Human brain development timeline
||The lead section of this article may need to be rewritten. (January 2013)|
In the 1950s, animal research showed development in the sensory regions after birth. During sensitive periods, the environment plays a major role in normal development.
This research indicated that from early postnatal time through the next several months or years, the brain went through synaptogenesis followed by synaptic pruning which represent the creation and elimination of synapses during growth.
In the 1960-70s, studies done on human brains revealed that myelination continues to develop past the early childhood years and into adolescence, especially in the prefrontal cortex.
Synaptic reorganization takes place most predominantly during childhood and adolescence. During these periods the brain becomes sensitive to change which allows it to develop in unique ways dependent upon the individuals age, gender, and environment along with many other variables.
The concept of "self-organization" indicates that the brain actually organizes itself based on the individuals experiences.
In 2012, a team of scientists created a statistical model that could predict the age of an individual under the age of 20 from an MRI scan with 92% accuracy. The model measures 231 biomarkers of brain anatomy and was constructed with data from 885 people. This work provides a uniquely holistic view of adolescent brain development and suggests that the responsible processes are more strongly genetically pre-programmed than is typically thought.
- 1 Descriptors
- 2 Neuroimaging
- 3 Current Research
- 4 Further Research
- 5 See also
- 6 References
- 7 External links
The human brain development timeline is easier to understand when the data is organized into charts and images however there are discrepancies between researchers about the exact time frame of each developmental stage. The images and charts provided on this page are approximate. Multiple charts and images are provided for disambiguation.
|33||posterior commissure appears||Ashwell et al. (1996)|
|33||medial forebrain bundle appears||Ashwell et al. (1996)|
|44||mammillothalamic tract appears||Ashwell et al. (1996)|
|44||stria medullaris thalami appears||Ashwell et al. (1996)|
|51||axons in optic stalk||Dunlop et al. (1997)|
|56||external capsule appears||Ashwell et al. (1996)|
|56||stria terminalis appears||Ashwell et al. (1996)|
|60||optic axons invade visual centers||Dunlop et al. (1997)|
|63||internal capsule appears||Ashwell et al. (1996)|
|63||fornix appears||Ashwell et al. (1996)|
|70||anterior commisure appears||Ashwell et al. (1996)|
|77||hippocampal commissure appears||Ashwell et al. (1996)|
|87.5||corpus callosum appears||Ashwell et al. (1996)|
|157.5||eye opening||Clancy et al. (2007)|
|175||ipsi/contra segregation in LGN and SC||Robinson and Dreher (1990)|
Neuroimaging is responsible for great advancements in understand how the brain develops. EEG and ERP are effective imaging processes used mainly on babies and young children since they are more gentle. Infants are generally tested with fNIRS. The MRI and fMRI are widely used for research on the brain due to the quality of images and analysis possible from them.
Magnetic Resonance Imaging
MRI's are helpful in analyzing many aspects of the brain. The magnetization-transfer ratio (MTR) measures integrity using magnetization. Fractional anisotropy (FA) measures organization using the diffusion of water molecules. Additionally, mean diffusivity (MD) measures the strength of white matter tracts.
Structural Magnetic Resonance Imaging
Using structural MRI, quantitative assessment of a number of developmental processes can be carried out including defining growth patterns, and characterizing the sequence of myelination. These data complement evidence from Diffusion Tensor Imaging (DTI) studies that have been widely used to investigate the development of white matter.
Functional Magnetic Resonance Imaging
fMRI's test mentalising which is the theory of the mind by activating a network. The posterior superior temporal sulcus (pSTS) and temporo-parietal junction (TPJ) are helpful in predicting movement. In adults, the right pSTS showed greater response than the same region in adolescents when tested on intentional causality. These regions were also activated during the "mind in the eyes" exercise where emotion must be judged based on different images of eyes. Another key region is the anterior temporal cortex (ATC) in the posterior region. In adults, the left ATC showed greater response than the same region in adolescents when tested on emotional tests of mentalising. Finally, the medial prefrontal cortex (MPFC) and the anterior dorsal MPFC (dMPFC) are activated when the mind is stimulated by psychology.
