Skip to main content
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2015 Jan 1.
Published in final edited form as: Ann N Y Acad Sci. 2013 Dec 19;1308:89–106. doi: 10.1111/nyas.12314

Issues in the timing of integrated early interventions: contributions from nutrition, neuroscience and psychological research

Theodore D Wachs a, Michael Georgieff b, Sarah Cusick b, Bruce McEwen c
PMCID: PMC4075015  NIHMSID: NIHMS565175  PMID: 24354763

Abstract

A central issue when designing multi-dimensional biological and psychosocial interventions for children who are exposed to multiple developmental risks is identification of the age period(s) in which such interventions will have the strongest and longest lasting impact (sensitive periods). In this paper we review nutritional, neuroscience and psychological evidence on this issue. Nutritional evidence is used to identify nutrient sensitive periods of age-linked dimensions of brain development, with specific reference to iron deficiency. Neuroscience evidence is used to assess the importance of timing of exposures to environmental stressors for maintaining neural, neuroendocrine and immune systems integrity. Psychological evidence illustrates the sensitivity of cognitive and social-emotional development to contextual risk and protective influences encountered at different ages. Evidence reviewed documents that the early years of life are a sensitive period where biological or psychosocial interventions or exposure to risk or protective contextual influences can produce unique long-term influences upon human brain, neuroendocrine and cognitive or psychosocial development. However, the evidence does not identify the early years as the sole sensitive time period within which to have a significant influence upon development. Choice of age(s) to initiate interventions should be based on what outcomes are targeted and what interventions are used.

Keywords: Early Intervention, sensitive periods, iron deficiency, stress, developmental risk, brain

Introduction

Evidence documents that children from low income families in both wealthy1 and low income countries 2 have greater exposure to multiple biological and psychosocial risks that can significantly compromise their development. These findings emphasize the importance of integrating and implementing multi-dimensional biological and psychosocial interventions to compensate for exposure to multiple risks.3 A critical and long-standing question involves identifying the age period(s) in which such interventions can have the strongest and longest lasting impact. The concept that the early years of life are a time when children are particularly sensitive to extrinsic influences has deep seated roots, dating back to the writings of Plato.4 In the present era questions involving timing of events and change over time in relations between contextual elements are central issues in major developmental theories such as Developmental Systems Theory 5. and the Bio-ecological Model. 6.

Initial empirical support about the importance of the early years of life came from 20th century embryological research on fetal development and ethological research on imprinting, which culminated in the concepts of “critical” and “sensitive periods” of development. While both concepts refer to age periods characterized by plasticity in development, where the impact of exposure to facilitative experiences or developmental risks is particularly strong and lasting, the concepts are not identical.7 Critical periods are characterized by enhanced sensitivity to exposures which are restricted to a sharply defined time period such that the impact of exposures during this time period are irreversible. In contrast, when sensitive periods are occurring the exposure time windows for enhanced sensitivity are broader, there can be continued, though reduced, plasticity both before and after the sensitive period and exposure during sensitive time windows are not necessarily irreversible. 8

Evidence from human level studies favors the operation of sensitive rather than critical periods.912 Research findings also indicate that there may be multiple sensitive periods depending upon the domains of development assessed. 9,10,13 Illustrating the operation of multiple sensitive periods is evidence that sensitive periods for neural development may be narrower than sensitive periods for behavioral development 14,15 and that different sensitive period windows are seen for cognitive/academic versus social-emotional outcomes16,17. For example, the impact of exposure to poverty (or to interventions designed to reduce poverty) upon later cognitive or academic outcomes appears to be strongest in the period from infancy to early childhood, whereas such exposure appears to adversely impact on social-emotional development or behavior problem outcomes across the age span from infancy through adolescence 18. One implication of this pattern of findings is that different time periods may be needed for biological versus psychosocial interventions or for different psychosocial outcomes.

The primary question addressed by this paper is whether the early years of life are a sensitive time period for implementing integrated biological and psychosocial interventions to promote the development of children living in poverty in low and middle income countries? To address this question we will review evidence from nutrition, neuroscience and developmental psychology on timing of exposures to biological or psychosocial influences and neural, physiological and behavioral outcomes. In this paper the early years are defined as the time span between fertilization and the end of the fifth year of life. Our rationale for age 5 years is based on evidence that lower developmental trajectories during this time period are a significant precursor for poor school readiness and subsequent inadequate school performance as well as later cognitive and social-emotional problems.2 In addition, although we relate defined time periods to specific outcome dimensions, we recognize the validity of the conclusion drawn by developmental systems theorists that different outcome dimensions are linked in such a way that changes in one outcome can result in changes in other outcomes.5

The Timing of Nutrition and Brain Development

Optimal overall brain development in the prenatal period and early years of life depends on providing sufficient quantities of key nutrients during specific sensitive time periods. While all nutrients are important for brain development, certain nutrients (eg, protein, long chain polyunsaturated fatty acids, iron, copper, zinc, iodine, folate, choline and vitamins A, B6 and B12) have particularly large effects early in life and exhibit critical or sensitive periods for neurodevelopment (for more details see supplementary on-line table A). These periods coincide with the times when specific brain regions are developing most rapidly and have their highest nutrient requirements. Because the brain is not a homogeneous organ there is not a single common growth trajectory or a single sensitive period.9 Rather, different brain regions (e.g., the hippocampus, striatum, cortex) and brain processes (e.g., myelination) exhibit growth trajectories that span and peak at different times, each with specific nutrient requirements. These periods of peak growth are also those times when the deficiency of a specific nutrient, particularly one that supports basic neuronal/glial metabolic processes (e.g., protein, iron, glucose), is most deleterious. Supplementation of a deficient nutrient after these sensitive windows of development have passed usually results in incomplete correction of the brain insult and thus in an increased risk of long-term neurodevelopmental deficits. Defining the timing of these peak periods of nutrient requirement for certain brain areas is critical for the successful implementation of nutritional interventions to prevent harmful, potentially permanent effects of deficiency on brain development.

Sensitive periods for specific nutrients (Table 1) are typically identified in controlled studies of pre-clinical models at different stages of early development and subsequently validated with successful nutritional intervention studies in humans that yield beneficial neurobehavioral outcomes in the domains identified in the preclinical models. The literature on early iron nutrition serves as an example of how such multi-disciplinary studies work in concert to demonstrate that timing affects a nutrient’s relationship with the developing brain.

Table 1.

Brain regions affected by critical nutrients for brain development in the first 1000 days*

Nutrient Period(s) of particularly high brain demand for nutrient Principal brain region or circuitry affected
Protein
  1. Gestation

  2. 4 – 12 months postnatal

  1. Global, hippocampus, striatum, myelin, cerebellum

  2. Cortex (esp prefrontal), myelin

LCPUFAS Last trimester of gestation- 2–3 months postnatal Global, retina
Iron
  1. Last trimester of gestation

  2. 6 months-3 years postnatal

  1. Myelin, striatum, hippocampus

  2. Myelin, frontal cortex, basal ganglia (motor)

Zinc
  1. Last four months of gestation

  2. 6 months – 10 years

  1. Autonomic nervous system, cerebellum, hippocampus

  2. Cortex

Iodine
  1. First trimester of gestation

  2. Last trimester of gestation

  3. Infancy-12 years

  1. Global

  2. Cortex, striatum, cerebellum, hippocampus

  3. Myelin, prefrontal cortex

Copper Last trimester of gestation Occipital and parietal cortex, striatum, cerebellum, hippocampus
*

All nutrients listed are critical in the first 1000 days and have their largest effects on brain development at that time; some nutrient-brain developmental time frames extend into middle childhood with milder effects of effects on different neural systems.

Iron Deficiency

Iron deficiency is the most common nutritional deficiency worldwide, with an estimated one billion people having iron deficiency anemia.19 The developing brain requires iron for enzymes and hemoproteins that regulate cellular processes, including fatty acid production, dopamine neurotransmitter synthesis, and neuronal energy production.20,21 The peak periods of brain vulnerability to iron deficiency are those where a high demand for iron coincides with a time period when iron balance is likely to be negative (Table 1). This includes the fetal/neonatal period and infancy/toddlerhood (6 months to 3 years), two time periods where iron deficiency has profound and long-lasting effects and where supplementation has proven to be an effective deterrent of later impairment. It is important to note that while early adulthood is also a period of high risk for negative iron balance, brain development at this time is slower, and thus brain demand for iron is relatively low. Accordingly, iron deficiency in women between 18 and 35 years may cause acute effects, but these effects appear to resolve with restoration of iron status, with no apparent long-term neurobehavioral consequences.22

Newborn infants with iron deficiency from late gestation demonstrate recognition memory deficits indicative of impaired hippocampal function, slower processing speed potentially indicative of reduced myelination, and altered temperament characterized by poorer infant-mother interaction and suggestive neurobiologically of altered dopamine metabolism.2325 Infants with postnatal iron deficiency anemia show fewer learning and memory effects, but do display slower speeds of neural transmission in auditory brain stem responses and visual evoked potentials, consistent with hypomyelination.26, 27 Iron deficiency later in toddlerhood leads to impaired social emotional behavior, including maintaining closer proximity to caregivers, increased irritability, and decreased positive affect.28,29 Iron deficiency at this time appears to particularly affect the brain’s monoaminergic system, i.e. neurochemistry, and these behavioral changes may not be remediable with iron therapy.20, 30

Animal studies corroborate the effect that the timing of iron deficiency in infancy vs. toddlerhood has on neurobehavioral outcomes. Rodent models of gestational/lactational vs postnatal dietary iron deficiency reveal variable impairments in spatial navigation, trace fear conditioning, and procedural memory, all consistent with functional and structural abnormalities in the hippocampus and striatum, as well as abnormalities in myelin formation and monoamine regulation based on the timing of the deficiency.3139 A differential timing effect is also seen in rhesus monkeys, where late gestational iron deficiency results in a less fearful and more impulsive animal, while postnatal iron deficiency results in a more inhibited and anxious one.40

Iron Supplementation

Studies of iron supplementation in pregnancy and childhood reinforce these findings and demonstrate that the importance of timing in intervention studies cannot be overstated.4144 When the period of high brain demand for iron coincides with a period of high risk for iron deficiency, as in the fetal and toddler periods, neurodevelopmental consequences are more likely to occur. Accordingly, these periods are optimal for iron intervention (for more specific details see supplementary on-line table B). Prenatal iron supplementation appears to particularly set the stage for postnatal iron and brain health. Iron/folic acid supplementation during pregnancy results in significantly better scores in working memory, inhibitory control, and fine motor functioning in children at 7 to 9 years of age.43 In contrast, daily iron/folic acid with or without zinc supplementation of children from age 12 to age 35 months, whose mothers do not receive micronutrient supplementation during pregnancy, has no effect on intellectual, executive, or motor function at age 7–9 years.44 Moreover, supplementation of children from 12–36 months whose mothers receive iron/folic acid during pregnancy confers no additional cognitive benefit over prenatal iron/folic acid alone.42

While 12–36 months of age is both a period of peak vulnerability to iron deficiency and brain demand for iron (supplementary on-line table B), the brain system exacting the greatest need for iron at this age is the monoaminergic system. Iron supplementation between 12–36 months would thus potentially lead to improvements in socioemotional behavior, but significant improvements in cognitive, intellectual, and motor functioning-- the domains tested by the researchers—would necessitate earlier supplementation.

