Skip to main content
PMC home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2020 Apr 1.
Published in final edited form as: Front Neuroendocrinol. 2019 Jan 31;53:100736. doi: 10.1016/j.yfrne.2019.01.002

The Influence of Unpredictable, Fragmented Parental Signals on the Developing Brain

Laura M Glynn 1,2, Tallie Z Baram 3,4,5
PMCID: PMC6776465  NIHMSID: NIHMS1521767  PMID: 30711600

Abstract

Mental illnesses originate early in life, governed by environmental and genetic factors. Because parents are a dominant source of signals to the developing child, parental signals–beginning with maternal signals in utero–are primary contributors to children’s mental health. Existing literature on maternal signals has focused almost exclusively on their quality and valence (e.g. maternal depression, sensitivity). Here we identify a novel dimension of maternal signals: their patterns and especially their predictability/unpredictability, as an important determinant of children’s neurodevelopment. We find that unpredictable maternal mood and behavior presage risk for child and adolescent psychopathology. In experimental models, fragmented/ unpredictable maternal care patterns directly induce aberrant synaptic connectivity and disturbed maturation of cognitive and emotional brain circuits, with commensurate memory problems and anhedonia-like behaviors. Together, our findings across species demonstrate that patterns of maternal signals influence brain circuit maturation, promoting resilience or vulnerability to mental illness.

Keywords: brain circuits, maternal care, entropy, neurodevelopment, depression, anhedonia, prenatal, postnatal, unpredictability, adversity

1.1. Introduction: Why Parental (Especially Maternal) Signals?

Parental care (particularly that from the mother) is a primary determinant of child survival in humans (Pavard et al. 2005; Sear et al. 2002; Willfuhr and Gagnon 2013); so central is this care for the survival of the species, some have argued that the development of parental behavior may be one of the primary forces shaping the evolution of the mammalian brain (c.f. Hrdy 2000; MacLean 1990). Sensitive periods in early life largely overlap with developmental stages in which the child is dependent on the mother, thus providing a pathway through which maternal signals shape development (Kuzawa and Quinn 2009). Not only does the mother facilitate the survival of her young through the provisioning of sustenance and protection, but beginning in the prenatal period, maternal signals influence the developing brain, shaping its maturation, with implications for the child’s future cognitive and emotional function and trajectory of health or disease. Thus, the influence of maternal signals prenatally and postnatally on numerous aspects of brain development has far-reaching implications for mental health.

2.1. What Maternal Signals are Salient to Brain Maturation? Current Knowledge and Novel Principles

A robust empirical literature indicates that pre and postnatal maternal behaviors and emotional states are important determinants of risk for psychiatric disease. For example, building on the foundational work of Bowlby, 1950, the importance of a secure attachment relationship, which is scaffolded by sensitive maternal behavior, has widespread implications for cognitive and emotional development (Belsky and Fearon 2002; Masur, Flynn, and Eichorst 2005; NICHD ECCRN, 1999a, 1999b, 2003, 2006; Hane et al. 2010; Feldman 2007, 2015). Similarly, the profound adverse consequences of a lack of maternal care, and exposures to maternal depression during the pre and postnatal periods are well-established (Goodman 2007; Gunnar, 2010; Murray et al. 2011; Dawson et al. 2003; Feldman et al. 2009; Halligan et al. 2004; Beck 1998; Verbeek et al. 2012; Nelson et al., 2007). The documented effects on mental health are associated with altered maturation of neural circuits, which persists until adulthood (Soe et al. 2018; Wen et al. 2017; Lebel et al. 2016; Posner et al. 2016; Sandman etal. 2015). The accumulating evidence relating maternal behavior and psychological distress during the pre and postnatal periods to risk for mental health disorders demands the identification of specific components of maternal signals that shape the developing brain. Whereas a clearly identified role for valence of maternal signals- empathy, sensitivity, availability, etc., has been established as described above, recent work in both humans and animal models highlights the importance of patterns of maternal-derived cues to the developing brain in shaping the maturation of brain circuits (Davis et al. 2017; Baram et al. 2012; Molet, Heins, et al. 2016; Molet, Maras, et al. 2016; Evans et al. 2005).

Thus, in addition to the well-established roles of maternal mood levels and sensitive maternal behavior, patterns of maternal signals seem to influence the maturation and organization of brain circuitries. Notably, in both rodent models and humans, the effects of patterns and especially of unpredictable, fragmented maternal signals on brain and behavior appear to be additional to the quantity and quality of the same signals, underscoring the importance of patterns. Below we will illustrate these principles by describing new findings in human and experimental model studies, and propose that unpredictable, fragmented signals from the mother (FRAG) represent a critical influence on the developing brain with implications for mental health outcomes (Baram et al. 2012; Glynn et al. 2018a; Risbrough et al. 2018).

Our work with humans, presented in the following paragraphs, has focused on patterns of maternal inputs in two domains: maternal behavior and maternal mood. We find that aberrant patterns of these domains influence cognitive and emotional maturation including memory, self-control and risk for internalizing disorders in children (Glynn et al. 2018b; Davis et al. 2017). Emerging information suggests that the mechanisms involved in such behavioral phenotypes include aberrant maturation of the underlying brain circuits (Kopala-Sibley et al. 2018; Bolton, Molet, et al. 2018; Molet, Maras, et al. 2016; Fareri et al. 2017).

2.1.2. Patterns of Maternal Behavior and their fragmentation and unpredictability (Sensory FRAG)

Mental and cognitive capabilities are a result of the development and maturation of underlying brain circuits. These, built of neurons and neuronal ensembles connected via synapses perform the complex computational tasks underlying specific brain functions including memory, decision making, and emotion regulation. During development, these circuits are immature, and certain synaptic connections formed early are strengthened to become permanent whereas others are eliminated. In sensory circuits such as vision and hearing, important neurobiological principles have been established demonstrating the role of modality-specific patterns of sensory signals are required for the maturation of the circuit. Lack of sensory signals (e.g., sight) or aberrant sensory patterns (auditory) during sensitive developmental periods disrupt the sculpting and maturation of visual, somatosensory and auditory brain circuits, with commensurate functional deficits (Espinosa and Stryker 2012; Khazipov et al. 2004; Singh-Taylor et al. 2015; Wiesel and Hubel 1963; Hackett et al. 2011). However, it is not known whether analogous sensory signals and their patterns are important for the maturation of cognitive and emotional brain circuits. Because the dominant sensory signals to the developing organism are generated by the mother, and because maternal care per se has been shown as critical for neurodevelopmental outcomes, we tested the hypothesis that patterns of maternal-derived signals might influence the maturation of brain circuits underlying cognitive functions such as memory and related circuits underlying pleasure reward and affective functioning.

