Cited by 89Advanced Neuroimaging Techniques and ApplicationsNeurogenesis and neuroplasticity mechanismsFunctional Brain Connectivity Studies

The human cerebral cortex is neither one nor many: neuronal distribution reveals two quantitatively different zones in the gray matter, three in the white matter, and explains local variations in cortical folding

Open access
Ribeiro, Pedro F. M., Ventura-Antunes, Lissa, Gabi, Mariana, Mota, Bruno, Grinberg, Lea T., Farfel, José M., Ferretti-Rebustini, Renata E. L., Leite, Renata E. P. +2 more·2013·Frontiers in Neuroanatomy

Abstract

The human prefrontal cortex has been considered different in several aspects and relatively enlarged compared to the rest of the cortical areas. Here we determine whether the white and gray matter of the prefrontal portion of the human cerebral cortex have similar or different cellular compositions relative to the rest of the cortical regions by applying the Isotropic Fractionator to analyze the distribution of neurons along the entire anteroposterior axis of the cortex, and its relationship with the degree of gyrification, number of neurons under the cortical surface, and other parameters. The prefrontal region shares with the remainder of the cerebral cortex (except for occipital cortex) the same relationship between cortical volume and number of neurons. In contrast, both occipital and prefrontal areas vary from other cortical areas in their connectivity through the white matter, with a systematic reduction of cortical connectivity through the white matter and an increase of the mean axon caliber along the anteroposterior axis. These two parameters explain local differences in the distribution of neurons underneath the cortical surface. We also show that local variations in cortical folding are neither a function of local numbers of neurons nor of cortical thickness, but correlate with properties of the white matter, and are best explained by the folding of the white matter surface. Our results suggest that the human cerebral cortex is divided in two zones (occipital and non-occipital) that differ in how neurons are distributed across their gray matter volume and in three zones (prefrontal, occipital, and non-occipital) that differ in how neurons are connected through the white matter. Thus, the human prefrontal cortex has the largest fraction of neuronal connectivity through the white matter and the smallest average axonal caliber in the white matter within the cortex, although its neuronal composition fits the pattern found for other, non-occipital areas.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.3389/fnana.2013.00028

Cites

9 of 9 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Differences in cortical development between mice and humans limit the ability of mouse models to define mechanisms relevant to the human DS phenotype.

    “The field has typically relied on mouse models to gain understanding of the impact of T21, but differences in cortical development between mice and humans — including the complexity of progenitor types or the formation of a lissencephalic rather than gyrencephalic brain153–155 — limit their ability to define mechanisms relevant to the human phenotype.”

    From Consequences of trisomy 21 for brain development in Down syndrome (2024) · cites this paper as [154]

    The Discussion section on local variations in cortical folding discusses differences in gyrification across species and developmental mechanisms of folding. It does not identify the mouse cortex as lissencephalic, compare cortical progenitor types, or assess whether mouse models can define mechanisms of the human Down syndrome phenotype. No page number is available in our copy. (LLM, conf=0.73)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 58

