Christophe Antony Lounis

Vsevolod Peysakhovich

Mickaël Causse

Flight Eye Tracking Assistant (FETA): Proof of Concept

Keywords: Eye-Tracking, Aviation, Human Factors, Human Computer Interaction, Neuroergonomics, Flying Assistant, Assistive Technology

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

Introduction

Over the past 50 years, continuous technical and technological improvements in commercial aviation made it the safest modes of transportation [START_REF]Boeing. Statistical Summary of Commercial Jet Airplane Accidents Worldwide Operations, 1959-2014[END_REF]. Progress in cockpit systems and aircraft design [START_REF] Sarter | From tool to agent": The evolution of (Cockpit) automation and its impact on human-machine coordination[END_REF], in pilot training, in flight crew and air-traffic control procedures, are still essential to maintain a low accident rate despite an everincreasing traffic [START_REF] Button | Wings across Europe: towards an efficient European air transport system[END_REF]. Nevertheless, accidents always occur and a large part of them involve human error (approximatively 60 to 80 percent of accidents), as shown Figure 1. One critical solution provided by the industry to reduce crew's workload [START_REF] Lee | Human factors and ergonomics in automation design[END_REF] and to deal with human errors [START_REF] Chialastri | Automation in aviation[END_REF][START_REF] Lee | Human factors and ergonomics in automation design[END_REF] has been the introduction of automation. However, automation also shifted the role of the crew from controllers to supervisors [START_REF] Mumaw | Analysis of pilots' monitoring and performance on an automated flight deck[END_REF]. Unfortunately, automation is not always fully understood and nor correctly monitored [START_REF] Parasuraman | Complacency and bias in human use of automation: An attentional integration[END_REF]. It can induce complacency, overconfidence, and airline pilots sometimes rely too much on it [START_REF] Wickens | Complacency and automation bias in the use of imperfect automation[END_REF]. Therefore, increasing the pilot's ability to stay in the loop, in particular by promoting an appropriate monitoring of the cockpit instruments, is a main current safety challenge [START_REF] Berberian | The out-of-the-loop Brain: a neuroergonomic approach of the human automation interaction[END_REF]. This is particularly true during the approach and landing phases, critical periods of a flight, in which safety margin and tolerance on flight parameters deviations are very low [START_REF] Sumwalt | Examining How Breakdowns in Pilot Monitoring of the Aircraft Flight Path[END_REF]. A report of the active pilot monitoring working group published by the Flight Safety Foundation [START_REF] Sumwalt | Examining How Breakdowns in Pilot Monitoring of the Aircraft Flight Path[END_REF] investigated 188 cases involving monitoring issues leading to accidents. They find out that 66% of monitoring errors occurred during dynamic phases of flights (e.g., climb, descent, approach, and landing). They also identified the failure to cross-check instruments as a major cause of those monitoring errors that resulted in excessive deviations of the flight parameters (e.g., altitude, trajectory, or speed deviation). Recently, the Federal Aviation Administration (FAA) published a final training rule that requires enhanced pilot monitoring training to be included into existing air careers training programs [START_REF]A Practical Guide for Improving Flight Path Monitoring: Final Report of the Active Pilot Monitoring Working Group[END_REF]. Furthermore, the French accident investigation bureau (Bureau Enquêtes et Analyses, BEA) suggested to analyze pilots' monitoring with eye tracking to improve piloting procedures [START_REF][END_REF]. In this sense, a recent paper described the different ways in which eye tracking could be used to assist commercial pilots during the flight [START_REF]Study on Aeroplane State Awareness during Go-Around[END_REF].

Based on the latter studies, we propose the FETA system. It compares the current visual scan of a pilot with a database of "standard" visual circuits. If the current visual scan deviates too much from the database (e.g., the speed is not fixated during a too long period), FETA emits a vocal alarm (e.g., "check speed"). The current paper describes the development and the evaluation of the FETA (Flight Eye Tracking Assistant) system [START_REF] Peysakhovich | The neuroergonomics of aircraft cockpits: the four stages of eye-tracking integration to enhance flight safety[END_REF]. In particular, we evaluated the impact of FETA on situation awareness [START_REF] Lounis | Intelligent cockpit: eye tracking integration to enhance the pilot-aircraft interaction[END_REF], subjective workload, flight performance, and visual scans.

