Benjamin Somers

Fabien Dagnat

Jean-Christophe Bach

Modeling heterogeneous IT infrastructures: a

Keywords: IT infrastructure modeling, Collaborative infrastructure design, Heterogeneous models, Multi-viewpoint design, Model federation

HAL is

Introduction

The evolution of IT infrastructures has followed a tendency towards abstraction. This has enabled the development and deployment of enterprise-wide systems with greater flexibility and scalability, in order to fulfill a wider variety of needs. These advances come however at the cost of increased architectural complexity [START_REF] Urbach | Transformable IT Landscapes: IT Architectures Are Standardized, Modular, Flexible, Ubiquitous, Elastic, Cost-Effective, and Secure[END_REF]. In some domains such as banking, healthcare and defense, the lifetime of IT infrastructures can span over decades, combining the old and the new. Modern cloud-native IT infrastructures are therefore often built upon so-called legacy systems, with which they need to interact to perform business-critical operations.

Yet, IT infrastructures do not depend exclusively on the proper operation of hardware and software: the human factor and the good performance of business processes must be taken into account. This is where Enterprise Modeling comes into the picture [START_REF] Vernadat | Enterprise modelling: Research review and outlook[END_REF], with two major challenges. First, technical infrastructures tend to be stacked in technical layers (such as hardware, software, network...) while human organizations tend to be divided in business domains (financial, cloud operations, support...). These technical layers, however, do not necessarily match the business ones, and vice versa; this disalignment must be taken into account in the modeling. Second, all the stakeholders should be included in the modeling process [START_REF] Voinov | Modelling with stakeholders[END_REF], to properly capture the interactions within the infrastructure and the related responsibilities. The different stakeholders, however, have different skill sets, reflected in the use of different tools and a different jargon from other employees [START_REF] Amaral | Recent Advances in Multi-paradigm Modeling[END_REF], making model alignment process even more complex.

It is important to gather this knowledge around a common model to have a good overview of IT infrastructures and to conduct analyses covering several technical and administrative domains. This article proposes a component-oriented metamodel that takes into account the various perspectives of such infrastructures. We argue that a complete and correct model is achieved by considering all of these viewpoints and better integrating the responsibilities of each stakeholder.

To this end, we present the theoretical background of our study in section 2. Section 3 describes the metamodel we use. In section 4, we propose a collaborative methodological framework to encode business processes and technical jobs in this metamodel. We present a case study in section 5 to illustrate our approach by reasoning on a model. Finally, section 6 concludes this article.

Related work

Models convey domain-specific knowledge in a broad range of fields through abstraction of concepts. From technical to administrative areas, with various levels of granularity, they are an integral part of today's businesses [START_REF] Kulkarni | Modelling and enterprises -the past, the present and the future[END_REF]. IT infrastructure modeling combines the concepts of enterprise modeling and IT models, linked together to provide a complete picture.

Enterprise modeling

Enterprise modeling has undergone major evolutions since its inception [START_REF] Vernadat | Enterprise modelling: Research review and outlook[END_REF]. Models have become "active" [START_REF] Greenwood | Active Models in Business[END_REF], to manage the complexity of interactions across diverse business domains. Combined with formal methods, high-fidelity models can bring great value to companies adopting them [START_REF] Cohn | The rise of the model-driven enterprise[END_REF]. However, due to the wide range of professions and tools used within these companies, such models are complex to produce and discrepancies occur [START_REF] Van Der Aalst | Business Process Management: A Comprehensive Survey[END_REF].

Standards such as ISO 19439 [START_REF]International Organization for Standardization: ISO 19439:2006 -Enterprise integration -Framework for enterprise modelling[END_REF] propose to model enterprises in (at least) four views (process, information, infrastructure and organization) on three modeling levels (requirements, design and implementation). Such a layered approach is found in many modeling frameworks, among which we can mention RM-ODP [START_REF]Reference Model of Open Distributed Processing (RM-ODP[END_REF] and Archimate [START_REF]The Open Group: ArchiMate ® 3.1 Spec[END_REF]. These frameworks provide a high-level way to model enterprises and allow to represent the infrastructures on which businesses lie. However, the large amount of concepts and the lack of precise semantics regarding IT infrastructures make the frameworks difficult to use in IT domains for people whose main job is not enterprise modeling [START_REF] Lantow | On the Heterogeneity of Enterprise Models: ArchiMate and Troux Semantics[END_REF]. Moreover, the matricial aspects of their approaches and their division into layers are not always suitable [START_REF] Jørgensen | Enterprise Modeling -What We Have Learned, and What We Have Not[END_REF].

IT models

Many languages and representations exist to describe IT infrastructures, from hardware to software, including networks and processes. A datacenter can be described by rack diagrams, illustrating the layout of servers and network components. A software can be represented using UML diagrams [START_REF]OMG: Unified Modeling Language (UML)[END_REF], to show its structure and the different interactions at work, or can be described by its code. A network topology can be seen as mathematical objects [START_REF] Salamatian | Hidden Markov Modeling for Network Communication Channels[END_REF][START_REF] Park | Self-Similar Network Traffic: An Overview[END_REF], described by switch configurations, or as code in Software-Defined Networking [START_REF] Masoudi | Software defined networks: A survey[END_REF]. These areas also benefit from many contributions from the formal methods communities (e.g. Alloy [START_REF] Jackson | Software Abstractions: Logic, Language, and Analysis[END_REF] to verify specifications or Petri nets [START_REF] Murata | Petri nets: Properties, analysis and applications[END_REF] to model complex behaviors).

However, most of these frameworks cannot interact with one another. This limit appears if we ask questions that cross several domains, such as "which services become non-operational if we unplug this cable?" [START_REF] Neville-Neil | I Unplugged What?[END_REF] or "is my business domain impacted by this router vulnerability?".

Collaborative modeling

Domain-specific languages are adapted to their respective domains [6] and can represent in detail things that holistic frameworks cannot. But these languages are sometimes not understandable by other parties. It leads to many metamodels [START_REF] Kaczmarek | Ontologies in the realm of enterprise modeling -a reality check[END_REF], often sharing the same core concepts [START_REF] Breton | An Overview of Industrial Process Meta-Models[END_REF], being used to model enterprises.

The modeling process must come from a need and be undertaken by including all the professional disciplines concerned. However, due to a lack of modeling skills, some stakeholders are not able to participate in such a process [START_REF] Renger | Challenges in Collaborative Modeling: A Literature Review[END_REF]. Work in the model federation community [START_REF] Golra | Addressing Modularity for Heterogeneous Multi-model Systems using Model Federation[END_REF] (where we maintain links between models expressed in different metamodels) is a step towards including the expertise of such stakeholders. Other approaches, such as composition [START_REF] Fleurey | A Generic Approach for Automatic Model Composition[END_REF] (where we build a common metamodel to align models) and unification [31] (where we build a single model) are described in the literature.

