Gene expression-based modeling of human cortical synaptic density
Open accessAbstract
Postnatal cortical synaptic development is characterized by stages of exuberant growth, pruning, and stabilization during adulthood. How gene expression orchestrates these stages of synaptic development is poorly understood. Here we report that synaptic growth-related gene expression alone does not determine cortical synaptic density changes across the human lifespan, but instead, the dynamics of cortical synaptic density can be accurately simulated by a first-order kinetic model of synaptic growth and elimination that incorporates two separate gene expression patterns. Surprisingly, modeling of cortical synaptic density is optimized when genes related to oligodendrocytes are used to determine synaptic elimination rates. Expression of synaptic growth and oligodendrocyte genes varies regionally, resulting in different predictions of synaptic density among cortical regions that concur with previous regional data in humans. Our analysis suggests that modest rates of synaptic growth persist in adulthood, but that this is counterbalanced by increasing rates of synaptic elimination, resulting in stable synaptic number and ongoing synaptic turnover in the human adult cortex. Our approach provides a promising avenue for exploring how complex interactions among genes may contribute to neurobiological phenomena across the human lifespan.
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Aerobic glycolysis supports synaptic and neurite formation, as well as turnover, which likely persists throughout the lifespan.
“More specifically, we hypothesize that AG supports synaptic and neurite formation, as well as turnover, which likely persists throughout the lifespan”
From Aerobic glycolysis in the human brain is associated with development and neotenous gene expression (2014) · cites this paper as Goyal and Raichle, 2013
The Discussion speculates that aerobic glycolysis may coincide with high synaptic growth rates in early childhood, and the Abstract reports model-based evidence of ongoing synaptic turnover in the adult human cortex. The paper does not establish that aerobic glycolysis supports synaptic or neurite formation and turnover throughout the lifespan. (LLM, conf=0.86)
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Cites
Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources
Huang, D. W., Sherman, B. T., & Lempicki, R. A. (2009) · Nature Protocols · doi:10.1038/nprot.2008.211 · Cited by 35,890 · see citing works
WGCNA: an R package for weighted correlation network analysis
Langfelder, P., & Horvath, S. (2008) · BMC Bioinformatics · doi:10.1186/1471-2105-9-559 · Cited by 27,252 · see citing works
Cahoy, J. D., Emery, B., Kaushal, A., Foo, L. C., Zamanian, J. L., Christopherson, K. S., Xing, Y., Lubischer, J. L., Krieg, P. A., Krupenko, S. A., et al. (2008) · The Journal of Neuroscience · doi:10.1523/jneurosci.4178-07.2008 · Cited by 3,129 · see citing works
Mapping cortical change across the human life span
Sowell, E. R., Peterson, B. S., Thompson, P. M., Welcome, S. E., Henkenius, A. L., & Toga, A. W. (2003) · Nature Neuroscience · doi:10.1038/nn1008 · Cited by 2,328 · see citing works
Spatio-temporal transcriptome of the human brain
Kang, H. J., Kawasawa, Y. I., Cheng, F., Zhu, Y., Xu, X., Li, M., Sousa, A. M. M., Pletikos, M., Meyer, K. A., Sedmak, G., et al. (2011) · Nature · doi:10.1038/nature10523 · Cited by 2,084 · see citing works
Experience-dependent structural synaptic plasticity in the mammalian brain
Holtmaat, A., & Svoboda, K. (2009) · Nature Reviews Neuroscience · doi:10.1038/nrn2699 · Cited by 1,934 · see citing works
Plasticity in gray and white: neuroimaging changes in brain structure during learning
Zatorre, R. J., Fields, R. D., & Johansen-Berg, H. (2012) · Nature Neuroscience · doi:10.1038/nn.3045 · Cited by 1,685 · see citing works
Raichle, M. E., & Mintun, M. A. (2006) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.29.051605.112819 · Cited by 1,630 · see citing works
Self-citationPositron emission tomography study of human brain functional development
Chugani, H. T., Phelps, M. E., & Mazziotta, J. C. (1987) · Annals of Neurology · doi:10.1002/ana.410220408 · Cited by 1,548 · see citing works
Chen, M. S., Huber, A. B., van der Haar, M. E., Frank, M., Schnell, L., Spillmann, A. A., Christ, F., & Schwab, M. E. (2000) · Nature · doi:10.1038/35000219 · Cited by 1,445 · see citing works
Extraordinary neoteny of synaptic spines in the human prefrontal cortex
Petanjek, Z., Judaš, M., Šimić, G., Rašin, M. R., Uylings, H. B. M., Rakic, P., & Kostović, I. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1105108108 · Cited by 1,411 · see citing works
Concurrent overproduction of synapses in diverse regions of the primate cerebral cortex
Rakic, P., Bourgeois, J.-P., Eckenhoff, M. F., Zecevic, N., & Goldman-Rakic, P. S. (1986) · Science · doi:10.1126/science.3952506 · Cited by 1,133 · see citing works
McKerracher, L. J., David, S., Jackson, D. L., Kottis, V., Dunn, R. J., & Braun, P. E. (1994) · Neuron · doi:10.1016/0896-6273(94)90247-x · Cited by 1,125 · see citing works
Stoichiometric coupling of brain glucose metabolism and glutamatergic neuronal activity.