Higher resolution photography has allowed three-dimensional sonography to aid in identifying the human brain development during the embryonic stages. Studies report that three primary structures are formed in the sixth gestational week. These are the forebrain, the midbrain, and the hindbrain, also known as the prosencephalon, mesencephalon, and the rhombencephalon respectively. Five secondary structures from these in the seventh gestational week. These are the telencephalon, diencephalon, mesencephalon, metencephalon, and myelencephalon which later become the lateral ventricles, third ventricles, aqueduct, and upper and lower parts of the fourth ventricle from the telencephalon to the myelencephalon, during adulthood. 3-D sonographic imaging allows in-vivo depictions of ideal brain development which can aid in recognizing irregularities during gestation.
Spatio-temporal modeling of Brain Development
In early development (before birth and during the first few months), the brain undergoes more changes in size, shape and structure than at any other time in life. Improved understanding of cerebral development during this critical period is important for mapping normal growth, and for investigating mechanisms of injury associated with risk factors for maldevelopment such as premature birth. Hence, there is a need for dense coverage of this age range with a time-varying, age-dependent atlas. Such a spatio-temporal atlases can accurately represent the dynamic changes occurring during early brain development, and also can be used as a normative reference space.
White Matter Development
Using MRI, studies showed that while white matter increases from childhood (~9 years) to adolescence (~14 years), grey matter decreases. This was observed primarily in the frontal and parietal cortices. Theories as to why this occurs vary. One thought is that the intracortical myelination paired with increased axonal calibre increases the volume of white matter tissue. Another is that synaptic reorganization occurs from proliferation and then pruning.
Grey matter development
The rise and fall of the volume of grey matter in the frontal and parietal lobes peaked at ~12 years of age. The peak for the temporal lobes was ~17 years with the superior temporal cortex being last to mature. The sensory and motor regions matured first after which the rest of the cortex developed. This was characterized by loss of grey matter and it occurred from the posterior to the anterior region. This loss of grey matter and increase of white matter may occur throughout a lifetime though the more robust changes occur from childhood to adolescence.
Current research has been able to make new discoveries for various parts of the brain thanks to the noninvasive imaging available.
- Medial Prefrontal Cortex (MPFC)
In this region, more activity is noted in adolescents than in adults when faced with tests on mentalising tasks as well as communicative and personal intent. Decreased activity from adolescence to adulthood. In a mentalising task employing animation, the dMPFC was more stimulated in adults while the ventral MPFC was more stimulated in children. The can be attributed to the use of objective strategy associated with the dMPFC. Theories for decrease in activity from adolescence to adulthood vary. One theory is that cognitive strategy becomes more automatic with age and another is that functional change occurs parallel to neuroanatomical change which is characterized by synaptogenesis and pruning.
The MPFC is an example of one specific region that has become better understood using current imaging techniques. Current research provides many more findings like this.
The development of the brain will vary between males and females since each gender matures at different times and in different ways however the details are yet to be understood. An important bodily change like puberty was tested to show drastic effects on the peaks in cortical development. In preliminary studies, gray matter increased in the amygdala, a region linked to emotional response. In females, there was increased oestradiol and increased limbic gray matter. In males, there was increased testosterone and parietal cortex gray matter. A decrease in the volume of the hippocampus was also noted. These results need further support.
Differences in environment can affect how the brain develops and at what pace. The environment can include factors like location and surroundings as well as circumstances in those environments. Environment can also be identified as an individuals emotions or response to certain stimuli. In this case, the concept of "self-organization" which postulates that the brain organizes itself based on each individual, must be explored further.
Different regions of the brain depend on each other for specific functions. They communicate by their connectivity. Much is still unknown about these networks and how exactly they are connected to carry out the necessary functions. Most current research is taken from animal studies; testing in humans is vital to gain more information of this topic. Task-dependent connectivity can be analyzed functionally or effectively. A trend from some preliminary research implied there was a change in organization occurring with increasing age. Due to the complexity of the brain and the range of its capabilities, extensive research will have to be done to understand this phenomenon. The Resting-state functional MRI can be used to explore further into connectivity.
The effects of drugs on brain development has yet to be thoroughly understood. Potential research should be targeted to find the long-term effects of administering stimulants and the variables should include type of drug, dosage, and age of patient. Animal studies must be conducted first to get a thorough understanding of potential consequences and mechanisms.
Early life Stress
Early life stress is defined as exposure to circumstances during childhood that overwhelm a child’s coping resources and lead to sustained periods of stress. Results from multiple studies indicate that the effects of early life stress on the developing brain are significant and include, but are not limited to the following: increased amygdala volume, decreased activity in frontal cortical and limbic brain structures, and altered white matter structures.