To characterize patterns of sensory signals to the developing human infant, we applied a unique behavioral coding scheme to observations of mothers interacting with their infants in a prospective longitudinal cohort. Briefly, mothers were video-recorded interacting with their infants in a semi-structured 10-minute play episode in which they were given a standard set of age-appropriate toys and are instructed to play with their infant as they would at home (Davis et al. 2017). Using the Observer XT (Noldus Information Technology, 2008), maternal sensory signals were characterized in three domains: auditory (all maternal vocalizations, e.g., laughing, talking), tactile (all instances of physical contact, e.g. holding, touch) and visual (maternal manipulation of a toy or object while the infant was visually attending, Davis et al. 2017). Rather than coding these interactions for quality or valence (e.g. positive versus negative regard or sensitive versus intrusive), we classified the behaviors by sensory modality (auditory tactile, visual), coding behaviors in these three domains continuously in real time. We then analyzed the patterns of these behaviors, which allows the determination of whether for a given mother if specific patterns recur (e.g., holding a toy then smiling then putting the toy down) and the degree to which the patterns are random or unpredictable. The sequence of behaviors for each mother can be summarized by considering how often specific transitions occur; e.g., how many times touch is followed by speaking or speaking is following by concurrent touch and visual inputs. This summary index, termed sensory FRAG, is derived as follows: we focused on the conditional probabilities of transitioning between the visual, auditory and tactile signals. Predictability of a given transition from one behavior to another was examined considering all of the possible permutations, and quantified through an entropy rate (Vegetabile et al. in press). The entropy rate measures the randomness and unpredictability of the distribution of transitions with higher values indicating less predictable maternal signals (i.e., more sensory FRAG).

An initial examination of unpredictable maternal behavior was conducted in a prospective longitudinal study of 128 mother-child pairs in which sensory FRAG was assessed at 1-year year and cognitive development through six years of age (Davis et al. 2017). Children who were exposed to higher sensory FRAG during the first year of life exhibited less optimal cognitive development at 2-years of age (r = −.34; p < .01) and evidence of poorer performance on a hippocampus-dependent memory task at 6 years of age (r = −.27; p < .05). Importantly, these associations were independent of the quantity of sensory signals (i.e., the number of transitions) and persisted after consideration of possible third variable explanations including: maternal sensitivity, postpartum depression, duration of breastfeeding and family socioeconomic status. Additional analyses tested the hypothesis that sensory FRAG might partially mediate the relation between a more global observational measure of quality of maternal care (the widely used assessment of maternal sensitivity developed by the NICHD Study of Early Child Care and Youth Development (NICHD ECCRN 1999a). These analyses revealed that sensory FRAG might partially mediate the relation between maternal sensitivity and child cognitive function. Taken together, our findings provide evidence that predictability of maternal sensory signals evaluated on short time scales is associated with cognitive development and raise the possibility that sensory FRAG might also represent a more proximal process by which some previously established indicators of quality of maternal care may shape development.

2.1.3. Patterns of Maternal Mood (Mood FRAG)

The valence of maternal mood (e.g. depression), is clearly a critical determinant of children’s mental health. However, it is likely that variability or unpredictability of maternal mood influences children’s emotional and cognitive development, in addition to the effects of mood levels. Therefore, we have begun to examine patterns or predictability of maternal mood. Additional support for the premise of this approach is derived from work in the fields of emotion and personality psychology emphasizing the importance of intra-individual variability in mood (independent from level or valence) as a central component of affective experience (Wessman and Ricks 1966; Larsen and Diener 1987; Mischel and Shoda 1995; Fiske and Rice 1955) and from documented links between mood variability and mental health (Depue et al. 1981; Bonsall et al. 2012; Kuppens et al. 2007; Thompson, Berenbaum, and Bredemeier 2011). Interestingly, despite increasing interest in the role of emotion regulation and patterns of mood for mental health (c.f. Aldao, Nolen-Hoeksema, and Schweizer 2010; Fernandez, Jazaieri, and Gross 2016; Kring and Sloan 2010), the potential impact of this domain of maternal affective function and hence signals to her child—on cognitive and emotional development has received little attention.

When studying emotion dynamics it is possible to examine patterns over time (e.g. across days or weeks) or to focus on the dynamics at a single point in time, capturing a snapshot of emotional experience (Kuppens and Verduyn 2015). We chose the latter approach and quantified fragmentation and unpredictability of the item-by-item responses to standardized assessments of mood states (mood FRAG). Specifically, our measure of mood FRAG comprises an application of Shannon’s entropy to the distribution of responses on mood questionnaires (Cover and Thomas 2006). The responses at a single assessment of mood states (e.g. the Center for Epidemiologic Studies Depression Scale or the Profile of Mood States) were tabulated over the items within each scale into probability distributions based on the relative frequency of each response choice, and these distributions represent empirical estimates of the propensity of a participant to respond across items in a consistent way. In this sense, mood FRAG quantifies the degree of predictability or unpredictability of the item-specific response, with higher values denoting less predictability. As shown in Table 1, a participant who generally reports “never worried” or “always secure” on a state anxiety scale, for example, would be considered very predictable and thus have a very low entropy score (low mood FRAG), whereas a participant who completes the anxiety items entirely at random would have a very high entropy score (high mood FRAG). We have tested the convergent validity of mood FRAG by examining its association with affective instability, a time-based, momentary measure of mood variability. Importantly, we have shown that the measure of mood FRAG is positively associated with variability in mood as assessed in real time across hours and days with the use of ecological momentary assessments (r = .42; p < .01; Glynn et al. 2018b).

