Aging and the human neocortex
Abstract
Neurostereology has been applied to quantitative anatomical study of the human brain. Such studies have included the total neocortical number of neurons and glial cells, the estimated size distribution of neocortical neurons, the total myelinated fiber length in the brain white matter, the total number of synapses in the neocortex, and the effect of normal aging on these structural elements. The difference in total number of neurons was found to be less than 10% over the age range from 20 to 90 years, while the glial cell number in six elderly individuals, mean age 89.2 years, showed an average number of 36 billion glial cells, which was not statistically significantly different from the 39 billion glial cells in the neocortex of six young individuals with a mean age of 26.2 years. The total myelinated fiber length varied from 150,000 to 180,000 km in young individuals and showed a large reduction as a function of age. The total number of synapses in the human neocortex is approximately 0.15 x 10(15) (0.15 quadrillion). Although the effect of aging is seen in all estimated structural elements, the effect of sex is actually higher. The functional relevance of these differences in neuron numbers in both age and gender is not known.
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Published estimates of neocortical neuron number vary by as much as a factor of 2, between 14.7 and 32.0 billion neurons.
“published estimates of the number of neocortical neurons varies by as much as a factor of 2 (between 14.7 to 32.0 billion neurons) (Pakkenberg and Gundersen, 1997; Pakkenberg et al., 2003).”
From The Cellular and Molecular Landscapes of the Developing Human Central Nervous System (2016) · cites this paper as Pakkenberg et al., 2003
Published estimates of the number of synapses received by an individual neocortical neuron range from approximately 7,200 to 29,642.
“The published estimates for the number of synapses an individual neocortical neuron receives also vary between approximately 7,200 (Pakkenberg et al., 2003; Tang et al., 2001) and 29,642 synapses (DeFelipe et al., 2002).”
From The Cellular and Molecular Landscapes of the Developing Human Central Nervous System (2016) · cites this paper as Pakkenberg et al., 2003
Cites
GUNDERSEN, H. J. G., BENDTSEN, T. F., KORBO, L., MARCUSSEN, N., MØLLER, A., NIELSEN, K., NYENGAARD, J. R., PAKKENBERG, B., SØRENSEN, F. B., VESTERBY, A., et al. (1988) · APMIS · doi:10.1111/j.1699-0463.1988.tb05320.x · Cited by 2,929 · see citing works
Self-citationThe unbiased estimation of number and sizes of arbitrary particles using the disector
Sterio, D. C. (1984) · Journal of Microscopy · doi:10.1111/j.1365-2818.1984.tb02501.x · Cited by 2,533 · see citing works
The efficiency of systematic sampling in stereology—reconsidered
GUNDERSEN, H. J. G., JENSEN, E. B. V., KIÊU, K., & NIELSEN, J. (1999) · Journal of Microscopy · doi:10.1046/j.1365-2818.1999.00457.x · Cited by 1,317 · see citing works
Differences in the pattern of hippocampal neuronal loss in normal ageing and Alzheimer's disease
West, M. J., Coleman, P. D., Flood, D. G., & Troncoso, J. C. (1994) · The Lancet · doi:10.1016/s0140-6736(94)92338-8 · Cited by 1,267 · cited 2× · see citing works
“…aging and disease showed normal individuals to lose neurons in two regions of the hippocampus, subiculum and the hilus as a function of age, while patients with AD lost more neurons in these two regions than normal aging individuals (West and Gundersen, 1990; West et al., 1994; Simic et al., 1997).”
BackgroundUnbiased stereological estimation of the number of neurons in the human hippocampus
West, M. J., & Gundersen, H. J. G. (1990) · Journal of Comparative Neurology · doi:10.1002/cne.902960102 · Cited by 1,153 · cited 2× · see citing works
“…aging and disease showed normal individuals to lose neurons in two regions of the hippocampus, subiculum and the hilus as a function of age, while patients with AD lost more neurons in these two regions than normal aging individuals (West and Gundersen, 1990; West et al., 1994; Simic et al., 1997).”
