Angélique Peret

Claire Pléau

Edouard Pearlstein

Thomas Scalfati

Geoffrey Marti

François Michel

Valérie Crépel
email: valerie.crepel@inserm.fr

Mature dentate granule cells show different intrinsic properties depending on the behavioral context of their activation

Keywords: hippocampus, dentate gyrus, intrinsic excitability, initial segment, home cage, virtual reality

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Introduction

The dentate gyrus (DG), an input region of the hippocampal formation, plays a crucial role in learning, memory and spatial navigation [START_REF] Mcnaughton | Hippocampal synaptic enhancement and information storage within a distributed memory system[END_REF][START_REF] Baker | The Human Dentate Gyrus Plays a Necessary Role in Discriminating New Memories[END_REF].

Memory storage and recall involve a discrete population of cells within the DG region [START_REF] Goodsmith | Spatial Representations of Granule Cells and Mossy Cells of the Dentate Gyrus[END_REF][START_REF] Hainmueller | Parallel emergence of stable and dynamic memory engrams in the hippocampus[END_REF][START_REF] Tonegawa | Memory Engram Cells Have Come of Age[END_REF] . Only a small fraction of dentate granule cells (DGCs) is active during behavior, while the large majority remains silent [START_REF] Chawla | Sparse, environmentally selective expression of Arc RNA in the upper blade of the rodent fascia dentata by brief spatial experience[END_REF][START_REF] Diamantaki | Sparse activity of identified dentate granule cells during spatial exploration[END_REF][START_REF] Jung | Spatial selectivity of unit activity in the hippocampal granular layer[END_REF][START_REF] Neunuebel | Spatial Firing Correlates of Physiologically Distinct Cell Types of the Rat Dentate Gyrus[END_REF][START_REF] Pilz | Functional Imaging of Dentate Granule Cells in the Adult Mouse Hippocampus[END_REF][START_REF] Skaggs | Theta phase precession in hippocampal neuronal populations and the compression of temporal sequences[END_REF][START_REF] Stefanelli | Hippocampal Somatostatin Interneurons Control the Size of Neuronal Memory Ensembles[END_REF][START_REF] Tonegawa | Memory Engram Cells Have Come of Age[END_REF][START_REF] Hainmueller | Parallel emergence of stable and dynamic memory engrams in the hippocampus[END_REF]. Since the discovery of the DG's ability to generate new neurons throughout life [START_REF] Aimone | Resolving new memories: a critical look at the dentate gyrus, adult neurogenesis, and pattern separation[END_REF], it has been proposed that DG neurogenesis provides a substrate for spatial memory and pattern separation [START_REF] Aimone | Resolving new memories: a critical look at the dentate gyrus, adult neurogenesis, and pattern separation[END_REF][START_REF] Clelland | A functional role for adult hippocampal neurogenesis in spatial pattern separation[END_REF][START_REF] Kropff | Dynamic role of adult-born dentate granule cells in memory processing[END_REF][START_REF] Leutgeb | Pattern separation in the dentate gyrus and CA3 of the hippocampus[END_REF][START_REF] Mcnaughton | Hippocampal synaptic enhancement and information storage within a distributed memory system[END_REF][START_REF] Nakashiba | Young dentate granule cells mediate pattern separation, whereas old granule cells facilitate pattern completion[END_REF][START_REF] Neunuebel | CA3 retrieves coherent representations from degraded input: Direct evidence for CA3 pattern completion and dentate gyrus pattern separation[END_REF][START_REF] Sahay | Increasing adult hippocampal neurogenesis is sufficient to improve pattern separation[END_REF][START_REF] Kirschen | Active Dentate Granule Cells Encode Experience to Promote the Addition of Adult-Born Hippocampal Neurons[END_REF]. This role would be facilitated by the high excitability of newly generated neurons [START_REF] Aimone | Resolving new memories: a critical look at the dentate gyrus, adult neurogenesis, and pattern separation[END_REF][START_REF] Cameron | Adult neurogenesis produces a large pool of new granule cells in the dentate gyrus[END_REF][START_REF] Kempermann | Genetic influence on neurogenesis in the dentate gyrus of adult mice[END_REF][START_REF] Lopez-Rojas | Mature granule cells of the dentate gyrus-Passive bystanders or principal performers in hippocampal function?[END_REF][START_REF] Ninkovic | Distinct modes of neuron addition in adult mouse neurogenesis[END_REF]. On the other hand, recent studies have shown that mature dentate granule cells (mDGCs) play a major role in pattern completion [START_REF] Nakashiba | Young dentate granule cells mediate pattern separation, whereas old granule cells facilitate pattern completion[END_REF][START_REF] Hainmueller | Parallel emergence of stable and dynamic memory engrams in the hippocampus[END_REF] and are required for recall of familiar contexts [START_REF] Vukovic | Immature doublecortin-positive hippocampal neurons are important for learning but not for remembering[END_REF]. However, until now the intrinsic physiological properties of these mature cells remain poorly investigated in contrast to those of adult born DGCs. Notably it is yet unestablished whether mDGCs display uniform intrinsic properties or whether these properties could vary depending on the behavioral context of their activation.

Here, we examined ex vivo the intrinsic electrophysiological properties of activated DGCs from mice maintained in their home cage or trained in a virtual reality (VR) environment compared to the non-activated cells. The analysis of intrinsic properties of DGCs activated in these conditions was performed using a fluorescent cellular tagging approach based on a transgenic mouse model in which the synthesis of the fosGFP fusion protein is controlled by the promoter of gene c-fos. The latter activity-dependent gene expression is commonly used as a functional readout for the neuronal activation [START_REF] Barth | Alteration of neuronal firing properties after in vivo experience in a FosGFP transgenic mouse[END_REF][START_REF] Czajkowski | Encoding and storage of spatial information in the retrosplenial cortex[END_REF] Accordingly, activated neurons during a given behavioral task will express shortly and transiently the fluorescent protein enabling their identification for electrophysiological analysis on acute brain slices [START_REF] Barth | Alteration of neuronal firing properties after in vivo experience in a FosGFP transgenic mouse[END_REF]. Our data reveal that activated mDGCs, from mice in their familiar home cage, exhibit different intrinsic properties and a marked lower excitability when compared to the non-activated cells. Remarkably, activated mDGCs from mice trained in virtual environment display a more excitable state when compared to the home cage condition.

Therefore, our study shows that mDGCs do not constitute a uniform cell population since they display diverse intrinsic properties depending on the behavioral context of their activation. We propose that these properties could constitute the electrophysiological signature of different cell assemblies involved in specific contexts such as a familiar environment or spatial training.

Results

fosGFP + cells display characteristics of mature DGCs in home cage and training conditions

In a first set of experiments, we examined the fraction of DGCs activated in two different behavioral contexts. We studied the percentage of DGCs activated in the home cage condition and during training in a virtual linear track. For the training in a virtual linear track, the task was performed using a virtual reality (VR) system [START_REF] Dombeck | Functional imaging of hippocampal place cells at cellular resolution during virtual navigation[END_REF][START_REF] Hainmueller | Parallel emergence of stable and dynamic memory engrams in the hippocampus[END_REF][START_REF] Harvey | Intracellular dynamics of hippocampal place cells during virtual navigation[END_REF][START_REF] Ravassard | Multisensory Control of Hippocampal Spatiotemporal Selectivity[END_REF][START_REF] Schmidt-Hieber | Cellular mechanisms of spatial navigation in the medial entorhinal cortex[END_REF] (see Materials and Methods). Head-restrained mice were trained to run back and forth along a 150 cm-long bidirectional virtual linear track that had proximal and distal walls with varying patterns as visual cues for orientation (Figure 1A,B). The mice, which were water restricted throughout the experiment (see Materials and Methods), received a water reward at the end of the track after successfully traversing the full track length. After receiving a reward at the first reward zone, the mice then turned around for running in the opposite direction to receive the next reward at the end of the track. In the first sessions (up to 8), the mice moved slowly and erratically along the track as they learned the task. By 8-10 sessions, the mice moved consistently back and forth along the track, and they received rewards at increasing rates over time (1.14 ± 0.05 reward/min, n = 8 mice), they ran reliably back and forth along the track, and they slowed down before reaching the track end consistent with learning of the task (Figure 1C).

To analyse the fraction of DGCs activated in the different behavioral contexts (see above), we used a strain of transgenic mice in which the synthesis of fosGFP fusion protein is controlled by the promoter of the activity-dependent immediate early gene (IEG) c-fos (see Materials and Methods). These mice enabled an ex vivo characterization of activated cells with a recent history of elevated activity in vivo [START_REF] Barth | Alteration of neuronal firing properties after in vivo experience in a FosGFP transgenic mouse[END_REF][START_REF] Yassin | An embedded subnetwork of highly active neurons in the neocortex[END_REF] . In VR training conditions, examination of cells expressing fosGFP was performed ex vivo in hippocampal slices obtained shortly after the last training session (about 18-20 sessions, see Materials and Methods).