  1. Aboitiz F. Scheibel A. B. Fisher R. S. Zaidel E. (1992). Fiber composition of the human corpus callosum. Brain Res. 598, 143– 153 10.1016/0006-8993(92)90178-C 1486477 · DOI
  2. Andrews T. J. Halpern S. D. Purves D. (1997). Correlated size variations in human visual cortex, lateral geniculate nucleus, and optic tract. J. Neurosci. 17, 2859– 2868 9092607 PMC6573115
  3. Azevedo F. A. Carvalho L. R. Grinberg L. T. Farfel J. M. Ferretti R. E. Leite R. E. (2009). Equal numbers of neuronal and nonneuronal cells make the human brain an isometrically scaled-up primate brain. J. Comp. Neurol. 513, 532– 541 10.1002/cne.21974 19226510 · DOI · Paper page
  4. Barres B. A. Raff M. C. (1994). Control of oligodendrocyte number in the developing rat optic nerve. Neuron 12, 935– 942 10.1016/0896-6273(94)90305-0 8185952 · DOI
  5. Bishop K. M. Goudreau G. OLeary D. D. (2000). Regulation of area identity in the mammalian neocortex by Emx2 and Pax6. Science 288, 344– 349 10.1126/science.288.5464.344 10764649 · DOI
  6. Bush E. C. Allman J. M. (2004). The scaling of frontal cortex in primates and carnivores. Proc. Natl. Acad. Sci. U.S.A. 101, 3962– 3966 15007170 PMC374352
  7. Cahalane D. J. Charvet C. J. Finlay B. F. (2012). Systematic, balancing gradients in neuron density and number across the primate isocortex. Front. Neuroanat. 6: 28 10.3389/fnana.2012.00028 22826696 PMC3399120 · DOI
  8. Caminiti R. Ghaziri H. Galuske R. Hof P. R. Innocenti G. M. (2009). Evolution amplified processing with temporally dispersed slow neuronal connectivity in primates. Proc. Natl. Acad. Sci. U.S.A. 106, 19551– 19556 10.1073/pnas.0907655106 19875694 PMC2770441 · DOI · Paper page
  9. Chen C. H. Gutierrez E. D. Thompson W. Panizzon M. S. Jernigan T. L. Eyler L. T. (2012). Hierarchical genetic organization of human cortical surface area. Science 335, 163– 166 10.1126/science.1215330 22461613 PMC3690329 · DOI
  10. Collins C. E. Airey D. C. Young N. A. Leitch D. B. Kaas J. H. (2010). Neuron densities vary across and within cortical areas in primates. Proc. Natl. Acad. Sci. U.S.A. 107, 15927– 15932 10.1073/pnas.1010356107 20798050 PMC2936588 · DOI
  11. Collins C. E. Leitch D. B. Wong P. Kaas J. H. Herculano-Houzel S. (2013). Faster scaling of visual neurons in cortical areas relative to subcortical structures in non-human primate brains. Brain Struct. Funct. 218, 805– 816 10.1007/s00429-012-0430-5 22684638 PMC3727908 · DOI
  12. De Sousa A. A. Sherwood C. C. Mohlberg H. Amunts K. Schleicher A. MacLeod C. E. (2010). Hominoid visual brain structure volumes and the position of the lunate sulcus. J. Hum. Evol. 58, 281– 291 10.1016/j.jhevol.2009.11.011 20172590 · DOI
  13. Elston G. N. Benavides-Piccione R. DeFelipe J. (2001). The pyramidal cell in cognition: a comparative study in human and monkey. J. Neurosci. 21, RC163 11511694 PMC6763111
  14. Finlay B. L. Hersman M. N. Darlington R. B. (1998). Patterns of vertebrate neurogenesis and the paths of vertebrate evolution. Brain Behav. Evol. 52, 232– 242 10.1159/000006566 9787222 · DOI
  15. Fjell A. M. Westlye L. T. Amlien I. Espeseth T. Reinvang I. Raz N. (2009). High consistency of regional cortical thinning in aging across multiple samples. Cereb. Cortex 19, 2001– 2012 10.1093/cercor/bhn232 19150922 PMC2733683 · DOI
  16. Frahm H. Stephan H. Baron G. (1984). Comparison of brain structure volumes in insectivora and primates. V. Area Striata (AS). J. Hirnforsch. 25, 537– 557 6501869
  17. Fukuchi-Shimogori T. Grove E. A. (2001). Neocortex patterning by the secreted signaling molecule FGF8. Science 294, 1071– 1074 10.1126/science.1064252 11567107 · DOI
  18. Garel S. Huffman K. J. Rubenstein J. L. (2003). Molecular regionalization of the neocortex is disrupted in Fgf8 hypomorphic mutants. Development 130, 1903– 1914 10.1242/dev.00416 12642494 · DOI
  19. Grinberg L. T. Ferretti R. E. Farfel J. M. Leite R. Pasqualucci C. A. Rosemberg S. (2007). Brain back of the Brazilian aging brain study group – a milestone reached and more than 1,600 collected brains. Cell Tissue Bank. 8, 151– 162 10.1007/s10561-006-9022-z 17075689 · DOI · Paper page
