Analysis of data collected on 131 species of primates, bats, and insectivores showed that the sizes of brain components, from medulla to forebrain, are highly predictable from absolute brain size by a nonlinear function. The order of neurogenesis was found to be highly conserved across a wide range of mammals and to correlate with the relative enlargement of structures as brain size increases, with disproportionately large growth occurring in late-generated structures. Because the order of neurogenesis is conserved, the most likely brain alteration resulting from selection for any behavioral ability may be a coordinated enlargement of the entire nonolfactory brain.

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A general model of neural development is derived to fit 18 mammalian species, including humans, macaques, several rodent species, and six metatherian (marsupial) mammals. The goal of this work is to describe heterochronic changes in brain evolution within its basic developmental allometry, and provide an empirical basis to recognize equivalent maturational states across animals. The empirical data generating the model comprises 271 developmental events, including measures of initial neurogenesis, axon extension, establishment, and refinement of connectivity, as well as later events such as myelin formation, growth of brain volume, and early behavioral milestones, to the third year of human postnatal life. The progress of neural events across species is sufficiently predictable that a single model can be used to predict the timing of all events in all species, with a correlation of modeled values to empirical data of 0.9929. Each species' rate of progress through the event scale, described by a regression equation predicting duration of development in days, is highly correlated with adult brain size. Neural heterochrony can be seen in selective delay of retinogenesis in the cat, associated with greater numbers of rods in its retina, and delay of corticogenesis in all species but rodents and the rabbit, associated with relatively larger cortices in species with delay. Unexpectedly, precocial mammals (those unusually mature at birth) delay the onset of first neurogenesis but then progress rapidly through remaining developmental events.

To better understand the neurotoxic effects of diverse hazards on the developing human nervous system, researchers and clinicians rely on data collected from a number of model species that develop and mature at varying rates. We review the methods commonly used to extrapolate the timing of brain development from experimental mammalian species to humans, including morphological comparisons, "rules of thumb" and "event-based" analyses. Most are unavoidably limited in range or detail, many are necessarily restricted to rat/human comparisons, and few can identify brain regions that develop at different rates. We suggest this issue is best addressed using "neuroinformatics", an analysis that combines neuroscience, evolutionary science, statistical modeling and computer science. A current use of this approach relates numeric values assigned to ten mammalian species and hundreds of empirically derived developing neural events, including specific evolutionary advances in primates. The result is an accessible, online resource (http:// www.translatingtime.net/) that can be used to equate dates in the neurodevelopmental literature across laboratory species to humans, predict neurodevelopmental events for which data are lacking in humans, and help to develop clinically relevant experimental models.

How does evolution grow bigger brains? It has been widely assumed that growth of individual structures and functional systems in response to niche-specific cognitive challenges is the most plausible mechanism for brain expansion in mammals. Comparison of multiple regressions on allometric data for 131 mammalian species, however, suggests that for 9 of 11 brain structures taxonomic and body size factors are less important than covariance of these major structures with each other. Which structure grows biggest is largely predicted by a conserved order of neurogenesis that can be derived from the basic axial structure of the developing brain. This conserved order of neurogenesis predicts the relative scaling not only of gross brain regions like the isocortex or mesencephalon, but also the level of detail of individual thalamic nuclei. Special selection of particular areas for specific functions does occur, but it is a minor factor compared to the large-scale covariance of the whole brain. The idea that enlarged isocortex could be a “spandrel,” a by-product of structural constraints later adapted for various behaviors, contrasts with approaches to selection of particular brain regions for cognitively advanced uses, as is commonly assumed in the case of hominid brain evolution.