Implications for interventions

The established sensitive periods of brain development for each nutrient should guide the timing of implementation of nutrition interventions to ensure optimal brain development. Nutritional health of the offspring is related to maternal nutritional health even prior to the time the child is conceived. Many important brain systems (eg, the hippocampus, myelination, synaptogenesis) that are dependent on adequate nutritional supply are maturing in the fetus in the last trimester. Thus, nutritional, medical and social interventions that ensure a healthy, low-stress pregnancy optimize nutrient delivery to the developing fetal brain. In the postnatal period, earlier screening and identification of nutrient risks/deficits is critical since the preponderance of data shows earlier nutritional intervention is more effective in promoting long-term brain health. When developing these interventions, four key principles must also be considered to achieve significant neurobehavioral results:

  • 1). The nutritional intervention must be given concordantly with when the nutrient is most needed, e.g., iron supplementation during pregnancy or early infancy to achieve improved cognitive or motor outcomes in later childhood;

  • 2). The target population must not already be sufficient in the nutrient. No evidence exists that nutrient delivery greater than that which is needed to ensure sufficiency will provide additional neurobehavioral benefit;

  • 3). The behavioral or cognitive battery used to assess outcomes later in childhood must be appropriately specific (i.e., assess potentially affected neural circuits) and not be too global such that subtle differences will not be detected;

  • 4). The timing of the assessment battery must also be carefully considered. A null result in response to intervention may be found if the test is administered too late, and the child outgrew a previous nutritionally induced brain deficit, either by neural plasticity or catch-up growth.

Text Box 1. Nutrition and brain development in the first 1000 days.

  1. Brain growth and development is highly dependent on adequate nutritional substrates for that growth. While all nutrients are necessary for the growth of cells, including those in the brain, certain nutrients appear particularly influential: protein, energy sources including glucose, fats including long-chain polyunsaturated fatty acids (aka fish oils), iron, zinc, copper, iodine, folic acid, choline and vitamin A. Deficits in nutrients can cause the brain to function abnormally during the period of the deficit. These deficits appear to be related to alterations in brain metabolism.

  2. Some nutritional deficits confer long-term structural and functional abnormalities well beyond the period of deficit suggesting that the brain has been permanently altered. These deficits appear to be related to structural changes (i.e., not having built the brain correctly) and genomic (e.g., epigenetic) changes that alter long-term regulation of brain function.

  3. The brain is not a homogeneous organ. Rather it is characterized by interconnected regions each of which has a different developmental trajectory. The times of most rapid development (i.e., cell growth and differentiation) define the time of greatest nutrient needs. Thus, the timing of nutrient provision or deficiency determines how the structure develops and ultimately how it functions. A given nutrient deficit at one age may result in quite different developmental effects than the same nutrient deficit at another age. These findings imply that critical/sensitive windows exist for many of these systems and that these windows are tightly linked to periods of rapid regional brain growth and differentiation.

  4. The majority of brain growth that is nutrient sensitive occurs in the first 1000 days from conception. Ensuring the delivery of specific nutrients coincident with growth spurts that are dependent on those nutrients should shape dietary and nutritional intervention policy. As a blanket approach, overall nutrient sufficiency is most important for the pregnant woman, the newborn infant and the toddler to ensure long-term brain health in the offspring.

  5. Provision of nutrients represents only the supply side of the equation. The metabolic status of the recipient, including the presence of illness and psychological stress, will alter how growth factors are regulated and how nutrients are utilized. Thus, factors that mediate stress (see next section) are also important with respect to the effectiveness of nutritional therapy in promoting brain growth.

Timing of stress for brain and neuroendocrine development and function

Stressful experiences throughout the life-course and resulting health promoting or damaging behaviors have an impact on metabolism and can be regarded as “nutrition sensitive”. In response to a changing social and physical environment, the body and brain respond to novelty and potential threats by activating autonomic, neuroendocrine, metabolic and immune system responses that promote adaptation. This process, called “allostasis” helps to maintain homeostasis and is primarily dependent on the brain to perceive and react to novelty and potential threats and activate the coordinated mediators of allostasis. When this mechanism is overused by many stressful events, and especially when the balanced responses of the network of allostasis are dysregulated, then wear and tear on the body ensue, referred to as “allostatic load”. This concept has relevance to the intersection between metabolism, stress responsiveness, and malnutrition in the sense of both quality and quantity of food, are very much involved.

Text BOX 2. Allostasis and allostatic load.

The brain is a target of allostatic load as is the rest of the body. Depression, anxiety disorders and substance abuse are expressions of this load along with cardiovascular disease, Type 2 diabetes and metabolic syndrome and other disorders that reflect the consequences of chronic stress in terms of poor sleep, overeating, smoking, drinking and lack of physical activity.45 Allostatic load changes architecture of regions of the brain involved in cognition and emotional regulation, including shrinkage of the hippocampus that can be reversed by regular, moderate exercise.46,47 In animal models, chronic stress is also associated with shrinkage of dendrites in the medial prefrontal cortex as well as hippocampus and dendritic growth in the basolateral amygdala and orbitfrontal cortex.46,48

Chronic stress becomes toxic and has its greatest impact to produce the greatest allostatic load when the individual lacks sufficient control of his or her life due to inadequate social, emotional or material resources.49 Moreover, adverse events in early life predispose the brain and body to greater vulnerability to stress throughout the life course.49,50 Prenatal stress of the mother is known to increase anxiety behavior of the offspring and alter brain structure and function, including impaired development of the hippocampus.51,52 Prenatal stress in humans is associated with shorter telomeres in offspring along with behavioral and metabolic dysregulation that includes increased risk for metabolic disorders related to low birthweight.5355 There are also possible epigenetic effects that are transmitted from the parents to the offspring.56,57

Early life events related to maternal care in animals, as well as parental care in humans, play a powerful role in later mental and physical health, as demonstrated by the adverse childhood experiences (ACE) studies and other recent work. A summary of evidence on findings from animal studies are seen in supplementary on-line text box A. At the human level one of the consequences of ACE is an increased prevalence of metabolic disorders, obesity and diabetes that may reflect both quantity and quality of food as well as how the body processes it.50 Food insecurity may be an added factor 58 along with the stressful nature of an ugly and dangerous neighborhood living environment influencing obesity and increasing allostatic load.59,60 In studies on ACE in human populations, there are reports of increased inflammatory tone, not only in children, but also in young adults related to early life abuse, that includes chronic harsh language, as well as physical and sexual abuse.61,62 Chaos in the home is associated with development of poor self-regulatory behaviors, as well as obesity.63 An ACE study carried out in a middle class population indicates that poverty is not the only source of early life stressors.50

Nevertheless, low SES does increase the likelihood of stressors in the home and neighborhood, including toxic chemical agents such as lead and air pollution.6365 Low SES children are found to be more likely to be deficient in language skills, as well as self-regulatory behaviors and also in certain types of memory that are likely to be reflections of impaired development of parasylvian gyrus language centers, prefrontal cortical systems and temporal lobe memory systems.66,67 Low SES also correlates with smaller hippocampal volumes.68 Lower subjective SES, an important index of objective SES, is associated with reduction in prefrontal cortical gray matter.69 Growing up in a lower SES environment is accompanied by greater amygdala reactivity to angry and sad faces, which, as noted above, may be a predisposing factor for early cardiovascular disease that is known to be more prevalent at lower SES levels.70,71 Furthermore, depression is often associated with low SES, and children of depressed mothers, followed longitudinally, have shown increased amygdala volume while hippocampal volume was not affected.72

On the positive side, there are the “reactive alleles” that, in nurturing environments, lead to beneficial outcomes and even better outcomes compared to less reactive alleles, even though those same alleles can enhance adverse outcomes in a stressful early life environment.7375 Regarding adverse outcomes and good and bad ”environments”, it must be recognized, as stated in the Active Calibration Model, that allostatic processes are adjusted via epigenetic influences to optimize the individuals adaptation to, and resulting fitness for, a particular environment, whether more or less threatening or nurturing.76 Yet, there are “trade-offs” in terms of physical and mental health that, on the one hand, may increase the likelihood of passing on one’s genes by improving coping with adversity and enhancing mental health and overall reproductive success, but, on the other hand, may impair later health, e.g., by eating of “comfort foods”.77 Nowhere is this all more important that during adolescence, which is a time of transition in physiology and brain development and maturation.78,79

Adolescents have a propensity for risk-taking that is related to the capacity to exert self- control, as can be assessed by tests of delayed gratification such as the “marshmallow test” that have considerable predictive power for social, cognitive and mental health outcomes over the life course.80,81 The neural basis of self-regulation involves frontal-striatal circuitries that integrate motivational and control processes and appear to be stable for a lifetime, based upon studies of the same individuals over 4 decades.82 A key feature is an exaggerated ventral striatal representation of appetitive cues in adolescents relative to the ability to exert control. The connectivity within the ventral frontostriatal circuit including the inferior frontal gyrus and dorsal striatum is particularly important to the ability to exert self-regulation.83 Moreover, adolescents are typically somewhat impaired in fear learning but at the same time impaired in fear extinction, which implies that they may take more risks 84 and that, when there is a traumatic event, they may be more affected by this including the possibility that this carries over into adult life. 85,86

Box 3. Brain development transitions in adolescence.