Our proposal

In this article, we advocate a collaborative approach to IT infrastructure modeling. In this section, we present our metamodel (represented in figure 1) and detail its characteristics, before presenting its differentiating aspects.

Presentation of the metamodel

Our first contribution combines a responsibility-oriented metamodel (though simpler than what can be found in [START_REF] Feltus | ReMoLa: Responsibility model language to align access rights with business process requirements[END_REF]) and a component-oriented metamodel, all Fig. 1: Component-oriented metamodel within a reflexive unifying metamodel. Our metamodel is divided in three parts: the actors and their responsibilities (on figure 1a), the components and their instances (on figure 1c), and the additional links in the metamodel (on figure 1b).

Actors and responsibilities. In our metamodel, responsible entities have responsibilities over entities and can assume roles. Roles represent generic sets of responsibilities. They can be used to encode access rights on an information system or positions in a company's organizational chart for example. Actors represent the actual entities which can assume roles and have responsibilities.

They can be used for example to encode users, allowed to access specific servers because of their positions, or even a whole company, responsible for the proper functioning of the products it sells. Relationships represent all lines and arrows in models (and in the metamodel itself).

Components and instances. Our metamodel is focused on components. They provide services (actions or reactions to events) that can in turn use other components' actions through procotols. They can also contain resources, that may be entities providing services (components, such as a web server) or not (such as configuration files, web resources, or even the models themselves). Both resources and events can be the result of an action, so we decided to unify them under the effect kind. Three layers appear here:

-The Kind layer ( ), representing the core concepts of the metamodel; -The Type layer ( ), representing "types of" these concepts. For example, "physical server" is a type of component. To follow the UML notation, a component type is represented here as :Component. -The Instance layer ( ), representing "instances of" these concepts and types.

For example, a physical server is an instance of "physical server" and "check webpage availability" is a service instance. In our work, we have not encountered the need for service types. An instance of :Component is represented here as ::Component.

Metamodel links. Every Kind, Type and Instance of the metamodel is an instance of entity; it means that responsible entities can have responsibilities over them. For example, an actor can be responsible for the development of a software component (on the Type layer) and another can be responsible for its configuration and deployment (on the Instance layer).

A core feature of our metamodel is that responsible entities can be responsible of responsibilities themselves. It makes sense in a context of access management where a person may be responsible for the access right given to another person. As models can themselves be resources in the metamodel, it is easy to represent situations like an employee responsible for modeling a particular component. We think that both characteristics are distinctive features of our approach.

For consistency, responsible entities are also components and their behaviors can be encoded as services.

Collaborative modeling

Our second contribution is a collaborative modeling framework using this metamodel relying on three principles: non-intrusiveness, refinability and correctness.

Non-intrusiveness. Most technical IT domain have their own sets of tools and representations to convey information and model systems [START_REF] Amaral | Recent Advances in Multi-paradigm Modeling[END_REF]. Two persons in the same domain can understand one another thanks to this common jargon, but may struggle to interact with people in areas with which they are not familiar.

Working on small models whose boundaries are clearly defined enables experts to work collaboratively and still preserve the integrity and coherence of each model. Furthermore, these experts can take advantage of the most appropriate tools and techniques for their particular domains. Our metamodel intends to provide a framework adapted to linking these tools together, rather than replacing them. By allowing employees to work locally with their peers on models, we avoid the issues raised by [START_REF] Renger | Challenges in Collaborative Modeling: A Literature Review[END_REF] in the first phases of modeling. Refinability. To reflect the actual design process of its components, people involved in infrastructure modeling should be able to gradually refine their models. This can be done either in a top-down (where one adds details) or bottom-up (where one abstracts them) approach, or a combination of both, as shown in figure 2. In this example, a top-down approach would describe what a "service checker" is, by dividing it into sub-components (Service checker, made of Checker API and Logger) and then refining their services (check, history...). A bottom-up approach would be to describe the services wanted for a "service checker" and combining them into components and super-components providing them.

Iterative conception goes through a succession of incomplete models. When working on a new piece of software, a common approach consists in letting the end-users describe their needs and iteratively producing code that meets these needs [START_REF] Ruparelia | Software Development Lifecycle Models[END_REF]. The initial need may be very imprecise and high-level and may require several refinement steps during the project's lifespan.

"Holes" in models can also arise from blackbox software or hardware, or even legacy components whose knowledge has been lost, for example due to employee turnover. Even though the knowledge of an IT infrastructure is partial, properties can still be deduced. By allowing imprecision, the benefit is threefold:

-Coherence: instead of making wrong assumptions, modelers can express their lack of knowledge, limiting the number of inconsistencies between models; -Reconciliability: employees should not attempt to refine a component they are not responsible for, simplifying the reconciliation phase and ensuring that responsibilities are respected; -"Fail-early"-ness: as properties can be proved early in a project modeling, safety and security issues can be addressed from the first stages of development.

Correctness. In order to get a detailed view of an IT infrastructure, it is crucial to involve all its stakeholders in the process. Indeed, the sum of local viewpoints is not enough to produce an overall model: the reconciliation and the resulting links between models are essential. As the literature shows [START_REF] Nuseibeh | ViewPoints: meaningful relationships are difficult![END_REF], model reconciliation is a complex task, this is why we advocate to start the collaborative modeling process as soon as possible.

If the modeling is done according to these principles, model reconciliation is mainly a matter of refining black boxes in other models and linking them together, as illustrated in figure 3. Here, we have three points of view, from three teams. On the left, the organizational structure of a human resources department is represented. In the middle, we have the design of a web application using a remote database (not modeled) allowing some HR people (unknown at modeling time) to manage employees. On the right, there is a simple model of said database.

During model reconciliation, the teams align their vocabularies (Human resources and HR), specify black boxes (? becomes Personnel administration) and combine knowledge (10.2.42.1:3306 refers to mysql-02-01). There is no universal method to solve modeling conflicts, but we think that modeling in incremental steps avoids solving them on larger models. The reconciliation process itself may be modeled using our metamodel, by assigning responsibilities to the employees performing the reconciliation. When a model is updated, it becomes easy to know who performed the reconciliation and notify them to review whether the change invalidates their work or not. This idea, which to our knowledge has not been explored, ensures that the overall model remains correct in the long term.

Encoding business processes and technical jobs

Modern IT companies have a combination of business processes, which are more administrative in nature, and very detailed technical workflows. Choosing a holistic modeling framework that can cover all these aspects in detail seems unrealistic. A federated approach [START_REF]International Organization for Standardization: ISO 14258:1998 -Industrial automation systems and integration -Concepts and rules for enterprise models[END_REF] enables to take advantage of everyone's skills, while Fig. 4: An example of model reuse achieving a more thorough modeling. Such an approach keeps the metamodels and models of each stakeholder and proceeds by establishing semantics links between models. Yet, model interdependencies and inconsistencies can arise and hinder collaboration, especially when changes are made in one model that affect other models. As already stated in section 3.2, our framework makes it possible to detect and react to these changes.