Early life stress is believed to produce changes in brain development by interfering with neurogensis, synaptic production, and pruning of synapses and receptors. Interference with these processes could result in increased or decreased brain region volumes, potentially explaining the findings that early life stress is associated with increased amygdala volume and decreased anterior cingulate cortex volume.
From the literature, several important conclusions have been drawn. Brain areas that undergo significant post-natal development, such as those involved in memory and emotion are more vulnerable to effects of early life stress. For example, the hippocampus continues to develop after birth and is a structure that is affected by childhood maltreatment. Early life stress seems to interfere with the overproduction of synapses that is typical in childhood, but does not interfere with synaptic pruning in adolescence. This results in smaller hippocampal volumes, potentially explaining the association between early life stress and reduced hippocampal volume. This volume reduction may be associated with the emotion regulation deficits seen in those exposed to early life stress.
The amygdala is particularly vulnerable to early life stress. The amygdala also undergoes significant development during childhood, is structurally and functionally altered in individuals that have experienced early life stress, and is associated with the socioemotional difficulties linked with early life stress.
Receptor type is another consideration when determining whether or not a brain region is sensitive to the effects of early life stress. Brain regions with a high density of glucocorticoid receptors are especially vulnerable to the effects of early life stress, likely because glucocorticoids bind to these receptors during stress exposure, facilitating the development of survival responses at the cost of other important neural pathways. Some examples of brain regions with high glucocorticoid receptor density are the hippocampus and cerebellar vermis. Stress activates the HPA axis, and results in the production of glucocorticoids. Increased glucocorticoid production results in increased activation of these brain regions, facilitating the development of certain neural pathways at the cost of others.
Abnormalities in brain structure and function are often associated with deficits that may persist for years after the stress is removed, and may be a risk factor for future psychopathology. The brain regions most sensitive to early life stress are those undergoing developmental changes during the stress exposure. As a result, stress alters the developmental trajectory of that brain region, producing long-lasting alterations in structure and function.
Common types of early life stress in the literature include maltreatment, neglect, and previous institutionalization. However, recent research has indicated that more normative experiences, such as living in poverty can influence brain function in ways similar to other stressors. These findings encourage the expansion of what is considered to be early life stress.
- Andersen SL (2003). "Trajectories of brain development: point of vulnerability or window of opportunity?". Neurosci Biobehav Rev. 27 (1–2): 3–18. doi:10.1016/S0149-7634(03)00005-8. PMID 12732219.
- Blakemore SJ (Jun 2012). "Imaging brain development: the adolescent brain". Neuroimage 61 (2): 397–406. doi:10.1016/j.neuroimage.2011.11.080. PMID 22178817.
- Lewis MD (2005). "Self-organizing individual differences in brain development". Developmental Review 25 (3–4): 252–277. doi:10.1016/j.dr.2005.10.006.
- "Brain scans don't lie about age". EurekAlert. 16 August 2012. Retrieved 2013-01-12.
- Brown TT, Kuperman JM, Chung Y, et al. (25 Sep 2012). "Neuroanatomical assessment of biological maturity". Curr Biol. 22 (18): 1693–1698. doi:10.1016/j.cub.2012.07.002. PMC 3461087. PMID 22902750.
- Tau GZ, Peterson BS (2010). "Normal Development of Brain Circuits". Neuropsychopharmacology 35 (1): 147–168. doi:10.1038/npp.2009.115. PMC 3055433. PMID 19794405.
- Ashwell KW, Waite PM, Marotte L (1996). "Ontogeny of the projection tracts and commissural fibres in the forebrain of the tammar wallaby (Macropus eugenii): timing in comparison with other mammals". Brain Behav. Evol. 47 (1): 8–22. doi:10.1159/000113225. PMID 8834781.
- Dunlop SA, Tee LB, Lund RD, Beazley LD (1997). "Development of primary visual projections occurs entirely postnatally in the fat-tailed dunnart, a marsupial mouse, Sminthopsis crassicaudata". J. Comp. Neurol. 384 (1): 26–40. doi:10.1002/(SICI)1096-9861(19970721)384:1<26::AID-CNE2>3.0.CO;2-N. PMID 9214538.
- Clancy B, Kersh B, Hyde J, Darlington RB, Anand KJS, Finlay BL (2007). "Web-based method for translating neurodevelopment from laboratory species to humans". Neuroinformatics 5 (1): 79–94. PMID 17426354.