FETA system development

The main purpose of the FETA system is to warn the pilot when he looks not sufficiently at an instrument. In order to calibrate the "not sufficiently", the threshold beyond which visual scans become "abnormal", we built a database of standard visual circuits in the cockpit with a sample of 16 airline pilots. They performed approachlanding phases in flight simulator while their eye movements were recorded. We also ensured that their flight performance remained in the standard safety thresholds.

Participants

Sixteen male professional airline pilots (ATPL: Airline Transport Pilot License or CPL: Commercial Pilot License) volunteered to participate in this study. Their mean age was 34 years old (range: 23-59). Their total flight experience ranged from 1,600 to 13,000 hours (M = 4,321.73 hrs, SD = 2,911.41 hrs). They were not paid for their participation. They had normal or corrected-to-normal vision. The experiment was approved by the Research Ethics Committee (CER, n°2019-131).

Procedure

Each participant signed a consent form and provided demographic information, their flight qualifications (type of aircraft), and their total flight experience hours. Pilots were briefed on the study and receive instruction about the flight scenario and the goal of this experiment. They filled a fatigue questionnaire. Next, pilots were installed in the flight simulator and were submitted to the eye-tracking calibration procedure. Participants took the captain position and performed a training consisting in two approach-landings scenarios in order to familiarize themselves with the flight simulator. Then, they performed the two experimental approach-landings scenarios.

Flight simulator

The study was conducted in the PEGASE (Platform for Experiments on Generic Aircraft Simulation Environment) flight simulator of the ISAE-SUPAERO (Toulouse, France), illustrated in figure 2. It simulates an Airbus A320 with a glass cockpit. The simulator includes pilots' seats, sidestick controllers, throttles, trim wheels, and rudder pedals.

Eye-tracking measurements

Eye tracking data was collected with a Smart eye System embedded in the cockpit. The Smart eye System consists of 5 deported cameras (0° -2° of accuracy), with a sampling frequency of 60 Hz. Furthermore, the cockpit has been divided into several Areas of Interests (AOIs), as presented in figure 3. They correspond to the main flight displays. These AOIs are used by the FETA system to evaluate online current visual scans. We also used these AOIs during the human factor evaluation to examinate the impact of FETA on visual scans. The threshold for detecting a fixation on an AOI was set at 100 ms [17].

Experimental conditions

The 16 pilots performed two times the same flight scenario. The flight scenario consisted of a manual approach-landing task to Toulouse-Blagnac Airport Runway LFBO 14R. Flight began at coordinates 1.2159 longitude and 43.7626 Latitude. During the scenario, the pilot had to comply with some specific instructions. In particular: maintain a vertical speed between +500 ft/min and -800ft/min, a speed of 130 knots, and a heading of 143°.

Flight parameters

Firstly, we checked flight performance of the pilots, assuming that correct flight performance is likely correlated to an efficient cockpit monitoring. Figure 5 shows the mean flight parameters deviation for vertical speed, speed and heading during the landing task. Flight performance of each pilot was adequate. Average vertical speed was in the correct range, and average speed and average heading were very close to the target values.

Visual Behavior Database and notification threshold

The Visual Behavior Database (VBD) has been established with the eye recordings made on the 16 pilots that performed the two approach-landing scenarios. Mean nondwell times were calculated for each AOI. While dwell times represent the time during which an individual gaze inside an AOI [START_REF] Russi-Vigoya | Analysis of pilot eye behavior during glass cockpit simulations[END_REF], non-dwell times correspond to the period of time during which an individual does not look at an AOI, see Figure 6. We used the "non-dwells times" of the 16 expert pilots as the metric for the FETA notification threshold. More precisely, the thresholds consisted of the averages of the non-dwell time for each AOI plus a standard deviation, as presented in [START_REF]Boeing. Statistical Summary of Commercial Jet Airplane Accidents Worldwide Operations, 1959-2014[END_REF].