Sibson, N. R., Dhankhar, A., Mason, G. F., Rothman, D. L., Behar, K. L., & Shulman, R. G. (1998) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.95.1.316 · Cited by 897 · see citing works
Regulation of synaptic connectivity by glia.
Eroglu, C., & Barres, B. A. (2010) · Nature · doi:10.1038/nature09612 · Cited by 813 · cited 4× · see citing works
“, C1QA), another related to fragile X syndrome (FMR1), and others that also have been implicated in the processes of synaptic elimination (21).”
BackgroundInfluentialSynaptogenesis in human visual cortex—evidence for synapse elimination during normal development
Huttenlocher, P. R., de Courten, C., Garey, L. J., & Van der Loos, H. (1982) · Neuroscience Letters · doi:10.1016/0304-3940(82)90379-2 · Cited by 745 · see citing works
Neural activity triggers neuronal oxidative metabolism followed by astrocytic glycolysis
Kasischke, K. A., Vishwasrao, H. D., Fisher, P. J., Zipfel, W. R., & Webb, W. W. (2004) · Science · doi:10.1126/science.1096485 · Cited by 742 · see citing works
Regional aerobic glycolysis in the human brain
Vaishnavi, S. N., Vlassenko, A. G., Rundle, M. M., Snyder, A. Z., Mintun, M. A., & Raichle, M. E. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1010459107 · Cited by 738 · see citing works
Self-citationProlonged myelination in human neocortical evolution
Miller, D. J., Duka, T., Stimpson, C. D., Schapiro, S. J., Baze, W. B., McArthur, M. J., Fobbs, A. J., Sousa, A. M. M., Šestan, N., Wildman, D. E., et al. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1117943109 · Cited by 699 · see citing works
Bhatt, D. H., Zhang, S., & Gan, W.-B. (2009) · Annual Review of Physiology · doi:10.1146/annurev.physiol.010908.163140 · Cited by 380 · see citing works
Sleep and waking modulate spine turnover in the adolescent mouse cortex
Maret, S., Faraguna, U., Nelson, A. B., Cirelli, C., & Tononi, G. (2011) · Nature Neuroscience · doi:10.1038/nn.2934 · Cited by 315 · see citing works
Kennedy, C., & Sokoloff, L. (1957) · Journal of Clinical Investigation · doi:10.1172/jci103509 · Cited by 300 · see citing works
Strategies for aggregating gene expression data: The collapseRows R function
Miller, J. A., Cai, C., Langfelder, P., Geschwind, D. H., Kurian, S. M., Salomon, D. R., & Horvath, S. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-322 · Cited by 300 · see citing works
(2000) · Choice Reviews Online · doi:10.5860/choice.38-1236 · Cited by 257 · see citing works
Lund, J. S., Boothe, R. G., & Lund, R. D. (1977) · The Journal of Comparative Neurology · doi:10.1002/cne.901760203 · Cited by 226 · see citing works
THE DENSITY OF SYNAPSES AND NEURONS IN NORMAL, MENTALLY DEFECTIVE AND AGEING HUMAN BRAINS
Cragg, B. G. (1975) · Brain · doi:10.1093/brain/98.1.81 · Cited by 225 · see citing works
y Cajal, S. R. (1989) · The MIT Press eBooks · doi:10.7551/mitpress/5817.001.0001 · Cited by 222 · see citing works
Developmental changes of cerebral blood flow and oxygen metabolism in children.