BackgroundPakkenberg, B. (1990) · Archives of General Psychiatry · doi:10.1001/archpsyc.1990.01810230039007 · Cited by 566 · see citing works
Self-citationMILLER, A. K. H., Alston, R. L., & Corsellis, J. (1980) · Neuropathology and Applied Neurobiology · doi:10.1111/j.1365-2990.1980.tb00283.x · Cited by 351 · see citing works
The total number of neurons in the human neocortex unbiasedly estimated using optical disectors
Brændgaard, H., Evans, S. M., Howard, C. V., & Gundersen, H. J. G. (1990) · Journal of Microscopy · doi:10.1111/j.1365-2818.1990.tb02967.x · Cited by 282 · see citing works
Quantitation of cerebral atrophy in preclinical and end-stage Alzheimer’s disease
de la Monte, S. M. (1989) · Annals of Neurology · doi:10.1002/ana.410250506 · Cited by 229 · see citing works
A quantitative study of cerebral atrophy in old age and senile dementia
Hubbard, B. M., & Anderson, J. M. (1981) · Journal of the Neurological Sciences · doi:10.1016/0022-510x(81)90048-4 · Cited by 223 · see citing works
Regeur, L., Jensen, G. B., Pakkenberg, H., Evans, S. M., & Pakkenberg, B. (1994) · Neurobiology of Aging · doi:10.1016/0197-4580(94)90030-2 · Cited by 168 · see citing works
Self-citationTang, Y., & Nyengaard, J. R. (1997) · Journal of Neuroscience Methods · doi:10.1016/s0165-0270(97)02228-0 · Cited by 99 · see citing works
Hyman, B. T., & Gómez‐Isla, T. (1994) · Neurobiology of Aging · doi:10.1016/0197-4580(94)90031-0 · Cited by 74 · see citing works
Cerebral atrophy in AIDS: a stereological study
OSTER, S., Christoffersen, P., Gundersen, H. J. G., Nielsen, J. O., Pakkenberg, B., & Pedersen, C. (1993) · Acta Neuropathologica · doi:10.1007/bf00334671 · Cited by 47 · see citing works
Self-citationRegeur, L. (2000) · European Journal of Neurology · doi:10.1046/j.1468-1331.2000.00017.x · Cited by 44 · see citing works
Size of neocortical neurons in control subjects and in Alzheimer's disease
Bundgaard, M. J., Regeur, L., Gundersen, H. J. G., & Pakkenberg, B. (2001) · Journal of Anatomy · doi:10.1046/j.1469-7580.2001.19840481.x · Cited by 28 · see citing works
Self-citationSix billion neurons lost in AIDS
OSTER, S., Christoffersen, P., Gundersen, H. J. G., Nielsen, J. O., Pedersen, C., & PAKKENBERG, R. (1995) · Apmis · doi:10.1111/j.1699-0463.1995.tb01401.x · Cited by 28 · see citing works
Analysis of organic and biological surfaces
Echlin, P. (1984) · Wiley eBooks · Cited by 26 · see citing works
Thoughts on no neocortical neuronal loss but loss of volume in AD
Flood, D. G. (1994) · Neurobiology of Aging · doi:10.1016/0197-4580(94)90035-3 · Cited by 10 · see citing works
Regional rather than global pathology decides symptoms in senile dementia of Alzheimer's type
Brun, A. E. (1994) · Neurobiology of Aging · doi:10.1016/0197-4580(94)90036-1 · Cited by 7 · see citing works
?imi?, G., Kostovi?, I., Winblad, B., & Bogdanovi?, N. (1997) · The Journal of Comparative Neurology · doi:10.1002/(sici)1096-9861(19970324)379:4
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Essen, D. C. V., Uǧurbil, K., Auerbach, E. J., Barch, D. M., Behrens, T. E. J., Bucholz, R. D., Chang, A., Chen, L., Corbetta, M., Curtiss, S. W., et al. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.02.018 · Cited by 2,605 · see citing works
Saver, J. L. (2005) · Stroke · doi:10.1161/01.str.0000196957.55928.ab · Cited by 2,197 · see citing works
The connectomics of brain disorders
Fornito, A., Zalesky, A., & Breakspear, M. (2015) · Nature Reviews Neuroscience · doi:10.1038/nrn3901 · Cited by 1,734 · see citing works
Building high-level features using large scale unsupervised learning
Le, Q. V. (2013) · 2013 IEEE International Conference on Acoustics, Speech and Signal Processing · doi:10.1109/icassp.2013.6639343 · Cited by 1,695 · see citing works
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Fjell, A. M., & Walhovd, K. B. (2010) · Reviews in the Neurosciences · doi:10.1515/revneuro.2010.21.3.187 · Cited by 1,085 · see citing works
