Lynda Ait Oubelli
email: lynda.ait-oubelli@onera.fr

Yamine Ait Ameur
email: yamine@enseeiht.fr

Judicaël Bedouet
email: judicael.bedouet@onera.fr

Benoit Chausserie-Lapree
email: benoit.chausserie-lapree@cnes.fr

Beatrice Larzul
email: beatrice.larzul@cnes.fr

Automating the Evolution of Data Models for Space Missions. A Model-Based Approach

Keywords: Model driven engineering (MDE), Data model comparison, Data model evolution, Data migration, Composite evolution operators, Semantic transformation patterns

In space industry, model-driven engineering (MDE) is a key technique to model data exchanges with satellites. During the preparation of a space mission, the associated data models are often revised and need to be compared from one version to another. Thus, due to the undeniably growth of changes, it becomes difficult to track them. New methods and techniques to understand and represent the differences, as well as commonalities, between different model's revisions are highly required. Recent research works address the evolution process between the two layers (M2/M1) of the MDE architecture. In this research work, we have explored the use of the layers (M1/M0) of the same architecture in order to define a set of atomic operators and their composition that encapsulate both data model evolution and data migration. The use of these operators improves the quality of data migration, by ensuring full conservation of the information carried by the data.

Introduction

Context Space agencies in Europe like CNES, the French space agency, have been involved in data modelling and in the standardization of data modelling techniques for more than 20 years. They have defined some Consultative Committee for Space Data Systems (CCSDS) recommendations. These recommendations are related to syntactic and semantic data description techniques, long term data preservation, data producer and archive interface. Furthermore, CNES, jointly with the European Space Agency (ESA), have also developed tools to support the recommended approaches and to support data engineering for various space projects.

One of these tools is the BEST [START_REF]National Center for Space Studies (CNES) and The European Space Agency (ESA)[END_REF] workbench that came beyond EAST, the Enhanced Ada SubseT. It allows a non-ambiguous description of data formats including syntactic and semantic information. This tool is used in the frame of space projects by scientists and engineers. It allows them to easily describe their data formats and make them evolve, to quickly produce test data conform to the format specification, to access and interpret data without having to write specific code. The formal description of space-related data is crucial and should be taken into account from the early stages of the mission when: -the space system (one or more satellites, a ground control center, a mission center, etc.) is designed; -the satellite, once launched, starts to send telemetries and the ground segment starts to send commands; -a large amount of data is produced, processed, transformed and sent to endusers during the life of the satellite;

All different stakeholders have to understand the data, to be able to interpret it and to make use of it. Any misunderstanding might cause important delays in mission planning. Arguably, a complete and non-ambiguous definition of any kind of data produced is a key factor for meeting the deadlines of the project. This formal definition can be used to generate different pieces of code that will be used in the frame of the project (eg. on-board software, simulation software, etc.). Code generation is highly valuable for a project, since some parts of the application can be updated in a fast and efficient way with a minimum development cost [START_REF] Bedouet | GAMME, a meta-model to unify data needs in simulation modeling (WIP)[END_REF]. However, data generated (Data V1) with a particular release of the application (DataModel V1) are not necessarily compatible with another release (DataModel V2). Thus, starting the creation or the generation of data that conforms to (DataModel V2) from scratch is a tedious operation.

Motivations

Research into how complex industrial data models evolve from one version to another and how their data migrate still stands as one of the most scientific challenges to be addressed. This is due to the disability of existing solutions to face with the huge complexity of changes in terms of type and number. Interpreting comparison results of two small data models may be an easy thing. Meanwhile, interpreting comparison results of two huge data models is still a hard task.

Objectives

In this research study, we intend to tackle the following objectives:

-being able to recognize many differences as a unique composite operator; -ensuring evolution schema by finding structural and descriptive changes; -full data migration without any loss of information.

To achieve these objectives, we decide to investigate and improve several methods to compare data models (M1 level) governed by structured data-oriented metamodels (M2 level) as shown in Fig. 1. Then, we propose to transform the obtained differences to evolution operators working at data model level (M1) as well as At the data model level, an evolution operator defines a data model transformation capturing a common evolution [START_REF] Herrmannsdoerfer | An extensive catalog of operators for the coupled evolution of metamodels and models[END_REF]. At the data level, it defines a model transformation capturing the corresponding migration.

Depending on the final usability goals of data and depending on the modeller's skills, the same concept may be modeled in different ways. For example, to define the structure of data with BEST, only "composition" relationship can be used (e.g. in the XIF meta-model1 ) whereas "inheritance" relationship may be preferred (e.g. in the XTCE meta-model2 ). Such a difference in the way of modeling induces a huge number of syntactic differences between a XIF data model and a XTCE data model, even if they represent the same concept.

Understanding the semantic differences that may exist between the two data models is difficult because these real differences are hidden by a large number of syntactic differences. Once we can recognize these differences as a unique composite operator, we can clearly identify other syntactic differences. This is an extreme example of data model transformation but we often meet such cases while studying how data models evolve. Indeed, modellers often need to reconstruct their model to meet new end-users needs. But, they do not want to completely break the ancient model or make it obsolete. Even if ascendant compatibility is not kept, they try to preserve information contained in old data.

The remainder of this research paper is organized as follows. The next section summarizes the previous related work for the different approaches being used for meta-model evolution and model co-evolution. The theoretical description of the proposed approach to formalize space data model evolution and data migration using semantic composite operators is presented in Sect. 3. A case study is discussed in Sect. 4 followed with conclusions and future work.

Many researchers studied the discipline of evolution, which was defined by [START_REF] Lehman | Software evolution[END_REF] as ≪Al l programming activity that is intended to generate a new software version from an earlier operational version≫. This section summarises the different existing approaches for model-based comparison, evolution and migration. Capturing and formalizing evolution stages for composite systems is a challenging task, due to the nature and characteristics of such systems. Furthermore, the rapid changes, the density and the type of differences, which change according to the evolution of end-user's requirements, are the reasons of proposition of a variety of algorithms, methods and evolution laws by scientific community.

Model Comparison

The history of comparing algorithms is composed of three stages. At the beginning, we compared character to character. For instance, diff programs [START_REF] Hunt | An Algorithm for Differential File Comparison[END_REF], are used to solve the longest common subsequence problem (LCS). They are based on finding the lines that do not change between files. Then, we leveled up by focusing on the attributes and nodes of a structured document. A number of advanced algorithms are being available to capture differences for XML such as XDiff [START_REF] Wang | X-Diff: an effective change detection algorithm for XML documents[END_REF] or Aladin [START_REF]Aladin[END_REF]. However, even if they give logical results, they lack the ability to recognise a huge number of complex changes in a reasonable time. Finally, we are interested in the meaning of these nodes and attributes by comparing a class node with a class node, an attribute node with an attribute node, for instance, with the assistance of the well accepted EMF framework [START_REF] Toulmé | Intaloi Inc. Presentation of EMF compare utility[END_REF].

Evolution Operators

The process of capturing and constructing evolution operators have been investigated by [START_REF] Herrmannsdoerfer | An extensive catalog of operators for the coupled evolution of metamodels and models[END_REF]. Authors of this paper have worked on the definition of 61 reusable evolution operators (30 are atomic and 31 are composite) with the aim of treating the coupled-evolution of meta-models (M2) and models (M1). The authors have provided a catalog based on EMOF meta-modeling formalism in which they have outlined a set of migration rules specified at a model level. However, even if they tackled the generation of evolution operators, the authors did not discuss the detection mechanism of these operators. This was treated later in [START_REF] Khelladi | Detecting complex changes and refactorings during (Meta) model evolution[END_REF], where the authors proposed a detection engine of complex changes. They have addressed the two challenges of variability and overlap between evolution operators. A research prototype named COPE [START_REF] Herrmannsdoerfer | COPE -automating coupled evolution of metamodels and models[END_REF] was extended into a transformation tool tailored for the migration of models in response to meta-model co-evolution named Edapt [START_REF] Vissers | Maintenance of specification models in industry using Edapt[END_REF]. Furthermore, authors in [START_REF] Kehrer | Understanding model evolution through semantically lifting model differences with SiLift[END_REF] introduced the Silift, generic tool environment able to lift incomprehensible low level differences derived from EMF Compare into representations of user-level edit operations.

Data Migration

Nowadays, the majority of research teams [START_REF] Khelladi | Detecting complex changes and refactorings during (Meta) model evolution[END_REF][START_REF] Herrmannsdoerfer | COPE -automating coupled evolution of metamodels and models[END_REF][START_REF] Vissers | Maintenance of specification models in industry using Edapt[END_REF][START_REF] Kehrer | Understanding model evolution through semantically lifting model differences with SiLift[END_REF] have tackled the phenomenon based on the M2/M1 architecture layers of MDE, where the authors presented a semi-automatic process to co-evolve model-to-model transformations upon metamodel evolution. Authors in [START_REF] Rose | A comparison of model migration tools[END_REF] introduced a comparison study between various model migration tools such as AML, COPE, Ecore2Ecore and Epsilon Flock. Indeed, each one of them has a set of criteria, that will support users to select the most appropriate tool according to their needs.

Proposed Approach

To ease the migration process of data in a critical domain, where each data value vulnerability is so height, a solid protocol for controlling the evolution of data models is required. Therefore, deriving and capturing the set of changes might even be largely different from one version to another. In this paper, we propose a novel model-based approach for automating the evolution of data models. An overview of the proposed approach is illustrated in Fig. 2, where three different successive processes (data models comparison, evolution operator's flattening and data migration) should be able to reuse the results of each other. We introduce a pipeline protocol in order to guarantee the conservation of data values during the migration.

It is worth noting that the proposed scenario based on three processes can be applied to other domains, where the main goal of the migration process is data preservation. However, in other cases, where the main purpose is functionalities conservation processing, many other approaches that tackled the comparison, evolution and co-evolution processes are available. They act separately at M2/M1 level in different ways with different tools.

