Age-related changes in the microstructural organization of the human posterior associative cortex from birth to age 12 years

Tatiana A. Tsekhmistrenko , Dmitry K. Obukhov , Sami Omar

Morphology ›› 2023, Vol. 161 ›› Issue (1) : 5 -17.

PDF
Morphology ›› 2023, Vol. 161 ›› Issue (1) : 5 -17. DOI: 10.17816/morph.562844
Original Study Articles
research-article

Age-related changes in the microstructural organization of the human posterior associative cortex from birth to age 12 years

Author information +
History +
PDF

Abstract

BACKGROUND: Human posterior associative cortex, including its temporoparietal–occipital subarea, is important in cognitive control, verbal activity, sensory stimuli processing, and attention regulation, visuomotor responses, and situational decision making. Despite data suggesting the prolonged formation of these higher mental functions during postnatal ontogeny, the posterior associative cortex has been insufficiently characterized with respect to microstructural transformations in its individual functionally specialized zones during childhood development.

AIM: This study aimed to examine age-related changes in the cytoarchitecture of functionally differentiated zones of the posterior associative cortex in the temporal and occipital lobes of the cerebral hemispheres from birth to 12 years of age.

MATERIALS AND METHODS: The study analyzed 73 left cerebral hemispheres of male children from birth to age 12 years who died because of an accident. Computerized morphometry was employed to measure cortical thickness, outer pyramidal plate thickness, and pyramidal neuron profile field area on Nissl-stained paraffin sections of the cortex taken in the temporoparietal–occipital subarea (subareas 37ac, 37a, and 37d) and area 19 of the occipital region. Quantitative data were analyzed at annual intervals.

RESULTS: The thickness of the posterior associative cortex increased on the lateral surface of the temporal and occipital lobes at the ages of 1, 4, and 7 years; on the inferior medial surface of the temporal lobe at the ages of 1 and 6 years; and on its medial surface at the ages of 1 and 7 years. The layer III thickness in subareas 37ac, 37a, and 37d significantly increased synchronously with the increase in cortical cross-sectional area, and in area 19, it continued from the age of 4 to 7 years after the stabilization of the group-average indicators of cortical thickness in this field. All areas examined were characterized by a two-step growth of cortical thickness, which exceeded the growth rate of layer III thickness in relation to the total cortical cross-section. The size of the pyramidal neurons in subareas 37ac and 37d increased in two stages, whereas those in subarea 37a and area 19 increased in three stages of different durations.

CONCLUSIONS: Microstructural changes in the posterior associative cortex in children are heterochronic, heterodynamic, and specialized not only in topographically and functionally distinct cortical areas but also in separate cytoarchitectonic fields, subfields, and level of cytoarchitectonic layers and intracortical microstructural components. The most significant morphofunctional transformations are observed during the first year of life and at the ages of 3–4, 6–7, and 10 years.

Keywords

children / posterior associative cortex / morphometry / postnatal ontogenesis

Cite this article

Download citation ▾
Tatiana A. Tsekhmistrenko, Dmitry K. Obukhov, Sami Omar. Age-related changes in the microstructural organization of the human posterior associative cortex from birth to age 12 years. Morphology, 2023, 161(1): 5-17 DOI:10.17816/morph.562844

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

Brodmann K. Brodmann’s localisation in the cerebral cortex the principles of comparative localisation in the cerebral cortex based on cytoarchitectonics by Dr. K. Brodmann. New York–London: Springer Science; 2006. 295 p.

[2]

Brodmann K. Brodmann’s localisation in the cerebral cortex the principles of comparative localisation in the cerebral cortex based on cytoarchitectonics by Dr. K. Brodmann. New York–London : Springer Science, 2006. 295 p.

[3]

Atlas citoarhitektoniki kory bol’shogo mozga cheloveka. SA Sarkisov, IN Filimonov, EP Kononova, i dr., editors. Moscow: Medgiz; 1955. 278 p. (In Russ).

[4]

Атлас цитоархитектоники коры большого мозга человека / под ред. С.А. Саркисова, И.Н. Филимонова, Е.П. Кононовой, и др. Москва : Медгиз, 1955. 278 с.

