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The brain circuits controlling the immunosuppressive response to the pro-inflammatory cytokine IL1ß

Ferdinand Jagot

To cite this version:

Ferdinand

  Jagot. The brain circuits controlling the immunosuppressive response to the proinflammatory cytokine IL1ß. Immunology. Université Paris Cité, 2021. English.


Irene Balelli
email: irene.balelli@inria.fr

Aude Sportisse
email: aude.sportisse@inria.fr

Francesco Cremonesi
email: francesco.cremonesi@inria.fr

Pierre-Alexandre Mattei
email: pierre-alexandre.mattei@inria.fr

Marco Lorenzi
email: marco.lorenzi@inria.fr

Fed-MIWAE: Federated Imputation of Incomplete Data via Deep Generative Models

Keywords: Missing data, Federated learning, Federated pre-processing, Variational autoencoders, Deep Learning

Federated learning allows for the training of machine learning models on multiple decentralized local datasets without requiring explicit data exchange. However, data pre-processing, including strategies for handling missing data, remains a major bottleneck in real-world federated learning deployment, and is typically performed locally. This approach may be biased, since the subpopulations locally observed at each center may not be representative of the overall one. To address this issue, this paper first proposes a more consistent approach to data standardization through a federated model. Additionally, we propose Fed-MIWAE, a federated version of the state-of-the-art imputation method MIWAE, a deep latent variable model for missing data imputation based on variational autoencoders. MIWAE has the great advantage of being easily trainable with classical federated aggregators. Furthermore, it is able to deal with MAR (Missing At Random) data, a more challenging missing-data mechanism than MCAR (Missing Completely At Random), where the missingness of a variable can depend on the observed ones. We evaluate our method on multi-modal medical imaging data and clinical scores from a simulated federated scenario with the ADNI dataset. We compare Fed-MIWAE with respect to classical imputation methods, either performed locally or in a centralized fashion. Fed-MIWAE allows to achieve imputation accuracy comparable with the best centralized method, even when local data distributions are highly heterogeneous. In addition, thanks to the variational nature of Fed-MIWAE, our method is designed to perform multiple imputation, allowing for the quantification of the imputation uncertainty in the federated scenario.

Introduction and previous work

Digital health revolution relies heavily on the massive development and deployment of data-driven machine learning (ML) and deep learning (DL) methods. Nevertheless, in order to insure reliability, trustworthiness and generalizability of such models, their parameters need to be learned from a sufficiently large curated dataset, representative of the whole data distribution. These conditions are rarely met within a single research hospital, and data centralization is often not possible due to current privacy regulations (e.g. the European GDPR 1 ).

Federated learning (FL) is an emerging learning paradigm based on multiple decentralized datasets. FL has been identified as a promising solution for healthcare applications [START_REF] Rieke | The future of digital health with fl[END_REF][START_REF] Matthew G Crowson | A systematic review of federated learning applications for biomedical data[END_REF], due to its improved robustness and generalization properties. Indeed, the final aggregated FL model gathers information provided by multiple hospitals through distributed optimization. Moreover, FL implicitly alleviates data governance burden and naturally improves privacy guarantees with respect to classical centralized ML methods. The growing interest in FL is demonstrated by the several FL-based research projects [START_REF] Sadilek | Privacy-first health research with federated learning[END_REF][START_REF] Matthew G Crowson | A systematic review of federated learning applications for biomedical data[END_REF][START_REF] Rieke | The future of digital health with fl[END_REF][START_REF] Pati | Federated learning enables big data for rare cancer boundary detection[END_REF], and FL infrastructures such as Flare [Roth et al.], OpenFL [Foley et al.], SubstraFL 2 , and Fed-BioMed 3 , which are specifically tailored for biomedical applications. Despite the potential of FL for cross-institutional medical research, data pre-processing, including data standardization and management of missing entries in the database, is generally carried out locally by each participating site. The importance of establishing reliable data pre-processing pipelines in a federated manner has already been emphasized [START_REF] Sekhar | Overview of federated facility to harmonize, analyze and management of missing data in cohorts[END_REF], and is of paramount importance in real-life FL deployment.

In order to prevent the deletion of incomplete rows in the dataset, with consequent critical loss of information (especially in the high-dimensional setting) and the potential induction of a biased analysis, one common approach to handle missing values involves data imputation [START_REF] Roderick | Statistical analysis with missing data[END_REF]. Besides the naive mean imputation, which consists in replacing missing values with the mean across the observed population, several alternatives and more elaborate imputation methods have been proposed in the literature, including based on probabilistic models [START_REF] Roderick | Statistical analysis with missing data[END_REF], low-rank models [START_REF] Mazumder | Spectral regularization algorithms for learning large incomplete matrices[END_REF] or random forests [START_REF] Daniel | Missforest-non-parametric missing value imputation for mixed-type data[END_REF]. Some methods are specifically designed for multiple imputation, such as mice [START_REF] Van Buuren | mice: Multivariate imputation by chained equations in r[END_REF], to account for imputation uncertainty Austin et al. [2021]. More recently, imputation methods based on deep latent variable models have been proposed, for example by building on generative adversarial networks (GANs) [START_REF] Cheng-Xian | Learning from incomplete data with generative adversarial networks[END_REF], Yoon et al., 2018] or on variational autoencoders (VAEs) [START_REF] Mattei | MIWAE: Deep generative modelling and imputation of incomplete data sets[END_REF][START_REF] Nazabal | Handling incomplete heterogeneous data using vaes[END_REF][START_REF] Bruun Ipsen | not-MIWAE: Deep generative modelling with missing not at random data[END_REF]. However, none of these imputations methods are specifically designed for the FL setting. The theoretical behaviour of learning with hidden variables in a federated context has been addressed in Dieuleveut et al. [2021] through an Expectation Maximization (EM) based optimization scheme, and applied to federated missing values imputation for biodiversity monitoring. In [START_REF] Balelli | A probabilistic framework for modeling the variability across federated datasets[END_REF], authors proposed a Bayesian latent variable model for multiple views data assimilation and completely missing modalities imputation in a federated setting [START_REF] Balelli | A probabilistic framework for modeling the variability across federated datasets[END_REF], which also relies on the EM algorithm for local parameters' optimization. Recently, two papers [Zhou et al., 2021, Yao andZhao, 2022] have extended the imputation method based on GANs [Yoon et al., 2018] to the FL framework. Nevertheless, the theoretical results behind these methods have only been provided in the Missing Completely At Random (MCAR) setting [Yoon et al., 2018], and are not suited for the more complex and yet relevant case of Missing At Random (MAR) data, where the missingness of a variable depends on the observed ones, or the most challenging Missing Not At Random (MNAR) setting, where the missingness can even depend on the unobserved variable itself [START_REF] Mohan | On handling self-masking and other hard missing data problems[END_REF][START_REF] Peter C Austin | Missing data in clinical research: a tutorial on multiple imputation[END_REF] (Table 1). It is worth mentioning that it is not possible to assess whether the missing pattern is MCAR or MAR solely based on the data (i.e. without additional clinical knowledge), hence the need in favouring methods able to handle both cases, and possibly explicitly account for the uncertainty on the imputed variable, for instance through multiple imputation. In this work, we first propose a pipeline for data pre-processing through multisite data, including federated standardization as well as imputation of missing entries, performed through Fed-MIWAE, the federated version of MIWAE [START_REF] Mattei | MIWAE: Deep generative modelling and imputation of incomplete data sets[END_REF], a deep latent variable model for missing data imputation based on variational inference. The VAEs-based structure of MIWAE makes it easily trainable with classical federated aggregators, such as FedAvg [START_REF] Mcmahan | Communication-efficient learning of deep networks from decentralized data[END_REF], FedProx [START_REF] Li | Federated optimization in heterogeneous networks[END_REF] or Scaffold [START_REF] Sai | Scaffold: Stochastic controlled averaging for federated learning[END_REF]. By relying on the framework of MIWAE, Fed-MIWAE is naturally designed to handle both MCAR and MAR data, while allowing for multiple imputation. In Sec. 2 we describe our proposed approach and its implementation, and in Sec. 3 we applied it to multi-modal medical imaging data and clinical scores from the ADNI database, distributed to multiple sites to simulate a federated setting. Fed-MIWAE shows higher generalization and improved performance compared to all tested local imputation methods, and comparable with the centralized sce-nario, even in presence of simulated MAR data and high heterogenity accross local datasets in terms of patients distribution.