von Bartheld, C. S., Bahney, J., & Herculano‐Houzel, S. (2016) · Journal of Comparative Neurology · doi:10.1002/cne.24040 · Cited by 1,063 · see citing works
Age-related myelin breakdown: a developmental model of cognitive decline and Alzheimer’s disease
Bartzokis, G. (2004) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2003.03.001 · Cited by 968 · see citing works
Xin, H., Li, Y., Buller, B., Katakowski, M., Zhang, Y., Wang, X., Shang, X., Zhang, Z. G., & Chopp, M. (2012) · Stem Cells · doi:10.1002/stem.1129 · Cited by 897 · see citing works
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Silbereis, J. C., Pochareddy, S., Zhu, Y., Li, M., & Sestan, N. (2016) · Neuron · doi:10.1016/j.neuron.2015.12.008 · Cited by 818 · see citing works
Farahani, F. V., Karwowski, W., & Lighthall, N. R. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.00585 · Cited by 719 · see citing works
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Chen, R., Canales, A., & Anikeeva, P. (2017) · Nature Reviews Materials · doi:10.1038/natrevmats.2016.93 · Cited by 655 · see citing works
Overview of the SpiNNaker System Architecture
Furber, S. B., Lester, D., Plana, L. A., Garside, J. D., Painkras, E., Temple, S., & Brown, A. D. (2012) · IEEE Transactions on Computers · doi:10.1109/tc.2012.142 · Cited by 648 · see citing works
Gene expression changes in the course of normal brain aging are sexually dimorphic
Berchtold, N. C., Cribbs, D. H., Coleman, P. D., Rogers, J., Head, E., Kim, R., Beach, T., Miller, C., Troncoso, J., Trojanowski, J. Q., et al. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0806883105 · Cited by 626 · see citing works
Neocortical glial cell numbers in human brains
Pelvig, D. P., Pakkenberg, H., Stark, A. K., & Pakkenberg, B. (2008) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2007.04.013 · Cited by 601 · see citing works
Self-citationStereological tools in biomedical research
Mandarim‐de‐Lacerda, C. A. (2003) · Anais da Academia Brasileira de Ciências · doi:10.1590/s0001-37652003000400006 · Cited by 595 · see citing works
Lemaître, H., Goldman, A. L., Sambataro, F., Verchinski, B. A., Meyer‐Lindenberg, A., Weinberger, D. R., & Mattay, V. S. (2010) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2010.07.013 · Cited by 545 · see citing works
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Amunts, K., & Zilles, K. (2015) · Neuron · doi:10.1016/j.neuron.2015.12.001 · Cited by 533 · see citing works
Evolution of the Human Nervous System Function, Structure, and Development
Sousa, A. M. M., Meyer, K. A., Santpere, G., Gulden, F. O., & Sestan, N. (2017) · Cell · doi:10.1016/j.cell.2017.06.036 · Cited by 517 · see citing works
A synopsis on aging—Theories, mechanisms and future prospects
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Verkhratsky, A., Olabarria, M., Noristani, H. N., Yeh, C., & Rodriguez, J. J. (2010) · Neurotherapeutics · doi:10.1016/j.nurt.2010.05.017 · Cited by 453 · see citing works
Small-world human brain networks: Perspectives and challenges
Liao, X., Vasilakos, A. V., & Yong He, A. (2017) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2017.03.018 · Cited by 431 · see citing works
The impact of aging and gender on brain viscoelasticity
Sack, I., Beierbach, B., Wuerfel, J., Klatt, D., Hamhaber, U., Papazoglou, S., Martus, P., & Braun, J. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.02.040 · Cited by 423 · see citing works
Environment and Brain Plasticity: Towards an Endogenous Pharmacotherapy
Sale, A., Berardi, N., & Maffei, L. (2014) · Physiological Reviews · doi:10.1152/physrev.00036.2012 · Cited by 402 · see citing works
The Basic-Systems Model of Episodic Memory
Rubin, D. C. (2006) · Perspectives on Psychological Science · doi:10.1111/j.1745-6916.2006.00017.x · Cited by 374 · see citing works
