{'fraction_non_alphanumeric': 0.04073767449827286, 'fraction_numerical': 0.02786262810813897, 'mean_word_length': 4.741681691525979, 'pattern_counts': {'":': 0, '<': 3, '<?xml version=': 0, '>': 15, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 1, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Effects of spaced learning in the water maze on development of dentate granule cells generated in adult mice

New dentate granule cells (GCs) are generated in the hippocampus throughout life. These adult‐born neurons are required for spatial learning in the Morris water maze (MWM). In rats, spatial learning shapes the network by regulating their number and dendritic development. Here, we explored whether such modulatory effects exist in mice. New GCs were tagged using thymidine analogs or a GFP‐expressing retrovirus. Animals were exposed to a reference memory protocol for 10–14 days (spaced training) at different times after newborn cells labeling. Cell proliferation, cell survival, cell death, neuronal phenotype, and dendritic and spine development were examined using immunohistochemistry. Surprisingly, spatial learning did not modify any of the parameters under scrutiny including cell number and dendritic morphology. These results suggest that although new GCs are required in mice for spatial learning in the MWM, they are, at least for the developmental intervals analyzed here, refractory to behavioral stimuli generated in the course of learning in the MWM. © 2015 Wiley Periodicals, Inc.


INTRODUCTION
One of the major challenges in neurobiology is to understand the cellular mechanisms underlying long-term memory. The hippocampal formation (HF) is a major structure involved in spatial learning that produces long-term memory traces. Altering the integrity of the HF function by different means impairs learning and vice versa, structural and functional changes occurring in the hippocampus in the course of learning, such as synaptic remodeling and long-term potentiation, are key signatures of long-term memory processes (Bailey and Kandel, 1993;Moser, 1999). These adjustments have an adaptive value as they enlarge our behavioral repertoire by adding new skills, or altering previously acquired ones, and thus allow us to adapt to any new life situation.
The discovery of a de novo hippocampal production of neurons in the adult brain has raised the fascinating hypothesis that the new generated neurons are involved in hippocampal-dependent memory (Altman, 1962;Gross, 2000). In support of this hypothesis, there is abundant literature showing that new neurons are recruited by spatial learning in the Morris water maze (MWM) (Kee et al., 2007) and that ablating new neurons impairs spatial learning (Dupret et al., 2008;Garthe et al., 2009). Moreover, spatial learning itself regulates adult neurogenesis in complex ways (see Table 1). Indeed, it promotes the survival of adult-born neurons that are relatively mature (generated one week before training) (Gould et al., 1999;Hairston et al., 2005;Drapeau et al., 2007;Dupret et al., 2007;Sisti et al., 2007;Epp et al., 2011), induces the death of cells (Ambrogini et al., 2004) that are more immature (Dupret et al., 2007), and stimulates proliferation of precursors (Dupret et al., 2007). In addition spatial learning promotes the dendritic development of the surviving neurons in a homeostatic manner; these effects persist for several months, are specific to neurons born during adulthood and depend on the cognitive demand . All these learning-evoked changes in cell number and dendritic arbors rely on the activation of NMDA receptors Curlik and Shors, 2011). One question that remains to be answered is how learning-induced activity modulates adult neurogenesis.
To answer this question and motivated by the thought that genetically engineered mice will be used, we verified in a first step whether similar regulation exists in mice, the work cited above being carried out exclusively in rats. Using validated protocols in rats, we therefore studied the effect of spatial learning on cell survival, cell death, cell proliferation, and dendritic development in mice. Day: D. Month: M. Training: T. The sign "-" refers to "before training". If not indicated, animals received a spaced training.