Phase 1. Data Models Comparison

As shown in Fig. 3, the comparison process is the first step in our approach. It requires two data models (M1 level) that conform to the same meta-model (M2 level). After customizing EMF Compare's matching, differencing and filtering engines, the comparison results will be a syntactic delta which contains equals i.e. commonalities as well as differences (ResourceAttachementChange, Attribute Change, ReferenceChange and FeatureMapChange). These are based on four atomic operators:Add, Delete, Change and Move. Lot of efforts are being made by [14] to improve the algorithms of EMF Compare in order to help the developers to customize the engines according to their requirements. But the framework provides low level comparison results that are not always logic or difficult to understand by human as mentioned in [START_REF] Kehrer | Understanding model evolution through semantically lifting model differences with SiLift[END_REF].

Another alternative to get the differences between the old and the new data models would be to record end-user's actions on the editor (UAR) using action observers. These methods immediately produce a complete evolution schema.

Phase 2. Evolution Operators Flattening

The kinds of differences delivered by the previous phase are at a low level. They are not easy to interpret. Thus, we transform the differences to a set of atomic operators. These operators are independent of the previous phase, defined according to the treated metal-model and help to move to upper level. Most of the time, each difference is represented by one atomic operator. Each cascading difference is represented by the most priority operator. For example: Add Attribute A to Class C and Add Class C to Model M can be considered as one atomic operator: Add Class C to Model M. Indeed, adding a new class implies the addition of all its properties. Furthermore, sometimes we can find differences that cause the same change. For instance, in the case of eOpposite references in Ecore meta-model, modifying one of the two references will automatically update the other side of the opposition. Both changes will be detected as two differences and can be seen as one atomic operator. Once atomic operators are found, we instantiate composite operators (see Fig. 4) as compositions of atomic ones, in order to rise from syntactic differencing delta to semantic one. In practice, we can find implicitly one or many composite operators derived from a composition of many atomic operators. For example, Pull up attribute A is considered as a composite operator that is composed of the following atomic operators: Delete attribute A from class C, Add attribute A to class C'. Another interpretation of this composite operator is: Pull up attribute is composed of the atomic operator Move attribute A from Class C to its mother class C'.

In the proposed approach, our goal is to build an evolution schema with the maximum number of composite semantic operators and minimum number of atomic syntactic operators. Each atomic operator is specified by a precondition an apply and a migrate operation. For example, the atomic operator Add attribute A to class C requires the existence of class C. Each composite operator is characterized by four properties: a formula, a precondition, an apply and a migrate:

-a formula is a sequence of atomic operators that define a composite operator.

Identical formulas do not necessarily lead to a composite operator or to the same composite operator, depending on the precondition. -a precondition responsible for managing the dependency between atomic operators in term of existence, order and priority. For example: Add Literal L requires the precondition Exist Enumeration E, which checks the existence of the enumeration E in the old data model or the existence of the operator Add Enumeration E. -an apply is used to make evolve the old data model. Apply calls are used to check if the new data model is equivalent to the final evolved old data model. -a migrate is a property where each composite evolution operator encapsulates its own migration behavior in order to ensure data conservation.

Moreover, to avoid conflicts, we suggest to integrate the end-user to resolve ambiguity during the building of composite operators, as shown in Fig. 5(a). He/she can modify the found evolution schema and apply a checking operation on his/her evolution schema; after calling the different apply, the final evolved data model M must be identical to the original new data model. If some differences are found between these two data models, the evolution schema based on atomic and composite operators is wrong and needs to be corrected by the end-user.

Phase 3. Data Conservation

In our study, we classify the operators into two categories: safe operators without impact and operators with impact. The impact is considered at the M0 level. For example, the operator Add Attribute may need or not a migration of existing and corresponding object instances.

Furthermore, inspired by information theory concepts defined by [START_REF] Mcdonough | Information Economics and Management Systems[END_REF], where the author distinguish between data representing cost side of the system, and information representing the value side, we propose a sub-classification of the operators with impact into two other sub-categories: operators where migration is data-conservative and operators where it is not. Figure 6(a) illustrates the concept by an evolution example of a UML composition relationship to an aggregation UML relationship. In this case we change the data structure but we do not lose any information and we do not need to create additional pieces of information as shown in (Fig. 7).

The last phase of our approach is the migration process as shown in (Fig. 6(b)). We migrate the existing instance attributes in data from the source M0 to the target M0'. We define M0 as instances of the model M1 and S0 as physical data, which represent the M0 instances into different formats. In this section, we will see how the previous approach is applied to a particular revision of a data model used in the MICROSCOPE project [START_REF] Baghi | Gaussian regression and power spectral density estimation with missing data: the MICROSCOPE space mission as a case study[END_REF]. This project studies the universality of free fall in space thanks to two differential accelerometers supplied by the French National Aerospace Research Center, ONERA, and embedded in a microsatellite from the CNES. While CNES models the telecommands (TC) and the telemeasures (TM), ONERA models the processing of telemeasures. In both cases, there is an intensive use of model-driven engineering to handle the complexity of such project thanks to two respective in-house metamodels Best [START_REF]National Center for Space Studies (CNES) and The European Space Agency (ESA)[END_REF] and GAMME [START_REF] Bedouet | GAMME, a meta-model to unify data needs in simulation modeling (WIP)[END_REF]. In the following, we detail the comparison, evolution and migration strategies that could be used to co-evolve parameters used during the telemeasures processing, following the pipeline architecture.

This case study interests in the way of combining two telemeasures: class Signaux with two attributes signal1 and signal2. Both attributes were typed by an enumeration called Signal, identifying the different telemeasures. Then, it was decided that it is possible to combine other signals than a telemeasure. As shown in Fig. 8

Comparison Process

After using the default engines of EMF Compare 3.0.1, we find many falsenegatives (i.e. undetected correspondences) and false-positives (i.e. unexpected correspondences) in the matching results. 247 differences are obtained. However, when moving to release 3.2.0 of EMFCompare, we get better matching results. Moreover, after customizing the matching engine policy (based on id), we get only 17 differences. The idea is to find a unique name to identify each element in the model (for example, the unique name of an attribute is its name concatenated to the name of the container class). EMFCompare considers the two compared versions as two graphs. At the beginning, it makes a matching between ancestors. Each matching is composed of zero or many sub-matches. In other words, EMFCompare uses a top-down matching approach. Next, a differencing engine is employed to obtain all the differences using all the matched elements. Then, we decide to keep some interesting differences: when a data model element is updated or when an added data model element has an impact on data. Therefore, we neglect hidden changes given by cascading EMF diffs. Finally, we get 7 differences.

Evolution Process

In fact, the seven differences provided by EMF Compare (Table 1) are satisfying in term of correctness and precision. However, it is not easy to exploit them. Thus, we decide to transform these low level differences to atomic operators (Table 2) by applying a java transformation template on EMFCompare differencing engine and by running an evolution operator's defined at the meta-model level.

In the end, we obtain three ADD Classes as atomic operators and two composite operators ChangeTypeofStructuralFeature, that concern type changes of the attributes signal1 and signal2 from Signal to Data Abstract. In this case, our evolution schema is hybrid, i.e. it is a mixture of atomic and composite operators. In the evolution meta-model (Fig. 8) we define four atomic operators ADD-Class, ADDAttribute, ChangeTypeofStructuralFeature and ADDReferenceType. Each composite operator is a transformation pattern composed of a set of atomic operators. For example, Complex1 as shown in Table 3 is composed of two atomic operators: ChangeTypeofStructuralFeature and ADDReferenceType. The sequence of both operators is the formula. As shown in Fig. 8, a precondition checks the dependency between atomic and composite operators where:

-F' respresents the new gStructuralFeature in the gClass DonneesSession: signal ; -new((f.gType).allFeatures) represents the new gType of the two new gStruc-turalFeature signal1 and signal2: Data Abstract; -F'.gType represents the gType of the gattribute signal: Signal ; We use this precondition to check our evolution schema with five operators. In the end, if the precondition is true, a full data conservation is immediately achieved. By calling the apply() of each operator, we can compare the transformed old (the final old data model) with the new version used during the comparison process. In our case the results are positive, i.e. the proposed evolution schema based on evolution operators is correct.

Migration Process

In our case, migration concerns composite operators only. We need to migrate the values of the gattributes as shown in Fig. 9. Initially, we have signaux an instance of the gclass Signaux. In the end, we arrive to three instances, one is instantiated from the gclass Signaux and the two others are instantiated from the gclass DonneesSession. The data models in space industry evolve very rapidly from simple data models to more and more complex ones. A number of technologies, methodologies and platforms have emerged to ease and formalize the comparison between different revisions, the synchronization between the evolutions of UML, EMF metamodels (M2) and the co-evolution of models. In fact, there are many model evolution and migration tools and each one has its own characteristics. But it is still difficult for end-users to extract the correct evolution. Moreover, most of these tools do not interest in the evolution of data models (M1) and co-evolution of data model instances (M0). Thus, in this research study, we propose a protocol at M1/M0 level to automate the migration process of our data based on the comparison and on the evolution of their data models. Because reconstruction of composite operators based on the atomic ones provide a rich source of evolution strategy, an approach is being investigated to formalize comparison, evolution and migration processes using a model-based setting. This can be a major milestone for data model evolution with data values conservation as a primary priority during the migration.

Several other research directions to pursue our work can be investigated. First, we are interested in extending our approach to handle during the evolution process semantic meaning of elements by the integration of end-user via a graphical user interface (GUI) and a definition of a thesaurus, that concerns space data models. The thesaurus contains words that belong to the same lexical field. During the comparison process, it is used to check weather the meaning of data model elements is kept or not, in case we have syntactic changes. Furthermore, we want to validate our approach on more complex data models in term of number and type of changes. We also want to tackle the conservation of APIs. Instead of working on a complex formula to recognize an evolutionary operator, we could look at whether the data API is conserved.

Fig. 1 .

 1 Fig. 1. Four-layer architecture of MDE

Fig. 2 .

 2 Fig. 2. The pipeline architecture of model-based approach for data migration

Fig. 3 .

 3 Fig. 3. Overview of the input/output for the comparison process.

Fig. 4 .

 4 Fig. 4. Evolution atomic and composite operator's meta-model

Fig. 5 .

 5 Fig. 5. The passage from syntactic comparison delta to semantic one

Fig. 6 .Fig. 7 .