[5]

Braunlich K, Love BC. Occipitotemporal representations reflect individual differences in conceptual knowledge. J Exp Psychol Gen. 2019;148(7):1192–1203. doi: 10.1037/xge0000501

[6]

Braunlich K., Love B.C. Occipitotemporal representations reflect individual differences in conceptual knowledge // J Exp Psychol Gen. 2019. Vol. 148, N 7. P. 1192–1203. doi: 10.1037/xge0000501

[7]

Maffei V, Indovina I, Mazzarella E, et al. Sensitivity of occipito-temporal cortex, premotor and Broca’s areas to visible speech gestures in a familiar language. PLoS One. 2020;15(6):e0234695. doi: 10.1371/journal.pone.0234695

[8]

Maffei V., Indovina I., Mazzarella E., et al. Sensitivity of occipito-temporal cortex, premotor and Broca’s areas to visible speech gestures in a familiar language // PLoS One. 2020. Vol. 15, N 6. P. e0234695. doi: 10.1371/journal.pone.0234695

[9]

Yeon J, Shekhar M, Rahnev D. Overlapping and unique neural circuits are activated during perceptual decision making and confidence. Sci Rep. 2020;10(1):20761. doi: 10.1038/s41598-020-77820-6

[10]

Yeon J., Shekhar M., Rahnev D. Overlapping and unique neural circuits are activated during perceptual decision making and confidence // Sci Rep. 2020. Vol. 10, N 1. P. 20761. doi: 10.1038/s41598-020-77820-6

[11]

Conel JLR. The postnatal development of the human cerebral cortex. Vol. 1. The cortex of the newborn. Harvard Univ. Press. 1939.

[12]

Conel J.L.R. The postnatal development of the human cerebral cortex. Vol. 1. The cortex of the newborn. Harvard Univ. Press. 1939.

[13]

Baum GL, Flournoy JC, Glasser MF, et al. Graded variation in T1w/T2w ratio during adolescence: measurement, caveats, and implications for development of cortical myelin. J Neurosci. 2022;42(29):5681–5694. doi: 10.1523/JNEUROSCI.2380-21.2022

[14]

Baum G.L., Flournoy J.C., Glasser M.F., et al. Graded variation in T1w/T2w ratio during adolescence: measurement, caveats, and implications for development of cortical myelin // J Neurosci. 2022. Vol. 42, N 29. P. 5681–5694. doi: 10.1523/JNEUROSCI.2380-21.2022

[15]

Tamnes CK, Ostby Y, Fjell AM, et al. Brain maturation in adolescence and young adulthood: regional age-related changes in cortical thickness and white matter volume and microstructure. Cereb Cortex. 2010;20(3):534–548. doi: 10.1093/cercor/bhp118

[16]

Tamnes C.K., Ostby Y., Fjell A.M., et al. Brain maturation in adolescence and young adulthood: regional age-related changes in cortical thickness and white matter volume and microstructure // Cereb Cortex. 2010. Vol. 20, N 3. P. 534–548. doi: 10.1093/cercor/bhp118

[17]

Wandell BA, Dumoulin SO, Brewer AA. Visual field maps in human cortex. Neuron. 2007;56(2):366–383. doi: 10.1016/j.neuron.2007.10.012

[18]

Wandell B.A., Dumoulin S.O., Brewer A.A. Visual field maps in human cortex // Neuron. 2007. Vol. 56, N 2. P. 366–383. doi: 10.1016/j.neuron.2007.10.012

[19]

Barton JJS. Face processing in the temporal lobe. Handb Clin Neurol. 2022;187:191–210. doi: 10.1016/B978-0-12-823493-8.00019-5

[20]

Barton J.J.S. Face processing in the temporal lobe // Handb Clin Neurol. 2022. Vol. 187. P. 191–210. doi: 10.1016/B978-0-12-823493-8.00019-5

[21]

Leech R, Sharp DJ. The role of the posterior cingulate cortex in cognition and disease. Brain. 2014;137(Pt 1):12–32. doi: 10.1093/brain/awt162

[22]

Leech R., Sharp D.J. The role of the posterior cingulate cortex in cognition and disease // Brain. 2014. Vol. 137(Pt 1). P. 12–32. doi: 10.1093/brain/awt162

[23]

Sheth BR, Young R. Two visual pathways in primates based on sampling of space: exploitation and exploration of visual information. Front Integr Neurosci. 2016;10:37. doi: 10.3389/fnint.2016.00037

[24]

Sheth B.R., Young R. Two visual pathways in primates based on sampling of space: exploitation and exploration of visual information // Front Integr Neurosci. 2016. Vol. 10. P. 37. doi: 10.3389/fnint.2016.00037

[25]

Glanc S. Primer of biostatistics. Moscow: Praktika; 1998. 459 p.