1. The properties of single cells in striate cortex of the rhesus monkey, representing the visual field 2 degrees -5 degrees from the fovea, were examined quantitatively with stationary and moving stimuli. Three distinct classes of cells were identified: S type, CX type, and T type. 2. S-type cells were defined as those oriented cells which to the optimal direction of movement in their receptive fields exhibited one or more spatially separate subfields within each of which a response was obtained to either a light or dark edge, but not to both. Several different types of S-cells were distinguished: a) S1-type cells for which moving edges revealed a single excitatory area within which a response was elicited by either a light or a dark edge but not by both. Most of these cells were unidirectional. b) S2-type cells for which moving edges revealed two spatially separate response areas, one of which was excited by a light edge and the other by a dark edge. Both regions responded to the same direction of movement. c) S3-type cells which had two response areas, one of which was excited by a stimulus moving in one direction (at right angles to the axis of orientation) and the other, of opposite contrast, which responded in the opposite direction, d) S4-type cells which to one direction of movement showed two spatially separate regions sensitive to a light and dark edge and which in the other direction of movement had only one responsive area (either light or dark). e) Cells which had multiple spatially separate subfields (S5-7 types). 3. CX-type cells were defined as those oriented cells which in their receptive fields exhibited no spatial separation for light- and dark-edge responses; they discharged to both edges in the same direction of movement and in the same spatial area. Flashing stimuli elicited both on and off responses throughout the receptive field. CX-type cells were predominantly of two types: those which were selective for direction of stimulus movement and those which were not. 4. A third class of cells (T-type) were those which were excited by only one sign of contrast change and responded in a sustained fashion even when there was no contour within the receptive field. These cells were poorly or not at all oriented; some of them were selective to wavelength. 5. Quantitative comparisons showed the following differences between S-type and CX-type cells: a) S-type cells had smaller receptive fields than CX-type cells but the populations over-lapped considerably. Receptive-field size was smallest in layer 4c. In all other layers S-type cells had the same size fields. CX-type cells, by contrast, tended to have larger fields in layer 5-6 than 2-3. b) The spatial separation between light and dark response areas was the best criterion for distinguishing S-type and CX-type cells. The distribution of this measure disclosed two populations of cells with relatively limited overlap. c) In layers 2 and 3, both S-type and CX-type cells had low spontaneous activity...

Biomedical researchers and medical professionals are regularly required to compare a vast quantity of neurodevelopmental literature obtained from an assortment of mammals whose brains grow at diverse rates, including fast developing experimental rodent species and slower developing humans. In this article, we introduce a database-driven website, which was created to address this problem using statistical-based algorithms to integrate hundreds of empirically derived developing neural events in 10 mammalian species (http://translatingtime.net/). The site, based on a statistical model that has evolved over the past decade, currently incorporates 102 different neurodevelopmental events obtained from 10 species: hamsters, mice, rats, rabbits, spiny mice, guinea pigs, ferrets, cats, rhesus monkeys, and humans. Data are arranged in a Structured Query Language database, which allows comparative brain development measured in postconception days to be converted and accessed in real time, using Hypertext Preprocessor language. Algorithms applied to the database also allow predictions for dates of specific neurodevelopmental events where empirical data are not available, including for the human embryo and fetus. By designing a web-based portal, we seek to make these comparative data readily available to all those who need to efficiently estimate the timing of neurodevelopmental events in the human fetus, laboratory species, or across several different species. In an effort to further refine and expand the applicability of this database, we include a mechanism to submit additional data.

Several patterns of brain allometry previously observed in mammals have been found to hold for sharks and related taxa (chondrichthyans) as well. In each clade, the relative size of brain parts, with the notable exception of the olfactory bulbs, is highly predictable from the total brain size. Compared with total brain mass, each part scales with a characteristic slope, which is highest for the telencephalon and cerebellum. In addition, cerebellar foliation reflects both absolute and relative cerebellar size, in a manner analogous to mammalian cortical gyrification. This conserved pattern of brain scaling suggests that the fundamental brain plan that evolved in early vertebrates permits appropriate scaling in response to a range of factors, including phylogeny and ecology, where neural mass may be added and subtracted without compromising basic function.T he allometric relationship of brain parts to overall brain size has been studied and debated extensively (1-7). At the core of the debate lies the question of whether the brain is best characterized as a collection of independently varying structures/devices evolved for particular behavioral requirements or niches or as a single coordinated processing structure/device in which adaptation for species-specific behavioral capacities occurs without the production of delineable modules (8, 9). Many methodological issues have arisen as well, including what about a brain should be quantified [cells or volumes (10)], what should be compared and how, and how to take into account the statistical dependence of both structural and species relationships (11).Until recently, a single data corpus comprising primates, bats, and insectivorous mammals was the sole source for comparison (2), leaving the question of whether these mammals represented all vertebrates, or even all other mammals, unresolved. The addition of carnivorous mammals (including marine mammals), ungulates, xenarthrans, and the manatee demonstrated that the original conclusions drawn from primates, bats, and insectivores could be extended to this larger data set (8, 12). These studies revealed that mammalian brain structure exhibits a pattern of variation containing two principal components. The first component, accounting for ≈96% of the total variance of related brain parts to total brain size, loads most highly on neocortex and cerebellum. The second component loads most highly on the olfactory bulb and associated limbic structures and accounts for ≈3% of the original variance. Each brain part also has a characteristic slope with respect to absolute brain size, such that every large mammalian brain is composed disproportionately of neocortex and cerebellum. The remaining 1% of the variance must subsume all remaining sources, including niche, sex and individual differences, and measurement error. This 1% contribution is large in one sense: In two species with the same brain size, a single structure might differ by a factor of 2.5. The total range of structure sizes may differ by a factor of 100,000 or more b...