Animals
We used three-month-old male C57BL/6J mice (n 5 101, batch 1-3, Charles River France), three-month-old female C57BL/6J mice (n 5 36, batch 5, Charles River France) or CD1 male mice (n 5 19, batch 4, Charles River France). An additional group of 7 week-old females (n 5 16, batch 6, Leloir institute) was used (see Table 2). Males individually housed and female housed in groups of 4 per cage were maintained under a 12 h light/12 h dark cycle (lights on from 8:00 A.M. to 8:00 P.M.) in a temperature-(22 6 3 C) and humiditycontrolled facility. Animals had ad libitum access to food and water for 2 weeks after arrival. Experiments were performed in accordance with the European Union (2010/63/UE), the French National Committee local recommendations (Batches 1-5th), and the Institutional Animal Care and Use Committee of the Fundaci on Instituto Leloir (Batch 6th).

Thymidine Analog Injections
Newly born cells were labeled by the incorporation of synthetic thymidine analogs (XdU [where X represents Cl, or I]). The learning groups received a single injection of IdU and of CldU at different time points before the onset of training (see Table 2), both at equimolar doses of 50 mg BrdU/kg (CldU: 42.76 mg/kg/10 mL; IdU: 57.65 mg/kg/10 mL, ip). The different control groups were injected with XdU within the same period. CldU (Sigma) was dissolved in NaCl (0.9%) and IdU (Sigma) was dissolved in 400 mL 1N NH4OH and 9.6 mL of NaCl.

Production of Viral Vectors
Retroviral particles based on the Moloney murine leukemia virus were assembled using three plasmids containing the envelope (CMV-vsvg), viral proteins (CMV-gag/pol), and GFP as in previous work (Laplagne et al., 2006). Plasmids were transfected into 293T cells using deacylated polyethylenimine. Virus-containing supernatant was harvested 48 h after transfection and concentrated by two rounds of ultracentrifugation. Virus titer was typically 10 5 particles/mL.

Stereotaxic Surgery for Retroviral Delivery
Seven or seventeen days before behavioral training (Table 2), mice were anesthetized (150 mg ketamine 1 15 mg xylazine in 10 mL of saline/g) and virus (1 mL at 0.15 mL/min) was infused into the right DG using sterile microcapillary calibrated pipettes (Drummond Scientific) at the following coordinates: AP: 22 mm, L: 21.5 mm lateral, V: 21.9 mm from Bregma.

Morris Water Maze Training
Mice were tested in a MWM as described previously (Dupret et al., 2008). The MWM consisted of a circular tank built of white plastic (150 cm diameter, 60 cm height) filled with water (20 6 1 C) that had been made opaque by the addition of a nontoxic white cosmetic adjuvant. During the pretraining session, mice were allowed to swim for 60 s in the Male or female mice (C57BL/6J or CD1) housed individually or in groups were injected with IdU (7, 15, or 28 days) or CldU (3, 10, or 21 days) or GFPexpressing retrovirus (7 or 17 days) before learning. Two days after completion of training, animals were sacrificed for immunohistochemistry. Days: D, Training 5 T. The sign "-" refers to "before training". water maze without a platform. Then, they were placed upon the platform raised at the surface of the water where they were required to stay at least for 15 s. Finally, they were allowed to swim for a 30 s period that was ended by a climbing trial onto the hidden platform. The platform was never localized in the quadrant used for the training sessions. At the end of the pretraining, all mice swam actively and were able to climb onto the platform and stay on it for 15 s. During training, mice in the Learning group (L) were required to locate the hidden platform (1.5 cm under the water in a fixed location) from variable random start positions. They received 3 daily trials separated by a 5 min intertrial interval. A trial terminated when the animal climbed onto the platform. Mice that failed to find the platform within a 60 s cut-off time were placed onto the platform by the experimenter and had to stay there for 15 s before being placed back in their home cage for the 5 min intertrial interval. The releasing point (starting point) differed for each trial and different sequences of releasing points were used day to day. Twenty-four hours after completion of training, the hidden platform was removed and memory for the platform location was assessed during a probe test. During this test, mice were allowed to freely swim in the water maze for 60 s and performances were assessed by time spent in the target quadrant where the platform was located, and by total number of crossings in the exact platform location. In the first experiments (Table 2), one control group was used (C) consisting of animals that were transferred to the testing room at the same time and with the same procedures as the learning group but that were not exposed to the water maze. In experiment 200926th, an additional group of mice that was trained to find a visible platform (VP) in a fixed location was used.