Table 1.

Item responses for three hypothetical respondents on a standardized mood scale demonstrating the concept of mood FRAG.

1 2 3 4 5 6 7 8 9 10
Participant 1 a a a a a a a a a a
Participant 2 b a a c b a b d c c
Participant 3 d c c d d d d c d c
Open in a new tab

Note: Suppose that a mood scale has a set of possible responses given by the letters a, b, c and d. This table shows simulated outcomes for three hypothetical respondents on such a scale. These participant responses are tabulated over items into probability distributions based on the relative frequency of each choice (i.e., the relative frequency of endorsing a, b, c or d), and we view these distributions as empirical estimates of the propensity of a participant to respond to mood items in a particular way. For example, if this were a state anxiety scale, a respondent who reports “never worried,” and “always secure” (similar to Participant 1) may be said to respond particularly consistently across items, while a respondent who reports “never worried”, “sometimes calm,” and “rarely secure” (similar to Participant 2) responds less consistently. Shannon’s entropy of these probability distributions are then calculated for each respondent and normalized to provide an index of mood FRAG. In our hypothetical example, Participant 1 would have a normalized entropy score of 0 (low mood FRAG), Participant 2 would have a normalized entropy score of .95 (high mood FRAG) and Participant 3 would be assigned a score of .49 (moderate mood FRAG).

Employing this instrument, we examined the predictive utility of the mood FRAG index in two independent, prospectively studied cohorts of mothers and children (N’s = 227 and 180, Glynn et al. 2018b). Risk for internalizing disorders was assessed by maternal report of fearful temperament (a prodromal risk factor for the development of internalizing disorders) at 1, 2 and 7 years and by child report of his or her own anxiety symptoms at 10 years of age and depressive symptoms at 13 years of age. Higher prenatal maternal mood FRAG predicted increased child negative affectivity at 12-months (r = .36; p < .01). The positive association was also observed at 24 months (r =.31, p < .01) and at 7 years of age (r =.32, p < .01). Consistent with these maternal reports, higher prenatal mood FRAG predicted increased child self-report of anxiety symptoms at age 10 (r = .24, p < 01) and depressive symptoms at 13 (r = .29; p < .01). It is important to note that all of these effects persisted after statistical adjustment for both pre and postnatal mood levels (e.g., depressive symptoms), as well as after adjusting for other established indicators of early life adversity including gestational age at birth, socioeconomic status, cohabitation with the child’s father. A further point worth underscoring (Glynn et al. 2018b), is that these effects are specific to mood FRAG – we calculated an entropy score from answers given on a non-mood related questionnaire (one related to physical activity) and these entropy scores did not predict child outcomes at any age. Additionally, both pre and postnatal mood FRAG were independent and statistically significant predictors of risk for anxiety and depression, suggesting that exposures to mood FRAG in both periods may meaningfully influence emotional development. Thus, in a prospective sample followed for 13 years from pregnancy through early adolescence, unpredictable maternal mood was associated with internalizing problems during infancy and childhood and symptoms of anxiety and depression in adolescence.

The mechanisms through which unpredictable patterns of maternal signals (sensory and mood FRAG) lead to alterations in children’s cognitive and emotional phenotypes have yet to be established and these types of mechanistic studies are challenging in human cohorts. An exciting aspect of our findings in infants and children are the robust parallels with observations in controlled experimental systems. In these rodent models, we can design studies that allow detailed probing of both causality and mechanisms, providing a strong translational system in which to further our understanding of the role of early life unpredictability and fragmentation of maternal-derived signals in brain maturation.

3.1. How Do Maternal Signals Influence the Developing Brain?: Insight from Experimental Models

Brain maturation spans prenatal and early postnatal (infancy) periods, and the sculpting of a number of important brain circuits continues to adulthood. Processes involved in brain circuit-maturation include axonal and dendritic growth, synaptic formation, stabilization and pruning (Garey 1984; Speh and Moore 1993; Hoeijmakers, Lucassen, and Korosi 2014; Woo et al. 1997; Maras and Baram 2012; Neniskyte and Gross 2017). The perinatal period therefore represents a critical stage of development, rendering the brain particularly vulnerable environmental influences (Chen and Baram 2016). Environmental signals critically contribute to the evolution of brain circuits. Thus, light and visual patterns, and sound and tones are required for the establishment and refinement of visual and auditory circuits, respectively (Espinosa and Stryker 2012; Sun et al. 2018). However, the environmental signals that might drive the maturation of ‘cognitive’ and ‘emotional’ circuits remain unknown.

In mammals, including humans, monkeys and rodents, maternal input has perhaps the most significant influence on the type of environment experienced during development (Rincon-Cortes and Sullivan 2014; Baram et al. 2012; Bowlby 1950; Sanchez, McCormack, and Howell 2015; Kundakovic and Champagne 2015; Seay, Hansen, and Harlow 1962). The role of parental and especially maternal care in influencing offspring outcome has been a topic of intense study in humans (Gunnar 2010; Nelson et al. 2007; Heim and Binder 2012), primates (Drury, Sanchez, and Gonzalez 2016; Seay, Hansen, and Harlow 1962) and rodents (Malter Cohen et al. 2013; Raineki et al. 2012; Dalle Molle et al. 2012; Rice et al. 2008; Champagne et al. 2003). Specifically, a compelling existing body of work has linked the presence (Gunnar 2010; Nelson et al. 2007) and certain features of maternal care (Rilling and Young 2014) to emotional outcome in both children and rodents. Thus, it is tempting to consider that maternal signals might contribute to the maturation of emotional and cognitive brain circuits. However, the fundamental properties of maternal signals that are perceived by the developing brain and influence the developing limbic networks to promote advantageous versus pathological outcomes remain enigmatic (Bale et al. 2010; Baram et al. 2012; Champagne et al. 2003; Heim and Binder 2012; NIMH Workgroup 2009). Our recent findings in experimental models support a direct causal and relation of maternal signals and their patterns in the maturation of cognitive and emotional brain circuits (Ivy et al. 2010; Molet, Heins, et al. 2016; Molet, Maras, etal. 2016; Bolton, Molet, etal. 2018; Bolton, Ruiz, etal. 2018).