2 An approach to federated data pre-processing

Missing data importance-weighted autoencoder (MIWAE)

In this work we extend the MIWAE method [START_REF] Mattei | MIWAE: Deep generative modelling and imputation of incomplete data sets[END_REF] in order to be able to train it in a federated manner. MIWAE is defined by a deep generative model from a latent Gaussian variable z to the original data x, where t ∼ p θ (x|z), and the conditional distribution p θ is parametrized using a neural network (NN) with weights θ. Due to the presence of missing values in the data, the observed likelihood we would like to maximise is intractable [START_REF] Roderick | Statistical analysis with missing data[END_REF]. Similarly to variational autoencoders, the idea of MIWAE is to propose a conditional distribution of the latent variables given the observed data, x observed , q γ (z|x observed ), parameterized as well by a NN with weights γ. A stochastic lower bound of the observed likelihood can then be computed and optimized using SGD-like approaches. Finally, the imputation step is done by computing the conditional expectation of the missing variables given the observed ones, E[x missing |x observed ], estimated via self-normalised importance sampling.

Proposed method

We propose a federated pre-processing approach, described in Algorithm 1. Firstly, we perform federated standardization in presence of missing values through a single communication round (Stage 1). This step only requires the communication to the central server of all local feature-wise means and standard deviations to analytically evaluate the global mean and std. Each center will be able to use this information to perform its data standardization with respect to the whole federated dataset. Secondly, we propose to train Fed-MIWAE using the same FL platform (Stage 2 (i) of Algorithm 1), over the decentralised standardised datasets, across R iterative communication rounds. In this stage, the local parameters' optimization and aggregation can be performed using classical FL optimization and aggregation schemes, such as Federated Averaging (FedAvg) [START_REF] Mcmahan | Communication-efficient learning of deep networks from decentralized data[END_REF], FedProx [START_REF] Li | Federated learning: Challenges, methods, and future directions[END_REF] or Scaffold [START_REF] Sai | Scaffold: Stochastic controlled averaging for federated learning[END_REF], which are designed to be more robust to data heterogeneity. Finally, missing values can be imputed locally by each center using the final global model (Stage 2 (ii) of Algorithm 1).

Application to the Alzheimer's Disease Neuroimaging

Initiative dataset (ADNI)

Data

We consider 311 participants extracted from the ADNI dataset, composed by 104 cognitively normal individuals (NC) and 207 patients diagnosed with Alzheimer

Algorithm 1 Federated data pre-processing disease (AD), coming from 55 centers. All participants are associated with multiple data views: cognitive scores including MMSE, CDR-SB, ADAS-Cog-11 and RAVLT, Magnetic resonance imaging (MRI), Fluorodeoxyglucose-PET and AV45-Amyloid PET images. MRI morphometrical biomarkers were obtained as regional volumes using the cross-sectional pipeline of FreeSurfer v6.0 based on the Desikan-Killiany parcellation [START_REF] Fischl | [END_REF]. Measurements from AV45-PET and FDG-PET were estimated by co-registering each modality to their respective MRI, normalizing by the cerebellum uptake and by computing regional amyloid load and glucose hypometabolism using PetSurfer pipeline [START_REF] Douglas N Greve | Cortical surface-based analysis reduces bias and variance in kinetic modeling of brain pet data[END_REF] based on the same parcellation. Features were corrected beforehand with respect to intra-cranial volume, sex and age using a multivariate linear model. Overall, the features dimension for a fully observed patient is 130. Further details are provided in Supp. Material, Table S1.

Scenarios

We consider two federated scenarios:

Natural split scenario: we take into account the information concerning the site to perform the split. Due to the high number of participating sites, we artificially create 4 datasets by gathering the contribution of about 14 ADNI sites per simulated center (see details in Supp. Material, Table S2). Not-IID scenario: we generated 3 centers, one containing only AD patients (Client 1, N 1 = 171), one containing only NC patients (Client 2, N 2 = 77), and the third one, kept as external testing dataset, containing samples from both groups (N 3 = 63).

We further simulate 30% of missing data in each resulting local dataset, considering either the MCAR or MAR settings. For the natural split scenario, we perform 5 times a 4-fold cross-validation: each time, three datasets are used as the training centers, while the fourth one is used in testing phase only. For the not-IID scenario, the training is always performed (5 times) on Client 1 and Client 2, with testing on Client 3. Numerical experiments with Fed-MIWAE were executed using version v4.2 of the Fed-BioMed software4 . Experiment results were logged using Fed-BioMed's logging facility and manually collected for analysis. Due to space limitations, we will only show results on the more challenging MAR setting in Sec. 3.4. Similar conclusions have been obtained in the MCAR setting, and are provided in the Supp. Materials (Table S5, Figure S1).

Benchmark

We compare our proposed method trained using FedProx [START_REF] Li | Federated learning: Challenges, methods, and future directions[END_REF] S3). In addition, we assess the performance of classical imputation methods, by training them either locally or using the centralized dataset: naive mean imputation (mean), nonparametric method with random forests (rf) Stekhoven and Bühlmann [2012] and iterative chained equations method (ice) [START_REF] Van Buuren | mice: Multivariate imputation by chained equations in r[END_REF], from the impute module of the scikit-learn library [START_REF] Pedregosa | Scikit-learn: Machine learning in Python[END_REF]. To evaluate the imputation accuracy of all considered methods, we compute the normalized Mean Squared Error (MSE).

Results

In Figure 1, we assess the performance of the imputation methods through MSE in both considered scenarios, with respect to the task of imputing missing data both on the external testing dataset (upper row) and on datasets which have been seen during training (bottom row). In all cases, our method optimized with FedProx outperforms all local MIWAE models (Local cli). Moreover, one can see that performing data standardisation in a federated manner allows to reach improved results compared to the case where the standardization step has been performed locally (cf FedProx compared to FedProx loc), especially in the more challenging non-iid scenario. Interestingly, the MIWAE method in the centralized setting displays a lower performance compared to Fed-MIWAE. This can be attributed to the potential issue of overfitting, and can be prevented for instance by deploying drop-out and early stopping strategies, which has not been done here for the sake of a fair comparison throughout the methods. Among the considered benchmark approaches, one can see that the classical Random Forests imputation method (rf) achieves the highest performance, More details can be found in Table 2 and Supp. Table S4 when tested on both an external dataset and the completion of missing entries from the training data. The naive mean-values imputation method mean is not included in Figure 1 due to its very poor performance (see for instance Table 2), while the method ice, is significantly less accurate than rf and our proposed approach, despite being trained in a standard centralized manner.