Immunohistochemistry
One day after the probe test, trained animals were perfused transcardially with 30 mL PBS, pH 7.3, containing heparin (5.10 4 IU/mL), followed by 30 mL of 4% paraformaldehyde in 0.1M of phosphate buffer, pH 7.3. The different agematched control groups were killed the same day. After 1 week of postfixation in PAF, brains were sliced using a vibratome (batchs 1-5th) or a cryostat (batch 6th). Sections were conserved at 220 C in cryoprotectant solution until processing. Free-floating coronal sections (40 mm for batches 1-5 and 60 mm for batch 6) were processed using a standard immunohistochemical procedure (Dupret et al., 2007(Dupret et al., , 2008 to visualize the thymidine analogs (CldU, IdU) in alternating one-in ten sections using different anti-BrdU antibodies from different vendors (For CldU: rat primary at 1/1000, Accurate Chemical and Scientific Corporation; For IdU: mouse primary at 2000, BD Biosciences). Bound antibodies were visualized with biotinlabeled donkey anti-rat antibodies (1/1000, Jackson for CldU) or biotin-labeled horse-antibodies (1/200, Abcys for IdU). Cell proliferation and cell death were studied using rabbit antibodies directed against the phosphorylated form of histone 3 (pH 5 3; 1/500, Upstate, Lake Placid, NY) or against the activated-caspase3 (1/400, Cell signaling). Bound antibodies were visualized with biotin-labeled goat antirabbit antibodies (1/200, DAKO). For each antibody, sections from all animals were processed in parallel, and immunoreactivities were visualized by the biotin-streptavidin technique (ABC kit; Dako) using 3,3-diaminobenzidine as chromogen.

Analysis of Cell Numbers on DAB Stained Sections
Cell counting was performed on coded sections when the experimenter performed the behavioral training and on noncoded sections when the experimenter was na€ ıve to the training procedure. The number of X-immunoreactive (IR) cells in the supragranular and infragranular blades of the left DG was estimated on counts made by systematic random sampling of every tenth section along the rostrocaudal axis of the HF using a modified version of the optical fractionator. All of the X-IR cells were counted on each section and the resulting numbers were tallied and multiplied by the inverse of the sections sampling fraction (1/ssf 5 20). When counting was performed in both sides, the numbers were tallied and multiplied by 1/10.

Analysis of the Dendritic Arbor on DAB Stained Sections
The dendritic arbor was first analyzed in batch 1 by analyzing IdU-doublecortin (DCX) double labeled cells. Briefly, sections were first incubated for 48 h with a rabbit anti-DCX primary antibody (1/2000, Abcam); upon incubation with a biotinylated goat anti-rabbit secondary antibody, immunoreactivity was visualized with DAB as chromogen, leading to brown staining of IR cells. Sections were then treated with 2N HCl 37 C for 30 min and incubated for 48 h with a rat anti-BrdU (1/500, BD Biosciences) antibody. Biotinylated secondary antibodies (goat antirat; 1/1000, Jackson) were revealed with Vector SG as chromogen, leading to a blue staining.
Morphometric analysis of IdU-DCX and virus-labeled neurons were performed with a 3100 objective, using a semiautomatic neuron-tracing system (Neurolucida; Microbrightfield, Colchester, VT). Briefly, neurons were selected based on the following criteria: (i) neurons exhibited vertically orientated dendrites that extended into the dentate molecular layer and (ii) dendrites of selected neurons had minimal overlap with the dendrites of adjacent cells to unambiguously trace the dendritic tree. Data for various metric measurements were calculated, including the cell body area and total dendritic length.

