Neurovascular coupling is optimized to compensate for the increase in proton production from nonoxidative glycolysis and glycogenolysis during brain activation and maintain homeostasis of pH, pCO2 , and pO2.

During transient brain activation cerebral blood flow (CBF) increases substantially more than cerebral metabolic rate of oxygen consumption (CMRO2 ) resulting in blood hyperoxygenation, the basis of BOLD fMRI contrast. Explanations for the high CBF vs. CMRO2 slope, termed neurovascular coupling (NVC) constant, focused on maintainenance of tissue oxygenation to support mitochondrial ATP production. However, paradoxically the brain has a 3-fold lower oxygen extraction fraction (OEF) than other organs with high energy requirements, like heart and muscle during exercise. Here, we hypothesize that the NVC constant and the capillary oxygen mass transfer coefficient (which in combination determine OEF) are co-regulated during activation to maintain simultaneous homeostasis of pH and partial pressure of CO2 and O2 (pCO2 and pO2 ). To test our hypothesis, we developed an arteriovenous flux balance model for calculating blood and brain pH, pCO2 , and pO2 as a function of baseline OEF (OEF0 ), CBF, CMRO2 , and proton production by nonoxidative metabolism coupled to ATP hydrolysis. Our model was validated against published brain arteriovenous difference studies and then used to calculate pH, pCO2, and pO2 in activated human cortex from published calibrated fMRI and PET measurements. In agreement with our hypothesis, calculated pH, pCO2, and pO2 remained close to constant independently of CMRO2 in correspondence to experimental measurements of NVC and OEF0 . We also found that the optimum values of the NVC constant and OEF0 that ensure simultaneous homeostasis of pH, pCO2, and pO2 were remarkably similar to their experimental values. Thus, the high NVC constant is overall determined by proton removal by CBF due to increases in nonoxidative glycolysis and glycogenolysis. These findings resolve the paradox of the brain's high CBF yet low OEF during activation, and may contribute to explaining the vulnerability of brain function to reductions in blood flow and capillary density with aging and neurovascular disease.

White matter volume and white/gray matter ratio in mammalian species as a consequence of the universal scaling of cortical folding.

Because the white matter of the cerebral cortex contains axons that connect distant neurons in the cortical gray matter, the relationship between the volumes of the 2 cortical compartments is key for information transmission in the brain. It has been suggested that the volume of the white matter scales universally as a function of the volume of the gray matter across mammalian species, as would be expected if a global principle of wiring minimization applied. Using a systematic analysis across several mammalian clades, here we show that the volume of the white matter does not scale universally with the volume of the gray matter across mammals and is not optimized for wiring minimization. Instead, the ratio between volumes of gray and white matter is universally predicted by the same equation that predicts the degree of folding of the cerebral cortex, given the clade-specific scaling of cortical thickness, such that the volume of the gray matter (or the ratio of gray to total cortical volumes) divided by the square root of cortical thickness is a universal function of total cortical volume, regardless of the number of cortical neurons. Thus, the very mechanism that we propose to generate cortical folding also results in compactness of the white matter to a predictable degree across a wide variety of mammalian species.

Similar Microglial Cell Densities across Brain Structures and Mammalian Species: Implications for Brain Tissue Function.

Microglial cells play essential volume-related actions in the brain that contribute to the maturation and plasticity of neural circuits that ultimately shape behavior. Microglia can thus be expected to have similar cell sizes and even distribution both across brain structures and across species with different brain sizes. To test this hypothesis, we determined microglial cell densities (the inverse of cell size) using immunocytochemistry to Iba1 in samples of free cell nuclei prepared with the isotropic fractionator from brain structures of 33 mammalian species belonging to males and females of five different clades. We found that microglial cells constitute ∼7% of non-neuronal cells in different brain structures as well as in the whole brain of all mammalian species examined. Further, they vary little in cell density compared with neuronal cell densities within the cerebral cortex, across brain structures, across species within the same clade, and across mammalian clades. As a consequence, we find that one microglial cell services as few as one and as many as 100 neurons in different brain regions and species, depending on the local neuronal density. We thus conclude that the addition of microglial cells to mammalian brains is governed by mechanisms that constrain the size of these cells and have remained conserved over 200 million years of mammalian evolution. We discuss the probable consequences of such constrained size for brain function in health and disease.SIGNIFICANCE STATEMENT Microglial cells are resident macrophages of the CNS, with key functions in recycling synapses and maintaining the local environment in health and disease. We find that microglial cells occur in similar densities in the brains of different species and in the different structures of each individual brain, which indicates that these cells maintain a similar average size in mammalian evolution, suggesting in turn that the volume monitored by each microglial cell remains constant across mammals. Because the density of neurons is highly variable across the same brain structures and species, our finding implies that microglia-dependent functional recovery may be particularly difficult in those brain structures and species with high neuronal densities and therefore fewer microglial cells per neuron.

