Different scaling of white matter volume, cortical connectivity, and gyrification across rodent and primate brains

Ventura-Antunes, Lissa; Mota, Bruno; Herculano‐Houzel, Suzana

doi:10.3389/fnana.2013.00003

Cited by 105 publications

(83 citation statements)

References 27 publications

(77 reference statements)

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“…In contrast, Lewitus et al (5), who did not analyze our data on artiodactyls, arbitrarily defined two groups as those with folding index above 1.5 (the larger primates in their sample, including humans) and below 1.5 (rodents and the smallest primates, which includes lissencephalic species). This is a finding that essentially replicated our previous demonstration that the relationship between folding index and number of neurons differs across primates and rodents (6), without giving credit to it. Lewitus et al (5) went on to propose that this arbitrary threshold of 1.5 corresponds to~10 9 neurons.…”

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confidence: 79%

Response to Comments on “Cortical folding scales universally with surface area and thickness, not number of neurons”

Mota

Herculano‐Houzel

2016

Science

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De Lussanet claims that our model that accounts for the degree of folding of the cerebral cortex based on the product of cortical surface area and the square root of cortical thickness is better reduced to the product of gray-matter proportion and folding index. Lewitus et al., in turn, claim that the assumptions of our model are in conflict with experimental data; that the model does not accurately fit the data; and that the ancestral mammalian brain was gyrencephalic. Here, we show that both claims are inappropriate. In our original Report (1), we showed for the first time that the relationship between total surface area (A T ) and exposed surface area (A E ) of the mammalian cerebral cortex can be universally described, across lissencephalic and gyrencephalic species alike, by the power, where T is the average thickness of the cortical gray matter (Fig. 1A). Our model predicts the degree of folding of the mammalian cerebral cortex (that is, the relationship between A T and A E ) with an r 2 of 0.998; that is, 99.8% of the variance in A T T 1/2 across species is explained by the variation in kA E 1.305 . The empirical function is very close to our theoretical function A T T 1/2 = kA E 1.25 that expresses the relationship between A T and A E , given T, that minimizes the effective free energy of a growing cortical volume-that is, the function that describes the most stable conformation of a growing, elastic, deformable cerebral cortex given its surface area and thickness. We thus propose that folding occurs as the expanding cortex, during development, settles at each moment in time into the most energetically favorable conformation, one that depends simply on its current combination of A T and T, not number of neurons.Based on the calculation from our data of values of k for the theoretical exponent of 1.25, de Lussanet notes that the residuals of the function, that is, k, vary systematically with increasing A E across species (2). This indicates a (presumably hidden) significant discrepancy between the exponent for A E predicted by our effective free energy-minimization theory and the empirical exponent 1.305 (excluding cetaceans) that is found in the data. Our original Report makes it clear that, with a value of 1.305 ± 0.010, the exponent for A E that best fits the data is close to, but statistically distinguishable (given the standard error) from, the theoretical exponent of 1.250, which is not surprising for a mean-field model based on a number of simplifying assumptions.The origin of the discrepancy of 0.055 in the scaling exponent is an important question. In purely theoretical terms, the use of A E as an order parameter was a necessary simplification, based on the expected relationship V T~AE 3/2 [made explicitly in the supplementary information in (1), and a good empirical approximation, given that we obtain V T~AE 1.52 ]. Whether modifications to correct for Gaussian curvature of the surface and the presence of deep nuclei would suffice to close the gap of 0.055 between theoretical and empirical e...

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confidence: 79%

Response to Comments on “Cortical folding scales universally with surface area and thickness, not number of neurons”

Mota

Herculano‐Houzel

2016

Science

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“…Where they exist, patterns of genetic covariance may even suggest counterintuitive patterns of covariance. For example, Rogers et al [53] report a negative genetic correlation between cerebral volume and gyrification in both Papio and humans despite their positive evolutionary relationship during primate brain evolution [54] (but see [55]). Anatomical covariation [56] and genome-wide association studies in humans provide further evidence of independence in brain component variability [57,58].…”