Takahashi, T., Shirane, R., Sato, S., & Yoshimoto, T. (1999) · PubMed Central · Cited by 194 · see citing works
Spinogenesis and Pruning Scales across Functional Hierarchies
Elston, G. N., Oga, T., & Fujita, I. (2009) · Journal of Neuroscience · doi:10.1523/jneurosci.5216-08.2009 · Cited by 160 · see citing works
Miller, J. H., McKinstry, R. C., Philip, J. V., Mukherjee, P., & Neil, J. J. (2003) · American Journal of Roentgenology · doi:10.2214/ajr.180.3.1800851 · Cited by 94 · see citing works
Oligodendrocytes as Regulators of Neuronal Networks during Early Postnatal Development
Doretto, S., Malerba, M., Ramos, M., Ikrar, T., Kinoshita, C., Mei, C. D., Tirotta, E., Xu, X., & Borrelli, E. (2011) · PLoS ONE · doi:10.1371/journal.pone.0019849 · Cited by 54 · see citing works
Stages of increased cerebral blood flow accompany stages of rapid brain growth
Epstein, H. T. (1999) · Brain and Development · doi:10.1016/s0387-7604(99)00066-2 · Cited by 48 · see citing works
Synaptophysin protein and mRNA expression in the human hippocampal formation from birth to old age
Eastwood, S. L., Weickert, C. S., Webster, M. J., Herman, M. M., Kleinman, J. E., & Harrison, P. J. (2006) · Hippocampus · doi:10.1002/hipo.20194 · Cited by 40 · see citing works
Huttenlocher (1997) · doi:10.1002/(sici)1096-9861(19971020)387:2
Flagged first, then by citations
Cited by
Sorted by citations
Aerobic glycolysis in the human brain is associated with development and neotenous gene expression
Goyal, M. S., Hawrylycz, M., Miller, J. A., Snyder, A. Z., & Raichle, M. E. (2014) · Cell Metabolism · doi:10.1016/j.cmet.2013.11.020 · Cited by 459 · see citing works
“More specifically, we hypothesize that AG supports synaptic and neurite formation as well as turnover, which most likely persists throughout the lifespan (Goyal and Raichle, 2013; Marder and Goaillard, 2006).”
Self-citationThe restless brain: How intrinsic activity organizes brain function
Raichle, M. E. (2015) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2014.0172 · Cited by 433 · see citing works
Self-citationA Synergistic Core for Human Brain Evolution and Cognition
Luppi, A. I., Mediano, P. A. M., Rosas, F. E., Holland, N., Fryer, T. D., O’Brien, J. T., Rowe, J. B., Menon, D. K., Bor, D., & Stamatakis, E. A. (2022) · Nature Neuroscience · doi:10.1038/s41593-022-01070-0 · Cited by 314 · see citing works
Loss of Brain Aerobic Glycolysis in Normal Human Aging
Goyal, M. S., Vlassenko, A. G., Blazey, T. M., Su, Y., Couture, L. E., Durbin, T. J., Bateman, R. J., Benzinger, T. L.-S., Morris, J. C., & Raichle, M. E. (2017) · Cell Metabolism · doi:10.1016/j.cmet.2017.07.010 · Cited by 318 · see citing works
Self-citationUpregulated energy metabolism in the Drosophila mushroom body is the trigger for long-term memory
Plaçais, P.-Y., de Tredern, É., Scheunemann, L., Trannoy, S., Goguel, V., Han, K.-A., Isabel, G., & Préat, T. (2017) · Nature Communications · doi:10.1038/ncomms15510 · Cited by 211 · see citing works
Brain Fuel Utilization in the Developing Brain
Steiner, P. (2019) · Annals of Nutrition and Metabolism · doi:10.1159/000508054 · Cited by 159 · see citing works
Goyal, M. S., Venkatesh, S., Milbrandt, J., Gordon, J. I., & Raichle, M. E. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1511465112 · Cited by 141 · see citing works
Self-citationBianchi, S., Stimpson, C. D., Duka, T., Larsen, M. D., Janssen, W. G. M., Collins, Z., Bauernfeind, A. L., Schapiro, S. J., Baze, W. B., McArthur, M. J., et al. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1301224110 · Cited by 136 · see citing works