Analysis of the Dendritic Arbor and Spines using Immunofluorescence
For batch 6, one-in-six sections (thickness 60 mm) were incubated with rabbit polyclonal antibodies directed against GFP (1/500; Invitrogen) and bound antibodies were visualized

LEARNING-INDUCED ADULT HIPPOCAMPAL NEUROGENESIS IN MICE
with a donkey anti-rabbit Cy3 (1/250; Jackson ImmunoResearch). Dendritic length and spine density were analyzed using confocal microscopy (Zeiss LSM 510). For dendritic length measurements, images were acquired (403; NA, 1.3; oil-immersion) taking z-series including 35 -50 optical slices, airy unit 5 1 at 0.8 mm intervals. Dendritic length was measured from projections of three-dimensional reconstructions onto a single plane using Zeiss LSM image Browser software. For spine counts, selected GFP-IR dendritic segments were located in the middle third of the molecular layer. High-resolution images were acquired using a 633 objective (NA, 1.4; oil-immersion) and taking z-series of 40 -200 optical slices of airy unit 5 1 at 0.1 mm intervals as previously described (Morgenstern et al., 2008). All dendritic spines within 2 mm from the shaft were counted on projections onto a single plane. Four to nine dendritic segments (40-50 mm in length) were imaged per DG region per mouse (with 6 2 10 mice per experiment), choosing fragments at a distance 150 mm from each other to avoid sampling multiple dendrites from single neurons. A similar number of fragments were chosen from the infrapyramidal and suprapyramidal blades.

Analysis of Cellular Phenotypes
To examine the phenotype of XdU-IR cells, one-in-ten sections were incubated with BrdU antibodies from different vendors (For IdU: mice antibodies from Becton Dickinson at 1/ 2000; For CldU: rat antibodies from Accurate at 1/11,000), which were revealed using CY3-anti-mice or CY3-anti-rat antibodies (1/1000; Jackson Immunoresearch). Sections were then incubated with goat anti-DCX antibodies (1/1000; Santa Cruz Biotechnology), which were visualized with an Alexa-488 antigoat IgG (1/1000; Jackson). The percentage of XdU-labeled cells expressing DCX was determined throughout the DG using a confocal microscope with helium-neon and argon lasers (DMR TCSSP2AOBS; Leica).

GENERAL PROCEDURES
In the first batch of animals (first experiment), we examined whether spatial learning (L) influences the survival of neurons born 7 or 3 days before training, cell death and cell proliferation in male C57BL/6J mice. Animals were sacrificed one day after a probe test performed at the end of the learning phase. The Control group (C), which was not exposed to the task, was killed at the same time.
In the second batch of animals (second experiment), we examined whether spatial learning (L) influences the survival of neurons born 15 or 10 days before training, as well as cell death and cell proliferation in male C57BL/6J mice. Animals (L, C) were sacrificed as previously described.
In the third batch of animals (third experiment), we examined whether spatial learning (L) influences the survival of neu-rons born 28 or 21 days before training in male C57BL/6J mice. Animals (C, VP, L) were sacrificed as previously described.
In the fourth batch of animals (fourth experiment), we examined whether spatial learning (L) influences the survival of neurons born 15 or 3 days before training in CD1 outbred mice. Animals and their controls (C) were sacrificed as previously described.
In the fifth batch of animals (fifth experiment), we examined whether spatial learning (L) influences the dendritic development of genetically labeled neurons born 7 days before training in female C57BL/6J mice. Animals (C,VP,L) were sacrificed as previously described.
In the sixth batch of animals (sixth experiment), the influence of spatial learning (L) on the dendritic development of genetically labeled neurons born 17 days before training in female C57BL/6J mice. Animals (C,VP,L) were sacrificed as previously described.

STATISTICAL ANALYSIS
Differences between groups were analyzed with an ANOVA or a Student t test. Relationships between behavioral scores and cell numbers were evaluated using the Pearson correlation test.

