What is the term used for a specific time of development when a given event or lack of an event has the greatest impact?

139.A __________ is a specific time during development when a given event, or lack of an event, hasthe greatest impact.A. cohortB. nonnormative eventC. critical periodD. developmental milestone

This fact sheet is about the critical periods of development and types of birth defects that can result from exposures at different stages of pregnancy. This information should not take the place of medical care and advice from your healthcare provider.

What are critical periods of development?

In pregnancy, each part of the baby’s body forms at a specific time. During these times, the body can be very sensitive to damage caused by some medications, alcohol, or other harmful exposures. This specific time is called the “critical period of development” for that body part.

Does the chance for different types of birth defects change throughout pregnancy?

Every pregnancy starts out with a 3-5% chance of having a birth defect. This is called the background risk. If an exposure can increase the chance for birth defects, the chance depends on what body part is developing at the time of exposure. Once a body part has formed, it is no longer at risk to develop major birth defects. Some exposures could still affect a body part’s growth and/or function.

The chart in this fact sheet shows the critical periods of development for different parts of the body. The chart starts from the time of conception when the egg and sperm join. The weeks listed on the chart are the “embryonic age” or “fetal age” of a pregnancy. Note that this is different from a common way of dating a pregnancy called “gestational age.” Gestational age begins with the first day of a person’s last menstrual period. This day is usually about two weeks before a baby is conceived. This means that you can change gestational age to embryonic/fetal age by subtracting two weeks. For example, 12 gestational weeks (since the first day of your last period) is the same as 10 fetal weeks (since the first day of conception).

The solid bars on the chart show when each part is most sensitive to harmful exposures and at risk for major birth defects. Birth defects are typically classified as “major” if they cause significant medical problems and need surgery or other treatment. Heart defects, spina bifida, and clubfeet are examples of major birth defects.

The striped bars show periods when the body parts are still at risk to develop minor birth defects and functional defects. “Minor” birth defects by themselves do not cause significant medical problems and usually do not require treatment or surgery. Minor birth defects can also be variations of typical development. Wide-set eyes and large ears are examples of minor birth defects.

Both major and minor birth defects are physical or structural changes. However, “functional defects” change how a part of the body works without changing its physical structure. Intellectual disability and hearing loss are both examples of functional defects.

The chart also shows the location of the most common birth defects that can occur during each week. In general, major defects of the body and internal organs are more likely to occur between 3 to 12 embryo / fetal weeks. This is the same as 5 to 14 gestational weeks (weeks since the first day of your last period). This is also referred to as the first trimester. Minor defects and functional defects including those affecting the brain are also able to occur later in pregnancy.

What is the term used for a specific time of development when a given event or lack of an event has the greatest impact?

*Adapted from Moore 1993, and the National Organization of Fetal Alcohol Syndrome (NOFAS) 2009.

What is the greatest risk from a harmful exposure during very early pregnancy?

Harmful exposures during very early pregnancy have the greatest risk of causing miscarriage. A fertilized egg divides and attaches to the inside of the uterus during the first two weeks of embryo development. Very harmful exposures during this period (first four weeks after the first day of your last period) may interfere with the attachment of the embryo to the uterus. Harmful exposures during this time can also damage most or all of the cells of the growing embryo. Problems with uterine attachment and severe cell damage can both result in a miscarriage. Sometimes this miscarriage is before a person even realizes that they are pregnant.

Less severe exposures during this time may only damage a few of the embryo’s cells. The cells of the embryo have a greater ability to recover at this early stage than they do later on in pregnancy. If an individual does not have a miscarriage, it is expected that the exposures during this time are not likely to cause a birth defect.

The first four weeks of gestation are called the “all or none period.” “All” refers to high exposures damaging all of the embryo’s cells. This damage can cause early miscarriage. “None” refers to exposures that are not high enough to have a significant effect on the pregnancy. The rule of the “all or none period” can be used to determine the chance of many different types of exposures. However, there are some important exceptions to this rule. Please contact MotherToBaby to discuss your specific exposure with our experts.

What are the greatest risks from harmful exposures during the first trimester of pregnancy?

The first trimester of pregnancy is defined as up to the 14th week of pregnancy (13 weeks and 6 days) counting since the first day of your last menstrual period. Harmful exposures during the first trimester have the greatest chance of causing major birth defects. This is because many important developmental changes take place during this time. The major structures of the body form in the first trimester. These include the spine, head, arms and legs. The baby’s organs also begin to develop. Some examples of these organs are the heart, stomach and lungs. While the heart and stomach completely form during the first trimester, the lungs continue to develop past the first trimester.

What are the greatest risks from harmful exposures during the second and third trimesters of pregnancy?

Harmful exposures during the second and third trimesters can cause growth problems and minor birth defects. Growth is an important part of the second and third trimester. The structures and organs that developed during the first trimester grow larger. Babies with growth problems may be much smaller or much larger than average. This size difference can put babies at risk for certain health problems.

Harmful exposures during the second and third trimesters can also cause functional defects like learning problems. The brain is part of the central nervous system and it develops during the entire pregnancy. Major, structural brain development lasts until about 16 fetal weeks (18 gestational weeks). However, the brain continues to develop for the rest of the pregnancy, after birth and through young adulthood.

While usually less well studied, some exposures in the second or third trimester might cause other pregnancy complications, such as preterm delivery (delivery before 37 weeks gestation) or low levels of amniotic fluid (the fluid that surrounds the developing baby in the uterus).

The use of certain medications and substances at the end of pregnancy can cause withdrawal in some newborns. You should always tell your healthcare provider about all of the medications, supplements and/or other substances that you take.

Does this mean that an exposure might be harmful at certain times during pregnancy but not at other times?

Yes. Imagine your healthcare provider gives you a new medication to take during your third trimester. We will call this “Medication A.” You read that Medication A increases the chance for heart defects. This means that babies may have a higher chance for major heart defects if they are exposed to this medication during the heart’s critical period of development. The heart’s critical period of development is from 3 to 6 embryonic weeks (5 to 8 gestational weeks). This means that using this medication in the third trimester cannot cause a major heart defect. Always talk to your healthcare provider before making any changes to how you take your medication.

Please click here for references.

OTIS/MotherToBaby encourages inclusive and person-centered language. While our name still contains a reference to mothers, we are updating our resources with more inclusive terms. Use of the term mother or maternal refers to a person who is pregnant. Use of the term father or paternal refers to a person who contributes sperm.

View PDF Fact Sheet

Early life events can exert a powerful influence on both the pattern of brain architecture and behavioral development. In this paper a conceptual framework is provided for considering how the structure of early experience gets “under the skin.” The paper begins with a description of the genetic framework that lays the foundation for brain development, and then to the ways experience interacts with and modifies the structures and functions of the developing brain. Much of the attention is focused on early experience and sensitive periods, although it is made clear that later experience also plays an important role in maintaining and elaborating this early wiring diagram, which is critical to establishing a solid footing for development beyond the early years.

Our current understanding of the role of experience in influencing the course of human development arises in large part from studies of atypical patterns and disorders of development, including disorders that arise due to adverse early experience. However, whereas studies of atypical development has done an excellent job of describing dimensional phenotypes that underlie disrupted development, it is imperative that we integrate these descriptive concepts with biological constructs of neurodevelopment in order to understand the relevant brain architecture and neurochemical constituents that determine responses to experience that ultimately lead to typical and atypical change (see Nelson & Jeste, 2008, for recent review). In this paper we seek to elucidate the myriad ways the structure of experience weaves its way into the structure of the developing brain.

