In order to limit the need to contrast statistical methodologies of successive papers within the text, the following description of species, neural structures, developmental span, and mathematical models employed in this research project follows, including immediately relevant work of several other laboratories. First, the current database for the "Translating Time" model with tables of species, structures and sources, a description of the current model, and a utility to translate or predict a developmental equivalent day between any two species in the model can be found at www.translatingtime.org (Clancy et al., 2007). The initial comparison of neurogenetic schedules in rhesus monkey, cat, four rodents and a marsupial, extending from onset of neurogenesis to approximately birth in the monkey, using principal components analysis, is described in Finlay and Darlington (1995) and an extended discussion of statistical considerations, principally phylogenetic covariation can be found in Finlay et al. (2001). Darlington et al. (1999) and Clancy et al. (2000) bring the number of species to nine eutherian (placental) mammals including humans and six metatherians (marsupials), principally using regression analyses. Clancy et al. (2001) extend the neurodevelopmental events past neurogenesis to include synaptogenesis, cell death, ocular dominance columns and the like, using regression and the general linear model (see also Clancy et al., 2007; Finlay and Clancy, 2008). The relationship of individual variability to between-species variability is discussed in Finlay et al. (2011), and specifically in humans in Charvet et al. (2013). The current iterative model deriving the "event scale" of maturation developed in Workman et al. (2013) brings the number of mammalian species to 18, the number of developmental events to 271, including myelination, volume change, and early behavioral events, extending to humanequivalent of the third postnatal year. Particularly relevant to the present paper, patterns in the neural maturation of altricial versus precocial species are contrasted. A demonstration of the problems arising from a failure to account for allometric concerns can be found in "Human exceptionalism" (Finlay and Workman, 2013). Early behavioral development and related neuroplasticity are integrated with translating time in Finlay and Uchiyama (2017), and finally, evolution of life histories, including events like weaning and menopause in Hawkes and Finlay (2018). Readers are directed to the early work of Passingham (1985), and Garwicz et al. (2009), who use similar methods to examine early independent ambulation, as well as that of Halley's studies of the growth of initial primordia and brain across a wide range of mammals (Halley, 2016, 2017). More recently, the advent of single cell RNA sequencing provides an exciting opportunity to investigate developmental trajectories of neural subpopulations across species (Habib et al., 2017; Iacono et al., 2017; Fan et al., 2018; Zhong et al., 2018). We here broaden the maturational range of neurodevelopmental ages of studies in our database to capture late stages of hippocampal neurogenesis across species.

#### Allometry of Brain and Brain Parts

The general form of scaling of neural mass or neuron numbers in any brain region compared to the whole brain, has been studied for many years (Jerison, 1973; Gould, 1975; Fleagle, 1985). Overall consensus exists about general features of brain and body scaling, though subject to the normal continuing debate about optimal ways to quantify statistical variation in large and complex datasets (Finlay et al., 2001; Freckleton et al., 2002). We will take the particular example of cross-species comparisons of the volume and number of neurons in the neocortex, and particularly the frontal cortex (the allometric study of the brain), to introduce the related and less familiar topic of scaling of developmental duration across species, which we term developmental allometry.

If scaling of neocortical volume (or "isocortex") is the focus for consideration, the fact that the human brain has a disproportionately large cortex compared to primates and most other mammals is quite "obvious" – for example, the human cortex comprises over 80% of its total brain mass, compared to around 20% in shrews and rodents (Finlay and Darlington, 1995). The correct empirical observation of the apparently

1http://www.translatingtime.org

disproportionate size of the cortex along with its persistent misinterpretation is the prototypical example of a problem we will call "human exceptionalism" (Finlay and Workman, 2013). The disproportionate volume of the human neocortex suggested to multiple researchers alike – anthropologists, embryologists, neuroscientists and psychologists – that it must be the result of special selection compared to the rest of the brain. Since the cortex was thus thought to be the subject of selection within the brain, every cognitive alteration or adaptation in evidence in humans has typically been typically credited to its superior computational prowess. But it's not necessarily so. Although we have an unusually large brain, our cortex is the size it should be for a brain of our absolute size when cross-species cortex volume or cell numbers are represented on logarithmic scales (Jerison, 1973; Hofman, 1989; Finlay and Darlington, 1995; Kaas and Herculano-Houzel, 2017).

#### Linear Scales, Logarithmic Scales, and the Allometric Equation

A "proper" comparison of variations across species of different sizes and developmental durations requires care (this section is abridged from Hawkes and Finlay, 2018 to which the reader is directed for a more extensive discussion). Even with "all else equal" in such factors as a species' niche, number of brain components, sex and age, still, the laws of geometry, and of physics and chemistry, impose lawful changes in both form and process with increase in brain mass. The intrinsic geometry of physical relationships results in variable allometric relationships (e.g., doubling the volume of a sphere only increases its radius by 1.26 times). After such geometric constants are understood, any two structures or processes changing in size or duration across species could show non-linear scaling relationships, scale linearly, or might show no predictable scaling, depending on the mechanisms or functions that are relevant. For example, the divisions, doubling and redoubling of stem cell pools are best described by non-linear equations. Some features change linearly: for example, if multiplied by the appropriate constant, cross-sectional diagrams of mice and rat eyes are superimposable even though the rat's eye is twice as big, as both are solving a linear optical problem with the same materials (Remtulla and Hallet, 1985). Some features do not scale at all with brain mass (considering mammals only here), such as the diameter of the cell bodies of neurons, the time to complete the first generation of a mature neuron, or the duration of action potentials. Such variable geometrical and biological scaling relations can coexist for different aspects of the same structure. Finally, datasets of interest often have underlying geometries that can mislead graphical comparisons. Consider a typical Mercator projection of the earth's landmasses, where the continents of Africa and Greenland appear approximately equal in size, but when measured in its correct spherical coordinates, Africa is more than 10 times larger than Greenland.

Allometries are conventionally represented as scaling relationships. If the relationship between two features that correlate with each other in size, say 'x' and 'y,' is represented as y = kxa where 'k' is some constant, and the exponent 'a'

represents the rate at which 'y' changes with respect to a change in 'x.' If exponent 'a' is more or less than 1 then a change in 'y' is associated with a geometrical change in 'x.' Such geometrical or exponential relationships can be plotted and visualized as linear ones by logarithmic transformation: log y = alog x + log k. In such log plots, the exponent 'a' now appears as the slope of the increase in 'y' with respect to x. Using this representation of cortex mass relative to the whole brain on a logarithmic scale, it is clear that the human neocortex is exactly the size it "should" be (**Figure 1**). The human brain is absolutely large compared to other primates, but given this large brain, each part falls onto its "expected" position, from hindbrain to cortex (Hofman, 1989). The cortex has "positive allometry" with respect to the rest of the brain, its slope greater than one, which is the "linear scaling reference" of **Figure 1**. Inevitably, therefore, with different brain components each increasing in mass at different rates, larger mammalian brains become "disproportionately" composed of cortex. The exact exponent of cortical positive allometry might vary with whether neurons, all cells, surface area or volume is measured, and shows some taxon-specific differences, but none reduce the positive exponent to one or less (a sampling of a large literature: Jerison, 1973, 1989; Hofman, 1989; Herculano-Houzel et al., 2007; Reep et al., 2007; Charvet et al., 2013).

Because of the regular, predictable relationships of the relative sizes of brain parts at all absolute brain volumes, lacking other information, our large cortex cannot be attributed to special selection for that feature, as it comes "for free" with selection on the whole brain, or in fact, could arise by leverage by selection on any part of the brain (Finlay and Darlington, 1995). It is interesting, to be sure, that over evolutionary time that the cortex, and the cerebellum are the two brain regions where disproportionate neuron number, volume and

energy consumption are routinely allocated (Finlay et al., 2011). Comparison of relative cortical and cerebellar volume between any two mammals of different brain size will reveal this feature, not only comparison of the human brain with all others. The most telling evidence is that those several mammalian brains which are absolutely larger in mass than the human brain, including several cetaceans and ungulates, continue the allometric equation of the cortex, so that they have proportionately even more cortex than humans do (**Figure 1**).

#### The Evolutionary Question at Issue: The Case of the Prefrontal Cortex

Questions involving allometric scaling are in no way historical debates as a similar controversy is ongoing about whether a specific region of cortex, the prefrontal cortex, is "allometrically unexpected" in humans (Sherwood and Smaers, 2013). Just as the cortex has a particular exponent of enlargement with respect to the rest of the brain, every cortical area (e.g., prefrontal, primary visual) has its own exponent (or slope in the logtransformed equation) showing its change in relative volume compared to overall cortex volume. Both the prefrontal and parietal cortex regions have an exponent that is larger than the cortex's overall exponent, showing a positive allometry (Jerison, 1997). The issue under debate is whether the frontal cortex in humans is larger still than would be expected from its already high positive allometry (Semendeferi et al., 2002; Barton and Venditti, 2013; Chaplin et al., 2013; Passingham and Smaers, 2014). As before, however, when we discussed preferential allocation of "excess" neural mass for cortex and cerebellum versus the rest of the brain, it is interesting that it is frontal and parietal cortex that are preferentially enlarged in the cortical sheet when brains increase in volume across mammals.

Why should these researchers care about this issue? If researchers claim a region's volume is "allometrically unexpected" in humans, they are claiming that it must have been the target of selection, typically because of special importance of the function ascribed to that brain region in that species. In the case of the frontal cortex, the cognitive features usually evoked are cognitive control, the ability to choose reasonable behavioral solutions from competing possibilities, or to evaluate choices with respect to goals distant in space or time. Thus, the claim that the frontal cortex is allometrically unexpected in humans is a claim that humans have been selected on a behavioral feature like cognitive control, which in turn is improved with the relative volume of frontal cortex. Structures that change their volume according to regular, cross-species allometric rules, however, even if they look disproportionate on a linear scale, require no special explanation. If the entire brain has been under special selection for larger size in any species, every single change in the proportionality of its parts is generated by its change in size. We'll make no ruling on this claim, except to note that the deviation in human frontal cortex volume, if it exists, is small enough to make it susceptible to relatively minor differences in methodology between research groups.

It remains interesting and important that brains enlarge in particular ways, and that predictable patterns of reorganization, both behavioral and computational, are associated with cortical enlargement (Finlay and Uchiyama, 2017). Mammals with large brains are certain to show evidence of a disproportionate contribution of frontal cortex (Passingham and Smaers, 2014). Allometric regularities in structural scaling, whether in the cortex, or in the hippocampus we will soon be discussing, require that we investigate coordinated mechanisms outside the structures of interest, and should make us skeptical of causal accounts that depend on selection on hypothesized special adaptations of the particular species of animal.

An important mechanism of volumes and neuron number coordination in several cases studied so far appears to be the coordinated control of duration of neurogenesis, as applied to every part of mammalian brains (e.g., Finlay and Darlington, 1995; Dyer et al., 2009; Cahalane et al., 2014; Charvet and Finlay, 2014). As the duration of hippocampal neurogenesis is the subject of the empirical component of this paper, we will now turn to issues in the allometry of development.

## The Allometry of Developmental Duration: Basic Requirements The Need for Data From Multiple Species: Why Attempts to "Norm" Measurements Between Only Two Species Will Be Ineffective

The formal properties of "allometrically expected" changes in mass also apply to translations of developmental time from one species to another. The appropriate coordinate system to represent time translations will depend on the data to be represented, and the representation desired. The relationship of developmental timing between species cannot be presumed to be best represented on a linear scale. In order to fairly compare developmental durations between animals, enough data must be collected from a number of relevant species to support generating an allometric equation with credible confidence intervals for its slope and intercept. For example, taking a first example from volume allometry, if you hypothesized that special selection in humans for language ability resulted in a comparatively larger Broca's area, it is necessary to show that the size of Broca's area in humans exceeds its expected allometric position compared to Broca's area in other primates (Schoenemann, 2006). A "control structure" such as primary visual cortex, a subcortical structure, or the rest of the brain cannot be used to "normalize" the volume of Broca's area, as allometric relationships in brain volumes can be expected to be non-linear. Broca's area will be disproportionately large in humans versus rhesus monkeys, but it will also be disproportionately large in rhesus monkeys versus marmosets, or in horses versus sheep, where relative language competence will not apply. If Broca's area has positive allometry compared to visual cortex, every contrast of a large and small mammalian brain will always show disproportionate volume increase in Broca's area in the larger brain. Similarly, the question of whether hippocampal neurogenesis and maturation is unusually early or late in humans depends on whether the timing of hippocampal maturation deviates from its expected developmental allometry.

Inappropriate norming procedures applied to developmental timing questions will produce the identical errors to those produced by inappropriately norming allometric comparisons of volume. You cannot, for example, compare the time from birth to adolescence in chimpanzees versus humans, see that the duration is longer in humans, and conclude that humans have been specially selected for a longer childhood. The duration may be entirely predictable from the time required to generate a large brain, intrinsic correlation with longevity or some other superordinate feature of life history. The "translating time" database was collected, in part, to be able to understand such comparisons in a larger cross-species context. A major surprise of this work was the extreme regularity of neural development in mammals, which in addition to the interest of the regularity alone, gives us a reliable set of brain-based benchmarks to understand the relative maturation of each species with respect to life-history events like birth or weaning (Hawkes and Finlay, 2018).