Animal models are giving important clues. During adolescence, chronic juvenile stress consisting of 6h daily restraint from postnatal day 20 to 41, produced depressive-like behavior and significant neuronal remodeling of brain regions likely involved in these behavioral alterations, namely, the hippocampus, prefrontal cortex and amygdala. Chronically stressed males and females exhibited anhedonia, increased locomotion when exposed to novelty, and altered coping strategies when exposed to acute stress. Coincident with these behavioral changes, there was stress-induced shrinkage of dendrites in the hippocampus and prefrontal cortex and concurrent hypertrophy of dendrites in the amygdala and impaired development of the hippocampus carrying into adult life. 87,88

The human prefrontal cortex undergoes a prolonged course of maturation that continues well after puberty and parallels a slowly emerging ability for flexible social behavior.89,90 Interestingly, there are differences within the cerebral cortex in heritability. Primary sensory and motor cortex, which develop earlier, show relatively greater genetic effects earlier in childhood, whereas the later developing dorsal prefrontal cortex and temporal lobes show increasingly prominent genetic effects with maturation.91

It is also noteworthy that the prefrontal cortex (PFC) to amygdala connectivity changes from positive to negative between early childhood and adolescence and young adulthood.70 Indeed, young children are wary of strangers as secure attachment to the mother develops. One index of this sensitive period is that, early in life, ambiguous facial expressions are perceived as conveying negative meaning.92 However, during adolescence, there is a restriction on extinction of fear learning, suggesting that negative experiences may have greater impact during that developmental period, although it is not yet known whether fearful events during adolescence may be more difficult to extinguish later in adult life.85

Finally, it is important to note that early life adversity in rhesus and humans impairs development of the PFC, among other effects in the brain and body. In rhesus, peer rearing causes changes in 5HT1A receptor density in a number of brain regions including PFC and is associated with an enlarged vermis, dorsomedial PFC, and dorsal anterior cingulate cortex without any apparent differences in the corpus callosum and hippocampus.93,94 In humans, adverse childhood experiences were associated with smaller PFC, greater activation of the HPA axis, and elevation in inflammation levels compared to non-maltreated children, while adults with a history of childhood maltreatment showed smaller PFC and hippocampal volume, greater activation of the HPA axis, and elevation in inflammation levels compared to non-maltreated individuals.95

There is also increased risk for obesity and metabolic disorders, including Type 2 diabetes. Indeed the developing as well as adult brain is vulnerable to metabolic dysregulation such as occurs in Type 2 diabetes and pre-diabetes. The brain responds to metabolic hormones such as insulin, leptin, ghrelin and IGF-1.96 In adults both pre-Type 2 diabetes and diabetes causes the hippocampus, a brain region important for learning and memory and mood regulation, to shrink and, with it, there is impairment of memory and mood.9799 There is also increased risk for later Alzheimer’s disease.100 Moreover, many of these problems begin in childhood and teenagers with pre-Type 2 diabetes and diabetes have impaired neural architecture and cognitive function.101,102 This has potentially huge implications for success in school and acquiring skills for the increasingly technical workforce, with a growing impact on national competitiveness as well as soaring health care costs.

Major conclusions- stress and adaptation

  1. The brain is the central organ of stress and adaptation to stress and does so through the autonomic, neuroendocrine, immune and metabolic systems, via the active process of allostasis. The brain is itself a target of the dysregulation and overuse of allostasis resulting in allostatic load and overload, which also is manifested in the body as cardiovascular disease, diabetes, arthritis and other disorders that commonly increase with age.

  2. Brain architecture is altered by stress so as to weaken brain regions involved in learning, memory and self-regulation but strengthen brain regions important for anxiety and aggression. However, the brain is normally resilient and able to recover after stress, but this resilience is impaired with aging and also in mood and anxiety disorders.

  3. Adverse experiences in childhood exert lasting effects on physical as well as mental health. Animal models reveal long lasting changes in brain architecture via epigenetic processes that involve behavioral transmission from the parent to the child as well as modifications of DNA without changing the genetic code that are passed on in the germ cells and in utero in the developing fetus. Early life adversity also increases the level of inflammation in the body that lasts into adulthood and contributes to increased incidence of mood and anxiety disorders, substance abuse, sexual precocity, cardiovascular disease and diabetes.

  4. Adolescence is a time of major changes in brain architecture, particularly the prefrontal cortex that controls self-regulatory behaviors and, as a result, adolescence is a time of vulnerability to stress. Childhood obesity and diabetes that may result, in part, from early life adversity, affects brain development, cognitive function and learning ability as well as increasing the risk for dementia later in life.

  5. Metabolic dysregulation related to poor quality of diet and also stress-related patterns of health behaviors, including how ongoing stress and resulting allostatic load alters food consumption and metabolic processing, have profound effects on brain development and function that are only now beginning to be appreciated.

Implications for intervention

Interventions that create a stable, consistent and nurturing parent-child bond foster the development of vital self-regulatory behaviors in which the late-developing prefrontal cortex plays a key role. The continuing plasticity of the brain offers some hope that behavioral intervention may have some beneficial effect throughout the life-course. In addressing the growing problem of obesity and diabetes, beginning in childhood, it must be recognized that these disorders take a toll on the brain, affecting the ability of individuals to function in our complex society. A promising strategy to prevent obesity involves teaching self-regulation to Head Start preschoolers 103, although including parents in such therapy is also important 104. In addition, programs such as the conditional cash transfer in Oportunidades in Mexico offer some hope in helping poor families rid themselves of infections and adopt healthier life styles, with some reported improvements in developmental markers of cognition and improved mental health 105,106, although such programs have shown uneven effects on educational learning outcomes 107.

Timing issues in contextual contributions to cognitive or social- emotional development

As discussed previously significant neural development continues after the early years of life, particularly during the adolescent period. 84,108 Similarly, later neural changes can be influenced by current contextual characteristics.14, 109,110 A parallel pattern of findings emerges when we consider the impact of contextual influences on cognitive and social-emotional development. Evidence on contextual influences illustrates that: (a) important developmental landmarks or precursors for later development occur both in the early years and at later ages; (b) both early and later contextual influences or interventions can influence subsequent functioning; (c) later influences or interventions may sometimes be necessary to maintain the impact of early influences or interventions. Each of these conclusions is documented in the following sections.

Rates of behavioral development

The early years. Examples of important developmental landmarks appearing during the first two years as well as precursors of later development which emerge over the first 5 years of life are shown in table 2.

Table 2.

Ages of emergence of critical developmental landmarks and precursors of critical developmental markers.

Early appearing developmental landmarks (birth-24 months): Normally developing visual function such as visual acuity (primarily first 6 months, with gradual improvement to 4 years) and eye movements following repetitive movement through the visual field (optokinetic nystagmus-3–24 months). 13
Certain domains of language such as phononetic perception (seen in the first 10 months) 111
Acquisition of taste preferences (first 3 months). 112
Acquisition of basic trust and attachment (primarily seen in the 6–12 month period). 113
Early appearing precursors (preschool and early childhood years) of later developmental landmarks Internalization of committed compliance to adult requests as a precursor for effortful self-regulation (initially seen in the time period between 14–56 months). 114.
Developing a “theory of mind” as a precursor for taking another person’s perspective (emerges around 4 years of age).115
Understanding of the “wrongness” of moral transgressions as a precursor for later moral reasoning (initially seen in the time period from 2.5–4 years).116
Language based perceptual categories as a precursor for later word learning (appearing around 18 months). 111
Deferred imitative play as a precursor to the development of abstract thinking (appears between 18–24 months).117
Development of an internalized conscience or inhibiting aggressive outbursts as a precursor to effortful self-regulation (seen between 4–7 years) 118.
Later appearing developmental functions appearing in middle childhood, adolescence or adulthood. Evaluating the comparative values of risks versus rewards as a marker of effortful self-regulation (appears between 12–20 years).84,108.
Orientation to future goals and considering long- term consequences (appears between 11–17 years). 120
Interpersonal competencies such as taking another person’s perspective (12–15 years).117
Distinguishing between effort versus ability as primary causes or outcome success or failure (9–12 years). 121
Cognitive competencies such as: working memory (7–15+ years). 122,123
Knowledge based cognitive dimensions- “crystallized intelligence” (peaks in middle adulthood). 124,125

Later onset of important developmental characteristics

While precursors of later development can be seen in the infancy and toddler periods early specific skills or behaviors can be lost, expanded or replaced by later developing skills. 119 For example, experience-dependent brain development in adolescence and early adulthood is thought to mediate the emergence of and increases in later appearing social-emotional, communication and cognitive functions. Developmental characteristics appearing past the early years are also shown in table 2.

Do early contextual influences or interventions impact upon children’s development?

Evidence for a direct impact

As seen in table 3 findings from early intervention studies document that the early years of life are a sensitive period for preventing long-term sensory problems, for facilitating social-emotional development and for promoting child cognitive and academic competence. Results from meta-analytic studies, reviews and randomized control trials also document that interventions involving parents and carried out during the first several years of a child’s life can significantly improve parental sensitivity, cognitive stimulation, discipline strategies and supportive warm parenting.113, 128,130,131, 134 Findings also emphasize the potential importance of intervention quality 130 or parent involvement (for home based interventions) 135, given that long exposure to a sub-standard early intervention program may have limited benefits.

Table 3.

Impact of early and later interventions or exposures upon children’s development.

Child outcomes: Early exposures or interventions (infancy-early childhood): Later exposures or interventions (middle childhood-adolescence):
Perception. Cochlear implants for children with severe hearing deficits can have maximal impact on promoting normal sound reactions if implanted before age 3 ½ years, with diminishing gains thereafter.126
Social-emotional development Meta-analytic findings involving previously institutionalized adopted infants document the latter half of the first year as a sensitive period for promoting attachment security.113,127 Findings suggest that the early years of life are a particularly salient time period for preventative interventions to reduce negative emotionality and behavioral problems or promote self- regulation or pro-social behaviors128,129 Adverse long-term consequences associated with exposure in childhood or adolescence to developmental risks such as: Societal violence;149; Alcohol;150
Culturally based socialization for aggressive behavior;151
Positive consequences associated with exposure in childhood or adolescence to positive developmental influences such as: Social support, which facilitates children’s resilience after occurrence of a major natural disaster;152
Community acceptance, which supports the adjustment of former child soldiers;153 Treatment programs for abused children;154 Programs to increase child pro-social behavior and reduce aggression;155 School based programs to promote better inhibitory control; 144
Drug prevention programs156
Programs for reducing the impact of parental divorce on offspring.157
Cognitive/academic competence. Meta-analytic and review findings document that intervention during the early years carried out in either high or low-medium income countries can have long-term cognitive-academic benefits.129132
Meta-analytic findings and results from individual studies show at least partial benefits in cognitive and academic performance for institutionalized children adopted into high quality homes in the early years of life. 127, 133
Attending high quality elementary schools, can promote academic achievement for children who did not attend preschool programs.158
Validated programs to reduce learned helplessness or increase self-efficacy beliefs in children with poor academic achievement. 121
Validated interventions to promote reading skills in elementary school children.159
School feeding programs promote some aspects of educational performance.160

While findings from some studies suggest that social-emotional development may be particularly sensitive to interventions or experiences experienced during the first 3 years 113, 127129 other studies indicate that there is no specific time window during the first 5–6 years where cognitive or social-emotional intervention effects are uniquely strong. 129,132, 136, 137 Isolation of unique sensitive time windows during the early years of life are complicated by evidence indicating that significant early intervention effects may not show up until well after the intervention has been completed. 136, 138,139

Evidence supporting an indirect impact

Early contextual influences also may have long-term consequences through constraining or enhancing later reactivity either epigenetically or through neural mechanisms. 49,50, 140 Similarly, developmental researchers have described five behavioral processes through which early exposure to stressors or protective factors can influence later reactivity:141,142

  • Facilitation: positive early experiences increase the child’s receptivity to positive later experiences;

  • Buffering: positive early experiences protect the individual against later stress;

  • Sensitization: early risk exposure increases the individual’s reactivity to later occurring risks;

  • Steeling occurs when successfully dealing with early stress increases later stress resistance;

  • Blunting: exposure to early risks also can reduce the ability of the individual to benefit from subsequent positive influences.