The foundations of brain architecture are established early in life through a continuous series of dynamic interactions between genetic influences and environmental conditions and experiences (Friederici, 2006; Grossman, 2003; Hensch, 2005; Horn, 2004; Katz & Shatz, 1996; Majdan & Shatz, 2006; Singer 1995). There is increasing evidence that environmental factors play a crucial role in coordinating the timing and pattern of gene expression, which in turn determines initial brain architecture. Because specific experiences potentiate or inhibit neural connectivity at key developmental stages, these time points are referred to as sensitive periods (Hess, 1973; Knudsen, 2004). Each one of our perceptual, cognitive, and emotional capabilities is built upon the scaffolding provided by early life experiences. Examples can be found in both the visual and auditory systems, where the foundation for later cognitive architecture is laid down during sensitive periods for basic neural circuitry. The capacity to perceive stereoscopic depth requires early experience with binocular vision, (Crawford, Pesch, & von Noorden, 1996), which at a later point in development may have implications for perceptual and cognitive development. Likewise, the capacity to perceive a range of tones requires variation in the tonal environment, and exposure to such variation later leads to language processing and proficiency (Kuhl, 2004; Newport, Bavelier, & Neville, 2001; Oyama, 1990; Weber-Fox & Neville 2001). Just as a square house cannot be built on a round foundation, so must later refinements in adult perceptions reflect the architectural foundation developed through early experiences. Although early experiences are reflected in behavior, behavioral measures tend to underestimate (in part because of a lack of sensitivity and specificity) the magnitude and persistence of the effects of early neuronal development (Knudsen, 2004). In order to explore the role of timing and quality of early experiences on later cognitive function, we must therefore return to the genetic framework of the developing brain.

Refinements in the neural circuits that mediate sensory, emotional and social behaviors are driven by experience (Feldman & Knudsen, 1998). Specifically, postnatal experiences drive a protracted process of maturation at the structural and functional level, but the very ability of such developmental processes to occur successfully is dependent in large part on the prenatal establishment of the fundamental brain architecture that provides the basis for receiving, interpreting, and acting on information from the world around us (Hammock, 2006). While the term “blueprint” has been utilized in the past to describe a fixed set of genes with inflexible interactions, the term is used here as an analogy to a rough draft, or design – the framework from which a more defined structure will evolve. The emergence of the architecture in all vertebrate species begins early, when the fundamental cardinal axes are established in the neural plate by sets of different transcription factors that specify molecular differences, particularly growth factor signaling molecules (e.g. fibroblast growth factors, wnts) antero-posteriorly, medio-laterally and dorso-ventrally (Puelles, 2001). In humans, this occurs within the first two months post-fertilization (Levitt, 2000, 2003). The initial focus of brain patterning was on the nature of specification of segmented regions of the nervous system, such as the brainstem and spinal cord, in which highly conserved homeobox-containing transcription factors control ‘downstream’ gene networks that specify motor and sensory neuron types in each segment. It has become clear, however, that developmental gene networks are a highly conserved strategy for specifying the initial plan of the central nervous system. Thus, they are utilized even in the more complex, nonsegmented anterior regions of the brain that include the thalamus, hypothalamus, basal forebrain and cerebral cortices (Sur & Rubenstein, 2005). The gene networks in large part encode transcription factors, proteins that control (through positive [enhancing] and negative [repressing] activity), the expression of genes downstream that mediate the major histogenic events that include progenitor cell proliferation, cell fate choices, cell migration, axon and dendritic growth and synapse formation. Collectively, these highly structured events build the early plan for brain architecture. The genetic mechanisms are thought to be highly conserved, because when examined, the networks are expressed in similar patterns across rodents and humans. Experimentally, however, the genetic mechanisms through which regional patterns and connections are established prenatally have been defined in animal models, primarily rodents and non-human primates (O’Leary, Chou, & Sahara, 2007; Rakic, 2006). In the context of understanding clinical disorders that have a prenatal origin, the challenge has been to extrapolate that information from different species in the context of the timing of human brain development, an exercise that has received considerable attention with new database tools (e.g., http://www.translatingtime.net/). The onset of histogenesis during prenatal development provides the cellular framework necessary for establishing and modifying later-developing circuitry.

The cerebral cortex has garnered substantial attention in defining key developmental features across species. This is due in part to the technical advantages of studying a well-organized, layered structure, and the functional relevance of linking typical and atypical maturation of complex behaviors and neurodevelopment. The neocortex in all mammalian species comprisessix layers of neurons, the architecture, connectivity and chemistry of which are distinct depending upon their location. The neocortex is organized to receive information from the organism’s surrounding environment, typically through connections with the thalamus. It does so by integrating information within and across architecturally distinct functional domains, and then relays this information to other brain centers that generate an appropriate functional response. There are two major organizing principles of the neocortex that are guided by gradients of expressed gene networks that establish an evolutionarily conserved design. First, the precursors of different functional areas emerge during the time period when neurons are produced and are displayed in the tangential domain (roughly the first and secondtrimester of pregnancy in the human; see O’Leary et al., 2007; Sur & Rubenstein, 2005). Regional specification is not absolute, but does involve transcriptional networks controlling the expression of axon guidance molecules that control the initial input and output wiring plan (see below). Expansion of the size of the neocortex during evolution (e.g. 1000-fold between mouse and human) occurs mostly in this tangential domain (Rakic, 2005). Extending through the six-layered neocortex is the most fundamental of functional units, the radial column (Rakic, 2006). Each column of cells contains well-organized networks of local circuit and projection neurons, and are built in an orderly fashion in which the neurons destined to occupy deep layers are produced, or ‘born’ first, followed by more superficially displaced neurons. Though there are some differences between rodents and primates, the projection neurons, which utilize the excitatory neurotransmitter glutamate, and the local circuit neurons, many of which use the inhibitory neurotransmitter Gamma-amino butyric acid (GABA) arise in part from different regions of the forebrain (Wonders, 2007). The regions of the forebrain proliferative zones from which glutamatergic and GABAergic neurons arise are specified genetically to give rise to the unique neuron types by different sets of transcription factors. For example, glutamatergic neurons arise from the dorsal pallium, the roof of the forebrain in which cells uniquely express transcription factors like Emx1 and Pax6. GABAergic neurons destined for the cerebral cortex, joining their glutamateric neuron partners, arise from the subpallium which also gives rise to the basal ganglia. This region expresses transcription factors in the Dlx and Nkx family. The genetic influence of these initial neurodevelopmental events is illustrated by experiments in which these different transcription factors are mis-expressed in different regions will result in the anomalous production of projection or interneurons in the wrong location (Hebert & Fishell, 2008).

The ‘inside-out’ pattern of neuron production and migration provides the basis for building essentially similar ontogenetic radial units across functional areas, with minor variations in the ratio of excitatory to inhibitory neurons in different regions. In fact, this conserved radial organization provides the framework for later-developing refinement of circuits that are influenced extensively by patterns of physiological activity through experience-dependent mechanisms (see below). The tangential patterning of the neocortex into different functional domains that are prepared to receive specific thalamic input begins as soon as the first neurons are produced, and is due largely to distinctive gradients of expression of transcription factors such as emx1 and 2, pax6 and lhx2, which regulate the production of signaling molecules that include the fibroblast growth factors (fgf), bone morphogenetic proteins (bmp) and wnt proteins (O’Leary et al. 2007). Experiments in genetically manipulated mice demonstrate that by eliminating or altering the expression of just one transcription factor, the functional fate of cortical regions can be changed (Cholfin & Rubenstein, 2007). For example, emx2 controls the expression of fgf8 near the anterior end of the cerebrum. Fgf8 alone is sufficient to specify the cortical regions that will eventually receive connections that are typical of frontal and somatosensory cortices (Fukuchi-Shimogori & Grove, 2001,Fukuchi-Shimogori & Grove, 2003). This type of early genetic respecification is functionally relevant. For example, Fgf17 is responsible for initial patterning of different frontal cortex areas (Cholfin & Rubenstein, 2007). The early specification of the neocortex by genetic mechanisms is powerful because downstream from these signaling factors are axon guidance molecules that serve as the important chemical cues for getting axons to grow into their correct target region prior to beginning the extended process of synapse formation (for example, see Alcamo et al., 2008). Gene regulatory networks also can influence the initial size of cortical areas by modulating the number of neurons that are produced. As noted above, key to the flexibility of this early architecture is the conserved nature of the radial organization. Thus, the long-distance circuit projections that help to define functional cortical areas, and even functional differences in superficial and deep projecting neurons, are altered when the disruption of early gene networks modify guidance cues so that atypical connections are made. Though we tend to think that genetic mechanisms are immutable, it is important to stress that expression of early gene networks can be perturbed not only by catastrophic genetic mutations that disrupt important regulatory genes, but also by prenatal environmental influences, such as drugs, alcohol, toxins, and inflammatory responses. These may have less profound impacts on brain patterning, but nonetheless can result in long-term disruption of cellular differentiation and behavioral development (Stanwood & Levitt, 2008).