In this section, we propose a methodological framework adaptable to concrete business processes, to build thorough yet accurate models using our metamodel.

Component catalogs

Companies usually design their systems by using external components. For hardware systems, most of them include commercially available off-the-shelf components sold by other companies. In the software domain, third-party libraries and packages are an integral part of modern systems.

This decentralized aspect of system design can be applied to infrastructure modeling: manufacturers can produce models for their systems and users can integrate these models into their infrastructure models. Such models can be made available in catalogs available internally within companies and externally to clients or for public use. The benefit is twofold. First, responsibilities and knowledge are better distributed: the models are produced by system designers, not the users. Second, the modeling process is sped up: model reuse, as would code reuse in software development, allows designers to build systems faster.

The two modeling steps, design and use, are illustrated in figure 4, where a company's Server 1 is build from other companies' Product α and OS 2. One can then instantiate this Server 1 architecture in their models without redesigning it.

A posteriori modeling

Understanding the orchestration of a company's business processes can help optimize the existing, as well as build the new, in a better controlled way. In the Fig. 5: A posteriori modeling. Step 1 corresponds to the inventory, step 2 is the modeling process and step 3 is the reconciliation.

banking industry for example, the use of legacy systems imposes technical choices that cannot be made without a good knowledge of existing architectures. Within the company's departments, this knowledge exists in diagrams, source code, configuration files... which first need to be identified (step 1 of figure 5). This step comes along with hardware inventories, if needed. Then, the identified elements are mapped onto our metamodel. Care must be taken to assign responsibilities to the model entities in the early stages of modeling (who owns which product, who develops which service, who is in charge of modeling which component...).

Step 2 of figure 5 illustrates this process. In an iterative way, business processes interacting with the modeled elements must be identified. Agile collaboration frameworks should be used to implement this process, as they promote collaborative, quick and iterative changes. This is represented in step 3 of figure 5.

A priori modeling

Accurate and complete models allow to better evaluate the financial and technical costs of projects, to optimize infrastructure dimensioning and to create safe and secure by design systems. Throughout the life of a project, it is important to ensure that a system does not deviate from its specification, for example due to a lack of communication, a misunderstanding or an urge to move too quickly. Verification of expert-defined properties on these models allows to ensure the conformity of such systems before their realization and helps to select a technical solution rather than another. For example, in a banking infrastructure, we could check that only certified personnel can access sensitive cardholder data.

A specification is seen in our approach as a model interface that technical proposals must implement. This extends the concepts from Object-Oriented Programming to infrastructure modeling. This concept of model typing is explored in [START_REF] Steel | On model typing[END_REF]. Our metamodel ensures the syntactic conformance of the models to the specification, but the semantic conformance must be verified by domain experts.

Case study

To illustrate our approach, let us now consider a fake banking company, called eBank. eBank provides banking and payment services to consumers and businesses. One of its flagship product, ePay, acts as a payment processor for companies and as an instant payment and expense sharing tool for consumers. The employees of the company want to have a better understanding of its overall processes and decided to use our approach to this end. In this section, we first make an inventory of the company's models. Then, we link these models together into our metamodel. Finally, we use the resulting big picture for a cross-model case study.

Heterogeneous models...

The company decided to start its modeling by focusing on ePay's environment, namely the company's organizational structure, the business processes around the product and the technical architecture. Each department uses domain-specific modeling tools, leading to different views of the overall infrastructure.

Organizational structure. eBank is structured in two directions: Technical and Administrative, each subdivided into structures, divided themselves into departments. An organizational chart of the company is given in figure 6.

Business processes. The company's activities are guided by various business processes. For the sake of brevity, we consider here only the equipment purchase process, represented in figure 7. For clarity, we do not show the exclusive gateways and assume requests to be automatically accepted. eBank has been using a task management solution for many years to track how many person-hours are needed for which projects. The solution is also used to know who is working on what at a given time. By reusing this software's database, employees created a catalog of common company tasks to predict their durations and help project planning. This catalog ranges from technical tasks, for example "commission a server", to administrative ones, for example "open a position". These two tasks are represented in figure 8.

Lastly, all financial transactions are managed by the finance department.

Technical architecture. eBank manages a datacenter hosting the hardware necessary for its activities. Some services are hosted on dedicated machines and others are on an internal cloud infrastructure. Due to time constraints, ePay has not been migrated to a modern cloud infrastructure yet. The service follows an active/passive architecture, where only one node operates at a time.

To check the proper functioning of its services in real time, the company has a monitoring infrastructure that measures availability and several key performance indicators. For business clients, ePay must process its requests within three seconds 99.9% of the time and must pay penalties in case of non-compliance.

... linked together

After several rounds of modeling, eBank's employees came up with the representation shown in figure 9. First, the organizational structure (figure 6) is partially mapped to the eBank component and its four sub-components representing structures and departments. The BPMN diagram (figure 7) adds the Hardware component type, along with the Maintenance and Usage responsibilities that the Cloud & Software structure and Hardware department have on this Hardware. The task catalog (figure 8) adds knowledge about the Hiring department and its Hiring responsibility. The task management solution (Task manager) keeps track of the time spent on the Usage and Maintenance of the Hardware and on the Hiring process, highlighting the particular nature of responsibilities in our metamodel. Finally, the Finance department is responsible for employees' Wage payment, for the Invoice payment of Hardware and the company's Financial obligation regarding its Payment processing's SLAs.

Exploiting the model

The company's real time monitoring has recently identified slowdowns in ePay on peak hours. Following our model, we can see that there is a potential impact on the Finance department of its Financial obligations. Some employees have suggested scaling the infrastructure before such slowdowns violate SLAs. One way to do so is to setup new Physical servers and change the overall architecture of the services. This new architecture is expected to mobilize part of the Cloud & Software structure for several months. The Human Resources structure proposes to either ignore the potential problem or to assign its teams on the scaling project (by hiring new staff, outsourcing some of the work or reassigning staff without additional hiring or outsourcing).

In figure 10, we explore our model to trace the paths between the slowdown and the Finance department, to identify potential financial impacts. To make its decision, the company has to compare (a) the financial impact if nothing is done (resulting from the Financial obligation) to (b) the financial impact of the improved architecture (resulting from Invoice payments and the personnel cost). The company does not have an outsourcing process, so the analysis of the Outsource branch, represented by "?" in the figure, cannot be performed. We have not detailed the Reassign branch because it is outside the scope of this article. However, its analysis is valuable to the company since the reassignment of staff would change the time allocated in the Task manager. This would consequently have indirect impacts on the Finance department (for example, failure to deliver new features to clients due to a lack of time, leading to increased customer churn).

