Neonatal Transitions in Social Behavior and Their Implications for Autism

Abstract

Within the context of early infant-caregiver interaction, we review a series of pivotal transitions that occur within the first 6 months of typical infancy, with emphasis on behavior and brain mechanisms involved in preferential orientation towards, and interaction with, other people. Our goal in reviewing these transitions is to better understand how they may lay a necessary and/or sufficient groundwork for subsequent phases of development, and also to understand how the breakdown thereof, when development is atypical and those transitions become derailed, may instead yield disability. We review these developmental processes in light of recent studies documenting disruptions to early-emerging brain and behavior mechanisms in infants later diagnosed with autism spectrum disorder, shedding light on the brain-behavior pathogenesis of autism.

Keywords: autism pathogenesis; infant learning; infant–caregiver adaptation; neonatal transitions; neurodevelopmental transitions; newborn predispositions.

Figure 1.. Neonatal Preferential Orientation to Caregiver Sounds, Smells, and Sights.

Preferential orienting is observed already in the first days after birth in the auditory domain (A), in response to olfactory cues (B), and in visual attention to faces (C), biological motion (D), and face-like stimuli (E). At 5 days, neonates show distinct preference for looking at faces that have eyes open rather than closed and even distinguish, despite relatively poor visual acuity so soon after birth, between faces with eyes directed towards them rather than away. Similarly, neonates distinguish and prefer their own mother’s voice to that of an unknown woman, but prefer the sound of even an unknown woman’s voice to that of silence. In addition, neonates recognize their mother’s smell and will selectively head-turn at 2–7 days after birth towards the scent of their own mother’s breast. Examples of more preferred stimuli are pictured in the left column while less preferred stimuli are pictured in the right column. Stimulus descriptions and citations are given at far right. Data are from references [,,–,,–136,150].

Figure 2.. Developmental Milestones in Infants’ First 6 Months.

Plotted data, showing the percentage of infants who display each listed behavior between birth and 6 months. Behaviors are plotted sequentially, with behaviors that emerge (and, in some cases, decline) earlier in development at the top and behaviors emerging later in development at the bottom. This sequence of developmental milestones highlights the cascading process of infant development, whereby an infant’s own emerging abilities lead the infant towards new ways of exploring and experiencing the world, prompting further development in an iterative process. Developmental milestone data were scanned or transcribed from published texts and manuals [,,,–142]; data were fitted with a sigmoid function to describe the cumulative proportion of children displaying each behavior (common milestones were averaged across sources). The fitted functions are color-scaled from generally absent (black) to present in 50% of children (red) to present in most children (yellow).

Figure 3.. Transitions from Reflex-like Actions to Volitional Behaviors.

Behavioral transitions in the first6months of life, with implications for early social behavior in autism. (A) Data from references [,,,–142] showing examples of declining reflex-like actions (unbroken lines) and emerging volitional behaviors (broken lines) in the vocal, visual, and motor domains. Approximate transition times are marked by vertical lines. (B) Top, trajectories of reflex-like actions and volitional behaviors from (A) are plotted against chronological age. Bottom, trajectories are aligned at the time of transition to illustrate the idea that learned, volitional behaviors may replace, emerge from, and/or depend upon behaviors that were initially spontaneous or reflexive: as new volitional behaviors are acquired, the adaptive value of simpler reflex-like actions decreases, as does the action. (C) In typical face perception, existing normative data [44,45,81,92] suggest a similar transition at approximately 2 months: reflex-like eye-looking declines (unbroken gray line) while volitional eye-looking increases (broken gray line). Reflexive eye-looking is believed to be experience-expectant and subcortically mediated, while volitional eye-looking is believed to be experience-dependent and largely cortically mediated. (D) A corresponding reduction in amount of eye-looking by typically developing (TD) infants has been observed at the hypothesized time of transition, shown in the top panel (blue line, sum of percentage fixation on eyes). Observed data from months 2–8 replotted from [32]. Trajectories from 0–2 months represent hypothesized levels of eye-looking in keeping with existing normative data [96,97]. In observed data from [32], infants with autism spectrum disorder (ASD) exhibited relatively high levels of eye-looking at 2 months, which then declined. By contrast, TD infants showed relatively low levels of eye-looking at 2 months, which then increased. Relatively high levels of eye-looking at 2 months in ASD (red) suggests reflex-like eye-looking that is not supplanted by volitional eye-looking and, instead, persists atypically. Rather than an outright failure of cortically controlled voluntary preferential attention in ASD, eye-tracking data suggest a co-opting of experience-dependent cortical mechanisms by attention to other, nonsocial features in the environment. As a result, reflex-like eye-looking gradually declines as it is supplanted by attention to other (non-eye) features. Abbreviation: ATNR, asymmetrical tonic neck reflex.