Quantification of cells expressing fosGFP was performed in the dorsal hippocampus, which is known to play a crucial role in spatial memory [START_REF] Moser | Spatial learning impairment parallels the magnitude of dorsal hippocampal lesions, but is hardly present following ventral lesions[END_REF]. When mice were maintained in their home cage, a discreet fraction of DGCs (Prox1-positive, Figure 2C) was activated, since they expressed fosGFP (fosGFP + cells) (1.06 ± 0.1% of DGCs, n = 25 slices, n = 6 mice) (see Figure 1D,E, Materials and Methods) . In line with previous observations [START_REF] Liu | Optogenetic stimulation of a hippocampal engram activates fear memory recall[END_REF][START_REF] Kirschen | Active Dentate Granule Cells Encode Experience to Promote the Addition of Adult-Born Hippocampal Neurons[END_REF][START_REF] Shevtsova | Early-Age Running Enhances Activity of Adult-Born Dentate Granule Neurons Following Learning in Rats[END_REF][START_REF] Stefanelli | Hippocampal Somatostatin Interneurons Control the Size of Neuronal Memory Ensembles[END_REF], we observed a significant higher fraction of fosGFP + cells in the dorsal hippocampus in mice trained in VR (1.92 ± 0.16% of DGCs, n = 19 slices, n = 5 mice, p< 0.0001) (Figure 1D,E).

We then explored the distribution of fosGFP + cells across the DG in dorsal hippocampus in the different experimental conditions. To address this question, we plotted the localization of fosGFP + cells in DG and investigated their lateral and radial distributions. DG cell layer extends laterally from the upper (suprapyramidal) blade to the lower (infrapyramidal) blade and from the outer layer (near the molecular layer) to the inner layer (near the hilus) in a radial direction [START_REF] Altman | Migration and distribution of two populations of hippocampal granule cell precursors during the perinatal and postnatal periods[END_REF]. In order to reliably quantify the position across data we used normalized coordinates (see Materials and Methods). We plotted the distribution and the count of fosGFP + cells (Figure 2A, B). Our data showed that for both experimental conditions, a majority of fosGFP + cells were located in the upper blade of dentate granule cell layer (67 ± 4.7%, n = 25 slices, n = 6 mice in home cage condition; 62 ± 3.2%, n = 19 slices, n = 5 mice trained in VR) and near the molecular layer (74 ± 2.9%, n = 25 slices, n = 6 mice in home cage condition; 67 ± 2%, n = 19 slices, n = 5 mice trained in VR) (Figure 2A, B). Furthermore, immunohistochemistry performed with Prox1 antibody confirmed that most fosGFP + cells corresponded to DGCs; we found that more than 95% of these neurons were co-labelled with EGFP and Prox1 antibodies (98.5%, n = 130 cells, n = 5 mice trained in VR; 97.2%, n = 36 cells, n = 3 mice in home cage condition) (Figure 2C). This preferential location near the molecular layer suggested that these fosGFP + DGCs corresponded to mDGCs, since adult-born neurons are preferentially located near the hilus [START_REF] Zhao | Mechanisms and functional implications of adult neurogenesis[END_REF]. To test that hypothesis, we identify adult-born DGCs and we used an anti doublecortin (DCX) antibody, a specific marker of newly born neurons [START_REF] Zhao | Mechanisms and functional implications of adult neurogenesis[END_REF]. We did not find any fosGFP + DGC co-labelled with DCX antibodies in the two behavioral contexts (Figure 2C), confirming that fosGFP + cells did not correspond to new-born neurons in our experimental conditions. Overall, our data reveal that fosGFP + DGCs activated in home cage condition or during training correspond to mDGCs. Furthermore, we observe that training in VR induces an increase in the number of fosGFP + DGCs compared to the home cage condition.

fosGFP + DGCs display a low intrinsic excitability in mice maintained in home cage

We then asked if intrinsic properties of activated and non-activated DGCs in home cage condition were similar. To address this question, we examined the intrinsic excitability of fosGFP + and fosGFP -DGCs from mice in their home cage was examined ex vivo in slices using patch-clamp recordings (see Materials and Methods). We observed a marked lower excitability in fosGFP + DGCs compared with the fosGFP -cells as shown by the f/I plots (see Materials and Methods) (p<0.0001) (Figure 3A). This difference in firing frequency was not associated with a modification of the action potential (AP) threshold, amplitude and half-width (Tables 1,2).

We then analysed the passive membrane properties of fosGFP -(n = 27 cells) and fosGFP + DGCs (n = 17 cells). We observed a lower input resistance (Rin) in fosGFP + DGCs when compared to fosGFP -DGCs (185.36 ± 11.54 MΩ and 336.36 ± 12.74 MΩ in fosGFP+ and fosGFP-DGCs, respectively, p< 0.0001), associated with a higher rheobase (70.59 ± 8.07 pA and 34.81 ± 2.29 pA in fosGFP + and fosGFP -DGCs, respectively, p< 0.0001) (Figure 3B, C) (Tables 1,2), without a significant change in resting membrane potential (RMP), threshold, amplitude and half-width of AP (Tables 1,2).

In conclusion, our data show that fosGFP + DGCs activated in the home cage condition display a lower intrinsic excitability compared to fosGFP -DGCs.

fosGFP + DGCs display a different excitable state when mice are trained in VR

Our histological analysis has revealed that training in VR induced an important increase in the number of fosGFP + DGCs (see above). In order to study the intrinsic properties of this subset of activated neurons, we examined their firing pattern and intrinsic properties ex vivo in slices (see Materials and Methods). In these cells, we observed a higher excitability (Figure 3A) when compared to fosGFP + cells in home cage condition (Figure 3A); this was associated with a higher input resistance (Rin) (237.93 ± 13.48 MΩ in 13 fosGFP + DGCs, p=0.0174) and a lower rheobase (43.08 ± 4.99 pA in 13 fosGFP + DGCs, p=0.0338) (Figure 3B,C), without a significant change in resting membrane potential (RMP), threshold, amplitude and half-width of AP (Tables 1,2). Remarkably, these fosGFP + DGCs still remained less excitable than fosGFP -cells (Figure 3A,B,C) (Tables 1,2). This revealed that fosGFP + DGCs trained in VR displayed an "intermediate" state of excitability between DGCs activated in home cage and non-activated DGCs. Finally, our data unveiled that fosGFP -DGCs constituted a homogenous population since they displayed similar excitability and intrinsic properties across experimental conditions (Figure 3) (Tables 1,2) showing that the diversity of intrinsic properties is restricted to fosGFP + DGCs.

Taken together, our data show that activated DGCs from mice trained in VR display a more excitable state when compared to those from mice in home cage condition.

fosGFP + DGCs display a different AP waveform and AIS length compared with fosGFP -

DGCs

Besides passive and voltage-dependent conductances, intrinsic excitability is tightly regulated by the site of spike initiation in the axon initial segment (AIS) [START_REF] Araki | Response of single motoneurons to direct stimulation in toad's spinal cord[END_REF][START_REF] Bean | The action potential in mammalian central neurons[END_REF][START_REF] Bender | The physiology of the axon initial segment[END_REF][START_REF] Coombs | The generation of impulses in motoneurones[END_REF][START_REF] Debanne | Axon physiology[END_REF]Grubb and Burrone, 2010a;[START_REF] Kole | Signal Processing in the Axon Initial Segment[END_REF][START_REF] Petersen | Plasticity of the Axon Initial Segment: Fast and Slow Processes with Multiple Functional Roles[END_REF][START_REF] Scott | Neuronal adaptation involves rapid expansion of the action potential initiation site[END_REF][START_REF] Wefelmeyer | Homeostatic Plasticity of Subcellular Neuronal Structures: From Inputs to Outputs[END_REF]. When it is recorded at the somatic level, AP waveform generally comprises two distinct components [START_REF] Colbert | Axonal action-potential initiation and Na+ channel densities in the soma and axon initial segment of subicular pyramidal neurons[END_REF][START_REF] Coombs | The generation of impulses in motoneurones[END_REF][START_REF] Fuortes | Steps in the production of motoneuron spikes[END_REF][START_REF] Grace | Intracellular and extracellular electrophysiology of nigral dopaminergic neurons--1. Identification and characterization[END_REF][START_REF] Häusser | Axonal initiation and active dendritic propagation of action potentials in substantia nigra neurons[END_REF], the first peak reflecting the spike initiation in AIS and the second peak the somato-dendritic spike [START_REF] Bean | The action potential in mammalian central neurons[END_REF]. To test a putative modification of AP initiation in AIS in our experimental conditions, we examined the AP waveform in fosGFP + and fosGFP -DGCs using phase plot analysis consisting in the secondderivative of the somatic membrane potential (d 2 V/dt 2 ) as a function of somatic membrane potential (Figure 4A) (see Materials and Methods); the second-derivative analysis allowed a good resolution of the two components of the AP phase plot (AIS peak vs. somato-dendritic peak) [START_REF] Kress | High threshold, proximal initiation, and slow conduction velocity of action potentials in dentate granule neuron mossy fibers[END_REF][START_REF] Meeks | Action potential initiation and propagation in CA3 pyramidal axons[END_REF]. We then evaluated the fraction of fosGFP -and fosGFP + DGCs exhibiting an AP phase plot with one or two peaks (Figure 4A); the first peak was discriminated from the second one using the analysis of the stationary inflection point [START_REF] Kress | High threshold, proximal initiation, and slow conduction velocity of action potentials in dentate granule neuron mossy fibers[END_REF]. We showed that a majority of fosGFP -DGCs displayed a phase plot with two peaks across all experimental conditions (86%, 25 out of 29 cells in home cage condition; 88%, 23 out of 26 cells in mice trained in VR) (Figure 4B). On the contrary, only around half of fosGFP + displayed a phase plot with two peaks; a significant fraction (p=0.0002) of fosGFP + DGCs displayed a phase plot with a different dynamic, since the first peak could not be detected in this neuronal subset across all experimental conditions (43%, 9 out of 21 cells in home cage condition; 44%, 7 out of 16 cells in mice trained in VR) (Figure 4B). These results reveal a modified axonal component in the AP waveform in fosGFP + cells which could result from a structural modification of the AIS. In order to test this hypothesis, we performed an immunofluorescent labelling for the scaffolding molecule ankyrin-G (AnkG) to quantify AIS length in fosGFP -and fosGFP + DCGs (Figure 5A). Experiments were performed ex vivo from mice maintained in home cage condition and from mice trained in VR (see above and Materials and Methods). The analysis was done in DGCs located in the outer part of the molecular layer of the DG (Figure supplement 2B), since we showed that fosGFP + DGCs were preferentially distributed in this area across both experimental conditions (see above and Figure 2). We observed that fosGFP + DGCs displayed a similar shorter AIS length across experimental conditions compared to fosGFP -DGCs (Figure 5C) (23 ± 0.67 µm in fosGFP -n = 47 vs 18.2 ± 0.6 µm in fosGFP + n = 47 in home cage condition, p<0.0001; 24.1 ± 0.63 µm in fosGFP -n = 57 vs 18.9 ± 0.62 µm in fosGFP + n = 57 in mice trained in VR, p<0.0001). This was due to a leftward shift of AIS length distribution towards smaller values (Figure 5B). Interestingly, the shorter AIS length is not associated with a significant relocation of AIS (Figure 5C).