By calculating the cost of each decision branch, which is partly automatable, the company can make a decision regarding this particular problem. In this article, we have presented a collaborative approach for IT infrastructure modeling. This approach consists of a generic metamodel aimed at linking models together, a better control of each stakeholder's responsibilities and methods inspired by software engineering to guide the modeling process. Our approach is not intended to replace established methods within companies, but rather to allow for analyses spanning across multiple models. Through model federation, we think that business modeling can include more stakeholders, while preserving the tools and models they are used to working with.

The validation of our approach is still a work in progress. To this end, we have developed an infrastructure modeling language and its compiler that can be linked to model checkers such as Z3 [START_REF] De Moura | Z3: An Efficient SMT Solver[END_REF]. While we have been able to verify consistency (for example, whether a set of configuration constraints is satisfiable) and safety properties (for example, whether a system is fault-tolerant) on small models, we are now focusing on scaling our tool to larger models. An important step in the validation of our approach is an industrial experiment, covering broad business domains, that we are currently drafting.

Fig. 2 :

 2 Fig. 2: Model for a generic service checker

Fig. 3 :

 3 Fig. 3: Model reconciliation with three points of view Legend. Reconciliation

Fig. 7 :

 7 Fig. 6: eBank's organizational structure

Fig. 8 :

 8 Fig. 8: Task catalog Legend. a b c d Sub-task (a: title, b: department, c: people, d: duration), End

Fig. 9 :Fig. 10 :

 910 Fig. 9: ePay's big picture. To help the reader, instantiations are blue () and responsibility links and their association classes are red ( and ).Payment processing Slowdown detected


MD Jean-Philippe Salaün
email: salaunjeanphilippe@gmail.com

PhD Audrey Chagnot

PhD Arnaud Cachia

Nicolas Poirel Phd

MD Valérie Datin-Dorrière

MSc Cléo Dujarrier

PhD Eloïse Lemarchand

MD Marine Rolland

PhD Lisa Delalande

MD PhD Pierre Gressens

MD Bernard Guillois

PhD Olivier Houdé

MSc Damien Levard

MD Clément Gakuba

Marine Moyon Phd

PhD Mikael Naveau

MD PhD François Orliac

MD PhD Gilles Orliaguet

MD Jean-Luc Hanouz

PhD Véronique Agin

PhD Grégoire Borst

Vivien Denis

Phd

Consequences of general anesthesia in infancy on behavior and brain structure. Running head: Pediatric anesthesia: lasting effects on brain

Question: Does early exposure to general anesthesia impact long-term behavior and brain structure in mice and humans?

• Findings: This is the first translational study that suggests lasting effects of early life exposure to anesthetics on later emotional control behaviors and brain structures, both in rodents and humans.

• Meaning: These considerations should stimulate the development of less brain toxic anesthetic agents and to avoid early exposure to general anesthesia whenever it is possible.

ABSTRACT

Background

One in seven children needs general anesthesia before three years old. Brain toxicity of anesthetics is controversial. Our objective was to clarify whether exposure to general anesthesia of the developing brain could lead to lasting behavioral and structural brain changes.

Methods

A first study was performed in mice. The behaviors (fear conditioning, Y-Maze and actimetry) and brain anatomy (high-resolution Magnetic Resonance Imaging) of six to eight weeks old Swiss mice exposed or not to general anesthesia from 4 to 10 days-old were evaluated. A second study was a complementary analysis from the pre-existing APEX cohort to assess the replicability of our data in humans. The behaviors (Behavior Rating Inventory of Executive Function emotional control and working memory score, Backward Digit Span and Raven 36) and brain anatomy (high-resolution Magnetic Resonance Imaging) were compared in 102 children 9 to 10 years exposed or not to a single general anesthesia (surgery) during infancy.

Results

The animal study revealed chronic exacerbated fear behavior in the adult mice (95% CI: 4 to 80; p=0.03) exposed to postnatal general anesthesia; this was associated with a 11% (95% CI: 7.5 to 14.5%) reduction of the periaqueductal grey matter (p=0.046). The study in humans suggested lower emotional control (95% CI: 0.33 to 9.10; p=0.06) and a 6.1% (95% CI: 4.3 to 7.8%) reduction in the posterior part of the right inferior frontal gyrus (p=0.019) in the children who had been exposed to a single general anesthesia.

Conclusion

The preclinical and clinical findings of these independent studies suggest lasting effects of early life exposure to anesthetics on later emotional control behaviors and brain structures.

5

KEYWORDS

developing brain; brain toxicity; pediatric anesthesia.

INTRODUCTION

Increasing preclinical studies suggest that general anesthesia (GA) has negative effects on the developing brain 1 . Exposures to GABA agonists or NMDA antagonists resulted in structural and functional brain impairments including behavioral changes [START_REF] Jevtovic-Todorovic | Exposure of Developing Brain to General Anesthesia: What Is the Animal Evidence?[END_REF] . The issue of the clinical relevance of general anesthetics brain toxicity in children was highlighted in the last decade but gathered less consistent proofs. Some cohort studies reported an association between GA and impaired neurodevelopment 3 while others do not [START_REF] Mccann | Neurodevelopmental outcome at 5 years of age after general anaesthesia or awake-regional anaesthesia in infancy (GAS): an international, multicentre, randomised, controlled equivalence trial[END_REF] . In one hand, preclinical studies are mandatory even though species differences may preclude any clinical translation. On the other hand, it is practically difficult to design a randomized clinical study on the effect of GA on the developing brain. Human cohort analysis remains an interesting way to examine the question raised. Although the morphologies of rodent and human brains are different, neural circuits involved in cognitive and emotional functions are beginning to be better understood [START_REF] Russell | All-optical interrogation of neural circuits in behaving mice[END_REF][START_REF] Poirel | Number conservation is related to children's prefrontal inhibitory control: an fMRI study of a piagetian task[END_REF] .

We used a translational approach. We present the result of 1) an experimental study focused on behavioral and anatomical changes which occur following postnatal exposure to GA in mice, and 2) the analysis of data recorded an ongoing French cohort study of developing children ("APprentissages EXécutifs et cerveau chez les enfants d'âge scolaire"; APEX). The authors had access to behavior and magnetic resonance brain imaging (MRI) of 9-10 years old children exposed or not to a single GA for minor surgery during infancy.

The main objective of the study was to examine behavioral and structural brain changes in mice and children at a distance from the exposure of the developing brain to general anesthesia. The main objective of the preclinical study was to examine behavioral and structural brain changes in mice at a distance from exposure to postnatal GA.

METHODS

The main objective of the APEX data analysis was to examine long-term behavioral and structural brain imaging according to the exposure or not to GA in infancy. The secondary objectives of the APEX data analysis were to examine the relationship between (1) behavioral changes and structural brain abnormalities (2) the age at exposure to anesthesia and the magnitude of behavioral and structural brain changes.