#### Setting Zero, or Onset of Neurodevelopment: Birth Is Not a Reliable Indicator of Brain Maturation

All allometric equations have a slope and an intercept, but in developmental allometry, the intercept often suggests a real-world developmental meaning, for example, the onset of neurogenesis, conception, or birth. Even though a real-world event like conception may appear to be a likely candidate for "zero" in an allometric equation, this must be mathematically determined, not stipulated. In "translating time," the best fit for "day zero" to the empirically measured neuroembryological data first proved to be a point located between conception and first production of mature neurons, possibly implantation (Finlay and Darlington, 1995; Finlay et al., 2001; Workman et al., 2013). Although birth is often chosen as a natural zero in anthropological work, and especially for research on late hippocampal neurogenesis to be discussed here, for the good theoretical reason that it marks the beginning of the independent life of the organism, and for the practical reason that prenatal measurements often hard to come by, still, this choice can be very misleading when attempting to compare developmental schedules (**Figure 2**). We will explain the derivation of the axes and the maturational progress represented on this graph in more detail in the next section, but for the moment, the x scale, the "event scale" is a multivariate measure of overall maturational state of the nervous system, with the generation of the first neurons near "0," with "1" corresponding to about 2 years postnatal in humans, with embryological features

like achievement of 80% of adult brain volume and variable progress of myelination. The y-axis is post-conception days of development on a linear scale – on a log scale, the allometric equation of each curve plotted would become a straight line (**Figure 3**). Post-conception days are plotted on a linear scale in this graph to emphasize the extreme divergences in absolute days to maturity in the species plotted here.

We have stressed the importance of two basic features of developmental allometric analysis critical for interpreting the presence or absence of "postnatal" or "adult neurogenesis." The first is obtaining developmental data from enough species to generate reliable allometric equations, and the second is locating a true "zero" from which to scale maturational events in the same equations. The Translating Time database and model can supply both necessities. Exploring "postnatal" neurogenesis in the hippocampus will be reporting on very different phenomena if mice, precocial guinea pigs, or humans are compared.

### A Brief Review of Our Specific Methodology for Comparing Neurodevelopmental Sequences Across Species

Over the past 20 years, a database and methodology to compare the progress of neural development across species have been elaborated (see footnote 1). The multiple statistical considerations leading to this representation can be found in the series of papers detailed in the first section, and a full description of the model in Workman et al. (2013). The original purpose of this work was to describe a mammalian "Bauplan" for neural development, and thus identify deviations from this plan that might mark taxon- or species-specific alterations corresponding to evolutionary adaptations, which is exactly how we will employ it for to examine the hippocampal data we have collected. The present model includes 18 species, and 271 "events" of mixed type, including neurogenesis in particular structures or cell classes (e.g., Layer 4 of striate cortex; Purkinje cells in the cerebellum; onset of synaptogenesis in a thalamic nucleus; emergence of some minimal behavioral reactivity, and transitions capturing continuous processes such as increases in brain volume or myelination).

The model from Workman et al. (2013) is reproduced in **Figure 2**, and extends to a maturational stage equal to approximately 2 years postnatal in humans. Only events in brain and some early behavioral capacities are included to model the event scale and each species' regression line – no measures of body or organ maturation or volume, or interactional, life history events like birth or weaning are included in this version. The "event scale," which is the best order and interval relationship of the 271 distinct neurodevelopmental events in the 18 species, is fit iteratively to all the data, (x-axis, **Figure 2**). The speed of progress of each individual species through these events is given as a regression equation, in days on a log scale (y-axis; compare the linear scale in **Figure 2** of the same functions). It is more typical to plot time on the x-axis in developmental studies, and it is important to remember this difference in representation. Days are on the y-axis because we are interested in duration as a function of maturational state. For example, for species with different sized brains, how long will it take them to reach equivalent maturational states? The differences in each species' slope show differences in maturational rate, with steeper slopes meaning slower progress through maturational stages in absolute time: the mouse takes only about 30 days to execute its 271 neurodevelopmental events, while the human takes 1,000 days, as humans generate greater numbers of neurons and volumes of connectivity per event.

The fit of model results to empirically measured results is astonishingly close, 0.9929, which reflects an extreme, and initially unexpected conservation of developmental sequences in mammals. Only two interaction terms are necessary to produce taxon-specific differences in these data so far, which are the black-circled points floating above the larger number of points of the corresponding color. The first term corresponds to an extension in corticogenesis in primates, some marsupial species and carnivores (n.b: this can be equally well represented as an advance in initiation and termination of neurogenesis in the "rest of the brain" –Clancy et al., 2001; Workman et al., 2013; Charvet et al., 2017a,b). The second represents a delay in neurogenesis in the retina of the nocturnal cat and ferret (also owl monkey, Dyer et al., 2009). Extensions in cortical neurogenesis produce a disproportionate expansion of cortical and, in particular, upper layer neuron numbers in primates (Cahalane et al., 2014; Charvet et al., 2015, 2017a,b), and a greater number of rods and rodassociated neurons in carnivores and owl monkeys.

### **Birth can intersect quite different developmental events in different species**

As noted earlier, birth may occur at a wide range of stages in neural development in different species. For example, cortical and cerebellar neurogenesis is ongoing at birth in some rodents, but in primates, both are largely concluded at that time. No obvious inflections, halts or accelerations near birth can be found in basic central nervous system construction. There is one event, a whole-brain surge of synaptogenesis, which appears to just antedate either birth or burrow exit in the four mammals studied to date instead of conforming the otherwise monolithic neurodevelopment program (reviewed in Finlay and Uchiyama, 2017).

#### **Other evidence for regular mammalian neurodevelopment**

Empirical support for the surprising claim of an extremely conserved mammalian neurodevelopmental schedule can be found in several independent sources. Mammalian brains continue to grow after birth, and Passingham (1985) first noted that if the volume of the brain at birth is plotted against gestation length for an eclectic set of eutherian mammals, including rats, pigs and dolphins (log transformed), a straight line results, suggesting brain mass is produced generally at the same rates in all species, smaller brains simply ceasing their growth earlier (Passingham, 1985). Halley (2016, 2017), in a much larger and more closely measured data set of changes in brain volume post-conception, recently confirmed the same notion. We have also successfully modeled the development of neuron number in the cortex combining information on kinetics of neurogenesis with adult neuron numbers in multiple species (Charvet and Finlay, 2014; Cahalane et al., 2014).

Other observations of single maturational phenomena give other insights, and underline further unexpected consequences of this conserved neurodevelopmental rate.

#### **Two surprising findings about precocial animals**

In mammals, the onset of walking is predicted by neural maturation (which is conserved) but not birth or any known niche variable. The time of the first unsupported step is highly predictable from a developmental allometric equation derived from adult brain mass, including one interaction term slightly accelerating the time of first step for those species with a plantigrade standing position (Garwicz et al., 2009), which fits seamlessly into the translating time model. This monolithic nature of the neurodevelopmental program, and its close correlation with brain size puts an interesting constraint on precocial mammals. Relatively large-brained, precocial ungulates like sheep and elk, who must be ready to run just after birth, accomplished this evolutionarily by extending gestation and delaying birth in their large offspring to match conserved parameters of brain development. They do not selectively advance the general rate of brain maturation nor push forward the maturation of circuitry closely associated with ambulation apart from the rest of the brain, which might seem to be a less stressful solution.

 While it might seem a reasonable strategy to make the most of every possible second for brain maturation available in utero in precocial species, to allow fine tuning of the coordinated behavior required immediately after birth, the conserved pace of brain maturation seems to rule this out. Since these animals must also produce large, mature bodies, which appear to require more time than the brain, the onset of neural development as marked by the first postmitotic neurons is substantially delayed, not stretched to fill the available time, allowing somatic maturation a head start (Workman et al., 2013). We will discuss whether a similar situation is present in marmosets, born with some precocial features.

# Applying "Translating Time" to the Question of Late Hippocampal Neurogenesis

The first reports of neurogenesis in adult humans and other mammals produced much excitement, in that it contradicted the central dogma that no new neurons, are generated in adulthood and offered a possible avenue for brain rehabilitation and repair. At first, the presence of new neurons was reported widely throughout the forebrain, but in time, unambiguous neurogenesis was finally limited to two locations, the hippocampus and the olfactory bulb via the "rostral migratory stream," mostly from work in rodents, but with confirmation in humans (Ming and Song, 2005). Recently, however, the existence of significant adult hippocampal neurogenesis has been questioned (Dennis et al., 2016; Andreae, 2018; Kempermann et al., 2018; Lee and Thuret, 2018; Sorrells et al., 2018). A report by Sorrells et al. (2018) concluded that neurogenesis in the human hippocampal dentate gyrus drops to undetectable levels during childhood, suggesting that human hippocampal neurogenesis is unlike that of other mammals (Knoth et al., 2010). A concurrent study (Boldrini et al., 2018) also investigated adult neurogenesis in the human hippocampus (14–79 years of age) and contradicts the first study. Using quite similar methodologies, the second group argued that adult hippocampal neurogenesis is in fact present throughout the lifespan. In such cases of contradiction, consultation with the animal model literature is of major help. A problem that has plagued this work is the absence of a robust and reliable way to compare time courses of events in different species. Adult hippocampal neurogenesis of any species could represent the tail end of a normal embryonic period of neurogenesis, or a truly indeterminate phenomenon, as is seen in virtually all non-mammalian vertebrates, or perhaps a targeted rekindling of neurogenesis for a particular purpose in adulthood. Because of the methodological similarity of the two studies, it may not be possible to rule in favor of one or the other on reported evidence, but a better idea of where errors might lie is a natural outcome of quantitative developmental modeling.

### Specific Objectives 1: Extending the Translating Time Model and Representing Species on a Common Scale

As we described previously, mammalian species vary in the length of both neural and somatic development, the positioning of birth with respect to neural maturation, and the relative length of neurogenesis in different structures. Comparing humans to macaques and mice, human neurodevelopment is much longer (duration correlating close with brain volume, as does the duration of lifespan). Humans are born at a slightly earlier stage of neural maturation than macaques, and at much later stage than rats and mice. Rhesus monkeys and humans also curtail neurogenesis in limbic structures relatively earlier than rodents (Workman et al., 2013), corresponding to the fact that as limbic structures are systematically relatively smaller (that is, scale with a smaller exponent) in primates compared to rodents (Reep et al., 2007). The translating time model at present does not have good data representation for late developmental stages to allow close comparisons in adulthood. We are therefore adding new data, and one new species to extend the model farther into the lifespan, but without any substantive change in its basic structure. We find appropriately transformed envelopes of neurogenesis across species to be very similar, and continuous.

## Specific Objectives 2: Closer Examination of Human Hippocampal Neurogenesis and the Problems of Detecting Non-scaling Cellular Events in a Non-linearly Scaling Lifespan

We consider the allometric nature of developmental schedules in humans to identify how hippocampal neurogenesis should vary if the duration of hippocampal neurogenesis in humans is similar to that of rodents. Further, the ability to align timetables allows us to investigate an intrinsic problem of detecting a

cellular signal in scaling situations, which is that organismal variables of size and duration show robust scaling, but cellular phenomena like action potentials, the length of the cell cycle and so forth rarely do. A rat may expect to live around 700 days post-adolescence, while an approximate comparable human figure is 25,000 days. If the cellular processes associated with an occasion of neurogenesis are transitory, and almost certainly do not scale with lifespan, the probability of simple detection falls radically in the long lifespan. We will discuss this aspect of scaling both as a methodological problem, and as a question about the importance of extremely low-probability events.

# MATERIALS AND METHODS

# Species and Sources

In order to extend the current neurodevelopmental model to later developmental stages, we added some additional data on the timing of developmental milestones in two rodent species (i.e., rats and mice), and three primate species (i.e., macaques, marmosets, and humans). "Developmental events" capture rapid transformations, such as onset of neurogenesis, any other process, or arbitrary divisions of continuous processes into epochs (e.g., 20%, 40%, 60%, and 80% of a structure's adult volume). Examples of developmental events include birth-dating of cell types, synaptogenesis, myelination, changes in protein and RNA expression. The new types of data added were those capturing temporal changes in cell proliferation from markers (i.e., DCX and Ki67) in the hippocampus. We only include developmental events present in at least two species, and at least one rodent species.

We identified variation in proliferative and newly born neuron numbers over the course of prenatal and postnatal development in primates and in rodents. More specifically, we collected previously published data where the number of Ki67+ (proliferative) and newly born (DCX+) cells relative to the total number of hippocampal granule cells was quantified at several stages of development in rodents and in primates. We defined as epochs when Ki67+ cells decline to 2%, 0.7%, 0.5%, 0.3%, 0.2%, and 0.1% of total granule cells in primates and rodents. We also identified when the number of DCX+ cells reach 3, 2.5, 2, 1.5, 1, and 0.5% of total granule cells in rodents and in primates. To do so, we fit a linear regression between the natural-logged values of age and the relative number of cell markers to compare the duration of the decline in late hippocampal neurogenesis between primates and rodents (**Figure 3**). We only selected age ranges in which there is a sharp decline in the relative number of Ki67 and DCX+ cells over time as assessed on a natural-log scale. This permits fitting a linear regression through the data for each species (**Figure 3**). These data are from Merrill et al. (2003), Rao et al. (2006), Ben Abdallah et al. (2010), Jabès et al. (2010), Amrein et al. (2011), Amrein et al. (2015), and Hochgerner et al. (2018). For rats, we considered the number of Ki67+ and DCX+ cells from Rao et al. (2006) and total granule cell numbers from Merrill et al. (2003). We consider studies that normalize the total number of proliferative and immature cells relative to total granule cells rather than those that consider the number of proliferative and immature neurons per mm2 of tissue.