 67 Fig. 6. Conservation of data values during the migration process

  the modeller decided to reconstruct the data model by replacing the two attributes by two composition relationships towards a new class, called DataAbstract. The original attributes signal1 and signal2 are factorized in a class DonneesSession, inheriting from DataAbstract and owning a signal attribute of typeSignal. In this way, end-users can combine two telemeasures, a telemeasure with another signal or two signals. As a result: -a new abstract class named DataAbstract is added; -new two classes named DonneesSession, AutresDonnees inheriting from DataAbstract are added; -a new attribute named signal of type Signal is added to the class DonneesSession; -a new attribute named signalExt of type String is added to the class Autres-Donnees; -the types of signal1 and signal2 are changed from Signal to DataAbstract.

Fig. 8 .

 8 Fig. 8. Evolution from the old to new data model

Fig. 9 .

 9 Fig. 9. Data conservation during the migration process

Table 1 .

 1 Syntactic comparison results between two data models obtained by EMF compare customization.

	EMFCompare diff	Kind	Matching
	ReferenceChangeSpec CHANGE gType (GStructuralFeature: signal1 in
			GClass: Signaux) matched to
			(GStructuralFeature: signal1 in
			GClass: Signaux)
	AttributeChangeSpec CHANGE type	(GMetaModel Element property: null
			in GAttribute: Signal1) matched to
			(GMetaModel Element property:
			Composition in GReference: Signal1)
	ReferenceChangeSpec CHANGE gType (GStructuralFeature: signal2 in
			GClass: Signaux) matched to
			(GStructuralFeature: signal2 in
			GClass: Signaux)
	AttributeChangeSpec CHANGE type	(GMetaModel Element property: null
			in GAttribute: Signal2) matched to
			(GMetaModel Element property:
			Composition in GReference: Signal2)
	ReferenceChangeSpec ADD gContents (GClass: null in GModel:
			Lap1nDataModel) matched to
			(GClass: DataAbstract in GModel:
			Lap1nDataModel)
	ReferenceChangeSpec ADD gContents (GClass: null in GModel:
			Lap1nDataModel) matched to
			(GClass: DonneesSession in GModel:
			Lap1nDataModel)
	ReferenceChangeSpec ADD gContents (GClass: null in GModel:
			Lap1nDataModel) matched to
			(GClass: AutreDonnees in GModel:
			Lap1nDataModel)

Table 2 .

 2 From differences to atomic operators.

	Atomic Operator	GModelElement Value
	ADDgContents	GClass	DataAbstract
	ADDgContents	GClass	DonneesSession
	ADDgContents	GClass	AutreDonnees
	ADDReferenceType	type	Composition
	ChangeTypeofStructuralFeature GAttribute	DataAbstract
	ADDReferenceType	type	Composition
	ChangeTypeofStructuralFeature GAttribute	DataAbstract

-old(F).gType represents the old gType of the two old gStructuralFeature sig-nal1 and signal2: Signal.

Table 3 .

 3 Evolution schema based on atomic and composite operators

			Comp osite	GMo delElement	Value
	Atomic Operator GMo delElement Value ADDgCon tents GClass DataAbstract	Operator Complex1	ADDReferenceT ype ChangeTypeofStructuralFea-	Comp osition DataAbstract
	ADDgCon tents GClass	DonneesSession		ture
	ADDgCon tents GClass	AutreDonnees	Complex2	ADDReferenceT ype ChangeTypeofStructuralFea-	Comp osition DataAbstract
				ture

The underlying language used to formally define data is XML formatted according to a CNES standard named XIF. XIF is a join implementation of two CCSDS standards: CCSDS 644.0-B-3 Data Description Language EAST Specification and CCSDS 647.1-B-1 Data Entity Dictionary Specification Language.

[START_REF] Bedouet | GAMME, a meta-model to unify data needs in simulation modeling (WIP)[END_REF] When exchanging satellite database including telemetry and telecommand definitions with satellite manufacturers or other space partners, we may use another CCSDS standard: 660.0-B-1 XML Telemetric and Command Exchange (XTCE).

Acknowledgments. Authors would like to express their gratitude to all members of the TCS team: Patrice Carle, Romain Kervarc, Rémi Lafage, Antoine Ferlin and Rémi Plantade from ONERA. This paper relies on their excellent comments and their constructive suggestions.


Geneviève Rougon
email: genevieve.rougon@univ-amu.fr.

Sophie Brasselet

Franck Debarbieux

Advances in Intravital Non-Linear Optical Imaging of the Central Nervous System in Rodents

Keywords: Central nervous system, intravital imaging, multi-photon microscopy, fluorescent reporter mice, genetically encoded indicators, label-free optical imaging, inflammation, neurodegeneration, myelin

Purpose of review: Highly coordinated cellular interactions occur in the healthy or pathologic adult rodent central nervous system (CNS). Until recently, technical challenges have restricted the analysis of these events to largely static modes of study such as immuno-fluorescence and electron microscopy on fixed tissues. The development of intravital imaging with subcellular resolution is required to probe the dynamics of these events in their natural context, the living brain.

Recent findings: This review focuses on the recently developed live non-linear optical imaging modalities, the core principles involved, the identified technical challenges that limit their use and the scope of their applications. We highlight some practical applications for these modalities with a specific attention given to Experimental Autoimmune Encephalomyelitis (EAE), a rodent model of a chronic inflammatory disease of the CNS characterized by the formation of disseminated demyelinating lesions accompanied by axonal degeneration. Summary: We conclude that label-free nonlinear optical imaging combined to two photon imaging will continue to contribute richly to comprehend brain function and pathogenesis and to develop effective therapeutic strategies.

INTRODUCTION

Efforts to understand how the brain works require tools for observing cells, their interactions and their outcome as well as neural circuits in action. For example, in recent years, accumulated evidence has suggested that neuroinflammation was a major component of several central nervous system (CNS) pathologies such as stroke, Multiple Sclerosis [START_REF] Miron | Macrophages and CNS remyelination[END_REF], Alzheimer's disease and Parkinson's disease [START_REF] Weiner | Inflammation and therapeutic vaccination in CNS diseases[END_REF][START_REF] Schwartz | Macrophages and microglia in central nervous system injury: Are they helpful or harmful? Journal of Cerebral Blood Flow and Metabolism[END_REF], but also in schizophrenia and malignant CNS neoplasms (e.g., glioblastoma multiforme) [START_REF] Kore | Inflammatory cytokines, interleukin-1 beta and tumor necrosis factor-alpha, upregulated in glioblastoma multiforme, raise the levels of CRYAB in exosomes secreted by U373 glioma cells[END_REF], among many others. A prerequisite for a more comprehensive description of immunological processes in the brain is a thorough characterization of the dynamics and function of the different involved immune cells types. This can only be achieved at the systemic level in animal models by long-term visualization and mapping of neuro-immune cellular dynamic interactions in the living brain and their interactions with neural cells. Moreover, destabilization of myelin membrane assembly leading to swelling, vesiculation and desintegration of the myelin structure is also a common underlying feature of these neurological disorders. In the corresponding animal models, a coordinated interplay seems to exist between, immune cells and myelin [START_REF] Klein | Myelin and macrophages in the PNS: An intimate relationship in trauma and disease[END_REF]. Indeed, oligodendrocytes, the myelinating glial cells of the CNS, are, due to their high specialization and metabolic needs, highly vulnerable to various insults although they are also capable of immune-modulation and to interact with immune cells in acute and chronic CNS inflammation [START_REF] Zeis | The immunomodulatory oligodendrocyte[END_REF]. Finally, the exploration in animal models of pathologies of the relationships between neuronal activity, cognition and behavior cannot be explored in simple preparations or even in anesthetized animals, as the neural basis of behavior in normal or pathological conditions must be explored in awake behaving subjects.

The architecture of neuronal networks has been studied by different imaging techniques. Each of them has its own advantages and drawbacks. For instance, the magnetic resonance imaging (MRI) as well as its derivatives (magnetic resonance spectroscopy (MRS), magnetization transfer imaging (MTI), diffusion-weighted imaging (DWI)), although extremely powerful techniques able to map the white matter density in the whole brain and spinal cord with approximately 200 m resolution [START_REF] Kozlowski | In vivo longitudinal Myelin Water Imaging in rat spinal cord following dorsal column transection injury[END_REF], does no provide a sufficient specific information on the cellular composition of the imaged area. Positron emission tomography (PET), by the use of exogenous radioactive labeling agents, provides ≈ 1 mm resolution images that are more specific since based on direct labeling [START_REF] Stankoff | Imaging of CNS myelin by positron-emission tomography[END_REF]. These techniques, providing threedimensional (3D) reconstruction, are important tools for the diagnosis of various nervous system abnormalities especially in humans. Due to the lack of high spatial [START_REF] Vigeveno | Shifting imaging targets in multiple sclerosis: From inflammation to neurodegeneration[END_REF], and temporal resolution, the produced information, however, is poorly correlated with functional deficits and also insufficiently accurate to properly evaluate the relative amount of neurodegeneration and demyelination in rodents.

We will focus on in vivo optical microscopy techniques that have recently become a gold standard in studies involving small animals including (a) multiphoton microscopy that has found a niche in the world of biological imaging as the best noninvasive means of fluorescence microscopy in living animals. Coupled with transgenic mouse models and genetically encoded fluorescent indicators, its use is now increasing exponentially, (b) several labelfree optical methods currently available for in vivo imaging with high resolution of myelin architecture and myelin-producing cells. Fluorescence tomography and opto-acoustic tomography [START_REF] Arranz | Advances in optical imaging for pharmacological studies[END_REF] that do not provide cellular resolution will not be discussed. Finally, we will illustrate that information on tissue morphology, cell behavior or diseases' states can be obtained by combining different contrasts for instance multiphoton fluorescence with coherent label-free microscopes [START_REF] Kwan | Optical visualization of Alzheimer's pathology via multiphoton-excited intrinsic fluorescence and second harmonic generation[END_REF].