Spatial Learning Does Not Affect Survival of Neurons Born One Week before Training
In the first experiment (Table 2, batch 1), newborn granule cells (GCs) were labeled with two different thymidine analogs according to a protocol established in rats (Dupret et al., 2007): male C57BL/6J mice were injected with IdU and CldU 7 days and 3 days before training, respectively. Animals learned the task as shown by the decrease in latency (Fig. 1A, F(9,144) 5 9.84, P < 0.001) or distance travelled (data not shown, F(9,144) 5 7.29, P < 0.001) to find the platform. Cells labeled with IdU and CldU were counted to monitor changes in survival induced by learning ( Fig. 2A,B). No differences were found in the number of IdU-IR (Fig. 1B, t 28 5 1.25, P > 0.05) and of CldU-IR cells (Fig. 1C, t 28 5 0.78, P > 0.05) between the learning and the control groups. As a consequence, the number of apoptotic cells expressing activated-caspase3 (Fig. 2C) was similar between groups (Fig. 1D, t 28 5 1.28, P > 0.05). In addition, the number of proliferating cells revealed by the number of cells expressing pH 3 (Fig. 2D) was not influenced either by spatial learning (Fig. 1E; t 28 5 0.31, P > 0.05). Finally, we examined the phenotype of IdU and CldU cells and found that >90% of these cells colocalized with the immature neuronal marker DCX (Fig. 2E,F; Table 3). Survival of cells generated 7 or 3 days before training is not affected by spatial learning. A: Schematic representation of the experiment (upper panel) and latency to find the escape platform (bottom panel). B: Number of IdU-IR cells (born 7 days before training). C: Number of CldU-IR cells (born 3 days before training). D: Apoptotic cell death measured by the number of activated-caspase3-IR cells. E: Cell proliferation measured by pH3-IR cells. C: control animals not exposed to the pool; L: animals trained to find a hidden platform. This observation indicated that thymidine analog labeled-cells were mostly neurons and that learning did not influence neuronal differentiation. Given the existence of large interindividual differences in the rate of learning, we searched for correlations between behavioral scores and cell numbers. Independently of the way the behavioral scores were calculated (mean of the latency or mean of the distance over the entire training period or over the last three days of training) no correlation was found with IdU or CldU-labeled cells (Fig. 3) or the number of apoptotic or proliferating cells (data not shown).
The hypothesis behind this study is that learning would influence development and survival of newborn cells through activation of inputs and/or outputs that might influence their integration within the network. In the experiment described above, none of the parameters used to study different aspects of neurogenesis was influenced by learning. Given that adult-born neurons are slower maturing in mice than rats (Snyder et al., 2009), we modified our protocol in order to tag older cells at the time of training (Table 2, batch 2). Then, a similar experiment was carried out, now with GCs labeled 15 or 10 days before training (Fig. 4). Although animals learned the task (latency: F(9,153) 5 9.84, P < 0.001; distance: F(9,153) 5 7.94), no differences were found between control and trained groups in the number of neurons labeled at any age (CldU: t 29 5 0.08, P > 0.05; IdU: t 29 5 1.24, P > 0.05), or the level of apoptosis (t 29 5 0.51, P > 0.05) or proliferation (t 29 5 1.07, P > 0.05). There was no correlation either between behavioral scores and cell numbers (data not shown).
In a third experiment ( Table 2, batch 3), we again modified the protocol of injection of IdU and CldU (respectively, 28 and 21 days before MWM training) and included a VP group, where mice were allowed to swim toward a VP. We found no effect on cell survival (Fig. 5, CldU: F(2,37) 5 0.19, P > 0.05; IdU: F(2,37) 5 0.097 P 5 0.91). Apoptosis and cell proliferation were not measured, since in the previous experiment these parameters were not influenced by learning.
In summary, these four experiments showed that spatial learning did not modify the survival of cells generated within one month before training, did not eliminate new cells from the dentate network, and did not induce a compensatory increase in cell proliferation. Given that, we have shown in rats  that spatial learning can regulate neuronal dendritic development without influencing cell survival (Lemaire et al., 2012), we then focused on dendritic arbors.