Examples of each of these processes are found in supplementary on-line table C. What these five processes illustrate is that we cannot understand the impact of later occurring contextual influences or interventions without also considering the nature of the child’s early context.

Are early influences or interventions uniquely sufficient?

While early experiences or early interventions can have direct or indirect long-term consequences, the evidence also shows that experiences or interventions occurring well after the early years of life also can alter subsequent development.143 For example, interventions such as computer or martial arts training designed to promote children’s executive functioning appear to have more benefit when used with 8–12 year old children than when used with 4–5 year olds. 144 Research reviews also document that increased levels of schooling can promote knowledge based skills (crystallized intelligence), biologically based information processing skills (“fluid intelligence) and specific components of intelligence such as reasoning and memory) for children from both high and low-middle income countries. 145148 Additional findings illustrating the impact of later occurring experiences or interventions are also seen in table 3.

Are later influences necessary to maintain the impact of prior influences?

Evidence from follow-up studies

Findings from both meta-analyses and systematic reviews, encompassing both U.S, and non-U.S. small and large scale intervention studies, indicate that the stability of long-term cognitive gains, even if still remaining significant, tends to weaken over time. 131, 132, 138 Attenuation of initial cognitive gains following early intervention may result from non-intervention children catching-up in cognitive skills once they start attending primary school 131 or, alternatively, from a fadeout of initial gains by intervention children from low income groups if they attend low-quality primary schools 138 In either case primary school influences are implicated as relevant to the stability of intervention based early cognitive gains. Educational, economic and behavioral gains or reductions in anti-social behavior resulting from exposure to small scale high dosage early interventions are more likely to be maintained over time 161 whereas gains in these areas associated with large scale shorter dosage programs are more likely to attenuate, though still remaining significant for some outcomes. 129, 132, 138,

In addition to program dosage and scale child characteristics also play a significant role in influencing stability of early intervention gains. While children at higher levels of biological or psychosocial developmental risk have greater need for, and perhaps greater responsivity to, early intervention programs 130 there also is evidence suggesting that the impact of early intervention programs may be attenuated for children with higher levels of biological or psychosocial risk. 3, 128,162 The fading of early intervention gains in high risk populations is consistent with evidence showing that high levels of developmental risk can overwhelm the impact of normally protective influences.163 One implication of these findings is that it may be necessary to continue interventions or provide follow-up interventions beyond the first 5 years for children with significant levels of cumulative biological or psychosocial risk exposure or children with a history of compromised development. 128, 164167 For children with high levels of cumulative risk exposure there is a greater likelihood that initial intervention gains are more likely to be lost over time without some type of subsequent high quality follow-up intervention experience,158, 168, 169 particularly when such children were enrolled in scaled up lower dosage early interventions. 169

The role of “causal chains”

Whether or not there is a long term impact of early interventions also may depend on the degree to which the early intervention initiates causal chains of later occurring events which serve to maintain the impact of the early event.142 For example longitudinal findings show not only how children’s participation in a quality preschool program directly enhances cognitive skills at age 6 but also how, over time, intervention children also had a higher probability of subsequently receiving more parental and teacher educational support and involvement, were more likely to attend higher quality schools, and were are at lower risk for parent abuse or neglect, repeatedly changing schools or grade retention. 170 Path analyses illustrate how these later naturally occurring parental and school causal chain links associated with early interventions serve to influence the child’s educational attainment through early adulthood.

Other examples of naturally occurring causal chains include evidence that; (a) early exposure to developmental risk factors can increase the probability of children encountering other risks later in life 142,171; (b) early exposure to developmental risks or protective influences can shut down or open up later opportunities 128; (c) Children’s participation in early enrichment programs can increase the probability of young children being involved in follow-up interventions and can initiate changes in parental rearing styles, such as more reading to their child, or parental life changes, such as getting more education, all of which can in turn promote children’s subsequent development.128,172 When the long-term impact of early interventions depends upon exposure to later supportive experiences the concept of a single early time bounded sensitive period becomes problematical. This is because causal chains mean that the child also must be sensitive to later occurring events if the impact of the early intervention is to be maintained.

Developmental contributions to the question of sensitive periods: conclusions

  1. Critical aspects of neural and social-emotional development or precursors for later development occur during the first 5 years of life.

  2. There are long-term consequences for both concurrent and later cognitive-educational and social-emotional functioning from experiences or interventions occurring during the first 5 years. Such consequences can result from either direct or indirect influences of early experiences or interventions.

  3. More intense early interventions during the first 5 years or longer duration interventions may be necessary to increase the probability that early gains will be maintained over time. This is particularly true for children with a history of high levels of exposure to biological or psychosocial developmental risk factors.

  4. There are significant changes in cognitive and social-emotional development occurring at least through adolescence which are linked to later developing brain regions.

  5. There can be significant experience driven enhancement of cognitive and social-emotional competence in later childhood, adolescence and adulthood.

  6. Some of the long-term impact of early experiences or interventions will depend on subsequent changes in the child’s proximal context.

Implications for intervention

The first several years of life may be a sensitive period for promoting social-emotional development and parenting quality. The overall pattern of evidence also suggests that for cognitive/academic outcomes interventions could start during the preschool years without necessarily impacting on their effectiveness. However, when designing interventions to promote positive parenting, cognitive development or school competence a wider time window may be necessary to maintain initial gains when dealing with high risk children or multi-risk contexts. For high risk children or high risk families, the impact of early psychosocial interventions will be stronger and more durable when there are built-in experiences or follow-up interventions during the early school years as well.

Integrated conclusions and implications

Evidence from multiple disciplines documents that there can be unique long-term influences upon human neural growth, health and cognitive or social-emotional development from early biological or psychosocial interventions and exposure to risk or protective contextual characteristics. The first 3–5 years of life (including the prenatal period) appear to be a sensitive time window for ensuring adequate nutrition to promote brain development, for promoting consistent, responsive, sensitive parenting, for promoting social-emotional competencies and for providing cognitive stimulation to promote school readiness.

However, the evidence does not support the hypothesis that the early years are the sole sensitive time period within which to have a significant influence upon human development. Adolescence also is a sensitive period for continued growth of the prefrontal cortex, for vulnerability to stress and for the development of critical dimensions of executive function, perspective taking and abstract thought. Rather than a single sensitive period the evidence indicates multiple sensitive periods, with the sensitive time windows depending on rate of development of specific neural regions or behavioral functions, outcomes assessed, and the nature of the experiences encountered or interventions provided. The implication from findings reviewed here suggests that choice of age at which to begin interventions should be based on what outcomes are targeted and what interventions are used.173 For example, for interventions involving iron supplementation (table 1) or promoting secure attachments 113 it will be important to start as early as possible, certainly within the first year, whereas starting around age 3 years would not be too late for interventions involving stimulation to promote school readiness. 128

Further, for children living in high stress environments, or encountering multiple high risk events, or receiving lower early intervention dosages there may need to be systematic follow-up interventions to maintain the gains resulting from early interventions. For example, the functional consequences of gains in early brain development resulting from early nutritional supplementation may require building in subsequent psychosocial stimulation experiences if the nutritional intervention is to impact upon the child’s school readiness and subsequent school performance. Finally, in evaluating the long-term impact of interventions it will be critical to look for both main effects and person x intervention interactions, given evidence showing that children with different developmental histories, different genotypes or different individual characteristics may react in very different ways to the same intervention package 3.