MULTIPHOTON EXCITED FLUORESCENCE MICROSCOPY

Principle and properties of two photon microscopy

Recent developments in fluorescent imaging technology together with the generation of transgenic mice expressing fluorescent proteins linked to specific endogenous proteins make the mouse a powerful model organism to study cell populations in the brain. In two-photon excitation fluorescence, two near-infrared photons (700 to 1200 nm wavelength) are absorbed simultaneously, where each photon provides half of the energy, which is normally required to excite the fluorophore into a higher electronic state. Therefore, emission of fluorophores in wavelengths that fall in visible light or UV region can be induced with low-energy near-infrared photons. The near-infrared excitation spectrum is unique as the linear absorption and scattering coefficients contributed by cells and tissues is low in this wavelength range, which leads to a high light penetration depth [START_REF] Niesner | Intravital two-photon microscopy: Focus on speed and time resolved imaging modalities[END_REF][START_REF] Hoover | Advances in multiphoton microscopy technology[END_REF]. Two-photon excitation fluorescence, however, is achieved only at very high photon concentration in space and time, requiring extremely high near infra-red laser intensities. The development of ultra-short pulsed lasers can now provide transient intensities of GW/cm 2 in a pulsed form, with the pulse duration of 100 femtoseconds to picoseconds range, at a high pulse rate of 80-90 MHz. As a result two-photon excited fluorescence signals can be generated effectively at average laser powers lower than 5 mW incident on the tissue. In addition, the twophoton absorption occurs only in the plane of focus, minimizing background scatter from regions outside focus. In particular, two photon excited fluorescence microscopy allows subcellular-scale 3D imaging of complex media over depths of several hundreds of micrometers (typically 1000 in the cortex and 150 in the spinal cord), and it has become an indispensable tool for volume and live-tissue studies. It thus provides deeper penetration of live tissues with reduced phototoxicity and photobleaching in comparison to classical confocal microscopy. The technique is qualified as non or minimally invasive, which makes it useful for imaging the physiology, morphology, and cell-cell interactions in neural tissue of living animals with high spatial and temporal resolution.

At the cost of depth penetration, the imaging speed can be increased up to 1000 images/seconds on the most fluorescent objects using multibeam excitation of the fluorescence [START_REF] Qu | Recent Progress in Multifocal Multiphoton Microscopy[END_REF].

Moreover, nanomolar concentrations of fluorophores can be detected in vivo in volumes as small as few m 3 [START_REF] Malcor | Chemical optimization of new ligands of the low-density lipoprotein receptor as potential vectors for central nervous system targeting[END_REF], comparable to PET scan sensitivity of femtomolar concentrations in mm 3 .

Studies including analysis of single neurons as well as neural network are challenging. The small size of neuronal dendrites and spines combined with the high speed of neurophysiological signals, such as transients in membrane potential or ion concentration, necessitates that functional study of these structures uses recording methods with not only high spatial but also high temporal resolutions. In this regard, conventional two-photon microscopy, in combination with fluorescent indicators sensitive to physiological parameters, has proven to be only a partial solution by providing near-diffraction-limited spatial resolution even when imaging structures are deep inside light-scattering tissue. This is because the relatively slow beam-scanning methods used severely limit the extent to which functional data can be recorded. In this regard, a recent method of laser scanning for multiphoton microscopy overcomes the temporal limitations of previous approaches and allows for what is known as 3D Random Access Multiphoton (3D RAMP) microscopy, an imaging technique that supports full 3D recording of many sites of interest on physiologically relevant time scales [START_REF] Reddy | Randomaccess multiphoton microscopy for fast three-dimensional imaging[END_REF].

Multicolor imaging

Until recently, implemented methodology did not provide simultaneous, efficient and independent multiphoton excitation of more than two spectrally distinct chromophores whereas integrative biological studies require to visualize and track various cell populations. The main challenges toward this goal are adding multiple non descaned detectors with suited band pass filters or ultimately using detector arrays to acquire and analyze photons over the whole spectrum, labeling cells of interest by expression of new fluorescent proteins with non-overlapping emission spectra, finding optimal excitation strategies to reveal the fluorescence of all markers. Recent significant improvements have been made [START_REF] Mahou | Multicolor two-photon tissue imaging by wavelength mixing[END_REF]. Recently, Ricard & Debarbieux [START_REF] Ricard | Six-color intravital two-photon imaging of brain tumors and their dynamic microenvironment[END_REF] described a multiphoton imaging set up (Fig. 1) to dynamically characterize the interactions between five cellular components in a clinically relevant glioblastoma multiforme (GBM) model designed in reporter mice with targeted cell populations labeled by fluorescent proteins of different colors. They made non-invasive longitudinal and multi-scale observations of cell-to-cell interactions on a daily basis from volumes of interest, covering most of the mouse parietal cortex at subcellular resolution (Fig. 2).

FLIM and FRET imaging

Technological advances have made also a significant contribution to the ability to extend fluorescent two photon imaging techniques beyond that of simple morphological analysis. Three microscopic methodologies take advantage of fluorescent probe properties: fluorescence recovery after photobleaching (FRAP), fluorescence lifetime imaging microscopy (FLIM), and fluorescence resonance energy transfer (FRET). FRAP provides valuable information about the mobility of molecules on surfaces and within cells, and can be used to monitor molecular assemblies and the dynamics of complex domains over time. FLIM uses a short pulsed light source (femtoseconds to picoseconds) to excite the sample and the subsequent fluorescence emission decay is recorded by high-speed detectors. FLIM monitors the environment around fluorophores by altering the characteristic lifetime of those molecules. As an example, Rusakov's team [START_REF] Zheng | Time-resolved imaging reveals heterogeneous landscapes of nanomolar Ca(2+) in neurons and astroglia[END_REF] has implemented and validated the method of monitoring low resting [Ca 2+ ] and [Ca 2+ ] dynamics in neurons and astroglia in situ at high resolution using two-photon excitation FLIM of the common Ca 2+ indicator OGB-1. They unveiled some previously unrecognized features of intracellular Ca 2+ homeostasis that might have fundamental implications for cell function.

FRET monitors the transfer of energy between two fluorophores with significant spectral overlap, when they come within nanometer distances (2-10 nm) of one another. Several sensors have been developed to use FRET to study in a direct manner a wide variety of cell based dynamic biological events ( [START_REF] Lam | Improving FRET dynamic range with bright green and red fluorescent proteins[END_REF][START_REF] Maysinger | Nanoparticle-based and bioengineered probes and sensors to detect physiological and pathological biomarkers in neural cells[END_REF]; see also section 3).

FRET can be combined to FLIM to simultaneously assess the biochemical environment. FRET measurements are dependent on the acceptor/donor ratio, but FLIM-FRET behaves independently of the donor concentration given that fluorescence lifetime is inherent to each fluorophore and its surrounding environment in a concentration independent manner. Although the in vivo FLIM-FRET technique is still in its infancy it has already captivated researchers due to its unique live imaging ability [START_REF] Ueda | Application of FRET probes in the analysis of neuronal plasticity[END_REF][START_REF] Byrne | The use of two-photon FRET-FLIM to study protein interactions during nuclear envelope fusion In Vivo and In Vitro[END_REF].

Preparations for chronic imaging

Non invasive chronic imaging of the same area in the CNS for long periods of time (i.e. days) had required to design techniques minimizing invasiveness to access the cortex or the spinal cord. Concerning the cortex, the thinned skull and cranial window techniques dominate in the literature. Thinned skull technique consists of drilling the skull enough to allow imaging of the underlying brain [START_REF] Drew | Chronic optical access through a polished and reinforced thinned skull[END_REF]. The proposed advantage of this method is the reduced risks of tissue injury and microglial activation, but the inconvenient is the inhomogeneity of the overlying bone layer, hence the inhomogeneous optical properties and the limited depth accessible. Cranial window technique consists of the full removal of part of the skull and its replacement by a thin glass window. Sensitivity and imaging depth are much improved with full removal of the skull. Careful consideration of both sterile technique and surgical expertise are paramount to a successful imaging experiment regardless of the approach taken since both techniques can lead to transient inflammation. A very comprehensive review comparing and contrasting the pros and cons of each method had been recently published [START_REF] Dorand | Comparison of intravital thinned skull and cranial window approaches to study CNS immunobiology in the mouse cortex[END_REF]. Similarly, a few techniques have been developed for implanting and maintaining windows over the spinal cords of adult mice (Fig. 3) for highthroughput imaging data acquisition [START_REF] Farrar | Chronic in vivo imaging in the mouse spinal cord using an implanted chamber[END_REF][START_REF] Fenrich | Long-term in vivo imaging of normal and pathological mouse spinal cord with subcellular resolution using implanted glass windows[END_REF][START_REF] Fenrich | Implanting glass spinal cord windows in adult mice with experimental autoimmune encephalomyelitis[END_REF].

Limitations of multiphoton microscopy

Although multiphoton microscopy is clearly a very promising technology, it faces some challenges. First of all, it requires the presence of fluorescence in the sample that has to be introduced in the living animals as discussed in the following section. Then, because each image is acquired pixel by pixel, the volume of tissue that can be imaged in a reasonable time (typically one hour) is small (i.e. a few mm 3 ). Therefore, the density of fluorescent objects of interest within the imaged tissue must be high enough to ensure that they will be present in the imaged area. Nevertheless, parcimonious density of fluorescent objects is preferable to uniform distribution in the whole tissue since the contrast is optimal for objects surrounded CFP: Astrocytes by dark or different colors. Another limitation is the presence of physiological respiratory and heart movements that impede the desired micrometric resolution of cell processes. These movements can however be partially compensated by image acquisition gating [START_REF] Taylor | Real-time optical gating for threedimensional beating heart imaging[END_REF], on line autofocussing and post-acquisition registration [START_REF] Rohde | Correction of motion artifact in cardiac optical mapping using image registration[END_REF].

