Multivariate neural connectivity patterns in early infancy predict later autism symptoms.

Abstract Background Functional brain connectivity is altered in children and adults with autism spectrum disorder (ASD). Functional disruption during infancy could provide earlier markers of ASD, thus providing a crucial opportunity to improve developmental outcomes. Using a whole-brain multivariate approach, here we asked whether electroencephalography (EEG) measures of neural connectivity at 3 months of age predict autism symptoms at 18 months. Methods Spontaneous EEG data were collected from 65 infants with and without familial risk for ASD at 3 months of age. Neural connectivity patterns were quantified using phase coherence in the alpha range (6-12Hz). Support vector regression (SVR) analysis was used to predict ASD symptoms at age 18 months, with ASD symptoms quantified by the Autism Diagnostic Observation Schedule-Toddler Module. Results ADOS scores predicted by SVR algorithms trained on 3-month EEG data correlated highly with ADOS scores measured at 18 months (r=0.76, p=0.02, root mean square error=2.38). Specifically, lower frontal connectivity and higher right temporo-parietal connectivity at 3 months predicted higher ASD symptoms at 18 months. The SVR model did not predict cognitive abilities at 18 months (r=0.15, p=0.36), suggesting specificity of these brain patterns to ASD. Conclusions Using a data-driven, unbiased analytic approach, neural connectivity across frontal and temporo-parietal regions at 3 months predicted ASD symptoms at 18 months. Identifying early neural differences that precede an ASD diagnosis could promote closer monitoring of infants who show signs of neural risk and provide a crucial opportunity to mediate outcomes through early intervention.