Fed-MIWAE shows a very good performance in all considered settings, compared to the centralized approaches tested. In particular, the imputation quality of our federated method is remarkable when considering the not-IID scenario and the ability of the trained model to infer missing entries in an external dataset (Figure 1 (d) and Table 2), outperforming the best centralized model. In addi-tion, one can appreciate the consistent performance exhibited by Fed-MIWAE in the not-IID setting, where it achieves a MSE similar to the one obtained in the natural split scenario, where patients' distribution across centers is more balanced. In contrast, the performance of all local models (Local cli) in imputing missing values of the external testing dataset drops significantly in this extremely non-heterogeneous scenario. Finally, our approach is even able to outperform the best centralized model when applied to the smaller sized local datasets (i.e. Client 4 in the natural split scenario and Client 2 in the non-iid one), underscoring the importance of performing the imputation task by exploiting the federated framework. Finally, we should stress that an additional major advantage of our method consists in its ability to perform multiple imputation, by drawing i.i.d. samples from the estimated posterior probability p Fed-MIWAE (x missing |x observed ). This allows us to quantify the uncertainty of the imputation. In Figure 2 (a) we show an example of multiple imputation on a randomly selected subject in the testing dataset obtained by drawing 30 samples, while in Figure 2 (b) one can see that a significantly lower variance is associated to p Fed-MIWAE (x missing |x observed ) compared to p(x missing ), leading us to the conclusion that x observed do provide relevant information for the imputation of x missing .

Conclusion

In this work, we propose an efficient pipeline to handle MCAR and MAR values in the FL framework, which provides highly generalizable results and comparable performance with respect to centralized methods. We show that our method is accurate even when applied in highly heterogeneous settings, which should be carefully taken into account in real-world FL contexts. Several improvement of the proposed method can be envisaged in the future, spanning from addressing the complex problem of handling MNAR data, by incorporating the not-MIWAE method [START_REF] Bruun Ipsen | not-MIWAE: Deep generative modelling with missing not at random data[END_REF] in our pipeline, to improve privacy requirements for instance by using secure aggregation or differential privacy techniques. In addition, data pre-processing pipelines in the federated framework should be further explored since they are key for the successful training of FL in real clinical studies. Jinsung Yoon, James Jordon, and Mihaela Schaar. Gain: Missing data imputation using generative adversarial nets. In International conference on machine learning, pages 5689-5698. PMLR, 2018. Xu Zhou, Xiaofeng Liu, Gongjin Lan, and Jian Wu. Federated conditional generative adversarial nets imputation method for air quality missing data. Knowledge-Based Systems, 228:107261, 2021.

Input:

  Rounds R, Epochs E, MIWAE hyperparameters Stage 1: Federated standardization Each center c computes its local mean and std (µ (c) , σ (c) ). Server aggregates them and sends the global mean and std (µ, σ). Each center c standardizes its local dataset with µ and σ. Stage 2: Fed-MIWAE (i) Train step for r = 1 to R do Each center c initializes its local model with (θ r-1 , γ r-1 ). Each center performs E epochs of local MIWAE bound optimization, and sends the updated local parameters ((θ (c) ) t+1 , (γ (c) ) t+1 ) to the Server. Server aggregates ((θ (c) ) t+1 , (γ (c) ) t+1 )c and sends the global parameter (θ r , γ r ) back to the centers. end for (ii) Missing values imputation Each center c dispose of the final global model (θ R , γ R ) for missing data imputation.

FFig. 1 :

 1 Fig. 1: ADNI data, MAR setting. Comparison of federated and local models measured on imputing missing entries of a previously unseen testing dataset (upper row) and of local datasets used for training (bottom row, where colors denotes the employed model, following the same color code as in the upper row figures). Left column: Natural split scenario. Right column: Not-IID scenario.More details can be found in Table2and Supp. TableS4

Fig. 2 :

 2 Fig. 2: ADNI data, MAR setting. (a) Example of multiple imputation on the test data using Algorithm 1, and (b) corresponding comparison between p Fed-MIWAE (x missing |x observed ) with p(x missing ).

F

  Fig. S1: ADNI data, MCAR setting. Comparison of federated and local models measured on imputing missing entries of a previously unseen testing dataset (upper row) and of local datasets used for training (bottom row, where colors denotes the employed model, following the same color code as in the upper row figures). Left column: Natural split scenario. Right column: Not-IID scenario.

Table 1 :

 1 Missing data classification with examples

	MCAR Accidental loss/damage of a sample, non-systematic personnel error in a sam-
	ple's acquisition/reporting.
	MAR The acquisition of a feature (e.g. a clinical score test) depends on a fully ob-
	served variable (e.g. the age): the observed systematic difference in incomplete
	features can be explained by differences in observed variables.
	MNAR The reason for a missing sample can depend both on observed and unobserved
	variables, including

the missing sample itself (e.g. self-censored data)

  (simply denoted FedProx in Sec. 3.4, or FedProx loc, if the data standardization is done locally and only Stage 2 of Algorithm 1 is performed), with the local training of MIWAE in each site (Local cli for the site i) as well as the centralized training of MIWAE (Centralized). To ensure a fair comparaison, the chosen hyperparameters of MIWAE are the same, regardless of how the model is trained (Supp. Material, Table

Table 2 :

 2 ADNI data, MAR setting, not-IID split. Comparison of all considered methods on the testing dataset (mean (std)).

	Training		Federated	Centralized
	Model	FedProx	FedProx loc mean ice	random forests
	MSE ↓ 0.4299 (0.005) 0.5529 (0.005) 1.0933 1.081 (0.0233) 0.4559 (0.0042)

Table S5 :

 S5 ADNI data, MCAR setting, not-IID split. Testing dataset.

	Training			Federated	Centralized
	Model		FedProx	FedProx loc mean ice	random forests
	MSE	↓	0.4477 (0.0053) 0.627 (0.008) 1.1717 0.7157 (0.006) 0.5167 (0.0009)
	mean (std)

https://gitlab.inria.fr/fedbiomed/fedbiomed/-/tree/v4.2.4

Supplementary Material


Flore Lormant

Vitor Hugo

Bessa Ferreira

Julie Lemarchand

Fabien Cornilleau

Paul Constantin

Céline Parias

Aline Bertin

Léa Lansade

Christine Leterrier

Frédéric Lévy

INRAE Ludovic Calandreau
email: ludovic.calandreau@inrae.fr

Training level reveals a dynamic dialogue between stress and memory systems in birds

Keywords: bird cognition, chronic stress, spatial memory, cue-based memory, overtraining, Japanese quail

de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

It has long been recognized that chronic stress strongly modulates learning and memory performance [START_REF] Conrad | A critical review of chronic stress effects on spatial learning and memory[END_REF][START_REF] Luksys | Neural mechanisms and computations underlying stress effects on learning and memory[END_REF][START_REF] Sandi | Stress and cognition[END_REF]. However, the relationship between chronic stress and memory are still controversial: some studies reported an improving effect of chronic stress on memory performance [START_REF] Conboy | Stress at Learning Facilitates Memory Formation by Regulating AMPA Receptor Trafficking Through a Glucocorticoid Action[END_REF][START_REF] Conrad | Repeated restraint stress facilitates fear conditioning independently of causing hippocampal CA3 dendritic atrophy[END_REF][START_REF] Goodman | Impaired spatial memory and enhanced habit memory in a rat model of post-traumatic stress disorder[END_REF], whereas others reported a negative effect [START_REF] Goodman | Impaired spatial memory and enhanced habit memory in a rat model of post-traumatic stress disorder[END_REF][START_REF] Moreira | Impact of chronic stress protocols in learning and memory in rodents: Systematic review and meta-analysis[END_REF][START_REF] Wright | Enriched environment prevents chronic stress-induced spatial learning and memory deficits[END_REF]. Overall, chronic stress generally provokes memory impairment for complex tasks and improves simple tasks [START_REF] Sandi | Stress and cognition[END_REF]. These discrepancies support the fact that memory is not a single unit. Different parts of the brain are responsible for different memory systems that process different types of information and are differently affected by stress [START_REF] Packard | Factors that influence the relative use of multiple memory systems[END_REF].