FLUORESCENT LABELS

Exogenous fluorescent labels

Exogenous fluorescent labels (Table 1) can be used to label vascularization or cells of interest ex-vivo prior to reinjection in the animal. For example blood vessels can be highlighted by injection in a tail vein of a fluorescent marker such as 70kDa dextran, conjugated with either fluorescein or rho-damineB isothiocyanate or Cascade Blue (example in Fig. 2). Interest of fluorescent dextrans is that they can be uploaded by macrophages from the plasmatic compartment within a few hours, resulting in a long term intravital labeling of macrophagic populations. For selective labeling of the vascular compartment, quantum dots have to be preferred to labeled dextran as they barely accumulate in phagocytic cells [START_REF] Fenrich | Long-term in vivo imaging of normal and pathological mouse spinal cord with subcellular resolution using implanted glass windows[END_REF]. Quantum dots are small, inorganic nanoparticles that emit a specific wavelength of light depending on their size, from ultraviolet to near infrared. Moreover, these nanoparticles are promising scaffolds for imaging applications, for example designing quantum-dot surfaces with various functionalities, for use as nanosensors or as fluorescent probes [START_REF] Maysinger | Nanoparticle-based and bioengineered probes and sensors to detect physiological and pathological biomarkers in neural cells[END_REF]. In the context of studying neuroinflammation, some of the earliest studies to visualize the immune response in situ were performed using adoptive transfer of exogenously labeled cells. Cell populations collected in vivo were purified and labeled with fluorescent dyes with non-overlapping emission spectra, such as CFSE [START_REF] Koyanagi | A comparative study of colorimetric cell proliferation assays in immune cells[END_REF], SNARF [START_REF] Singh | Use of SNARF-1 to measure murine T cell proliferation in vitro and its application in a novel regulatory T cell suppression assay[END_REF], CMAC [START_REF] Tauskela | Evaluation of glutathione-sensitive fluorescent dyes in cortical culture[END_REF] or DiI [START_REF] Vandepitte | An improved orthotopic rat bladder tumor model using Dil-loaded fluorescent AY-27 cells[END_REF]. Although it has been useful for short-term trafficking and migration studies, cell division however resulted in the dilution of dyes and made them difficult to track.

Cell-type-specific expression of fluorophores

Genetically encoded proteins (Table 1) represent fantastic new tools for long term labeling, manipula-tion and characterization of inflammatory cells in real time and with cellular specificity. Since the breakthrough discovery of the green fluorescent protein (GFP) in 1978 a protein that can be excited with blue light to emit green fluorescence, a large number of other fluorescent proteins have been discovered and constantly optimized. Today, they comprise a palette of proteins spanning the spectrum from ultraviolet to far red, laying the basis for multicolor imaging [START_REF] Stepanenko | Modern fluorescent proteins: From chromophore formation to novel intracellular applications[END_REF]. Usually, they exhibit very high extinction coefficients and very high quantum yields, properties making them very bright. Genes coding for fluorescent proteins can be introduced into the mouse genome by either random transgenesis or targeted transgenesis via homologous recombination [START_REF] Hadjantonakis | Embryonic stem cells and mice expressing different GFP variants for multiple non-invasive reporter usage within a single animal[END_REF]. It is also possi- ble to introduce conditional genome alterations that are spatially and temporally restricted by combining the Cre/loxP site-specific recombination system with random transgenesis technologies [START_REF] Lobe | Z/AP, a double reporter for cre-mediated recombination[END_REF]. There was a report on labeling neurons with multiple colors using a mixture of several fluorescent proteins expressed under the neuron-specific Thy1 promoter, obtained by random Cre recombination, which produced more than 100 colorful hues that could be distinguished by fluorescence microscopy [START_REF] Livet | Transgenic strategies for combinatorial expression of fluorescent proteins in the nervous system[END_REF]. This technique, called Brainbow, uses Cre-mediated recombination to generate a random mix of several fluorescent proteins expressed in individual neurons and is expected to facilitate the deciphering of complex neuronal circuits. The fluorescent protein tags can be cytoplasmic, nuclear or membranous. Care should be taken that the localization of the large fluorescent protein does not impede the cell physiology. Experience seems to indicate that cytoplasmic expression is usually safe for the cells. Moreover, beside specific cell targeting, fluorescent molecules can be linked to multiple proteins with various functions, e.g. calcium [START_REF] Jiang | Imaging intracellular Ca(2)(+) signals in striatal astrocytes from adult mice using genetically-encoded calcium indicators[END_REF],

voltage [START_REF] Gong | The evolving capabilities of rhodopsin-based genetically encoded voltage indicators[END_REF], glutamate [START_REF] Marvin | An optimized fluorescent probe for visualizing glutamate neurotransmission[END_REF] or caspases [START_REF] Yan | Poly beta-cyclodextrin/TPdye nanomicelle-based twophoton nanoprobe for caspase-3 activation imaging in live cells and tissues[END_REF] sensors. These conjugated molecules allow the activation of the fluorescent protein when the sensor binds to the appropriate stimulus molecule.

Optical indicators for fast circuit imaging

Electrical signals are fundamental to cellular sensing, communication and motility. Understanding how brain functions emerge from these electrical signals requires a methodology to monitor membrane voltage transients from large numbers of cells at high spatiotemporal resolution. Voltage-sensitive dye imaging enables visualization of information processing in different areas of the brain with reasonable spatial and temporal resolution (see above). It employs different chemical compounds to transduce neural activity directly into the changes in intrinsic optical signal. Voltage-sensitive dyes are chemical probes that reside in the neural membrane and change their fluorescence or absorbance in response to membrane potential changes [START_REF] Zecevic | Imaging nervous system activity with voltage-sensitive dyes[END_REF]. However, by far the most widely used reporters of neural activity rely on detection of Ca 2++ . Action potentials lead to opening of voltage-gated calcium channels, and thus to an elevation in cytoplasmic Ca 2++ concentration, typically from 50 -100 nM to 5-10 microM; the Ca 2++ transient decays over 100-500 ms [START_REF] Grienberger | Imaging calcium in neurons[END_REF]. Development of cell permeable calcium dyes has contributed greatly to the understanding of intracellular calcium signaling. Dyes such as Fluo-5N with fast off rates have been commonly used to monitor calcium dynamics. However, the use of these small molecule dyes suffers from several limitations including invasive staining procedures and blindness toward cellular diversity. The field has recently gained a fresh powerful momentum with the development of genetically encoded calcium or voltage indicators [START_REF] Knopfel | Genetically encoded optical indicators for the analysis of neuronal circuits[END_REF][START_REF] Carandini | Imaging the awake visual cortex with a genetically encoded voltage indicator[END_REF] one recently useful example of which is the GCaMP6 family [START_REF] Chen | Ultrasensitive fluorescent proteins for imaging neuronal activity[END_REF].

TWO PHOTON IMAGING OF NEUROINFLAMMATION

Reporter fluorescent mice

In vivo two photon imaging data can assist in achieving an understanding of the dynamics of mechanisms involved in inflammation at the cellular level. The brain parenchyma is separated from peripheral tissue by the blood brain barrier, which prevents blood immune cells from entering brain under normal conditions. Many neurological disorders are associated with a blood-brain barrier disruption that strongly impact the integrity of the neurovascular unit, composed of cerebral blood vessels, glial cells and neurons.

The CNS has its own innate immune system and microglial cells are the resident macrophages of the brain [START_REF] Gomez Perdiguero | Tissue-resident macrophages originate from yolk-sac-derived erythro-myeloid progenitors[END_REF]. The generation of various reporter mice expressing fluorescent proteins under promoters specific for different myeloid cell types (e.g. dendritic cells, neutrophils, macrophages) has permitted long-term tracking of many different types of immune cells in vivo [START_REF] Alferink | Compartmentalized production of CCL17 in vivo: Strong inducibility in peripheral dendritic cells contrasts selective absence from the spleen[END_REF][START_REF] Lindquist | Visualizing dendritic cell networks in vivo[END_REF][START_REF] Faust | Insertion of enhanced green fluorescent protein into the lysozyme gene creates mice with green fluorescent granulocytes and macrophages[END_REF][START_REF] Hume | Applications of myeloid-specific promoters in transgenic mice support in vivo imaging and functional genomics but do not support the concept of distinct macrophage and dendritic cell lineages or roles in immunity[END_REF][START_REF] Jung | Analysis of fractalkine receptor CX(3)CR1 function by targeted deletion and green fluorescent protein reporter gene insertion[END_REF][START_REF] Wolf | Microglia, seen from the CX3CR1 angle[END_REF]. Nevertheless, until recently most of these mice have been generated using GFP thus limiting the analysis of several reporter genes in the same animal. For more information, there are a few databases of mouse lines that list available reporter mouse lines (http:// www.informatics.jax.org/ and http://jaxmice.jax.org/ research/fluorescent proteins or lacZ.html). However, it is still difficult for researchers to search for reporter mouse lines that meet their specific purposes and thus a new database of reporter mouse lines expressing fluorescent markers that would facilitate this search would be interesting.

Moreover, the most important consequence of neuroinflammation being damage to axons/neurons, it is particularly interesting to generate mice allowing their observation. In this line, the difference in fluorescence emission spectra for eGFP, eYFP and CFP allows for intravital identification of the three fluorophores in triple labeled Thy1-CFP//LysM-eGFP//CD11c-eYFP animals [START_REF] Fenrich | Longand short-term intravital imaging reveals differential spatiotemporal recruitment and function of myelomonocytic cells after spinal cord injury[END_REF]. In the context of research on neuroinflammation, these animals with CFP expression in subpopulations of neurons [START_REF] Feng | Imaging neuronal subsets in transgenic mice expressing multiple spectral variants of GFP[END_REF], eGFP expression in peripheral myelomonocytic cells including neutrophils, granulocytes, circulating macrophage precursors and activated infiltrating macrophages [START_REF] Faust | Insertion of enhanced green fluorescent protein into the lysozyme gene creates mice with green fluorescent granulocytes and macrophages[END_REF], and eYFP expression in a subset of myelomonocytic cells resident to the CNS including a subset of microglia [START_REF] Hume | Applications of myeloid-specific promoters in transgenic mice support in vivo imaging and functional genomics but do not support the concept of distinct macrophage and dendritic cell lineages or roles in immunity[END_REF] are particularly appropriate to study the contribution of resident and peripheral immune cells in neuropathology.