We have employed a paradigm rearing mice or rats for one postnatal week in ‘simulated poverty’, using cages with limited bedding and nesting, and observed both maternal behaviors and the outcomes of the pups (Molet et al. 2014; Molet, Heins, et al. 2016; Rice et al. 2008; Ivy et al. 2008). To analyze dam behavior we assessed the durations of maternal nurturing behaviors as well as several qualitative aspects of dam behavior known to influence outcome (Champagne et al. 2003). In addition, we analyzed the patterns and sequences of maternal care and examined their predictability and fragmentation. We employed analyses of entropy rates similar to those reported above for human sensory FRAG.

Surprisingly, the quantity and several typical qualitative measures of maternal nurturing behaviors (e.g. arched-back nursing; Champagne et al. 2003) did not predict emotional outcome in the pups. However, novel analyses of the patterns of maternal behavior revealed that individual nurtuing events were short and fragmented, and the sequences of distinct behaviors were unpredictable in the limited-beding cages (Molet, Heins, et al. 2016).These aberrant patterns were quantified using entropy rates as described for human behaviors (Molet, Heins, et al. 2016). Notably, these aberrant patterns of maternal caring behaviors- i.e., the major source of sensory input to the pups- led to abnormal emotional outcome in the pups as they reached adolescence Specifically, a reduced capacity to experience pleasure (anhedonia) was observed (Bolton, Ruiz, et al. 2018; Molet, Heins, et al. 2016). More recently, aberrant maturation of the pleasure-reward circuitry in these ‘graduates’ of unpredictable maternal care has been identified (Bolton, Molet, et al. 2018). Thus, it appears that at least one crucial brain circuit is directly modulated by the patterns of maternal–origin sensory signals during sensitive early-life periods.

The mechanisms by which environmental signals modulate circuit formation and refinement involve, in part, activity-dependent strengthening of engaged synapses and pruning of others (Woo et al. 1997; Neniskyte and Gross 2017; Paolicelli et al. 2011; Schafer et al. 2012; Comery et al. 1997). It is not yet known of synaptic development or pruning are affected in the reward circuitry of pups exposed to unpredictable (high entropy) maternal care. Predictable sequences of events engage the dopaminergic reward system (Berns et al. 2001) that is not fully mature until the third postnatal week in rodents (Voorn et al. 1988) and is sensitive to the influence of early-life experiences (Pena et al. 2014; Ventura et al. 2013). Thus, we propose that predictable sensory-signals may be critical for the maturation of these circuits (Singh-Taylor et al. 2015; Singh-Taylor et al. 2018), and unpredictable early-life sensory signals may disrupt these developmental processes, provoking anhedonia (Molet, Heins, et al. 2016).

In addition to anhedonia, and congruent with findings described above in children, impaired memory was observed in graduates of the unpredictable maternal behaviors (Ivy et al. 2010; Molet, Maras, et al. 2016; Davis et al. 2017), associated with aberrantl maturation of hippocampal / limbic circuit organization (Molet, Maras, et al. 2016; Ivy et al. 2010). Further, there is also evidence that highly predictable maternal signals influence synaptic growth and persistence in brain circuits subserving stress-resilience (Singh-Taylor et al. 2018). Thus, there is converging evidence that patterns of maternal-derived sensory signals to the developing rodent brain influence synapse stabilization and circuit maturation in limbic and cognitive brain networks, with consequent cognitive and emotional sequelae. Importantly, these effects on brain and behavior are additional to those related to the quantity and quality of the maternal signals, underscoring the importance of patterns of unpredictability in shaping the immature brain.

4.1. Conclusions and Therapeutic Opportunities

Cognitive and emotional health, as well as vulnerability to cognitive and emotional disorders, derive from interactions between genes and environment, especially during sensitive developmental periods. We have limited control over genetic susceptibility. Thus, an emphasis on understanding and mitigating early-life environmental factors is warranted.

There is compelling evidence for broad and persisting consequences on mental health outcomes of exposure to early life adversity. Many of the circumstances of early-life adversity (war, displacement, poverty, discrimination) are difficult to modify. Here we identify aberrant patterns of sensory input from the mother as an important and potentially modifiable factor, and hence a feasible target for intervention. Future work will be required to assess and delineate the precise critical periods of vulnerability to unpredictable maternal signals, and to the crafting of interventions aimed to enhance patterns promoting optimal brain maturation and mental health outcomes.

  • Mothers provide signals to the developing brain pre-and postnatally

  • Parental signals profoundly influence a child’s mental and cognitive outcomes

  • Patterns-specifically predictability-of these signals influence neurodevelopment

  • Aberrant maternal signal patterns disrupt brain circuit maturation in rodents

  • In humans, unpredictable maternal signals are linked to child mental illness risk

Acknowledgments

Funding: The authors’ work is supported by the National Institutes of Health (MH-096889; MH73136; NS28912).

Footnotes

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

The authors have no competing interests to declare.

