SignificanceThe evolution of brain processing capacity has traditionally been inferred from data on brain size. However, similarly sized brains of distantly related species can differ in the number and distribution of neurons, their basic computational units. Therefore, a finer-grained approach is needed to reveal the evolutionary paths to increased cognitive capacity. Using a new, comprehensive dataset, we analyzed brain cellular composition across amniotes. Compared to reptiles, mammals and birds have dramatically increased neuron numbers in the telencephalon and cerebellum, which are brain parts associated with higher cognition. Astoundingly, a phylogenetic analysis suggests that as few as four major changes in neuron-brain scaling in over 300 million years of evolution pave the way to intelligence in endothermic land vertebrates.

Department of Zoology Faculty of Science Charles University CZ 12844 Prague Czech Republic

Prague Zoo CZ 17100 Prague Czech Republic

Zobrazit více v PubMed

Tsuboi M., et al. , Breakdown of brain-body allometry and the encephalization of birds and mammals. Nat. Ecol. Evol. 2, 1492–1500 (2018). PubMed

Ksepka D. T., et al. , Tempo and pattern of avian brain size evolution. Curr. Biol. 30, 2026–2036.e3 (2020). PubMed

Smaers J. B., et al. , The evolution of mammalian brain size. Sci. Adv. 7, eabe2101 (2021). PubMed PMC

Jerison H., Evolution of the Brain and Intelligence (Academic Press, 1973).

Font E., García-Roa R., Pincheira-Donoso D., Carazo P., Rethinking the effects of body size on the study of brain size evolution. Brain Behav Evol 93, 182–195 (2019). PubMed

Herculano-Houzel S., Numbers of neurons as biological correlates of cognitive capability. Curr. Opin. Behav. Sci. 16, 1–7 (2017).

Němec P., Osten P., The evolution of brain structure captured in stereotyped cell count and cell type distributions. Curr. Opin. Neurobiol. 60, 176–183 (2020). PubMed PMC

Williams R. W., Herrup K., The control of neuron number. Annu. Rev. Neurosci. 11, 423–453 (1988). PubMed

Dicke U., Roth G., Neuronal factors determining high intelligence. Philos. Trans. R. Soc. Lond. B Biol. Sci. 371, 20150180 (2016). PubMed PMC

Herculano-Houzel S., Lent R., Isotropic fractionator: A simple, rapid method for the quantification of total cell and neuron numbers in the brain. J. Neurosci. 25, 2518–2521 (2005). PubMed PMC

Jardim-Messeder D., et al. , Dogs have the most neurons, though not the largest brain: Trade-off between body mass and number of neurons in the cerebral cortex of large carnivoran species. Front. Neuroanat. 11, 118 (2017). PubMed PMC

Kverková K., et al. , Sociality does not drive the evolution of large brains in eusocial African mole-rats. Sci. Rep. 8, 9203 (2018). PubMed PMC

Herculano-Houzel S., et al. , Microchiropterans have a diminutive cerebral cortex, not an enlarged cerebellum, compared to megachiropterans and other mammals. J. Comp. Neurol. 528, 2978–2993 (2020). PubMed

Olkowicz S., et al. , Birds have primate-like numbers of neurons in the forebrain. Proc. Natl. Acad. Sci. U.S.A. 113, 7255–7260 (2016). PubMed PMC

Massen J. J. M., et al. , Brain size and neuron numbers drive differences in yawn duration across mammals and birds. Commun. Biol. 4, 503 (2021). PubMed PMC

Elizondo Lara L. C., Young J., Schliep K., De León L. F., Brain allometry across macroevolutionary scales in squamates suggests a conserved pattern in snakes. Zoology (Jena) 146, 125926 (2021). PubMed

Ngwenya A., Patzke N., Manger P. R., Herculano-Houzel S., Continued growth of the central nervous system without mandatory addition of neurons in the Nile crocodile (Crocodylus niloticus). Brain Behav Evol 87, 19–38 (2016). PubMed

Kverková K., Polonyiová A., Kubička L., Němec P., Individual and age-related variation of cellular brain composition in a squamate reptile. Biol. Lett. 16, 20200280 (2020). PubMed PMC