  20. Haug H. (1987). Brain sizes, surfaces, and neuronal sizes of the cortex cerebri: a stereological investigation of man and his variability and a comparison with some mammals (primates, whales, marsupials, insectivores, and one elephant). Am. J. Anat. 180, 126– 142 10.1002/aja.1001800203 3673918 · DOI · Paper page
  21. Herculano-Houzel S. (2011a). Brain evolution, in Human Neuroanatomy, eds Em Mai J. Paxinos G. (Nova Iorque: Elsevier), 2– 13
  22. Herculano-Houzel S. (2011b). Not all brains are made the same: new views on brain scaling in evolution. Brain Behav. Evol. 78, 22– 36 10.1159/000327318 21691045 · DOI
  23. Herculano-Houzel S. (2012b). The isotropic fractionator: a fast, reliable method to determine numbers of cells in the brain or other tissues, in Springer Neuromethods: Neuronal Network Analysis: Concepts and Experimental Approaches, Vol. 67, eds fellin T. Halassa M. M. (New York, NY: Humana Press), 391– 403 10.1007/7657_2011_13 · DOI
  24. Herculano-Houzel S. Collins C. Wang P. Kaas J. (2007). Cellular scaling rules for building primate brains. Proc. Natl. Acad. Sci. U.S.A. 104, 3562– 3567 10.1073/pnas.0611396104 17360682 PMC1805542 · DOI · Paper page
  25. Herculano-Houzel S. Collins C. Wong P. Kaas J. H. Lent R. (2008). The basic nonuniformity of the cerebral cortex Proc. Natl. Acad. Sci. U.S.A. 105, 12593– 12598 10.1073/pnas.0805417105 18689685 PMC2527956 · DOI
  26. Herculano-Houzel S. Lent R. (2005). Isotropic fractionator: a simple, rapid method for the quantification of total cell and neuron numbers in the brain. J. Neurosci. 25, 2518– 2521 10.1523/JNEUROSCI.4526-04.2005 15758160 PMC6725175 · DOI · Paper page
  27. Herculano-Houzel S. Mota B. Lent R. (2006). Cellular scaling rules for rodent brains. Proc. Natl. Acad. Sci. U.S.A. 103, 12138– 12143 16880386 PMC1567708
  28. Herculano-Houzel S. Mota B. Wong P. Kaas J. H. (2010). Connectivity-driven white matter scaling and folding in primate cerebral cortex. Proc. Natl. Acad. Sci. U.S.A. 107, 19008– 19013 10.1073/pnas.1012590107 20956290 PMC2973896 · DOI
  29. Herculano-Houzel S. Ribeiro P. F. M. Campos L. da Silva A. V. Torres L. B. Catania K. C. (2011). Updated neuronal scaling rules for the brains of Glires (rodents/lagomorphs). Brain Behav. Evol. 78, 302– 314 10.1159/000330825 21985803 PMC3237106 · DOI
  30. Hilgetag C. C. Barbas H. (2006). Role of mechanical factors in the morphology of the primate cerebral cortex. PLoS Comput. Biol. 2: e22 10.1371/journal.pcbi.0020022 16557292 PMC1409812 · DOI
  31. Hofman M. (1985). Size and shape of the cerebral cortex in mammals. I. The cortical surface. Brain Behav. Evol. 27, 28– 40 10.1159/000118718 3836731 · DOI
  32. Jacobs B. Schall M. Prather M. Kapler E. Driscoll L. Baca S. (2001). Regional dendritic and spine variation in human cerebral cortex: a quantitative golgi study. Cereb. Cortex 11, 558– 571 10.1093/cercor/11.6.558 11375917 · DOI · Paper page
  33. Jerison H. (1985). Animal intelligence as encephalization. Philos. Trans. R. Soc. Lond. B Biol. Sci. 308, 21– 35 10.1098/rstb.1985.0007 2858875 · DOI · Paper page
  34. Kaas J. H. (2000). Organizing principles of sensory representations. Novartis Found. Symp. 228, 188– 198 10.1002/0470846631.ch13 10929323 · DOI
  35. Kaas J. H. (2009). Encyclopedia of Neuroscience (Amsterdam: Elsevier), 793– 800 10.1016/B978-008045046-9.00948-7 · DOI
  36. Karbowski J. (2003). How does connectivity between cortical areas depend on brain size? Implications for efficient computation. J. Comput. Neurosci. 15, 347– 356 10.1023/A:1027467911225 14618069 · DOI
  37. Martin K. A. Whitteridge D. (1984). The relstionship of receptive field properties to the dendritic shape of neurones in the cat striate cortex. J. Physiol. 356, 291– 302 6520788 PMC1193164