Birds have primate-like numbers of neurons in the forebrain.

Some birds achieve primate-like levels of cognition, even though their brains tend to be much smaller in absolute size. This poses a fundamental problem in comparative and computational neuroscience, because small brains are expected to have a lower information-processing capacity. Using the isotropic fractionator to determine numbers of neurons in specific brain regions, here we show that the brains of parrots and songbirds contain on average twice as many neurons as primate brains of the same mass, indicating that avian brains have higher neuron packing densities than mammalian brains. Additionally, corvids and parrots have much higher proportions of brain neurons located in the pallial telencephalon compared with primates or other mammals and birds. Thus, large-brained parrots and corvids have forebrain neuron counts equal to or greater than primates with much larger brains. We suggest that the large numbers of neurons concentrated in high densities in the telencephalon substantially contribute to the neural basis of avian intelligence.

No relative expansion of the number of prefrontal neurons in primate and human evolution.

Human evolution is widely thought to have involved a particular expansion of prefrontal cortex. This popular notion has recently been challenged, although controversies remain. Here we show that the prefrontal region of both human and nonhuman primates holds about 8% of cortical neurons, with no clear difference across humans and other primates in the distribution of cortical neurons or white matter cells along the anteroposterior axis. Further, we find that the volumes of human prefrontal gray and white matter match the expected volumes for the number of neurons in the gray matter and for the number of other cells in the white matter compared with other primate species. These results indicate that prefrontal cortical expansion in human evolution happened along the same allometric trajectory as for other primate species, without modification of the distribution of neurons across its surface or of the volume of the underlying white matter. We thus propose that the most distinctive feature of the human prefrontal cortex is its absolute number of neurons, not its relative volume.

Cellular Scaling Rules for the Brains of Marsupials: Not as "Primitive" as Expected.

In the effort to understand the evolution of mammalian brains, we have found that common relationships between brain structure mass and numbers of nonneuronal (glial and vascular) cells apply across eutherian mammals, but brain structure mass scales differently with numbers of neurons across structures and across primate and nonprimate clades. This suggests that the ancestral scaling rules for mammalian brains are those shared by extant nonprimate eutherians - but do these scaling relationships apply to marsupials, a sister group to eutherians that diverged early in mammalian evolution? Here we examine the cellular composition of the brains of 10 species of marsupials. We show that brain structure mass scales with numbers of nonneuronal cells, and numbers of cerebellar neurons scale with numbers of cerebral cortical neurons, comparable to what we have found in eutherians. These shared scaling relationships are therefore indicative of mechanisms that have been conserved since the first therians. In contrast, while marsupials share with nonprimate eutherians the scaling of cerebral cortex mass with number of neurons, their cerebella have more neurons than nonprimate eutherian cerebella of a similar mass, and their rest of brain has fewer neurons than eutherian structures of a similar mass. Moreover, Australasian marsupials exhibit ratios of neurons in the cerebral cortex and cerebellum over the rest of the brain, comparable to artiodactyls and primates. Our results suggest that Australasian marsupials have diverged from the ancestral Theria neuronal scaling rules, and support the suggestion that the scaling of average neuronal cell size with increasing numbers of neurons varies in evolution independently of the allocation of neurons across structures.

Continued Growth of the Central Nervous System without Mandatory Addition of Neurons in the Nile Crocodile (Crocodylus niloticus).