Dendritic Morphology of New GCs Is Not Modified by Spatial Learning
In rats, we have shown that the dendritic arbor of cell generated one week before training is increased by spatial learning . So in a first step, we analyzed on representative animals of the first experiment (Table 2, Batch 1), the dendritic arbors of IdU-labeled cells expressing DCX (Fig. 2G) according to a previously described method . A quantitative analysis using Neurolucida revealed that dendritic length (C: 137.2 6 11.4; L: 156.36 10.8 mm, t 12 5 1.21, P > 0.05), and area of the cell body (C: 44 6 1.7; L: 43.2 6 1.4 mm 2 , t 12 5 0.33, P > 0.05) were not influenced by spatial learning. Survival of cells generated 15 or 10 days before training is not affected by spatial learning. A: Schematic representation of the experiment (upper panel) and latency to find the escape platform (bottom panel). B: Number of IdU-IR cells (born 15 days before training). C: Number of CldU-IR cells (born 10 days before training). D: Apoptotic cell death measured by the number of activated-caspase3-IR cells in a thionin counterstaining. E: Cell proliferation measured by pH3-IR cells. C: control animals not exposed to the pool; L: animals trained to find a hidden platform.

FIGURE 5.
Survival of cells generated 28 or 21 days before training is not affected by spatial learning. A: Schematic representation of the experiment (upper panel) and latency to find the escape platform (bottom panel). B: Number of IdU-IR cells (born 28 days before training). C: Number of CldU-IR cells (born 21 days before training. C: control animals not exposed to the pool; VP: animals trained to find a visible-platform; L: animals trained to find a hidden platform. We next performed a similar experiment but in this case the cells generated one week before training were labeled using a GFP-expressing retrovirus (Table 2, Batch 5, Fig. 2H). Given that the effect of learning on cell survival has been shown to be more pronounced in female rats compared to male (Dalla et al., 2009) and that individual housing (Martin and Brown, 2010) may reduce the ability of the brain to respond to environmental challenges, female C57BL/6J mice housed in groups of 4 were used. Although female mice learned the task (Fig.  7A), dendritic tree measurements revealed no differences between groups, confirming IdU-DCX results and indicating that dendritic length is not modified by learning in the MWM (Fig. 7B, F(2,23) 5 0.006, P > 0.05).
In the last experiment also performed in group-housed female mice GCs were labeled 17 days before training in the MWM (Table 2, Batch 6, Fig. 8A). Thus, morphological analysis was performed in four-week-old GCs (Fig. 8B). We found a similar dendritic length for all three groups (Fig. 8C, F(2,10) 5 0.55, P > 0.05). Dendritic spine density, the morphological correlate of glutamatergic synapses impinging onto GCs, was also analyzed in this experimental group (Fig. 2J). Learning did not modulate the number of input connections in a global manner (Fig. 8D, F(2,10) 5 1, P > 0.05).
Overall, these results strengthen the notion that learning does not influence the maturation of neurons generated within one month before training in adult mice.