  38. Mota B. Herculano-Houzel S. (2012). How the cortex gets its folds: a connectivity-based universal model of mammalian gyrification. Front. Neuroanat. 6: 3 10.3389/fnana.2012.00003 22347170 PMC3270328 · DOI
  39. Mullen R. J. Buck C. R. Smith A. M. (1992). NeuN, a neuronal specific nuclear protein in vertebrates. Development 116, 201– 211 1483388
  40. O'Leary D. D. (1989). Do cortical areas emerge from a protocortex? Trends Neurosci. 12, 400– 406 10.1016/0166-2236(89)90080-5 2479138 · DOI · Paper page
  41. O'Leary D. D. Chou S. J. Sahara S. (2007). Area patterning of the mammalian cortex. Neuron 56, 252– 269 10.1016/j.neuron.2007.10.010 17964244 · DOI
  42. Pillay P. Manger P. R. (2007). Order-specific quantitative patterns of cortical gyrification. Eur. J. Neurosci. 25, 2705– 2712 10.1111/j.1460-9568.2007.05524.x 17459107 · DOI
  43. Poth C. Fung C. Güntürkün O. Ridgway S. H. Oelschläger H. H. (2005). Neuron numbers in sensory cortices of five delphinids compared to a physeterid, the pygmy sperm whale. Brain Res. Bull. 66, 357– 360 16144614
  44. Prothero J. (1997). Scaling of cortical neuron density and white matter volume in mammals. J. Hirnforsch. 38, 513– 524 9476216
  45. Raz N. Rodrigue K. M. (2006). Differential aging of the brain: patterns, cognitive correlates and modifiers. Neurosci. Biobehav. Rev. 30, 730– 748 10.1016/j.neubiorev.2006.07.001 16919333 PMC6601348 · DOI · Paper page
  46. Rilling J. K. Insel T. R. (1999). The primate neocortex in comparative perspective using magnetic resonance imaging. J. Hum. Evol. 37, 191– 223 10.1006/jhev.1999.0313 10444351 · DOI
  47. Rockel A. J. Hiorns R. W. Powell T. P. (1980). The basic uniformity in structure of the neocortex. Brain 103, 221– 244 10.1093/brain/103.2.221 6772266 · DOI
  48. Schoenemann P. T. Sheehan M. J. Glotzer L. D. (2005). Prefrontal white matter volume is disproportionately larger in humans than in other primates. Nat. Neurosci. 8, 242– 252 10.1038/nn1394 15665874 · DOI
  49. Semendeferi K. Armstrong E. Schleicher A. Zilles K. Van Hoesen G. W. (2011). Prefrontal cortex in humans and apes: a comparative study of area 10. Am. J. Phys. Anthropol. 114, 224– 241 11241188
  50. Semendeferi K. Lu A. Schenker N. Damasio H. (2002). Humans and great apes share a large frontal cortex. Nat. Neurosci. 5, 272– 276 10.1038/nn814 11850633 · DOI
  51. Smaers J. B. Steele J. Case C. R. Cowper A. Amunts K. Zilles K. (2011). Primate prefrontal cortex evolution: human brains are the extreme of a lateralized ape trend. Brain Behav. Evol. 77, 67– 78 10.1159/000323671 21335939 · DOI · Paper page
  52. Stephan H. Frahm H. Baron G. (1981). New and revised data on volumes of brain structures in insectivores and primates. Folia Primatol. 35, 1– 29 10.1159/000155963 7014398 · DOI
  53. Teffer K. Semendeferi K. (2012). Human prefrontal cortex: evolution, development, and pathology. Prog. Brain Res. 195, 191– 218 10.1016/B978-0-444-53860-4.00009-X 22230628 · DOI
  54. Tomasi S. Caminiti R. Innocenti G. M. (2012). Areal differences in diameter and length of corticofugal projections. Cereb. Cortex 22, 1463– 1472 10.1093/cercor/bhs011 22302056 · DOI
  55. Welker W. (1990). Comparative structure and evolution of cerebral cortex, in Part II, Cerebral Cortex Vol. 8B, eds Jones E. G. Peters A. (New York, NY: Plenum Press), 3– 10
  56. Winkler A. M. Kochunov P. Blangero J. Almasy L. Zilles K. Fox P. T. (2010). Cortical thickness or grey matter volume? The importance of selecting the phenotype for imaging genetics studies. Neuroimage 53, 1135– 1146 10.1016/j.neuroimage.2009.12.028 20006715 PMC2891595 · DOI · Paper page
  57. Zhang K. Sejnowski T. J. (2000). A universal scaling law between gray matter and white matter of cerebral cortex. Proc. Natl. Acad. Sci. U.S.A. 97, 5621– 5626 10.1073/pnas.090504197 10792049 PMC25878 · DOI · Paper page
  58. Zilles K. Armstrong E. Schleicher A. Kretschmann H. J. (1988). The human pattern of gyrification in the cerebral cortex. Anat. Embryol. 179, 173– 117 10.1007/BF00304699 3232854 · DOI