In all mammalian species, this early period of specified patterning to generate a unique architecture is followed by an extended period of synapse formation, adjustment and pruning that typically extends from the last quarter of gestation through puberty (Bourgeois, Goldman-Rakic, & Rakic 1999; Huttenlocher & Dabholkar, 1997). There is a unique relationship that emerges between pre- and postsynaptic partners as physiological activity takes over from the activity-independent histogenic events to sculpt presynaptic terminals and postsynaptic dendrites and spines. Over the past decade, investigators have identified literally thousands of genes that encode proteins related to synapse formation, plasticity and stabilization (Akins & Biederer 2006; Sheng & Hoogenraad, 2007; Sudhof, 2008). While synapses can form under experimental conditions in the absence of physiological activity, experience is essential for the normally occurring regulation of the molecular basis for synapse formation. In this complex process, activity controls the expression of transcription factors that direct the downstream expression of structural proteins, receptors and signaling molecules that are needed for synapses to function properly (Flavell et al., 2006; Majdan & Shatz, 2006; Paradis et al., 2007; Sugiyama et al, 2008). Moreover, activity regulates the distribution of key proteins within the synapse, making them available for the important task of information processing during sensitive and critical periods of development (Shepherd & Huganir, 2007).

Because of dramatic differences in brain size, the developmental timing of the histogenic events across mammalian species is unique. The process begins at about 56 days post-conception and extends through the first years of life in humans. The events (e.g. cell type specification, axon guidance) that provide the initial brain blueprint, influenced by early genetic patterning, are completed early and relatively rapidly, by midgestation in the primates and the end of gestation in rodents (Levitt, 2003). The formation of neural connections, the development of unique cytoarchitecture (the structural correlate of functional areas), the growth of dendritic arbors and the peak formation of synapses is a far more time-extensive process, extending through the second and third postnatal years in humans, 180 days in the macaque monkey, and at around weaning (21 days) in rodents. Thus, through a well-defined developmental process, the basic connectivity of local and long-distance neurons is set up to take advantage of a highly flexible organization that is both activity-dependent and expectant postnatally. The continued growth of the neocortex occurs up to puberty by the addition of myelin, dendritic growth, non-neuronal cells, and a complex process of resculpting synapses whose numerical density across the entire neocortex is stable, but may change in their laminar distribution. The mechanisms that control this latter event are not clear, but are likely to be activity-dependent. Recent studies of synaptic resculpting demonstrate that highly novel gene-environment regulatory mechanisms are at play, such as the activity-dependent movement of transcription factors from pre- to postsynaptic neurons to control maturation of neurons in a non-cell autonomous fashion (Sugiyama et al, 2008).

Although our genetic code provides an important foundation for early development, it must be understood as a framework upon which many environmental factors influence future structure and function. This can best be illustrated through studies of the sensory systems, which demonstrate the crucial role of environment in the early development and maintenance of the nervous system. Such work also demonstrates the need for patterned physiologic activity during development, as well as refinement and maintenance of detailed sensory maps. For example, supporting cells in the developing rat cochlea spontaneously release Adenosine-5′-triphosphate (ATP), synchronizing the output of neighboring inner hair cells, which aids in the refinement of tonotopic maps. Spontaneous ATP-dependent signaling rapidly subsides after the onset of hearing, thereby preventing this experience-independent activity from interfering with the accurate encoding of sound. The initiation of electrical activity in auditory nerves before hearing suggests that peripheral, non-sensory cells may provide the necessary early environment for the development of central auditory pathways (Tritsch, Yi, Gale, Glowatski, & Bergles, 2007). In addition, auditory perception evolves according to the tonal rearing environment, with increased sensitivity to tones outside of those continuously experienced in the environment (Han, Kover, Insanally, Semerdjian, & Bao, 2007).

Similarly, in the landmark studies of vision by Wiesel and Hubel (1963, 1965), it was demonstrated that kittens reared with normal visual experience resulted in each eye having sole access to alternating columns of neurons in layer IV of the striate cortex. At birth, however, both eyes synapse on all neurons in layer IV. In order to assure that a neuron is stimulated by experience coming from only one eye, a competitive process occurs in which activation and neighboring inhibition result in an alternating pattern of connectivity between columns of neurons in layer IV and each eye (Wiesel & Hubel, 1965). When kittens were reared with one eye closed for a period of time after birth, the occluded eye became essentially functionally blind. This blindness is due to the elimination of connections of the closed eye to layer IV and the lack of exposure to patterned activity. If occlusion extends beyond a certain time period, the typical pattern of ocular representation cannot be recovered despite the restoration of visual input to both eyes (Wiesel & Hubel, 1965). It has been hypothesized that the initial ingrowth of axons from the thalamus to ocular dominance columns in visual cortex is governed by molecular cues (Crair, Horton, Antonini & Stryker, 2001; Crowley & Katz, 2000). That is not to say, however, that an atypical or plastic change in visual representation is not achievable (Amedi et al., 2007; Antonini, Fagiolini & Stryker, 1999; Flege, Munro, & MacKay, 1995; He, Ray, Dennis, & Quinlan, 2007; Miller, Keller, & Stryker, 1989). It has recently been shown, for example, that the decreased visual acuity seen in the adult rat suffering from chronic monocular deprivation is reversed if the adult rat is treated with dark exposure prior to removal of the occlusion (He, et al., 2007). The increased plasticity induced by the dark environment may be due to a lack of input to visual cortex through the functioning eye, and therefore a reduction in the strength of previously established connections. A similar restoration of visual acuity can also be induced with chronic administration of fluoxetine (Maya Vetencourt et al., 2008). Such dramatic changes in sensory system connectivity suggest that activity-dependent potentiation of these initial axons is required to maintain connections among cortical regions. In the case of primary visual cortex, local circuit neurons have been implicated in activity-dependent plasticity through GABAergic inhibition over a wide range of neighboring axonal paths. (Fagiolini, et al., 2004; Hensch & Stryker, 2004). An altered pattern of activity through one circuit can thus radically change neighboring circuits through an increase or decrease in inhibition of mediating cells. Furthermore, visual deprivation has been shown to alter the formation of dendritic spines in visual cortex once the pathways for normal vision have begun to potentiate (Oray, Majewska, & Sur, 2004; Trachtenburg & Stryker, 2001). Following deprivation, spines show increased structural motility – possibly allowing for plasticity of local connections – and later deterioration (Kim & Bonhoeffer, 1994; Mataga, Mizuguchi,& Hensch, 2004).

The early development of visual pathways may be likened to the laying of a foundation and scaffolding for a building. If the scaffolding pattern is changed, the building may not be constructed in its original form, though a functional alternative may be reached. Thus, irreversible changes at the synaptic level do not necessarily translate into irreversible changes in a complex behavior (Feldman & Knudsen, 1998). For example, we now understand that the sensitive period for visual representation reflects, predominantly, the critical period for thalamic input to layer IV (Antonini & Stryker, 1993; Miller, Keller, & Stryker, 1989; Pascual-Leone, Amedi, Fregni, & Merabet, 2005), but that plasticity of other sensory systems may allow a blind person to demonstrate normal – and possibly enhanced – spatial awareness (Amedi et al., 2007). Plasticity in higher regions involved in spatial awareness feeds back upon lower pathways, thus compensating for an abnormal visual representation.

Advanced perceptual processes are also dependent upon the normal development of basic visual systems. For example, early visual deprivation due to congenital cataracts can lead to subtle but persistent deficits in face processing, even when the cataracts are removed in the first months of life (Le Grand, Mondloch, Maurer, & Brent, 2001). Similarly, experience with specific faces, such as same vs. different species, powerfully shapes subsequent face specialization. For example, monkeys deprived of viewing faces since birth are capable of discriminating both monkey and human faces following the selective restoration of faces in the visual environment, but what kind of faces determines whether that same monkey will be able to subsequently discriminate human or monkey faces – thus, monkeys selectively exposed to human faces can only discriminate human faces not monkey faces, and monkeys selectively exposed to monkey faces can only discriminate monkey faces, not human faces (Sugita, 2008).