Bartzokis, G., Sultzer, D., Lu, P. H., Nuechterlein, K. H., Mintz, J., & Cummings, J. L. (2004) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2003.09.005 · Cited by 363 · see citing works
Nelson, P. T., Head, E., Schmitt, F. A., Davis, P. R., Neltner, J. H., Jicha, G. A., Abner, E. L., Smith, C. D., Van Eldik, L. J., Kryscio, R. J., et al. (2011) · Acta Neuropathologica · doi:10.1007/s00401-011-0826-y · Cited by 365 · see citing works
Drachman, D. A. (2005) · Neurology · doi:10.1212/01.wnl.0000166914.38327.bb · Cited by 367 · see citing works
Creative Innovation: Possible Brain Mechanisms
Heilman, K. M., Nadeau, S. E., & Beversdorf, D. Q. (2003) · Neurocase · doi:10.1076/neur.9.5.369.16553 · Cited by 354 · see citing works
Constitutional Aneuploidy in the Normal Human Brain
Rehen, S. K., Yung, Y. C., McCreight, M. P., Kaushal, D., Yang, A. H., Almeida, B. S. V., Kingsbury, M. A., Cabral, K. M. D. S., McConnell, M. J., Anliker, B., et al. (2005) · Journal of Neuroscience · doi:10.1523/jneurosci.4560-04.2005 · Cited by 320 · see citing works
Electrophysiological source imaging: A noninvasive window to brain dynamics
He, B., Sohrabpour, A., Brown, E., & Liu, Z. (2018) · Annual Review of Biomedical Engineering · doi:10.1146/annurev-bioeng-062117-120853 · Cited by 297 · see citing works
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Budday, S., Steinmann, P., & Ellen Kuhl, A. (2015) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2015.00257 · Cited by 317 · see citing works
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Scheff, S. W., PRICE, D. A., Schmitt, F. A., Scheff, M. A., & Mufson, E. J. (2011) · Journal of Alzheimer s Disease · doi:10.3233/jad-2011-101782 · Cited by 301 · see citing works
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Draganski, B., Ashburner, J., Hutton, C., Kherif, F., Frackowiak, R. S. J., Helms, G., & Weiskopf, N. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.01.052 · Cited by 288 · see citing works
Small, S. A., Chawla, M. K., Buonocore, M. H., Rapp, P. R., & Barnes, C. A. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0400285101 · Cited by 281 · see citing works
Li, S., Zeng, F., Chen, C., Liu, H., Tang, G., Gao, S., Song, C., Lin, Y., Pan, F., & Guo, D. (2013) · Journal of Materials Chemistry C · doi:10.1039/c3tc30575a · Cited by 276 · see citing works
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LINE-1 retrotransposons: mediators of somatic variation in neuronal genomes?
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Saeidi, M., Karwowski, W., Farahani, F. V., Fiok, K., Taïar, R., Hancock, P. A., & Aljuaid, A. M. (2021) · Brain Sciences · doi:10.3390/brainsci11111525 · Cited by 277 · see citing works
Aging‐dependent changes of microglial cells and their relevance for neurodegenerative disorders
Von Bernhardi, R., Tichauer, J. E., & Eugenín, J. (2010) · Journal of Neurochemistry · doi:10.1111/j.1471-4159.2009.06537.x · Cited by 258 · see citing works
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Brainin, M., Tuomilehto, J., Heiss, W., Bornstein, N. M., Bath, P. M., Teuschl, Y., Richard, E., Guekht, A. B., Quinn, T. J., & the Post Stroke Cognition Study Group (2014) · European Journal of Neurology · doi:10.1111/ene.12626 · Cited by 257 · see citing works
Poor sleep quality is associated with increased cortical atrophy in community-dwelling adults
Sexton, C. E., Storsve, A. B., Walhovd, K. B., Johansen‐Berg, H., & Fjell, A. M. (2014) · Neurology · doi:10.1212/wnl.0000000000000774 · Cited by 235 · see citing works
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Li, R., Yang, D., Fang, F. Z., Hong, K., Reiss, A. L., & Zhang, Y. (2022) · Sensors · doi:10.3390/s22155865 · Cited by 244 · see citing works
Building high-level features using large scale unsupervised learningPreprint