To explore how blood brain barrier disruption instigates and amplifies immune and degenerative responses, Ryu JK. et al. [START_REF] Ryu | Blood coagulation protein fibrinogen promotes autoimmunity and demyelination via chemokine release and antigen presentation[END_REF] developed an experimental setting for exploring the cascade of pathogenic events that directly follows from the leakage of plasma proteins in the white matter. They established that fibrinogen is a major protein in the blood that drives sustained neuro-inflammatory responses in the CNS. This was first substantiated in vivo, where a single stereotactic injection of fibrinogen in the corpus callosum induced recruitment and local differentiation of myelin antigen-specific Th1 cells leading to demyelination. Then the authors injected fibrinogen into Ccr2-RFP//Cx3cr1-GFP mice, which differentially label resident microglia (GFP + ) and inflammatory monocytes (RFP + ). They quickly detected RFP + cells after injection and propose that the extravascular fibrinogen triggers recruitment of inflammatory monocytes into the CNS.

Imaging neurodegeneration in the pathological spinal cord

Intravital multicolor imaging has also increased our understanding of events occurring during neurodegeneration. For example, Fenrich et al. [START_REF] Fenrich | Longand short-term intravital imaging reveals differential spatiotemporal recruitment and function of myelomonocytic cells after spinal cord injury[END_REF] performed a spinal cord lesion in the Thy1-CFP// LysM-eGFP//CD11c-eYFP mice. They provided a quantitative assessment of recruitment and redistribution dynamics of infiltrating LysM + cells and resident CD11c + cells over the long term in the same ani-mals, as well as their real-time interactions with axons after spinal cord injury (Fig. 4). The findings showed that infiltrating LysM + cells are rapidly recruited to injury sites and are associated with the collapse of distended axon terminals caudal of injury sites. Conversely, resident microglial CD11c + cells peaked at the injury sites later, and are more closely associated with clearing axon debris rostral of injury sites. The mice and protocol described in this study can be used for studying inflammation and cell interaction dynamics in other pathologies such as EAE [START_REF] Fenrich | Implanting glass spinal cord windows in adult mice with experimental autoimmune encephalomyelitis[END_REF].

Two photon Imaging of axonal transport in EAE

Intravital imaging has also increased our understanding of axonal pathology in neuroinflammation. Axonal transport impairment has been described in classical neurodegenerative diseases. Axonal transport is the process by which cargo shuttles long distances between the neuronal cell body and synapses along the axon; it allows axons to be supplied with lipids, proteins, and organelles from the soma (through anterograde transport), while components that require degradation or recycling are trafficked back to the cell body (through retrograde transport). Nikic et al. [START_REF] Nikic | A reversible form of axon damage in experimental autoimmune encephalomyelitis and multiple sclerosis[END_REF] using in vivo two-photon microscopy on an EAE mouse model observed for the first time that axonal abnormalities start with focal swellings, and while some axons degenerate, in others swellings are reversible resulting in spontaneous recovery. More recently, the same group [START_REF] Sorbara | Pervasive axonal transport deficits in multiple sclerosis models[END_REF] used mice in which the transport of single, fluorescentlylabeled organelles mitochondria (in Thy1-MitoCFP mice) and peroxisomes (in Thy1-PeroxiYFP mice) can be tracked along the spinal axons in vivo (Fig. 5). The group directly assayed organelle trafficking and microtubule stability along individual spinal axons following EAE induction. Intriguingly, they found that transport was not only reduced in the degenerating axons but also in the majority of normalappearing myelinated axons found in the lesions, indicating that transport disturbances may promote and demarcate the more pervasive axonal dysfunction that precedes progressive degeneration. A net deficiency of organelle delivery from the cell body to the synapses was observed as anterograde rather than retrograde transport was more significantly affected. This also coincided with organelle arrest in areas of neuroinflammation such that mitochondrial accumulation, for example, was observed within the lesions. Importantly, this study also showed that both a standard anti-inflammatory treatment, as well as a redox-scavenging protocol, efficiently reversed the transport deficit. Altogether their in vivo observations strongly suggested that reactive species released by activated immune cells in neuroinflammatory lesions acutely induce transport disruptions by altering the attachment of motor/cargo complexes to microtubules.

In a similar topics, Gonzalez et al. [START_REF] Gonzalez | In vivo time-lapse imaging of mitochondria in healthy and diseased peripheral myelin sheath[END_REF] used viral vectors to specifically transduce mito-dsRed2 that selectively labels mitochondria, in the sciatic nerve of living mice. They reliably imaged live animals under anesthesia for approximately 5 h. The parameters recorded in living animals were characteristic /indicative of healthy mitochondria under physiological condition. They also showed that changes in mitochondrial parameters correlate with alteration of mitochondria physiology and with mitochondrial diseases in peripheral nerves.

LABEL-FREE OPTICAL METHODS

Recently, several optical imaging modalities have been developed, which do not require fluorescence labeling, relying rather on local refractive index properties or intrinsic responses from the biological sample. Among those label-free methods, the most used exploit non linear optical signals directly provided by the biological molecules themselves. We will briefly cover those techniques and present optical coherence tomography (OCT) [START_REF] Arous | Single myelin fiber imaging in living rodents without labeling by deep optical coherence microscopy[END_REF][START_REF] Henry | In vivo optical microscopy of peripheral nerve myelination with polarization sensitive-optical coherence tomography[END_REF], third harmonic generation (THG) [START_REF] Farrar | In vivo imaging of myelin in the vertebrate central nervous system using third harmonic generation microscopy[END_REF][START_REF] Lim | Label-free imaging of Schwann cell myelination by third harmonic generation microscopy[END_REF], confocal reflectance [START_REF] Schain | Label-free in vivo imaging of myelinated axons in health and disease with spectral confocal reflectance microscopy[END_REF] and vibrational microscopy (Coherent Anti Stokes Raman -CARS-microscopy) [START_REF] Wang | Coherent anti-stokes Raman scattering imaging of axonal myelin in live spinal tissues[END_REF]. We will illustrate the knowledge that nonlinear microscopy brings on endogenous molecules and their architecture, in particular in collagen, myelin and myelin-producing cells.

Characteristic features of myelin

Since label free optical microscopy techniques dedicated to neuro-imaging essentially image myelin, we remind here a few essential properties that make myelin a source of label-free contrast. One of the most important features defining the functionality of myelin is its unique composition. The myelin sheath is composed of lipids (≈ 70% by dry weight) and proteins (≈ 30% by dry weight) with a low water content [START_REF] Quarles | Myelin Formation, Structure and Biochemistry[END_REF]. The components of the myelin sheath present the extraordinary ability to self-assemble into a highly ordered and stable structure. The balanced intermolecular forces between lipids as well as between lipids and proteins results in the formation of organized and tightly packet multi-bilayers, which makes it a high refractive index structure, moreover strongly birefringent. Myelin Basic Protein (MBP) by acting as an electrostatic glue between negatively charged bilayers and positively charged amino acids groups, maintain the structure and stability of the cytoplasmic region of the myelin sheath [START_REF] Min | Interaction forces and adhesion of supported myelin lipid bilayers modulated by myelin basic protein[END_REF]. Moreover, the adhesion between bilayers highly depends on the lipid composition [START_REF] Banquy | Adhesion and hemifusion of cytoplasmic myelin lipid membranes are highly dependent on the lipid composition[END_REF], in particular, the ratio between the saturated to unsaturated lipid molecules. Interestingly, the lipid composition differs between the healthy and EAE white matter and the ratio of charged to uncharged lipids [START_REF] Husted | In vivo detection of myelin phospholipids in multiple sclerosis with phosphorus magnetic resonance spectroscopic imaging[END_REF][START_REF] Lee | Lipid domains control myelin basic protein adsorption and membrane interactions between model myelin lipid bilayers[END_REF] is modified. A possibility is that alterations in the distribution of intermolecular forces decreases the myelin stability and provokes the swelling process. Moreover, such lipid abnormalities have been reported in the EAE white matter before the apparition of lesions [START_REF] Laule | Diffusely abnormal white matter in multiple sclerosis: Further histologic studies provide evidence for a primary lipid abnormality with neurodegeneration[END_REF].

Optical coherence tomography

Optical coherence tomography (OCT) is based on the evaluation of the interference between nearballistic backscattered photons from the sample, with unscattered light from a reference low-coherence beam. Thus, OCT provides information about the different reflective layers in a sample that are due to differences in refractive indexes. Many studies exploring its diagnostic feasibility for several tissue types have been conducted, particularly, real-time in vivo applications. A full Field OCT has been developed based on a large field of view imaging combined with high-frame-rate interferometry [START_REF] Arous | Single myelin fiber imaging in living rodents without labeling by deep optical coherence microscopy[END_REF]. This tool has been employed as a deep optical coherence microscope to measure the density of individual myelinated fibers in the rat cortex over a large volume of gray matter, providing perspectives for myelin chronic imaging in demyelinating diseases. Henry et al. [START_REF] Henry | In vivo optical microscopy of peripheral nerve myelination with polarization sensitive-optical coherence tomography[END_REF] demonstrated that polarization sensitive-OCT (PS-OCT) is capable of providing nondestructive and quantitative assessment of nerve health after injury. They assessed the sciatic nerve microenvironment through measurements of birefringence after applying a nerve crush injury in a rat model. Initial loss of function and subsequent recovery were demonstrated by calculating the sciatic function index. They reported that the PS-OCT phase retardation slope, which is proportional to birefringence, increased monotonically with the sciatic function index. Additionally, morphometric analysis of the myelin thickness showed that the PS-OCT slope is a good indicator of myelin health and recovery after injury. However, this technique, which measures the difference in the refractive index between tissues components, is very sensitive to experimental motion artifacts.

Second and third -harmonic generation microscopy

When illuminated by short pulses of high-peak power (typically around 100 femtosecond in time width, 80 MHz repetition rate, 10 to 100 mW average power) in the near infrared spectral range, ordered structures such as collagen, tubulin and myosin are responsible for the creation of a coherent radiation which photons exhibit double energy, in a process known as second harmonic generation (SHG). The characteristics of SHG originate from its requirements regarding the structure of the sample, which should not possess any center of symmetry (e.g. typically obtained from helical arrangement of amino acids in collagen) [START_REF] Campagnola | Second harmonic generation imaging microscopy: Applications to diseases diagnostics[END_REF]. SHG microscopy imaging thus provides an endogenous contrast modality that allows for morphological observation in collagenrich or microtubule-rich structures such as the brain and the lymph nodes [START_REF] Winkler | High resolution three-dimensional reconstruction of the collagenous matrix of the human optic nerve head[END_REF][START_REF] Guc | Long-term intravital immunofluorescence imaging of tissue matrix components with epifluorescence and twophoton microscopy[END_REF]. The lateral and axial resolutions for two-photon excited fluorescence as given above are also valid for SHG microscopy (see for example Fig. 2).