Hubel and Wiesel’s experiments involving visual deprivation brought about the concept of “sensitive” and “critical” periods in early cognitive development. “Sensitive” periods are defined as a time in development during which the brain is particularly responsive to experiences in the form of patterns of activity (Daw, 1997). Further, this timepoint may be termed a “critical” period if the presence or absence of an experience results in irreversible change (Newport, et al., 2001; Trachtenberg & Stryker, 2001). Those factors that allow a circuit underlying cognition to be plastic – or render it unchangable – are not yet well understood. In the area of speech and language, the “maturational hypothesis,” predicts that native language proficiency cannot be obtained when learning begins after puberty (Bruer, 2001; Werker & Tees, 2005). Studies supporting this theory have correlated the degree of accent in a second language to age at the time of acquisition of that language (Birdsong & Molis, 2001; Johnson & Newport, 1989). Adults exposed to a second language in early childhood were found to have native-like accents and pattern of tone (Gordon, 2000; Long, 1990; Oyama, 1990; Stein, et al., 2006). Other researchers have also found a negative correlation between age at acquisition and grammaticality judgments (Flege, MacKay, & Meador, 1999; Komarova & Nowak, 2001). These same studies, however, have failed to show any clear discontinuity in the relationship between accent and age over the period from childhood to adulthood, thus suggesting that although a sensitive period may exist, a “critical” period for language acquisition is unlikely (Flege, et al. 1999; Komarova & Nowak, 2001; Wartenburger et al., 2003; Weber-Fox & Neville, 2001). Many of these same investigators, however, argue that the decline in language learning facility may be confounded by educational level, and that involvement in a larger bilingual community may diminish the drive to find means for communication through a learned language (Flege, et al., 1999; Hoff & Naigles, 2002; Komarova & Nowak, 2001). It has further been hypothesized that enhancements in non-language cognitive systems, such as memory, may actually have deleterious effects on language learning (Newport, 1990; Newport, et al., 2001). If this is the case, then the neural system responsible for language may in fact remain open, though difficult to access through adult cognitive processing or with only typical effort (the extent to which considerable effort might force open the sensitive period can be found in training studies such as those focused on teaching native Japanese speakers to perceptually discriminate and correctly produce the R/L distinction; see Akahane-Yamada, Strange, Downs-Pruitt & Masuda, 1998; Guion, Flege, Akahane-Yamada, & Downs-Pruitt, 1998, 2000).

Several investigators have used the theory of neural networks, originally developed for vision research, to model the activity of individual neurons and/or groups of neurons in the brain during learning (Christiansen & Chater, 2001; Morton & Munakata, 2005). These neural network models are particularly useful for comparing the experience-independent and experience-based accounts of sensitive periods, because the network can be kept constant with regard to features affected by maturation, motivation, and amount of exposure. Returning to the work of Hubel and Wiesel, it is important to note that the loss of binocular function in the kitten did not arise simply because of the absence of input to the occluded eye. Occluding both eyes during the same time period of development was proven not to result in loss of binocular vision (Cynader & Mitchell, 1980). It is necessary for one eye to have access to layer IV of the visual cortex while the other eye is denied access, allowing exclusive connectivity of the unoccluded eye to striate cortex. The irreversible loss of binocular vision during development must therefore be due to a combination of environmental experience and cortical learning processes (Mataga, et al., 2004; Knudsen, 2004). The fact that the existence of a sensitive period can depend upon occurrence of a particular environment suggests that in early development, portions of networks become perceptually biased, making future modifications more difficult. For example, in the literature on both speech and face perception, the perceptual window through which faces and speech is initially processed is broadly tuned, then narrows with experience. For example, Pascalis, de Haan and Nelson (2002) demonstrated that six- and nine-month-old infants and adults can readily discriminate two human faces, but only 6-month-old infants can discriminate two monkey faces. Similarly, six month olds given three months of experience viewing monkey faces can readily discriminate monkey faces at nine months of age, whereas nine month old infants not afforded such experience cannot (Pascalis et al., 2005).

As a rule, circuits that process lower level information mature earlier than those that process higher level information (Burkhalter, Bernardo, & Charles, 1993; Scherf, Behrmann, Humphreys, & Luna, 2007). For example, in the neural hierarchy that analyzes visual information, low-level circuits that analyze the color, shape or motion of stimuli are fully mature long before the high-level circuits that analyze or identify biologically important stimuli, such as faces, food, or frequently used objects (Burkhalter, et al., 1993; Knudsen, 2004; Scherf et al., 2007). The process by which initial learning leads to a constraint on later learning is termed entrenchment, and is equally apparent in the development of speech (Munakata & Pfaffly, 2004; Seidenberg & Zevin, 2006). Several studies have shown, for example, that adults are often better at discriminating non-native phonetic contrasts when they differ substantially from phonemes of their native language (Best, McRoberts, & Sithole, 1988; Frieda, Walley, Flege & Sloane, 1999; Guion et al., 2000; Kuhl, 2004). Adults are poorer at discriminating when the phonetic contrasts are similar to phonetic contrasts of their native language. This is akin to the nature of the developing auditory system, which as previously noted, is more capable of discriminating tones outside of the tonal environment of rearing. At both the level of tone, and of speech phonetic discrimination, there is evidence for a fixed bias of the neural network. As previously discussed in the case of visual networks, however, neurons may be constantly modifying connectivity, allowing learning from new environments to compete against already existing tendencies. This is well demonstrated in animals altered neonatally to receive retinal projections to the auditory portions of the thalamus (von Melchner, Pallas, & Sur, 2000). Such animals reveal that auditory cortex may be modified by extrinsic activity to develop retinotopic maps similar to those seen in visual cortex. The role of environment and inputs to the brain may therefore be seen as critical in the bias of network formation during early life.

Altered patterns of enhancement and inactivity are thought to be the basis for neural plasticity, and have been suggested in humans by studies of tactile and auditory perception in the blind, where such systems may even activate “visual” cortex (Antonini & Stryker, 1993; Ellis & Lambon, 2000; Merabet, Rizzo, Amedi, Somers, & Pascual-Leone, 2005). It is likely that changes in experience have a greater impact on an untrained ‘young’ network as compared to the same experience on an ‘older’ trained network. This biasing feature is suggested by studies on aphasia that show that words learned earlier in life are more resistant to loss, and are more easily accessed in naming tasks as compared to words learned later (Greenough, Black, & Wallace, 1987).

It has been suggested that learning through experience leads to the capacity to understand specific environments and the responses needed for these environments (Scarr & McCartney, 1983; Anisman, Zaharia, Meaney, & Merali, 1998). Similarly, changes in the environment – particularly when they are dramatic and pervasive – may have the power to alter neural connectivity and cognitive processing between systems. Examples can be found in studies of sensory deprivation, such as blindfolding, as well as sensory enhancement through technology. In studies of deaf children receiving cochlear implants, it is clear that language learning improves with earlier correction (Tomblin, Barker, Spencer, Zhang, & Gantz, 2005). It remains to be determined, however, whether this effect upon learning is due to actual changes in cognitive capacity, or changes in the learning environment brought about by the ability to interact with others through spoken language.

The nature of our experiences, particularly during a time-limited period in early development, can profoundly affect the mental framework we use to understand the world around us. Sensitive periods in child development are of interest because they represent a timeframe in which our capabilities can be modified and perhaps enhanced. The quality of experiences during such periods – be they adverse or enhancing – is also of importance in understanding why it may be difficult to restore normal function once development has been altered. While explanatory models for the timing of early experiences have generally been based at the genetic or neural circuit level, our direct observations of the effects of early environments are often made at the behavioral level. Through the study of sensitive periods, we are better able to understand the impact that early experience may have upon development. To cite but one example, it has recently been demonstrated that otherwise-typically developing young children institutionalized at birth have IQs in the low 70s. However, placing such children in high quality foster care before the age of two years leads to a dramatic increase in IQ (Nelson et al., 2007). A similar trend also occurs for language (Windsor, Glaze, Koga & the BEIP Core Group, 2007) and the development of the EEG (Marshall, Reeb, Fox & the BEIP Core Group, in press), although in the case of the former, the sensitive period occurs around 16–18 months.