- Robinson SR, Dreher B (1990). "The visual pathways of eutherian mammals and marsupials develop according to a common timetable". Brain Behav. Evol. 36 (4): 177–195. doi:10.1159/000115306. PMID 2279233.
- Serag, A.; Aljabar, P.; Ball, G.; Counsell, S.J.; Boardman, J.P.; Rutherford, M.A.; Edwards, A.D.; Hajnal, J.V.; Rueckert, D. (2012). "Construction of a consistent high-definition spatio-temporal atlas of the developing brain using adaptive kernel regression". NeuroImage 59 (3): 2255–2265. doi:10.1016/j.neuroimage.2011.09.062. PMID 21985910.
- Serag, A.; Aljabar, P.; Counsell, S.J.; Boardman, J.P.; Hajnal, J.V.; Rueckert, D. (2011). "Tracking developmental changes in subcortical structures of the preterm brain using multi-modal MRI". Biomedical Imaging: From Nano to Macro, 2011 IEEE International Symposium on: 349–352. doi:10.1109/ISBI.2011.5872421.
- Kim MS, Jeanty P, Turner C, Benoit B (Jan 2008). "Three-dimensional sonographic evaluations of embryonic brain development". J Ultrasound Med. 27 (1): 119–124. PMID 18096737.
- Biswal BB, Mennes M, Zuo XN, et al. (9 Mar 2010). "Toward discovery science of human brain function". Proc Natl Acad Sci U S A 107 (10): 4734–4739. doi:10.1073/pnas.0911855107. PMC 2842060. PMID 20176931.
- Andersen SL (May 2005). "Stimulants and the developing brain". Trends Pharmacol Sci. 26 (5): 237–243. doi:10.1016/j.tips.2005.03.009. PMID 15860370.
- Pechtel, P., & Pizzagalli, D. A. (2011). Effects of early life stress on cognitive and affective function: an integrated review of human literature. Psychopharmacology, 214(1), 55–70. doi:10.1007/s00213-010-2009-2
- Mehta, M. A., Golembo, N. I., Nosarti, C., Colvert, E., Mota, A., Williams, S. C. R., … Sonuga-Barke, E. J. S. (2009). Amygdala, hippocampal and corpus callosum size following severe early institutional deprivation: The English and Romanian Adoptees Study Pilot. Journal of Child Psychology and Psychiatry, 50(8), 943–951. doi:10.1111/j.1469-7610.2009.02084.x
- Tottenham, N., Hare, T. A., Quinn, B. T., McCarry, T. W., Nurse, M., Gilhooly, T., … Casey, B. J. (2010). Prolonged institutional rearing is associated with atypically large amygdala volume and difficulties in emotion regulation: Previous institutionalization. Developmental Science, 13(1), 46–61. doi:10.1111/j.1467-7687.2009.00852.x
- Chugani, H. T., Behen, M. E., Muzik, O., Juhász, C., Nagy, F., & Chugani, D. C. (2001). Local Brain Functional Activity Following Early Deprivation: A Study of Postinstitutionalized Romanian Orphans. NeuroImage, 14(6), 1290–1301. doi:10.1006/nimg.2001.0917
- Eluvathingal, T. J. (2006). Abnormal Brain Connectivity in Children After Early Severe Socioemotional Deprivation: A Diffusion Tensor Imaging Study. PEDIATRICS, 117(6), 2093–2100. doi:10.1542/peds.2005-1727
- Baker, L. M., Williams, L. M., Korgaonkar, M. S., Cohen, R. A., Heaps, J. M., & Paul, R. H. (2013). Impact of early vs. late childhood early life stress on brain morphometrics. Brain Imaging and Behavior, 7(2), 196–203. doi:10.1007/s11682-012-9215-y
- Teicher, M. H., Andersen, S. L., Polcari, A., Anderson, C. M., Navalta, C. P., & Kim, D. M. (2003). The neurobiological consequences of early stress and childhood maltreatment. Neuroscience & Biobehavioral Reviews, 27(1-2), 33–44. doi:10.1016/S0149-7634(03)00007-1
- Kim, P., Evans, G. W., Angstadt, M., Ho, S. S., Sripada, C. S., Swain, J. E., … Phan, K. L. (2013). Effects of childhood poverty and chronic stress on emotion regulatory brain function in adulthood. Proceedings of the National Academy of Sciences, 110(46), 18442–18447. doi:10.1073/pnas.1308240110
- Translating Time — a website providing translation of brain developmental times among different species