We consider developmental transitions as the emergence of "plateaus" in the expression of multiple genes in single structures. We identified such plateaus in RNA expression from RNA sequencing data of bulk from the hippocampus in both species (Iacono et al., 2017). We identified when expressed genes reach a plateau in their expression across 14,417 orthologous genes as defined by the mouse genome database (Smith et al., 2018). We used a non-linear model with the software package R (easynls, model 3). Only orthologous expressed genes were considered. Age ranges were constrained to vary between 101 and 999 days in humans (n = 10) and between P1 to P30 in mice (n = 15) to compare roughly equivalent developmental time windows across these two species. We used normalized RNA sequencing expression made available by the Allen brain Institute. RNA expression from mice hippocampi was obtained from Iacono et al. (2017) (GEO: GSE79380). We selected only those models with p-values of coefficients less than 0.05 in humans and mice. This resulted in 34 genes in which plateaus were identified in both species. We averaged the age in which plateaus in RNA expression were identified in both species and include these data as one developmental event.

# Developmental Timing in Marmosets

We gathered available data on the timing of early neurodevelopmental events for the marmoset as we had done for other species. We matched our previously collected database on developmental event definitions, principally using anatomical changes from structural MRI scans (Hikishima et al., 2013), spatiotemporal changes in gene expression, as well as anatomical transformations from the literature. Examples of developmental events include morphological events such as first observation of retinal axons in the optic stalk, or when neurofilament heavy polypeptide (NEFH) expression emerges in the cortex. Because the marmoset is increasingly used as a model organism, we expect this inclusion to be useful past this study alone. To compute the timing of developmental events from MRIs, we noted the earliest age in which a event had occurred and the latest age in which the event had not yet occurred, as we had done previously (Charvet and Striedter, 2010; Workman et al., 2013). In total, we include 29 events for marmosets (**Supplementary Table S1**).

# Statistical Analyses

We include 213 developmental events from Workman et al. (2013) and Charvet et al. (2017b), eliminating events capturing the timing of cortical neurogenesis because cortical neurogenesis is extended in primates compared with rodents. We only included developmental events present in at least two of the species. Of the 213 events, 47 represented events or stages in limbic system development, including neurogenesis timing as well as the emergence of axonal pathways of limbic structures. We added 22 developmental events, 14 of which that capture the decline in late hippocampal neurogenesis (six Ki67, eight DCx; **Figure 3**).

A new 0–1 "event scale" was fit linearly to span this extended range, by subtracting the timing of each developmental

FIGURE 3 | The natural-logged values of DCX+ and Ki67+ cell numbers relative to hippocampal granule cell numbers are plotted against the natural-logged values of age in days post-conception in (A,B) marmosets, (C,D) mice, (E) macaques, and (F) rats. We performed a linear regression through these values to identify when the relative number of Ki67+ cells reaches 0.7, 0.5, 0.3, 0.2, and 0.1% of the total granule cell population. We also identify when DCX cell numbers reach 3, 2.5, 2, and 1.5% of the total granule cell population. With the exception of the marmoset, the relative number of DCX+ and Ki67+ to total granule cells were averaged at each age. (G) We use single cell RNA-seq to compute the number of immature granule cells relative to hippocampal neurons over the course of prenatal and postnatal development in mice. (H) Such an analysis shows that DCX+/granule cell numbers of mice fall within the 99% confidence intervals generated from single cell RNA-seq data. These findings demonstrate strong concordance between methods. Data are from Merrill et al. (2003), Rao et al. (2006), Ben Abdallah et al. (2010), Jabès et al. (2010), and Amrein et al. (2011, 2015). Data from single cell RNA-seq are from Hochgerner et al. (2018). Regressions were generated with software package IGOR.

event from the earliest event and divided these values by the difference between the latest event and the earliest event. We fit a linear regression through log-transformed values for each species against the event scale. We use the fitted values from the regression of human developmental event timing versus the event scale to predict the timing of late stages of human hippocampal neurogenesis timing. We omitted developmental events capturing cortical neurogenesis because a subset of cortical cell types are generated later than expected in primates (Clancy et al., 2001; Charvet et al., 2017a,b), which may increase error when predicting the duration of hippocampal neurogenesis across species.

We tested whether hippocampal neurogenesis occurs earlier than expected given the timing of other developmental events. We fit a linear model with the event scale as a continuous variable and developmental event timing as the response variable. To test whether limbic structures undergo neurogenesis earlier than expected relative to the timing of other events, we classified neurogenetic events as limbic or non-limbic. We tested whether the "limbic factor" as well as the interaction between the event scale and the "limbic factor" would account for a significant percentage of the variance.

# Single Cell RNA Sequencing to Identify Adult Hippocampal Neurogenesis

Because adult neurogenesis has recently been disputed in humans (Sorrells et al., 2018), we investigated whether adult neurogenesis could be observed from single cell RNA sequencing data extracted from the human hippocampus and prefrontal cortex aged 40–65 (Habib et al., 2017). We computed the relative number of cells expressing neural progenitor markers (DCX+, SOX2+, DPYSL3+) relative to the number of cells expressing PROX1+ in humans. We select PROX1 as a marker for granule cells because it is expressed by hippocampal granule cells but not by other cell types in the cortex. That is, the expression of PROX1 from bulk samples is higher in the hippocampus than in other cortical regions (**Supplementary Figure S1A**). PROX1 is expressed by hippocampal granule cells but not by isocortical cells (**Supplementary Figure S1B**). We selected SOX2, DCX, and DPYSL3 (aka TUC-4) because they are markers of immature neurons (Ngwenya et al., 2006; Cipriani et al., 2018). We considered PROX1 to be expressed if the gene count was greater than 0. To identify whether DCX+, SOX2+, and DPYSL3+ collocate with PROX1+ cells above chance level, we randomly reassigned PROX1 expression to different neuronal types 1,000 times. We then computed the number of DCX+, SOX2+, and DPYSL3+ cells relative to the number of PROX1+ cells. We assess whether the relative number of DCX+, SOX2+, and DPYSL3+ falls above the 95% confidence intervals generated from 1,000 permutations. Such an analysis permits investigating whether the number of immature neurons is present above chance level. Because we are focused on whether new neurons are generated in the adult hippocampus, we do not include cells belonging to clusters previously identified as glial, astrocytic, microglia, and endothelial. Data are from DroNc-Seq generated by Habib et al. (2017).

# RESULTS

# Initial Characterization of Late Hippocampal Neurogenesis

The initial step is to characterize how the number of dividing progenitors (Ki67+ cells) and immature neurons (DCX+) relative to granule cell numbers vary with post-conceptional day. **Figures 3A,C,E,F** show the measured values of Ki67+ cells expressed as a percent of total granule cells versus days postconception for the marmoset, mouse, macaque, and rat. Frames B and D show DCX+ labeled granule cells for marmoset and mouse only, again as a percent of total granule cells. Both scales are natural log scales, and the durations spanned vary considerably, from approximately 50 to 250 days post-conception in the mouse, versus approximately 150 to 3,000 days postnatal in macaque and marmoset. This enables calculating when the percentage of Ki67+ to total granule cells reach 2%, 0.7%, 0.5%, 0.3%, 0.2%, and 0.1%, and when the percentage of DCX+ to total granule cells reach 3%, 2.5%, 2%, 1.5%, 1%, and 0.5% in each species. The range for each species was constrained so that the natural-logged values of the relative number of Ki67+ and DCX+ to total granule cells systematically decline with age. This approach permitted fitting a linear regression through the data.

# Addition of Declining Hippocampal Neurogenesis Values Into the Overall Maturational Event Scale

In **Figure 3**, hippocampal neurogenesis indicators are described with relation to post-conception day in each species, but we would like to know how the decline in hippocampal neurogenesis relates to the common progress of brain maturation across species. Two ways of presenting "translating time" data can be used. In **Figure 4A**, the new data on late hippocampal neurogenesis for marmoset, macaque, and mouse, and the single rat point are plotted against the common "event scale." This type of data representation is optimal for visualizing overall slope and intercept similarities and differences between multiple species. As expected, the species with long early neurodevelopment periods show longer periods of adult hippocampal neurogenesis. No truncations, breaks or sudden accelerations in any particular species are in evidence, though there are interesting differences in the maturational path in marmosets versus macaques we will address subsequently.

It is also possible to use the translating time scale to express the events of one species in the time frame of a second species, "translate" the approximately 130 modeled days of the macaque to the 50 days of a mouse, which facilitates close comparisons of delay or advance of any class of events between the selected species (**Figures 4B,C**). For example, comparing nocturnal to diurnal mammals, the rods and other rod-related cells of the retina are generated later in nocturnal mammals, which would be visible in graphs like **Figures 4B,C** as an elevation of rodrelated points (nocturnal animals on the y-axis) (Dyer et al., 2009; Workman et al., 2013). In this case, we look for a difference in the implied intercept or slope of the "late hippocampal neurogenesis" points to determine if they show any signs of systematic variation

FIGURE 4 | (A) The timing of developmental events is plotted against an event scale in macaques, marmosets, rats, and mice. The timing of hippocampal neurogenesis was extrapolated from regressions capturing the decline in the number of DCX and Ki67+ cells relative to hippocampal granule cell numbers in primates and rodents. The timing of developmental events in macaques (B) and marmosets (C) are plotted against those in rats. (B,C) Developmental time points capturing late stages of hippocampal neurogenesis are highlighted (x).

from the common developmental scaling function. No such differences are apparent.

#### Marmoset Developmental Timing

fnins-12-00706 October 3, 2018 Time: 19:14 # 12

Early in development, equivalent events in marmosets occur later than in macaques (**Figure 4A**). At later time points, equivalent events occur earlier in marmosets than in macaques. A linear model with the event scale as a continuous variable and the logged values of developmental event timing as the predictor shows that the slope is lower in marmosets (y = 1.27x + 1.73, slope SE = 0.037, intercept SE = 0.02, R 2 = 0.976, p < 2.2e-16) than it is in macaques (y = 1.89x + 1.44, slope SE = 0.056, intercept SE = 0.016, R 2 = 0.903; p < 2.2e-16). In other words, marmosets initiate neural development late with respect to conception, close to day 90 compared to day 35 in macaques, but then progress through developmental events faster than macaques, producing a smaller brain by the end of neural development. The consequence of this late, accelerated developmental trajectory is that hippocampal neurogenesis wanes earlier in marmosets than in macaques. This is similar to the pattern previously observed in precocial mammals like guinea pigs, spiny mouse, and sheep (Workman et al., 2013) where neural development is delayed with respect to conception later, but once initiated, proceeds at a faster rate than in a number of altricial species. Equivalent developmental ages between marmosets and other species (i.e., mouse, rat, macaque, and human) are listed in **Supplementary Table S2**.

#### Somewhat Earlier Termination of Limbic Neurogenesis in the Macaque

The large sample size in macaques allows us to test whether limbic neurogenesis occurs earlier relative to the timing of other events in macaques (**Figure 5**). To that end, we fit a linear model with the logged values of developmental event timing as the predictor, the event scale as a continuous variable and a discrete categorical variable that classifies neurogenetic events as limbic or not. We also tested whether the interaction between the "limbic" factor and the event scale accounts for a significant percentage of the variance. The fitted model accounts for a significant percentage of the variance in developmental event timing for macaques (F = 416.7; R 2 = 0.91). The limbic factor is not significant (F = 2.065; p = 0.15) but the interaction between the limbic factor and the event scale is significant for macaques (F = 11.92; p < 0.05). These data demonstrate that the slope of the natural-logged values of late hippocampal neurogenesis versus the event scale is lower than expected in macaques considering the timing of other developmental events. In other words, hippocampal neurogenesis may cease slightly earlier than expected in macaques compared with rodents.

#### Hippocampal Neurogenesis in Humans

For humans, a linear regression of the timing of reported developmental milestones versus the event scale computed for humans by the translating time model (Workman et al., 2013) is plotted for the reduced dataset we used in this model, as a visual check and demonstration of the predictability of human data points, in **Figure 6A**. No new data are introduced in **Figure 6A**;

FIGURE 5 | Developmental milestones are plotted against the event scale in marmosets (A) and macaques (B). Milestones that capture the timing of limbic neurogenesis are in dark blue (marmosets) and in dark red (macaque). We fit a linear regression through the logged values of the timing of developmental milestones against the event scale. Late hippocampal neurogenesis consistently falls below the regression. In other words, late hippocampal neurogenesis may occur earlier than expected given the timing of most developmental milestones in macaques.

its intention is only to show the baseline variability against which we might introduce and compare other data (y = 2.44x + 1.53; slope SE = 0.12; intercept SE = 0.04, R 2 = 0.85, p < 2.2e-16). We then extrapolated predicted values from the linear model to see how late stages of hippocampal neurogenesis should vary if the timing of hippocampal neurogenesis were conserved across humans and mice (solid lines, **Figures 6B,C**) According to these predictions from mice, human hippocampal neurogenesis as assessed from the relative number of Ki67+ and DCX+ cells should drop sharply between prenatal stages up until to 8– 26 years of age and subsequently remain relatively invariant at later time points (**Figure 6B**). More specifically, the percentage of Ki67+ to total granule cells should drop up until about 8– 26 years of age (post-conception day 3,000 to 10,000; **Figure 6**) and remain relatively invariant thereafter. Similarly, the relative number of DCX+ to total granule cells should drop from birth to about 8–26 years of age (post-conception day 3,000 to 10,000; **Figure 6C**).