It is important to note recent work suggesting that the brain retains the capacity to adapt and change throughout the lifespan (Crawford, et al., 1996; Jones, 2000; Keuroghlian & Knudsen, 2007). However, the foundation of brain architecture must lie in the early developmental years, and that the influence of childhood environment is much more salient in such basic cognitive processes as sensory perception (Amedi, et al., 2007; Antonini & Stryker, 1993; Hensch, 2005; Hess, 1973; Grossman et al., 2003; Karmarkar & Dan, 2006; Knudsen, 2004; Pascual-Leone, et al., 2005). Each sensory and cognitive system reaches a unique sensitive period (Daw, 1997), and thus identical environmental conditions will result in very different cognitive and emotional experiences for a child, depending upon his or her age (Amedi, et al., 2007; Jones, 2000; Trachtenberg & Stryker, 2001; Tritsch et al., 2007, for reference see Bailey et. al (eds.), 2001).

Behavioral analysis can demonstrate the value of early experiences in the development of the brain. It must be remembered, however, that information is processed in a series of networks, each reflecting the effects of environment at varying timepoints. Higher level processing may mask modifications in lower levels networks (Daw, 1997; Feldman & Knudsen, 1998; Trachtenberg & Stryker, 2001). Thus, behavioral outcomes may be shaped by later experience, even though circuits at the lowest levels in a pathway remain irreversibly altered. In addition, studies of the plasticity of sensory processing reveal that similar information can be derived from alternative pathways (Akins, 2006; Antonini & Stryker, 1993; Ellis & Lambon, 2000; Pascual-Leone et al., 2005; von Melchner et al., 2000). For example, when using sound devices to asses space, blind individuals have been shown to activate lateral occipital cortex in the same manner as sighted individuals do through vision (Amedi et al., 2007). It has been suggested that loss sensory input – such as occurs in late blindness – may in fact lead to the unmasking and strengthening of alternative pathways stemming from multisensory integration regions of the brain (Pascuale-Leone et al., 2005). These pathways may not only substitute for the original sensory inputs, but may enhance previously existing capabilities. This form of sensory enhancement can often be seen in the highly tuned auditory and tactile perception of blind.

High-level neural circuits that carry out sophisticated mental functions depend on the quality of the information that is provided to them by lower level circuits. Low-level circuits whose architecture was shaped by healthy experiences early in life provide high-level circuits with precise, high-quality information. High-quality information, combined with sophisticated experience later in life, allows the architecture of circuits involved in higher functions to take full advantage of their genetic potential. Thus, early learning lays the foundation for later learning and is essential (though not sufficient) for the development of optimized brain architecture. Stated simply, rich early experience must be followed by rich and more sophisticated experience later in life, when high-level circuits are maturing, in order for full potential to be achieved (DeBello & Knudsen, 2004; Karmarkar & Dan, 2006; Nelson, deHaan & Thomas, 2006; Sabatini et al., 2007).

Although studies of the adverse effects of deprivation on brain development are powerful and compelling, they tell us little about the benefits of enrichment. Much of what we know about the impact of early experience on brain architecture comes from animal or human studies of deprivation. As we work to clarify further the patterns of genetic expression required for normal neural structure, we have also recognized that an optimallevel of environmental input, or “expectable” environment must exist in parallel. Increasing evidence suggests that this “expectable environment” of early development requires not only the variation in light necessary for vision, or the tones heard in a spoken language, but also the emotional support and familiarity of a caregiver (Nelson et al., 2007; Sánchez, Ladd & Plotsky, 2001). It is important to emphasize that the well-documented negative impacts of deprivation on brain circuitry do not mean that excessive enrichment produces measurable enhancements in brain function. A small number of case reports exist in which neglected children with very little language experience in early childhood were given enriched language exposure in a protective environment (Curtiss, 1977; Itard, 1932; Zingg, 1940). Longitudinal follow-up studies of these children demonstrated that, after several years of language exposure, they were unable to achieve adult-level native language abilities. In animal models, rat pups deprived of maternal care were shown to have reduced hippocampal volume as compared to pups with “enriched” maternal care (Bredy, Humpartzoomian, Cain, & Meaney, 2003). Placement of deprived pups into an “enriched care” environment resulted in learning and memory aptitude similar to high-care pups, however hippocampal volume did not change, suggesting that plastic mechanisms early in life allow for alternative pathways to form typical behavior despite lasting structural deficits. Most recently, early intervention to correct a deeply impoverished early environment has been shown to greatly improve cognitive, linguistic, and emotional capabilities in humans (Ghera et al., in press; Nelson et al., 2007; Windsor et al., 2007). Activity-dependent mechanisms of network formation, as described earlier, may be responsible for such changes when children are placed into a stimulating environment for learning and exploration. With continued research into the modification of sensitive periods, as well as the factors influencing cortical plasticity throughout life, we may remain optimistic about the possibility ofrecovery from earlydeprivation. Thisin turn may provide hope for children who lack the biological framework, or necessary environment required for optimal neural and cognitive growth.

Finally, the possibility of cognitive and neural rehabilitation leads to theories of enrichment beyond the norm to a level of enhanced development. Educational and environmental enrichment of preschool children from impoverished economic settings has been shown to improve cognitive measures through early adulthood (Campbell, Pungello, Miller-Johnson, Burchinal, & Ramey, 2001; Martin, Ramey C.T., & Ramey, S.L., 1990; see Faran, 2000 for review). Cognitive capabilities, however, may follow a pattern similar to the growth curve of the human body; that is, while it is possible to enhance the environment of a child to assume the pattern of the normal curve, it is not possible to exceed the predicted trajectory to a significant extent without causing some potential harm. This is suggested by large studies of children from varying socioeconomic status, which demonstrated an improvement in cognitive performance only in those born to a low socioeconomic class, with no significant difference between those of middle-class and high-income families (Jefferis, Power, & Hertzman, 2002). If the possibility for enhancement exists, it is perhaps related to forms of enrichment that lie outside access to unlimited resources – i.e., beyond the expectable environment. Creativity, for example, is a key component to enhanced cognitive functioning, yet we have not been able to define the neural processes or environmental attributes that can enrich this aspect of cognition, nor are there sure-fire ways of boosting creativity among the population at large. Similarly, exposure to art or music or great literature or horseback riding may not confer any evolutionary advantage (i.e., reproductive success), yet these activities may confer some advantage among certain strata of society. Thus, perhaps it would be useful to draw a distinction between enrichment as applied to those experiencing downward deviations from the expectable environment (such as those reared in situations of neglect or deprivation) and enhanced enrichment applied to those reared in typical (expectable) environments. Enrichment may lead to a restoration of typical development whereas enhanced enrichment may lead to exceeding the typical environment. Of course, a challenge here lies in accounting for individual differences, as some individuals have greater potential to benefit from art or music lessons than others. Individual heterogeneity may be under control of gene X gene, gene X environment and environment X environment factors. For example, animal studies show that epigenetic mechanisms - whereby environmental factors and experiences early in life can permanently alter the genome of an individual through chemical modification - will impact long-term cognitive and social-emotional functioning (Szyf, McGowan, & Meany, 2008). The field awaits translation of this type of mechanism into human experiences. Finally, how might the field of developmental psychology benefit from advances being made in developmental neuroscience? First, given that our genome contains many fewer genes than we surmised even a decade ago (approximately 20,000), and given advances being made in the field of epigenetics, renewed attention should be paid to the origins and elaboration of complex human behaviors. Second, those working in the field of intervention should take stock in what is now known about neural plasticity; for example, it is quite possible that we could witness a revolution in new treatment approaches based on what we know about the malleability of the human brain. Finally, for the millions of children around the world who begin their lives in adverse circumstances, we should be mindful of what is known about sensitive periods, and act with alacrity to improve the lives of these children before neural circuits become well-established and thus, difficult to modify. To borrow an analogy from economics, by investing early and well in our children’s development we increase the rate of return later in life, and in so doing improve not only the lives of individuals but of societies as well.

The writing of this paper was made possible by the Doris Duke Charitable Foundation Fellowship at Harvard Medical School (to Sharon Fox), the NIH (NS03445; MH078829), the John D. and Catherine T. Macarthur Foundation and the Richard David Scott Chair (to Charles A. Nelson), and the Annette Schaffer Eskind Chair, NIMH grant MH080759 and NICHD P30 core grant HD15052 (to Pat Levitt).

Sharon E. Fox, Harvard-MIT Division of Health Sciences and Technology.

Pat Levitt, Vanderbilt University.

Charles A. Nelson, III, Children’s Hospital Boston, Harvard Medical School.