φthreshold = μNDT + σNDT . ( 1
)
This metric indicates the maximum non-dwell time tolerance for each AOI (i.e., beyond which an insufficient monitoring is diagnosed).

FETA interface

Besides the Visual Behavior Database, the eye tracking system, and the vocal alarms, FETA also has an application permitting to visualize the activity from outside the cockpit. Coded in C#, the FETA interface has many features shown in Figure 7. The features of FETA are:

1. AOI Monitoring Panel (on the left of Figure 7) It shows the state of each AOI. The color turns from green to blue when the AOI is not monitored enough according to the VBD.

2. Show timer (center of Figure 7) User can tick the tick boxes of any of the AOIs to see the timer of each AOI. This timer shows the elapsed duration since the last monitoring (in seconds).

3. AOI Heat Map Panel (on the right of Figure 7) This heat map panel indicates the proportion of fixation times on the AOI since the beginning of the flight.

4. Timer (center of Figure 7) This feature shows the elapsed time until the beginning of the simulation in seconds.

5. AOI Text Alert and Current Area of Interest Annunciator (at the bottom left of Figure 7) The AOI Text Alert will show the name of the AOI that needs to be monitored. If more than one AOI needs to be monitored, this alert will only show the name of the AOI with the highest priority. The Current Area of Interest Annunciator shows the currently monitored AOI.

6. Flight Parameter Indicators (at the bottom left of Figure 7) This feature shows the several flight parameters that affect the dynamic of the database.

7. Start/Stop Tracking Button and Show/Hide Heat Map Button (centred at the bottom of Figure 7) The Start/Stop Tracking Button starts or stops FETA, while the Show/Hide Heat Map Button shows or hides the AOI heat map.

8. Audio Alarm (cannot be shown) FETA will emit an audio alarm that corresponds to the AOI Text Alert (e.g. "check speed").

FETA system assessment

The second part of this paper focuses on the evaluation of the FETA system. In particular, its impact on mental workload, situational awareness, flight performances, and cockpit monitoring. As a preliminary assessment, five pilots were submitted to three different scenarios varying in terms of monitoring difficulty.

Participants

Five male professional pilots (ATPL, CPL) volunteered to participate in this study. They had normal or corrected-to-normal vision. Their mean age was 39 years old (range: 33-50). Their total flight experience ranged from 2,500 to 8,500 hours (M = 3,176 hrs, SD = 2,645 hrs). Pilots were not paid for their participation. The experiment was approved by the Research Ethics Committee (CER, n°2019-131).

Procedure

Procedure was essentially the same than during the FETA calibration, except that the new 5 pilots performed four additional landings. During this evaluation, FETA auditory notifications (in case of abnormal monitoring) were restricted to three instruments: speed, vertical speed, and heading. These instruments were chosen because they corresponded to the flight parameter values that pilots had to maintain. Possible auditory alarms emitted by FETA were: "check speed"; check vertical speed", check heading".

Apparatus

This experiment also took place in the PEGASE flight simulator, using the same eye tracking system.

Experimental conditions

Pilots performed two times three different randomized landing scenarios. The first scenario (Scenario 1) was identical to the one performed by the pilots for the building of the VBD. In the second and the third scenarios, we increased monitoring difficulty. During these two scenarios, pilots were asked to read aloud the distance between the aircraft and a specific radio beacon (information displayed in the ND-zone) either every 0.5 Nm (scenario 2) or every 0.2 Nm (scenario 3). The pilots had to comply with the same speed, vertical speed, and heading constraints than during the VBD building. At the end of the simulation, pilots filled out 2 subjective questionnaires: situational awareness measures using SART [START_REF] Goldberg | Eye tracking in web search tasks: design implications[END_REF] and workload Instantaneous Self-Assessment [START_REF] Tattersall | An experimental evaluation of instantaneous selfassessment as a measure of workload[END_REF], which is a subjective scale ranging from 1 to 5. The latter allows assessing overall workload. After the flight scenarios, open interviews were conducted to garner the various opinions of the pilots according to the system.