Supplementary Material

supplemental

References

  • 1.Evans G. The environment of childhood poverty. Am Psychol. 2004;59:77–92. doi: 10.1037/0003-066X.59.2.77. [DOI] [PubMed] [Google Scholar]
  • 2.Walker S, Wachs TD, Grantham-McGregor S, et al. Inequality begins by early childhood: Risk and protective factors for early child development. Lancet. 2011;378:1325–1338. doi: 10.1016/S0140-6736(11)60555-2. [DOI] [PubMed] [Google Scholar]
  • 3.Wachs TD, Rahman A. The Nature and Impact of Risk and Protective Influences on Children’s Development in Low Income Countries. In: Britto P, Engle P, Super C, editors. Handbook of Early Childhood Development Research and Its Impact on Global Policy. Oxford University Press; New York: pp. 85–122. 2113. [Google Scholar]
  • 4.Waterfield R, translator. Plato. Republic 377 a–b. New York: Oxford University Press; 1993. [Google Scholar]
  • 5.Lerner R. Structure and process in relational, developmental systems theories: A commentary on contemporary changes in the understanding of developmental changes across the life span. Hum Dev. 2011;54:34–43. [Google Scholar]
  • 6.Bronfenbrenner U, Morris P. The bioecological model of Human Development. In: Lerner R, editor. Theoretical Models of Human Development: Vol 1 of the Handbook of Child Psycholog. 6. Hoboken NJ: Wiley; 2006. pp. 793–828.pp. 793–828. [Google Scholar]
  • 7.Bruer J. A critical and sensitive period primer. In: Bailey D, Bruer J, Symons F, Lichtman J, editors. Critical Thinking About Critical Periods. Brookes Publishing; Baltimore, MD: 2001. pp. 3–26. [Google Scholar]
  • 8.Bornstein MH. Sensitive periods in development: Structural characteristics and causal interpretations. Psychol Bull. 1989;105:179–197. doi: 10.1037/0033-2909.105.2.179. [DOI] [PubMed] [Google Scholar]
  • 9.Johnson M. Sensitive periods in functional brain development: Problems and Prospects. Dev Psychobiol. 2005;46:287–292. doi: 10.1002/dev.20057. [DOI] [PubMed] [Google Scholar]
  • 10.Armstrong VL, Brunet PM, He C, et al. What is so critical?: A commentary on the reexamination of critical periods. Dev Psychobiol. 2006;48:326–331. doi: 10.1002/dev.20135. [DOI] [PubMed] [Google Scholar]
  • 11.Colombo J. The critical period concept: Research, Methodology and theoretical issues. Psychol Bull. 1982;91:260–275. [PubMed] [Google Scholar]
  • 12.Michel G, Tyler A. Critical period: A history of the transition from questions of when, to what, to how. Dev Psychobiol. 2005;46:163–183. doi: 10.1002/dev.20058. [DOI] [PubMed] [Google Scholar]
  • 13.Lewis TL, Maurer D. Multiple sensitive periods in human visual development: Evidence from visually deprived children. Dev Psychobiol. 2005;46:163–183. doi: 10.1002/dev.20055. [DOI] [PubMed] [Google Scholar]
  • 14.Fox SE, Levitt P, Nelson CA., III How the timing and quality of early experiences influence the development of brain architecture. Child Dev. 2010;81:28–40. doi: 10.1111/j.1467-8624.2009.01380.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Knudsen EI. Sensitive periods in the development of the brain and behavior. J Cognitive Neurosci. 2004;16:1412–1425. doi: 10.1162/0898929042304796. [DOI] [PubMed] [Google Scholar]
  • 16.Bradley RH, Caldwell BM, Rock SL. Home environment and school performance: A ten-year follow-up and examination of three models of environmental action. Child Dev. 1988;59:852–867. doi: 10.1111/j.1467-8624.1988.tb03239.x. [DOI] [PubMed] [Google Scholar]
  • 17.Landry SH, Smith KE, Swank PR, Guttentag PRC. A responsive parenting intervention: The optimal timing across early childhood for impacting maternal behaviors and child outcomes. Dev Psychol. 2008;44:1335–1353. doi: 10.1037/a0013030. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Huston A, Bentley A. Human Development in societal context. Ann Rev Psychol. 2010;61:411–37. doi: 10.1146/annurev.psych.093008.100442. [DOI] [PubMed] [Google Scholar]
  • 19.World Health Organization. Micronutrient deficiencies. http://www.who.int/nutrition/topics/ida/en/index.html.
  • 20.Lozoff B, Beard J, Connor C, et al. Long-lasting neural and behavioral effects of iron deficiency in infancy. Nutr Rev. 2006;64:S34–43. doi: 10.1301/nr.2006.may.S34-S43. discussion S72–91. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Fuglestad AJ, Ramel SE, Georgieff MK. Miconutrient needs of the developing brain: priorities and assessment. In: Packer L, editor. Micronutrients and brain health, Oxidative stress and disease. Vol. 434. Boca Raton: CRC Press; 2010. p. Xxi. [Google Scholar]
  • 22.Murray-Kolb LE, Beard JL. Iron treatment normalizes cognitive functioning in young women. Am J Clin Nutr. 2007;85:778–787. doi: 10.1093/ajcn/85.3.778. [DOI] [PubMed] [Google Scholar]
  • 23.Amin SB, Orlando M, Eddins A, et al. In utero iron status and auditory neural maturation in premature infants as evaluated by auditory brainstem response. J Pediatr. 2010;156:377–381. doi: 10.1016/j.jpeds.2009.09.049. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Wachs TD, Pollitt E, Cueto S, et al. Relation of neonatal iron status to individual variability in neonatal temperament. Dev Psychobiol. 2005;46:141–153. doi: 10.1002/dev.20049. [DOI] [PubMed] [Google Scholar]
  • 25.Burden MJ, Westerlund A, Armony-Sivan R, et al. An event-related potential study of attention and recognition memory in infants with iron-deficiency anemia. Pediatrics. 2007;120:e336–345. doi: 10.1542/peds.2006-2525. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Algarin C, Peirano P, Garrido M, et al. Iron deficiency anemia in infancy: long-lasting effects on auditory and visual system functioning. Pediatr Res. 2003;53:217–223. doi: 10.1203/01.PDR.0000047657.23156.55. [DOI] [PubMed] [Google Scholar]
  • 27.Roncagliolo M, Garrido M, Walter T, et al. Evidence of altered central nervous system development in infants with iron deficiency anemia at 6 months: delayed maturation of auditory brainstem responses. Am J Clin Nutr. 1998;68:683–690. doi: 10.1093/ajcn/68.3.683. [DOI] [PubMed] [Google Scholar]
  • 28.Lozoff B, Clark K, Jing Y, et al. Dose-response relationships between iron deficiency with or without anemia and infant social-emotional behavior. J Pediatr. 2008;152:696–702. doi: 10.1016/j.jpeds.2007.09.048. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Lozoff B, Corapci F, Burden M, et al. Preschool-aged children with iron deficiency anemia show altered affect and behavior. J Nutr. 2007;137:683–689. doi: 10.1093/jn/137.3.683. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Beard JL, Connor JR. Iron status and neural functioning. Annu Rev Nutr. 2003;23:41–58. doi: 10.1146/annurev.nutr.23.020102.075739. [DOI] [PubMed] [Google Scholar]
  • 31.Beard, Felt JLB, Schallert T, et al. Moderate iron deficiency in infancy: biology and behavior in young rats. Behav Brain Res. 2006;170:224–232. doi: 10.1016/j.bbr.2006.02.024. [DOI] [PubMed] [Google Scholar]
  • 32.Felt BT, Beard J, Schallert T, et al. Persistent neurochemical and behavioral abnormalities in adulthood despite early iron supplementation for perinatal iron deficiency anemia in rats. Behav Brain Res. 2006;171:261–270. doi: 10.1016/j.bbr.2006.04.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Felt BT, Lozoff B. Brain iron and behavior of rats are not normalized by treatment of iron deficiency anemia during early development. J Nutr. 1996;126:693–701. doi: 10.1093/jn/126.3.693. [DOI] [PubMed] [Google Scholar]
  • 34.Georgieff MK. Nutrition and the developing brain: nutrient priorities and measurement. Am J Clin Nutr. 2007;85:614S–620S. doi: 10.1093/ajcn/85.2.614S. [DOI] [PubMed] [Google Scholar]
  • 35.McEchron MD, Cheng A, Liu H, et al. Perinatal nutritional iron deficiency permanently impairs hippocampus-dependent trace fear conditioning in rats. Nutr Neurosci. 2005;8:195–206. doi: 10.1080/10284150500162952. [DOI] [PubMed] [Google Scholar]
  • 36.McEchron MD, Goletiani CJ, Alexander DN. Perinatal nutritional iron deficiency impairs noradrenergic-mediated synaptic efficacy in the CA1 area of rat hippocampus. J Nutr. 2010;140:642–647. doi: 10.3945/jn.109.114702. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Rao R, Tkac I, Townsend E, et al. Perinatal iron deficiency alters the neurochemical profile of the developing rat hippocampus. J Nutr. 2003;133:3215–3221. doi: 10.1093/jn/133.10.3215. [DOI] [PubMed] [Google Scholar]
  • 38.Connor JR, Menzies SL. Relationship of iron to oligodendrocytes and myelination. Glia. 1996;17:83–93. doi: 10.1002/(SICI)1098-1136(199606)17:2<83::AID-GLIA1>3.0.CO;2-7. [DOI] [PubMed] [Google Scholar]
  • 39.Ward KL, Tkac I, Jing Y, et al. Gestational and lactational iron deficiency alters the developing striatal metabolome and associated behaviors in young rats. J Nutr. 2007;137:1043–1049. doi: 10.1093/jn/137.4.1043. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Golub MS, Hogrefe C, Germann S, et al. Behavioral consequences of developmental iron deficiency in infant rhesus monkeys. Neurotoxicol Teratol. 2006;28:3–17. doi: 10.1016/j.ntt.2005.10.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Chang S, Zeng L, Brouwer F, et al. Effect of Iron Deficiency Anemia in Pregnancy on Child Mental Development in Rural China. Pediatrics. 2013;131:e755–763. doi: 10.1542/peds.2011-3513. [DOI] [PubMed] [Google Scholar]
  • 42.Christian P, Morgan M, Murray-Kolb L, et al. Preschool iron-folic acid and zinc supplementation in children exposed to iron-folic acid in utero confers no added cognitive benefit in early school-age. J Nutr. 2011;141:2042–2048. doi: 10.3945/jn.111.146480. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Christian P, Murray-Kolb L, Khatry S, et al. Prenatal micronutrient supplementation and intellectual and motor function in early school-aged children in Nepal. JAMA. 2010;304:2716–2723. doi: 10.1001/jama.2010.1861. [DOI] [PubMed] [Google Scholar]
  • 44.Murray-Kolb LE, Khatry S, Katz J, et al. Preschool micronutrient supplementation effects on intellectual and motor function in school-aged Nepalese children. Arch Pediatr Adolesc Med. 2012;166:404–410. doi: 10.1001/archpediatrics.2012.37. [DOI] [PubMed] [Google Scholar]