DISCUSSION
In this work, we have observed that the MWM learning paradigm produces no effects on the proliferation, survival/death, and morphology of newborn GCs of the adult mouse dentate gyrus. To study the influence of spatial learning on the different steps of neurogenesis, systemic injection of thymidine analogs followed by exposure to the MWM was used and combined to specific markers of cell proliferation or death. To investigate whether spatial learning modulates more subtle neuronal characteristics that could be revealed by morphology, we either analyzed the morphology of IdU-DCX labeled cells or performed retroviral labeling of GCs to expose them to the MWM at different ages. In both cases, dendritic arborization  Development of the dendritic arbor of neurons born one week before training is not influenced by spatial learning. A. Schematic representation of the experiment (upper panel) and latency to find the escape platform (bottom panel). B. Dendritic tree length of GFP-labeled neurons. C: control animals not exposed to the pool. VP: animals trained to find a visibleplatform. L: animals trained to find a hidden platform.
was similar for all neurons of a given age, whether or not exposed to the MWM. In addition, spine density was not either influenced by training in the water maze. So, independently of the method used, no differences were found between mice that learned spatial information and those that did not.
These results are surprising given the previous observations that learning the water maze regulates cell numbers in a complex way (see new Table 1). These effects were not observed when animals were exposed to the pool without a platform or when they were trained to find a VP, a task that does not require the integrity of the hippocampus indicating that stress or a "light" exercise (swimming for a few minutes) are not involved in the learning-induced regulation of cell birth/cell death and cell proliferation. Spatial learning also promotes an increase in dendritic tree length and spine density in immature and mature adult-born neurons. All these studies were done in rats (male outbred SD singly housed in our experiments or housed in groups of 5-6 (Gould et al., 1999;Shors et al., 2012), which are not averse to aquatic environments (Finlay and Sengelaub, 1981). It is to note that in female SD (Chow et al., 2013) and in male Long-Evans rats the most frequently used strain for memory studies (Snyder et al., 2005;Epp et al., 2011), spatial learning did not influence cell survival. In another study, it was suggested that such lack of effect may be due to the regiment of BrdU labeling (Mohapel et al., 2006).
One possible explanation for the discrepancy observed between rats and mice is linked to the task that we used, which may have induced a greater stress response in mice and thus mask a potential prosurviving effect. Indeed, mice are primarily terrestrial (Whishaw and Tomie, 1996) and display poorer performance than rats in wet mazes that are more stressful for them (Lipp et al., 1987;Whishaw and Tomie, 1996). Support-ing this view, it has been shown that learning-induced increase in corticosterone levels is higher in the MWM compared with the dry Barnes maze (Harrison et al., 2009). In fact, the Barnes maze induced a modest increase in corticosterone that facilitated proficient learning. In contrast, the MWM induced a modest increase in corticosterone in some mice but a larger increase in others that were less efficient to find the hidden platform; this observation suggests that the elevated corticosterone impaired learning in mice with a greater stress response (Harrison et al., 2009). In addition to the task itself, the water temperature that we used (20 C) might be stressful for mice (in contrast to what has been observed in rats) given their small body size. As a consequence, the stressing conditions (and the more prolonged release of corticosterone) induced by the wet maze (in our experimental conditions) might have counterbalanced an accelerated maturation induced by learning in mice.
Another confounding variable could be the influence of physical exercise (or fatigue). Indeed, an increase in the survival of cells generated 8 days before exposure to the task was reported using a mass training protocol (Trouche et al., 2009). These effects were, however, significantly different from those observed in the swim control group only one month after learning (Trouche et al., 2009). More recently, an increase in cell survival was observed using a delayed matching to place protocol in the water maze that is more cognitively demanding (Haditsch et al., 2013), but it is unclear whether this effect is specific since control groups for stress or physical exercise were lacking.