Human factors assessment

Due to the low number of participants, we only present descriptive statistics for subjective assessments, and flight performance. However, eye tracking data allows us to use inferential statistics regarding the comparison with and without FETA. In particular by taking into account the difficulty of scenarios (1, 2, 3) as a covariate.

Subjective results

Figure 9 shows the SART results. A higher SART score indicates a better situational awareness. On average, FETA seemed to disturb the situational awareness when flying context was easy (scenario 1 and 2), but it tended to be the opposite when flying context was more complex (scenario 3). As presented in figure 9 (at right), ISA workload indicator did not show marked difference with or without the FETA system. However, in an easy flying context (scenario 1), the FETA system seems to induce more workload and this trend is reversed when flying context is more difficult (scenario 2 and 3).

Flight performance results

The figure 8 shows flight parameters deviations. During the easy scenario (scenario 1), pilots had higher speed deviations with FETA than without. Concerning the heading in the difficult condition (scenario 3), pilots had on average lower heading deviations with the FETA system than without.

Eye tracking results

Figure 10 shows the percentage dwell times on each AOI for all scenarios with and without FETA system. The Wilcoxon-Mann-Whitney nonparametric test shows a significant effect (p<0.05) of FETA vs. without FETA condition on the AOIs according to speed, vertical speed, heading, flight mode annunciator and out the window.

Discussion and conclusions

The purpose of this study was on the one hand to present the concept and the development of a flight eye-tracking assistant (FETA) calibrated thanks to eyemovement recordings from 16 airline pilots. On the other hand, we also proposed a user-centered evaluation (e.g., situation awareness, mental workload) of the first version of this assistant together with an assessment of its impact on the cockpit monitoring. This evaluation was performed with 5 other airline pilots.

Overall, this first version of FETA demonstrated mixed results. First, results showed that there was no clear improvement of the flight parameters that had to be maintain during the landing (speed, vertical speed, and heading). There was an increased speed deviation during the easier landing and on the contrary an improvement of heading accuracy during the most difficult landing scenario. Consistently, subjective results tend to show that FETA was not detrimental only when flight scenario was difficult. In particular, situation awareness seemed slightly improved by FETA in the scenario 3. Eye tracking results were more favorable to FETA, with an increase of the time spent on some instruments subjected to the FETA audio notification in case of insufficient visual consultation. In presence of FETA, pilots checked more often the speed, the vertical speed, and the heading. This additional time gazing these instruments impacted the time spend on the window. Most likely, FETA was efficient to redirect attention toward the critical flight instruments thanks to the vocal alarm triggered when visual circuit deviated too much from the database. Despite this positive result, our experiment shed to light several issues that should be addressed in the future. Open interviews with the pilots allowed revealing some areas of improvements. For example, the use of the auditory modality is not necessary the best one. This channel is already used by the synthetic voice in the cockpit, and also during the exchanges between pilots and air traffic control. To overcome this problem, other notifications methods could be explored, such as visual and/or haptic modalities. Another important improvement would be to both integrate flight parameters values and eye movements in FETA. Indeed, it would be more appropriate to trigger notifications when both visual scans and flight parameters deviates too much from standards. For example, when speed decline too fast etc. This would help avoid triggering spurious notification (useless auditory notification from FETA), which was one of the main problems raised by the pilots during the debriefing. More generally, the FETA system should consider other eye tracking metrics when considering the landing task; for example, it could analyze the visual patterns (transitions between AOIs, not only the fixation on each AOI) and correct them when they deviate from established standards, using artificial intelligence. Furthermore, FETA could take into account other flight phases, automatically identified considering the flight data (e.g., altitude, speed, flight mode…). Then, this would enable to adapt eye-tracking metrics to the given flight phases. For example, cockpit monitoring is much less intense during the cruise, but this phase is more prone to drowsiness or fatigue. FETA could integrate metrics based on the percentage of eye closure [START_REF] Taylor | Situational awareness rating technique (SART): The development of a tool for aircrew systems design[END_REF] or considering the frequency of eye blinks [START_REF] Sommer | Evaluation of PERCLOS based current fatigue monitoring technologies[END_REF]. Future study should consider these improvements and assessing FETA during complex flight phases with a higher number of pilots.