  • 45.McEwen BS. Protective and damaging effects of stress mediators: central role of the brain. Dial Clin Neurosci Stress. 2006;8:367–381. doi: 10.31887/DCNS.2006.8.4/bmcewen. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46.McEwen BS, Gianaros PJ. Stress- and allostasis-induced brain plasticity. Annu Rev Med. 2011;62:431–445. doi: 10.1146/annurev-med-052209-100430. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Erickson KI, Voss MW, Prakash RS, et al. Exercise training increases size of hippocampus and improves memory. Proc Natl Acad Sci USA. 2011;108:3017–3022. doi: 10.1073/pnas.1015950108. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Vyas A, Mitra R, Rao BSS, Chattarji S. Chronic stress induces contrasting patterns of dendritic remodeling in hippocampal and amygdaloid neurons. J Neurosci. 2002;22:6810–6818. doi: 10.1523/JNEUROSCI.22-15-06810.2002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Shonkoff JP, Boyce WT, McEwen BS. Neuroscience, molecular biology, and the childhood roots of health disparities. JAMA. 2009;301:2252–2259. doi: 10.1001/jama.2009.754. [DOI] [PubMed] [Google Scholar]
  • 50.Anda RF, Butchart A, Felitti VJ, Brown DW. Building a framework for global surveillance of the public health implications of adverse childhood experiences. Am J Prev Med. 2010;39:93–98. doi: 10.1016/j.amepre.2010.03.015. [DOI] [PubMed] [Google Scholar]
  • 51.Weinstock M. Alterations induced by gestational stress in brain morphology and behaviour of the offspring. Prog Neurobiol. 2001;65:427–451. doi: 10.1016/s0301-0082(01)00018-1. [DOI] [PubMed] [Google Scholar]
  • 52.Mairesse J, Vercoutter-Edouart AS, Marrocco J, et al. Proteomic characterization in the hippocampus of prenatally stressed rats. J Proteomics. 2012;75:1764–1770. doi: 10.1016/j.jprot.2011.12.017. [DOI] [PubMed] [Google Scholar]
  • 53.Entringer S, Epel ES, Kumsta R, et al. Stress exposure in intrauterine life is associated with shorter telomere length in young adulthood. Proc Natl Acad Sci USA. 2011;108:E513–518. doi: 10.1073/pnas.1107759108. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 54.Barker DJP. The fetal origins of coronary heart disease. Acta Paediatr Suppl. 1997;422:78–82. doi: 10.1111/j.1651-2227.1997.tb18351.x. [DOI] [PubMed] [Google Scholar]
  • 55.Entringer S, Buss C, Wadhwa PD. Prenatal stress and developmental programming of human health and disease risk: concepts and integration of empirical findings. Curr Opin Endocrinol Diabetes Obes. 2010;17:507–516. doi: 10.1097/MED.0b013e3283405921. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 56.Pankevich DE, Mueller BR, Brockel B, Bale TL. Prenatal stress programming of offspring feeding behavior and energy balance begins early in pregnancy. Physiol Behav. 2009;98:94–102. doi: 10.1016/j.physbeh.2009.04.015. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 57.Vucetic Z, Kimmel J, Totoki K, et al. Maternal high-fat diet alters methylation and gene expression of dopamine and opioid-related genes. Endocrinology. 2010;151:4756–4764. doi: 10.1210/en.2010-0505. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 58.Pan L, Sherry B, Njai R, Blanck HM. Food insecurity is associated with obesity among US adults in 12 states. Journal of the Academy of Nutrition and Dietetics. 2012;112:1403–9. doi: 10.1016/j.jand.2012.06.011. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 59.Chang VW, Hillier AE, Mehta NK. Neighborhood Racial Isolation, Disorder and Obesity. Social forces; a scientific medium of social study and interpretation. 2009;87:2063–92. doi: 10.1353/sof.0.0188. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 60.Theall KP, Drury SS, Shirtcliff EA. Cumulative neighborhood risk of psychosocial stress and allostatic load in adolescents. Am J Epidemiol. 2012;176(Suppl 7):S164–74. doi: 10.1093/aje/kws185. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 61.Danese A, Moffitt TE, Harrington H, et al. Adverse childhood experiences and adult risk factors for age-related disease: depression, inflammation, and clustering of metabolic risk markers. Arch Pediatr Adolesc Med. 2009;163:1135–114365. doi: 10.1001/archpediatrics.2009.214. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 62.Miller GE, Chen E. Harsh family climate in early life presages the emergence of a proinflammatory phenotype in adolescence. Psychol Sci. 2010;21:848–856. doi: 10.1177/0956797610370161. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 63.Evans GW, Gonnella C, Marcynyszyn LA, et al. The role of chaos in poverty and children’s socioemotional adjustment. Psychol Sci. 2005;16:560–565. doi: 10.1111/j.0956-7976.2005.01575.x. [DOI] [PubMed] [Google Scholar]
  • 64.Diez Roux AV, Mair C. Neighborhoods and health. Ann N Y Acad Sci. 2010;1186:125–145. doi: 10.1111/j.1749-6632.2009.05333.x. [DOI] [PubMed] [Google Scholar]
  • 65.McEwen BS, Tucker P. Critical biological pathways for chronic psychosocial stress and research opportunities to advance the consideration of stress in chemical risk assessment. Am J Public Health. 2011;101(Suppl 1):S131–139. doi: 10.2105/AJPH.2011.300270. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 66.Farah MJ, Shera DM, Savage JH, et al. Childhood poverty: Specific associations with neurocognitive development. Brain Res. 2006;1110:166–174. doi: 10.1016/j.brainres.2006.06.072. [DOI] [PubMed] [Google Scholar]
  • 67.Hart B, Risley TR. Meaningful Differences in the Everyday Experience of Young American Children. Baltimore, MD: Brookes Publishing Company; 1995. [Google Scholar]
  • 68.Hanson JL, Chandra A, Wolfe BL, Pollak SD. Association between income and the hippocampus. PLoS One. 2011;6:e18712. doi: 10.1371/journal.pone.0018712. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 69.Gianaros PJ, Horenstein JA, Cohen S, et al. Perigenual anterior cingulate morphology covaries with perceived social standing. Soc Cogn Affect Neurosci. 2007;2:161–173. doi: 10.1093/scan/nsm013. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 70.Gee DG, Humphreys KL, Flannery J, et al. A developmental shift from positive to negative connectivity in human amygdala-prefrontal circuitry. J Neurosci. 2013;33:4584–4593. doi: 10.1523/JNEUROSCI.3446-12.2013. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 71.Adler NE, Boyce TW, Chesney MA, et al. Socioeconomic Inequalities in Health. JAMA. 1993;269:3140–3145. [PubMed] [Google Scholar]
  • 72.Lupien SJ, Parent S, Evans AC, et al. Larger amygdala but no change in hippocampal volume in 10-year-old children exposed to maternal depressive symptomatology since birth. Proc Natl Acad Sci USA. 2011;108:14324–14329. doi: 10.1073/pnas.1105371108. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 73.Boyce WT, Ellis BJ. Biological sensitivity to context: I. An evolutionary-developmental theory of the origins and functions of stress reactivity. Dev Psychopathol. 2005;17:271–301. doi: 10.1017/s0954579405050145. [DOI] [PubMed] [Google Scholar]
  • 74.Caspi A, Sugden K, Moffitt TE, et al. Influence of life stress on depression: Moderation by a polymorphism in the 5-HTT gene. Science. 2003;301:386–389. doi: 10.1126/science.1083968. [DOI] [PubMed] [Google Scholar]
  • 75.Suomi SJ. Risk, resilience, and gene x environment interactions in rhesus monkeys. Ann NY Acad Sci. 2006;1094:52–62. doi: 10.1196/annals.1376.006. [DOI] [PubMed] [Google Scholar]
  • 76.Del Giudice M, Ellis BJ, Shirtcliff EA. The Adaptive Calibration Model of stress responsivity. Neurosci Biobehav R. 2011;35:1562–1592. doi: 10.1016/j.neubiorev.2010.11.007. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 77.Dallman MF, Pecoraro NC, la Fleur SE. Chronic stress and comfort foods: Self-medication and abdominal obesity. Brain Behav Immunity. 2005;19:275–280. doi: 10.1016/j.bbi.2004.11.004. [DOI] [PubMed] [Google Scholar]
  • 78.Eiland L, Romeo RD. Stress and the developing adolescent brain. Neuroscience. 2012 doi: 10.1016/j.neuroscience.2012.10.048. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 79.Sisk CL, Foster DL. The neural basis of puberty and adolescence. Nature Neurosci. 2004;7:1040–1047. doi: 10.1038/nn1326. [DOI] [PubMed] [Google Scholar]
  • 80.Mischel W, Ebbesen EB, Zeiss AR. Cognitive and attentional mechanisms in delay of gratification. J Pers Soc Psychol. 1972;21:204–218. doi: 10.1037/h0032198. [DOI] [PubMed] [Google Scholar]
  • 81.Mischel W, Ayduk O, Berman MG, et al. ‘Willpower’ over the life span: decomposing self-regulation. Soc Cogn Affect Neurosci. 2011;6:252–256. doi: 10.1093/scan/nsq081. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 82.Casey BJ, Somerville LH, Gotlib IH, et al. Behavioral and neural correlates of delay of gratification 40 years later. Proc Natl Acad Sci USA. 2011;108:14998–15003. doi: 10.1073/pnas.1108561108. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 83.Somerville LH, Hare T, Casey BJ. Frontostriatal maturation predicts cognitive control failure to appetitive cues in adolescents. J Cogn Neurosci. 2011;23:2123–2134. doi: 10.1162/jocn.2010.21572. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 84.Steinberg L. Cognitive and affective development in adolescence. TRENDS Cogn Sci. 2005;9:69–74. doi: 10.1016/j.tics.2004.12.005. [DOI] [PubMed] [Google Scholar]
  • 85.Pattwell SS, Bath KG, Casey BJ, Ninan I, Lee FS. Selective early-acquired fear memories undergo temporary suppression during adolescence. Proc Natl Acad Sci U S A. 2011;108:1182–7. doi: 10.1073/pnas.1012975108. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 86.Pattwell SS, Duhoux S, Hartley CA, Johnson DC, Jing D, et al. Altered fear learning across development in both mouse and human. Proc Natl Acad Sci U S A. 2012;109:16318–23. doi: 10.1073/pnas.1206834109. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 87.Eiland L, Ramroop J, Hill MN, et al. Chronic juvenile stress produces corticolimbic dendritic architectural remodeling and modulates emotional behavior in male and female rats. Psychoneuroendocrino. 2012;37:39–47. doi: 10.1016/j.psyneuen.2011.04.015. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 88.Isgor C, Kabbaj M, Akil H, Watson SJ. Delayed effects of chronic variable stress during peripubertal-juvenile period on hippocampal morphology and on cognitive and stress axis functions in rats. Hippocampus. 2004;14:636–648. doi: 10.1002/hipo.10207. [DOI] [PubMed] [Google Scholar]