Two memory systems have had a considerable amount of scientific interest in the last decades as they present strong parallels between humans and animals. The first one is spatial memory, a form of declarative memory based on the hippocampus and used mainly to establish a relationship between different cues from the environment, which allow the creation of a cognitive mental map [START_REF] O'keefe | The hippocampus as a cognitive map[END_REF][START_REF] Morris | Place navigation impaired in rats with hippocampal lesions[END_REF]. As the cues from the environment are treated in relation to each other, the spatial memory is considered to be more complex than the cue-based memory, a non-declarative form of memory that results from a simple association between a salient cue and the target and is based on the striatum [START_REF] Packard | Factors that influence the relative use of multiple memory systems[END_REF][START_REF] White | Multiple Parallel Memory Systems in the Brain of the Rat[END_REF][START_REF] Lormant | Research Note: Role of the hippocampus in spatial memory in Japanese quail[END_REF]. From the first week of life, chicks can already be trained to use both their spatial and cue-based memory.

When the environmental information allows the use of both memory systems, chicks were seen to rely on their cue-based memory [START_REF] Morandi-Raikova | The use of spatial and local cues for orientation in domestic chicks (Gallus gallus)[END_REF]. We recently showed that, in quail, individual differences in behavioral traits, such as fearfulness and stress-coping, alters the way birds use these different systems. Indeed, fearful and easily stressed quail use their spatial memory preferentially, while less fearful/stressed individuals prefer to rely on their cue-based memory [START_REF] Lormant | A trait for a high emotionality favors spatial memory to the detriment of cue-based memory in Japanese quail[END_REF]. The same occurs for the relationship between individual exploratory level and memory systems in free-range chickens: less exploratory chickens, known to be easily startled, have better spatial memory than more exploratory chickens [START_REF] Ferreira | Relationship between ranging behavior and spatial memory of free-range chickens[END_REF][START_REF] Ferreira | Social motivation and the use of distal, but not local, featural cues are related to ranging behavior in free-range chickens (Gallus gallus domesticus)[END_REF][START_REF] Taylor | Frequent range visits further from the shed relate positively to free-range broiler chicken welfare[END_REF].

Besides intrinsic factors, external insults, such as hippocampal lesions or chronic stress, may also influence which memory system an individual will preferentially use. Hippocampal lesioned and chronic stressed quail present similar patterns of behavior when different memory systems are requested: while these animals present important impairments on spatial memory, they have their cuebased memory spared and, in some cases, even improved [START_REF] Lormant | Research Note: Role of the hippocampus in spatial memory in Japanese quail[END_REF][START_REF] Lormant | Emotionality modulates the impact of chronic stress on memory and neurogenesis in birds[END_REF].

Beyond chronic stress, the training level can also influence the use of multiple memory systems [START_REF] Packard | Factors that influence the relative use of multiple memory systems[END_REF][START_REF] Hicks | Effects of Overtraining on Acquisition and Reversal of Place and Response Learning[END_REF][START_REF] Packard | Anxiety, cognition, and habit: A multiple memory systems perspective[END_REF][START_REF] Packard | Exhumed from thought: Basal ganglia and response learning in the plus-maze[END_REF][START_REF] Yin | The role of the basal ganglia in habit formation[END_REF]. The majority of the knowledge on this area comes, however, from studies on rodents. Indeed, rats trained to learn the location of a reward in a maze preferentially use their spatial memory after a few days of training and shift to a cue-based dominant response with overtraining [START_REF] Packard | Inactivation of hippocampus or caudate nucleus with lidocaine differentially affects expression of place and response learning[END_REF].

Training and stress levels may also interact: the initial differences in memory performance due to stress may disappear with overtraining [START_REF] Sandi | Stress and cognition[END_REF]. Other studies suggest training itself may be a source of stress and reduce hippocampal neurogenesis [START_REF] Ehninger | Paradoxical effects of learning the Morris water maze on adult hippocampal neurogenesis in mice may be explained by a combination of stress and physical activity[END_REF][START_REF] Aztiria | Extensive training in a maze task reduces neurogenesis in the adult rat dentate gyrus probably as a result of stress[END_REF], which could explain the shift between memory systems. While much is known on chronic stress effects on declarative spatial or non-declarative cuebased memory performance, the influence of the level of training and its interaction with chronic stress influence remains poorly understood. Furthermore, the understanding of these interactions and their applicability to other taxa, such as birds, is still limited and merits further investigation.

Using the Japanese quail (Coturnix coturnix) as the animal model, in this work, we investigated whether and how chronic stress could differentially impact spatial and cue-based memories according to the level of training. In previous work, we developed reliable protocols to study the effects of chronic stress on different quail behavior and cognitive abilities. In quail, chronic stress is known to increase individuals' emotional reactivity (to both novel objects and humans, for example) and disrupt feeding patterns [START_REF] Favreau-Peigné | Unpredictable and repeated negative stimuli increased emotional reactivity in male quail[END_REF][START_REF] Favreau-Peigné | Emotionality modulates the effect of chronic stress on feeding behaviour in birds[END_REF][START_REF] Laurence | Environmental enrichment reduces behavioural alterations induced by chronic stress in Japanese quail[END_REF]. These negative effects may be long lasting, during up to seven weeks after the termination of the stress procedure [START_REF] Laurence | Short-and long-term effects of unpredictable repeated negative stimuli on Japanese quail's fear of humans[END_REF]. While our results suggested that chronic stress strongly impair quail spatial memory, with lesser impacts on cue-based memory [START_REF] Lormant | Emotionality modulates the impact of chronic stress on memory and neurogenesis in birds[END_REF],

we did not take into consideration the possible effects of training level and their interactions with chronic stress. In order to fill this knowledge gap, here, control and chronically stressed Japanese quail were trained in a dual spatial/cued task, which could be solved using the spatial or cue-based memories and tested for their performances on each type of memory after five and fifteen training days. Additionally, as an animal's immediate psychological state may interact with chronic stress and impact the relative use of different memory systems and memory performance [START_REF] Lormant | Emotionality modulates the impact of chronic stress on memory and neurogenesis in birds[END_REF][START_REF] Deschaux | Postextinction fluoxetine treatment prevents stress-induced reemergence of extinguished fear[END_REF][START_REF] Izquierdo | Stress hormones enhance retrieval of fear conditioning acquired either one day or many months before[END_REF][START_REF] Marin | Effects of an acute stressor on fear and on the social reinstatement responses of domestic chicks to cagemates and strangers[END_REF], a series of tests were conducted after an emotional challenge that consisted of exposing quail to an unfamiliar environment (open field). Finally, birds were submitted to a test to assess whether they learn to find the target cup by a simple association between the target cup's color and the reward or by detecting the difference of colors between the target cup and the remaining cups. Based on the literature, we expected chronically stressed quail to show lower performance in the spatial memory test than control ones after initial training (five days of training). These differences were expected to lower or disappear after overtraining (fifteen days of training) and reinstated after the emotional challenge. Concerning the cue-based memory, we expected no differences between chronically stressed and control quail.

Since this is a simple form of memory, we expected the emotional challenge would not disrupt it.