It is generally believed that animals with larger bodies require larger brains, composed of more neurons. Across mammalian species, there is a correlation between body mass and the number of brain neurons, albeit with low allometric exponents. If larger bodies imperatively require more neurons to operate them, then such an increase in the number of neurons should be detected across individuals of a continuously growing species, such as the Nile crocodile. In the current study we use the isotropic fractionator method of cell counting to determine how the number of neurons and non-neurons in 6 specific brain regions and the spinal cord change with increasing body mass in the Nile crocodile. The central nervous system (CNS) structures examined all increase in mass as a function of body mass, with allometric exponents of around 0.2, except for the spinal cord, which increases with an exponent of 0.6. We find that numbers of non-neurons increase slowly, but significantly, in all CNS structures, scaling as a function of body mass with exponents ranging between 0.1 and 0.3. In contrast, numbers of neurons scale with body mass in the spinal cord, olfactory bulb, cerebellum and telencephalon, with exponents of between 0.08 and 0.20, but not in the brainstem and diencephalon, the brain structures that receive inputs and send outputs to the growing body. Densities of both neurons and non-neurons decrease with increasing body mass. These results indicate that increasing body mass with growth in the Nile crocodile is associated with a general addition of non-neurons and increasing cell size throughout CNS structures, but is only associated with an addition of neurons in some structures (and at very small rates) and not in those brain structures directly connected to the body. Larger bodies thus do not imperatively require more neurons to operate them.

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

Mammals sleep between 3 and 20 h d(-1), 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 sleep-inducing 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.

How to count cells: the advantages and disadvantages of the isotropic fractionator compared with stereology.

The number of cells comprising biological structures represents fundamental information in basic anatomy, development, aging, drug tests, pathology and genetic manipulations. Obtaining unbiased estimates of cell numbers, however, was until recently possible only through stereological techniques, which require specific training, equipment, histological processing and appropriate sampling strategies applied to structures with a homogeneous distribution of cell bodies. An alternative, the isotropic fractionator (IF), became available in 2005 as a fast and inexpensive method that requires little training, no specific software and only a few materials before it can be used to quantify total numbers of neuronal and non-neuronal cells in a whole organ such as the brain or any dissectible regions thereof. This method entails transforming a highly anisotropic tissue into a homogeneous suspension of free-floating nuclei that can then be counted under the microscope or by flow cytometry and identified morphologically and immunocytochemically as neuronal or non-neuronal. We compare the advantages and disadvantages of each method and provide researchers with guidelines for choosing the best method for their particular needs. IF is as accurate as unbiased stereology and faster than stereological techniques, as it requires no elaborate histological processing or sampling paradigms, providing reliable estimates in a few days rather than many weeks. Tissue shrinkage is also not an issue, since the estimates provided are independent of tissue volume. The main disadvantage of IF, however, is that it necessarily destroys the tissue analyzed and thus provides no spatial information on the cellular composition of biological regions of interest.

Mammalian Brains Are Made of These: A Dataset of the Numbers and Densities of Neuronal and Nonneuronal Cells in the Brain of Glires, Primates, Scandentia, Eulipotyphlans, Afrotherians and Artiodactyls, and Their Relationship with Body Mass.

Comparative studies amongst extant species are one of the pillars of evolutionary neurobiology. In the 20th century, most comparative studies remained restricted to analyses of brain structure volume and surface areas, besides estimates of neuronal density largely limited to the cerebral cortex. Over the last 10 years, we have amassed data on the numbers of neurons and other cells that compose the entirety of the brain (subdivided into cerebral cortex, cerebellum, and rest of brain) of 39 mammalian species spread over 6 clades, as well as their densities. Here we provide that entire dataset in a format that is readily useful to researchers of any area of interest in the hope that it will foster the advancement of evolutionary and comparative studies well beyond the scope of neuroscience itself. We also reexamine the relationship between numbers of neurons, neuronal densities and body mass, and find that in the rest of brain, but not in the cerebral cortex or cerebellum, there is a single scaling rule that applies to average neuronal cell size, which increases with the linear dimension of the body, even though there is no single scaling rule that relates the number of neurons in the rest of brain to body mass. Thus, larger bodies do not uniformly come with more neurons--but they do fairly uniformly come with larger neurons in the rest of brain, which contains a number of structures directly connected to sources or targets in the body.