Storks L., Powell B. J., Leal M., Peeking inside the lizard brain: Neuron numbers in Anolis and its implications for cognitive performance and vertebrate brain evolution. Integr. Comp. Biol. icaa129 (2020). PubMed

Herculano-Houzel S., Coordinated scaling of cortical and cerebellar numbers of neurons. Front. Neuroanat. 4, 12 (2010). PubMed PMC

Uyeda J. C., Harmon L. J., A novel Bayesian method for inferring and interpreting the dynamics of adaptive landscapes from phylogenetic comparative data. Syst. Biol. 63, 902–918 (2014). PubMed

Macrì S., Savriama Y., Khan I., Di-Poï N., Comparative analysis of squamate brains unveils multi-level variation in cerebellar architecture associated with locomotor specialization. Nat. Commun. 10, 5560 (2019). PubMed PMC

Striedter G. F., Northcutt R. G., Brains through Time: A Natural History of Vertebrates (Oxford University Press, 2019).

Marhounová L., Kotrschal A., Kverková K., Kolm N., Němec P., Artificial selection on brain size leads to matching changes in overall number of neurons. Evolution 73, 2003–2012 (2019). PubMed PMC

Schulze L., et al. , Transparent Danionella translucida as a genetically tractable vertebrate brain model. Nat. Methods 15, 977–983 (2018). PubMed

Gerhardt H. C., Tucker M. A., von Twickel A., Walkowiak W., Anuran vocal communication: Effects of genome size, cell number and cell size. Brain Behav Evol (2021). PubMed

Isler K., van Schaik C. P., The expensive brain: A framework for explaining evolutionary changes in brain size. J. Hum. Evol. 57, 392–400 (2009). PubMed

Isler K., van Schaik C., Costs of encephalization: The energy trade-off hypothesis tested on birds. J. Hum. Evol. 51, 228–243 (2006). PubMed

Armstrong E., Bergeron R., Relative brain size and metabolism in birds. Brain Behav. Evol. 26, 141–153 (1985). PubMed

Yu Y., Karbowski J., Sachdev R. N., Feng J., Effect of temperature and glia in brain size enlargement and origin of allometric body-brain size scaling in vertebrates. BMC Evol. Biol. 14, 178 (2014). PubMed PMC

Herculano-Houzel S., Scaling of brain metabolism with a fixed energy budget per neuron: Implications for neuronal activity, plasticity and evolution. PLoS One 6, e17514 (2011). PubMed PMC

Niven J. E., Laughlin S. B., Energy limitation as a selective pressure on the evolution of sensory systems. J. Exp. Biol. 211, 1792–1804 (2008). PubMed

Hyder F., Rothman D. L., Bennett M. R., Cortical energy demands of signaling and nonsignaling components in brain are conserved across mammalian species and activity levels. Proc. Natl. Acad. Sci. U.S.A. 110, 3549–3554 (2013). PubMed PMC

Else P. L., Hulbert A. J., Comparison of the “mammal machine” and the “reptile machine”: Energy production. Am. J. Physiol. 240, R3–R9 (1981). PubMed

Nagy K. A., Girard I. A., Brown T. K., Energetics of free-ranging mammals, reptiles, and birds. Annu. Rev. Nutr. 19, 247–277 (1999). PubMed

Goldman B. D., Goldman S. L., Lanz T., Magaurin A., Maurice A., Factors influencing metabolic rate in naked mole-rats (Heterocephalus glaber). Physiol. Behav. 66, 447–459 (1999). PubMed

Larson J., Park T. J., Extreme hypoxia tolerance of naked mole-rat brain. Neuroreport 20, 1634–1637 (2009). PubMed

Barton R. A., Embodied cognitive evolution and the cerebellum. Philos. Trans. R. Soc. Lond. B Biol. Sci. 367, 2097–2107 (2012). PubMed PMC

Smaers J. B., Turner A. H., Gómez-Robles A., Sherwood C. C., A cerebellar substrate for cognition evolved multiple times independently in mammals. eLife 7, e35696 (2018). PubMed PMC