References

  1. Aldao A, Nolen-Hoeksema S, and Schweizer S. 2010. ‘Emotion-regulation strategies across psychopathology: A meta-analytic review’, Clin Psychol Rev, 30: 217–37. [DOI] [PubMed] [Google Scholar]
  2. Bale TL, Baram TZ, Brown AS, Goldstein JM, Insel TR, McCarthy MM, Nemeroff CB, Reyes TM, Simerly RB, Susser ES, and Nestler EJ. 2010. ‘Early life programming and neurodevelopmental disorders’, Biol Psychiatry, 68: 314–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Baram TZ, Davis EP, Obenaus A, Sandman CA, Small SL, Solodkin A, and Stern H. 2012. ‘Fragmentation and unpredictability of early-life experience in mental disorders’, Am J Psychiatry, 169: 907–15. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Beck CT 1998. ‘The effects of postpartum depression on child development’, Arch Psychiatr Nurs, 12: 12–20. [DOI] [PubMed] [Google Scholar]
  5. Belsky J, and Fearon RM. 2002. ‘Early attachment security, subsequent maternal sensitivity, and later child development: does continuity in development depend upon continuity of caregiving?’, Attach Hum Dev, 4: 361–87. [DOI] [PubMed] [Google Scholar]
  6. Berns GS, McClure SM, Pagnoni G, and Montague PR. 2001. ‘Predictability modulates human brain response to reward’, J Neurosci, 21: 2793–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Bolton JL, Molet J, Regev L, Chen Y, Rismanchi N, Haddad E, Yang DZ, Obenaus A, and Baram TZ. 2018. ‘Anhedonia Following Early-Life Adversity Involves Aberrant Interaction of Reward and Anxiety Circuits and Is Reversed by Partial Silencing of Amygdala Corticotropin-Releasing Hormone Gene’, Biol Psychiatry, 83: 137–47. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Bolton JL, Ruiz CM, Rismanchi N, Sanchez GA, Castillo E, Huang J, Cross C, Baram TZ, and Mahler SV. 2018a. ‘Early-life adversity facilitates acquisition of cocaine self-administration and induces persistent anhedonia’, Neurobiol Stress, 8: 57–67. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Bonsall MB, Wallace-Hadrill SM, Geddes JR, Goodwin GM, and Holmes EA. 2012. ‘Nonlinear time-series approaches in characterizing mood stability and mood instability in bipolar disorder’, Proc Biol Sci, 279: 916–24. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Bowlby John. 1950. ‘Research into the origins of delinquent behavior’, BMJ, 1: 570–73. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Champagne FA, Francis DD, Mar A, and Meaney MJ. 2003. ‘Variations in maternal care in the rat as a mediating influence for the effects of environment on development’, Physiol Behav, 79: 359–71. [DOI] [PubMed] [Google Scholar]
  12. Chen Y, and Baram TZ. 2016. ‘Toward Understanding How Early-Life Stress Reprograms Cognitive and Emotional Brain Networks’, Neuropsychopharmacology, 41: 197–206. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Comery TA, Harris JB, Willems PJ, Oostra BA, Irwin SA, Weiler IJ, and Greenough WT. 1997. ‘Abnormal dendritic spines in fragile X knockout mice: maturation and pruning deficits’, Proc Natl Acad Sci U S A, 94: 5401–4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Cover TM, and Thomas JA. 2006. Elements of Information Theory (John Wiley and Sons: New York: ). [Google Scholar]
  15. Dalle Molle R, Portella AK, Goldani MZ, Kapczinski FP, Leistner-Segal S, Salum GA, Manfro GG, and Silveira PP. 2012. ‘Associations between parenting behavior and anxiety in a rodent model and a clinical sample: relationship to peripheral BDNF levels’, Transl Psychiatry, 2: e195. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Davis EP, Stout SA, Molet J, Vegetabile B, Glynn LM, Sandman CA, Heins K, Stern H, and Baram TZ. 2017. ‘Exposure to unpredictable maternal sensory signals influences cognitive development across species’, Proc Natl Acad Sci U S A, 114: 10390–95. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Dawson G, Ashman SB, Panagiotides H, Hessl D, Self J, Yamada E, and Embry L. 2003. ‘Preschool outcomes of children of depressed mothers: role of maternal behavior, contextual risk, and children’s brain activity’, Child Dev, 74: 1158–75. [DOI] [PubMed] [Google Scholar]
  18. Depue RA, Slater JF, Wolfstetter-Kausch H, Klein D, Goplerud E, and Farr D. 1981. ‘A behavioral paradigm for identifying persons at risk for bipolar depressive disorder: a conceptual framework and five validation studies’, J Abnorm Psychol, 90: 381–437. [DOI] [PubMed] [Google Scholar]
  19. Drury SS, Sanchez MM, and Gonzalez A. 2016. ‘When mothering goes awry: Challenges and opportunities for utilizing evidence across rodent, nonhuman primate and human studies to better define the biological consequences of negative early caregiving’, Horm Behav, 77: 182–92. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Espinosa JS, and Stryker MP. 2012. ‘Development and plasticity of the primary visual cortex’, Neuron, 75: 230–49. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Evans GW, Gonnella C, Marcynyszyn LA, Gentile L, and Salpekar N. 2005. ‘The role of chaos in poverty and children’s socioemotional adjustment’, Psychol Sci, 16: 560–5. [DOI] [PubMed] [Google Scholar]
  22. Fareri DS, Gabard-Durnam L, Goff B, Flannery J, Gee DG, Lumian DS, Caldera C, and Tottenham N. 2017. ‘Altered ventral striatal-medial prefrontal cortex resting-state connectivity mediates adolescent social problems after early institutional care’, Dev Psychopathol, 29: 1865–76. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Feldman R 2007. ‘Parent-infant synchrony and the construction of shared timing; physiological precursors, developmental outcomes, and risk conditions’, J Child Psychol Psychiatry, 48: 329–54. [DOI] [PubMed] [Google Scholar]