Overall, our data reveal that a significant fraction of fosGFP + DGCs (when compared to fosGFP -DGCs) display a change in the dynamics of their AP waveform associated with a shorter AIS length, which is independent from the behavioral context of their activation.

Discussion

Our study performed ex vivo identifies subsets of mDGCs activated in two different behavioral contexts (home cage condition and training in VR), using a fluorescent cellular tagging approach based on a transgenic mouse model in which the expression of EGFP is controlled by the promoter of the activity-dependent IEG c-fos [START_REF] Barth | Alteration of neuronal firing properties after in vivo experience in a FosGFP transgenic mouse[END_REF]. Our main finding is that activated mDGCs do not constitute a homogenous neuronal population in terms of intrinsic properties since they display different states of excitability depending on their context of activation. Remarkably, our data also reveal that activated mDGCs always exhibit lower excitability than the non-activated mDGCs across all experimental conditions. In keeping with this, our study shows that AIS length distribution is shifted towards lower values in activated mDGCs. This cellular mechanism could play a role in dampening their basal excitability as previously reported by in vitro experiments (Grubb and Burrone, 2010b).

It is well established that the expression of IEGs such as c-fos is selectively upregulated in subsets of neurons in specific brain regions associated with learning and memory formation [START_REF] Minatohara | Role of Immediate-Early Genes in Synaptic Plasticity and Neuronal Ensembles Underlying the Memory Trace[END_REF]. Notably, many studies have reported that, in these regions, neurons expressing an upregulation of c-fos encode and store information required for memory recall [START_REF] Minatohara | Role of Immediate-Early Genes in Synaptic Plasticity and Neuronal Ensembles Underlying the Memory Trace[END_REF][START_REF] Tonegawa | Memory Engram Cells Have Come of Age[END_REF]. Therefore, IEGs have been largely used to tag neurons involved in memory function and behavioral tasks. This enables targeting of sparse engram neurons [START_REF] Tonegawa | Memory Engram Cells Have Come of Age[END_REF][START_REF] Ryan | Engram cells retain memory under retrograde amnesia[END_REF]) to describe their functional features [START_REF] Barth | Experimental evidence for sparse firing in the neocortex[END_REF][START_REF] Pignatelli | Engram Cell Excitability State Determines the Efficacy of Memory Retrieval[END_REF]. Using fosGFP mice, we identified ex vivo a sparse population of DGCs activated in the dorsal hippocampus activated in different behavioral contexts. Notably our findings revealed that training in VR induces a significant increase in the number activated DGCs compared with the home cage condition [START_REF] Liu | Optogenetic stimulation of a hippocampal engram activates fear memory recall[END_REF][START_REF] Kirschen | Active Dentate Granule Cells Encode Experience to Promote the Addition of Adult-Born Hippocampal Neurons[END_REF][START_REF] Shevtsova | Early-Age Running Enhances Activity of Adult-Born Dentate Granule Neurons Following Learning in Rats[END_REF][START_REF] Stefanelli | Hippocampal Somatostatin Interneurons Control the Size of Neuronal Memory Ensembles[END_REF]. These data are in keeping with previous observations showing a sparse recruitment of DGCs during a spatial behavioral experience [START_REF] Chawla | Sparse, environmentally selective expression of Arc RNA in the upper blade of the rodent fascia dentata by brief spatial experience[END_REF]. Remarkably, we showed that, in our experimental conditions, activated DGCs display features of mDGCs since (i) the examination of their radial distribution across the cell layer revealed a majority of fosGFP + cells located near the molecular layer, (ii) we did not observe cells that were co-labelled with EGFP and DCX antibodies, and (iii) they exhibited a mean value of Rin characteristic of mature DGCs [START_REF] Overstreet-Wadiche | Functional maturation of adult-generated granule cells[END_REF][START_REF] Dieni | Low excitatory innervation balances high intrinsic excitability of immature dentate neurons[END_REF][START_REF] Save | Temporal Embryonic Origin Critically Determines Cellular Physiology in the Dentate Gyrus[END_REF]. These observations reinforce the notion that mDGCs are not dormant cells [START_REF] Alme | Hippocampal granule cells opt for early retirement[END_REF] but comprise active neurons involved in specific tasks [START_REF] Nakashiba | Young dentate granule cells mediate pattern separation, whereas old granule cells facilitate pattern completion[END_REF][START_REF] Vukovic | Immature doublecortin-positive hippocampal neurons are important for learning but not for remembering[END_REF].

We then questioned whether these subsets of mDGCs activated in the home cage condition or during training could have distinct electrophysiological properties. Indeed, there is a growing body of evidence showing that modulation of intrinsic excitability plays a central role in neuronal plasticity and learning processes [START_REF] Barth | Visualizing circuits and systems using transgenic reporters of neural activity[END_REF][START_REF] Daoudal | Long-term plasticity of intrinsic excitability: learning rules and mechanisms[END_REF][START_REF] Sehgal | Learning to learn -intrinsic plasticity as a metaplasticity mechanism for memory formation[END_REF][START_REF] Zhang | The other side of the engram: experience-driven changes in neuronal intrinsic excitability[END_REF]. Most of the studies investigating a change in intrinsic excitability induced by learning have reported an increase in the firing rate [START_REF] Daoudal | Long-term plasticity of intrinsic excitability: learning rules and mechanisms[END_REF][START_REF] Epsztein | Intracellular Determinants of Hippocampal CA1 Place and Silent Cell Activity in a Novel Environment[END_REF]. Surprisingly, we observed an opposite scheme, as activated mDGCs display an overall lower intrinsic excitability than the non-activated cells.

Besides this major observation, our findings reveal that activated DGCs do not constitute a homogenous population in terms of intrinsic properties since they exhibit different excitable states depending on the behavioral context of their activation. Indeed, we observed a significant higher excitability in activated DGCs from the mice trained in VR when compared to the cells from mice in home cage condition. Therefore, when comparing intrinsic properties of active neurons, our observations are in keeping with previous observations reporting an increase in excitability during training or learning processes [START_REF] Barth | Visualizing circuits and systems using transgenic reporters of neural activity[END_REF][START_REF] Daoudal | Long-term plasticity of intrinsic excitability: learning rules and mechanisms[END_REF][START_REF] Pignatelli | Engram Cell Excitability State Determines the Efficacy of Memory Retrieval[END_REF][START_REF] Sehgal | Learning to learn -intrinsic plasticity as a metaplasticity mechanism for memory formation[END_REF][START_REF] Titley | Toward a Neurocentric View of Learning[END_REF][START_REF] Yiu | Neurons Are Recruited to a Memory Trace Based on Relative Neuronal Excitability Immediately before Training[END_REF][START_REF] Zhang | The other side of the engram: experience-driven changes in neuronal intrinsic excitability[END_REF]. We then observed that the difference in excitability is related to the value of input resistance. Indeed, in home cage condition activated mDGCs exhibit a much lower input resistance associated with a lower excitability than the non-activated cells. Input resistance and excitability become significantly higher in activated mDGCs. Interestingly, in both conditions this difference in input resistance is not associated with a significant change in RMP. It is well established that input resistance is set by a variety of leak and voltage-dependent conductances and can be modulated by several intracellular signalling pathways [START_REF] Brickley | Adaptive regulation of neuronal excitability by a voltage-independent potassium conductance[END_REF]Lesage, 200;[START_REF] Marder | Variability, compensation and homeostasis in neuron and network function[END_REF]. Therefore, further experiments should be performed to elucidate the mechanisms involved in the setting of input resistance in mDGCs depending on their context of activation.