The remarkable, yet not extraordinary, human brain as a scaled-up primate brain and its associated cost.

Neuroscientists have become used to a number of "facts" about the human brain: It has 100 billion neurons and 10- to 50-fold more glial cells; it is the largest-than-expected for its body among primates and mammals in general, and therefore the most cognitively able; it consumes an outstanding 20% of the total body energy budget despite representing only 2% of body mass because of an increased metabolic need of its neurons; and it is endowed with an overdeveloped cerebral cortex, the largest compared with brain size. These facts led to the widespread notion that the human brain is literally extraordinary: an outlier among mammalian brains, defying evolutionary rules that apply to other species, with a uniqueness seemingly necessary to justify the superior cognitive abilities of humans over mammals with even larger brains. These facts, with deep implications for neurophysiology and evolutionary biology, are not grounded on solid evidence or sound assumptions, however. Our recent development of a method that allows rapid and reliable quantification of the numbers of cells that compose the whole brain has provided a means to verify these facts. Here, I review this recent evidence and argue that, with 86 billion neurons and just as many nonneuronal cells, the human brain is a scaled-up primate brain in its cellular composition and metabolic cost, with a relatively enlarged cerebral cortex that does not have a relatively larger number of brain neurons yet is remarkable in its cognitive abilities and metabolism simply because of its extremely large number of neurons.

Metabolic constraint imposes tradeoff between body size and number of brain neurons in human evolution.

Despite a general trend for larger mammals to have larger brains, humans are the primates with the largest brain and number of neurons, but not the largest body mass. Why are great apes, the largest primates, not also those endowed with the largest brains? Recently, we showed that the energetic cost of the brain is a linear function of its numbers of neurons. Here we show that metabolic limitations that result from the number of hours available for feeding and the low caloric yield of raw foods impose a tradeoff between body size and number of brain neurons, which explains the small brain size of great apes compared with their large body size. This limitation was probably overcome in Homo erectus with the shift to a cooked diet. Absent the requirement to spend most available hours of the day feeding, the combination of newly freed time and a large number of brain neurons affordable on a cooked diet may thus have been a major positive driving force to the rapid increased in brain size in human evolution.

The glia/neuron ratio: how it varies uniformly across brain structures and species and what that means for brain physiology and evolution.

It is a widespread notion that the proportion of glial to neuronal cells in the brain increases with brain size, to the point that glial cells represent "about 90% of all cells in the human brain." This notion, however, is wrong on both counts: neither does the glia/neuron ratio increase uniformly with brain size, nor do glial cells represent the majority of cells in the human brain. This review examines the origin of interest in the glia/neuron ratio; the original evidence that led to the notion that it increases with brain size; the extent to which this concept can be applied to white matter and whole brains and the recent supporting evidence that the glia/neuron ratio does not increase with brain size, but rather, and in surprisingly uniform fashion, with decreasing neuronal density due to increasing average neuronal cell size, across brain structures and species. Variations in the glia/neuron ratio are proposed to be related not to the supposed larger metabolic cost of larger neurons (given that this cost is not found to vary with neuronal density), but simply to the large variation in neuronal sizes across brain structures and species in the face of less overall variation in glial cell sizes, with interesting implications for brain physiology. The emerging evidence that the glia/neuron ratio varies uniformly across the different brain structures of mammalian species that diverged as early as 90 million years ago in evolution highlights how fundamental for brain function must be the interaction between glial cells and neurons.

Folding of the cerebellar cortex is clade-specific in form but universal in degree.