Another explanation may be linked to differences in the strength of learning that may be weaker in mice, as reflected by the long training they have to go through to reach performances similar to those observed in rats. Indeed, we and others have shown that learning increases cell survival (or cell death depending of the age of the cell at the time of training) only when that learning is successful. This conclusion was reached either by using group level analysis that consists in categorizing animals either as good learners or poor learners (D€ obr€ ossy et al., 2003;Drapeau et al., 2003Drapeau et al., , 2007 or by relating individual differences in learning group (only) to the number of proliferating or surviving cells (Drapeau et al., 2003). As discussed recently by Lazic et al. 2014, group level associations cannot be used to infer individual level association and individual level analysis should not be performed through all of the data, without regard for the experimental groups. Using an individual level analysis (in the learning groups for each experiment separately), we were unable to find any correlation (positive or negative) between the behavioral scores and cell numbers.
Furthermore, it has been shown that learning a conditioned response will preferentially rescue new neurons from death when learning is difficult to achieve (Curlik and Shors, 2011). Our results do not fit with these observations. Indeed, when compared to our own data obtained in rats the MWM is considerably more difficult to learn for mice, as a much longer interval is required for mastering the task. Even under those circumstances, we did not observe modulatory effects of learning on cell survival. To strengthen this notion, rats trained in the water maze with spaced trials-that tend to learn faster and remember longer-did not possess significantly more cells than animals exposed to the same amount of massed trials (Sisti et al., 2007).
Alternatively, adult-generated neurons may be less important for navigation through space in mice compared to rats. This hypothesis is supported by the observation that spatial learning in the water maze causes a greater increase in the proportion of newborn neurons expressing Zif268 in rats compared to mice (Snyder et al., 2005) and that impaired adult neurogenesis in mice has rendered highly variable outcomes in the water maze (Koehl and Abrous, 2011;Marin-Burgin and Schinder, 2012). In contrast, so far all studies performed in mice have shown that ablating adult neurogenesis impairs behavioral spatial pattern separation (Clelland et al., 2009;Sahay et al., 2011;Tronel et al., 2010). These data suggest that adult generated neurons in mice may be more specialized in paradigms related to spatial pattern separation. Clearly, studies directly comparing the relationship between neurogenesis and learning processes in rats and mice are missing.
It is important to note that developing GCs of the adult mouse hippocampus are, under specific conditions, highly sensitive to network activity. In fact, there are physiological and pathological conditions that modulate the development of adult born GCs. Voluntary running, seizures and chronic antidepressants are all conditions that accelerate neuronal maturation (van Praag et al., 1999;Santarelli et al., 2003;Overstreet-Wadiche et al., 2006;Piatti et al., 2011;Steib et al., 2014), in a manner that is strongly dependent of the electrical activity of the network (Ge et al., 2006;Piatti et al., 2011). However, given that adult-generated neurons in mice mature slower than in rats, we cannot exclude that the critical period within which cell survival might be influenced by learning extends beyond 4 weeks or/and the effects of learning are delayed in time, i.e. appeared several weeks after the end of training. In support of this, a specific pro-surviving effect of massed training in the water maze has been observed one month after training (Trouche et al., 2009).
The difference illustrated here between mice and rats is not unprecedented. For example, learning to find a hidden platform using a protocol similar to that used in the present study (and aimed to measure reference memory) induces growth of the mossy fiber system in rats (Ramirez-Amaya et al., 2001), but not in mice (Rekart et al., 2007). Interestingly, mice and rats rely on different strategies to solve the water maze, which could be linked to different implementation of the hippocampal circuitry. Thus mice were described to primarily use egocentric non spatial cues to solve the maze (Whishaw, 1995). Other differences in their hippocampal circuitry have been reported, and for instance the entorhinal cortex of rats project contra-and ipsi-laterally to the hippocampus whereas that of mice projects only ipsi-laterally (van Groen et al., 2002). Evolutionary divergence between mice and rats has also been reported for the olfactory bulb (Hendriksen et al., 2014). These differences could be due to the evolutionary distance between mice and rats, only 90% of the rat genome having its counterpart in the mouse genome (Gibbs et al., 2004). The adage that "a mouse is not a small rat" when studying memory is also relevant in the search of models and treatments in psychiatry disorders such as depression (Cryan and Mombereau, 2004).
In conclusion, these data reveal that the structural plasticity of adult-born dentate neurons in response to learning differs dramatically between mice and rats. These findings, together with previous observations (Snyder et al., 2009), emphasize the need to carefully consider species differences when studying adult neurogenesis.