On these predicted functions, we overlay the number of DCX+ and Ki67+ cells compared to total granule cells as reported by Boldrini et al. (2018). Our predictions are very generally consistent with those of Boldrini et al. (2018), in that we predict that both markers should remain relatively invariant from 8 to 26 years of age onward, but the added data do appear

more variable, and the Ki67+ cell numbers higher than would be expected. Other data potentially addressing this timetable, that of Sorrells et al. (2018), could not be plotted against this representation because number of proliferative cells/mm2 was assessed rather than relative to the total granule cell numbers determined in the rodent studies.

To investigate whether hippocampal neurogenesis timing in humans should deviate from that of rodents, we compare temporal changes in DCX expression in humans and mice. These data offer a slightly different perspective on the temporal pattern of late stages of hippocampal neurogenesis between species. We first note similarities between DCX RNA expression and the relative number of immature hippocampal granule cells assayed from single cell RNA sequencing data (**Supplementary Figure S2**). A qualitative investigation of DCX expression from multiple datasets in mice suggests that RNA sequencing from bulk data mirrors the temporal changes in the relative number of immature granule cells. As the relative number of granule cells declines in mice, DCX expression from bulk samples also declines sharply. At roughly 38–50 days post-conception, the relative number of immature granule cells varies relatively compared to earlier time points. That is, DCX expression is relatively invariant from 1 to 4 months of age in mice. In humans, DCX expression also decreases from prenatal time points up until around post-conception 316 (50 days after birth) and subsequently remains relatively invariant. According to the translating time model, 38–50 days post-conception in mice is roughly equivalent to 445–700 days post-conception in humans. In other words, the end in the abrupt decline in DCX expression might occur slightly earlier than expected in humans.

Because the presence of hippocampal neurogenesis has recently been questioned, we investigate whether hippocampal neurogenesis can be observed from single cell RNA sequencing obtained from the human hippocampus and prefrontal cortex (**Figure 7A**). We compare the number of cells expressing DPYSL3, DCX, and SOX2 relative to the number of PROX1 cells (**Figures 7B–E**). PROX1 is used as a marker of hippocampal granule cells and its expression is observed in previously identified excitatory hippocampal granule cells (cluster 8) and GABAergic cells (cluster 7). We computed the number of DCX+, SOX2+, and DPYSL3+ cells relative to the number of PROX1+ cells. We assess whether these values lie above chance level by comparing these values to those generated by permutation-based significance thresholds. Such an analysis shows that the SOX2+ and DPYSL3+ cell numbers relative to PROX1+ cell numbers occurs above the 99% confidence intervals of distributions generated from permutations (**Figures 7F–H**). However, the number of DCX+ to PROX1+ cells falls within the 99% confidence intervals generated from permutations. These findings suggest that human hippocampal neurogenesis is present at low but detectable numbers in the adult human brain but that DCX expression may drop to such low levels in adulthood that human hippocampal neurogenesis may be difficult to conclusively identify with DCX RNA expression.

# DISCUSSION

# Late Hippocampal Neurogenesis as an Extension of Development

When the dates and magnitudes of the long tail of declining late hippocampal neurogenesis are represented on the common maturational scale of the translating time procedure, it is clear that these events are continuous with early hippocampal neurogenesis, with little or no convincing evidence or hints of breaks or inflections. The translation of a maturational state to a particular duration of development is consistent with the normal translation seen in smaller versus larger brains.

FIGURE 7 | (A) t\_SNE plots of RNA expression from single cells extracted from the hippocampus and the prefrontal cortex of the human brain identifies clusters of cell populations. (B) PROX1 is used as a marker of hippocampal granule cells and its expression is observed in previously identified excitatory hippocampal granule cells (cluster 8) and GABAergic cells (cluster 7). (C) PROX1 co-localizes with genes expressed by immature neurons [(D) DPYSL3 (E) DCX (F) SOX2], which suggests that new granule cells are born in the human hippocampus. To identify whether DCX+, SOX2+, and DPYSL3+ collocate with PROX1+ cells above chance level, we randomly reassigned PROX1 expression to different neuronal types 1,000 times. We then extracted the number of DCX+, SOX2+, and DPYSL3+ cells relative to the number of PROX1+ cells 1,000 times (F–H). Such an analysis shows that the SOX2+ (F) and DPYSL3+ (H) cell numbers relative to PROX1+ cell numbers occurs above the 99% confidence intervals generated from permutations. However, the number of DCX+ to PROX1+ cells falls within the 99% confidence intervals generated from permutations (G). We removed cells belonging to clusters 9–15 from the analyses because they are identified as glia, astrocytic, microglia, and endothelial cell populations. We omit these cell types because our analysis is focused on identifying whether neurons rather than glial and endothelial cells are generated in the adult hippocampus. Data and identified clusters are from DroNc-Seq generated by Habib et al. (2017). Abbreviations: NSC, neural stem cells; exPFC, excitatory neurons in the prefrontal cortex; exCA1-3, excitatory neurons in CA1-3; exDG, excitatory neurons in dentate gyrus; ODC, oligodendrocytes; ASC, astrocytes; OPC, oligodendrocyte progenitors; MG, microglia; END, endothelial cells.

A structural correlate of duration of neurogenesis in the embryonic brain lends additional support to the conclusion that late hippocampal neurogenesis is an aspect of developmental neurogenesis in the brain. The embryonic brain first appears as a plate, with its caudal-to-rostral dimension comprised of repeating segments, the familiar spinal segments which undergo relatively little reorganization from embryo to adult, rhombomeres to the level of the midbrain (Lumsden and Krumlauf, 1996), and prosomeres in the telencephalon (Puelles et al., 2013; Albuixech-Crespo et al., 2017). The rhombomeric and prosomeric segments have repeating structural similarities, but undergo prolonged neurogenesis compared to the spinal cord, producing major changes in their appearance due to simple mass of neurons and neuronal migration. Important for the present purposes, the basal-to-alar dimension of the original neural plate is also a gradient in neurogenesis duration, shortest medially and longest laterally corresponding to, but not completely accounting for the number of neurons in each segment derived from each germinal position (Finlay et al., 1998; Workman et al., 2013). At the most lateral margin of the most anterior segments that produce the pallium, we find the zones that generate the olfactory bulb, the hippocampus, and the neocortex. This region collectively generates neurons for the longest duration, the first two continuing to add neurons well past the early developmental period. Thus, extended neurogenesis is a feature of the embryonic origin of the hippocampus, not a feature applied to an unpredictable location.

#### Limitations of the Database

Several caveats are in order, some about the translating time approach in general, and some about the particular procedures we used to incorporate this atypical corpus of data. While the translating time database is presently the only source integrating multiple aspects of developmental information over a large number of species, from a phylogenetic perspective, those species are anything but systematically or randomly chosen, featuring a large number of rodents and marsupials, relatively few primates, with the first New World primate appearing with this article, few large ungulates or carnivores and no cetaceans. As additions of new taxonomic groups or functionally defined groups, such as the precocial mammals in Workman et al. (2013) typically reveal new ways that neural development can be altered, blanket statements about "mammalian" neurogenesis should be avoided. So far, however, a few generalities can be made. Neurogenesis can begin very rapidly after the completion of the early germinal tissue, or it can be delayed while other tissues begin proliferation as is seen in some precocial mammals, but it always moves en bloc, and we have never observed breaks introduced into the overall sequence, as none were observed in this analysis. The onset and offset of neurogenesis in identified groups can be shifted, most frequently seen for the limbic versus neocortex shift described earlier, a neural variation extending back to sharks and rays (Finlay and Darlington, 1995; Reep et al., 2007; Yopak et al., 2010). Finally, while duration of neurogenesis is a very important aspect of brain evolution, it is important to keep in mind it is not the only source of variation, with medial–lateral axis location, for example, only accounting for about 50% of the variance in neuron number (Finlay et al., 1998).

We estimated the relative timing of the decline in hippocampal neurogenesis not by a complete recomputation of the model to include the new observations, but rather by extrapolating the former model, duration extending almost by a factor of 2 in mice and more in the larger species, a substantial amount. It is possible that this procedure could mis-estimate the slope of the decline fairly substantially, but it seemed reasonable to attempt a first description. We did note that macaques appeared to begin initial hippocampal neurogenesis slightly earlier and end earlier than expected given the timing of surrounding, non-hippocampal events. Ideally, other late developmental events should be used to anchor these observations, at which point the overall model will be recalculated, but defined points become harder to identify in later development. Continued reduction of neuron density in most structures in later development as well as spatiotemporal changes in RNA expression are potential candidates, but these approaches have rarely been employed systematically across a broad range of species.

# Developmental Timing in Marmosets

The inclusion of marmosets in the present study was intended to allow better comparisons between primate species, particularly because information on late hippocampal neurogenesis was available for it. We were somewhat thwarted in this enterprise, however, because we did not observe the simple translation for production of a smaller brain expected from the pattern laid out in rhesus macaques. Rather, early developmental events were delayed with respect to conception, then maturation proceeded rapidly, consistent with the marmoset's smaller brain, and finally, late developmental events occurred earlier than predicted. A delay followed by rapid maturation was a pattern we had observed before, however, in precocial rodents and ungulates (Workman et al., 2013). Why the rate of neural development does not simply slow to take advantage of the extra time in utero is unclear. We have not yet observed any case of slowing of rate of neural production in eutherian mammals, although marsupials generate neural tissue at a slower rate overall (Darlington et al., 1999). Marmosets do have small brains compared to macaques, and perhaps to have time to generate the body, it is necessary to delay the onset of generation of the brain, to avoid producing a post-mature brain while still in utero if no change in its rate of development is possible. In a prior study of retinal neurogenesis in the owl monkey, Aotus, compared to the capuchin monkey Cebus apella, we had notice that gestational lengths were longer than we had anticipated from earlier work in Old World monkeys (Dyer et al., 2009). The reason for this potential difference in life history parameters will require more observations in the marmoset, and other New World monkeys as well.

#### The Timing of Late Stages of Human Hippocampal Neurogenesis and the Problems of Detecting Rare Events

We used the timing of developmental transformation across non-human mammalian species to predict the timing of late stages of hippocampal neurogenesis in humans. If the timing

of hippocampal neurogenesis is conserved across humans, marmosets, and rodents, hippocampal neurogenesis as assessed from the relative number of DCX+ and Ki67+ to total granule cells should drop sharply up until 8–26 years of age and remain low and invariant at later time points. Sorrells et al. (2018) showed that the relative number of Ki67+ and DCX+ cells drop sharply during childhood up until 7–13 years of age, consistent with our predictions in humans. The question is whether human hippocampal neurogenesis ends earlier than expected given the developmental allometries of late developmental events. It is presently difficult to determine whether human hippocampal neurogenesis does indeed deviate from predictions generated from rodents. However, the work of Boldrini et al. (2018), using similar techniques demonstrates low and invariant human hippocampal neurogenesis between the ages of 14 and 79 years, but a markedly higher incidence than the prior studies.

We found inconsistent evidence for late hippocampal neurogenesis in humans within our own study. Our analysis of RNA sequences from single cells showed that the relative number of immature neurons to PROX1 (i.e., a marker of granule cells) were observed at greater than chance levels in adult humans. However, the relative number of DCX+/PROX1 is unusually low and fell below chance levels as assessed from our permutation-based significance thresholds. Whether DCX expression is expressed at high enough levels for it to be reliably detected in the adult human brain is unclear. Although the number of potential confounds to detection of immature neurons are many, including retention of immature neuron morphology in displaced populations until adulthood (Piumatti et al., 2018); unusual levels of genetic variation (Kaushal et al., 2003) as well as all of the problems of processing human tissue, developmental scaling plays a role as well.