Like the cerebralcortex, the surface of the cerebellum is repeatedly folded. Unlike the cerebralcortex, however, cerebellar folds are much thinner and more numerous; repeatthemselves largely along a single direction, forming accordion-like folds transverseto the mid-sagittal plane; and occur in all but the smallest cerebella. We haveshown previously that while the location of folds in mammalian cerebral cortex isclade-specific, the overall degree of folding strictly follows a universalpower law relating cortical thickness and the exposed and total surface areas predictedfrom the minimization of the effective free energy of an expanding, self-avoidingsurface of a certain thickness. Here we show that this scaling law extends tothe folding of the mid-sagittal sections of the cerebellum of 53 speciesbelonging to six mammalian clades. Simultaneously, we show that each clade hasa previously unsuspected distinctive spatial pattern of folding evident at themid-sagittal surface of the cerebellum. We note, however, that the mammaliancerebellum folds as a multi-fractal object, because of the difference betweenthe outside-in development of the cerebellar cortex around a preexisting coreof already connected white matter, compared to the inside-out development ofthe cerebral cortex with a white matter volume that develops as the cerebralcortex itself gains neurons. We conclude that repeated folding, one of the mostrecognizable features of biology, can arise simply from the interplay betweenthe universal applicability of the physics of self-organization and biological,phylogenetical clade-specific contingency, without the need for invokingselective pressures in evolution.

Faster scaling of auditory neurons in cortical areas relative to subcortical structures in primate brains.

Allometric studies in primates have shown that the cerebral cortex, cerebellum, and remaining brain structures increase in size as a linear function of their numbers of neurons and nonneuronal cells across primates. Whether such scaling rules also apply to functionally related structures such as those of the auditory system is unknown. Here, we investigate the scaling of brain structures in the auditory pathway of six primate species and the closely related tree shrew. Using the isotropic fractionator method to estimate the numbers of neurons and nonneuronal cells in the inferior colliculus, medial geniculate nucleus, and auditory cortex (Ac), we assessed how they scaled across species and examined the relative scaling relationships among them. As expected, each auditory structure scales in mass as a linear function of its number of neurons, with no significant changes in neuronal density across species. The Ac scales proportionately with the cerebral cortex as a whole, maintaining a relative mass of approximately 1% and a relative number of neurons of 0.7%. However, the Ac gains neurons faster than both subcortical structures examined. As a result, larger primate brains have increased ratios of cortical to subcortical neurons involved in processing auditory information.

Connectivity-driven white matter scaling and folding in primate cerebral cortex.

Larger brains have an increasingly folded cerebral cortex whose white matter scales up faster than the gray matter. Here we analyze the cellular composition of the subcortical white matter in 11 primate species, including humans, and one Scandentia, and show that the mass of the white matter scales linearly across species with its number of nonneuronal cells, which is expected to be proportional to the total length of myelinated axons in the white matter. This result implies that the average axonal cross-section area in the white matter, a, does not scale significantly with the number of neurons in the gray matter, N. The surface area of the white matter increases with N(0.87), not N(1.0). Because this surface can be defined as the product of N, a, and the fraction n of cortical neurons connected through the white matter, we deduce that connectivity decreases in larger cerebral cortices as a slowly diminishing fraction of neurons, which varies with N(-0.16), sends myelinated axons into the white matter. Decreased connectivity is compatible with previous suggestions that neurons in the cerebral cortex are connected as a small-world network and should slow down the increase in global conduction delay in cortices with larger numbers of neurons. Further, a simple model shows that connectivity and cortical folding are directly related across species. We offer a white matter-based mechanism to account for increased cortical folding across species, which we propose to be driven by connectivity-related tension in the white matter, pulling down on the gray matter.

Theropod dinosaurs had primate-like numbers of telencephalic neurons.