  • Akahane-Yamada R, Strange W, Downs-Pruitt JC, Masuda Y. Modification of L2 vowel production by intensive perception training as evaluated by acoustic analysis and native speakers. Journal of the Acoustical Society of America. 1998;103:3089–3096. [Google Scholar]
  • Akins MR, Biederer T. Cell-cell interactions in synaptogenesis. Current Opinion in Neurobiology. 2006;16:83–89. [PubMed] [Google Scholar]
  • Alcamo EA, Chiriella L, Dautzenberg M, Dobreva G, Farinas I, Grosschedl R, et al. Satb2 regulates callosal projection neuron identity in the developing cerebral cortex. Neuron. 2008;57:364–377. [PubMed] [Google Scholar]
  • Amedi A, Stern WM, Camprodon JA, Bermpohl F, Merabet L, Rotman S, et al. Shape conveyed by visual-to-auditory sensory substitution activates the lateral occipital complex. Nature Neuroscience. 2007;10:687–689. [PubMed] [Google Scholar]
  • Anisman H, Zaharia MD, Meaney MJ, Merali Z. Do early-life events permanently alter behavioral and hormonal responses to stressors? International Journal of Developmental Neuroscience. 1998;16:149–164. [PubMed] [Google Scholar]
  • Antonini A, Fagiolini M, Stryker MP. Anatomical correlates of functional plasticity in mouse visual cortex. The Journal of Neuroscience. 1999;19:4388–4406. [PMC free article] [PubMed] [Google Scholar]
  • Antonini A, Stryker MP. Rapid remodeling of axonal arbors in the visual cortex. Science. 1993;260:1819–1821. [PubMed] [Google Scholar]
  • Best CT, McRoberts GW, Sithole NM. Examination of perceptual reorganization for nonnative speech contrasts: Zulu click discrimination by English-speaking adults and infants. Journal of Experimental Psychology: Human Perception and Performance. 1988;14:345–360. [PubMed] [Google Scholar]
  • Birdsong D, Molis M. On the Evidence for Maturational Constraints in Second-Language Acquisition. Journal of Memory and Language. 2001;44:235–49. [Google Scholar]
  • Bourgeois J-P, Goldman-Rakic PS, Rakic P. Formation, elimination, and stabilization of synapses in the primate cerebral cortex. In: Gazzaniga MS, editor. The New Cognitive Neurosciences. Cambridge, MA: MIT Press; 1999. pp. 45–53. [Google Scholar]
  • Bredy TW, Humpartzoomian RA, Cain DP, Meaney MJ. Partial reversal of the effect of maternal care on cognitive function through environmental enrichment. Neuroscience. 2003;118:571–576. [PubMed] [Google Scholar]
  • Bailey DB, Bruer JT, Symons FJ, Lichtman JW, editors. Critical thinking about critical periods. Baltimore, MD: Paul H. Brookes Publishing Company; 2001. [Google Scholar]
  • Burkhalter A, Bernardo KL, Charles V. Development of local circuits in human visual cortex. The Journal of Neuroscience. 1993;13(5):1916–1931. [PMC free article] [PubMed] [Google Scholar]
  • Campbell FA, Pungello EP, Miller-Johnson S, Burchinal M, Ramey CT. The development of cognitive and academic abilities: Growth curves from and early childhood educational experiment. Developmental Psychology. 2001;37:231–242. [PubMed] [Google Scholar]
  • Cholfin JA, Rubenstein JLR. Patterning of frontal cortex subdivisions by fgf17. Proceedings of the National Academy of Sciences. 2007;104:7652–7657. [PMC free article] [PubMed] [Google Scholar]
  • Christiansen MH, Chater N. Connectionist psycholinguistics: capturing the empirical data. Trends in Cognitive Sciences. 2001;1:82–88. [PubMed] [Google Scholar]
  • Crair MC, Horton JC, Antonini A, Stryker MP. Emergence of occular dominance columns in cat visual cortex by 2 weeks of age. Journal of Comparative Neurology. 2001;430:235–249. [PMC free article] [PubMed] [Google Scholar]
  • Crawford ML, Pesch TW, von Noorden GK. Excitatory binocular neurons are lost following prismatic binocular dissociation in infant monkeys. Behavioural Brain Research. 1996;79:227–232. [PubMed] [Google Scholar]
  • Crowley JC, Katz LC. Early development of ocular dominance columns. Science. 2000;290:1321–1324. [PubMed] [Google Scholar]
  • Curtiss SG. A psycholinguistic study of a modern-day “wild child”. New York: Academic Press; 1977. [Google Scholar]
  • Cynader M, Mitchell DE. Prolonged sensitivity to monocular deprivation in dark-reared cats. Journal of Neurophysiology. 1980;43:1026–1040. [PubMed] [Google Scholar]
  • Daw NW. Critical periods and strabismus: what questions remain? Optometry and Vision Science. 1997;74:690–694. [PubMed] [Google Scholar]
  • DeBello WM, Knudsen EI. Multiple sites of adaptive plasticity in the owl’s auditory localization pathway. The Journal of Neuroscience. 2004;24:6853–6861. [PMC free article] [PubMed] [Google Scholar]
  • Ellis AW, Lambon RMA. Age of acquisition effects in adult lexical processing reflect loss of plasticity in maturing systems: insights from connectionist networks. Journal of Experimental Psychology: Learning, Memory & Cognition. 2000;26:1103–1123. [PubMed] [Google Scholar]
  • Fagiolini M, Fritschy JM, Low, Mohler H, Rudolph U, Hensch TK. Specific GABA circuits for visual cortical plasticity. Science. 2004;303:1681–1683. [PubMed] [Google Scholar]
  • Farran DC. Another decade of interventions for children who are low income or disabled: What do we know now? In: Shonkoff JP, Meisels SJ, editors. Handbook of early childhood intervention. 2. New York: Cambridge University Press; 2000. pp. 510–548. [Google Scholar]
  • Feldman DE, Knudsen EI. Experience-dependent plasticity and the maturation of glutamatergic synapses. Neuron. 1998;20:1067–1071. Review. [PubMed] [Google Scholar]
  • Flavell SW, Cowan CW, Kim TK, Greer PL, Lin X, Paradis S, et al. Activity-dependent regulation of MEF2 transcription factors suppresses excitatory synapse number. Science. 2006;311:1008–1012. [PubMed] [Google Scholar]
  • Flege JE, Munro MJ, MacKay IR. Factors affecting strength of perceived foreign accent in a second language. Journal of the Acoustical Society of America. 1995;97:3125–3134. [PubMed] [Google Scholar]
  • Frieda EM, Walley AC, Flege JE, Sloane ME. Adults’ perception of native and nonnative vowels: implications for the perceptual magnet effect. Perception & Psychophysics. 1999;61:561–577. [PubMed] [Google Scholar]
  • Friederici AD. The neural basis of language development and its impairment. Neuron. 2006;52:941–952. [PubMed] [Google Scholar]
  • Fukuchi-Shimgori T, Grove EA. Neocortex Patterning by the Secreted Signaling Molecule FGF8. Science. 2001;294:1071–1074. [PubMed] [Google Scholar]
  • Fukuchi-Shimgori T, Grove EA. Emx2 patterns the neocortex by regulating FGF positional signaling. Nature Neuroscience. 2003;6:825–831. [PubMed] [Google Scholar]
  • Ghera M, Marshall P, Fox N, Zeanah C, Nelson CA, Smyke A. Social Deprivation and Young Institutionalized Children’s Attention and Expression of Positive Affect: Effects of a Foster Care Intervention. Journal of Child Psychology and Psychiatry (in press) [PubMed] [Google Scholar]
  • Golari G, Ghahremani DG, Whitfield-Gabrieli S, Reiss A, Eberhardt JL, Gabrieli JDE, Grill-Spector K. Differential development of high-level visual cortex correlates with category-specific recognition memory. Nature Neuroscience. 2007;10:512–522. [PMC free article] [PubMed] [Google Scholar]
  • Gordon N. The acquisition of a second language. European Journal of Pediatric Neurology. 2000;4:3–7. Review. [PubMed] [Google Scholar]
  • Greenough WT, Black JE, Wallace CS. Experience and brain development. Child Development. 1987;58:539–559. [PubMed] [Google Scholar]
  • Grossman AW, Churchill J, McKinney BC, Kodish IM, Otte SL, Greenough WT. Experience effects on brain development: possible contributions to psychopathology. Journal of Child Psychology and Psychiatry. 2003;44:33–63. [PubMed] [Google Scholar]