Methods

Animals

All Japanese quail were bred and maintained at the Pôle d'

Chronic stress procedure

To stress quail chronically, we followed the same procedures as described in [START_REF] Lormant | Emotionality modulates the impact of chronic stress on memory and neurogenesis in birds[END_REF]. When quail reached 21 days old, they were transferred to individual cages. They were then divided into two treatment groups: a control and a chronic stress group. Quail from the chronic stress group were submitted to chronic unpredictable stress (CUS) for three weeks consisting of multiple negative stimulations or stressors (from five to six) per day. Each stressor occurred at unpredictable times each day/night to improve the unpredictability of the stressor delivery and decrease habituation to the stress procedure. This CUS procedure has been fully described and was shown to induce a state of chronic stress in quail [START_REF] Lormant | Emotionality modulates the impact of chronic stress on memory and neurogenesis in birds[END_REF][START_REF] Favreau-Peigné | Unpredictable and repeated negative stimuli increased emotional reactivity in male quail[END_REF][START_REF] Favreau-Peigné | Emotionality modulates the effect of chronic stress on feeding behaviour in birds[END_REF][START_REF] Laurence | Short-and long-term effects of unpredictable repeated negative stimuli on Japanese quail's fear of humans[END_REF][START_REF] Calandreau | Effect of one week of stress on emotional reactivity and learning and memory performances in Japanese quail[END_REF][START_REF] Guibert | Unpredictable mild stressors on laying females influence the composition of Japanese quail eggs and offspring's phenotype[END_REF]. Stressors were delivered directly in the quail's home cage: 30 min of confinement of the bird in the corner of its home cage, 30 min of food frustration by placing transparent devices on the feeder just before light on, 2×15 min of soft home cage shaking, interspaced by 15 min, unpredictable sounds (100 dB) with or without biological significance for quail. Outside disturbances were also delivered, such as a passage of a plastic stick on the home cage rods (twice a day for 2 min), sprayings of air or water on feathers (2 sprays interspaced by 2 min), and 2-min of waving a plastic flag in front of the home cage. Birds were also moved outside their homecage and individually transferred into a new environment for 30 min.

The control group was left undisturbed and underwent similar routine husbandry procedures as CUS birds. The experimenters regularly visited the control quail room to ensure they have spent the same amount of time with control and chronically stressed birds. Systematically, when stressors prevented CUS birds from feeding, an opaque device was also placed on the feeder of control birds to prevent them from feeding for an equivalent amount of time.

Learning and memory task

The following procedures and the arena used for the task were previously used and validated by our group in chickens and quail [START_REF] Lormant | Research Note: Role of the hippocampus in spatial memory in Japanese quail[END_REF][START_REF] Lormant | A trait for a high emotionality favors spatial memory to the detriment of cue-based memory in Japanese quail[END_REF][START_REF] Ferreira | Social motivation and the use of distal, but not local, featural cues are related to ranging behavior in free-range chickens (Gallus gallus domesticus)[END_REF][START_REF] Lormant | Emotionality modulates the impact of chronic stress on memory and neurogenesis in birds[END_REF].

At the end of the CUS procedure, 17 control and 20 chronically stressed birds were submitted to a training procedure followed by a series of memory tests (Figure 1). The CUS procedure was stopped before the start of the training for two main reasons: the first one was to dissociate the effects of chronic stress from those of a possible acute, immediate stress that could occur if animals were stressed just before the training/testing sessions. The second reason is that, since the behavioral and cognitive effects of CUS in quails can be seen long after the procedure stopped [START_REF] Favreau-Peigné | Emotionality modulates the effect of chronic stress on feeding behaviour in birds[END_REF][START_REF] Laurence | Short-and long-term effects of unpredictable repeated negative stimuli on Japanese quail's fear of humans[END_REF][START_REF] Calandreau | Higher inherent fearfulness potentiates the effects of chronic stress in the Japanese quail[END_REF], the stopping of CUS allowed us to test for the long-term consequences and implications of the procedure in animals' learning and memory.

After preliminary phases dedicated to familiarizing birds to the food reward (mealworms), the cups and the arena, control and chronically stressed birds were submitted to a dual spatial/cued task during five days that consisted of learning to find food (mealworm) hidden in a single cup (target cup) in an arena that contained eight opaque cups. On each trial, birds were introduced in the arena by a different starting point among three possibilities. The location of the target cup was constant among trials and days of training, allowing animals to solve the task using spatial memory. Moreover, the target cup was black, whereas other non-target cups were white. Thus, birds could also solve this task using their cue-based memory, based on the color of the cup.

The day after the fifth day of training, birds were submitted to a spatial test followed by a to keep birds motivated. The day after, birds were submitted to a new test, called the "color test", to assess whether birds found the target cup by a simple association between its color (black) and the reward or by learning a more general rule that the target cup was of a different color compared to the remaining cups.

During all the memory tests, behaviors of quail were recorded by a camera placed above the apparatus and computerized by a tracking video system (Ethovision XT; Noldus IT, The Netherlands).

2.4.

The dual spatial/cued task

Mealworms familiarization

One week before the beginning of training, a brown ceramic cup (6x7cm) was placed in each quail's home cage. For seven days, three times per day (spaced about 3h), 3 or 4 mealworms were deposited in the cup to familiarize birds with the cups and mealworms.

Habituation

After familiarization, birds were submitted to the habituation phase. Birds were food-deprived one hour before each habituation session. During five days, once a day, each quail was introduced into the center of a beige octagonal arena (120cm long; 50cm high) surrounded by a blue curtain (1.90m high) and lighted by a bulb at the ceiling (18 Lux). Eight black cues were disposed of in the arena, four on the walls and four on the curtain. Eight ceramic cups similar to those used for the familiarization were placed in the arena and contained mealworms. Four cups were wrapped with white paper, whereas black paper wrapped another four cups. In each habituation session, exploration of the arena and the cups was allowed until the bird found and ate all mealworms or after a maximum time of 600 s. Between each quail and day of habituation, the position of black and white cups was randomly moved. The number of cups visited was recorded on each day to estimate the level of exploration and habituation of the birds (Figure 1).

Training

On each day of training, birds were submitted to 2 training trials per day (1 hour between each trial). On each trial, birds were introduced in the arena by a different starting point among three possibilities (Figure 1). Only one cup was rewarded and contained three mealworms (target cup). The target cup was always black and at a fixed position on each trial, whereas the seven other cups were non-rewarded and white. When the bird reached the target cup and ate the mealworms, the trial was ended, or after a maximum time of 300 s. If the bird did not find the target cup, the experimenter gently guided it and allowed it to eat the mealworms. The bird returned to its home cage between each trial. The latency to find and eat the food in the rewarded black cup was recorded on each trial.

The spatial test

This test was used to assess the impact of chronic stress on spatial memory. Each bird was allowed to freely explore the arena and the 8 cups for 1 minute. All cups were empty and, more importantly, of similar color (white). The number of cups visited (number of errors) before reaching the target cup was scored. Moreover, the arena was virtually divided into four equal quadrants, and the time spent in each quadrant (spatial quadrant and three other quadrants) during the test was measured using the tracking video system (Figure 1).

The spatial test was conducted after 5 and 15 days of training (initial training and overtraining, henceforth) and after an emotional challenge by exposing birds to an open field (emotional challenge, henceforth).

The displacement test

The displacement test was used to assess the impact of chronic stress on the use of spatial and cue-based memory systems. It was systematically conducted 30 min after the spatial test. During this test, similarly to the training phase, the arena was equipped with one black cup and seven white cups, but all cups were empty of food. The black cup was displaced to a new position (Figure 1). The new position was changed for each test conducted after initial training, overtraining, and emotional challenge. All birds were allowed to explore the arena and the cups for 1 min freely. The number of errors before reaching the spatial and displaced cup was scored. Similar to the spatial test, the arena was virtually divided into four equal quadrants (spatial quadrant, displaced cup quadrant, and two other quadrants), and the time spent in each quadrant during the test was measured using the tracking video system. The time spent on the two other quadrants was averaged.

Tests after an emotional challenge

The third occasion of both tests (spatial and displacement tests) was conducted after an emotional challenge. This emotional challenge consisted of exposing the birds to an open field test, which is known to provoke anxiety-like quail behaviors [START_REF] Lormant | Emotionality modulates the impact of chronic stress on memory and neurogenesis in birds[END_REF][START_REF] Boulay | A higher inherent trait for fearfulness is associated with increased anxiety-like behaviours and diazepam sensitivity in Japanese quail[END_REF][START_REF] Parois | The influence of a probiotic supplementation on memory in quail suggests a role of gut microbiota on cognitive abilities in birds[END_REF]. Each quail was individually placed in the center of a square arena (80 × 80 × 80 cm) of white wood with a beige linoleum floor. The arena was placed into a new experimental room, surrounded by a green curtain and strongly lighted (50 lux).