Section: (A) Selective Decoupling Of Coevolving Traitsmentioning

confidence: 99%

Brain evolution and development: adaptation, allometry and constraint

2016

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Phenotypic traits are products of two processes: evolution and development. But how do these processes combine to produce integrated phenotypes? Comparative studies identify consistent patterns of covariation, or allometries, between brain and body size, and between brain components, indicating the presence of significant constraints limiting independent evolution of separate parts. These constraints are poorly understood, but in principle could be either developmental or functional. The developmental constraints hypothesis suggests that individual components (brain and body size, or individual brain components) tend to evolve together because natural selection operates on relatively simple developmental mechanisms that affect the growth of all parts in a concerted manner. The functional constraints hypothesis suggests that correlated change reflects the action of selection on distributed functional systems connecting the different sub-components, predicting more complex patterns of mosaic change at the level of the functional systems and more complex genetic and developmental mechanisms. These hypotheses are not mutually exclusive but make different predictions. We review recent genetic and neurodevelopmental evidence, concluding that functional rather than developmental constraints are the main cause of the observed patterns. How brains evolve: the importance of scaling relationshipsThe components of any adaptive complex by definition undergo coordinated evolution. Brains, bodies and individual brain components therefore exhibit distinctive patterns of correlated evolution. But what do these patterns tell us about the roles of adaptation and constraint in shaping phenotypes? In particular, how and to what extent do constraints imposed by shared developmental programmes dictate allometric relationships between components, limiting their response to selection? These questions have shaped two key debates central to how we view brain evolution: the functional relevance of brain size and the adaptive potential of brain structure [1][2][3]. These debates hinge on whether observed patterns of scaling relationships, between brain and body size or different brain components, are the product of selection to maintain functional correspondence or constraints imposed by shared developmental programmes. Crucially, however, a sound understanding of the significance of scaling relationships in brain evolution has been limited by a lack of data on the genetic and developmental mechanisms that regulate brain size and structure. Here we discuss how recent discoveries about the genetic control of neural development shed new light on the issue.(a) Brain: body coevolution and the importance of size

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“…Here I examine this second prediction that total sleep duration decreases together with neuronal density mm 22 across mammalian species and in their development. To this end, I present an analysis of a set of 24 species belonging to six mammalian clades for which cortical numbers of neurons, neuronal density and surface area were available [18][19][20][21][22][23][24][25][26][27][28][29], as well as data for total number of sleep hours per day [7]. Data are provided in table 1.…”

Section: Introductionmentioning

confidence: 99%

Decreasing sleep requirement with increasing numbers of neurons as a driver for bigger brains and bodies in mammalian evolution

Herculano‐Houzel

2015

Proc. R. Soc. B.

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Mammals sleep between 3 and 20 h d 21 , but what regulates daily sleep requirement is unknown. While mammalian evolution has been characterized by a tendency towards larger bodies and brains, sustaining larger bodies and brains requires increasing hours of feeding per day, which is incompatible with a large sleep requirement. Mammalian evolution, therefore, must involve mechanisms that tie increasing body and brain size to decreasing sleep requirements. Here I show that daily sleep requirement decreases across mammalian species and in rat postnatal development with a decreasing ratio between cortical neuronal density and surface area, which presumably causes sleepinducing metabolites to accumulate more slowly in the parenchyma. Because addition of neurons to the non-primate cortex in mammalian evolution decreases this ratio, I propose that increasing numbers of cortical neurons led to decreased sleep requirement in evolution that allowed for more hours of feeding and increased body mass, which would then facilitate further increases in numbers of brain neurons through a larger caloric intake per hour. Coupling of increasing numbers of neurons to decreasing sleep requirement and increasing hours of feeding thus may have not only allowed but also driven the trend of increasing brain and body mass in mammalian evolution.

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Different scaling of white matter volume, cortical connectivity, and gyrification across rodent and primate brains

Cited by 105 publications

References 27 publications

Response to Comments on “Cortical folding scales universally with surface area and thickness, not number of neurons”

Response to Comments on “Cortical folding scales universally with surface area and thickness, not number of neurons”

Brain evolution and development: adaptation, allometry and constraint

Decreasing sleep requirement with increasing numbers of neurons as a driver for bigger brains and bodies in mammalian evolution

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