[26]

Гланц С. Медико-биологическая статистика / пер. с англ. Москва : Практика, 1998. 459 c.

[27]

Potapova IG, Katinas GS, Stefanov SB. Otsenka i sravnenie srednih velichin s uchetom variabel‘nosti pervichnyh izmeryaemyh ob“ektov i individual‘noj izmenchivosti. Neuroscience and Behavioral Physiology Neuroscience Translations. 1983;85(9):86–92. (In Russ).

[28]

Потапова И.Г., Катинас Г.С., Стефанов С.Б. Оценка и сравнение средних величин с учетом вариабельности первичных измеряемых объектов и индивидуальной изменчивости // Архив анатомии, гистологии и эмбриологии. 1983. Т. 85, № 9. С. 86–92.

[29]

Lemeshko BJu. Neparametricheskie kriterii soglasija. Moscow: INFRA-M; 2014. 163 p. (In Russ).

[30]

Лемешко Б.Ю. Непараметрические критерии согласия. Москва : ИНФРА-М, 2014. 163 с.

[31]

Lidzba K, Ebner K, Hauser TK, Wilke M. Complex visual search in children and adolescents: effects of age and performance on fMRI activation. PLoS One. 2013;8(12):e85168. doi: 10.1371/journal.pone.0085168

[32]

Lidzba K., Ebner K., Hauser T.K., Wilke M. Complex visual search in children and adolescents: effects of age and performance on fMRI activation // PLoS One. 2013. Vol. 8, N 12. P. e85168. doi: 10.1371/journal.pone.0085168

[33]

Barsingerhorn AD, Boonstra FN, Goossens J. Development of symbol discrimination speed in children with normal vision. Invest Ophthalmol Vis Sci. 2018;59(10):3973–3983. doi: 10.1167/iovs.17-23168

[34]

Barsingerhorn A.D., Boonstra F.N., Goossens J. Development of symbol discrimination speed in children with normal vision // Invest Ophthalmol Vis Sci. 2018. Vol. 59, N 10. P. 3973–3983. doi: 10.1167/iovs.17-23168

[35]

Vancleef K, Janssens E, Petré Y, et al. Assessment tool for visual perception deficits in cerebral visual impairment: development and normative data of typically developing children. Dev Med Child Neurol. 2020;62(1):111–117. doi: 10.1111/dmcn.14303

[36]

Vancleef K., Janssens E., Petré Y., et al. Assessment tool for visual perception deficits in cerebral visual impairment: development and normative data of typically developing children // Dev Med Child Neurol. 2020. Vol. 62, N 1. P. 111–117. doi: 10.1111/dmcn.14303

[37]

Machinskaja RI, Krupskaja EV. Mozgovaja organizacija raspoznavanija detalej i celogo pri vosprijatii slozhnyh izobrazhenij u detej predshkol’nogo i mladshego shkol’nogo vozrasta. In: Mozgovye mehanizmy formirovanija poznavatel’noj dejatel’nosti v predshkol’nom i mladshem shkol’nom vozraste. Moscow: NOU VPO «MPSU»; Voronezh: MODJeK; 2014. P. 95–133. (In Russ).

[38]

Мачинская Р.И., Крупская Е.В. Мозговая организация распознавания деталей и целого при восприятии сложных изображений у детей предшкольного и младшего школьного возраста. В кн.: Мозговые механизмы формирования познавательной деятельности в предшкольном и младшем школьном возрасте. Москва : НОУ ВПО «МПСУ»; Воронеж : МОДЭК, 2014. С. 95–133.

[39]

Downing PE, Peelen MV. Body selectivity in occipitotemporal cortex: causal evidence. Neuropsychologia. 2016;83:138–148. doi: 10.1016/j.neuropsychologia.2015.05.033

[40]

Downing P.E., Peelen M.V. Body selectivity in occipitotemporal cortex: causal evidence // Neuropsychologia. 2016. Vol. 83. P. 138–148. doi: 10.1016/j.neuropsychologia.2015.05.033

Funding

Министерство науки и высшего образования РФMinistry of Science and Higher Education of the Russian Federation(Программа стратегического академического лидерства РУДН «Приоритет-2030», тема № 030209-0-000)

RIGHTS & PERMISSIONS

Eco-Vector

AI Summary AI Mindmap
PDF

135

Accesses

0

Citation

Detail

Sections
Recommended

AI思维导图

/