Preclinical investigations

Animals

Experiments were performed on 6-8 weeks Swiss male mice (Janvier Labs, Le Genest-Saint-Isle, France). The animal care facilities were the Centre Universitaire de Ressources Biologiques (CURB, Caen Normandy University, France; approval n° A14118015).

Body weight was measured before behavioral evaluations. All behavioral tests were performed between 8AM-5PM in a room with dim illumination (6 lx) by an investigator blinded to the allocation group.

Mice were randomly allocated to one of these 2 groups:

-the GA-exposed group: mice were exposed to 1.3% isoflurane (corresponding to 1 MAC in mice), in 100% O2 during 90 min each day from 4 to 10 days of life).

-the control group: mice were exposed to 100% O2, during 90 min each day from P4 to P10.

The exposition occurred in a plastic anesthesia chamber of 19x19x11.5cm with a fresh gas inlet (3 L.min -1 ) on its lid, an aspiration on one of its sides and a humidification system. A warm pad was placed in the anesthesia chamber to maintain the rectal temperature of the neonatal mice to 37°C during the procedure. A gas analyzer was applied to monitor gas concentrations (Capnomac Ultima®, Datex Engstrom). Hypnosis for mice placed under general anesthesia was established by the control of loss of righting reflex, absence of limb withdrawal when applying pressure to the paw and monitoring of heart and respiratory rates. Monitoring of heart and respiratory rates (Small Animal Monitoring and Gating System from SA Instruments Inc®) were performed during each period of exposure to anesthetics and during each procedure of MRI. Awakening of the anesthetized mice after purging volatile anesthetic from the ventilation system was assessed by paraclinical (plethysmography) and clinical observation (responses to tactile stimulations). Finally, these pups were smeared with own bedding and sent back to their dams. At 6-8 weeks old, behavioral experiments and brain imaging were performed (Table 1).

Behavioral assessment

The following behavioral tests were used for motor, emotional and cognitive functions assessment: actimetry 7 , fear conditioning 8 and Y-maze [START_REF] Dellu | Genetic differences in response to novelty and spatial memory using a two-trial recognition task in mice[END_REF] .

Actimetry. Spontaneous locomotor activity was quantified by using a rack of eight activity cages equipped with horizontal infrared beams located across the long axis of the cage (Imetronic®, Pessac, France). Mice were placed in individual acrylic chambers (30 x 20 x 20 cm) for 60 min. The number of horizontal and vertical movements was determined by breaks in movement-sensitive photo-beams that were then converted into locomotor activity counts. This test evaluates the hypo-or hyperactivity.

Contextual fear conditioning. Contextual fear memory was investigated in a conditioning chamber with white walls (67x53x55 cm, BIOSEB, Chaville, France). Signal generated by the mice movement was recorded and analyzed through a high sensitivity weight transducer system. The analogical signal was transmitted to the Freezing software module through the load cell unit for recording purposes and posterior analysis in terms of activity/immobility (Freezing). On the training day, animals were first individually placed into the conditioning chamber during a 4 min acclimatizing session. During the subsequent training (acquisition) session, two shocks (0.4 mA, 2 sec duration, 2 min inter-trial interval) were administered in the mice's feet. Thirty seconds after the final shock, the mice were returned in their home cages.

Twenty-four hours or 1 month after the training session, contextual fear memory was assessed by returning the mice to the conditioning chamber and measuring freezing behavior during a 4 min recall test. The measurement of fear was performed by considering the freezing time, defined as immobility (i.e. the absence of all movements with the exception of those related to respiration) for at least a period of 500 msec.

Y-maze.

Mice were submitted to a two-trial place recognition task in a Y-maze (34 x 8 x 15 cm; BMP Chaudronnerie, Bretteville-sur-Odon, France) based on a novelty free-choice exploration paradigm previously developed to study spatial cognition processes in rodents.

Extra-maze distal cues were suspended on the black curtains of the room walls at a distance of 123 cm from the maze (from the top of the cues to the center of the maze). During the first trial (acquisition session), one of the three arms of the maze was closed. Each mouse was then placed at the end of the starting arm (Arm 1), and was allowed to visit the accessible portions of the maze (Arm 1 / Arm 2) for 5 min. The mouse was then returned to its cage for three hours, before being tested for a second trial (recall session), in which they had free access to all three arms (Arm 1 / Arm 2 / New arm) for 5 min. Spatial performance was assessed through the comparison of the number of visits in each arm. It has been shown that control mice spend more time in the New arm than in the familiar arms (Arm 1 / Arm2) therefore indicating processing of remote spatial cues.

Brain Imaging

We used a 7T preclinical magnetic resonance imaging (Bruker, Pharmascan, Advance III) to acquire a 3D isotropic T2 weighted image of the brain of each mouse (TR=13.5s, TE=48ms, RARE acceleration factor=8, matrix=90x90x90, isotropic resolution=0.2mm 3 ). The acquisitions were realized on mice anesthetized with isoflurane (1-2% in 100% O2) over 20 minutes. Deformation-Based Morphometry 10 , and not Voxel-Based Morphometry (VBM), was used to analyze mouse brain anatomy as DBM overcome the VBM brain segmentation step, which is tedious with mouse.

The APEX cohort

Participants

The data for the clinical study were derived from the APEX study (2015-2019) which aimed at testing in healthy school-aged children (9-10 years) the behavioral and neural changes induced by an inhibitory control training or a metacognitive mindfulness meditation practice, compared to an active control training (details of APEX recruitment can be found in a previous report [START_REF] Delalande | Complex and subtle structural changes in prefrontal cortex induced by inhibitory control training from childhood to adolescence[END_REF] ).

Only baseline data, namely before intervention, were included in the present study. We included all participants (n=102) of the APEX cohort who were right-handed as determined by the Edinburgh Handedness Inventory 12 , born full-term, with normal or corrected-to-normal vision, no history of neurological diseases and cerebral abnormalities. Thus, 24 children who were exposed to a single general anesthesia for non-urgent minor surgery between 1-10 years (Fig. 1) constituted the GA-exposed group. Exposure to GA was evaluated by a standardized data form completed by the children's parents. We did not have access to the drugs and dosage of the used during GA. The 78 children who had not been exposed to GA constituted the control group.

Behavioral assessment

The following behavioral tests were used to assess working memory ability, non-verbal intelligence and emotional control.

Working memory. Children working memory abilities were assessed thanks to the backward digit span task [START_REF] Poirel | Number conservation is related to children's prefrontal inhibitory control: an fMRI study of a piagetian task[END_REF] . In this task, the children listened to series of discrete digits and were asked to recall the series of digits in the reverse order of presentation. The children first performed two series of two digits. The series of digits were incrementally increased by one digit every two trials. The task was stopped when the children failed to recall two trials with the same number of digits. The working memory score was defined as the number of series correctly recalled.