  24. Feldman R 2015. ‘Mutual influences between child emotion regulation and parent-child reciprocity support development across the first 10 years of life: Implications for developmental psychopathology’, Dev Psychopathol, 27: 1007–23. [DOI] [PubMed] [Google Scholar]
  25. Feldman R, Granat A, Pariente C, Kanety H, Kuint J, and Gilboa-Schechtman E. 2009. ‘Maternal depression and anxiety across the postpartum year and infant social engagement, fear regulation, and stress reactivity’, J Am Acad Child Adolesc Psychiatry, 48: 919–27. [DOI] [PubMed] [Google Scholar]
  26. Fernandez KC, Jazaieri H, and Gross JJ. 2016. ‘Emotion Regulation: A Transdiagnostic Perspective on a New RDoC Domain’, Cognit Ther Res, 40: 426–40. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Fiske DW, and Rice L. 1955. ‘Intra-individual response variability’, Psychol Bull, 52: 217–50. [DOI] [PubMed] [Google Scholar]
  28. Garey LJ 1984. ‘Structural development of the visual system of man’, Hum Neurobiol, 3: 75–80. [PubMed] [Google Scholar]
  29. Glynn LM, Stern HS, Howland MA, Risbrough VB, Baker DG, Nievergelt CM, … Davis EP 2018a. Measuring novel antecedents of mental illness: the Questionnaire of Unpredictability in Childhood. Neuropsychopharmacology. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Glynn LM, Howland MA, Sandman CA, Davis EP, Phelan M, Baram TZ, and Stern HS. 2018b. ‘Prenatal maternal mood patterns predict child temperament and adolescent mental health’, J Affect Disord, 228: 83–90. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Goodman SH 2007. ‘Depression in mothers’, Annu Rev Clin Psychol, 3: 107–35. [DOI] [PubMed] [Google Scholar]
  32. Gunnar MR 2010. ‘Reversing the Effects of Early Deprivation after Infancy: Giving Children Families may not be Enough’, Front Neurosci, 4: 170. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Hackett TA, Barkat TR, O’Brien BM, Hensch TK, and Polley DB. 2011. ‘Linking topography to tonotopy in the mouse auditory thalamocortical circuit’, J Neurosci, 31: 2983–95. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Halligan SL, Herbert J, Goodyer IM, and Murray L. 2004. ‘Exposure to postnatal depression predicts elevated cortisol in adolescent offspring’, Biol Psychiatry, 55: 376–81. [DOI] [PubMed] [Google Scholar]
  35. Hane AA, Henderson HA, Reeb-Sutherland BC, and Fox NA. 2010. ‘Ordinary variations in human maternal caregiving in infancy and biobehavioral development in early childhood: A follow-up study’, Dev Psychobiol, 52: 558–67. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Heim C, and Binder EB. 2012. ‘Current research trends in early life stress and depression: review of human studies on sensitive periods, gene-environment interactions, and epigenetics’, Exp Neurol, 233: 102–11. [DOI] [PubMed] [Google Scholar]
  37. Hoeijmakers L, Lucassen PJ, and Korosi A. 2014. ‘The interplay of early-life stress, nutrition, and immune activation programs adult hippocampal structure and function’, Front Mol Neurosci, 7: 103. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Hrdy Sarah B. 2000. Mother Nature: Maternal Instincts and How They Shape the Human Species (Ballantine Books).
  39. Ivy AS, Brunson KL, Sandman C, and Baram TZ. 2008. ‘Dysfunctional nurturing behavior in rat dams with limited access to nesting material: a clinically relevant model for early-life stress’, Neurosciann, 154: 1132–42. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Ivy AS, Rex CS, Chen Y, Dube C, Maras PM, Grigoriadis DE, Gall CM, Lynch G, and Baram TZ. 2010. ‘Hippocampal dysfunction and cognitive impairments provoked by chronic early-life stress involve excessive activation of CRH receptors’, J Neurosci, 30: 13005–15. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Khazipov R, Sirota A, Leinekugel X, Holmes GL, Ben-Ari Y, and Buzsaki G. 2004. ‘Early motor activity drives spindle bursts in the developing somatosensory cortex’, Nature, 432: 758–61. [DOI] [PubMed] [Google Scholar]
  42. Kopala-Sibley DC, Cyr M, Finsaas MC, Orawe J, Huang A, Tottenham N, and Klein DN. 2018. ‘Early Childhood Parenting Predicts Late Childhood Brain Functional Connectivity During Emotion Perception and Reward Processing’, Child Dev. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Kring Ann M. , and Sloan Denise M. (ed.)^(eds.) 2010. Emotion Regulation and Psychopathology: A transdiagnostic approach to etiology and treatment (Guildford Press: New York: ). [Google Scholar]
  44. Kundakovic M, and Champagne FA. 2015. ‘Early-life experience, epigenetics, and the developing brain’, Neuropsychopharmacology, 40: 141–53. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Kuppens P, Van Mechelen I, Nezlek JB, Dossche D, and Timmermans T. 2007. ‘Individual differences in core affect variability and their relationship to personality and psychological adjustment’, Emotion, 7: 262–74. [DOI] [PubMed] [Google Scholar]
  46. Kuppens P, and Verduyn P. 2015. ‘Looking at emotion regulation through the window of emotion dynamics’, Psychol Inq, 26: 72–79. [Google Scholar]
  47. Kuzawa CW, and Quinn EA. 2009. ‘Developmental Origins of Adult Function and Health: Evolutionary Hypotheses’, Annu Rev Anthropol, 38: 131–47. [Google Scholar]
  48. Larsen RJ, and Ed Diener. 1987. ‘Affect intensity as an individual difference characteristic: a review’, J Res Pers, 21: 1–39. [Google Scholar]