As mentioned at the outset of this discussion, different components of the same tissue may scale in altogether different ways with respect to brain size and developmental duration. As a rule of thumb, with the usual number of exceptions, cellbased properties do not scale with brain size or developmental duration. As cells essentially depend on diffusion for many critical metabolic factors, in at least one plane of section, neuron diameter cannot scale with brain size (long axons, but not fat ones, are acceptable). Oxidative metabolism, action potentials and most other cellular processes ignore animal mass. How about the cell cycle? The cell cycles of initial neurogenesis take similar amounts of time in small and large brains (Takahashi et al., 1994; Charvet and Striedter, 2008). The duration of the cell cycle becomes longer and longer as maturation proceeds, not by uniform elongation of every part, but particularly the quiescent period; in addition, fewer and fewer cells contribute to the cell cycle (Takahashi et al., 1994; Kornack and Rakic, 1998; Charvet and Striedter, 2008). We have claimed, though, that the termination of hippocampal neurogenesis looks quite similar in its overall envelope across the rodents, monkeys and the human we measured. This is with respect to these animals' maturational state, however, not their age in days. A back-of-napkin calculation of the duration of equivalent maturational periods from early "childhood" to death would be about 700 days for rats and about 25,000 days for a human. The initial spatial densities for Ki67 and DCX+ are roughly similar (**Figure 3**). It seems unlikely that generating a neuron would require 36 times longer in humans, or that the features of a young neuron would persist a similar long duration. On the other hand, the migration and integration of new neurons into circuits has been reported to take very much longer in adults, exceeding 6 months (Kohler et al., 2011). Thus, an empirical question remains, as it is unclear if these patterns represent a maintained state, or a transitory event. If they are time-limited events, the chance of registering such an event in the slice of time caught by a brain slice will be radically different in short- and long-lived mammals, and comparisons must take these basic scaling features into account.

# AUTHOR CONTRIBUTIONS

BF and CC designed the study, analyzed the data, and wrote the study. Both authors read and approved the manuscript.

# FUNDING

This work was supported by an NIH 1F32HD067011-01A1 fellowship to CC as well as an NSF grant, and by research chair support from the W. R. Kenan Jr. Charitable Trust to BF.

# ACKNOWLEDGMENTS

RNA sequencing data from human hippocampi were taken from the Allen Institute Website, which are supported by the NIH Contract HHSN-271-2008-00047-C to the Allen Institute for Brain Science. The opinions in this article are not necessarily those of the NIH. We thank Dr. Marnin Wolfe for helpful statistical discussions.

# SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnins.2018. 00706/full#supplementary-material

FIGURE S1 | PROX1 is more strongly expressed in the hippocampus than in other regions and is a marker of granule cells. (A) PROX1 expression from RNA-seq data from humans between 19 and 40 years of age is greater in the hippocampus than in prefrontal cortical regions. An ANOVA followed by a TUKEY HSD test shows that PROX1 is significantly more expressed in the hippocampus compared with prefrontal cortical regions in humans (ANOVA, TUKEY HSD, p < 0.01). (B) In situ hybridization through a sagittal and coronal section of mouse brain at post-natal day 56 and 24 months of age shows that Prox1 is strongly expressed in granule cells of the hippocampus but not in the isocortex. Abbreviations: HIP, hippocampus; dPFC, dorsal prefrontal cortex; vPFC, ventral prefrontal cortex; aPFC, anterior cingulate cortex; oPFC, orbital frontal cortex; ISO, isocortex. These data are from the Allen Brain Atlas.

FIGURE S2 | (A) Normalized DCX expression from bulk hippocampal samples and the number of immature granule cells as assessed from single cell RNA sequencing data in mice are plotted against the age in days post-conception in mice. We see strong concordance between RNA expression from bulk samples and the relative number of immature granule cells. DCX expression steadily declines with the relative number of immature granule cells. After 150 days of age,

# REFERENCES

the number of immature granule cells as well as DCX expression level off. (B) In humans, DCX expression falls abruptly until 1,000 days of post-conception.

TABLE S1 | Developmental event timing data-set.

TABLE S2 | Predicted developmental time points between marmosets, and other species.

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2018 Charvet and Finlay. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Effects of Strain and Species on the Septo-Temporal Distribution of Adult Neurogenesis in Rodents

Franziska Wiget 1 , R. Maarten van Dijk 1, 2, Estelle R. Louet 1 , Lutz Slomianka1, 3 and Irmgard Amrein1, 3 \*

1 Division of Functional Neuroanatomy, Institute of Anatomy, University of Zurich, Zurich, Switzerland, 2 Institute of Pharmacology, Toxicology and Pharmacy, Ludwig-Maximilian-University, Munich, Germany, 3 Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland

#### Edited by:

Paolo Peretto, Università degli Studi di Torino, Italy

#### Reviewed by:

Martine Migaud, INRA CNRS UMR7247, France María Llorens Martín, Universidad Autonoma de Madrid, Spain

> \*Correspondence: Irmgard Amrein<EMAIL_ADDRESS>
#### Specialty section:

This article was submitted to Neurogenesis, a section of the journal Frontiers in Neuroscience

Received: 16 October 2017 Accepted: 08 December 2017 Published: 19 December 2017

#### Citation:

Wiget F, van Dijk RM, Louet ER, Slomianka L and Amrein I (2017) Effects of Strain and Species on the Septo-Temporal Distribution of Adult Neurogenesis in Rodents. Front. Neurosci. 11:719. doi: 10.3389/fnins.2017.00719 The functional septo-temporal (dorso-ventral) differentiation of the hippocampus is accompanied by gradients of adult hippocampal neurogenesis (AHN) in laboratory rodents. An extensive septal AHN in laboratory mice suggests an emphasis on a relation of AHN to tasks that also depend on the septal hippocampus. Domestication experiments indicate that AHN dynamics along the longitudinal axis are subject to selective pressure, questioning if the septal emphasis of AHN in laboratory mice is a rule applying to rodents in general. In this study, we used C57BL/6 and DBA2/Crl mice, wild-derived F1 house mice and wild-captured wood mice and bank voles to look for evidence of strain and species specific septo-temporal differences in AHN. We confirmed the septal > temporal gradient in C57BL/6 mice, but in the wild species, AHN was low septally and high temporally. Emphasis on the temporal hippocampus was particularly strong for doublecortin positive (DCX+) young neurons and more pronounced in bank voles than in wood mice. The temporal shift was stronger in female wood mice than in males, while we did not see sex differences in bank voles. AHN was overall low in DBA and F1 house mice, but they exhibited the same inversed gradient as wood mice and bank voles. DCX+ young neurons were usually confined to the subgranular zone and deep granule cell layer. This pattern was seen in all animals in the septal and intermediate dentate gyrus. In bank voles and wood mice however, the majority of temporal DCX+ cells were radially dispersed throughout the granule cell layer. Some but not all of the septo-temporal differences were accompanied by changes in the DCX+/Ki67+ cell ratios, suggesting that new neuron numbers can be regulated by both proliferation or the time course of maturation and survival of young neurons. Some of the septo-temporal differences we observe have also been found in laboratory rodents after the experimental manipulation of the molecular mechanisms that control AHN. Adaptations of AHN under natural conditions may operate on these or similar mechanisms, adjusting neurogenesis to the requirements of hippocampal function.

Keywords: doublecortin, Ki67, Apodemus sylvaticus, Myodes glareolus, Mus domesticus

# INTRODUCTION

There has been a long-standing interest in differences between the septal and temporal hippocampus, which are expressed by specific behavioral effects after septal or temporal lesions (e.g., Hughes, 1965; Moser et al., 1995), differences in the relative abundance of cell types (e.g., Gaarskjaer, 1978; Jinno and Kosaka, 2006), or efferent and afferent connections (Ishizuka et al., 1990; Agster and Burwell, 2013; Prasad and Chudasama, 2013). Such differences were typically thought to reflect gradual changes along an anatomical and functional septo-temporal continuum. Recently, detailed genomic studies (Thompson et al., 2008; Dong et al., 2009; Cembrowski et al., 2016a,b) suggest a functional differentiation within hippocampal principal cell populations (reviewed by Fanselow and Dong, 2010; Strange et al., 2014), which may subserve different functions in different septo-temporal segments. The septal hippocampus has been associated with cognition (Moser et al., 1995; Bannerman et al., 1999; Kjelstrup et al., 2008; Reichel et al., 2017). The intermediate hippocampus has been proposed to integrate the septally mediated visuospatial information with the motivational behavioral control for precise place learning which is translated into behavior (Bast, 2007; Bast et al., 2009; Barker et al., 2017). The interconnection of the temporal hippocampus with the amygdala (Felix-Ortiz et al., 2013) and its control of the HPAaxis (Herman et al., 1995; Lowry, 2002; Belujon and Grace, 2015) emphasizes its role in stress coping and anxiety-related behaviors (Fanselow and Dong, 2010).

In view of the functional differentiation of the hippocampal formation itself, it is not surprising that septo-temporal differences are also observed in one of the phenomena that it hosts—adult hippocampal neurogenesis (AHN). In most species that have been investigated, AHN is higher in the septal than in the temporal dentate gyrus (Uchida et al., 2005; Jinno, 2011b; Snyder et al., 2012; Amrein et al., 2015; Lowe et al., 2015). Interestingly, domestication selectively increases the number and distribution of doublecortin-positive neuroblasts and differentiating young neurons in the temporal dentate gyrus of foxes, a species in which temporal AHN is also higher than septal AHN (Huang et al., 2015). We have recently shown that AHN is a strong differentiator between rodent species (van Dijk et al., 2016a), in which the extent of AHN may relate to factors as diverse as habitat conditions (Cavegn et al., 2013) or social status (Oosthuizen and Amrein, 2016). The differentiation by AHN between species living under different natural conditions strongly implies an adaptive value. Considering the ontogenetic plasticity of AHN in response to experimental manipulation of the external or internal environment, it seems reasonable to expect that septotemporal gradients in baseline AHN may be affected as well. Such gradients can become phylogenetically fixed if they are adaptive to the environment that represents a species niche. In this context, defining species-specific septo-temporal profiles for AHN in wild and laboratory-bred rodents may pinpoint, in which functional domain of the hippocampus one ought to look for the adaptive value of AHN.

Our data have, so far, been equivocal with regard to septotemporal phylogenetic effects. While we did see such effects associated with domestication in foxes (Huang et al., 2015), we also found rather similar septo-temporal distributions of proliferating cells and differentiating young neurons in a comparison of C57BL/6 mice with New World monkeys (Amrein et al., 2015). In the present study, we therefore focused on two wild rodent species, which we previously found to differ from each other (Amrein et al., 2004a,b), that is bank voles (Myodes glareolus) and long-tailed wood mice (Apodemus sylvaticus). We also used F1 offspring of wild-captured house mice (Mus domesticus) to study if domestication may impact septo-temporal features of AHN in mice. Large differences in AHN were seen between different strains of laboratory mice (Kempermann et al., 1997; Hayes and Nowakowski, 2002; Kempermann and Gage, 2002b). We included C57BL/6 and DBA strains to compare them to the wild species and their wild-derived house mouse conspecifics. In DBA mice, far fewer new neurons are generated than in C57BL/6 mice (Kempermann et al., 2006; van Dijk et al., 2016b), and AHN reacts differently to experimental interference (Overall et al., 2013). Such strain differences may also extend to septo-temporal differences in AHN. For all mice, proliferation and neuronal differentiation were studied using Ki67 and doublecortin (DCX) as markers for proliferation and neuronal differentiation. To facilitate comparisons at different septo-temporal levels in different strains and species, these markers were applied to sections of extracted and physically extended hippocampi.

# MATERIALS AND METHODS

# Animals

Eighty four rodents of different strains and species were investigated (**Table 1**). C57BL/6, DBA2/Crl and house mice were 4.5–5.5 month old, while the age of wild trapped mice was unknown. All experimental work was conducted under the permits #26394 (laboratory and house mice) and #27034 (wild mice) of the Canton of Zürich veterinary office.

# Trapping and Housing

Wild bank voles and wood mice were trapped at three locations (Zürich, Rifferswil, Trüllikon) in the canton of Zürich, Switzerland. Baited Sherman traps were set along hedges and scrubs from late afternoon to 4 h into darkness, traps were checked at 2–3 h intervals. Under isoflurane anesthesia, trapped animals were implanted subcutaneously with identification transponders (Planet ID GmbH, Germany) in the dorsal neck region, checked for health, gender, and signs of pregnancy, and treated against ectoparasites (Stronghold 15 mg, Selamectin, one drop). Animals were then single housed for 3 weeks. During this period, animals were fed ad libitum with anti-endoparasitic mouse food (Ivermectin, Kliba SA, Switzerland). Afterwards, animals were grouped according to species and sex. Groups of maximal nine adult animals were housed in a set of two to four tube-connected cages. All cages contained ample bedding and nesting material (tissue, hay, cardboard boxes) and ad libitum access to commercial mouse food and water. Group housed laboratory and house mice were kept in the same animal facility as wild mice. Lighting conditions corresponded to the

TABLE 1 | Study animals.

natural light cycle in late summer (light on 07:00–19:00). All mice underwent the same behavioral testing in IntelliCages (Galsworthy et al., 2005) for 31 days before perfusion. Analysis of the behavioral tests is ongoing.

# Perfusion and Dissection

Animals were deeply anesthetized with pentobarbital-Na intraperitoneally (50 mg/kg body weight) and perfused transcardially with 1% paraformaldehyde (PFA) solution containing 15% picric acid. Hippocampi were dissected, straightened and post-fixed in 4% PFA in this position for 5 h as described before (Amrein et al., 2015). The left and right hippocampi of each animal were randomly assigned to further processing.