Figure 4.. Developmental Methods: Quantifying Individualized Timescales of Growth.

Functional data analysis is a relatively new method of analyzing time series data that places greater emphasis on individual trajectories of data, considering the trajectory itself to be a single observation and quantifying that function’s variability in terms of both timing and scale [143,144]. This is an important methodological as well as conceptual shift in how longitudinal data are analyzed and understood, with exciting implications for studies of child development and for the conclusions we may draw about underlying biological processes. A good example of this shift can be seen when analyzing a very literal example of a child’s growth: change in height. (A) Shows height measurements of girls from 1 to 18 years of age (from the classic Berkeley Growth Study [145]). The underlying biological process of growth is of course nearly identical in all children, but exactly when the pubertal growth spurt occurs and how large it is varies considerably by individual. (B) Individual differences can be expected in timing (when a particular change occurs), in scale (how large or small a given change may be), and in both timing and scale. With conventional growth curve modeling, (C) fitting individual data with a power function (left panels) yields a relatively good fit in statistical terms (R² > 0.98 for each of the three example curves); however, it also eradicates all signs of the pubertal growth spurt, as seen especially in the plots of change in height and in rate of change in height (the1stand 2nd derivatives, respectively). Fitting thesamedatawitha5th order polynomial [right panels of (C)] improves the picture somewhat, but parameter estimates of when the pubertal spurt occurs in individual children (colored dots) are as much as 2 years earlier than estimates observed in a more data-driven fashion, as in (D), using B-spline basis functions. (D) In functional data analysis, variation in both timing and scale are quantified, and curve shape is determined empirically. (E) Rather than being confounded by individual differences in maturational rate (individualized developmental timescale), functional data analysis measures the extent of these differences as ‘warping’ or registration functions, explicitly comparing and correcting for differences in chronological time versus individual maturational time. (F) When data are analyzed as functional trajectories, registered according to measures of individual difference in developmental timing, the ability to estimate the shape of the actual developmental process improves substantially.

Figure 5.. Longitudinal Expression of Genes Associated With Neurodevelopmental Processes.

Recent advances in studying the spatio-temporal dynamics of gene expression in the human brain augur a new frontier for studies of infant and child development, connecting well-studied behavioral and cognitive milestones to multiple measures of infant brain biology. One example is the BrainSpan Atlas of the Developing Human Brain [104], offering a transcriptional architecture of the human brain from early fetal development through adulthood. While longitudinal expression patterns for more than 17,500 genes were analyzed, the gene expression trajectories plotted here are for genes whose differential expression is associated with key neurodevelopmental processes. (A) Fourteen brain regions in which expression was measured are highlighted. (B) Five sets of genes are highlighted, the expression levels of which are associated with neurogenesis, synaptogenesis, dendrite development, axon development, and myelination, respectively. (C) Longitudinal expression levels of genes associated with each process are plotted, for each brain region, as a percentage of the minimum and maximum lifetime levels of expression. Brain regions are sorted according to peak expression levels between 0 and 24 postnatal months of age. Note the clear waves of expression during these periods of early child development: high levels of expression associated with neurogenesis continue through the first 6 months of life, with marked decline thereafter, followed by waves of increased expression associated with synaptogenesis, dendrite development, axon development, and finally myelination. Given the enormous developmental change occurring in these time periods, data like these, coupled with densely sampled behavioral and neuroimaging data, are likely to transform understanding of infant development in the coming years. See online supplemental materials for additional related references.