Multivariate Neural Connectivity Patterns in Early Infancy Predict Later Autism Symptoms

Area of Science:

• Neuroscience
• Developmental Psychology
• Pediatrics

Background:

• Autism spectrum disorder (ASD) is characterized by altered functional brain connectivity in children and adults.
• Early functional disruption in infancy may serve as predictive markers for ASD.
• Identifying these markers can lead to improved developmental outcomes through timely intervention.

Purpose of the Study:

• To investigate if electroencephalography (EEG) measures of neural connectivity at 3 months of age can predict autism symptoms at 18 months.
• To utilize a whole-brain multivariate approach to analyze neural connectivity patterns.
• To establish early markers for ASD to facilitate early intervention strategies.

Main Methods:

• Collected spontaneous electroencephalography (EEG) data from 65 infants at 3 months of age.
• Quantified neural connectivity using phase coherence in the alpha frequency range (6-12 Hz).
• Employed support vector regression to predict ASD symptoms at 18 months using the Autism Diagnostic Observation Schedule, Second Edition (ADOS-2).

Main Results:

• EEG-based neural connectivity at 3 months accurately predicted ASD symptoms at 18 months (r = .76, p = .02).
• Lower frontal connectivity and higher right temporoparietal connectivity at 3 months were associated with increased ASD symptoms at 18 months.
• The model did not predict general cognitive abilities, indicating specificity to ASD symptoms (r = .15, p = .36).

Conclusions:

• A data-driven analysis revealed that neural connectivity in frontal and temporoparietal regions at 3 months predicts later ASD symptoms.
• Early identification of neural differences preceding an ASD diagnosis is crucial.
• This approach can guide closer monitoring of at-risk infants and enable early intervention for better outcomes.