Le, Q. V., Ranzato, M., Monga, R., Devin, M., Chen, K., Corrado, G. S., Dean, J., & Ng, A. Y. (2011) · arXiv (Cornell University) · doi:10.48550/arxiv.1112.6209 · Cited by 236 · see citing works
Aging of the brain, entropy, and Alzheimer disease
Drachman, D. A. (2006) · Neurology · doi:10.1212/01.wnl.0000240127.89601.83 · Cited by 230 · see citing works
García‐Cabezas, M. Á., John, Y. J., Barbas, H., & Zikopoulos, B. (2016) · Frontiers in Neuroanatomy · doi:10.3389/fnana.2016.00107 · Cited by 227 · see citing works
Drachman, D. A. (2014) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2013.11.003 · Cited by 224 · see citing works
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Freeman, S. H., Kandel, R., Cruz, L., Rozkalne, A., Newell, K. L., Frosch, M. P., Hedley‐Whyte, E. T., Locascio, J. J., Lipsitz, L. A., & Hyman, B. T. (2008) · Journal of Neuropathology & Experimental Neurology · doi:10.1097/nen.0b013e31818fc72f · Cited by 212 · see citing works
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Vawter, M. P., Evans, S. J., Choudary, P. V., Tomita, H., Meador-Woodruff, J., Molnar, M., Li, J. Z., López, J. F., Myers, R. M., Cox, D. R., et al. (2003) · Neuropsychopharmacology · doi:10.1038/sj.npp.1300337 · Cited by 204 · see citing works
Hermann, D., Weber‐Fahr, W., Sartorius, A., Hoerst, M., Frischknecht, U., Tunc‐Skarka, N., Perreau‐Lenz, S., Hansson, A. C., Krumm, B., Kiefer, F., et al. (2011) · Biological Psychiatry · doi:10.1016/j.biopsych.2011.07.034 · Cited by 197 · see citing works
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Essen, D. C. V., & Uǧurbil, K. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.01.032 · Cited by 192 · see citing works
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Hertz, L. (2008) · Neuropharmacology · doi:10.1016/j.neuropharm.2008.05.023 · Cited by 187 · see citing works
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Fu, X., Cheng, W., Wan, G., Yang, Z., & Tee, B. C. K. (2024) · Chemical Reviews · doi:10.1021/acs.chemrev.4c00049 · Cited by 172 · see citing works
Stereologic investigation of the posterior part of the hippocampus in schizophrenia
Schmitt, A., Steyskal, C., Bernstein, H., Schneider-Axmann, T., Parlapani, E., Schaeffer, E. L., Gattaz, W. F., Bogerts, B., Schmitz, C., & Falkai, P. (2008) · Acta Neuropathologica · doi:10.1007/s00401-008-0430-y · Cited by 167 · see citing works
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Hugenschmidt, C. E., Peiffer, A. M., Kraft, R. A., Casanova, R., Deibler, A. R., Burdette, J. H., Maldjian, J. A., & Laurienti, P. J. (2008) · Cerebral Cortex · doi:10.1093/cercor/bhm080 · Cited by 166 · see citing works
Gemmell, E., Bosomworth, H. J., Allan, L. M., Hall, R., Khundakar, A. A., Oakley, A. E., Deramecourt, V., Polvikoski, T. M., O'Brien, J. T., & Kalaria, R. N. (2011) · Stroke · doi:10.1161/strokeaha.111.636498 · Cited by 165 · see citing works
Cellular correlates of cortical thinning throughout the lifespan
Vidal-Pineiro, D., Parker, N., Shin, J., French, L., Grydeland, H., Jackowski, A. P., Mowinckel, A. M., Patel, Y., Pausova, Z., Salum, G., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-78471-3 · Cited by 154 · see citing works
Yu, F., Zhu, L. Q., Gao, W. T., Fu, Y. M., Xiao, H., Tao, J., & Zhou, J. (2018) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.8b03274 · Cited by 157 · see citing works
Structure out of chaos: Functional brain network analysis with EEG, MEG, and functional MRI
van Straaten, E. C. W., & Stam, C. J. (2012) · European Neuropsychopharmacology · doi:10.1016/j.euroneuro.2012.10.010 · Cited by 157 · see citing works
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Boltzmann brains and the scale-factor cutoff measure of the multiverse
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Ismail, L. E., & Karwowski, W. (2020) · IEEE Access · doi:10.1109/access.2020.3018995 · Cited by 150 · see citing works
Age-related changes in the central auditory system