# Gelatin Embedding

For immunohistochemistry, one hippocampus of each animal was transferred into 20% glycerol in 0.1M phosphate-buffered saline (PBS, pH = 7.2) for cryoprotection overnight at 4◦C. A gelatin matrix was used for tissue embedding as described in Smiley and Bleiwas (2012). Gelatin-egg albumin solution in 0.9% NaCl was prepared and mixed with 37% formaldehyde as hardener of the matrix. A 2.5M lysine solution and 25% glutaraldehyde at a ratio of 1:1 served as a cross-linking reagent. Mixed solution was poured as a base layer into embedding forms, four hippocampi were placed under the surface of the base layer. After setting of the base layer, a top layer of gelatin matrix was added. The hardened gelatin blocks were immersed in 20% glycerol. 40µm sections were cut on a sliding microtom (HM 430, Thermo Fisher Scientific, Waltham, MA) equipped with a freezing stage. Sections were cut perpendicular to the hippocampal longitudinal (septo-temporal) axis and collected in 10 series. One series was collected in PBS in well plates and mounted in correct anatomical order (reference series). These sections were Giemsa stained (Iñiguez et al., 1985) (Giemsa stock solution 1.09204.1000. Merck, Darmstadt, Germany). Nine series were stored in cryoprotective solution (CPS) at −20◦C until further processing.

# Immunohistochemistry

We used the endogenous proliferation marker Ki67 (Starborg et al., 1996; Cuylen et al., 2016; Sobecki et al., 2016) and the young neuron marker doublecortin (DCX; Matsuo et al., 1998). Test sections of all species and strains were used to titrate antibody concentrations that generated optimal signal-to-noise ratios and a saturated signal for the strongest stained cells. Every tenth section (one series) of each hippocampus was processed free-floating. Sections were rinsed in Tris-Triton (Tris-buffered saline (TBS), pH = 7.4 with 0.05% Triton). For epitope retrieval, sections were heat-treated for 45 min at 90◦C in citrate buffer (Dako REAL, Glostrup, Denmark) diluted 1:10 in distilled water. Pre-incubation was done for 1 h in 2% normal horse serum (NHS) for Ki67 and 2% normal rabbit serum (NRS) for DCX in Tris-Triton with 0.2% Triton. Incubation with primary antibody in pre-incubation buffer followed over night at 4◦C (Ki67: monoclonal mouse-anti-Ki67, BD Pharming, 1:1,000–1:3,000; DCX: polyclonal goat-anti-DCX, Santa Cruz, 1:4,000–1:8,000; for DBA mice in addition monoclonal mouse-anti-DCX, Santa Cruz, 1:50, and polyclonal rabbit-anti-DCX, Abcam, 1:1,000 were used). Thereafter, rinsed sections were incubated in biotinylated secondary antibody (all Vectastain Elite kits, Vector Laboratories, Burlingame, CA, USA, 1:1,000) diluted in TBS, 2% serum and 0.1% bovine serum albumin (BSA). Incubation in avidinbiotin-peroxidase complex (Vectastain Elite kits) was followed by staining with diaminobenzidine and H2O2 (SigmafastTM, D4418-50SET, Sigma-Aldrich, Steinheim, Germany) in distilled water. Mounted sections were embedded with Mowiol 4-88 (Ki67) diluted in glycerol and PBS (v/v) in a ratio of 1:3 (Sigma-Aldrich, Steinheim, Germany), or counterstained with hematoxylin solution (DCX) (Sigma-Aldrich, Steinheim, Germany) and embedded with Eukitt.

# Cell Quantification

Quantification of the two markers Ki67 and DCX was performed applying design-based stereological methods (West et al., 1991, for stereology-specific parameters see **Table 2**). The investigations were carried out using the StereoInvestigator software v10.50 (MBF Bioscience, Williston, Vermont, USA) on a Zeiss Axio Imager 2 microscope, and blinded with regard to mouse identity, strain and species.

For the estimation of the total number of DCX-positive cells (DCX+), the optical fractionator of the StereoInvestigator software was used (West et al., 1991). Every tenth section was analyzed (section sampling fraction (ssf) = 1/10). Step sizes between sampling locations were determined using data from estimates of the area of the region of interest in pilot animals. Counting frame sizes (see **Table 2**) that returned, on

TABLE 2 | Total estimated cell numbers (unilateral) and associated stereological parameters.

average, more than one count from the sampling locations were determined by trials of differently sized frames. Cells were counted in the anatomically ordered sections using a 63× oil immersion objective (Zeiss, Plan-Apochromat/1.4 oil DIC). Nuclei of DCX+ cells were counted throughout the entire section thickness of 40µm. To avoid oversampling, DCX+ cells that had their counterstained nucleus in focus in the focal plane that first touched the uppermost surface of the section were not counted. Following Disector counting rules (Sterio, 1984; West et al., 1991), only the nuclei of DCX+ cells that appeared in focus in the subsequent focal planes were included into the counts. We found DCX+ cells not only in the subgranular layer, but in some animals also within the granule cell layer. In eight randomly chosen animals, DCX+ cells in the subgranular layer were assessed separately from more superficial DCX+ cells between granule cells. Estimation of total cell number (N) for P DCX+ cells was calculated using the following formula: N = Q − · 1 asf · 1 ssf , where P Q − is the total number of counted cells. The area sampling fraction (asf) is calculated from the area of a counting frame, divided by the area associated with every step in x- and y direction ( a(counting frame) a(x,y step) ).

Ki67-positive (Ki67+) proliferating cells in the subgranular layer of the dentate gyrus were counted exhaustively using a 63× oil immersion objective (Zeiss, Plan-Apochromat/1.4 oil DIC), again excluding Ki67+ nuclei that appeared in focus in the uppermost focal plane of a section. The anatomical order of the sections was determined by comparison of the sections with the reference series. Estimates of total Ki67+ cell number per hippocampus were calculated by multiplying the total counts by the section sampling fraction (ssf) of 10.

# Definition of the Septal, Intermediate and Temporal Part of the Hippocampus

Acquisition of cell numbers were performed in the dissected and straightened hippocampi. Data of Ki67+ and DCX+ cells in each section in the correct anatomical order from septal to temporal were standardized for all animals into 12 virtual sections (bins) as described by Amrein et al. (2015). In short, raw data from each real section was divided into 12 sub-bins, corresponding to the desired number of virtual sections. Cell numbers for each virtual section was generated by successively pooling the cell numbers of these sub-bins. The number of pooled sub-bins corresponded to the number of the real sections available for an animal.

The division into equally sized septal, intermediate and temporal subdivision of the hippocampus was then applied on these 12 virtual sections. One third representing the septal (virtual sections 1–4), one third representing the intermediate (virtual sections 5–8) and one third representing the temporal (virtual sections 9–12) subdivisions of the hippocampus were used for graphical presentation and analysis.

# Statistical Analysis

To assess estimate precision, the Coefficient of Error (CE) was calculated (Gundersen et al., 1999; Slomianka and West, 2005) for cell counts.

Statistical analyses and graphics were compiled using R software (version 3.3.2). The standardized data of Ki67+ and DCX+ cells within the septal, intermediate and temporal subdivision were used. To account for age-related differences between and within species and strains, values in the virtual sections were recalculated as the percentage of the total estimated cell number for each animal. Nested two-way ANOVA with gender as covariate was used to test for species/strain-specific differences in the relative distribution of Ki67+ and DCX+ cells in the three septo-temporal subdivisions. Tukey post-hoc analyses were performed when the main effect was found to be significant. Within species, the distribution of DCX+ cells and gender related differences was tested using a nested one-way ANOVA.

# RESULTS

# Species-Specific Rates of AHN

Estimates of the total numbers of proliferating cells and young neurons were not statistically tested between species and strains, as the age of wild trapped animals was not assessed. The numerical data obtained (**Table 2**) do however confirm previously found species and strain differences. Wild house mice showed lower AHN than laboratory C57BL/6 (Klaus et al., 2012). Bank voles, lower in cell proliferation when compared to wood mice, had high numbers of DCX+ cells. This observation has been made previously in the comparison of bank voles with yellow-necked wood mice (Amrein et al., 2004b; van Dijk et al., 2016a), and may be due to attenuated cell death observed in this species (Amrein et al., 2004a). Proliferation in DBA laboratory mice was equal to previously reported data in this strain and at this age (van Dijk et al., 2016b), however DCX counts were unexpectedly lower. Repeating the estimates using three different DCX antibodies yielded similar results.

# Differences in Septo-Temporal AHN Gradients

We observed striking differences in the distribution of proliferating Ki67+ cells and young neurons (DCX+ cells) along the septo-temporal axis in the five rodent species and strains (**Figure 1**). While C57BL/6 showed an emphasis on neurogenesis in the septal subdivision, all other rodent species had more neurogenesis toward the temporal hippocampus. Statistical comparisons on standardized data revealed a significant main effect for species and strains in the distribution of DCX+ cells (**Figure 2A**) along the hippocampal axis [F(4, 77) = 11.36, p < 0.0001]. Furthermore, a strong interaction was found for species/strains and septo-temporal subdivisions [F(8, 154) = 9.05, p < 0.0001]. Post-hoc comparison in the septal subdivision showed C57BL/6 to have higher DCX values than all other species (comparison wood mouse p = 0.004; bank vole, house mouse and DBA all < 0.0001), while DBA, bank voles, wood mice and house mice did not differ from each other. In the intermediate subdivision, bank voles showed higher DCX values than C57BL/6, DBA and house mice (p = 0.029, 0.01, and 0.004, respectively), the difference to wood mice did not reach significance (p = 0.07). All other pairwise comparisons were non-significant in the intermediate subdivision. C57BL/6 differ again in the temporal subdivision, by having significantly lower DCX values than all other species (wood mouse p = 0.049, bank vole 0.024, DBA and house mouse p < 0.0001). No further significant species differences were found in the temporal subdivision. We found no overall effect of species and strains in the distribution of Ki67+ cells (**Figure 2B**) along the septo-temporal axis [F(4, 77) = 1.52, p = 0.21]. Interaction between species/strains and septo-temporal subdivision however was significant [F(8, 154) = 7.46, p < 0.0001]. Within the septal

FIGURE 1 | Distribution of neurogenesis-related absolute cell counts along the septo-temporal axis. Neurogenesis assessment in dissected, straightened hippocampi in laboratory C57BL/6 (A) follows a general septal>temporal gradient, whereas DBA (B), house mice (C), wood mice (D), and bank voles (E) show a septal<temporal gradient for young neurons (DCX), and, except for DBA, also for cell proliferation (Ki67). Raw data of cell counts were re-distributed into 12 virtual sections to allow direct comparisons within and between species/strains, gray areas indicate 95% confidence intervals. (F) Shows representative images of hematoxylin-counterstained DCX+ young neurons (A') and Ki67+ cells (B') in the subgranular layer of the dentate gyrus. Scale bar = 20µm.

subdivision, C57BL/6 showed higher Ki67 values compared to bank voles and house mice (p = 0.04 and p = 0.004). House mice on the other hand have significantly fewer Ki67+ cells than DBA and wood mice (p = 0.005 and 0.02). In the intermediate subdivision, bank voles had higher Ki67 values than all other species (p = 0.002 to < 0.0001). In the temporal subdivision, house mice differed from all other species in having the highest Ki67 values (p = 0.006 to < 0.0001). All animals had more DCX+ and Ki67+ cells in the intermediate part than in the septal or temporal subdivision (**Table 3**).

# Differences in the DCX/Ki67 Ratio

As a proxy for young neuron survival, we also assessed the ratios of DCX+ cells to Ki67+ cells (**Figure 2C**). Overall, there are significant differences between strains and species [F(4, 77) = 9.51, p < 0.0001] and an interaction between strain/species differences in the DCX/Ki67 ratio in the hippocampal subdivisions [F(8, 154) = 10.97, p < 0.0001]. Post-hoc comparison for the septal subdivision suggests a lower survival rate in DBA compared to bank voles, house mice and C56BL/6 (p = 0.041, 0.028, and 0.006 respectively). In the intermediate subdivision, bank voles have a higher DCX/Ki67 ratio than wood mice and DBA (p = 0.009 and 0.003). Temporally, the DCX/Ki67 ratio of bank voles exceeded that of all other species (all p < 0.0001). All other comparisons did not show significant differences.

# Radial Position of DCX+ Cells in Wild Mice

Among all 84 quantified animals, some animals showed widely scattered DCX+ cells along the radial axis of the dentate gyrus. DCX+ cells were found in the subgranular zone (SGZ, **Figures 3A,A'**) and in the dentate granule cell layer proper (GCL, **Figures 3B,B'**). Eight animals with this phenotype were randomly selected when animal identity was still blinded, and DCX+ cells were assessed separately in the SGZ and GCL. Out of the five investigated species, the selected animals exhibiting this phenotype were exclusively either wood mice or bank voles. The binomial probability of the sample to only contain wood mice and bank voles (n = 39) if this trait was distributed at random in all species and strains (n = 84) would be (39/84)8 = 0.002. Overall, we found in these two species a significant main effect

TABLE 3 | Percentage of neurogenesis-related cells in the three hippocampal subdivision.

of the radial axis distribution of DCX+ cells [F(2, 14) = 17.8, p < 0.001] and a significant interaction between the radial distribution of young neurons and hippocampal subdivision (p < 0.0001). Post-hoc tests revealed that DCX+ cells in the SGZ predominate in the septal [F(1, 14) = 50.7, p < 0.001] and intermediate [F(1, 14) = 14.7, p = 0.002] subdivision, while in the temporal subdivision DCX+ cells found in the GCL proper are more common [F(1, 14) = 36.2, p < 0.001, **Figure 3C**]. With the caveats of small samples and unequal group sizes, there was no difference between the two wood mice and six bank voles within this sample (data not shown).