Another remarkable feature observed is the different dynamics of the phase plot of AP in activated DGCs compared to non-activated cells. Indeed, our data show that nearly all nonactivated DGCs displayed a phase plot with two peaks accordingly to previous observations [START_REF] Kress | High threshold, proximal initiation, and slow conduction velocity of action potentials in dentate granule neuron mossy fibers[END_REF] across both experimental conditions. On the contrary, an important fraction (almost half) of activated DGCs display a phase plot where the first peak could not be detected by the analysis of the stationary inflection point [START_REF] Kress | High threshold, proximal initiation, and slow conduction velocity of action potentials in dentate granule neuron mossy fibers[END_REF]. These data, that suggested a change in the axonal component of the AP waveform, could result from a structural modification of AIS. The AIS is a key element involved in the regulation of neuronal excitability since this cellular compartment is responsible for action potential initiation [START_REF] Araki | Response of single motoneurons to direct stimulation in toad's spinal cord[END_REF][START_REF] Bean | The action potential in mammalian central neurons[END_REF][START_REF] Bender | The physiology of the axon initial segment[END_REF][START_REF] Coombs | The generation of impulses in motoneurones[END_REF][START_REF] Debanne | Axon physiology[END_REF]Grubb and Burrone, 2010a;[START_REF] Kole | Signal Processing in the Axon Initial Segment[END_REF][START_REF] Petersen | Plasticity of the Axon Initial Segment: Fast and Slow Processes with Multiple Functional Roles[END_REF][START_REF] Scott | Neuronal adaptation involves rapid expansion of the action potential initiation site[END_REF][START_REF] Wefelmeyer | Homeostatic Plasticity of Subcellular Neuronal Structures: From Inputs to Outputs[END_REF]. Moreover, it is now well accepted that AIS is not just a rigid structure, but it continuously adapts to its surrounding environment. These changes occur on different time scales ranging from hours to days. AIS modifications can be long-lasting, bidirectional, and are correlated with changes in intrinsic excitability [START_REF] Evans | Rapid Modulation of Axon Initial Segment Length Influences Repetitive Spike Firing[END_REF]Grubb and Burrone, 2010b;[START_REF] Kuba | Presynaptic activity regulates Na(+) channel distribution at the axon initial segment[END_REF][START_REF] Wefelmeyer | Homeostatic Plasticity of Subcellular Neuronal Structures: From Inputs to Outputs[END_REF]. A structural plasticity of AIS has been reported after several days in auditory nucleus neurons [START_REF] Kuba | Presynaptic activity regulates Na(+) channel distribution at the axon initial segment[END_REF]. This plasticity, associated with alterations in excitability, contributes to long-term control of AP firing (Grubb and Burrone, 2010a;[START_REF] Kole | Signal Processing in the Axon Initial Segment[END_REF][START_REF] Kuba | Presynaptic activity regulates Na(+) channel distribution at the axon initial segment[END_REF][START_REF] Wefelmeyer | Activity-dependent mismatch between axo-axonic synapses and the axon initial segment controls neuronal output[END_REF]. The precise cellular mechanisms involved are not well understood but recent studies revealed that AIS plasticity depends on calcineurin signalling pathway [START_REF] Evans | Rapid Modulation of Axon Initial Segment Length Influences Repetitive Spike Firing[END_REF]. We now show that activated mDGCs display a shorter AIS length than the non-activated cells. As we observed similar reduced AIS length across all experimental conditions, we propose that this difference constitutes a common cellular signature of activated mDGCs independently of their context of activation, which could lead to an overall dampening of excitability via an homeostatic phenomenon [START_REF] Wefelmeyer | Homeostatic Plasticity of Subcellular Neuronal Structures: From Inputs to Outputs[END_REF]. Further experiments should be performed to clarify this point. We hypothesize that this prominent hypo-excitability could reinforce the sparse activity [START_REF] Chawla | Sparse, environmentally selective expression of Arc RNA in the upper blade of the rodent fascia dentata by brief spatial experience[END_REF][START_REF] Diamantaki | Sparse activity of identified dentate granule cells during spatial exploration[END_REF][START_REF] Jung | Spatial selectivity of unit activity in the hippocampal granular layer[END_REF][START_REF] Neunuebel | Spatial Firing Correlates of Physiologically Distinct Cell Types of the Rat Dentate Gyrus[END_REF][START_REF] Pilz | Functional Imaging of Dentate Granule Cells in the Adult Mouse Hippocampus[END_REF][START_REF] Skaggs | Theta phase precession in hippocampal neuronal populations and the compression of temporal sequences[END_REF][START_REF] Stefanelli | Hippocampal Somatostatin Interneurons Control the Size of Neuronal Memory Ensembles[END_REF][START_REF] Tonegawa | Memory Engram Cells Have Come of Age[END_REF] and the function of DGCs as coincidence detectors [START_REF] Schmidt-Hieber | Subthreshold dendritic signal processing and coincidence detection in dentate gyrus granule cells[END_REF].

Taken together, our findings highlight a remarkable feature of activated mDGCs. We found that these neurons show different intrinsic properties depending on their behavioral contexts of activation, besides an overall dampening of excitability. We propose that these properties could constitute the neural signature of cell assemblies involved in specific contexts such as a familiar environment or spatial training.

Materials and methods

Ethics

All experiments were approved by the Institut National de la Santé et de la Recherche Médicale (INSERM) animal care and use committee and authorized by the Ministère de l'Education Nationale, de l'Enseignement Supérieur et de la Recherche, following evaluation by a local ethical committee (agreement number APAFIS#9896-201605301121497v11) in accordance with the European community council directives (2010/63/UE).

Mice

Adult males (n = 91, 100 ± 3 days old, 25.7 ± 0.3 g weight) fosGFP heterozygous mice (B6.Cg-Tg (Fos/EGFP) 1-3Brth/J, Jackson Laboratory, Bar Harbor, ME; RRID = IMSR_JAX:014135) were used for experiments. These mice were generated by fusing the c-fos promoter and the cfos coding region, including exons and introns, to the coding region for EGFP, creating a fosGFP C-terminus fusion protein [START_REF] Barth | Alteration of neuronal firing properties after in vivo experience in a FosGFP transgenic mouse[END_REF]. All mice were housed in standard conditions (12 hours light/dark cycles at 22 to 24°C, light off at 7:30 a.m., housed one per cage, and food ad libitum) and water restricted (1 ml a day ). Mice were handled before recording sessions to limit stress and experiments were performed during the dark cycle.

Surgical procedures

After the mice being genotyped, a head-bar was implanted. Before the surgery, fosGFP mice were anaesthetized with xylazine (13 mg/kg) / ketamine (66 mg/kg) in 0.9% saline and placed into a stereotactic frame. The skull was exposed and cleaned. Two screws were driven through small holes drilled in the bones and the head-bar was glued to the skull and fixed with bone cement (Heraeus Kulzer GmbH, Hanau, Germany). After 2-3 days of recovery animals were habituated to handling (1-2 days) and were water restricted (approximately 1 ml water/day). If mice weight dropped below 80% of pre-water restriction weight, they had access to food and water ad libitum and were discarded from the study.

Virtual reality set-up

In the present study we trained mice in virtual reality in order to create a context different from the home environment. Mice were first habituated to be head-restrained on an air-flowsupported Styrofoam ball and trained for 18-20 sessions (30 min per session, 2 sessions per day) (Phenosys GmbH; Berlin, Germany; Figure 1). Animals were running in a 150 cm-long linear track with visual cues on the side-walls (black dots, white dots, vertical green stripes and vertical black stripes) and distal visual cues on both sides provided through six TFT monitors surrounding the animal (JetBall-TFT, Phenosys GmbH), allowing them to identify their position throughout the linear track. Although mice were allowed to move the JetBall in all directions, only the movements along the long axis of the maze were recorded by the system.

Upon arrival to the end of the maze, the ball was immobilized by brakes and mice received a small (approx. 4 μl) water drop through a delivery tube; consecutive rewards at the same end were not available. After having obtained the reward, mice were then able to go back by turning the jet-ball to reach the next reward (Figure 1). When the animals performed sufficient number of session (18-20), they were anaesthetised with xylazine (13 mg/kg) / ketamine (66 mg/kg) and sacrificed. Analyses of the speed of the mice along the linear track and quantification of the number of rewards were performed a posteriori using custom-developed software written in MATLAB (MathWorks, Natick, MA).

Quantification of fosGFP + cells

The examination of cells expressing fosGFP (fosGFP + ) was performed in hippocampal slices from animals maintained in their home cage, or animals that achieved the last VR training session. Mice were deeply anesthetized with xylazine (13 mg/kg) / ketamine (66 mg/kg) prior to decapitation. The brain was then rapidly removed and hippocampi were dissected and fixed overnight (home cage: n = 6 mice, trained in VR: n = 5 mice). Transverse 80 µm thick slices were cut using a Leica VT1000S vibratome (Leica Microsystems, Wetzlar, Germany). Slices were then permeabilized in blocking solution containing 5% normal goat serum (NGS, Sigma-Aldrich, Merck KGaA, Darmstadt, Germany) in 0.5% Triton for 1 hour at room temperature.