  • 89.Casey BJ, Giedd JN, Thomas KM. Structural and functional brain development and its relation to cognitive development. Biol Psychol. 2000;54:241–257. doi: 10.1016/s0301-0511(00)00058-2. [DOI] [PubMed] [Google Scholar]
  • 90.Nelson EE, Guyer AE. The development of the ventral prefrontal cortex and social flexibility. Dev Cogn Neurosci. 2011;1:233–245. doi: 10.1016/j.dcn.2011.01.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 91.Lenroot RK, Schmitt JE, Ordaz SJ, et al. Differences in genetic and environmental influences on the human cerebral cortex associated with development during childhood and adolescence. Human Brain Mapping. 2009;30:163–174. doi: 10.1002/hbm.20494. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 92.Tottenham N, Phuong J, Flannery J, et al. A negativity bias for ambiguous facial-expression valence during childhood: Converging evidence from behavior and facial corrugator muscle responses. Emotion. 2013;13:92–103. doi: 10.1037/a0029431. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 93.Spinelli S, Chefer S, Carson RE, et al. Effects of early-life stress on serotonin(1A) receptors in juvenile Rhesus monkeys measured by positron emission tomography. Biol Psychiatr. 2010;67:1146–1153. doi: 10.1016/j.biopsych.2009.12.030. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 94.Spinelli S, Chefer S, Suomi SJ, et al. Early-life stress induces long-term morphologic changes in primate brain. Arch Gen Psychiatr. 2009;66:658–665. doi: 10.1001/archgenpsychiatry.2009.52. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 95.Danese A, McEwen BS. Adverse childhood experiences, allostasis, allostatic load, and age-related disease. Physiol Behav. 2012;106:29–39. doi: 10.1016/j.physbeh.2011.08.019. [DOI] [PubMed] [Google Scholar]
  • 96.McEwen BS. Physiology and neurobiology of stress and adaptation: Central role of the brain. Physiol Rev. 2007;87:873–904. doi: 10.1152/physrev.00041.2006. [DOI] [PubMed] [Google Scholar]
  • 97.Convit A. Links between cognitive impairment in insulin resistance: An explanatory model. Neurobiol Aging. 2005;26S:S31–S35. doi: 10.1016/j.neurobiolaging.2005.09.018. [DOI] [PubMed] [Google Scholar]
  • 98.Convit A, Wolf OT, Tarshish C, de Leon MJ. Reduced glucose tolerance is associated with poor memory performance and hippocampal atrophy among normal elderly. Proc Natl Acad Sci USA. 2003;100:2019–2022. doi: 10.1073/pnas.0336073100. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 99.Gold SM, Dziobek I, Sweat V, et al. Hippocampal damage and memory impairments as possible early brain complications of type 2 diabetes. Diabetologia. 2007;50:711–719. doi: 10.1007/s00125-007-0602-7. [DOI] [PubMed] [Google Scholar]
  • 100.Rasgon NL, Kenna HA. Insulin resistance in depressive disorders and Alzheimer’s disease: Revisiting the missing link hypothesis. Neurobiol Aging. 2005;26S:S103–S107. doi: 10.1016/j.neurobiolaging.2005.09.004. [DOI] [PubMed] [Google Scholar]
  • 101.Yates KF, Sweat V, Yau PL, et al. Impact of metabolic syndrome on cognition and brain: a selected review of the literature. Arterioscl Throm Vas. 2012;32:2060–2067. doi: 10.1161/ATVBAHA.112.252759. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 102.Yau PL, Castro MG, Tagani A, et al. Obesity and metabolic syndrome and functional and structural brain impairments in adolescence. Pediatrics. 2012;130:e856–864. doi: 10.1542/peds.2012-0324. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 103.Miller AL, Horodynski MA, Herb HE, Peterson KE, Contreras D, et al. Enhancing self-regulation as a strategy for obesity prevention in Head Start preschoolers: the growing healthy study. BMC public health. 2012;12:1040. doi: 10.1186/1471-2458-12-1040. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 104.Onnerfalt J, Erlandsson LK, Orban K, Broberg M, Helgason C, Thorngren-Jerneck K. A family-based intervention targeting parents of preschool children with overweight and obesity:conceptual framework and study design of LOOPS- Lund overweight and obesity preschool study. BMC public health. 2012;12:879. doi: 10.1186/1471-2458-12-879. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 105.Fernald LC, Gertler PJ, Neufeld LM. 10-year effect of Oportunidades, Mexico’s conditional cash transfer programme, on child growth, cognition, language, and behaviour: a longitudinal follow-up study. Lancet. 2009;374:1997–2005. doi: 10.1016/S0140-6736(09)61676-7. [DOI] [PubMed] [Google Scholar]
  • 106.Ozer EJ, Fernald LC, Weber A, Flynn EP, VanderWeele TJ. Does alleviating poverty affect mothers’ depressive symptoms? A quasi-experimental investigation of Mexico’s Oportunidades programme. Int J Epidemiol. 2011;40:1565–76. doi: 10.1093/ije/dyr103. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 107.Lomeli EV. Conditional Cash Transfers as Social Policy in Latin America: An Assessment of their Contributions and Limitations. Annu Rev Sociol. 2008;34:475–99. [Google Scholar]
  • 108.Crews F, He J, Hodge C. Adolescent cortical development: A critical period of vulnerability for addiction. Pharmacol Biochem Behav. 2007;86:189–199. doi: 10.1016/j.pbb.2006.12.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 109.Feldman DE, Knudsen EI. Experience-dependent plasticity and the maturation of glutamatergic synapses. Neuron. 1998;20:1067–1071. doi: 10.1016/s0896-6273(00)80488-2. [DOI] [PubMed] [Google Scholar]
  • 110.Lupien S, McEwen B, Gunnar M, Helm C. Effects of stress throughtout the lifespan on the brain, behavior and cognition. Nat Rev Neurosci. 2009;10:434–445. doi: 10.1038/nrn2639. [DOI] [PubMed] [Google Scholar]
  • 111.Werker JF, Tees RC. Speech perception as a window for understanding plasticity and commitment in language systems of the brain. Dev Psychobiol. 2005;46:233–251. doi: 10.1002/dev.20060. [DOI] [PubMed] [Google Scholar]
  • 112.Trabulsi JC, Mennella JA. Diet, sensitive periods in flavor learning, and growth. Int Rev Psychiatr. 2012;24:219–230. doi: 10.3109/09540261.2012.675573. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 113.Bakermans-Kranenburg MJ, van IJzendoorn MH, Juffer F. Less is more: Meta-analyses of sensitivity and attachment interventions in early childhood. Psychol Bull. 2003;129:195–215. doi: 10.1037/0033-2909.129.2.195. [DOI] [PubMed] [Google Scholar]
  • 114.Kochanska G, Coy KC, Murray KT. The development of self-regulation in the first four years of life. Child Dev. 2001;72:1091–1111. doi: 10.1111/1467-8624.00336. [DOI] [PubMed] [Google Scholar]
  • 115.Barr R. Developing social understanding in a social context. In: McCartney K, Phillips D, editors. Blackwell Handbook of Early Childhood Development. Malden Ma: Blackwell; 2008. pp. 188–207. [Google Scholar]
  • 116.Smetana JG, Rote WM, Jambon M, et al. Developmental changes and individual differences in young children’s moral judgments. Child Dev. 2012;83:683–696. doi: 10.1111/j.1467-8624.2011.01714.x. [DOI] [PubMed] [Google Scholar]
  • 117.Baird AA. The Terrible Twelves. In: Zelazo PD, Chandler M, Crone E, editors. Developmental Social Cognitive Neuroscience. Psychology Press; New York, NY: 2010. pp. 191–207. [Google Scholar]
  • 118.Rothbart M, Posner M, Kieras J. Temperament, attention and the development of self-regulation. In: McCartney K, Phillips D, editors. Blackwell Handbook of Early Childhood Development. Malden Ma: Blackwell; 2008. pp. 338–357. [Google Scholar]
  • 119.Rutter M. Transitions and turning points in developmental psychopathology: As applied to the age span between childhood and mid-adulthood. Int J Behav Dev. 1996;19:603–626. [Google Scholar]
  • 120.Steinberg L, Graham S, O’Brien L, Woolard J, Cauffman E, Banich M. Age differences in future orientation and delay discounting. Child Dev. 2009;80:28–44. doi: 10.1111/j.1467-8624.2008.01244.x. [DOI] [PubMed] [Google Scholar]
  • 121.Eccles J, Roeser R, Vida M, Fredricks J, Wigfield A. Motivational and Acievement Pathways through Middle Childhood. In: Balter L, Tamis-LeMonda C, editors. Child Psychology: A Handbook of Contemporary Issues. 2. New York: Psychology Press; 2006. pp. 325–355. [Google Scholar]
  • 122.Huizinga M, Dolan CV, van der Molen MW. Age-related change in executive function: Developmental trends and a latent variable analysis. Neuropsychologia. 2006;44:2017–2036. doi: 10.1016/j.neuropsychologia.2006.01.010. [DOI] [PubMed] [Google Scholar]
  • 123.Lambek R, Shevlin M. Working memory and response inhibition in children and adolescents: Age and organization issues. Scand J Psychol. 2011;52:427–432. doi: 10.1111/j.1467-9450.2011.00899.x. [DOI] [PubMed] [Google Scholar]
  • 124.McArdle JJ, Ferrer-Caja E, Hamagami F, Woodcock R. Comparative longitudinal structural analyses of the growth and decline of multiple intellectual abilities over the life span. Dev Psychol. 2002;38:115–142. [PubMed] [Google Scholar]
  • 125.Tucker-Drob EM. Differentiation of cognitive abilities across the life span. Dev Psychol. 2009;45:1097–1118. doi: 10.1037/a0015864. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 126.Tomblin JB, Barker BA, Hubbs S. Developmental constraints on language development in children with cochlear implants. Int J Audiol. 2007;46:512–523. doi: 10.1080/14992020701383043. [DOI] [PubMed] [Google Scholar]
  • 127.van IJzendoorn MH, Juffer F. The Emanuel Miller Memorial Lectures 2006: Adoption as intervention. Meta-analytic evidence for massive catch-up and plasticity in physical, socio-emotional, and cognitive development. J Child Psychol Psychiatr. 2006;47:1228–1245. doi: 10.1111/j.1469-7610.2006.01675.x. [DOI] [PubMed] [Google Scholar]
  • 128.Love JR, Chazen-Cohen R, Raikes H, Brooks-Gunn J. What makes a difference: Early Head Start evaluation findings in a developmental context. Monogr Soc Res Child Dev. 2013;78 doi: 10.1111/j.1540-5834.2012.00699.x. serial # 306. [DOI] [PubMed] [Google Scholar]