Each quail was allowed to explore the arena for 5 min before being withdrawn from the arena. Thirty

minutes after the open field test, quail were submitted to a spatial test followed by a displacement test.

The color test

This test aimed to assess whether birds learn to find the target cup by a simple association between its color (black) and the reward or by detecting the difference of colors between the target cup and the remaining cups (Figure 1). Each bird was allowed to freely explore the arena and the eight cups for 1 minute. All cups were empty and black, except for one white cup and not located in the spatial position used for training. The number of visits before reaching the white cup was scored.

Moreover, the number of visits before reaching the black cup located in the spatial location used during training was scored and compared to those to reach the white cup and other black cups. As for previous tests, the time spent in the four equal quadrants (white cup quadrant, spatial black cup quadrant, and two other black cup quadrants) of the arena was measured. This test was performed after a second recall training day conducted the day after the third test occasion.

Statistical analysis

Data from habituation (number of cups visited) and from training (latency to reach the target cup) were analyzed by parametric analyses of variance (ANOVA repeated measures) with stress group (control and chronically stressed quail) as between-subject factors and days (mean values for both training trials within days) as within-subject factors.

For spatial, displacement, and color tests, the number of errors (or visits) before reaching the target cup(s) and the time spent in the different quadrant of the arena were analyzed by ANOVA with groups (control and stressed) as between-subject factors and test occasions (test after initial training, test after overtraining and test after the emotional challenge) as within-subject factors. Additional within-subject factors (e.g., quadrants or cups considered) were also included, dependent on the variables under consideration. Exploratory analyses showed that when the time spent on quadrants was averaged (the three "other quadrants" during the spatial test and the two "other quadrants" during the displacement and color tests), the results were similar than when quadrants were analyzed separately.

To facilitate data comprehension and visualization, we therefore kept and included only the averaged time in other quadrants in our analyses. When main effects or interactions were significant, analyses were followed by multiple comparisons corrected by Tukey HSD.

When an individual did not find a target cup and visited less than 4 cups, it was attributed a number of 8 errors and an equivalent time of 15 sec in each quadrant. All statistical analyses were performed using IBM SPSS 21 and R version 3.6.1. Statistical significance was set at p < 0.05.

RESULTS

Effect of chronic stress on habituation and training in the dual spatial/cued task

During habituation, both groups visited more cups and ate significantly more mealworms over days without any differences between control and chronically stress quail (effect of days: F 4,140 =18.15, p<0.001; effect of stress: F 1,35 =0.13, p=0.72; interaction days x stress: F 4,140 =1.11, p=0.35; Figure 2a).

During the initial training period, both groups similarly learned the location of the target cup since the latency to find the target cup significantly decreased over days (effect of days: F 4,140 =19.60, p<0.001; effect of stress: F 1,35 =0.28, p=0.60; interaction days x stress: F 4,140 =0.93, p=0.45; Figure 2).

Similarly, during the overtraining period, the latency to reach the target cup significantly decreased without any effect of chronic stress (effect of days: F 9,315 =5.89, p<0.001; stress effect: F 1,35 =0.13, p=0.73; interaction days x stress: F 9,315 =0.58, p=0.81; Figure 2b).

The latency between the last day of overtraining (the fifteenth day of training) and the recall period between tests (last two additional days) was not different between days, nor between control and chronically stressed quail (effect of days: F 2,70 =1.12, p=0.33; effect of stress: F 1,35 =0.65, p=0.42; interaction days x stress: F 2,70 =1.10, p=0.33), indicating that animals reached a learning plateau.

Effect of chronic stress during the spatial test

The number of errors made before reaching the spatial cup was significantly increased in chronically stressed quail (effect of stress: F 1,35 =5.62, p=0.023, Figure 3a), independently of the test occasion and the interaction with stress (effect of test occasion: F 2,70 =1.45, p=0.24; interaction test occasion x stress: F 2,70 =2.72, p=0.072).

The time spent in the spatial quadrant compared to other quadrants was affected by the quadrant considered, stress, and the interaction quadrant, test occasion, and stress (effect of quadrant:

Effect of chronic stress during the displacement test

Analyses on the number of errors made before reaching the target cups (either the spatial cup or the displaced cup) showed that animals were more performant to reach the displaced cup, independent of their treatment group. There was also a significant interaction between the test occasion and the target cups (effect of target cup: F 4a). Between test occasions and within-target cup, post-hoc comparisons revealed that animals made significantly fewer errors before reaching the displaced cup during the tests after overtraining and after the emotional challenge, compared to the test after initial training (p=0.001 and p=0.005, respectively, Figure 4a).

Analyses on time spent in different quadrants showed that animals did not spend their time evenly between quadrants. They spent significantly more time than the chance level in the displaced cup quadrant independent of their treatment group. There was also a significant interaction between the test occasion and the time spent in different quadrants (effect of quadrants: F in the displaced cup quadrant was higher for all test occasions compared to those spent on the spatial quadrant and other quadrants (p<0.05, for the tests performed after initial training, overtraining, and just after the emotional challenge, respectively, Figure 4b).

Effect of chronic stress during the color test

During the colour test, independently of chronic stress, the number of visits made before reaching a cup was different depending on the cup considered (effect of cup: F 2,70 =18.60, p<0.0001; effect of stress: F 1,35 =0.03, p=0.87; interaction cup x stress: F 2,70 =0.06, p=0.95; Figure 5). Post-hoc analyses showed that the number of visits before reaching the spatial black cup was significantly lower than those performed before reaching other black cups or the white cup (p=0.02 and p<0.001, respectively, Figure 5a).

Finally, the time spent in the different quadrants was also significantly dependent on the quadrant considered (effect of quadrants: F 2,70 =3.62, p=0.032). Post-hoc analyses revealed a significantly larger amount of time spent in the black spatial cup quadrant compared to other black cups and white cup quadrants (p=0.01). No differences were found between the treatment groups, nor for the interaction quadrants and treatment groups (effect of stress: F 1,35 =1.02, p=0.31; interaction quadrants x stress: F 2,70 =0.59, p=0.55, Figure 5b). (n control quail = 17, n chronically stressed quail = 20)

DISCUSSION

Since the majority of the studies investigating the relationship between chronic stress and training level comes from studies on rodents, until now, we have had little understanding on how these interactions apply to other taxa, such as birds. To fill this gap, using Japanese quail as the animal model, the main objective of the present study was to address whether chronic stress can differentially impact two forms of long-term memory, the declarative spatial and the non-declarative cue-based memories, according to the level of training. To this end, control and chronically stressed Japanese quail were trained in a task that could be solved using spatial and cue-based memory, the dual spatial/cued task, and tested for their performances on each type of memory after 5 and 15 days of training (initial training and overtraining, respectively). Our results showed that the spatial memory of quail was sensitive to the deleterious effect of chronic stress. Performances of chronically stressed birds during the spatial test after initial training were impaired compared to that of control birds.

During the test after overtraining, however, these differences were lowered. Interestingly, during the test after an emotional challenge, when birds were exposed to an open field, the negative impact of chronic stress on spatial memory performance seemed to be fully reinstated. These findings indicate that the relationships between chronic stress, training level, and spatial memory are complex and dynamic. By contrast, cue-based memory was not affected in chronically stressed birds and control birds on any test occasion, which indicates that this form of memory is relatively resistant to the negative effect of chronic stress. Finally, a test conducted to better qualify birds' cue-based memory performance, the color test, provided support that chronically stressed birds were still able to efficiently remember the spatial location of the target cup when this location was signaled by a color similar to that used during training. This suggests that chronically stressed birds cannot efficiently recall stored spatial information in a context different from the one experienced during training, which highlights that chronic stress reduces the ability to use spatial memory flexibly.