  49. Lebel C, Walton M, Letourneau N, Giesbrecht GF, Kaplan BJ, and Dewey D. 2016. ‘Prepartum and Postpartum Maternal Depressive Symptoms Are Related to Children’s Brain Structure in Preschool’, Biol Psychiatry, 80: 859–68. [DOI] [PubMed] [Google Scholar]
  50. MacLean Paul D. 1990. The triune brain in evolution: role in paleocerebral functions (Plenum Press: New York: ). [DOI] [PubMed] [Google Scholar]
  51. Malter Cohen M, Jing D, Yang RR, Tottenham N, Lee FS, and Casey BJ. 2013. ‘Early-life stress has persistent effects on amygdala function and development in mice and humans’, Proc Natl Acad Sci U S A, 110: 18274–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Maras PM, and Baram TZ. 2012. ‘Sculpting the hippocampus from within: stress, spines, and CRH’, Trends Neurosci, 35: 315–24. [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Masur EF, Flynn V, and Eichorst DL. 2005. ‘Maternal responsive and directive behaviours and utterances as predictors of children’s lexical development’, J Child Lang, 32: 63–91. [DOI] [PubMed] [Google Scholar]
  54. Mischel W, and Shoda Y. 1995. ‘A cognitive-affective system theory of personality: reconceptualizing situations, dispositions, dynamics, and invariance in personality structure’, Psychol Rev, 102: 246–68. [DOI] [PubMed] [Google Scholar]
  55. Molet J, Heins K, Zhuo X, Mei YT, Regev L, Baram TZ, and Stern H. 2016. ‘Fragmentation and high entropy of neonatal experience predict adolescent emotional outcome’, Transl Psychiatry, 6: e702. [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Molet J, Maras PM, Avishai-Eliner S, and Baram TZ. 2014. ‘Naturalistic rodent models of chronic early-life stress’, Dev Psychobiol, 56: 1675–88. [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Molet J, Maras PM, Kinney-Lang E, Harris NG, Rashid F, Ivy AS, Solodkin A, Obenaus A, and Baram TZ. 2016. ‘MRI uncovers disrupted hippocampal microstructure that underlies memory impairments after early-life adversity’, Hippocampus, 26: 1618–32. [DOI] [PMC free article] [PubMed] [Google Scholar]
  58. Murray L, Arteche A, Fearon P, Halligan S, Goodyer I, and Cooper P. 2011. ‘Maternal postnatal depression and the development of depression in offspring up to 16 years of age’, J Acad Child Adoles Psychiatry, 50: 460–70. [DOI] [PubMed] [Google Scholar]
  59. Nelson CA 3rd, Zeanah CH, Fox NA, Marshall PJ, Smyke AT, and Guthrie D. 2007. ‘Cognitive recovery in socially deprived young children: the Bucharest Early Intervention Project’, Science, 318: 1937–40. [DOI] [PubMed] [Google Scholar]
  60. Neniskyte U, and Gross CT. 2017. ‘Errant gardeners: glial-cell-dependent synaptic pruning and neurodevelopmental disorders’, Nat Rev Neurosci, 18: 658–70. [DOI] [PubMed] [Google Scholar]
  61. NICHD Early Child Care Research Network. 1999a. ‘Child care and mother-child interaction in the first 3 years of life. ‘, Dev Psychol, 35: 1399–413. [PubMed] [Google Scholar]
  62. NICHD Early Child Care Research Network. 1999b. ‘Chronicity of maternal depressive symptoms, maternal sensitivity, and child functioning at 36 months. NICHD Early Child Care Research Network’, Dev Psychol, 35: 1297–310. [DOI] [PubMed] [Google Scholar]
  63. NICHD Early Child Care Research Network. 2003. ‘Does amount of time spent in child care predict socioemotional adjustment during the transition to kindergarten?’, Child Dev, 74: 976–1005. [DOI] [PubMed] [Google Scholar]
  64. NICHD Early Child Care Research Network. 2006. ‘Infant-mother attachment classification: risk and protection in relation to changing maternal caregiving quality’, Dev Psychol, 42: 38–58. [DOI] [PubMed] [Google Scholar]
  65. NIMH Workgroup. 2009. “Transformative neurodevelopmental research in mental illness: Results of the NIMH Workgroup.”
  66. Noldus Information Technology. 2008. The Observer XT Reference Manual Version 11.0.
  67. Paolicelli RC, Bolasco G, Pagani F, Maggi L, Scianni M, Panzanelli P, Giustetto M, Ferreira TA, Guiducci E, Dumas L, Ragozzino D, and Gross CT. 2011. ‘Synaptic pruning by microglia is necessary for normal brain development’, Science, 333: 1456–8. [DOI] [PubMed] [Google Scholar]
  68. Pavard S, Gagnon A, Desjardins B, and Heyer E. 2005. ‘Mother’s death and child survival: the case of early Quebec’, J Biosoc Sci, 37: 209–27. [DOI] [PubMed] [Google Scholar]
  69. Pena CJ, Neugut YD, Calarco CA, and Champagne FA. 2014. ‘Effects of maternal care on the development of midbrain dopamine pathways and reward-directed behavior in female offspring’, Eur J Neurosci, 39: 946–56. [DOI] [PubMed] [Google Scholar]
  70. Posner J, Cha J, Roy AK, Peterson BS, Bansal R, Gustafsson HC, Raffanello E , Gingrich J, and Monk C. 2016. ‘Alterations in amygdala-prefrontal circuits in infants exposed to prenatal maternal depression’, Transl Psychiatry, 6: e935. [DOI] [PMC free article] [PubMed] [Google Scholar]
  71. Raineki C, Cortes MR, Belnoue L, and Sullivan RM. 2012. ‘Effects of early-life abuse differ across development: infant social behavior deficits are followed by adolescent depressive-like behaviors mediated by the amygdala’, J Neurosci, 32: 7758–65. [DOI] [PMC free article] [PubMed] [Google Scholar]
  72. Rice CJ, Sandman CA, Lenjavi MR, and Baram TZ. 2008. ‘A novel mouse model for acute and long-lasting consequences of early life stress’, Endocrinol, 149: 4892–900. [DOI] [PMC free article] [PubMed] [Google Scholar]