Ouda, L., Profant, O., & Syka, J. (2015) · Cell and Tissue Research · doi:10.1007/s00441-014-2107-2 · Cited by 146 · see citing works
Emerging Materials for Neuromorphic Devices and Systems
Kim, M., Park, Y., Kim, I., & Lee, J. (2020) · iScience · doi:10.1016/j.isci.2020.101846 · Cited by 145 · see citing works
Liang, K. J., & Carlson, E. S. (2019) · Neurobiology of Learning and Memory · doi:10.1016/j.nlm.2019.01.004 · Cited by 131 · see citing works
PET imaging of synaptic density: A new tool for investigation of neuropsychiatric diseases
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Bhalala, U. S., Koehler, R. C., & Kannan, S. (2015) · Frontiers in Pediatrics · doi:10.3389/fped.2014.00144 · Cited by 128 · see citing works
MXene Ti3C2 memristor for neuromorphic behavior and decimal arithmetic operation applications
Wang, K., Chen, J., & Yan, X. (2020) · Nano Energy · doi:10.1016/j.nanoen.2020.105453 · Cited by 128 · see citing works
Towards cortex sized artificial neural systems
Johansson, C., & Lansner, A. (2006) · Neural Networks · doi:10.1016/j.neunet.2006.05.029 · Cited by 128 · see citing works
Diversity of astroglial responses across human neurodegenerative disorders and brain aging
Ferrer, I. (2017) · Brain Pathology · doi:10.1111/bpa.12538 · Cited by 123 · see citing works
Yan, X., Li, X., Zhou, Z., Zhao, J., Wang, H., Wang, J., Zhang, L., Ren, D., Zhang, X., Chen, J., et al. (2019) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.9b04443 · Cited by 124 · see citing works
Aging of the cerebral cortex differs between humans and chimpanzees
Sherwood, C. C., Gordon, A. D., Allen, J. S., Phillips, K. A., ERWIN, J. M., Hof, P. R., & Hopkins, W. D. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1016709108 · Cited by 122 · see citing works
Lu, P. H., Lee, G. J., Raven, E. P., Tingus, K., Khoo, T., Thompson, P. M., & Bartzokis, G. (2011) · Journal of Clinical and Experimental Neuropsychology · doi:10.1080/13803395.2011.595397 · Cited by 121 · see citing works
Changes in the osteocyte lacunocanalicular network with aging
Tiede-Lewis, L. M., & Dallas, S. L. (2019) · Bone · doi:10.1016/j.bone.2019.01.025 · Cited by 122 · see citing works
Calcium signaling in Parkinson’s disease
Calí, T., Ottolini, D., & Brini, M. (2014) · Cell and Tissue Research · doi:10.1007/s00441-014-1866-0 · Cited by 119 · see citing works
Liu, W. F., Laudert, S., Perkins, B. S., MacMillan-York, E., Martin, S., Graven, S. N., & for the NIC/Q 2005 Physical Environment Exploratory Group (2007) · Journal of Perinatology · doi:10.1038/sj.jp.7211844 · Cited by 120 · see citing works
Sulcal variability, stereological measurement and asymmetry of Broca's area on MR images
Keller, S. S., Highley, J. R., García‐Fiñana, M., Sluming, V., Rezaie, R., & Roberts, N. (2007) · Journal of Anatomy · doi:10.1111/j.1469-7580.2007.00793.x · Cited by 115 · see citing works
Long, X., Liao, W., Jiang, C., Liang, D., Qiu, B., & Zhang, L. (2012) · Academic Radiology · doi:10.1016/j.acra.2012.03.006 · Cited by 114 · see citing works
Age-related myelin dynamics revealed by increased oligodendrogenesis and short internodes
Lasiene, J., Matsui, A., Sawa, Y., Wong, F., & Horner, P. J. (2009) · Aging Cell · doi:10.1111/j.1474-9726.2009.00462.x · Cited by 111 · see citing works
Quantitative sodium MR imaging: A review of its evolving role in medicine
Thulborn, K. R. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.11.056 · Cited by 110 · see citing works
Oligodendrocytes in the aging brain
Sams, E. C. (2021) · Neuronal Signaling · doi:10.1042/ns20210008 · Cited by 107 · see citing works
Mapping Connections in Humans and Non-Human Primates
Essen, D. C. V., Jbabdi, S., Sotiropoulos, S. N., Chen, C., Dikranian, K. T., Coalson, T., Harwell, J., Behrens, T. E. J., & Glasser, M. F. (2014) · Elsevier eBooks · doi:10.1016/b978-0-12-396460-1.00016-0 · Cited by 107 · see citing works