Our results from habituation showed that the number of mealworms eaten increased over days without any significant effect of chronic stress. Moreover, the latency to reach the target cup decreased over days of training in both groups. These findings indicate that both control and chronically stressed birds similarly habituated to the arena and the cups and similarly learned the dual spatial/cued task.

They also indicate differences between groups observed during the different tests performed could not be attributed to evident motor or motivational differences and instead reflect stress-related changes in memory performance.

During the spatial test, the number of errors before reaching the spatial location of the target cup was higher in all test occasions (initial training, overtraining, after an emotional challenge) for chronically stressed birds compared to control birds, evidencing the negative impact of chronic stress on spatial memory performance. Although not significant (p=0.07), the interaction between test occasion and stress suggests that the performance of both groups during overtraining tests was similar, compared to initial training and after emotional challenge tests, when their performances differed more. Confirming this tendency, the time spent in different quadrants revealed some specificities of the animals' performance over the different test occasions. During spatial tests performed after initial training, chronically stressed birds spent their time evenly between the quadrants, while control birds spent significantly more time in the spatial quadrant. The same did not occur after overtraining.

Indeed, performances of both control and chronically stressed birds were relatively low during the spatial test performed after overtraining. Contrasting the spatial tests, chronic stress did not affect cuebased memory performance in all displacement tests performed. On all test occasions, chronically stressed birds and control had the same performance and followed the displaced black cup preferentially. These findings show that, similar to what is already known in rodents, chronic stress in birds differentially impacts memory depending on the memory system, with the spatial memory being much more sensitive to the adverse effects of chronic stress than cue-based memory [START_REF] Conrad | A critical review of chronic stress effects on spatial learning and memory[END_REF][START_REF] Goodman | Impaired spatial memory and enhanced habit memory in a rat model of post-traumatic stress disorder[END_REF][START_REF] De Quervain | Stress, glucocorticoids and memory: Implications for treating fear-related disorders[END_REF]. They also confirm that more complicated tasks are more impacted by chronic stress than simple tasks [START_REF] Sandi | Stress and cognition[END_REF],

corroborating our previous work on chickens and quail where we found different level of cognitive performance between animals from different groups (hippocampal lesioned vs non-lesioned, stressed vs non-stressed, fearful vs non-fearful, exploratory vs non-exploratory) only during complex tasks, which are more cognitively demanding, rather than during simple tasks, where animals may use stimulus-response strategies [START_REF] Lormant | Research Note: Role of the hippocampus in spatial memory in Japanese quail[END_REF][START_REF] Ferreira | Relationship between ranging behavior and spatial memory of free-range chickens[END_REF][START_REF] Ferreira | Social motivation and the use of distal, but not local, featural cues are related to ranging behavior in free-range chickens (Gallus gallus domesticus)[END_REF][START_REF] Lormant | Emotionality modulates the impact of chronic stress on memory and neurogenesis in birds[END_REF][START_REF] Ferreira | Uninhibited chickens: ranging behaviour impacts motor self-regulation in free-range broiler chickens ( Gallus gallus domesticus )[END_REF][START_REF] Ferreira | Range use is related to free-range broiler chickens' behavioral responses during food and social conditioned place preference tests[END_REF].

The low spatial memory performance after overtraining for control individuals may be explained by a progressive acquisition of a predominant cue-based strategy to solve the spatial test over days of training. In mammals, it is well documented that the involvement of memory systems is a dynamic process that can vary over time of training. In particular, overtraining in mammals favours cue-based memory [START_REF] Hicks | Effects of Overtraining on Acquisition and Reversal of Place and Response Learning[END_REF][START_REF] Packard | Anxiety, cognition, and habit: A multiple memory systems perspective[END_REF][START_REF] Yin | The role of the basal ganglia in habit formation[END_REF][START_REF] Packard | Inactivation of hippocampus or caudate nucleus with lidocaine differentially affects expression of place and response learning[END_REF]. In line with this, the number of errors to reach the black target cup during the displacement tests decreased with training, evidencing an improvement of this type of memory over time. These results highlight the importance of taking training level into account when testing memory in birds, since, similar to mammals a progressive shift from declarative, spatial memory use to a non-declarative, cue-based memory use may occur.

An alternative explanation for these results could be that extensive exposure to the task was perceived, by the control animals, as stressful on its own, in other words, control quail may have become stressed with overtraining which caused a worsening in their performance. In previous work we confirmed that stressed quail have poor spatial memory, but improved cue-based memory [START_REF] Lormant | Emotionality modulates the impact of chronic stress on memory and neurogenesis in birds[END_REF],

which gives support to this alternative hypothesis. If indeed stress from overtraining is behind the worsening in spatial memory for control individuals, it is noteworthy to state that the different stressors (overtraining vs chronic stress) were probably perceived differently by the birds, since the differences between control and chronically stressed quail on spatial memory performance were restored after an emotional challenge. Once more, our results give us further understanding of the relationship between chronic stress and memory in birds and reveal it is not that different from that of mammals: previous studies in rodents highlighted that acute stress or stress hormone injections experienced just before testing can induce the reemergence of an extinguished memory [START_REF] Deschaux | Postextinction fluoxetine treatment prevents stress-induced reemergence of extinguished fear[END_REF][START_REF] Izquierdo | Stress hormones enhance retrieval of fear conditioning acquired either one day or many months before[END_REF].

Another possibility for the suddenly use of spatial information in control birds is that, exposure to an open field is also an exposure to a novel spatial environment, that induces hippocampus dependent mechanisms. Such process may contribute to reactivate hippocampus dependent spatial memory in control birds. More studies need to be done to disentangle these two hypotheses. In any case, the worsening in performance of control birds after an extending period of training seems to not be due to an elimination of the spatial memory, which can re-emerge depending on the more immediate psychological state of the quail. Moreover, the reappearance, even after overtraining, of chronic stress adverse effects on the spatial memory of stressed quail could indicate a long-lasting sensibility of this memory system.

Finally, our study highlighted that chronic stress affected specific characteristics of spatial memory but did not make it impossible for stressed animals to have this form of memory. Indeed, in the last test, the color test, control, and chronically stressed birds made fewer visits before reaching the black cup located in the same spatial location used during training. Chronically stressed birds seemed thus able to remember the location of the target cup when this cup was black, as during training. It suggests that chronic stress specifically impaired the capacity of birds to remember a previously learned location when this location was no more signaled by the cue (black color). This capacity is crucial to perform the spatial test efficiently. Numerous studies conducted in chicks and quail evidenced that spatial memory critically requires the hippocampus [START_REF] Lormant | Research Note: Role of the hippocampus in spatial memory in Japanese quail[END_REF][START_REF] Lormant | Emotionality modulates the impact of chronic stress on memory and neurogenesis in birds[END_REF][START_REF] Morandi-Raikova | Selective activation of the right hippocampus during navigation by spatial cues in domestic chicks (Gallus gallus)[END_REF]. In mammals, the hippocampus was shown to be critically involved in forming distinct memories from events with a high level of similarities (pattern separation) and to re-establish previously acquired memory by using incomplete information as recall cues (pattern completion) [START_REF] Johnston | Paradox of pattern separation and adult neurogenesis: A dual role for new neurons balancing memory resolution and robustness[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]. During spatial tests, the spatial location was no more signaled by the cue (all cups were white). Birds should re-establish the complete spatial-cued information learned during training (one rewarded black cup among seven non-rewarded white cups) from this incomplete information. Under this framework, chronic stress, which negatively affects the hippocampus's functioning in quail [START_REF] Lormant | Emotionality modulates the impact of chronic stress on memory and neurogenesis in birds[END_REF], may impair hippocampus-dependent processing of pattern completion.

