity. These early attention differences predict later social development and ASD diagnosis.
Area of Science:
- Neuroscience
- Developmental Psychology
- Autism Spectrum Disorder Research
Background:
- Early attention difficulties in social settings may impact learning and social development.
- Autism Spectrum Disorder (ASD) is characterized by social interaction challenges.
- Understanding early neurodevelopmental markers for ASD is crucial.
Purpose of the Study:
- To investigate early attention engagement differences in infants with and without a family history of ASD.
- To examine if brain activity patterns in infancy predict later ASD diagnosis and social functioning.
- To explore the relationship between brain states and social development trajectories.
Main Methods:
- Multichannel electroencephalography (EEG) was used in 8-month-old infants during a face/non-face paradigm.
- Event-related potential (ERP) component, the Nc, and brain microstates were analyzed.
- Machine learning identified microstate features predicting ASD and social adaptive skills at age 3.
Main Results:
- Infants with a family history of ASD who later received an ASD diagnosis (FH-ASD) showed shorter Nc latency.
- Brain state timing predicted categorical ASD outcome, while brain state strength predicted dimensional social functioning.
- Reduced Nc amplitude difference and attentive microstate strength to faces predicted social skill variations.
Conclusions:
- Atypical attention engagement in infancy precedes social difficulties and ASD emergence.
- Spatio-temporal brain state characteristics in infancy offer a novel approach to understanding ASD neurodevelopment.
- Early attention markers identified via EEG may aid in predicting ASD risk and social development.
Abstract:
Early difficulties in engaging attentive brain states in social settings could affect learning and have cascading effects on social development. We investigated this possibility using multichannel electroencephalography during a face/non-face paradigm in 8-month-old infants with (FH, n = 91) and without (noFH, n = 40) a family history of autism spectrum disorder (ASD). An event-related potential component reflecting attention engagement, the Nc, was compared between FH infants who received a diagnosis of ASD at 3 years of age (FH-ASD; n = 19), FH infants who did not (FH-noASD; n = 72) and noFH infants (who also did not, hereafter noFH-noASD; n = 40). 'Prototypical' microstates during social attention were extracted from the noFH-noASD group and examined in relation to later categorical and dimensional outcome. Machine-learning was used to identify the microstate features that best predicted ASD and social adaptive skills at three years. Results suggested that whilst measures of brain state timing were related to categorical ASD outcome, brain state strength was related to dimensional measures of social functioning. Specifically, the FH-ASD group showed shorter Nc latency relative to other groups, and duration of the attentive microstate responses to faces was informative for categorical outcome prediction. Reduced Nc amplitude difference between faces with direct gaze and a non-social control stimulus and strength of the attentive microstate to faces contributed to the prediction of dimensional variation in social skills. Taken together, this provides consistent evidence that atypical attention engagement precedes the emergence of difficulties in socialization and indicates that using the spatio-temporal characteristics of whole-brain activation to define brain states in infancy provides an important new approach to understanding of the neurodevelopmental mechanisms that lead to ASD.
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