Gutiérrez-Ibáñez C., Iwaniuk A. N., Wylie D. R., Parrots have evolved a primate-like telencephalic-midbrain-cerebellar circuit. Sci. Rep. 8, 9960 (2018). PubMed PMC

Schmahmann J. D., Guell X., Stoodley C. J., Halko M. A., The theory and neuroscience of cerebellar cognition. Annu. Rev. Neurosci. 42, 337–364 (2019). PubMed

Stacho M., et al. , A cortex-like canonical circuit in the avian forebrain. Science 369, eabc5534 (2020). PubMed

Jarvis E. D., et al. , Global view of the functional molecular organization of the avian cerebrum: Mirror images and functional columns. J. Comp. Neurol. 521, 3614–3665 (2013). PubMed PMC

Güntürkün O., Bugnyar T., Cognition without cortex. Trends Cogn. Sci. 20, 291–303 (2016). PubMed

Sokolov A. A., Miall R. C., Ivry R. B., The cerebellum: Adaptive prediction for movement and cognition. Trends Cogn. Sci. 21, 313–332 (2017). PubMed PMC

Overington S. E., Morand-Ferron J., Boogert N. J., Lefebvre L., Technical innovations drive the relationship between innovativeness and residual brain size in birds. Anim. Behav. 78, 1001–1010 (2009).

Sayol F., et al. , Environmental variation and the evolution of large brains in birds. Nat. Commun. 7, 13971 (2016). PubMed PMC

Kabadayi C., Taylor L. A., von Bayern A. M. P., Osvath M., Ravens, New Caledonian crows and jackdaws parallel great apes in motor self-regulation despite smaller brains. R. Soc. Open Sci. 3, 160104 (2016). PubMed PMC

Smaers J. B., Mongle C. S., Safi K., Dechmann D. K. N., Allometry, evolution and development of neocortex size in mammals. Prog. Brain Res. 250, 83–107 (2019). PubMed

Reiner A., et al. ; Avian Brain Nomenclature Forum, Revised nomenclature for avian telencephalon and some related brainstem nuclei. J. Comp. Neurol. 473, 377–414 (2004). PubMed PMC

Mullen R. J., Buck C. R., Smith A. M., NeuN, a neuronal specific nuclear protein in vertebrates. Development 116, 201–211 (1992). PubMed

Herculano-Houzel S., Collins C. E., Wong P., Kaas J. H., Cellular scaling rules for primate brains. Proc. Natl. Acad. Sci. U.S.A. 104, 3562–3567 (2007). PubMed PMC

Neves K., et al. , Cellular scaling rules for the brain of afrotherians. Front. Neuroanat. 8, 5 (2014). PubMed PMC

Kazu R. S., Maldonado J., Mota B., Manger P. R., Herculano-Houzel S., Cellular scaling rules for the brain of Artiodactyla include a highly folded cortex with few neurons. Front. Neuroanat. 8, 128 (2014). PubMed PMC

Gabi M., et al. , Cellular scaling rules for the brains of an extended number of primate species. Brain Behav Evol 76, 32–44 (2010). PubMed PMC

Dos Santos S. E., et al. , Cellular scaling rules for the brains of marsupials: Not as “primitive” as expected. Brain Behav Evol 89, 48–63 (2017). PubMed

Sarko D. K., Catania K. C., Leitch D. B., Kaas J. H., Herculano-Houzel S., Cellular scaling rules of insectivore brains. Front. Neuroanat. 3, 8 (2009). PubMed PMC

Herculano-Houzel S., et al. , The elephant brain in numbers. Front. Neuroanat. 8, 46 (2014). PubMed PMC

Herculano-Houzel S., et al. , Updated neuronal scaling rules for the brains of Glires (rodents/lagomorphs). Brain Behav Evol 78, 302–314 (2011). PubMed PMC

Ribeiro P. F., Manger P. R., Catania K. C., Kaas J. H., Herculano-Houzel S., Greater addition of neurons to the olfactory bulb than to the cerebral cortex of eulipotyphlans but not rodents, afrotherians or primates. Front. Neuroanat. 8, 23 (2014). PubMed PMC

Gittleman J. L., Carnivore olfactory bulb size: Allometry, phylogeny and ecology. J. Zool. (Lond.) 225, 253–272 (1991).