Slices were then incubated with the polyclonal rabbit anti-EGFP antibody (Fisher Scientific, Hampton, NH) at 1:1000 in 5% NGS in 0.5% Triton x-100 overnight at 4°C. Slices were then incubated for 2 hours with the Alexa Fluor 488 secondary antibody (Invitrogen, Carlsbad, CA, 1:500) and counterstained with Neurotracer fluorescent Nissl (Invitrogen; RRID:AB_2620170) at 1:250 then coverslipped in fluoro-gel (Electron Microscopy Sciences, Hatfield, PA).

Fluorescent images were acquired using a confocal microscope (TCS SP5 X, Leica Microsystems) and image analysis was assessed by using ImageJ (NIH). We confirmed that expression of the fosGFP transgene correlated with the synthesis of the endogenous c-fos protein using a c-fos specific antibody (Synaptic Systems GmbH, Göttingen, Germany; RRID: AB_2106755, see below); we found that almost all fosEGFP + neurons were co-labelled with EGFP and c-fos antibodies (100 %, n = 18 cells, n = 2 mice in home cage condition; 89.7 %, n = 146 cells, n = 3 mice trained in VR) (Figure supplement 1). The density of fosGFP + cells was quantified (as cells.mm -3 ) by calculating DG area and counting the number of GFP + cells in each slice. The percentage of fosGFP+ DGCs was estimated by reporting the density of fosGFP+ neurons to the total density of granule cells (i.e. 967 442 cells/mm3), which was calculated from the total number of granule cells in the DG [START_REF] Buckmaster | Rapamycin suppresses axon sprouting by somatostatin interneurons in a mouse model of temporal lobe epilepsy[END_REF] and the volume of the DG [START_REF] Van Praag | Running increases cell proliferation and neurogenesis in the adult mouse dentate gyrus[END_REF]. Spatial distribution of fosGFP + neurons were examined through the DG cell layer. This neuronal layer extends laterally from the upper (suprapyramidal) blade to the lower (infrapyramidal) blade and from the outer layer (near the molecular layer) to the inner layer (near the hilus) in a radial direction [START_REF] Altman | Migration and distribution of two populations of hippocampal granule cell precursors during the perinatal and postnatal periods[END_REF][START_REF] Muramatsu | Neonatally born granule cells numerically dominate adult mice dentate gyrus[END_REF]. In the lateral axis, we defined the tip of DG lower blade as 0 and the tip of DG upper blade as 1. Similarly, we defined in the radial direction the border between granule cell layer and the hilus as 0 and the border between granule cell layer and molecular layer as 1. Therefore, each fosGFP + DGC was assigned two values between 0 and 1 corresponding to its respective position along the radial and lateral axis. The distribution of fosGFP + was then plotted (Figure 2A). The quantification of fosGFP + cells was performed on the dorsal hippocampus, as this region has been shown to be tightly associated with learning and memory [START_REF] Fanselow | Are the dorsal and ventral hippocampus functionally distinct structures?[END_REF]

Immunohistochemistry

For EGFP, cfos and Prox1 staining, slices were fixed then permeabilized in blocking solution containing 5% normal goat serum (NGS) (Sigma-Aldrich) and 0.5% Triton for 1 hour at room temperature. The slices were incubated with polyclonal chicken anti-EGFP (Abcam, Cambridge, UK; RRID:AB_300798) at 1:1000 and either with polyclonal Guinea pig anti-cfos antibody (Synaptic Systems; RRID: AB_2106755) at 1:500 or polyclonal rabbit anti-Prox1 antibody (Millipore; Merck KGaA, Darmstadt, Germany, RRID:AB_177485) at 1:2000 or polyclonal rabbit anti-DCX antibody (Abcam; RRID:AB_2088478) at 1:1000 in 5% NGS in 0.5% Triton overnight at 4°C. Slices were then incubated for 2 hours with secondary antibodies Alexa488 or Alexa555 (Invitrogen 1:500) and then coverslipped in fluoro-gel. Specific protocol has been used for Ankyrin G immunostaining (NeuroMab; UC Davis/NIH NeuroMab Facility, Davis, CA; RRID: AB_10673030). Trained mice were first deeply anesthetized with an i.p. injection of xylazine (13 mg/kg) / ketamine (66 mg/kg), then briefly intracardiacally perfused (flowrate: 8-10 ml/min for 2-5 min) with cold 1X phosphate buffer (PBS, pH 7.4), followed by 10 min of cold 1% antigenfix (Diapath, Martinengo, Italy). Brains were removed carefully and post-fixed in antigenfix for 24 h at 4°C and then cryopreserved in 20-30% sucrose / PBS at 4°C. Brains were allowed to completely sink to the bottom of the container before sectioning. Then, they were embedded in OCT compound (Tissue-Tek R, TedPella Inc., Redding, CA) and placed at -80°C. Frozen brains were sectioned transversally into 30 μm thick slices at -20°C to -25°C using a Leica CM 3050S cryostat (Leica Microsystems). Floating brain sections were washed with 1X PBS, and then pre-incubated with permeabilizing agent containing 0.5% Triton, 0.2% Tween-20 and 3% BSA. Then slices were washed with 0.2% Tween-20 and incubated with a blocking buffer consisting of 5% NGS (Sigma-Aldrich) and monoclonal mouse anti-ankyrinG antibody (1:500) (NeuroMab) overnight at 4°C. Slices were washed and incubated with biotin-conjugated secondary antibody (1:500) (Invitrogen) followed by incubation with Cy3-conjugated streptavidin (1:500) (Sigma Aldrich), then coverslipped with mounting medium containing DAPI (Vector laboratories, Burlingame, CA). Image acquisition with a Leica confocal microscope was performed to associate each AIS labelled with anti-ankyrinG antibody with the corresponding fosGFP + cell via EGFP staining and fosGFP -cell via DAPI staining. These images were also used for the reconstruction of the soma and the AIS with Neurolucida software. Image acquisition with a Leica confocal microscope (40 X oil immersion objective; using a laser scanning with appropriate excitation and emission filters) was performed to associate each AIS labelled with anti-ankyrinG antibody with the corresponding fosGFP + cell via EGFP staining and fosGFP -cell via DAPI staining. These images were also used for a 3D-reconstruction of the soma and the AIS with Neurolucida software. The 'start' and 'end' positions of AIS were the proximal and distal axonal points, respectively, at which the fluorescence profile dipped to 33% of its maximum peak (Figure supplement 2A) (Grubb and Burrone, 2010a).

Acute slice preparation

Hippocampal slices were prepared from heterozygous fosGFP mice. The slices were cut 45 min after the last training session in VR (18-20 sessions). Animals were deeply anesthetized with xylazine (13 mg/kg) / ketamine (66 mg/kg) prior to decapitation. The brain was then rapidly removed, and transverse 350 µm thick slices were cut using a Leica VT1200S vibratome in icecold oxygenated (95% O2 and 5% CO2) modified artificial cerebrospinal fluid (ACSF) containing the following (in mM): 132 choline, 2.5 KCl, 1.25 NaH2PO4, 25 NaHCO3, 7 MgCl2, 0.5 CaCl2, and 8 D-glucose. Slices were transferred to rest at room temperature in oxygenated (95% O2 and 5% CO2) solution of ACSF containing the following (in mM): 126 NaCl, 3.5 KCl, 1.2 NaH2PO4, 26 NaHCO3, 1.3 MgCl2, 2.0 CaCl2, and 10 D-glucose, pH 7.4.

Electrophysiological recordings

Individual slices were transferred to an upright microscope and neurons were visualized with infrared differential interference contrast microscopy (SliceScope Pro 3000M, Scientifica, Uckfield, UK). Slices were placed in a submerged chamber and perfused with oxygenated ASCF (30-32°C) at a flow rate of 2 to 3 ml/min. Whole cell recordings of DGCs were obtained using patch clamp technique. Glass electrodes (resistance 6-8 MΩ) were filled with an internal solution containing the following (in mM): 130 KMeSO4, 5 KCl, 10 4-(2-hydroxyethyl)-1piperazi-methanesulfonic acid, 2.5 MgATP, 0.3 NaGTP, 0.2 ethyleneglycoltetraacetic acid, 10 phosphocreatine, and 0.3 to 0.5% biocytin, pH 7.25. Access resistance ranged between 15 and 30 MΩ, and the results were discarded if the access resistance changed by >20%. Whole cell recordings were performed in current-clamp mode using a Multiclamp 700B amplifier (Molecular Devices, Sunnyvale, CA). Data were filtered at 2 kHz, digitized (20 kHz) with a Digidata 1440A (Molecular Devices) to a personal computer, and acquired using Clampex 10.1 software (PClamp, Molecular Devices). The recordings alternated between targeting fosGFP - and fosGFP + neurons during the course of an experiment. To avoid EGFP bleaching, the tissue was illuminated for a short period of time, typically around 5-10 s, to focus and record the image of targeted neuron.