  • Guion SG, Flege JE, Akahane-Yamada R, Downs-Pruitt JC. Acquisition of English phonetic categories by Japanese speakers: Evidence from categorical discrimination tests. Journal of the Acoustical Society of America. 1998;103:3089–3090. [Google Scholar]
  • Guion SG, Flege JE, Akahane-Yamada R, Pruitt JC. An investigation of current models of second language speech perception: the case of Japanese adults’ perception of English consonants. Journal of the Acoustical Society of America. 2000;107:2711–2724. [PubMed] [Google Scholar]
  • Hammock EAD, Levitt P. The discipline of neurobehavioral development: the emerging interface that builds processes and skills. Human Development. 2006;49:294–309. [Google Scholar]
  • Hanm YK, Kover H, Insanally MN, Semerdjian JH, Bao S. Early experience impairs perceptual discrimination. Nature Neuroscience. 2007;10:1191–1197. [PubMed] [Google Scholar]
  • He HY, Ray B, Dennis K, Quinlan EM. Experience-dependent recovery of vision following chronic deprivation amblyopia. Nature Neuroscience. 2007;10:1134–1136. [PubMed] [Google Scholar]
  • Hebért JM, Fishell G. The genetics of early telecephalon patterning: Some assembly required. Nature Reviews Neuroscience. 2008;9:678–685. [PMC free article] [PubMed] [Google Scholar]
  • Hensch TK, Stryker MP. Columnar architecture sculpted by GABA circuits in developing cat visual cortex. Science. 2004;303:1678–1681. [PMC free article] [PubMed] [Google Scholar]
  • Hensch TK. Critical period mechanisms in developing visual cortex. Current Topics in Developmental Biology. 2005;69:215–237. [PubMed] [Google Scholar]
  • Hess EH. Imprinting: Early experience and the developmental psychobiology of attachment. New York, NY: Van Nostrand Reinhold Company; 1973. [Google Scholar]
  • Hoff E, Naigles L. How children use input to acquire a lexicon. Child Development. 2002;73:418–433. [PubMed] [Google Scholar]
  • Horn G. Pathways of the past: the imprint of memory. Nature Reviews Neuroscience. 2004;5:108–120. [PubMed] [Google Scholar]
  • Huttenlocher PR, Dabholkar AS. Regional differences in synaptogenesis in human cerebral cortex. Journal of Comparative Neurology. 1997;387:167–78. [PubMed] [Google Scholar]
  • Itard JMG. In: The wild boy of Aveyron, trans. Humphrey G, Humphrey M, translators. New York: Century; 1932. 1967. (Original work published 1801) [Google Scholar]
  • Jeffaris H, Power C, Hertzman C. Birth weight, childhood socioeconomic environment and cognitive development in the 1958 British birth cohort study. British Medical Journal. 2002;325:305–311. [PMC free article] [PubMed] [Google Scholar]
  • Johnson JS, Newport EL. Critical period effects in second language learning: the influence of maturational state on the acquisition of English as a second language. Cognitive Psychology. 1989;21:60–99. [PubMed] [Google Scholar]
  • Jones EG. Cortical and subcortical contributions to activity-dependent plasticity in primate somatosensory cortex. Annual Review of Neuroscience. 2000;23:1–37. [PubMed] [Google Scholar]
  • Karmarkar UR, Dan Y. Experience-dependent plasticity in adult visual cortex. Neuron. 2006;52:577–585. [PubMed] [Google Scholar]
  • Katz LC, Shatz CJ. Synaptic activity and the construction of cortical circuits. Science. 1996;274:1133–1138. [PubMed] [Google Scholar]
  • Keuroghlian AS, Knudsen EI. Adaptive auditory plasticity in developing and adult animals. Progress in Neurobiology. 2007;82:109–121. [PubMed] [Google Scholar]
  • Kim DS, Bonhoeffer T. Reverse occlusion leads to precise restoration of orientation preference maps in visual cortex. Nature. 1994;370:370–372. [PubMed] [Google Scholar]
  • Knudsen EI. Sensitive periods in the development of the brain and behavior. Journal of Cognitive Neuroscience. 2004;16:1412–1425. [PubMed] [Google Scholar]
  • Komarova NL, Nowak MA. Natural selection of the critical period for language acquisition. Proceedings of The Royal Society of London. Series B, Biological Sciences. 2001;268:1189–1196. [PMC free article] [PubMed] [Google Scholar]
  • Kuhl PK. Early language acquisition: cracking the speech code. Nature Reviews Neuroscience. 2004;5:831–843. [PubMed] [Google Scholar]
  • Le Grand R, Mondloch CJ, Maurer D, Brent HP. Early visual experience and face processing. Nature. 2001;410:890. [PubMed] [Google Scholar]
  • Levitt P. Development of the Vertebrate Nervous System. In: Wong-Riley M, editor. Neuroscience Secrets. Philadelphia: Hanley & Belfus, Inc; 2000. pp. 46–57. [Google Scholar]
  • Levitt P. Structural and functional maturation of the developing primate brain. Journal of Pediatrics. 2003;143:S35–S45. [PubMed] [Google Scholar]
  • Long MH. Maturational constraints on language development. Studies in Second Language Acquisition. 1990;12:251–285. [Google Scholar]
  • Majdan M, Shatz CJ. Effects of visual experience on activity-dependent gene regulation in cortex. Nature Neuroscience. 2006;9:650–659. [PubMed] [Google Scholar]
  • Marshall P, Reeb BC, Fox NA the BEIP Core Group. Effects of early intervention on EEG power and coherence in previously institutionalized children in Romania. Development and Psychopathology (in press) [PMC free article] [PubMed] [Google Scholar]
  • Martin SL, Ramey CT, Ramey SL. The prevention of intellectual impairment in children of impoverished families: Findings of a randomized trial of educational daycare. American Journal of Public Health. 1990;80:844–847. [PMC free article] [PubMed] [Google Scholar]
  • Mataga N, Mizuguchi Y, Hensch TK. Experience-dependant pruning of dendritic spines at visual cortex by tissue plasminogen activator. Neuron. 2004;44:1031–1041. [PubMed] [Google Scholar]
  • Maya Vetencourt JJ, Sale A, Viegi A, Baroncelli L, De Pasquale R, O’Leary OF, et al. The antidepressant fluoxetine restores plasticity in the adult visual cortex. Science. 2008;320:385–388. [PubMed] [Google Scholar]
  • Merabet LB, Rizzo JF, Amedi A, Somers DC, Pascual-Leone A. What blindness can tell us about seeing again: merging neuroplasticity and neuroprostheses. Nature Reviews Neuroscience. 2005;6:71–77. Review. [PubMed] [Google Scholar]
  • Miller KD, Keller JB, Stryker MP. Ocular dominance column development: analysis and simulation. Science. 1989;245:605–615. [PubMed] [Google Scholar]
  • Morton JB, Munakata Y. What’s the difference? Contrasting modular and neural network approaches to understanding developmental variability. Journal of Development and Behavioral Pediatrics. 2005;26:128–139. [PubMed] [Google Scholar]
  • Munakata Y, Pfaffly J. Hebbian Learning and development. Developmental Science. 2004;7:141–148. [PubMed] [Google Scholar]
  • Nelson CA. A neurobiological perspective on early human deprivation. Child Development Perspectives. 2007;1:13–18. [Google Scholar]
  • Nelson CA, de Haan M, Thomas KM. Neural bases of cognitive development. In: Damon W, Lerner R, Kuhn D, Siegler R, editors. Handbook of Child Psychology. Vol. 2. New Jersey: John Wiley & Sons, Inc; 2006. pp. 3–57. [Google Scholar]
  • Nelson CA, Jeste S. Neurobiological perspectives on developmental psychopathology. In: Rutter M, Bishop D, Pine D, Scott S, Stevenson J, Taylor E, et al., editors. Textbook on Child and Adolescent Psychiatry. 5. Blackwell Publishing; London, UK: 2008. pp. x–y. [Google Scholar]
  • Nelson CA, Zeanah CH, Fox NA, Marshall PJ, Smyke A, Guthrie D. Cognitive Recovery in socially deprived young children: The Bucharest Early Intervention Project. Science. 2007;318:1937–1940. [PubMed] [Google Scholar]