Glycolytic Metabolism, Brain Resilience, and Alzheimer’s Disease
Zhang, X., Alshakhshir, N., & Zhao, L. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.662242 · Cited by 134 · see citing works
Kyzar, E. J., Floreani, C. V., Teppen, T. L., & Pandey, S. C. (2016) · Frontiers in Neuroscience · doi:10.3389/fnins.2016.00222 · Cited by 89 · see citing works
Brain aerobic glycolysis and motor adaptation learning
Shannon, B. J., Vaishnavi, S., Vlassenko, A. G., Shimony, J. S., Rutlin, J. R., & Raichle, M. E. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1604977113 · Cited by 80 · see citing works
Self-citationBrain atrophy progression in Parkinson’s disease is shaped by connectivity and local vulnerability
Tremblay, C., Rahayel, S., Vo, A., Morys, F., Shafiei, G., Abbasi, N., Markello, R. D., Gan‐Or, Z., Mišić, B., & Dagher, A. (2021) · Brain Communications · doi:10.1093/braincomms/fcab269 · Cited by 66 · see citing works
Decreasing-Rate Pruning Optimizes the Construction of Efficient and Robust Distributed Networks
Navlakha, S., Barth, A. L., & Bar‐Joseph, Z. (2015) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004347 · Cited by 68 · see citing works
On the central role of brain connectivity in neurodegenerative disease progression
Iturria‐Medina, Y., & Evans, A. C. (2015) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2015.00090 · Cited by 61 · see citing works
Glucose Requirements of the Developing Human Brain
Goyal, M. S., & Raichle, M. E. (2018) · Journal of Pediatric Gastroenterology and Nutrition · doi:10.1097/mpg.0000000000001875 · Cited by 59 · see citing works
Self-citationPrimiani, C. T., Ryan, V. H., Rao, J. S., Cam, M. C., Ahn, K., Modi, H. R., & Rapoport, S. I. (2014) · PLoS ONE · doi:10.1371/journal.pone.0110972 · Cited by 56 · see citing works
Brain Nutrition: A Life Span Approach
Goyal, M. S., Iannotti, L. L., & Raichle, M. E. (2018) · Annual Review of Nutrition · doi:10.1146/annurev-nutr-082117-051652 · Cited by 56 · see citing works
Self-citationThe iPSC perspective on schizophrenia
Räsänen, N., Tiihonen, J., Koskuvi, M., Lehtonen, Š., & Koıstınaho, J. (2021) · Trends in Neurosciences · doi:10.1016/j.tins.2021.11.002 · Cited by 50 · see citing works
Portable, field-based neuroimaging using high-density diffuse optical tomography
Fishell, A. K., Arbeláez, A. M., Valdés, C. P., Burns‐Yocum, T. M., Sherafati, A., Richter, E. J., Torres, M., Eggebrecht, A. T., Smyser, C. D., & Culver, J. P. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.116541 · Cited by 46 · see citing works
Gene networks show associations with seed region connectivity
Forest, M., Iturria‐Medina, Y., Goldman, J. S., Kleinman, C. L., Lovato, A., Klein, K., Evans, A. C., Ciampi, A., Labbe, A., & Greenwood, C. M. T. (2017) · Human Brain Mapping · doi:10.1002/hbm.23579 · Cited by 37 · see citing works
Isochronic development of cortical synapses in primates and mice
Wildenberg, G., Li, H., Sampathkumar, V., Sorokina, A., & Kasthuri, N. (2023) · Nature Communications · doi:10.1038/s41467-023-43088-3 · Cited by 31 · see citing works
Expression Profiling of RNA Transcripts during Neuronal Maturation and Ischemic Injury
Kaur, P., Karolina, D. S., Sepramaniam, S., Armugam, A., & Jeyaseelan, K. (2014) · PLoS ONE · doi:10.1371/journal.pone.0103525 · Cited by 30 · see citing works
Gene expression associated with individual variability in intrinsic functional connectivity
Li, L., Wei, Y., Zhang, J., Ma, J., Yi, Y., Gu, Y., Li, L. M. W., Lin, Y., & Dai, Z. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118743 · Cited by 20 · see citing works