Electrophysiological parameters were tested in whole-cell patch-clamp, when resting membrane potential (RMP) was stable for at least 5 min. Electrophysiological parameters were measured from responses to step current injections (500 ms duration) increasing from negative to positive values, and applied from a fixed membrane potential of -60 mV. Input resistance (Rin) was determined by plotting the membrane potential variations induced by hyperpolarising 500 ms steps of current (from -60 pA to 0 pA) and measuring the slope of the fitted linear regression curve. Firing frequency was studied by injecting 500 ms pulses of depolarizing current (I: from 20 pA up to 100 pA) into the cell and plotting the spike frequency (f) as a function of the current intensity (f/I plot). The AP waveform was analysed on the first AP elicited at suprathreshold potential (Mini Analysis Program, Synaptosoft, Decatur, GA). AP threshold was defined as the membrane potential when dV/dt > 10 mV/ms [START_REF] Kress | High threshold, proximal initiation, and slow conduction velocity of action potentials in dentate granule neuron mossy fibers[END_REF][START_REF] Naundorf | Unique features of action potential initiation in cortical neurons[END_REF]. AP amplitude was the difference between the AP threshold and the AP peak. AP width was measured from the half-height of the AP amplitude. Second-derivative (denoted d 2 V/dt 2 ) of the somatic membrane potential was calculated using Clampfit analysis tools. Phase plots were constructed by plotting d 2 V/dt 2 as a function of somatic membrane potential. Stationary inflection point refers to the point where the second derivative displays a slope of zero [START_REF] Kress | High threshold, proximal initiation, and slow conduction velocity of action potentials in dentate granule neuron mossy fibers[END_REF]. (C) Bar graphs displaying the AIS length (left) and the distance from the soma (right) of fosGFP -and fosGFP + DGCs in HC and VR conditions. Note that the length of AIS is significantly shorter in fosGFP + DGCs.

Figure legends Figure 1

 legends1 Figure legends

Figure 2 .

 2 Figure 2. fosGFP + cells display characteristics of mature DGCs (A) Top: Schematic illustration of DG from confocal image of Figure 1D with fosGFP + cells

Figure 3 .

 3 Figure 3. fosGFP + DGCs display different intrinsic properties depending on the

Figure 4 .

 4 Figure 4. fosGFP + DGCs display a modified AP waveform

Figure 5 .

 5 Figure 5. fosGFP + DGCs display a short AIS length (A) Projection of confocal images of fosGFP -(left) and fosGFP + (right) DGCs showing

  µm) AISLength (µm)

Table 1 Intrinsic electrophysiological properties of fosGFP -and fosGFP + DGCs

 1

				HC
		R input (MΩ)	Rheobase (pA)	RMP (mV)	AP amplitude (mV)	AP Threshold (mV)	APHW (ms)
	fosGFP -	Mean 336.40 sem 12.74 n 27	34.81 2.29 27	-78.30 1.10 23	77.65 2.02 27	-42.26 1.14 27	1.22 0.04 27
	fosGFP +	Mean 185.36 sem 11.54 n 17	70.59 8.07 17	-75.32 1.23 13	75.56 2.35 17	-39.43 1.95 17	1.25 0.03 17
				VR
		R input (MΩ)	Rheobase (pA)	RMP (mV)	AP amplitude (mV)	AP Threshold (mV)	APHW (ms)
	fosGFP -	Mean 332.40 SEM 11.29 n 24	30.83 2.40 24	-79.25 1.35 23	78.39 1.52 24	-41.39 0.93 24	1.18 0.03 24
	fosGFP +	Mean 237.93 sem 13.48 n 13	43.08 4.99 13	-77.86 1.83 11	73.66 2.84 13	-40.19 1.78 13	1.12 0.06 13

Table 2 Intrinsic electrophysiological properties of fosGFP -and fosGFP + DGCs - statistical comparisons

 2

	Two-way
	ANOVA
	p value	R input Rheobase	RMP	AP	AP	APHW
					amplitude	Threshold
	HC fosGFP -/	<0.0001	<0.0001	0.0795	0.6946	0.1775	0.954
	HC fosGFP +
	VR fosGFP -/	<0.0001	<0.0001	0.6133	0.1411	0.2375	0.6023
	VR fosGFP +
	HC fosGFP -/	0.9006	0.1069	0.9551	0.7459	0.9340	0.3336
	VR fosGFP -

Acknowledgments

We thank B. Poucet, D. Robbe, A. Malvache, J. Epsztein, J. Koenig, I. Bureau, R. Cossart, A.

Represa, R. Khazipov, S. X. Ho, and T. Marissal for helpful comments on this manuscript; and S. Varpula, S. Moussa, T. Tressard, and L. Petit for technical assistances. S. Corby-Pellegrino for heading the animal house facility. This work was supported by the Institut National de la Santé et de la Recherche Médicale (INSERM), Aix-Marseille Université (AMU), the Agence Nationale de la Recherche (ANR) (ANR 13-BSV4-0012-02 to V.C.), and the french Ministère de l'Enseignement Supérieur et de la Recherche (MESR to C.P.).

Statistical analysis

All values are given as means ± SEM. Statistical analyses were performed using Graphpad Prism 7 (GraphPad Software, La Jolla, CA). The normality of data distribution was assessed using the Shapiro-Wilk normality test. For comparison between two groups with normal distribution, the two-sample unpaired Student's t test was used, otherwise we used the unpaired Mann-Whitney test. To investigate the relationship between two parameters, the Pearson's correlation test was used. All tests were two-sided. For the comparison of multiple groups of two factors, we used two-way ANOVA test. Bonferroni post hoc test was used when adequate.

When data were not normally distributed, the unpaired Mann-Whitney rank-sum test was used.

The   or the Fisher's exact tests were used to compare proportions. Statistics are provided in the text and tables. The level of significance was set at P < 0.05; exact P values are given, unless P < 0.0001 or P > 0.9999. Analyses were performed blind to experimental groups.

Competing interests

No competing interest 0.0174 0.0338 0.2666 0.1274 0.7644 0.369

Supplementary figure 1

Representative double immunostaining using anti-EGFP antibody associated with anti-c-fos antibody in mice in home cage (HC) and trained in VR; scale bar 5 μm.

Supplementary figure 2


Anne Dutranois

Wan-Chiu Li

Paradigm Jean-Claude Dulac

Jean-Francois Lecomte

Jean-Paul Callot

Jean-Luc Rudkiewicz

Breakthrough in basin modeling using time/space frame Coupling space/time framework and 3D restoration revolutionizes basin modeling

A new way to model basins that couples the new space-time mathematical framework (defined as the UVT transform) and 3D restoration allows for easy and realistic construction of 4D models.

Using models built with the UVT transform, basin modelers will not only include faults and erosional surface properly in all structural settings, but also restore them using a 3D geomechanical finite element engine to model the proper paleo-basin geometries. As the UVT model is being restored, a "hybrid" grid carrying the static and dynamic properties is fully restored. The basin simulation software then takes all the time-dependent geological models and performs its computations on the 4D grid.

The goals of basin modeling are to find out whether the oil window was reached, to locate possible traps, and to estimate the volume and quality of hydrocarbons initially generated, migrated, and trapped, as well as to estimate pressure and temperatures in the reservoir (Rudkiewicz et al., 2000).

To achieve this, a full subsurface geologic model must be constructed and restored from the source rock to the current topography. Currently, a 4D basin model is a simple horizon based layer cake grid where faults are not represented explicitly as discontinuities. The reconstruction through time is based on the vertical back-stripping of layers. This is limited because the displacement of the blocks along the fault planes cannot be modeled and the material on both sides of reverse faults cannot be represented.

The estimation of migration paths across faults can be inaccurate especially in compressive basins or in extensive basins with long offset listric faults.

In addition to the limitations in properly representing the model at a given time step, the evolution of the basin as a function of time is done through the simple back-stripping and flattening of layers. So, this paleo-basin geometry can be inaccurate.

A new 3D framework

This new approach is based on the concept of the space/time mathematical framework introduced by Mallet (2004[START_REF] Mallet | Numerical Earth Models[END_REF]. This transformation can associate any point of the (x,y,z) space into a (u,v,t) space. The model is computed in the UVT space, with faults included as discontinuities, where T represents the geological time and U, V represents the paleo-geographic coordinates of each T surface. Thus, in the UVT space, a horizon is a plane, which is easier to describe than the complex shape of a faulted and folded horizon as it is at present time in XYZ. . The workflow to construct the UVT model is as follows:

1. Interpret the faults and of the main stratigraphic events from the seismic data in the XYZ space 2. Build a stratigraphic column, listing the considered stratigraphic events and their relationships 3. Compute 3D UVT parameterization of the volume constrained by the given faults, the stratigraphic events and the stratigraphic column.

The advantages of the UVT space for this workflow are numerous:

 The UVT transform is computed on top of an unstructured mesh which honors the fault network.

Layers on each side of the fault can glide along the fault surface as required proper restoration.

In addition, the finite element mechanical restoration accepts various types of grid (hexahedra, tetrahedra, prism). Since the UVT model consists of a set of UVT tetrahedrons, the finite element mechanical restoration can share the same unstructured mesh element. Sharing existing grids gains time and simplifies workflow. The creation of the finite element mesh has been the bottleneck in 3D restoration. Fault transmissivity depending on the displacement of the fault, which varies through time, can be computed automatically.  The UVT transform is computed for all stratigraphic sequences independently. Further, the eroded part of the sequence is computed to ensure continuity in case of partial erosions of the structure.