Working memory were also investigated thanks to the Behavior Rating Inventory of Executive Function (BRIEF) parent form [START_REF] Baron | Behavior rating inventory of executive function[END_REF] , in order to investigate daily working memory abilities.

Non-verbal intelligence. We used the Raven's progressive matrices [START_REF] Raven | Manual for Raven's progressive matrices and vocabulary scales[END_REF] to assess non-verbal intelligence. This test is a nonverbal task typically used to measure abstract reasoning and estimate fluid intelligence. In each test item, children are asked to identify the missing element that completes a pattern.

Emotional control. Emotional control was assessed using the standard Behavior Rating Inventory of Executive Function (BRIEF) parent form [START_REF] Baron | Behavior rating inventory of executive function[END_REF] .

Brain Imaging

The APEX study included brain MRI in children aged 9-11 years old. Before the scans, children were familiarized with the MRI session noise in a fake MRI and were trained not to move during the acquisition. T1 MRI acquisitions were performed while they passively watched a mute cartoon on an MRI-compatible screen to reduce motion, provide a positive experience and decrease waiting times [START_REF] Lemaire | Impact of audio/visual systems on pediatric sedation in magnetic resonance imaging[END_REF] . We used the APEX cohort high-resolution isotropic 3T sagittal structural T1 MRI data (TE = 3.3; TR = 7.2; flip angle = 9; 1 x 1 x 1 mm) acquired at the Cyceron biomedical imaging platform (Archieva, Philips Medical System, The Netherlands).

Local grey matter volumes were automatically assessed on the whole brain based on the standard voxel-based morphometry (VBM) approach using the Statistical Parametric Mapping software (SPM12) (http://www.fil.ion.ucl.ac.uk/spm/) and the Computational Anatomy Toolbox (CAT12) (http://www.neuro.uni-jena.de/cat/), a fully automatic, toolbox for performing robust and accurate VBM analyses [START_REF] Ashburner | Computational anatomy with the SPM software[END_REF] . We used the CAT12 (CAT12.6) toolbox which is optimized for the segmentation of brains of children (robust initial affine registration and sample-specific template based on a diffeomorphic image registration algorithm (DARTEL) [START_REF] Ashburner | A fast diffeomorphic image registration algorithm[END_REF] .

Statistical Analysis

Data were analyzed using the GraphPad Prism software (GraphPad Software, San Diego, California, United States). All analyses were two-tailed.

Preclinical behavioral data

Analysis of the datasets via parametric approaches turned out to be inappropriate in some cases due to violation of normality (Shapiro-Wilk tests). Therefore, parametric or non-parametric approaches were used whenever appropriated. Behavioral performance was compared between groups using Unpaired Student's t-tests (Actimetry; Fear conditioning; Body Weight).

Behavioral performance was compared within groups using Paired Student's t-tests (Y-maze: acquisition session). Friedman's tests were used for multiple comparisons (Y-maze: recall session). When an appropriate significant effect was detected by Friedman's tests, we performed post-hoc analysis using Dunn's Multiple Comparison Tests.

Preclinical imaging data

Using Advanced Normalization Tools (ANTs), an anatomical template of the control mice was built. Estimating the volumetric differences between the animals, the Jacobian determinant of the deformation field needed to warp the individuals to the template was used. Using SPM12, a second level of analysis was performed of these Jacobian determinant images to test morphological differences between groups. This second level analysis consist of voxel-wise Ttests corrected for the Family Wise Error rate (FWE).

Clinical behavioral data

Shapiro-Wilk analysis of the whole dataset revealed that the data followed a gaussian distribution, thus enabling the use of standard univariate analyze of covariance (ANCOVA) for each clinical behavioral data. The models included the following covariables: age of the child at tests and monthly parental income, as a proxy of socio-economic status.

Clinical imaging data

Voxelwise statistical analyses were performed on the whole brain using SPM12 software, within the General Linear Model (GLM) framework. Analyses were performed on the smoothed (8 mm) and modulated grey matter maps with gender, age, parental income as a proxy of socio-economic status, and weight at birth as matching variables. All analyses were performed using a voxelwise threshold at p < 0.05, corrected for multiple testing using SPM12 Family Wise Error (FWE) method. We further investigated some possible associations of grey matter deficits with behavioral scores using small volume correction (SVC) with a 10 x 10 x 10 mm search volume centered at the VBM cluster max voxel.

Sample size justifications

We decided of the number of animals per group for neuroanatomy and behavior tasks based on our previous published studies and ethical recommendations. Because the clinical study was a secondary analysis of the APEX cohort, it was not possible to calculate statistical power a priori. All statistical powers were calculated a posteriori using G*Power.

Preclinical investigations: Post hoc power calculations were found equal at 0.59 for fear conditioning at one month recall session, 0.05 for actimetry horizontal activity, 0.05 for actimetry vertical activity and 0.24 for Y-maze recall session. Based on our group sizes for GA-exposed mice and controls, we determined the effect size required for 1-β=0.9 and α=0.05 at d=0.65 for fear conditioning at one month recall session, d=0.04 for actimetry horizontal activity, d=0.04 for actimetry vertical activity and d=0.37 for Y-maze recall session. Post hoc power was found equal at 0.99 for MRI cluster. Based on our group sizes for GA-exposed mice and controls, we determined the effect size required for 1-β=0.9 and α=0.05 at d=3.86 for MRI cluster.

The APEX cohort: Post hoc power calculations were found equal at 0.43 for BRIEF emotional control score, 0.13 for BRIEF working memory score, 0.05 for Raven's progressive matrices and 0.05 for Backward Digital Span. Based on our group sizes for GA-exposed children and controls, we determined the effect size required for 1-β=0.9 and α=0.05 at d=0.42 for BRIEF emotional control score, d=0.19 for BRIEF working memory score, d=0.005 for Raven's progressive matrices and d=0.03 for Backward Digital Span. Post hoc power was found equal at 0.99 for MRI cluster. Based on our group sizes for GA-exposed children and controls, we determined the effect size required for 1-β=0.9 and α=0.05 at d=2.86 for MRI cluster.

RESULTS

Preclinical investigations

No mortality was reported during experiments.

Control and GA-exposed mice showed similar body weight at 6 weeks of life (control mice: weight (g) = 39.5±2.8; GA-exposed mice: weight = 39.7±2.7).

MRI Deformation based morphometry.

There was a 11% (95% CI: 7.5% to 14.5%) reduction in the volume of the periaqueductal grey matter in the GA-exposed group (Fig. 2; peak-level, t=7.32, dof=15; p-FWE = 0.046).

Actimetry. There was no significant difference between groups in horizontal locomotor activities (Fig. 3A) and vertical activities (Fig. 3B).

Contextual fear conditioning.