Bessi, V., Giacomucci, G., Mazzeo, S., Bagnoli, S., Padiglioni, S., Balestrini, J., Tomaiuolo, G., Piaceri, I., Carraro, M., Bracco, L., et al. (2020) · European Journal of Neurology · doi:10.1111/ene.14518 · Cited by 16 · see citing works
Zhang, C., Cui, L., He, W., Zhang, X., & Liu, H. (2020) · Experimental Cell Research · doi:10.1016/j.yexcr.2020.112420 · Cited by 15 · see citing works
Korade, Ž., & Mirnics, K. (2014) · Neuron · doi:10.1016/j.neuron.2014.01.006 · Cited by 9 · see citing works
Structural Insights Into the Dynamic Evolution of Neuronal Networks as Synaptic Density Decreases
Yuan, Y., Liu, J., Zhao, P., Fu, X., Huo, H., & Fang, T. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.00892 · Cited by 8 · see citing works
The Development of Synapses in Mouse and Macaque Primary Sensory CorticesPreprint
Wildenberg, G. A., Li, H., & Kasthuri, N. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.02.15.528564 · Cited by 12 · see citing works
Bulgarelli, C., Blasi, A., McCann, S., Milosavljevic, B., Ghillia, G., Mbye, E., Touray, E., Fadera, T., Acolatse, L., Moore, S. E., et al. (2026) · eLife · doi:10.1101/2024.01.02.573930 · Cited by 6 · see citing works
Bulgarelli, C., Blasi, A., McCann, S., Milosavljevic, B., Ghillia, G., Mbye, E., Touray, E., Fadera, T., Acolatse, L., Moore, S. E., et al. (2025) · eLife · doi:10.7554/elife.94194.2 · Cited by 6 · see citing works
Gelmez, P., Karakoc, T. E., & Ulucan, Ö. (2024) · OMICS A Journal of Integrative Biology · doi:10.1089/omi.2024.0167 · Cited by 4 · see citing works
Bulgarelli, C., Blasi, A., McCann, S., Milosavljevic, B., Ghillia, G., Mbye, E., Touray, E., Fadera, T., Acolatse, L., Moore, S. E., et al. (2024) · eLife · doi:10.7554/elife.94194.1 · Cited by 4 · see citing works
Guichet, C., Achard, S., Mermillod, M., & Baciu, M. V. (2025) · Imaging Neuroscience · doi:10.1162/imag.a.1091 · Cited by 4 · see citing works
Bulgarelli, C., Blasi, A., McCann, S., Milosavljevic, B., Ghillia, G., Mbye, E., Touray, E., Fadera, T., Acolatse, L., Moore, S. E., et al. (2024) · eLife · doi:10.7554/elife.94194 · Cited by 1 · see citing works
(2014) · Society for Neuroscience eBooks · doi:10.1523/hon.008010
Bulgarelli, C., Blasi, A., McCann, S., Milosavljevic, B., Ghillia, G., Mbye, E., Touray, E., Fadera, T., Acolatse, L., Moore, S. E., et al. (2026) · eLife · doi:10.7554/elife.94194.3
- Authors
- Goyal, M. S., & Raichle, M. E.
- Affiliations
- Washington University in St. Louis (US)
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2013-04
- Volume
- 110
- Issue
- 16
- Pages
- 6571-6576
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC3631628
- OpenAlex
- W2002701407
- Cited by
- 37
- References
- 34
- Citations by year
- 2026: 2 · 2025: 2 · 2024: 3 · 2023: 2 · 2022: 1 · 2021: 4 · 2020: 3 · 2019: 3 · 2018: 2 · 2017: 3 · 2016: 2 · 2015: 4 · 2014: 5 · 2013: 1
- FWCI
- 1.96
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neuropharmacology Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetics and Neurodevelopmental Disorders
- Life Sciences › Neuroscience › Developmental Neuroscience › Neurogenesis and neuroplasticity mechanisms
- Keywords
- synaptic development, synaptic pruning, synaptic growth, gene expression, kinetic modeling, synaptic turnover, regional variation, lifespan development
- MeSH terms
- Cerebral Cortex, Exons, Humans, Models, Neurological, Oligodendroglia, Synapses, Gene Expression Regulation, Developmental, Computational Biology, Gene Expression Profiling, Principal Component Analysis, Microarray Analysis
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