The UVT transform contains information to represent layer tops that have been eroded through time. Paleo-maps of horizons before erosion, including fault displacement, can be created. In addition, it is possible to "add back" some material, estimate volume before erosion, and restore so that the eroded layer can be restored.  The continuous UVT transform can be discretized into grids using different techniques, but, importantly, always linked to the UVT space. As the unstructured mesh carrying the UVT transform is restored, all objects created in the UVT transform will be restored. That is, the "magic" of the UVT transform. Because the evolution of the UVT transformation is described in 4D, location of cells can be tracked through time.

However, restoration is not done with the UVT transform. Although the UVT transform computes a flattened space, this space is not a "true" restored space. The restoration is computed through a finiteelement mechanical code, that allows for sliding fault blocks on fault surfaces and minimizes elastic deformation of the rock so the top of the layers reaches a (typically flat) target surface.

Gridding

Example of a 'hybrid' grid honoring the fault surfaces and the stratigraphic while mainly composed of hexahedral cells.

The new generation of basin modeling simulation software (and other similar simulation engines) requires grids which:

1. Follow the stratigraphy 2. Have mostly cubic (hexahedral) cells 3. Have cell faces parallel to the fault surfaces 4. Cells against faults need to be "nicely degenerated" from hexahedra to pyramids, wedges, and tetrahedra.

These requirements are met by so called 2.5D grids (pillar grids). Pillar grids cannot handle the complex faulting required to model sedimentary basins. A UVT model can be constructed inside any fault network, including systems of antithetic and synthetic faults. From the UVT model, a hybrid grid, containing non-cubic cells at the fault locations to honor both faults and stratigraphy, is created. These hybrid meshes are numerically efficient compared to pure tetrahedral or hexahedral meshes as the "nonhexahedral" elements are in topological continuity with non-faulted hexahedral neighbors.

As the hybrid grid is created from a UVT model, the relationship of every mesh node with the UVT is maintained. The fully unstructured mesh supporting the UVT model is restored at the same time as the hybrid grid. Also, the hybrid grid is created in the UVT space so eroded parts of the hybrid grid can be constructed as well, and then be given to the basin simulation software for pre-erosion computations.

Putting it all together

The illustration shows the sequential restoration of a UVT model and its associated hybrid grid as input data to basin modeling software. Each step is a layer removal followed by a flattening of the top of layer. Faults are kept in contact while the top layer is flattened.

From top to base (example from a synthetic model built from sandbox experiment): (A) geometry at current time (B) geometry at deposition time of the green unit (C) geometry before deposition of the green unit (D) geometry at the deposition of the blue unit.

The UVT transform enables the construction of 4D basin model geometry like never before. The 3D restoration of a UVT model is as easy as the construction of the UVT model because the unstructured mesh of the UVT is reused fully for the 3D restoration. The 4D model becomes a sequence of restored UVT models, where material eroded through time, also computed by the UVT transform, can be added.

As the UVT model is restored, a hybrid grid generated in the UVT model is restored for the basin simulation to run forward simulations.

The combination of the UVT transform and 3D restoration is a large step forward in 3D restoration for structural model analysis. The new generation of hybrid grids from a UVT model has direct applications not only in basin modeling, but also in coupled geomechanical and flow-simulations.


Ezequiel Orlandi

Jean-Luc Daridon

Hervé Carrier

Determination of bubble point pressure of two live oils with injected nitrogen by quartz crystal resonator

An experimental investigation of bubble pressure determination of two live oils with injected nitrogen is presented in this work. Conventional bubble point measurement is done with the aid of a constant expansion test. In such method the bubble point is characterized by an inflection in the PV curve as the oil becomes saturated. For some oils the PV curve is smoothed and it becomes difficult or impossible to accurately determine the bubble point. This work demonstrates that in such cases, the use of quartz crystal resonator technique makes it possible. This technique is successfully applied to determine the bubble point pressure of two live oils in which nitrogen is injected up to 40 mol%.

Introduction

Bubble point pressure of crude oils is a crucial parameter in field development and operations. When live oil sampling is possible, bubble point pressure determination can be performed by a constant mass expansion (CME) in a PVT laboratory [START_REF] Dake | Fundamentals of Reservoir Engineering[END_REF]. Bubble point is routinely determined from a sharp change in the PV curve due to change in compressibility once the oil becomes saturated. For volatile, near critical oils and oils that could have undergone secondary recovery processes the PV curve is smoothened and the accuracy of bubble point determination is jeopardized. For this reason numerical and experimental methods are constantly improved in order to accomplish a better crude oil characterization.

Our group recently published a detailed study on the bubble point pressure determination in bottomhole oils by using quartz crystal resonator technique [START_REF] Daridon | [END_REF]. It was shown that bubble point pressure of live oils can be estimated with a very good accuracy of 0.05 MPa by monitoring resonant properties (frequency and energy dissipation) of a quartz resonator immersed in a live oil.

a e-mail: ezequiel.orlandidesouzafreire@univ-pau.fr b e-mail: jean-luc.daridon@univ-pau.fr c e-mail: herve.carrier@univ-pau.fr 1 2 [START_REF] Whitson | Phase Behavior[END_REF] 4

Fig. 1. Schematics of the PVT cell equipped with a quartz crystal resonator (1); analyzer [START_REF] Daridon | [END_REF] and computer [START_REF] Whitson | Phase Behavior[END_REF] to record frequency and bandwidth data and to control the PVT cell pressure or volume; (4) a syringe pump utilized to inject nitrogen into the PVT cell.

Dissolved nitrogen is normally found in crude oil in small amount [START_REF] Whitson | Phase Behavior[END_REF]. Higher proportions can be a result of nitrogen miscible flooding. Nitrogen flooding is generally done to maintain reservoir pressure above saturation conditions and to enhance oil recovery [START_REF] Speight | The Chemistry and Technology of Petroleum[END_REF][START_REF] Clancy | Nitrogen for the enhanced revovery of oil and gas[END_REF]. Such kind of secondary recovery technique is applicable mostly for light oils and reservoirs with a depth capable of supporting pressures above the nitrogen minimum miscible pressure without causing formation damages.

Nitrogen and other flooding gases change crude oil bubble point pressure [START_REF] Vogel | The effect of nitrogen on the phase behavior and physical properties of reservoir fluids[END_REF][START_REF] Glasø | Generalized Pressure-Volume-Temperature Correlations[END_REF]. Nitrogen causes a huge increase in crude oil bubble point whereas other flooding gases such as CO 2 may cause a decrease of bubble conditions. Changes in bubble pressures related to gas injection must be determined with accuracy in order to design flooding process. However, the addition of nitrogen in live crude oil changes their compressibilities and makes smaller the discontinuity in the derivative of the volume versus pressure at the bubble point. In such cases the bubble point may not be determined with accuracy from usual PV method and more sensitive methods have to be applied. In this context, the aim of this work was to investigate the phase behavior of live oil with addition of nitrogen by using a quartz crystal resonator. To this end, measurements were carried out in two different crude oils with a nitrogen content ranging from 0 to 40 mol%.

Experimental section 2.1 Apparatus and live oils

The phase behavior of live oil + nitrogen system was measured up to 100 MPa using synthetic method with an indirect detection based on the monitoring of the resonant properties of a quartz crystal resonator. The apparatus, which has been described previously in detail [START_REF] Daridon | [END_REF] and shown in Figure 1, is made up of an automatic mobile piston cell closed at one end by a plug holding an acoustic wave sensor. It consists in a highly polished AT-cut beveled quartz disk with a fundamental frequency of 3 MHz and a blank diameter of 13.6 mm. Two electrodes were deposited on its faces on the central plano-plano portion by vacuum evaporation of an adhesive layer of titanium of 10 nm thickness followed by a 100 nm thick layer of gold. The external parts of the pins are connected to a network analyzer thanks to coaxial cable with SMA connectors. The cell is placed horizontally as well as the quartz sensor so as it probes only the liquid phase whatever the existence or nonexistence of a gas cap. The cylinder is fitted with two valves, one near to the end plug serving and inlet valve, and the other in the middle of the cylinder serves as discharging valve.

The live oil, received in a high pressure container, is brought to reservoir conditions (i.e. reservoir temperature and pressure) and is then transferred in small increments to the pre-evacuated PVT cell at the same temperature as the live oil container.

The oil that is transferred to the cell is maintained at reservoir pressure and temperature during 12 hours under stirring by a magnetic stirrer and three magnetic stir balls placed inside the cell. The volume occupied by the oil sample in the cell is measured to evaluate the mass of fluid introduced. The real amount of live oil introduced is determined according to PVT data provided by the oil company i.e. densities values and molar mass. This knowledge is crucial for the rest of the measurements since mixture composition is calculated according to that initial input. An accurate calibration of the dead volume of the cell is necessary. It has been done at the temperatures of interest in the pressure range 1-100 MPa (taking into account stainless steel dilatation with pressure and temperature) by introducing different mass of toluene, used as a reference compound for which densities are known with high accuracy (data are from NIST).