Fig. 1 .

 1 Fig. 1. Aeronautical accidents from 1969 to 2019 with human factors, technical failure and others causes as contributory factors. These data were retrieved from Bureau of Aircraft Accidents Archives (www.baaa-acro.com).

Fig. 2 .

 2 Fig. 2. The PEGASE flight simulator used during FETA development and assessment.

Fig. 3 .

 3 Fig. 3. Cockpit Display with AOIs and Sub-AOIS: (1) Primary Flight Display (PFD), (2) Navigation Display (ND),(3) Electronic Centralized Aircraft Monitoring (ECAM), (4) Out of Window (OTW), (5) Flight Control Unit (FCU), (6) Flight Mode Annunciator (PFD.FMA) , (7) Speed Tape (PFD.SPD), (8) Attitude Indicator (PFD.ATT), (9) Vertical Speed Tape (PFD.VS), (10) Heading Tape (PFD.HDG), (11) VOR tag reading area in ND (ND-zone).

Fig. 4 .

 4 Fig. 4. The landing scenario with the flight parameter values that pilots had to maintain.

Fig. 5 .

 5 Fig. 5. Violin plot of flight parameters deviations during the landing task. The red lines correspond to target values, as given by the experimenter before the flight scenarios. N = 16.

Fig. 6 .

 6 Fig. 6. Violin plot of non-dwell times during the landing task for the main AOIs. N = 16.

Fig. 7 .

 7 Fig. 7. FETA interface with its 7 different features.

Fig. 9 .

 9 Fig. 9. Left: SART results (higher the values, better the situational awareness); Right: ISA results (lower is the value and lower is the subjective workload). All three scenarios with and without the FETA system are showed. N = 5.

Fig. 8 .

 8 Fig. 8. Root Mean Square (RMS) of the flight parameters for each scenarios with and without FETA (the higher the value, the lower the performance). N = 5.

Fig. 10 .

 10 Fig. 10. Bar plot of percentage dwell times on each AOI. All three scenarios with and without the FETA system are showed. N = 5. (*p<0.05, Wilcoxon Mann-Whitney test).

Acknowledgments

This work was supported by a chair grant from Dassault Aviation ("CASAC", holder: Prof. Mickaël Causse)". The Authors thank the PEGASE simulator technical team and all the pilots who participated in this study.


domain: sdv sdv

text:
Alexandre Brenet

Julie Somkhit

Zsolt Csaba

Sorana Ciura

Edor Kabashi

Constantin Yanicostas

Dr Nadia Soussi-Yanicostas

Microglia mitigate neuronal activation in a zebrafish model of Dravet syndrome

Keywords: Microglia, Epilepsy, Seizure, Calcium imaging, Zebrafish, Seizures, Cytokines, Neuroprotection 2

It has been known for a long time that epileptic seizures induce brain neuroinflammation through microglia activation. Nevertheless, these cells have not yet received the attention they deserve in epilepsy research and the consequences of this microglial response on subsequent neuronal activity remain poorly understood. Here, we sought to fill this gap and gain a larger understanding of the role of microglia in the pathophysiology of epilepsy, using an established zebrafish Dravet syndrome epilepsy model based on Scn1Lab sodium channel loss-offunction, combined with live microglia and neuronal Ca 2+ imaging, local field potential (LFP) recording and genetic microglial ablation. First, in scn1Lab-deficient larvae experiencing epileptiform seizures, microglia displayed morphological and biochemical features suggesting M1-like pro-inflammatory activation, including reduced branching, amoeboid-like morphology, and a marked increase in the number of microglia expressing pro-inflammatory cytokine Il1β.