There was no significant difference between groups in the percentage of freezing during the acclimatizing period (acquisition) session: all mice showed high levels of fear following the two aversive unconditioned stimuli without differences between groups (Fig. 3C). One month after the training session, there was a higher freezing time (ft) in the GA-exposed mice (ft = 115 ± 73 sec) as compared to the control group (ft = 73 ± 56 sec; 95% confidence interval: 4 to 80; p = 0.03; Fig. 3D).

Y-maze.

During the acquisition session, there was no significant difference between groups in the total number of visits in the two free access arms of the Y-maze (Fig. 3E and3F). During the recall session performed three hours later, the new arm of the maze was visited similarly in GA-exposed and Control groups (Fig. 3G and3H).

The APEX cohort

There was no difference between groups in main children's characteristics (Table 2). In the GA-exposed children, mean age at anesthesia was 4.0±2.3 years old (29% of patients were 3 years old and under, 54% were between 4 and 6 years old and 17% were over 6 years old).

BRIEF emotional control score. Figure 4A suggests lower emotional control in the GAexposed children (58±12) as compared to the Control ones (53±11; difference=5; 95% CI of the difference: 0.33 to 9.10; p=0.06).

BRIEF working memory score.

There was no significant difference between groups in the BRIEF working memory scores (Fig. 4B).

Raven's progressive matrices.

The was no significant difference between groups in the Raven's progressive matrices (Fig. 4C).

Backward Digit Span.

There was no significant difference between groups in the Backward Digit Span performance (Fig. 4D).

Multivariate analysis found no relationship between behavioral and memory scores and age at

the time of anesthesia.

MRI Voxel based morphometry (VB gr M) analysis.

In the GA-exposed children there was a 6.1% (95% CI: 4.3 to 7.8%) lower grey matter volume in a single cluster corresponding to the posterior part of the right inferior frontal gyrus, BA9, 'aal' atlas (MNI coordinates x,y,z = [51;3;27]; height threshold: Z = 4.85, p-FWE = 0.019; Fig. 4E,4F,4G,4H). There was no difference in local white matter volume between groups.

Furthermore, an SVC analysis centered at [51;3;27] revealed positive main effects of the BRIEF emotional control score (height threshold: Z = 2.65, p-FWE = 0.047) and of the age at GA (height threshold: Z = 2.94, p-FWE = 0.024) on the volume of grey matter in the posterior part of the right inferior frontal gyrus. Of note, the same analysis with the BRIEF working memory score did not yield any significant correlation.

DISCUSSION

Our preclinical study revealed exacerbated fear behavior associated with an atrophy of the periaqueductal grey matter in the adult mice exposed to postnatal GA. Our clinical study provides the first evidence that exposure to GA in childhood can lead to a grey matter atrophy in the right prefrontal gyrus which is more pronounced the earlier the exposure to anesthesia.

Our study revealed a periaqueductal grey matter atrophy in mice 6-8 weeks after repeated postnatal anesthesia with 1 MAC of isoflurane (day 4-10). The periaqueductal grey matter is a key component in context fear discrimination in mice [START_REF] Rozeske | Prefrontal-Periaqueductal Gray-Projecting Neurons Mediate Context Fear Discrimination[END_REF] and is involved in anxiety and depression [START_REF] Ho | Periaqueductal Gray Glutamatergic Transmission Governs Chronic Stress-Induced Depression[END_REF] . These results are consistent with the literature [START_REF] Satomoto | Neonatal Sevoflurane Exposure Induces Adulthood Fear-induced Learning Disability and Decreases Glutamatergic Neurons in the Basolateral Amygdala[END_REF] . The lack of difference in locomotor activity and working memory between mice neonatally exposed to isoflurane is consistent with the literature. Repeated isoflurane anesthesia exposure of mice during development (day 7 to 9) does not impact locomotor activity [START_REF] Rosenholm | Repeated brief isoflurane anesthesia during early postnatal development produces negligible changes on adult behavior in male mice[END_REF] . Moreover, performance in working memory was not impaired in adult mice exposed to three hours of 2.7% isoflurane (day 6) [START_REF] Kodama | Neonatal desflurane exposure induces more robust neuroapoptosis than do isoflurane and sevoflurane and impairs working memory[END_REF] . Human data obtained from the APEX cohort suggested that the GA-exposed children (age at anesthesia: 4.0±2.3 years old) presented lower grey matter volumes in the right inferior frontal gyrus, the more pronounced the earlier the anesthesia. Reductions in the inferior frontal gyrus volume have been associated with impaired emotion functions and later depression [START_REF] Zhang | Altered functional connectivity of right inferior frontal gyrus subregions in bipolar disorder: a resting state fMRI study[END_REF][START_REF] Luby | Association Between Early Life Adversity and Risk for Poor Emotional and Physical Health in Adolescence: A Putative Mechanistic Neurodevelopmental Pathway[END_REF] .

These neuroimaging results could be associated with impaired emotional control. Our results are consistent with a meta-analysis of the 3 main prospective clinical studies about anesthesiainduced brain toxicity, which showed an impaired BRIEF executive function global score (RR:

1.68 [95% CI 1.23-2.30]; p=0.001) in 841 children exposed to GA [START_REF] Ing | Prospectively assessed neurodevelopmental outcomes in studies of anaesthetic neurotoxicity in children: a systematic review and metaanalysis[END_REF] .

Strengths

These term born children of the APEX cohort, scheduled for minor non-urgent surgery, had no history of neurological and cerebral abnormalities that could influence the interpretation of the results [START_REF] Andropoulos | Changing expectations for neurological outcomes after the neonatal arterial switch operation[END_REF] . The analyses were controlled for socio-economic status. In our study, ear nose and throat surgeries (46.0%), digestive (20.8%) and urological surgeries (16.7%) were in decreasing order or frequency the most represented surgical specialties. Similar patterns of the indications of surgery were reported in the literature [START_REF] Habre | Incidence of severe critical events in paediatric anaesthesia (APRICOT): a prospective multicentre observational study in 261 hospitals in Europe[END_REF] . This clinical study provides the first evidence that exposure to GA in childhood (excluding the neonatal period) can lead to grey matter atrophy.

It would be interesting to re-evaluate the possible impact of GA on the parameters evaluated in the previous APEX related studies such as the cortex anatomy [START_REF] Delalande | Complex and subtle structural changes in prefrontal cortex induced by inhibitory control training from childhood to adolescence[END_REF] and the baseline resting-state function which may be intrinsically related to the local structure [START_REF] Salvia | The local properties of bold signal fluctuations at rest monitor inhibitory control training in adolescents[END_REF] . Only one study had yet reported a grey matter decrease after GA in childhood [START_REF] Backeljauw | Cognition and Brain Structure Following Early Childhood Surgery With Anesthesia[END_REF] . The correction for multiple comparisons was applied only to the voxels within the single region of interest and not for all the voxels of the brain as in our study. This statistical method could be associated with high false positive rates. In our study, the grey matter atrophy persisted after FWE-correction for multiple tests on the whole brain.