  73. Rilling JK, and Young LJ. 2014. ‘The biology of mammalian parenting and its effect on offspring social development’, Science, 345: 771–6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  74. Rincon-Cortes M, and Sullivan RM. 2014. ‘Early life trauma and attachment: immediate and enduring effects on neurobehavioral and stress axis development’, Front Endocrinol (Lausanne), 5: 33. [DOI] [PMC free article] [PubMed] [Google Scholar]
  75. Risbrough VB, Glynn LM, Davis EP, Sandman CA, Obenaus A, Stern HS, Keator DB, Yassa MA, Baram TZ, and Baker DG. 2018. ‘Does Anhedonia Presage Increased Risk of Posttraumatic Stress Disorder? : Adolescent Anhedonia and Posttraumatic Disorders’, Curr Top Behav Neurosci, 38: 249–65. [DOI] [PMC free article] [PubMed] [Google Scholar]
  76. Sanchez MM, McCormack KM, and Howell BR. 2015. ‘Social buffering of stress responses in nonhuman primates: Maternal regulation of the development of emotional regulatory brain circuits’, Soc Neurosci, 10: 512–26. [DOI] [PMC free article] [PubMed] [Google Scholar]
  77. Sandman CA, Buss C, Head K, and Davis EP. 2015. ‘Fetal Exposure to Maternal Depressive Symptoms Is Associated With Cortical Thickness in Late Childhood’, Biol Psychiatry, 77: 324–34. [DOI] [PMC free article] [PubMed] [Google Scholar]
  78. Schafer DP, Lehrman EK, Kautzman AG, Koyama R, Mardinly AR, Yamasaki R, Ransohoff RM, Greenberg ME, Barres BA, and Stevens B. 2012. ‘Microglia sculpt postnatal neural circuits in an activity and complement-dependent manner’, Neuron, 74: 691–705. [DOI] [PMC free article] [PubMed] [Google Scholar]
  79. Sear R, Steele F, McGregor IA, and Mace R. 2002. ‘The effects of kin on child mortality in rural Gambia’, Demography, 39: 43–63. [DOI] [PubMed] [Google Scholar]
  80. Seay B, Hansen E, and Harlow HF. 1962. ‘Mother-infant separation in monkeys’, J Child Psychol Psychiatry, 3: 123–32. [DOI] [PubMed] [Google Scholar]
  81. Singh-Taylor A, Korosi A, Molet J, Gunn BG, and Baram TZ. 2015. ‘Synaptic rewiring of stress-sensitive neurons by early-life experience: a mechanism for resilience?’, Neurobiol Stress, 1: 109–15. [DOI] [PMC free article] [PubMed] [Google Scholar]
  82. Singh-Taylor A, Molet J, Jiang S, Korosi A, Bolton JL, Noam Y, Simeone K, Cope J, Chen Y, Mortazavi A, and Baram TZ. 2018. ‘NRSF-dependent epigenetic mechanisms contribute to programming of stress-sensitive neurons by neonatal experience, promoting resilience’, Mol Psychiatry, 23: 648–57. [DOI] [PMC free article] [PubMed] [Google Scholar]
  83. Soe NN, Wen DJ, Poh JS, Chong YS, Broekman BF, Chen H, Shek LP, Tan KH, Gluckman PD, Fortier MV, Meaney MJ, and Qiu A. 2018. ‘Perinatal maternal depressive symptoms alter amygdala functional connectivity in girls’, Hum Brain Mapp, 39: 680–90. [DOI] [PMC free article] [PubMed] [Google Scholar]
  84. Speh JC, and Moore RY. 1993. ‘Retinohypothalamic tract development in the hamster and rat’, Brain Res Dev Brain Res, 76: 171–81. [DOI] [PubMed] [Google Scholar]
  85. Sun H, Takesian AE, Wang TT, Lippman-Bell JJ, Hensch TK, and Jensen FE. 2018. ‘Early Seizures Prematurely Unsilence Auditory Synapses to Disrupt Thalamocortical Critical Period Plasticity’, Cell Rep, 23: 2533–40. [DOI] [PMC free article] [PubMed] [Google Scholar]
  86. Thompson RJ, Berenbaum H, and Bredemeier K. 2011. ‘Cross-sectional and longitudinal relations between affective instability and depression’, J Affect Disord, 130: 53–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  87. Vegetabile BG, Davis EP, Stout S, Baram TZ, and Stern H. in press ‘Estimating the entropy rate of finite markov chains with application to behavior studies’, J Educ Behav Stat. [Google Scholar]
  88. Ventura R, Coccurello R, Andolina D, Latagliata EC, Zanettini C, Lampis V, Battaglia M, D’Amato FR, and Moles A. 2013. ‘Postnatal aversive experience impairs sensitivity to natural rewards and increases susceptibility to negative events in adult life’, Cereb Cortex, 23: 1606–17. [DOI] [PubMed] [Google Scholar]
  89. Verbeek T, Bockting CL, van Pampus MG, Ormel J, Meijer JL, Hartman CA, and Burger H. 2012. ‘Postpartum depression predicts offspring mental health problems in adolescence independently of parental lifetime psychopathology’, J Affect Disord, 136: 948–54. [DOI] [PubMed] [Google Scholar]
  90. Voorn P, Kalsbeek A, Jorritsma-Byham B, and Groenewegen HJ. 1988. ‘The pre- and postnatal development of the dopaminergic cell groups in the ventral mesencephalon and the dopaminergic innervation of the striatum of the rat’, Neuroscience, 25: 857–87. [DOI] [PubMed] [Google Scholar]
  91. Wen DJ, Poh JS, Ni SN, Chong YS, Chen H, Kwek K, Shek LP, Gluckman PD, Fortier MV, Meaney MJ, and Qiu A. 2017. ‘Influences of prenatal and postnatal maternal depression on amygdala volume and microstructure in young children’, Transl Psychiatry, 7: e1103. [DOI] [PMC free article] [PubMed] [Google Scholar]
  92. Wessman AE, and Ricks DF. 1966. Mood and Personality (Holt, Rinehart & Winston: New York: ). [Google Scholar]
  93. Wiesel TN, and Hubel DH. 1963. ‘Single-Cell Responses in Striate Cortex of Kittens Deprived of Vision in One Eye’, J Neurophysiol, 26: 1003–17. [DOI] [PubMed] [Google Scholar]
  94. Willführ Kai P., and Gagnon Alain. 2013. ‘Are stepparents always evil? Parental death, remarriage, and child survival in demographically saturated Krummhörn (1720-1859) and expanding Québec (1670-1750)’, Biodemography Soc Biol, 59: 191. [DOI] [PubMed] [Google Scholar]
  95. Woo TU, Pucak ML, Kye CH, Matus CV, and Lewis DA. 1997. ‘Peripubertal refinement of the intrinsic and associational circuitry in monkey prefrontal cortex’, Neuroscience, 80: 1149–58. [DOI] [PubMed] [Google Scholar]

RESOURCES