At a higher order, any material exhibits the possibility to emit a photon of triple energy of the incident photon under pulsed excitation regime, in a process called Third Harmonic Generation (THG). THG microscopy is able in particular to image structures that exhibit high refractive index [START_REF] Debarre | Imaging lipid bodies in cells and tissues using third-harmonic generation microscopy[END_REF], which is very strong in myelin [START_REF] Farrar | In vivo imaging of myelin in the vertebrate central nervous system using third harmonic generation microscopy[END_REF]. Using THG microscopy Lim et al. [START_REF] Lim | Label-free imaging of Schwann cell myelination by third harmonic generation microscopy[END_REF] visualized Schwann cell myelination in live culture and ex vivo tissue. THG is capable of imaging compact myelin as well as non-compact subcellular domains, including nodal segments, Schmidt-Lanterman incisures, and Cajal bands, thus attractive for elucidating their functional roles. So far, in vivo THG imaging of sciatic nerve in live mice prove to be more difficult. Indeed, although the brightness of the images obtained is comparable to the ex vivo conditions, the resolution of imaging is degraded by motion artifacts (Fig. 6). The movement of nerves as much as a few micrometers per second, especially in the axial direction, confound g-ratio analysis (a well known parameter to define myelin state). Nevertheless, nodes and incisures are visible in living mice. As for PS-OCT, suitable methods to immobilize in vivo tissue should further improve the overall image quality.

Spectral confocal reflectance microscopy

Spectral confocal reflectance microscopy takes advantage of the high refractive index contrast of lipid-rich myelin and uses a conventional laserscanning confocal system to generate images by merging the simultaneously reflected signals from multiple lasers of different wavelengths. Schain et al. [START_REF] Schain | Label-free in vivo imaging of myelinated axons in health and disease with spectral confocal reflectance microscopy[END_REF] described the striking color patterns generated that appear unique to individual myelinated fibers allowing their tracing in dense axonal areas. Although the precise mechanism for the multicolor reflection is not clear, it is likely that the thickness and number of layers of myelin may determine the wavelengths that are preferentially reflected, with further variability due to focal irregularities in myelin sheath thickness, lipid composition and other local cellular variables. In any case the patterns highlight nodes of Ranvier and Schmidt-Lanterman incisures and can be used to detect various myelin pathologies. For example, they carried out chronic brain imaging up to 400 m deep, capturing de novo myelination of mouse cortical axons in vivo. Confocal reflectance as THG, are specifically sensitive to the inner and outer interface of the myelin sheath and not necessarily to its volume. Complementary studies are therefore still needed to find more reliable optical contrasts.

Coherent anti-Stokes Raman Scattering imaging

Vibrational microscopy (Raman microscopy and infrared microscopy), which observes molecular vibrations, gives information of molecular species without staining. However, infrared radiation is absorbed with water, and the long wavelength (3-10 m) limits the spatial resolution to several micrometers. While Raman processes are known to be of very low scattering cross sections, Coherent anti-Stokes Raman scattering (CARS) relies on a stimulated Raman process induced by a nonlinear excitation by two incident synchronized pulsed lasers [START_REF] Zumbusch | Three-dimensional vibrational imaging by coherent anti-stokes raman scattering[END_REF]. The coherent nature of this process strongly increases the signal, while benefiting from the advantages of nonlinear microscopy (near infra red illumination penetration depth, sub-micrometric resolution in particular). The vibrational based con- trast CARS has been applied to visualize the myelin sheath for the first time by Wang et al. in 2005 [66]. They have demonstrated that CARS microscopy, sensitive to the CH2 vibration modes, permits to retrieve the molecular orientation and vibrational spectral profiles of myelin lipids in a live spinal tissue in epi-(E-CARS) and forward (F-CARS) directions. Since this time, CARS microscopy applied to neuronal tissues has been extensively developed. Myelin from the CNS or peripheral nervous system (PNS) was observed in vivo [START_REF] Fu | Ex vivo and in vivo imaging of myelin fibers in mouse brain by coherent anti-Stokes Raman scattering microscopy[END_REF][START_REF] Wang | Increasing the imaging depth of coherent anti-Stokes Raman scattering microscopy with a miniature microscope objective[END_REF][START_REF] Belanger | In vivo evaluation of demyelination and remyelination in a nerve crush injury model[END_REF]. Now, it is possible to perform 3D high resolution, video-rate CARS images [START_REF] Imitola | Multimodal coherent anti-Stokes Raman scattering microscopy reveals microglia-associated myelin and axonal dysfunction in multiple sclerosis-like lesions in mice[END_REF] in live animals over weeks [START_REF] Shi | Longitudinal in vivo coherent anti-Stokes Raman scattering imaging of demyelination and remyelination in injured spinal cord[END_REF]. CARS microscopy in combination with other techniques, such as electrophysiology for instance, have allowed to observe the myelin envelope behavior following high-frequency stimulation [START_REF] Huff | Realtime CARS imaging reveals a calpain-dependent pathway for paranodal myelin retraction during high-frequency stimulation[END_REF]. Although small details of the myelin morphology, such as the node of Ranvier or the Schmidt-Lanterman clefts, had been already observed by CARS microscopy [START_REF] Wang | Coherent anti-stokes Raman scattering imaging of axonal myelin in live spinal tissues[END_REF], the recently developed method for resolution enhancement and suppression of distortions [START_REF] Gasecka | Resolution and contrast enhancement in coherent anti-Stokes Raman-scattering microscopy[END_REF][START_REF] De Vito | RP-CARS: Label-free optical readout of the myelin intrinsic healthiness[END_REF] and polarization sensitivity [START_REF] Bioud | Structure of molecular packing probed by polarization-resolved nonlinear four-wave mixing and coherent anti-Stokes Raman-scattering microscopy[END_REF] has given a possibility to obtain even more insights into the structural information of myelin. With these advances, the mechanistic disruption of myelin in various models has been studied. Bégin et al. [START_REF] Begin | Local assessment of myelin health in a multiple sclerosis mouse model using a 2D Fourier transform approach[END_REF] have developed an approach to calculate the average orientation and the direc-tional anisotropy within a myelin image domain. They also introduced a new quantitative parameter, the corrected correlation parameter (CCP), used to assess myelin health locally on images taken from the spinal cord of mice at different stages of a EAE pathology. They showed that the CCP can be used to quantify the degree of organization/disorganization in the myelin structure. As a proof of concept they showed that in the EAE mice, the number of sites with a lower CCP increases as the disease progresses. The CCP could therefore be developed into a parameter for pre-symptomatic detection of myelin disruption. It has also been found, for instance, that degraded myelin has higher degree of lipids unsaturation and lower lipid-packing ordering as compare to normal myelin, and that the lipid conformation is not fully recovered in regenerated fibers. Moreover, analysis of tissue sections uncovered that the density of myelin is strongly affected with structures showing decreasing straightness with disease progression [START_REF] Imitola | Multimodal coherent anti-Stokes Raman scattering microscopy reveals microglia-associated myelin and axonal dysfunction in multiple sclerosis-like lesions in mice[END_REF][START_REF] Fu | Paranodal myelin retraction in relapsing experimental autoimmune encephalomyelitis visualized by coherent anti-Stokes Raman scattering microscopy[END_REF][START_REF] Begin | Automated method for the segmentation and morphometry of nerve fibers in largescale CARS images of spinal cord tissue[END_REF].

Pros and cons of the label free techniques

It is highly probable that the label-free techniques will have a significant role to play in the future of brain research as the required technology is currently available. However, presently their use on leaving animals is still to be improved. Among the above presented techniques CARS present many advantages, providing specificity to the molecular content of myelin rather than macroscopic refractive index changes that are detected by OCT and THG, and relying on endogenous contrast without need for chemical fluorescent markers like fluorescent two photon microscopy [START_REF] Romanelli | Cellular, subcellular and functional in vivo labeling of the spinal cord using vital dyes[END_REF] which can ease its transfer to in vivo application. A serious limitation of the technique is however the depth penetration. CARS signals from myelin reach up to ≈ 250 m depths for fresh and ≈ 100 m depths for fixed nervous tissues.

It is obvious however, that imaging of myelin wrapped around the axon only provides a partial information about the structure, dynamics and functionality of the CNS. Importantly, the possibility to image myelin by CARS together with another contrasts such as reflectance [START_REF] Imitola | Multimodal coherent anti-Stokes Raman scattering microscopy reveals microglia-associated myelin and axonal dysfunction in multiple sclerosis-like lesions in mice[END_REF], two-photon fluorescence [START_REF] Fu | Paranodal myelin retraction in relapsing experimental autoimmune encephalomyelitis visualized by coherent anti-Stokes Raman scattering microscopy[END_REF][START_REF] Ylera | Chronically CNS-injured adult sensory neurons gain regenerative competence upon a lesion of their peripheral axon[END_REF] or second harmonic generation (coming from fibrous filaments of glial cells) [START_REF] Fu | Second harmonic and sum frequency generation imaging of fibrous astroglial filaments in ex vivo spinal tissues[END_REF] opens a door for the future fast live imaging of the architecture of the white matter and its changes during neurological disorders.

OUTLOOK FOR THE FUTURE

Microscopy based on different nonlinear processes can provide distinctive contrast for observing diverse endogenous cellular events and biomarkers. In this focused review, some demonstrations provided by a restricted number of research groups worldwide, active in this developing field, have been highlighted. Although these works have proven that in vivo optical imaging is already mature for specific applications in the CNS the deployment of its full potential is yet to come.

Looking into the future, we expect to see the growth of optical imaging through pioneering work in several directions. Recent developments in technical instruments and fluorescent reporter dyes and technologies will provide researchers with previously unimagined possibilities. Intravital imaging by two-photon microscopy started with the observation of a singlecell population, now the tracking of interactions between several cell types become feasible. Imaging also proceeded to intravital imaging at greater depth (to 1000 m in the cortex), longer periods of observation and highest speed.