In conclusion, the present study presents evidence that chronic stress can negatively impact the birds' declarative spatial memory. It highlights that chronic stress specifically alters the ability to use spatial memory flexibly. Also, we show for the first time in birds that an extended period of training can lower the spatial memory differences between control and chronically stressed individuals, either by a progressive shift to cue-based memory or by the fact that overtraining can be in itself perceived as stressful by control animals, hindering their performance. These hypotheses should be tested further.

Before testing, an emotional challenge was capable of reinstating the differences in performance between chronically stressed and control quail, showing the spatial memory system's long-lasting sensibility. To our knowledge, this is the first work that highlights the dynamic dialogue between stress, training level, and memory systems in birds, increasing the scientific understanding of these interactions and their applicability to other taxa than mammals.

  displacement test. During the spatial test, all cups were white, and the task could only be solved by remembering the spatial location of the target cup during the training phase. During the displacement test, performed 30 min later, the black target cup was displaced to a new position (different from the position used during training). Thus, both spatial and cue-based memories could provide a solution to the test. Quail could go back either to the spatial location of the target cup during the training phase (spatial memory) or go directly to the displaced black cup (cue-based memory) [16,17,19]. After this first test occasion, birds were trained for ten additional days in the dual spatial/cued task before being submitted to the second test occasion of spatial and displacement tests. Following the second test occasion, birds were trained for an additional day (recall training) in the dual spatial/cued task to avoid that lack of food during the second test occasion reduced bird motivation in subsequent tests. After this first recall training day, all birds were challenged by exposure to an open field test and were submitted to the third occasion of spatial and displacement tests. On the following day, quail were submitted to a second additional training day (recall training)

Figure 1 :

 1 Figure 1: Schedule of the experiment and schematic representation of the apparatus used for training and testing. At three-weeks-old, quail were submitted to chronic unpredictable stress (CUS) for three weeks. At the end of the CUS procedure, stressed and control birds were trained in a dual spatial/cued learning task and submitted to spatial and displacement memory tests after 5 and 15 days of training. After an additional training day, quail were submitted to an emotional challenge (exposure to an open field) followed by a new series of tests. After the last training day, birds were submitted to the color test. Quail could enter the arena from three different starting points (dark arrows), three starting points during training, and one during testing. The dotted lines represent the division of apparatus into four different quadrants.

Figure 2 :

 2 Figure 2: Effects of chronic stress on habituation and training performances in the dual spatial/cued task. a) Number of visited cups over days of habituation in control and chronically stressed quail. During habituation, all eight cups were rewarded with mealworms. b) Latency to reach the location of the target cup over days of training in control and chronically stressed quail. Mean ± SEM are given. (n control quail = 17, n chronically stressed quail = 20)

F 1 ,

 1 35 =7.58, p=0.009; effect of stress: F 1,35 =6.13, p=0.018; interaction quadrant x test occasion x stress: F 2,70 =3.36, p=0.040). Post-hoc analyses revealed that, during the tests after the initial training and the emotional challenge, control birds spent significantly more time in the spatial quadrant compared to other quadrants (p<0.001 and p=0.001, for the tests after the initial training and the emotional challenge, respectively). In contrast, chronically stressed birds spent an equivalent time in the spatial quadrant compared to other quadrants (p=1 and p=1 for the tests after the initial training and the emotional challenge, respectively, Figure3b). No differences were found for the test after overtraining: both control and chronically stressed quail spent an equivalent time in the spatial quadrant compared with other quadrants (p=1 and p=1, for control and chronically stressed quail, respectively, Figure3b).

Figure 3 .

 3 Figure 3. Effects of chronic stress in spatial memory tests performed after initial training (5 days), overtraining (15 days), and after an emotional challenge (EC, exposure to an open field). a) Number of errors before reaching the spatial location of the target cup, and b) Time (in seconds) spent in different quadrants of the arena (spatial and other quadrants). † p≤0.05, significant difference between control and chronically stressed quail. ** p≤0.01; *** p≤0.001, significant difference between the spatial quadrant and other quadrants. Mean ± SEM are given. (n control quail = 17, n chronically stressed quail = 20)

Figure 4 .

 4 Figure 4. Effects of chronic stress in the displacement tests performed after initial training (5 days), overtraining (15 days), and after an emotional challenge (EC, exposure to an open field). a) Number of errors before reaching the target cups (spatial cup or displaced cup), and b) Time (in seconds) spent in different quadrants of the arena (either the spatial quadrant, SQ, the displaced cup quadrant, DQ, or the other quadrants, OQ). * p≤0.05; ** p≤0.01; *** p≤0.001, significant difference between the spatial cup and displaced cup or between spatial quadrant, displaced cup quadrant, and other quadrants. Mean ± SEM are given. (n control quail = 17, n chronically stressed quail = 20)

Figure 5 .

 5 Figure 5. Effects of chronic stress on the color test. a) Number of visits before reaching the white cup, the black spatial cup, or other black cups in chronically stressed and control birds and b) Time (in seconds) spent in different quadrants of the arena containing the white cup, the spatial black cup, other black cups in chronically stressed and control birds. * p≤0.05; ** p≤0.01; *** p≤0.001, significant difference between the white cup, black spatial cup, and other black cups. Mean ± SEM are given.

  These quail had a controlled and known genetic background, being selected over 60 generations to have intermediate, control-like levels of fear/anxiety responses (intermediate durations of tonic immobility)[START_REF] Calandreau | Higher inherent fearfulness potentiates the effects of chronic stress in the Japanese quail[END_REF][START_REF] Laurence | Japanese Quail's Genetic Background Modulates Effects of Chronic Stress on Emotional Reactivity but Not Spatial Learning[END_REF].One-day old chicks were placed on communal floor pens. When chicks reached 21 days old,

	they were sexed by feather dimorphism, and males were transferred to a single home cage (41×51×25
	cm), under a 12:12h light-dark schedule (light was on at 0800h). Artificial turf was available on the
	cage floor, allowing animals to dust bathe. The ambient temperature was kept at approximately
	20±2°C. Food and water were provided ad libitum.
	Animal care and experimental treatments followed the French Ministry of Agriculture
	guidelines and European regulations on animal experimentation (86/609/EEC). They also followed
	local animal regulations (C37-175-1) of the French Ministry of Agriculture under the EEC directive
	and under ethics committee approval (agreements N° 1789 and 1848, Val de Loire).

Expérimentation Avicole de Tours (UE PEAT, INRAE, 2018. Experimental Poultry Facility, DOI: 10.15454/1.5572326250887292E12) where the experiment took place.

  occasion x stress: F 4,140 =0.42, p=0.78). Post-hoc analyses revealed that the time spent

	2,70 =67.19, p<0.001;
	effect of test occasion: F 2,70 =0.82, p=0.44; effect of stress: F 1,35 =3.81, p=0.059; interaction quadrants x
	stress: F 2,70 =1.09, p=0.34; interaction quadrants x test occasion: F 4,140 =3.25, p=0.014. interaction
	quadrants x test

ACKNOWLEDGEMENTS

This work was supported by the Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (INRAE). FL and VHBF were supported by the French Region Centre Val de Loire and from JUNIA ISA, respectively. We are grateful to all the experimental unit PEAT members of INRAE (UE PEAT, INRAE, 2018. Experimental Poultry Facility, DOI: 10.15454/1.5572326250887292E12) and particularly to I. Grimaud for providing care to the birds.

BIBLIOGRAPHY