Understanding the neuronal composition of the brains of dinosaurs and other fossil amniotes would offer fundamental insight into their behavioral and cognitive capabilities, but brain tissue is only rarely fossilized. However, when the bony brain case is preserved, the volume and therefore mass of the brain can be estimated with computer tomography; and if the scaling relationship between brain mass and numbers of neurons for the clade is known, that relationship can be applied to estimate the neuronal composition of the brain. Using a recently published database of numbers of neurons in the telencephalon of extant sauropsids (birds, squamates, and testudines), here I show that the neuronal scaling rules that apply to these animals can be used to infer the numbers of neurons that composed the telencephalon of dinosaur, pterosaur, and other fossil sauropsid species. The key to inferring numbers of telencephalic neurons in these species is first using the relationship between their estimated brain and body mass to determine whether bird-like (endothermic) or squamate-like (ectothermic) rules apply to each fossil sauropsid species. This procedure shows that the notion of "mesothermy" in dinosaurs is an artifact due to the mixing of animals with bird-like and squamate-like scaling, and indicates that theropods such as Tyrannosaurus and Allosaurus were endotherms with baboon- and monkey-like numbers of telencephalic neurons, respectively, which would make these animals not only giant but also long-lived and endowed with flexible cognition, and thus even more magnificent predators than previously thought.

Changing numbers of neuronal and non-neuronal cells underlie postnatal brain growth in the rat.

The rat brain increases >6x in mass from birth to adulthood, presumably through the addition of glial cells and increasing neuronal size, without the addition of neurons. To test this hypothesis, here we investigate quantitatively the postnatal changes in the total number of neuronal and non-neuronal cells in the developing rat brain, and examine how these changes correlate with brain growth. Total numbers of cells were determined with the isotropic fractionator in the brains of 53 Wistar rats, from birth to young adulthood. We find that at birth, >90% of the cells in the rat brain are neurons. Following a dormant period of approximately 3 days after birth, the net number of neurons in the cerebral cortex, hippocampus, and remaining tissue (excluding cerebellum and olfactory bulb) doubles during the first week, then is reduced by 70% during the second postnatal week, concurrently with net gliogenesis. A second round of net addition of 6 million neurons is observed in the cerebral cortex over the following 2 weeks. During the first postnatal week, brain growth relates mainly to increased numbers of neurons of larger average size. In the second and third weeks, it correlates with increased numbers of non-neuronal cells that are smaller in size than the preexisting neurons. Postnatal rat brain development is thus characterized by dramatic changes in the cellular composition of the brain, whose growth is governed by different combinations of cell addition and loss, and changes in average cell size during the first months after birth.

Mammalian neurobiology: The elephant (brain) in the room.

Neuroscience research is understandably focused on highly tractable and lab-friendly mice and rats, but that emphasis obfuscates the biological beauty and intellectual richness that lies in animal diversity. The benefits of venturing further into that phylogenetic diversity are nicely illustrated by a new study on the elephant brain.

From a Demand-Based to a Supply-Limited Framework of Brain Metabolism.

What defines the rate of energy use by the brain, as well as per neurons of different sizes in different structures and animals, is one fundamental aspect of neuroscience for which much has been theorized, but very little data are available. The prevalent theories and models consider that energy supply from the vascular system to different brain regions is adjusted both dynamically and in the course of development and evolution to meet the demands of neuronal activity. In this perspective, we offer an alternative view: that regional rates of energy use might be mostly constrained by supply, given the properties of the brain capillary network, the highly stable rate of oxygen delivery to the whole brain under physiological conditions, and homeostatic constraints. We present evidence that these constraints, based on capillary density and tissue oxygen homeostasis, are similar between brain regions and mammalian species, suggesting they derive from fundamental biophysical limitations. The same constraints also determine the relationship between regional rates of brain oxygen supply and usage over the full physiological range of brain activity, from deep sleep to intense sensory stimulation, during which the apparent uncoupling of blood flow and oxygen use is still a predicted consequence of supply limitation. By carefully separating "energy cost" into energy supply and energy use, and doing away with the problematic concept of energetic "demands," our new framework should help shine a new light on the neurovascular bases of metabolic support of brain function and brain functional imaging. We speculate that the trade-offs between functional systems and even the limitation to a single attentional spot at a time might be consequences of a strongly supply-limited brain economy. We propose that a deeper understanding of brain energy supply constraints will provide a new evolutionary understanding of constraints on brain function due to energetics; offer new diagnostic insight to disturbances of brain metabolism; lead to clear, testable predictions on the scaling of brain metabolic cost and the evolution of brains of different sizes; and open new lines of investigation into the microvascular bases of progressive cognitive loss in normal aging as well as metabolic diseases.