After the equilibrium time a stepwise depressurization at constant temperature is performed along with the recording of frequency and bandwidth data by the quartz crystal resonator to check if the bubble point pressure matches the bubble point pressure from the reservoir. This guarantees the integrity of the sample after the transfer process. The system is re-pressurized and the injection of nitrogen is then performed with the aid of a syringe pump with temperature control. The pressure in the syringe pump is brought to a higher pressure than the cell and the pump is set to maintain such a pressure. The nitrogen density at pressure and temperature of the syringe pump is used to calculate the volume of nitrogen which is needed to be injected in the cell to attain the desired molar composition of the system. The initial pressure and volume at the determined temperature at the syringe pump is noted. The valve that connects the syringe pump to the cell is then opened and the syringe pump starts to inject nitrogen in the cell. Once the desired nitrogen volume is injected the valve is closed and the final volume of the syringe pump is noted when the pressure stabilizes. The experimental quantity of injected nitrogen is then calculated having the experimental injected volume and the density of nitrogen at the temperature and pressure on the syringe pump.

When nitrogen is injected the system is tested to check if the mixture is monophasic. The test consist of a gradual depressurization following the energy dissipation profile. If there is no sudden change in the slope of the dissipation profile the system can be considered monophasic. The inverse process can also be applied. By an increase of pressure, if the dissipation profile has no change in its slope the system is considered monophasic. The effect of a monophasic system in energy dissipation is regular and is dictated by the change in viscosity and density of the solution as pressure increases or decreases.

During experiments, the system is constantly mixed by using magnetic stirrers. Temperatures for the experiments of crude oil 1 and 2 were set to their reservoir conditions (347 and 368 K respectively). Depressurizations were normally done at a constant expansion rate of 0.3 MPa per minute. Compositional analysis of live oil 1 and 2 are shown in Table 1.

Quartz crystal resonator technique

Quartz crystals are piezoelectric materials which when cut on the proper crystallographic plane are able to oscillate in shear mode when an alternating current is applied across its faces. Its connection to a network analyzer makes it possible the two main parameters of the vibrating quartz, its resonant frequency (f ) and the half-band-halfwidth (Γ) to be recorded. The half-band-half-width of the conductance spectra is related to energy dissipation (ΔD) as expressed by equation ( 1)

ΔD = 2ΔΓ f . (1)
Quartz crystal resonator technique was first used as a means of micro-weighting or thickness evaluation of thin and elastic films by Sauerbray [START_REF] Sauerbrey | [END_REF]. Sauerbray showed that a frequency decrease on the vibrating system is directly proportional to the thickness increase of a thin and elastic overlayer. Later Nomura and Kanazawa [9] developed the experimental and theoretical basis for the use of quartz crystal immersed in Newtonian liquid. They found that the frequency was directly proportional to the square root of the density viscosity product of the fluid. Muramatsu et al. [10] showed that the energy dissipation (ΔD) of a quartz immersed in a Newtonian fluid is also directly to the square root of the density viscosity product of the fluid. Equation (2) brings the energy dissipation as expressed by the half-band-half-width of a quartz with both faces immersed in a Newtonian liquid.

ΔΓ = f 3/2 0 η l ρ l πμ q ρ q 1/2 (2)
where ΔΓ is the change in bandwidth compared to the bandwidth of the vibrating quartz under vacuum conditions and η l , ρ l , μ q , ρ q stand for liquid viscosity, liquid density, quarts shear modulus and quartz density respectively. f 0 is the fundamental resonant frequency as measured under vacuum.

In a system without or with negligible adsorption or deposition on the surface of the quartz, bandwidth response is only dependent on the density and viscosity of the bulk fluid. Therefore, bandwidth of a quartz crystal resonator can be used to sense any changes in density and or viscosity of a fluid in contact with quartz faces. This characteristics of the quartz makes the bandwidth a good parameter for probing bubble conditions. The use of resonant frequency is also possible to detect phase changes, but its sensitivity to adsorbed material and also to pressure changes lessens the accuracy of bubble point determination.

Quartz crystal resonator technique can be applicable with the fundamental and overtones resonating modes. Former studies had demonstrated [START_REF] Daridon | [END_REF] on the conductance spectra acquisition of the third overtone mode when compared to the fundamental and higher harmonics. The fundamental overtone is essentially influenced by the quartz dimensions and higher overtones are more prone to be disturbed by inharmonics signals that may interact with harmonic peaks when the quartz resonator is immersed in viscous fluids [11].

Results and discussions

The constant mass expansion test with the use of quartz crystal resonator for live oil 1 is shown in Figure 2. Above the saturation pressure bandwidth data monotonically decreases as pressure is reduced due to an overall decrease in density and viscosity of the fluid. Once the bubble point is attained there is a sharp minimum in bandwidth data. In such a point as gas is released from the live oil the increase in density and viscosity of the media drives the augment in bandwidth. The bubble point is then determined as the intersection of two straight lines fitted on both sides of the inflection region. On the under-saturated region the decompression leads to the decrease of density and viscosity of the system, however, on the saturated region two effects are occurring. The sole decompression leads to a further decrease on density and viscosity on the liquid phase which is contra balanced by the increase in density and viscosity due to gas liberation. The determination of the bubble point for live oil 1 without injected nitrogen can be easily obtained from a constant mass expansion test. However with the addition of nitrogen there is a continuous smoothening of the compressibility of the fluid on the under-saturated and saturated regions. PV data obtained from CME tests on live oil 1 with different nitrogen molar fraction are shown in Figure 3. For nitrogen molar fraction of 40% the effect of the nitrogen saturation on the proximities of the bubble point can be observed (Fig. 3). The high nitrogen content smoothens the PV curve making the bubble point determination very inaccurate. The use of quartz crystal and analysis of the bandwidth profile during decompression highlights bubble point pressure for all the studied nitrogen molar fractions as shown in Figure 4. The similar profile of the curves inclination in the under saturated regions puts in evidence the sole effect of density and viscosity reduction which is occurring in such a macroscopically monophasic fluid. The obtained results for the constant mass expansion test for live oil 2 is shown in Figure 5. Figure 6 brings the results obtained on the bandwidth analysis for the constant mass expansion of crude oil 2.

The bubble points as they were determined by the bandwidth data and PV method are summarized in Table 2 for live oil 1 and 2 with the different nitrogen molar fractions. The bubble point for the live oil 2 with 40 mol% nitrogen is not shown as it was higher than 100 MPa which is close to the highest pressure that the PVT cell was designed to support. The pseudo phase diagram of the systems were then plotted and are shown in Figures 7 and8.

Conclusion

In continuation of the former developed work [START_REF] Daridon | [END_REF] the present study confirms the successful applicability of quartz crystal resonator technique to determine bubble point in live crude oil samples with injected nitrogen. The analysis of the bandwidth is preferred as it is independent on pressure and less sensitive to adsorbed material. The third harmonic is the less affected by quartz dimensions and inharmonic modes. Further studies with this technique will be performed with the aim of evaluating the rheology of the systems before and after nitrogen injection. It is worthy to highlight L LV that the use of quartz crystal resonator technique can also be applied to evaluate the influence of other gases as methane or carbon dioxide on live oil bubble point.

Fig. 8 .

 8 Fig. 8. Bubble point pressure as a function of nitrogen content in N2 + Live oil 2 pseudo binary system.

  This work was carried out as a part of the Joint Industrial Programme (JIP) Asphaltenes consortium "Improved Mechanisms of Asphaltene Deposition and Precipitation to Minimize Irregularities in Production and Transport" sponsored by the following industrial partners -AkzoNobel, BP, Canada Natural Resources, Nalco Champion, Petrobras, Statoil and Total as well as by the Norwegian Research Council (234112/E30).

Table 1 .

 1 Molar compositional analysis and respective molecular weights from the compounds present in North Sea live oil 1 and 2.

	Component	Live oil 1	Live oil 2
		mol %	Mw	mol %	Mw
	N2	1.0	28.0	0.6	28.0
	CO2	0.1	44.0	0.1	44.0
	C1	19.6	16.0	19.7	16.0
	C2	5.6	30.1	5.1	30.1
	C3	8.5	44.1	6.2	44.1
	i -C4	1.4	58.1	1.4	58.1
	n -C4	5.6	58.1	3.3	58.1
	i -C5	2.1	72.2	1.4	72.2
	n -C5	2.9	72.2	1.9	72.2
	Fraction C6	3.8	84.9	2.6	84.2
	Fraction C7+	49.4	268.3	57.7	224.0

Table 2 .

 2 Bubble point pressure measured in Live oil + injected nitrogen pseudo binary systems.

	xN 2	T /K P /MPa	P /MPa	xN 2	T /K P /MPa	P /MPa
	(mol%)		Quartz PV method (mol%)		Quartz PV method
			N2 + Live oil 1			N2 + Live oil 2
	0	367.7	9.04	8.99	0	347.9	8.53	8.45
	9.95	368.1	19.52	19.47	11.68	348.1	21.87	21.78
	19.27	368.1	30.48	30.46	21.13	348.2	38.31	38.22
	29.41	368.4	48.64	49.15	30.33	348.1	62.00	62.37
	39.25	368.4	72.95	72.90	40.36	348.3	>100	>100

L LV

Fig. 7. Bubble point pressure as a function of nitrogen content in N2 + Live oil 1 pseudo binary system.