  • 129.Nores M, Barnett S. Benefits of early childhood interventions across the world: (Under) Investing in the very young. Econ Ed Rev. 2010;29:271–282. [Google Scholar]
  • 130.Engle P, Fernald L, Alderman H, et al. Strategies for reducing inequalities and improving developmental outcomes for young children in low-income and middle-income countries. Lancet. 2011;378:1339–1353. doi: 10.1016/S0140-6736(11)60889-1. [DOI] [PubMed] [Google Scholar]
  • 131.Barnett WS. Effectiveness of early educational intervention. Science. 2011;333:975–978. doi: 10.1126/science.1204534. [DOI] [PubMed] [Google Scholar]
  • 132.Camilli G, Vargas S, Ryan S, Barnett WS. Meta-analysis of the effects of early education interventions on cognitive and social development. Teachers College Record. 2010;112:579–620. [Google Scholar]
  • 133.Kreppner JM, Rutter M, Beckett C, et al. Normality and impairment following profound early institutional deprivation: A longitudinal follow-up into early adolescence. Dev Psychol. 2007;43:931–946. doi: 10.1037/0012-1649.43.4.93. [DOI] [PubMed] [Google Scholar]
  • 134.Van Zeijl J, Mesman J, Koot HM, et al. Attachment-based intervention for enhancing sensitive discipline in mothers of 1- to 3-year-old children at risk for externalizing behavior problems: A randomized controlled trial. J Consult Psychol. 2006;74:994–1005. doi: 10.1037/0022-006X.74.6.994. [DOI] [PubMed] [Google Scholar]
  • 135.Lagerberg D. Secondary prevention in child health: Effects of psychological intervention, particularly home visitation, on children’s development and other outcome variables. Acta Paediatr Suppl. 2000;434:43–52. doi: 10.1111/j.1651-2227.2000.tb03095.x. [DOI] [PubMed] [Google Scholar]
  • 136.Zehnah C, Gunnar M, McCall R, et al. Sensitive periods. Monogr Soc Res Child Dev. 2011;76:147–162. doi: 10.1111/j.1540-5834.2011.00631.x. serial # 301. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 137.Duyme M, Dumaret AC, Tomkiewica S. How can we boost IQs of “dull children”?: A late adoption study. Proc Natl Acad Sci USA. 1999;96:8790–8794. doi: 10.1073/pnas.96.15.8790. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 138.Ludwig J, Miller D. Does Head Start improve children’s life chances? Evidence from a regression discontinuity design. Quart J Econ. 2007;122:159–208. [Google Scholar]
  • 139.Kaminski RA, Stormshak EA, Good RH, III, Goodman MR. Prevention of substance abuse with rural Head Start children and families: Results of Project STAR. Psychol Addict Behav. 2002;16:S11–S26. doi: 10.1037/0893-164x.16.4s.s11. [DOI] [PubMed] [Google Scholar]
  • 140.Roth TL, Sweatt JD. Annual research review: Epigenetic mechanisms and environmental shaping of the brain during sensitive periods of development. J Child Psychol Psychiat. 2011;52:398–408. doi: 10.1111/j.1469-7610.2010.02282.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 141.Rutter M. Implications of resilience concepts for scientific understanding. In: Lester B, Masten A, McEwen B, editors. Ann NY Acad Sci. Vol. 1094. 2006. pp. 1–12. Resilience in Children. [DOI] [PubMed] [Google Scholar]
  • 142.Wachs TD. Necessary but not sufficient: The role of individual and multiple influences on human development. American Psychological Association Press; Washington, D.C: 2000. [Google Scholar]
  • 143.Werner E, Smith R. Overcoming the Odds: High risk children from birth to adulthood. Cornell University Press; Ithaca NY: 1992. [Google Scholar]
  • 144.Diamond A, Lee K. Interventions shown to aid executive function development in children 4 to 12 years old. Science. 2011;333:959–964. doi: 10.1126/science.1204529. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 145.Ceci SJ. How much does schooling influence general intelligence and its cognitive components? A reassessment of the evidence. Dev Psychol. 1991;27:703–722. [Google Scholar]
  • 146.Cliffordson C, Gustafsson JE. Effects of age and schooling on intellectual performance: Estimates obtained from analysis of continuous variation in age and length of schooling. Intelligence. 2008;36:143–152. [Google Scholar]
  • 147.Stelzl I, Merz F, Ehlers R, Remer H. The effect of schooling on the development of fluid and crystallized intelligence: A quasi-experimental study. Intelligence. 1995;21:279–296. [Google Scholar]
  • 148.Nisbett RE, Aronson J, Blair C, et al. Intelligence: New findings and theoretical developments. Am Psychol. 2012;67:130–159. doi: 10.1037/a0026699. [DOI] [PubMed] [Google Scholar]
  • 149.Qouta S, Punamäki RL, Sarraj EE. Child development and family mental health in war and military violence: The Palestinian experience. Int J Behav Dev. 2008;32:310–321. [Google Scholar]
  • 150.Guttmannova K, Bailey JA, Hill KG, et al. Sensitive periods for adolescent alcohol use initiation: Predicting the lifetime occurrence and chronicity of alcohol problems in adulthood. J Stud Alcohol Drugs. 2011;72:221–231. doi: 10.15288/jsad.2011.72.221. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 151.Quinlan R, Quinlan M. Parenting and cultures of risk: A comparative analysis of infidelity, aggression and witchcraft. Am Anthropol. 2007;109:164–179. [Google Scholar]
  • 152.Hestyani Y. Children survivors of the 2004 Tsunami in Aceh, Indonesia: A study of resilience. In: Lester B, Masten A, McEwen B, editors. Ann NY Acad Sci. Vol. 1094. 2006. pp. 303–307. Resilience in Children. [DOI] [PubMed] [Google Scholar]
  • 153.Betancourt T, Borisova I, Williams T, et al. Sierra Leone’s former child soldiers: A follow-up study of psychosocial adjustment and community integration. Child Dev. 2010;81:1077–1095. doi: 10.1111/j.1467-8624.2010.01455.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 154.MacLeod J, Nelson G. Programs for the promotion of family wellness and the prevention of child maltreatment: A meta-analytic review. Child Abuse Neglect. 2000;24:1127–1149. doi: 10.1016/s0145-2134(00)00178-2. [DOI] [PubMed] [Google Scholar]
  • 155.Flannery DJ, Vazsonyi AT, Liau AK, et al. Initial behavior outcomes for the PeaceBuilders Universal School-Based Violence Prevention Program. Dev Psychol. 2003;39:292–308. doi: 10.1037//0012-1649.39.2.292. [DOI] [PubMed] [Google Scholar]
  • 156.Tobler NS, Roona MR, Ochshorn P, et al. School-based adolescent drug prevention programs: 1998 meta-analysis. J Primary Prev. 2000;20:275–336. [Google Scholar]
  • 157.Pedro-Carroll JL, Sutton SE, Wyman PA. A two-year follow-up evaluation of a preventive intervention for young children of divorce. School Psychol Rev. 1999;28:467–476. [Google Scholar]
  • 158.Magnuson KA, Ruhm C, Waldfogel J. The persistence of preschool effects: Do subsequent classroom experiences matter? Early Child Res Quart. 2007;22:18–38. [Google Scholar]
  • 159.Vellutino FR, Scanlon DM, Pratt A, et al. Cognitive profiles of difficult-to-remediate and readily remediated poor readers: Early intervention as a vehicle for distinguishing between cognitive and experiential deficits as basic causes of specific reading disability. J Educ Psychol. 1996;88:601–638. [Google Scholar]
  • 160.Jomaa LH, McDonnell E, Probat C. School feeding programs in developing countries: Impacts on children’s health and educational outcomes. Nutr Rev. 2011;69:83–98. doi: 10.1111/j.1753-4887.2010.00369.x. [DOI] [PubMed] [Google Scholar]
  • 161.Campbell FA, Ramey CT, Pungello E, et al. Early childhood education: Young adult outcomes from the Abecedarian Project. Appl Dev Sci. 2002;6:42–57. [Google Scholar]
  • 162.Hill J, Brooks-Gunn J, Waldfogel J. Sustained effects of high participation in an early intervention for low-birth-weight premature infants. Devel Psychol. 2003;39:730–744. doi: 10.1037/0012-1649.39.4.730. [DOI] [PubMed] [Google Scholar]
  • 163.Sameroff A, Rosenbloom K. Psychosocial constraints on the development of resilience. Resilience in Children. In: Lester B, Masten A, McEwen B, editors. Ann NY Acad Sci. Vol. 1094. 2006. pp. 116–124. [DOI] [PubMed] [Google Scholar]
  • 164.Reynolds A, Temple J. Extended early childhood intervention and school achievement: Age thirteen findings from the Chicago Longitudinal Study. Child Dev. 1998;69:231–246. [PubMed] [Google Scholar]
  • 165.Temple JA, Reynolds AJ, Miedel WT. Can early intervention prevent high school dropout?: Evidence from the Chicago Child-Parent Centers. Urban Educ. 2000;35:31–56. [Google Scholar]
  • 166.Currie J, Thomas D. School quality and the longer-term effects of Head Start. J Hum Resour. 2000;35:755–774. [Google Scholar]
  • 167.Kagitcibasi C, Sunar D, Bekman D, et al. Continuing effects of early enrichment in adult life: The Turkish Early Enrichment Project 22 years later. J Appl Dev Psychol. 2009;30:764–779. [Google Scholar]
  • 168.Currie J, Thomas D. Does Head Start make a difference? Am Econ Rev. 1995;85:341–364. [Google Scholar]
  • 169.Reynolds AJ, Temple JA, Robertson DL, Mann EA. Long-term effects of an early childhood intervention on educational achievement and juvenile arrest: A 15-year follow-up of low-income children in public schools. J Amer Med Assoc. 2001;285:2339–2346. doi: 10.1001/jama.285.18.2339. [DOI] [PubMed] [Google Scholar]
  • 170.Ou SR. Pathways of long-term effects of an early intervention program on educational attainment: Findings from the Chicago longitudinal study. Appl Dev Psychol. 2005;26:578–611. [Google Scholar]
  • 171.Hertzman C, Boyce T. How experience gets under the skin to create gradients in developmental health. Ann Rev Publ Health. 2010;31:329–347. doi: 10.1146/annurev.publhealth.012809.103538. [DOI] [PubMed] [Google Scholar]
  • 172.Reynolds AJ, Robertson DL. School-based early intervention and later child maltreatment in the Chicago Longitudinal Study. Child Dev. 2003;74:3–26. doi: 10.1111/1467-8624.00518. [DOI] [PubMed] [Google Scholar]
  • 173.Bailey D. Are critical periods critical for early childhood education? The role of timing in early childhood pedagogy. Early Child Res Quart. 2002;17:281–294. [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

supplemental

RESOURCES