Baron G., Stephan H., Frahm H. D., Comparative Neurobiology in Chiroptera. Volume 1: Macromorphology, Brain Structures, Tables and Atlases (Birkhäuser Verlag, 1996).

Grisham W., et al. , Brain volume fractions in mammals in relation to behavior in carnivores, primates, ungulates, and rodents. Brain Behav Evol 95, 102–112 (2020). PubMed

Rosen G. D., Williams R. W., Complex trait analysis of the mouse striatum: Independent QTLs modulate volume and neuron number. BMC Neurosci. 2, 5 (2001). PubMed PMC

Keller D., Erö C., Markram H., Cell densities in the mouse brain: A systematic review. Front. Neuroanat. 12, 83 (2018). PubMed PMC

Ngwenya A., et al. , The continuously growing central nervous system of the Nile crocodile (Crocodylus niloticus). Anat. Rec. (Hoboken) 296, 1489–1500 (2013). PubMed

Hudson D. M., Lutz P. L., Salt gland function in the leatherback sea turtle, Dermochelys coriacea. Copeia 1986, 247–249 (1986).

Jirak D., Janacek J., Volume of the crocodilian brain and endocast during ontogeny. PLoS One 12, e0178491 (2017). PubMed PMC

Burger J. R., George M. A., Leadbetter C., Shaikh F., The allometry of brain size in mammals. J. Mammal. 100, 276–283 (2019).

Stephan H., Methodische Studien über den quantitativen Vergleich architektonischer Struktureinheiten des Gehirns. Z. Wiss. Zool. 164, 143–173 (1960).

Tonini J. F. R., Beard K. H., Ferreira R. B., Jetz W., Pyron R. A., Fully-sampled phylogenies of squamates reveal evolutionary patterns in threat status. Biol. Conserv. 204, 23–31 (2016).

Upham N. S., Esselstyn J. A., Jetz W., Inferring the mammal tree: Species-level sets of phylogenies for questions in ecology, evolution, and conservation. PLoS Biol. 17, e3000494 (2019). PubMed PMC

Kumar S., Stecher G., Suleski M., Hedges S. B., TimeTree: A resource for timelines, timetrees, and divergence times. Mol. Biol. Evol. 34, 1812–1819 (2017). PubMed

R Core Team, R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, 2020).

Revell L. J., phytools: An R package for phylogenetic comparative biology (and other things). Methods Ecol. Evol. 3, 217–223 (2012).

Smaers J. B., Mongle C. S., evomap: R Package for the Evolutionary Mapping of Continuous Traits (2018).

Yu G., Using ggtree to visualize data on tree-like structures. Curr. Protoc. Bioinformatics 69, e96 (2020). PubMed

Gelman A., Rubin D. B., Inference from iterative simulation using multiple sequences. Stat. Sci. 7, 457–472 (1992).

Pinheiro J., Bates D., DebRoy S., Sarkar D.; R Core Team, nlme: Linear and Nonlinear Mixed Effects Models (2021).

Adams D. C., Comparing evolutionary rates for different phenotypic traits on a phylogeny using likelihood. Syst. Biol. 62, 181–192 (2013). PubMed

Adams D. C., Otárola‐Castillo E., geomorph: An R package for the collection and analysis of geometric morphometric shape data. Methods Ecol. Evol. 4, 393–399 (2013).

Smaers J. B., Mongle C. S., Kandler A., A multiple variance Brownian motion framework for estimating variable rates and inferring ancestral states. Biol. J. Linn. Soc. Lond. 118, 78–94 (2016).

Expanded olfactory system in ray-finned fishes capable of terrestrial exploration

Neuron numbers link innovativeness with both absolute and relative brain size in birds

Zobrazit více v PubMed

figshare
10.6084/m9.figshare