Cited by

Sorted by citations

Authors
Ribeiro, P. F. M., Ventura-Antunes, L., Gabi, M., Mota, B., Grinberg, L. T., Farfel, J. M., Ferretti-Rebustini, R. E. L., Leite, R. E. P., Filho, W. J., & Herculano-Houzel, S.
Affiliations
  • Conselho Nacional de Desenvolvimento Científico e Tecnológico (BR)
  • Universidade Federal do Rio de Janeiro (BR)
  • Universidade de São Paulo (BR)
Venue
Frontiers in Neuroanatomy
Published
2013
Volume
7
Pages
28-28
ISSN
1662-5129 (ISSN-L)
OpenAlex
W1975328486
Cited by
89
References
9
Citations by year
2026: 6 · 2025: 6 · 2024: 10 · 2023: 6 · 2022: 11 · 2021: 2 · 2020: 4 · 2019: 2 · 2018: 6 · 2017: 3 · 2016: 10 · 2015: 9 · 2014: 11 · 2013: 2
FWCI
8.28
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Frontiers in Neuroanatomy (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • eScholarship (California Digital Library) (submittedVersion)
  • eScholarship (California Digital Library) (submittedVersion, cc-by)
Funders
  • James S. McDonnell Foundation
  • Fundação de Amparo à Pesquisa do Estado de São Paulo
  • Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
  • Conselho Nacional de Desenvolvimento Científico e Tecnológico
  • Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro
Topics
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
  • Life Sciences › Neuroscience › Developmental Neuroscience › Neurogenesis and neuroplasticity mechanisms
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
Keywords
human cerebral cortex, prefrontal cortex, neuronal distribution, cortical gray matter, cortical white matter, axon diameter, cortical connectivity, cortical folding, cortical thickness, occipital cortex, anterior-posterior axis

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review