Electrophysiological Signatures of Visual Statistical Learning in Three-Month Old Infants at Risk for Autism Spectrum Disorder

Thursday, May 12, 2016: 11:30 AM-1:30 PM
Hall A (Baltimore Convention Center)
A. Marin1, T. Hutman2, M. Dapretto3, C. Ponting1, S. P. Johnson4 and S. S. Jeste1, (1)Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, (2)University of California Los Angeles, Los Angeles, CA, (3)University of California, Los Angeles, Los Angeles, CA, (4)UCLA, Los Angeles, CA
Background: Visual statistical learning (VSL) refers to implicitly extracting commonalities and transitional probabilities within the visual environment (Bulf et al., 2011), and may be a precursor to later cognition and social communication (Romberg & Saffran, 2010). We previously demonstrated impairments in VSL in preschool-age children with ASD using a novel electrophysiological (EEG) paradigm (Jeste et al., 2015). No studies have examined VSL in infants at risk for ASD.  

Objectives: We asked whether EEG signatures of VSL could be quantified at 3-months of age, and whether VSL distinguished high- and low-risk infants (HR infants have an older sibling with ASD), and whether there was an association between VSL at 3-months and later cognitive function.  

Methods: Three-month-old infants (n=22, high-risk (HR): n=11, low-risk (LR): n=11) were exposed to a continuous stream of shapes based on a modified version of the Kirkham et al. (2002) VSL task. High density EEG was recorded (128 electrode, EGI inc) and the event-related potential (ERP) of interest was the frontal Positive Slow Wave (PSW). A general linear model evaluated within-subject effects of region and condition and between-subject effects of group with respect to PSW mean amplitude. Learning was operationalized as differentiation between conditions (expected vs. probabilistic). Whole group correlations between ERP markers of learning and cognitive skills at 6-months (Mullen Scales of Early Learning, 1995) were also performed.  

Results: There was a significant group by condition interaction (F(1, 20) = 7.393, p = .013, partial h2 = .270) and a significant main effect of region (F(2, 19) = 5.302, p = .015, partial h2 = .358). Post-hoc tests revealed greater mean amplitude within the middle region as (M = 2.560, SD = 4.162) compared to the right (M = 2.039, SD = 3.155) and left (M = .687, SD = 3.841). Post-hoc tests revealed that HR infants significantly differentiated conditions (t(10) = 2.967, p = .014), while LR infants did not (t(10) = -.793, p = .446). An independent samples t-test returned significant differences between HR (M = 3.282, SD = 3.669) and LR (M = -.809, SD = 3.384) groups in PSW difference amplitudes (t(20) = 2.719, p = .013). The absolute value PSW difference amplitudes correlated with six-month visual receptive skills (r(21) = .49, p = .02) and mental age (r(21) = .48, p= .03), but groups did not differ on mental age at 6 months.  

Conclusions: EEG correlates of VSL seem to differentiate HR from LR infants as early as 3-months of age, with HR infants displaying evidence of VSL. EEG signatures of learning predict both visual receptive skills and mental age at six months. The relationship between VSL and cognitive function suggest a role of pattern learning in later cognitive development. Group differences in VSL suggest that infants at risk for ASD demonstrate strengths in processing visual patterns—a strength that may occur at the expense of later social attention or learning. As we confirm diagnostic outcomes, we will better understand the predictive nature of this cognitive domain in infants at risk for ASD.