The most likely mechanism to explain the atrophy detected after early GA exposure is the apoptosis of GABAergic and glutamatergic neurons 2 . After exposing neural stem cell to 1 MAC of sevoflurane, cell viability decreased and cytotoxicity increased in time-dependent way [START_REF] Qiu | Effect of apoptosis in neural stem cells treated with sevoflurane[END_REF] . This is all the more likely because periaqueductal grey matter contains GABAergic interneurons [START_REF] Yin | A Central Amygdala-Ventrolateral Periaqueductal Gray Matter Pathway for Pain in a Mouse Model of Depression-like Behavior[END_REF] . Another possible mechanism is neuroinflammation [START_REF] Baud | Neuroinflammation in the Developing Brain: Risk Factors, Involvement of Microglial Cells, and Implication for Early Anesthesia[END_REF] . In monkeys, the interest of a non-invasive neuroimaging evaluation of glial activation after early sevoflurane exposition was pointed out by using Positron Emission Tomography [START_REF] Zhang | In Vivo Monitoring of Sevoflurane-induced Adverse Effects in Neonatal Nonhuman Primates Using Small-animal Positron Emission Tomography[END_REF] .

Limitations and perspectives

The first limitation is the lack of data about the drugs administered in children from the APEX study. It could be hypothesized that anesthesia involved sevoflurane, the most currently used anesthetics in pediatric anesthesia in France [START_REF] Apai | Anesthesia and the Developing Brain: A Review of Sevoflurane-induced Neurotoxicity in Pediatric Populations[END_REF] . Moreover, no perioperative anesthetic complications were reported in the health records. The second limitation is that the GA-exposed children were exposed at various ages. The third limitation is that mice underwent multiple exposures to approximate concentrations of isoflurane and that their mean arterial pressure during anesthesia could not be monitored for technical reasons. However, heart and respiratory rates were monitored and there was no death in the GA-exposed mice. The fourth limitation is that if rodents are the most commonly used mammalian models for studying the neurotoxicity of anesthetics, their brain morphologies are dissimilar to humans [START_REF] Chinn | Standards for preclinical research and publications in developmental anaesthetic neurotoxicity: expert opinion statement from the SmartTots preclinical working group[END_REF] . However, the affected regions by general anesthesia (1) in mice and (2) in humans (periaqueductal grey matter and the right inferior frontal gyrus, respectively), although not identical, are both involved in the control of emotions. For instance, Rozeske's study has identified a subpopulation of prefrontalperiaqueductal grey-projecting neurons that control switching between different emotional states during contextual fear discrimination in mice [START_REF] Rozeske | Prefrontal-Periaqueductal Gray-Projecting Neurons Mediate Context Fear Discrimination[END_REF] . Similarly, Blakemore's study has highlighted a cortico-subcortical network involving right inferior frontal gyrus and periaqueductal grey matter also implicated in emotional control in human [START_REF] Blakemore | Negative emotions facilitate isometric force through activation of prefrontal cortex and periaqueductal gray[END_REF] . Moreover, rodents brain activity during daily life situations mainly rests upon fear processes, an adaptative surviving behavior which depends largely on the periaqueductal grey matter [START_REF] Deng | Periaqueductal Gray Neuronal Activities Underlie Different Aspects of Defensive Behaviors[END_REF] . Similarly, the human prefrontal part of the brain is a core region mainly necessary for daily life activity, in order to accurately deal with emotional and behavioral regulation [START_REF] Zhang | Altered functional connectivity of right inferior frontal gyrus subregions in bipolar disorder: a resting state fMRI study[END_REF] .

Future preclinical and clinical studies should also investigate to what extent detrimental effects of GA could be driven by side effects occurring during GA such as hypotension, hypocapnia or hypoxia [START_REF] Weiss | Safe Anesthesia For Every Tot -The SAFETOTS initiative[END_REF] . The NECTARINE study recently highlighted difficulties to accurately monitor neonates and small infants [START_REF] Disma | Morbidity and mortality after anaesthesia in 29 early life: results of the European prospective multicentre observational study, neonate and children audit of anaesthesia practice in Europe (NECTARINE)[END_REF] . This is also difficult in newborn mice. Finally, intraoperative data are often missing in studies on anesthesia toxicity [START_REF] Baron | Behavior rating inventory of executive function[END_REF][START_REF] Warner | Neuropsychological and Behavioral Outcomes after Exposure of Young Children to Procedures Requiring General Anesthesia: The Mayo Anesthesia Safety in Kids (MASK) Study[END_REF] .

Conclusion

We present the first translational study that analyzes the behavioral and structural brain impact of childhood exposure even to a single GA, the result of a successful collaboration between preclinical and clinical researchers. It should stimulate the development of less brain toxic anesthetic agents, better monitoring anesthesia in children and setting up long-term monitoring of our cohort in order to determine whether children are prone to develop psychiatric disorders. (I,J,K) 3D views of the extended cluster (FWE-uncorrected). (G) Three-dimensional rendering of the cortical surface and VBM cluster in the posterior part of the right inferior frontal gyrus (BA9) (voxel maximum: Talairach's x, y, z = [51;3;27]). For illustration purpose and ease of visualization, the cluster is represented with uncorrected statistics (p-uncorrected < 0.001).

TABLES AND FIGURES LEGENDS

(H) Grey matter volume in the VBM cluster located in the posterior part of the right inferior frontal gyrus is lower in children subjected to a single general anesthesia (p-FWE < 0.05; controlled for gender, age, parental income as a proxy of socio-economic status, and weight at birth).

  The preclinical study was in accordance with the French ethical laws (Decree 2013-118, Approval n°8962) and the European Communities Council guidelines (2010/63/EU).The clinical study was approved by a French national ethic committee (IRB 2015-A00383-46, 21/05/2015) and carried out in line with the declaration of Helsinki(1964). Parents or legal guardians gave written consent and children agreed to participate.

  p-value: * < 0.05; ** < 0.01; *** < 0.001.

Figure 1 :

 1 Figure 1: Characteristics of the children exposed to anesthesia

Figure 2 :

 2 Figure 2: Preclinical imaging

Figure 3 :

 3 Figure 3: Preclinical behavioral testing

Figure 4 :

 4 Figure 4: Clinical imaging and behavioral testing

Table 1 : Number and age of mice used in the study.Table 2 : Human population characteristics.

 12

ACKNOWLEDGEMENTS

The authors thank the children who participated in the study and their families.

Funding statement

This work was supported by the INSERM (French National Institute for Health and Medical Research), the French Ministry for Research and upper Education, the Caen-Normandy University, The Institute Blood and Brain @ Caen-Normandie (BB@C), European FEDER funds "Fonds Européens de Développement Economique et Régional Normandie" and the French National Research Agency (grant/award number: ANR-14-CE30-0014-01 APEX).

Conflicts of interest/ Financial disclosures

None.

GLOSSARY OF TERMS

APEX