Moreover, multimodal microscopy that combines several imaging modalities can bring together more cellular and molecular information and contrast to bring about a more profound understanding of normal and diseased CNS. Indeed, different types of nonlinear microscopy can be integrated effectively to image the same sample at the same time because they all use ultrafast lasers and laser-scanning technology for imaging (see example Fig. 2). Femtosecond sources with their extraordinary features, such as broadband frequency coverage for multiplex imaging, higher signal levels, and versatility in pulse shaping are particularly suited. The coupling of multiphoton fluorescence, second-harmonic generation, and coherent anti-Stokes Raman scattering is very promising to investigate the dynamics of events occurring in demyelinating pathologies such as EAE [START_REF] Meyer | A compact microscope setup for multimodal nonlinear imaging in clinics and its application to disease diagnostics[END_REF]. It is likely that commercial systems combining all of the necessary elements into a single package will become available, opening up the techniques to a wider audience by making the setup and operation user-friendly.

Novel approaches are also being developed such as « two-photon fiberscopes » [START_REF] Helmchen | Miniaturization of twophoton microscopy for imaging in freely moving animals[END_REF] to overcome the confounding effects of anesthesia on animals. They should allow to image cell Ca 2++ and blood flow or interactions across pairs of brain areas in a way that simultaneously provides cellular resolution within each area in awake and behaving animals [START_REF] Lecoq | Visualizing mammalian brain area interactions by dualaxis two-photon calcium imaging[END_REF]. Novel achievements in transgenic mouse technology, such as genetically encoded biosensors for different signaling pathways, will make possible to gain information about molecular events taking place inside neural cells within their spatiotemporal context. Finally, the nexus of dramatic recent developments in optogenetic probes, genetically encoded activity sensors, and novel microscopies, together allow the activity of neural circuits to be recorded and manipulated entirely using light [START_REF] Emiliani | All-Optical Interrogation of Neural Circuits[END_REF].

Noteworthy, the fiber versions of these microscopies will ultimately facilitate the combination of optical techniques with existing preclinical and clinical imaging modalities, giving access to unprecedented characterization of the cellular substrates of the contrast acquired by MRI or CT.

The shortcoming however resides in the fact that these imaging techniques provide a huge amount of data that must be stored and processed to extract the relevant information and quantitative results. This demands the development of new and specific tools and there is a real need for progresses in quantitative analysis of the data. Such effort is worth given that these advances promise to illuminate many fundamental challenges in neuroscience, from understanding the outcome of cell-cell interactions in pathologies to finding the links between neural circuit activity and behavior.

Fig. 1 .

 1 Fig. 1. Schematic representation of the two-photon microscopy setup. The excitation beam is produced by a femtosecond pulsed infrared tunable (720-1020 nm) laser. The laser power is modulated by an Acousto-Optic Modulator (AOM). The beam is scanned in the xy direction by galvanometric mirrors present in the scan head of a Zeiss LMS 7 MP two-photon microscope. The beam then passes through a LP690 dichroic mirror and is focused in the brain of the anaesthetized animal by a 20X-1.0 NA water immersion objective. The emitted epifluorescence is collected and reflected by the LP690 mirror in a non-descanned mode. The fluorescence is finally splitted and filtered using a set of dichroic mirrors and filters and collected by a set of 5 non-descanned detectors mounted in cascade (NDD). The characteristics of the dichroic mirrors and filters are depicted on the scheme.

Fig. 2 .

 2 Fig. 2. Five-color intravital two-photon imaging of glioblastoma tumor and its dynamic environment. Upper panel : fluorescent mice. (A) 5-colors tile scan (3×3 images) of the GBM and its microenvironment; inset: macroscopic image of the cranial window with the area covered by the tile scan (large white square) and the area covered by a single plane acquisition (small dotted square). (B) Orthogonal reconstructions obtained from a stack acquired from 0 to 300 m below the glass coverslip with a z-step of 3 m. Each YZ image shows the maximum intensity projection over 10 microns along the X axis. (Note in B the vertical orientation of major brain vessels; dotted lines: the levels of xy-sections. (C) Apical dendrites GFP positive of neurons are visible (arrows). Also note the SHG signal (magenta) corresponding to the collagen fibers at the level of the dura-mater. CD11c-positive cells (yellow) have invaded the tumor but not surrounding healthy tissues. (D, E) xy-sections taken at 45 m and 145 m below the glass coverslip. Colors: blue: vasculature, cyan: astrocytes, green: neurons, yellow: CD11c-positive cells, magenta: SHG signal (dura-mater), red: tumor cells. Scale bars: 100 m (adapted from Ref.18).

Fig. 3 .

 3 Fig. 3. Spinal glass window for long term recurrent imaging of the spinal cord. Upper panel (right) Detailed view of spinal glass window.The edges of the vertebrae are shaped so that the window rests on both sides of the vertebral opening without compressing the spinal cord. Kwik-Sil is used as a physical barrier preventing the infiltration of opaque tissue between the window and spinal cord. Upper panel (left) Image of an anaesthetized mouse with an implanted window. The window structure is being supported by a clamp attached to a plastic base. The animal is freely breathing while in the support, but the window remains stationary relative to the clamp. Lower panel: Long-term spinal cord integrity after glass window implantation. Recurrent imaging sessions (from day 0 to day 350) of the same region of a Thy1-CFP mouse spinal cord after using Rhodamine-Dextran to visualize the vascular compartment. Note that individual axons can be followed for approximately one year. Scale bar, 50 m; Excitation wavelength 945 nm (adapted from Ref. 27).

Fig. 4 .

 4 Fig. 4. Time series showing cellular events occurring after spinal cord injury: (A) time-series images (60 min) of a LysM positive cell (arrow) that formed a close apposition with an injured axon with a tortuous morphology and irregular swellings (arrowhead). (B) time-series (9 h) images of a CD11c positive cell undergoing cell division at 3 days post-injury. (C) two-photon image stack near a lesion site showing LysM positive cells and CD11c positive cells with amoeboid morphology and containing CFP positive vacuoles (arrows) at 4 and 6 days post-injury, respectively. Thy1-CFP dorsal column axons, LysM-GFP recruited peripheral myeloid cells, CD11c-EYFP microglial cells, QDot655 in blood vessels and second harmonic generation (SHG) from collagen in dura mater. Scale bars: 20 m. (adapted from Ref. 55).

Fig. 5 .

 5 Fig. 5. In vivo of Organelle Transport in neuroinflammatory lesions (A) In vivo two-photon image of the spinal cord of a control Thy1-YFP-16×Thy1-MitoCFP-P mouse (top) and a Thy1-YFP-16×Thy1-MitoCFP-P mouse 2 days after onset of EAE (bottom; axons, white; nuclei labeled by in vivo application of Nuclear-ID Red, red; mitochondrial channel not shown). (B) Magnified views of axons from neuroinflammatory lesions illustrating different stages of axon morphology (N, axon from a control mouse; normal-appearing axon within an inflammatory lesion and swollen axon within an inflammatory lesion. (C and D) In vivo two-photon time-series images of control axons (top) and stage 0 axons imaged 2 days after onset of EAE (bottom) with moving mitochondria (C), in Thy1-YFP-16×Thy1-MitoCFP-P mice, and peroxisomes (D), in Thy1-OFP-3×Thy1-PeroxiYFP-376 mice, represented as pseudo-colored overlays (lines represent tracks during the indicated time period). Note that anterograde transport is more significantly affected than retrograde transport (adapted with requested permission from Ref. 58).

Fig. 6 .

 6 Fig. 6. Simultaneous SHG and THG imaging-intact murine sciatic nerves ex vivo and in vivo. (A) Simultaneous SHG and THG imaging of ex vivo tissue. (Scale bar, 50 m.) (B and C) Regions corresponding to the colored frames in A. The axons marked by asterisk (*) and double asterisks (**) are shown at higher magnification in D and E, respectively, focused at the level of the abaxonal membrane. Cajal bands (red arrowheads) and appositions (green arrows) are visible. The images of D and E are displayed on logarithmic intensity scales for easier identification. (G) Imaging of in vivo tissue. (Scale bar, 30 m.) (Lower) High-magnification view containing a node (arrow). (adapted from Ref. 64 with requested permission.).

Table 1

 1 Excitation and emission wavelengths of commonly used fluorescent dyes

	Visualization	Abbreviation	2 Photon	Conventional	Emission
	molecules		excitation	excitation
	Fluorescent proteins,	eBFP	780	380
	jellyfish-derived
		eCFP	860-920	433
		eGFP	880-930	488
		eYFP	960	513
	Fluorescent proteins;	Kaede (before	730	508
	coral-derived	conversion)
		Kaede (after	730	572
		conversion)
		tdTomato	900-1000	554
		DsRed	930-990	558
		mCherry	760 or 900-1000	587
	Chemical dyes	CMF2HC	780-800	371
		CFSE	780-820	492
		CMFDA	800	492
		FITC	780-800	494
		CMTMR	780-820	541
		SNARF	700-810	563
		CMTPX	780-800	577
		Texas Red	780-920	595
	Calcium indicator	Indo-1	700	346
		Fura-2	700-850	363
		Fura-4F	700-850	366
		Fluo-5N	800	520
	Organelle visualization	Hoechst	780	350
		DAPI	700	358
		FM 1-43	830	480
		Rhodamine 123	780-860	507
		Dil	700	549
		Sulforhodamine 101	840-890	586
		DiD	780	644

This article is published online with Open Access and distributed under the terms of the Creative Commons Attribution Non-Commercial License (CC BY-NC 4.0).

ACKNOWLEDGMENTS

This review would not have been possible if not for all the researchers worldwide who have and are still contributing immensely to establish in vivo non linear optical imaging as a valuable tool for CNS research. The authors apologize for the significant studies in the related field that have been overlooked. Studies in our laboratories are supported by CNRS, Aix-Marseille University, ANR (grant Neuroinflam-DYN n • ANR-15CE16-009-01 and MyDeepCARS n • ANR-15-CE19-0018-01), ARSEP and Institut National du Cancer (PLBIO n • 14-197).

CONFLICT OF INTEREST

The authors declare that they have no conflict of interest.