# Sex-Related Differentiation along the Septo-Temporal Axis in Wood Mice

In wood mice and bank voles, animals of both sexes were available, and gender differences in distribution of proliferating and differentiating cells were tested for. The percentage of DCX+ young neurons did show a gender effect in wood mice for the distribution along the septo-temporal axis [F(1, 14) = 9.91, p = 0.007, **Figure 4A**], and an interaction of gender with septotemporal subdivisions [F(2, 28) = 3.87, p = 0.033]. Post-hoc tests revealed that in the septal subdivision, female wood mice have lower DCX values then males (p = 0.03), the intermediate subdivisions did not differ (p = 0.62), and in the temporal subdivision females had higher DCX values (p = 0.0499). The percentage of Ki67+ cells did not show a gender effect [F(1, 14) = 2.44, p = 0.14] in wood mice. We also found no gender × subdivision interaction for Ki67 [F(2, 28) = 0.875, p = 0.43]. Likewise, bank vole female and males did not differ in the distribution of Ki67+ cells along the septo-temporal axis [F(1, 21) = 0.046, p = 0.83], and there was also no gender x subdivision interaction [F(2, 42) = 1.48, p = 0.24]. Gender differences for DCX in bank voles were absent as well [F(1, 21) = 0.45, p = 0.51, **Figure 4B**]. Neither could interactions between gender and septo-temporal subdivisions be found in bank voles [F(2, 42) = 1.42, p = 0.25].

# DISCUSSION

# Opposing Septo-Temporal Gradients of Hippocampal Neurogenesis in Closely Related Rodents

Structural and functional specificity along the longitudinal axis of the hippocampus have been reported repeatedly (Fanselow and Dong, 2010; Kesner, 2013; Poppenk et al., 2013; Strange et al., 2014), with gradients of adult hippocampal neurogenesis (AHN) fitting well into this framework (Wu et al., 2015). The age related decline of AHN (Ben Abdallah et al., 2010; Amrein et al., 2011) has septo-temporally different effects on the relative densities of progenitor cell populations and a larger overall effect in the temporal dentate gyrus (Jinno, 2011a). The maturation of septally generated cells is faster than that of temporally generated ones (Piatti et al., 2011; Snyder et al., 2012), which, in turn, can be selectively accelerated by voluntary exercise (Piatti et al., 2011). Ritalin (Lagace et al., 2006), seizures (Ferland et al., 2002; Häussler et al., 2012) or GABA-receptor blockade (Felice et al., 2012) increase AHN more temporally than septally. Unpredictable stress results in a larger temporal decrease in AHN (Tanti et al., 2012), while acute stress appears to increase septal AHN (Kirby et al., 2013). Rats that display learned helplessness after inescapable shocks show a decrease of septal AHN, whereas AHN is not affected in rats that do not display this behavior (Ho and Wang, 2010). Behavioral effects specific to the ablation of AHN in the septal or temporal dentate gyrus have also been found (Wu and Hen, 2014). Different septo-temporal effects may be explained by the distribution of progenitors with septotemporally differing stimulus sensitivities (Jhaveri et al., 2015) or suppressed activity of temporal stem cells (Sun et al., 2015).

Reports in rats, mice, gerbils and primates support a septal > temporal gradient for neurogenesis (Dawirs et al., 1998; Teuchert-Noodt et al., 2000; Snyder et al., 2009b, 2012; Jinno, 2011b; Amrein et al., 2015; Bekiari et al., 2015). Our data on C57BL/6 follow the same pattern. Neurogenesis is high septally and lowest in the temporal dentate gyrus. However, we observe the opposite gradient in DBA, house mice, wood mice and bank voles, in which neurogenesis is higher temporally than septally. The shift toward a higher temporal AHN is more pronounced in female wood mice as compared to male wood mice. The inverted gradient in these species is, to some extent, apparent in proliferation, neuronal differentiation and the ratio of DCX/Ki67 positive cells. As observed before in a larger and phylogenetically more diverse sample of species (Amrein et al., 2011), cell proliferation is less variable across species. It also appears more restricted with respect to a septotemporal differentiation, as we found no main effect in the distribution of Ki67-positive cells between species. The largest differences between strains and species are observed for DCX+ young neurons. This cell population may either modulate local network circuitry or regulate hippocampus-dependent behaviors

FIGURE 3 | Radial location of DCX positive cells in bank voles and wood mice. DCX+ cells in wild rodents are not only found in the subgranular zone (SGZ, A,A'), but are found dispersed throughout the granular cell layer (GCL, B,B'). Quantitative assessment of DCX+ cells in the SGZ vs. those cells within the GCL reveals differences along the septo-temporal axis (C). While DCX+ cells in the SGZ predominate in the septal and intermediate hippocampus, significantly more DCX+ cells are found within the granule cell layer in the temporal subdivision. Scale bare (A,B) = 50µm; (A',B') = 10µm. \*\*p < 0.01, and \*\*\*p < 0.001; for exact values see results.

attributed to the septal or temporal hippocampus (Wu et al., 2015) according to species-specific needs. In particular, bank voles show a strong emphasis on a contribution of young neurons in the temporal hippocampal function. This is likely due to an increased or extended survival of young neurons in the temporal dentate gyrus.

# Neurogenesis and the Hippocampal Longitudinal Axis

Gene expression studies (Thompson et al., 2008) do indicate a division into three domains, while behavioral (Moser and Moser, 1998) and electrophysiological studies (Patel et al., 2012, 2013) demarcate a temporal third from the (combined) septalintermediate two-thirds (summarized in Strange et al., 2014). A smooth long-axis gradient as described for hippocampal connectivity (Amaral and Witter, 1989; Witter, 1993) and place field sizes (Kjelstrup et al., 2008) could have been a possible approach to analysis as well. Our partition of the hippocampus into equally sized subdivisions (septal, intermediate and temporal) is somewhat arbitrary, but roughly corresponds to the divisions that have been suggested and is independent of anatomical landmarks that may depend on the orientation of the sections when the hippocampus is cut in situ. Tanti and Belzung (2013) have previously emphasized the problems associated with comparisons of experimental outcomes that arise from variable in situ demarcations between septal and temporal hippocampus. Our approach to analyze dissected hippocampi cut perpendicular to their long axis allowed for comparisons between species with similar body sizes but different brain and hippocampal sizes with minimal data distortion. This approach has also been used previously to overcome the ambiguities of other definitions (Gaarskjaer, 1978; Rapp and Amaral, 1988; Jinno and Kosaka, 2006; Snyder et al., 2009b; Amrein et al., 2015; Sun et al., 2015). We took care to present the data in graphs visualizing the entire longitudinal axis. Beyond a septo-temporal differentiation, our data also indicate that neurogenesis shows few differences between strains or species in an intermediate subdivision and that this subdivision harbors the highest percentages of proliferating cells and young neurons in all species, accounting for ∼50% of all Ki67+ and DCX+ cells. Research in gradients has focused primarily on the septal and/or temporal pole, either by investigating septal and temporal parts, or by dividing the hippocampus into two areas (Wu et al., 2015). To our knowledge, neurogenesis has not been experimentally manipulated selectively in the intermediate hippocampus. fMRI recordings of the optogenetically stimulated intermediate hippocampus (Weitz et al., 2015) indicate a special role of this hippocampal subdivision by its widespread yet distinct cortical and subcortical network activation. Specific inactivation of the intermediate hippocampus by lesions or pharmacogenetic deactivation in rats shows that the intermediate hippocampus is necessary for spatial components of episodic memory (Barker et al., 2017) and sufficient to translate rapid place learning into a behavioral response (Bast et al., 2009). Our finding of high neurogenesis in the intermediate hippocampus in all species and strains further emphasizes the importance of this subdivision in the context of AHN.

# Tame and Wild

Defensive behavior, independent of prior experience, and adequate unconditioned fear responses in rats rely on an intact temporal hippocampus (Kjelstrup et al., 2002). There is a large body of evidence that a stress response is mediated by projections from the temporal subiculum to the basolateral amygdala and dorsomedial hypothalamus (reviewed by Lowry, 2002; Belujon and Grace, 2015). It should be pointed out here that the behavior of wild rodents brought into the laboratory is radically different from that of laboratory strains. Wild rodents do everything to avoid human contact and take painful defensive measures if it

cannot be avoided. They are very skillful jumpers and will leap out of the cages for safety if given the smallest opportunity. Such behaviors are adaptive in the natural environment of wild mice, but selective pressures that result from the environment (e.g., predation) have been absent or even been replaced by selection for docility in the laboratory (Goto et al., 2013). The behavior of the F1 generation of wild house mice used here was indeed still very "wild-type" like. Differences in behavior should therefore be reflected in differences of temporal hippocampal function, and such differences apparently encompass a higher number of temporal generated young neurons in wild rodents used in this study. The unexpected wild-type-like temporal AHN in DBA mice may support this idea. Although poorer performers than C57BL/6 in many hippocampal-dependent spatial learning tasks (Ammassari-Teule et al., 1992; Jones et al., 2001), they show a better passive avoidance learning, which, in contrast to C57BL/6 mice, is not impaired by septal NMDA receptor antagonist infusion (Mineur et al., 2007; Baarendse et al., 2008). They also show less freezing in fear conditioning tasks than C57BL/6 mice (Baarendse et al., 2008) that is not altered by chronic mild stress (Mineur et al., 2007).

# DCX/Ki67 Ratio, Cell Maturation and Survival

Changes in the ratio between DCX+ and Ki67+ cell numbers may be generated by at least three different mechanisms. The duration of expressions of DCX or Ki67 in the cells may change. Ki67 is expressed during S, G2, and M phases of the cell cycle, but only late during G1. Considering the short duration of G1 in cycling progenitors (Lewis, 1978; Nowakowski et al., 1989; Overall et al., 2016), changes in Ki67 expression during G1 could not generate the DCX/Ki67 ratio differences seen in bank voles. Instead, there is evidence for differences between laboratory rats and mice in the rate of young neuron maturation (Snyder et al., 2009a). Furthermore, delayed maturation and extended expression of markers of young neurons has been described in other species (Amrein and Slomianka, 2010; Brus et al., 2013), including primates (Ngwenya et al., 2008, 2015; Kohler et al., 2011; Amrein et al., 2015). Differences in the speed of maturation of young neurons, depending on septo-temporal location, have also been reported in laboratory rodents (Piatti et al., 2011; Snyder et al., 2012). On this background, the high DCX/Ki67 ratio in the intermediate and, even more so, in the temporal dentate gyrus of bank voles suggest to us an extended period in which DCX is expressed in bank voles. In that a large fraction of newly generated neurons dies before expressing markers of adult neurons (Dayer et al., 2003; Kempermann et al., 2003), which coincides with the shut-down of DCX expression (Brown et al., 2003), the extended period of maturation may also represent an extended period of survival. An alternative explanation of differences in the DCX/Ki67 ratio would be differences between strains or species in the number of cells that will have either neuronal or glial fates (Kempermann and Gage, 2002a,b), although heritable traits have been found to account for only little of the differences in gliogenesis between strains (Kempermann et al., 2006). Our material does not allow the assessment of this possibility.

# Changes in the Distribution along the Radial Axis

In wild wood mice and bank voles, our data suggest septotemporal differences in the distance that DCX+ cells migrate along the radial axis, with DCX+ cells preferentially found in the SGZ in the septal and intermediate subdivision, and more DCX+ cells within the granule cell layer proper in the temporal hippocampus. In laboratory rodents, there is only a modest migration of newborn neurons into the dentate granule cell layer during the first week after their formation (Kempermann et al., 2003) and most of the newborn cells remain at the base or in the lower one-third of the granule cell layer. Both in sheep and monkeys, in which newly generated neurons retain immature characteristics far longer than in rodent, there is at least statistical evidence for a migration of new neurons into the granule cell layer (Kohler et al., 2011; Brus et al., 2013). Unfortunately, the illustrations provided do not allow a comparison with our observations in wood mice and bank voles.

While a prolonged period of migration may result in differences in radial positioning, genetic control of cell migration can also generate a distribution of newborn cell reminiscent of that seen in the temporal dentate gyrus of wood mice and bank voles (Namba et al., 2011; Teixeira et al., 2012; Schafer et al., 2015; Woodbury et al., 2015). In the case of miR-155 overexpression, this seems possible even though the survival of newborn cells is reduced (Woodbury et al., 2015). We cannot address which of these factors may be responsible for the radial distribution of newborn cells in wood mice and bank voles. However, these mice show that natural selection may operate on these or similar mechanisms to generate what must be believed to be adaptive species differences.