  • Newport EL. Maturational constraints on language learning. Cognitive Science. 1990;14:11–28. [Google Scholar]
  • Newport EL, Bavelier D, Neville HJ. Critical thinking about critical periods: Perspectives on a critical period for language acquisition. In: Doupoux E, editor. Language, brain and cognitive development: Essays in honor of Jacques Mehler. Cambridge, MA: MIT Press; 2001. pp. 481–502. [Google Scholar]
  • O’Leary DDM, Chou SJ, Sahara S. Area patterning of the mammalian cortex. Neuron. 2007;56:252–269. [PubMed] [Google Scholar]
  • Oray S, Majewska A, Sur M. Dedritic spine dynamics are regulated by monocular deprivation and extracellular matrix degradation. Neuron. 2004;44:1021–1030. [PubMed] [Google Scholar]
  • Oyama S. The idea of innateness: effects on language and communication research. Developmental Psychobiology. 1990;23:741–747. [PubMed] [Google Scholar]
  • Pascalis O, de Haan M, Nelson CA. Is face processing species specific during the first year of life? Science. 2002;296:1321–1323. [PubMed] [Google Scholar]
  • Pascalis O, Scott LS, Kelly DJ, Dufour RW, Shannon RW, Nicholson E, et al. Plasticity of Face Processing in Infancy. Proceedings of the National Academy of Sciences. 2005;102:5297–5300. [PMC free article] [PubMed] [Google Scholar]
  • Paradis S, Harrar DB, Lin Y, Koon AC, Hauser JL, Griffith EC, et al. An RNAi-based approach identifies molecules required for glutamatergic and GABAergic synapse development. Neuron. 2007;53:217–232. [PMC free article] [PubMed] [Google Scholar]
  • Pascual-Leone A, Amedi A, Fregni F, Merabet LB. The plastic human brain cortex. Annual Review of Neuroscience. 2005;28:377–401. [PubMed] [Google Scholar]
  • Puelles L. Evolution of the nervous system: brain segmentation and forebrain development in amniotes. Brain Research Bulletin. 2001;55:695–710. [PubMed] [Google Scholar]
  • Rakic P. Less is more: progenitor cell death and cortical size. Nature Neuroscience. 2005;8:981–982. [PubMed] [Google Scholar]
  • Rakic P. A century of progress in corticoneurogenesis: from silver impregnation to genetic engineering. Cerebral Cortex. 2006;16(supplement):1–17. [PubMed] [Google Scholar]
  • Sabatini MJ, Ebert P, Lewis DA, Levitt P, Cameron JL, Mirnics K. Amygdala gene expression correlates of social behavior in monkeys experiencing maternal separation. Journal of Neuroscience. 2007;27:3295–3304. [PMC free article] [PubMed] [Google Scholar]
  • Sánchez MM, Ladd CO, Plotsky PM. Early adverse experience as a developmental risk factor for later psychopathology: evidence from rodent and primate models. Developmental Psychopathology. 2001;13:419–449. [PubMed] [Google Scholar]
  • Scarr S, McCartney K. How People Make Their Own Environments: A Theory of Genotype Environment Effects. Child Development. 1983;54(2):424–435. [PubMed] [Google Scholar]
  • Scherf KS, Behrmann M, Humphreys K, Luna B. Visual category-selectivity for faces, places and objects emerges along different developmental trajectories. Developmental Science. 2007;10:F15–F30. [PubMed] [Google Scholar]
  • Seidenberg MS, Zevin JD. Connectionist models in developmental cognitive neuroscience: Critical periods and the paradox of success. In: Munakata Y, Johnson M, editors. Processes of Change in Brain and Cognitive Development. Attention and Performance XXI. Oxford, UK: Oxford University Press; 2006. [Google Scholar]
  • Sheng M, Hoogenraad CC. The postsynaptic architecture of excitatory synapses: a more quantitative view. Annual Review of Biochemistry. 2007;76:823–847. [PubMed] [Google Scholar]
  • Shepherd JD, Huganir RL. The cell biology of synaptic plasticity: AMPA receptor trafficking. Annual Review of Cell and Developmental Biology. 2007;23:613–643. [PubMed] [Google Scholar]
  • Singer W. Development and plasticity of cortical processing architectures. Science. 1995;270:758–764. [PubMed] [Google Scholar]
  • Stanwood GD, Levitt P. The effects of monoamines on the developing nervous system. In: Nelson CA, Luciana M, editors. Handbook of Developmental Cognitive Neuroscience. 2. MIT Press; Cambridge, MA: 2008. pp. 83–94. [Google Scholar]
  • Stein M, Dierks T, Brandeis D, Wirth M, Srtik W, Koenig T. Plasticity in the adult language system: a longitudinal electrophysiological study on second language learning. Neuroimage. 2006;33(2):774–783. [PubMed] [Google Scholar]
  • Sudhof TC. Neurotransmitter release. Handbook of Experimental Pharmacology. 2008;184:1–21. [PubMed] [Google Scholar]
  • Sugita Y. Face perception in monkeys reared with no exposure to faces. Proceedings of the National Academy of Sciences. 2008;105:394–398. [PMC free article] [PubMed] [Google Scholar]
  • Sugiyama S, Di Nardo AA, Aizawa S, Matsuo I, Volovitch M, Prochiantz A, et al. Experience-dependant transfer of Otx2 homeoprotein into the visual cortex activates post natal plasticity. Cell. 2008;134:508–520. [PubMed] [Google Scholar]
  • Sur M, Rubenstein JLR. Patterning and plasticity of the cerebral cortex. Science. 2005;310:805–810. [PubMed] [Google Scholar]
  • Szyf M, McGowan P, Meany MJ. The social environment and the epigenome. Environmental and Molecular Mutagenesis. 2008;49:46–60. [PubMed] [Google Scholar]
  • Tomblin JB, Barker BA, Spencer L, Zhang X, Gantz BJ. The effect of age at cochlear implant initial stimulation on expressive language growth in infants and toddlers. Journal of Speech, Language and Hearing Research. 2005;48:853–867. [PMC free article] [PubMed] [Google Scholar]
  • Trachtenberg JT, Stryker MP. Rapid anatomical plasticity of horizontal connections in the developing visual cortex. Journal of Neuroscience. 2001;15:3476–3482. [PMC free article] [PubMed] [Google Scholar]
  • Tritsch NX, Yi E, Gale JE, Glowatski E, Bergles DE. The origin of spontaneous activity in the developing auditory system. Nature. 2007;450:50–55. [PubMed] [Google Scholar]
  • von Melchner L, Pallas SL, Sur M. Visual behaviour mediated by retinal projections directed to the auditory pathway. Nature. 2000;404:871. [PubMed] [Google Scholar]
  • Wartenburger I, Heekeren HR, Abutalebi J, Cappa SF, Villringer A, Perani D. Early setting of grammatical processing in the bilingual brain. Neuron. 2003;9:159–170. [PubMed] [Google Scholar]
  • Weber-Fox C, Neville HJ. Sensitive periods differentiate processing of open- and closed-class words: an ERP study of bilinguals. Journal of Speech, Language, and Hearing Research. 2001;44:1338–1353. [PubMed] [Google Scholar]
  • Werker JF, Tees RC. Speech perception as a window for understanding plasticity and commitment in language systems of the brain. Developmental Psychobiology. 2005;46:233–251. [PubMed] [Google Scholar]
  • Wiesel TN, Hubel DH. Single-cell responses in striate cortex of kittens deprived of vision in one eye. Journal of Neurophysiology. 1963;26:1003–1017. [PubMed] [Google Scholar]
  • Wiesel TN, Hubel DH. Extent of recovery from the effects of visual deprivation in kittens. Journal of Neurophysiology. 1965;28:1060–1072. [PubMed] [Google Scholar]
  • Windsor J, Glaze LE, Koga SF the BEIP Core Group. Language acquisition with limited input: Romanian institution and foster care. Journal of Speech, Language, and Hearing Research. 2007;50:1365–1381. [PubMed] [Google Scholar]
  • Wonders CP, Anderson SA. The origins and specification of cortical interneurons. Nature Reviews Neuroscience. 2007;7:687–696. [PubMed] [Google Scholar]
  • Zingg RM. Feral Man and Extreme Cases of Isolation. The American Journal of Psychology. 1940;53(4):487–517. [Google Scholar]