Levy, W. B., & Calvert, V. G. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2008173118 · Cited by 105 · see citing works
Cristòfol, R. M., Porquet, D., Corpas, R., Coto‐Montes, A. M., Serret, J., Camins, A., Pallàs, M., & Sanfeliu, C. (2011) · Journal of Pineal Research · doi:10.1111/j.1600-079x.2011.00939.x · Cited by 101 · see citing works
Bridging the gap between MRI and postmortem research in autism
Schumann, C. M., & Nordahl, C. W. (2010) · Brain Research · doi:10.1016/j.brainres.2010.09.061 · Cited by 99 · see citing works
Microglia in the developing brain: from immunity to behaviour
Schlegelmilch, T., Henke, K., & Peri, F. (2010) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2010.08.004 · Cited by 99 · see citing works
Neurobiological origin of spurious brain morphological changes: A quantitative MRI study
Lorio, S., Kherif, F., Ruef, A., Melie‐García, L., Frackowiak, R. S. J., Ashburner, J., Helms, G., Lutti, A., & Draganski, B. (2016) · Human Brain Mapping · doi:10.1002/hbm.23137 · Cited by 96 · see citing works
The timing of cognitive plasticity in physiological aging: a tDCS study of naming
Fertonani, A., Brambilla, M., Cotelli, M., & Miniussi, C. (2014) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2014.00131 · Cited by 93 · see citing works
High Variability in Neuronal Loss
Desai, S. M., Rocha, M., Jovin, T. G., & Jadhav, A. P. (2018) · Stroke · doi:10.1161/strokeaha.118.023499 · Cited by 93 · see citing works
A Newcomer's Guide to Functional Near Infrared Spectroscopy Experiments
Almajidy, R. K., Mankodiya, K., Abtahi, M., & Hofmann, U. (2019) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2019.2944351 · Cited by 94 · see citing works
White matter and cognitive function in schizophrenia
Dwork, A. J., Mancevski, B., & Rosoklija, G. B. (2007) · The International Journal of Neuropsychopharmacology · doi:10.1017/s1461145707007638 · Cited by 90 · see citing works
Effect of age on neocortical brain cells in 90+ year old human females—a cell counting study
Fabricius, K., Jacobsen, J. S., & Pakkenberg, B. (2012) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2012.06.009 · Cited by 89 · see citing works
Self-citationAdvances in transcriptome analysis of human brain aging
Ham, S., & Lee, S. (2020) · Experimental & Molecular Medicine · doi:10.1038/s12276-020-00522-6 · Cited by 81 · see citing works
Synapse-to-neuron ratio is inversely related to neuronal density in mature neuronal cultures
Cullen, D. K., Gilroy, M. E., Irons, H. R., & LaPlaca, M. C. (2010) · Brain Research · doi:10.1016/j.brainres.2010.08.058 · Cited by 86 · see citing works
- Authors
- Pakkenberg, B.
- Affiliations
- Bispebjerg Hospital (DK)
- Corresponding author
- Pakkenberg, B
- ORCID
- Pakkenberg, B
- Venue
- Experimental Gerontology
- Publisher
- Elsevier BV
- Published
- 2003-01
- Volume
- 38
- Issue
- 1-2
- Pages
- 95-99
- ISSN
- 0531-5565 (ISSN-L)
- OpenAlex
- W2003837387
- Cited by
- 485
- References
- 22
- Citations by year
- 2026: 20 · 2025: 18 · 2024: 20 · 2023: 22 · 2022: 15 · 2021: 27 · 2020: 65 · 2019: 29 · 2018: 28 · 2017: 27 · 2016: 30 · 2015: 22 · 2014: 27 · 2013: 16 · 2012: 18
- FWCI
- 28.89
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Experimental Gerontology (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Anatomy › Medical and Biological Sciences
- Social Sciences › Psychology › Neuropsychology and Physiological Psychology › Aging and Gerontology Research
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neuropharmacology Research
- Keywords
- brain aging, human neocortex, neuron number, glial cells, white matter, sex differences, brain structure
- MeSH terms
- Adult, Aged, Aged, 80 and over, Aging, Brain, Cell Count, Female, Humans, Male, Middle Aged, Nerve Fibers, Myelinated, Neuroglia, Neurons, Synapses, Neocortex, Cell Size
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