# CONCLUDING REMARKS

The variability in AHN in terms of absolute cell numbers, their septo-temporal distributions, ratios between proliferating and maturing cells between rodent species may be surprising and bewildering. It is less surprising when one considers the role that AHN may play in differentiating hippocampal function across taxonomic units. Within a larger sample of rodent species, we found that AHN is a far better differentiator between species than other hippocampal principal cell populations (van Dijk et al., 2016a). It is only equaled by variation in hilar cell numbers, which, like newborn neurons, are involved in the control of information flow through the dentate gyrus. Septo-temporal differences in AHN and differential septotemporal effects of experimental manipulations suggest that AHN participates in the anatomical and functional septotemporal differentiation of the hippocampus (Sahay and Hen,

# REFERENCES

2007; Glasper et al., 2012; Tanti and Belzung, 2013; Jinno, 2016). Considering the power of AHN to differentiate between species, a species-specific septo-temporal modulation of AHN turns from surprise to expectation. Much could be said about the relative vices and virtues of working with laboratory or wild rodents. Without the countless studies performed in laboratory animals, our observation would be phenomenology without plausible mechanisms. On the other hand, observations in wild animals may turn what we perceive as anatomical aberrations and functional deficits after the experimental manipulations of these mechanisms into substrates of adaptive onto- and phylogenetic changes.

# AUTHOR CONTRIBUTIONS

IA, RMvD and LS planned experiments. FW, EL, RMvD and IA conducted experiments FW, LS and IA wrote the manuscript.

# FUNDING

IA and RMvD were funded by the EMDO foundation, Zurich.

# ACKNOWLEDGMENTS

Special thanks goes to Prof. Barbara König for providing the wild house mice, and to Elisabeth Jakob, Rifferswil and Silvana Ressegatti, Zürich, for their help in trapping mice. We also thank Sonìa Matos for expert technical assistance. This work was supported by a grant of the EMDO Stiftung, Zurich, to RMvD and IA.

and neurogenesis in the hippocampus of wild-living rodents. Hippocampus 14, 1000–1010. doi: 10.1002/hipo.20018

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2017 Wiget, van Dijk, Louet, Slomianka and Amrein. This is an openaccess article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Evolutionary Shaping of Adult Hippocampal Neurogenesis in Mammals–Cognitive Gain or Developmental Priming of Personality Traits?

Hans-Peter Lipp1, 2, 3 \*

*1 Institute of Evolutionary Medicine, University of Zurich, Zurich, Switzerland, 2 Institute of Anatomy, University of Zurich, Zurich, Switzerland, 3 Department of Physiology, School of Laboratory Medicine, University of Kwazulu-Natal, Durban, South Africa*

Keywords: adult neurogenesis, natural selection, evolution, hippocampal functions, cognition, comparative, personality, genetic assimilation

Adult hippocampal neurogenesis (AHN) in mammals peaks in early postnatal/juvenile periods and is strongly down-regulated thereafter. Depending on species, it may disappear rapidly in adult individuals, or persist at very low levels for a lifetime. Commonly, higher levels of AHN in mammals are thought to provide mental flexibility allowing for adapting to new ecological niches. But why does natural selection not prevent down-regulation of AHN, and why should a rudimentary proliferation rate in humans provide reproductive fitness even for aged individuals? The problem is compounded by species-specific behavioral manifestations of hippocampal functions that depend on brain size and ecological niches. Moreover, in laboratory rodents, proliferation levels of AHN and behavioral covariates appear unpredictable and context-sensitive. Conversely, one might ask why evolutionary mechanisms tolerate in nearly all mammals a certain level of early postnatal or subadult AHN. Specifically, the hypothesis of cognitive flexibility appears odd in species in which AHN is massively reduced in early infancy such as in humans. I suggest that early but not late AHN plays a hidden role in developing randomly different epigenetic personality traits in local populations. Such traits may counteract or enhance natural selection of the underlying genetic architecture—a process known as genetic assimilation.

In mammals, protracted neurogenesis occurs in subventrical zones (SVZ) from which neuroblasts migrate rostrally to the olfactory bulb (rostral migratory stream, RMS) and from a secondary proliferation zone in the dentate gyrus, the subgranular zone (SGZ). The ongoing postnatal proliferation there is denoted as "adult" hippocampal neurogenesis (AHN). Molecular markers for migration and differentiation are often not correlated with basic levels of AHN in many species. For example, doublecortin (DCX) is a reasonable proxy for estimating proliferation rates in mice and rats. In other species such markers persist for long periods after the cessation of proliferation or appear even generated de novo (Amrein, 2015; Penz et al., 2015; Lipp and Bonfanti, 2016). Therefore, AHN and its potential relation to natural selection will refer here to simple proliferation only. After all, it is the dogma-breaking role of persisting neurogenesis that dominates the public view of AHN.

# THE MAIN PROBLEMS

The evolutionary role of AHN in mammals is not understood. The most straightforward explanation shared by many has been offered by Kempermann (2012, 2016) who claims that AHN is an evolutionary recent addition since it is linked with the mammalian hippocampus, which itself is unique for mammals. As the human hippocampus is mediating

#### Edited by:

*Vincent Tropepe, University of Toronto, Canada*

# Reviewed by:

*Troy Ghashghaei, North Carolina State University, United States*

> \*Correspondence: *Hans-Peter Lipp<EMAIL_ADDRESS>
#### Specialty section:

*This article was submitted to Neurogenesis, a section of the journal Frontiers in Neuroscience*

Received: *15 March 2017* Accepted: *05 July 2017* Published: *21 July 2017*

#### Citation:

*Lipp H-P (2017) Evolutionary Shaping of Adult Hippocampal Neurogenesis in Mammals–Cognitive Gain or Developmental Priming of Personality Traits?. Front. Neurosci. 11:420. doi: 10.3389/fnins.2017.00420* complex forms of memory, the putative beneficial role of AHN for human memory is extrapolated from rodent studies to humans and to other mammalian species. Thus, cognitive flexibility originating from AHN should provide mammalian species with superior abilities for adapting to new environments. This view bears some problems, namely the role of the hippocampus in mammalian behavior, the different levels of AHN in various species, the differential down-regulation of AHN across species, and the process of natural selection in small populations.

# DOES NATURAL SELECTION ACT ON BEHAVIORAL TRAITS DEPENDING ON AHN?

To answer this, one would need to know what behaviors and abilities are clearly correlated with hippocampal structural traits including AHN. Perhaps surprisingly, the situation is not clear since even laboratory rodents show a bewildering variety of hippocampus-dependent behaviors, mostly ignored by AHN research. For one, they include natural behaviors such as food burrowing and nest building (Deacon et al., 2002), and social behavior (Ely et al., 1976). Better known are many learning paradigms including simple two-way avoidance (Lipp et al., 1989) and cognitive behaviors such as spatial navigation and pattern separation.

Why is such diversity not known for humans? The human hippocampus interacts chiefly with higher-order association cortex mostly lacking in rodents (**Figures 1A,B**), see also Dong (2008). In addition, the rodent hippocampus integrates less "cognitive" input with subcortical limbic structures (**Figures 1B,D**). In both species (**Figures 1C,D**), the hippocampal loops form the ultimate associative cortex. However, in rodents this structure must also handle functions relegated to specialized cortex regions in humans (Bergmann et al., 2016), being therefore multifunctional. Furthermore, the rodent hippocampus blends hypothalamic and basal forebrain activity into input/output loops connecting to fronto-limbic cortical areas. Thus, the hippocampus is likely to be involved in modulation of many species-typical behaviors. The prevailing uncritical bidirectional extrapolation from human hippocampal functions to rodents and vice versa has led to some ill-founded views.

For example, many researchers and editors believe now that AHN is specifically critical for complex tests involving pattern separation, the human ability to memorize fine-grained differences in spatial or contextual environments (Sahay et al., 2011). Yet the behavioral evidence for this conclusion is shaky as it was obtained in mice exposing them to test situations requiring gradually recognizing subtle differences in spatial arrangements of threatening situations. But small rodents cannot afford to discriminate subtleties of threats such as the color and size of a cat and must react immediately. Obviously, AHN-dependent pattern separation is useful for survival of rodents only if it works at once, and this demonstration is still lacking.

# CAN VARIABLE STRUCTURAL TRAITS IN GRANULE CELLS AND MOSSY FIBERS MEDIATE BEHAVIOR AND BEING TARGETED BY NATURAL SELECTION?

The extent of the so-called intra/infrapyramidal mossy fiber projection (IIP-MF) correlates genetically and individually with behavioral traits, not all of them being considered as "hippocampal" (Lipp et al., 1989). Variations of IIP-MF in mice co-vary positively with predictability of ongoing behaviors, and respond rapidly to selective breeding and natural selection (Lipp and Wolfer, 1995, 2002, 2013). Therefore, developmental variations in the distribution of the granule cell axons can predict behavioral traits of adults.

On the other hand, it is difficult to find in rodents consistent correlations between individual numbers of newly generated neurons and individual behaviors. A likely reason is that the extent of the IIP-MF is determined during the first postnatal days of rodent pups, and remains rather stable during adulthood. Conversely, AHN in rodents is extremely sensitive to environmental changes, activity and stress levels, and appears influenced by about 190 genes (Kempermann et al., 2006). It also changes strongly during life history as explained below. Thus, functional relations between AHN and behaviors in mice and rats are masked by excessive temporal variability of the structural substrate, possible interactions with learning, and by the multifunctionality of the rodent hippocampus. This may explain the huge number of behavioral AHN studies with discordant results (Lipp and Bonfanti, 2016). Exceptions with more predictable outcome are some naturalistic behaviors. Genetic suppression of AHN resulted in impairments of species-typical behaviors and sucrose preference in mice (Jedynak et al., 2012) and rats (Snyder et al., 2016). Studies of individual correlations showed positive correlations of AHN levels with sucrose preference (Hu et al., 2016), and with roaming in large enclosures (Freund et al., 2013). Conversely, reactions to novelty correlated negatively with the number of proliferating neurons (van Dijk et al., 2016). But even for species-typical behaviors, their relation to AHN appears unpredictable. Yet natural selection requires that behavioral phenotype and genotype should be strongly linked. It is thus difficult to see how natural selection could act on cognitive or species-typical behaviors linked unpredictably to AHN, and even more difficult to imagine selective pressure on increased AHN in species in which it is already sparse early in life.

# AHN IS STRONGLY DOWN-REGULATED BUT IS BOTTOMING OUT AFTER TWO YEARS, INDEPENDENT OF SPECIES

AHN diminishes with age, initially considered as a normal aging process. However, studies in mice showed that AHN is exponentially decreasing after 7 weeks of age till about 4 months—during peak conditions for reproduction—and leveling off afterwards (Ben Abdallah et al., 2010). A strong decline during the first year of life has now been documented repeatedly for humans (Knoth et al., 2010; Dennis et al., 2016, 2017).

reduction of cortical activity pattern for representation in the hippocampal formation (Lipp, 2015). (D) Corresponding view in rodents in which the input to the hippocampal loop system originates chiefly from non-associative cortex regions but also from subcortical structures. Random dispersal of newly born cells in the dentate gyrus may prime the early postnatal development into different behavioral phenotypes and personalities.

Weissleder et al. (2016) have reported a further decrease from years 21 till 91, yet starting from a low level. Other markers of proliferative activity in the human hippocampus did not show an age-dependent decline. Thus it makes sense to distinguish early vs. late AHN.

The belief in a functional role of AHN in aged humans is supported solely by a widely cited study by Spalding et al. (2013) even though it remains controversial among experts. Using the decay of radioactive C14 in human hippocampi, they calculated daily turnover rates of 700 cells per day among 20 millions granule cells. However, they miscalculated proliferation levels in mice and concluded—widely cited also—that AHN in 40 year-old humans is comparable in proliferation levels to correspondingly aged mice (for details see Lipp and Bonfanti, 2016).

In comparative terms, the work of Amrein et al. (2011) has shown that strong down-regulation of AHN occurs in many species regardless of life span and ecological conditions. A bottom in proliferative activity is reached in both mice and men after about 2 years, and AHN is continuing at minimal rates independent of the life span of the species. These findings make cross-species comparability of AHN and life history questionable.

For example, we had hypothesized that the decay of AHN in humans might follow a much slower pace, bottoming out at the age of about 30 years (Amrein and Lipp, 2009). This view might have explained some typical transitions in human life history such as the emergence of long-term memory at the expense of short-term memory in children (Yim et al., 2013; Akers et al., 2014). Likewise, it would have fitted the transition from exuberant and reckless juvenile behavior to cautious adult behavior—a characteristic of the maturation of the hippocampus as postulated by Altman et al. (1973). The general idea was that the input-output patterns of the hippocampal formation between the loops crossing the hippocampal formation (**Figures 1C,D**) would initially be kept malleable by the AHN-dependent production of juvenile excitable granule cells. The down-regulation of AHN would then entail a fixation of input-output relations associated with acquired and species-typical behaviors optimal for a given environment. This concept would make sense in species in which the initial reduction of AHN covers pre- and post-puberty and early adulthood such as in mice or rats. In these species, the altered hippocampal physiology might be of evolutionary adaptive value. Therefore, natural selection might favor strong early AHN followed by a period of fixing behavioral traits. However, in humans the decay period falls into a time window with reduced behavioral expression and cognitive abilities. Moreover, it is accompanied by an enormous (non-proliferative) growth of dendrites and connections in the forebrain. This raises the question whether early peaking of AHN and downregulation with late bottoming-out is useful for humans at all.

# WHY IS LATE HUMAN AHN NOT ENHANCED BY NATURAL SELECTION?