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In typical infant development, parents and their children jointly contribute to establishing frequent episodes of joint attention that boost language acquisition and shape social cognition. Here we used novel live eye-tracking technology to evaluate the degree to which autism spectrum disorder (ASD) is related to reduced responding to others' joint attention bids in infancy (RJA) and to a reduced tendency to initiate joint attention episodes (IJA). Because young infants use their gaze for both RJA and IJA, this approach allowed us to quantify these elusive processes early in life. The final sample consisted of 112 infants (54 boys and 58 girls), of whom 81 were at familial risk for ASD and 31 were typically developing low-risk infants. At follow-up (36 months of age), 22 children in the high-risk group were diagnosed with ASD. At 10 months of age, rates of IJA were lower in infants later diagnosed with ASD than in the comparison groups (effect sizes d = 0.78–0.95) and followed an atypical developmental trajectory from 10 to 18 months (p <.002). RJA distinguished infants based on familial ASD risk, albeit not ASD diagnosis. The differences in IJA could not be explained by overall looking time, social preference, eye movement latencies, or number of fixations. This live eye-tracking study suggests that during an important period for the development of social cognition (10–18 months of age), infants later diagnosed with ASD show marked atypicalities in IJA but not in RJA. The results indicate that IJA is an important target for future prodromal intervention trials. [ABSTRACT FROM AUTHOR]

Altered resting-state functional connectivity in the default mode network (DMN) is characteristic of both autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD). Standard analytical pipelines for resting-state functional connectivity focus on linear correlations in activation time courses between neural networks or regions of interest. These features may be insensitive to temporally lagged or nonlinear relationships. In a twin cohort study comprising 292 children, including 52 with a diagnosis of ASD and 70 with a diagnosis of ADHD, we applied nonlinear analytical methods to characterize periodic dynamics in the DMN. Using recurrence quantification analysis and related methods, we measured the prevalence, duration, and complexity of periodic processes within and between DMN regions of interest. We constructed generalized estimating equations to compare these features between neurotypical children and children with ASD and/or ADHD while controlling for familial relationships, and we leveraged machine learning algorithms to construct models predictive of ASD or ADHD diagnosis. In within-pair analyses of twins with discordant ASD diagnoses, we found that DMN signal dynamics were significantly different in dizygotic twins but not in monozygotic twins. Considering our full sample, we found that these patterns allowed a robust predictive classification of both ASD (81.0% accuracy; area under the curve = 0.85) and ADHD (82% accuracy; area under the curve = 0.87) cases. These findings indicate that synchronized periodicity among regions comprising the DMN relates both to neurotypical function and to ASD and/or ADHD, and they suggest generally that a dynamical analysis of network interconnectivity may be a useful methodology for future neuroimaging studies. [ABSTRACT FROM AUTHOR]