More importantly, scn1Lab-KD larvae fully lacking microglia showed a significantly increased neuronal activation compared to that seen in scn1Lab-KD individuals with microglia, as shown by LFP recording and Ca 2+ imaging, and also by the epileptiform seizure-related whirling swimming of larvae. These findings are evidence that despite a microglial activation and the synthesis of pro-inflammatory cytokines, microglia provide neuroprotection to epileptic neuronal networks, making these cells a promising therapeutic target in epilepsy.

Introduction

Epilepsy is the most frequent neurological disorder, affecting more than 1% of the world's population [START_REF] Fisher | ILAE Official Report: A practical clinical definition of epilepsy[END_REF][START_REF] Fisher | Redefining epilepsy[END_REF]. The disease is characterized by recurrent seizures caused by synchronized hyperactivation of neuronal networks of various sizes ranging from a limited brain region to the whole cortex, leading to focal or generalized seizures, respectively [START_REF] Thurman | Standards for epidemiologic studies and surveillance of epilepsy[END_REF][START_REF] Stafstrom | Seizures and epilepsy: An overview for neuroscientists[END_REF]World Health Organization, 2019). Potent anti-epileptic drugs (AEDs) have been developed over the last decades.

However, they are ineffective in approximately one-third of patients, especially children with developmental epileptic encephalopathies, such as Dravet syndrome (DS) [START_REF] Brunklaus | Comorbidities and predictors of healthrelated quality of life in Dravet syndrome[END_REF][START_REF] Kwan | Drug-Resistant Epilepsy[END_REF][START_REF] Wu | Incidence of dravet syndrome in a US population[END_REF]. In addition, they can cause significant side effects, such as teratogenicity in pregnant women [START_REF] Xue-Ping | Risk factors for drug-resistant epilepsy: A systematic review and meta-analysis[END_REF][START_REF] Marxer | A review of the evidence on the risk of congenital malformations and neurodevelopmental disorders in association with antiseizure medications during pregnancy[END_REF]. These limitations emphasize the need for novel anti-epileptic strategies.

Notably, most existing AEDs are "neurocentric", targeting neuronal proteins directly involved in neuronal network activation. At the same time, microglia have so far received little attention as a therapeutic target in epilepsy despite large amounts of data accumulated over the last decades, strongly suggesting that these cells are essential players in the pathophysiology of the disease [START_REF] Vezzani | Neuroinflammatory pathways as treatment targets and biomarkers in epilepsy[END_REF].

Microglial cells, the resident brain macrophages, have several essential functions, including immunoprotection, maintenance of neuron health and homeostasis, and maturation of neuronal networks through developmentally controlled apoptosis of supernumerary neurons and pruning excess synapses [START_REF] Lukens | Microglia and Neurodevelopmental Disorders[END_REF]. As immune cells, microglia also respond rapidly to brain injury or infection through complex reprogramming, referred to as microglia activation, which comprises morphological and biochemical changes, including the synthesis and release of pro-inflammatory cytokines [START_REF] Woodburn | The semantics of microglia activation: neuroinflammation, homeostasis, and stress[END_REF].

In epileptic patients, it has long been known that seizures cause the activation of microglial cells and synthesis of inflammatory mediators [START_REF] Ravizza | Innate and adaptive immunity during epileptogenesis and spontaneous seizures: Evidence from experimental models and human temporal lobe epilepsy[END_REF][START_REF] Vezzani | Glia as a source of cytokines: Implications for neuronal excitability and survival[END_REF][START_REF] Vezzani | Fetal brain inflammation may prime hyperexcitability and behavioral dysfunction later in life[END_REF][START_REF] Vezzani | Neuroinflammatory pathways as treatment targets and biomarkers in epilepsy[END_REF]. Analysis of post-mortem brain tissues from epileptic patients has indeed shown massive microglial activation, directly correlated with seizure severity [START_REF] Boer | Inflammatory processes in cortical tubers and subependymal giant cell tumors of tuberous sclerosis complex[END_REF][START_REF] Morin-Brureau | Microglial phenotypes in the human epileptic temporal lobe[END_REF]. Moreover, in various pharmacological rodent epilepsy models, pronounced morphological changes of microglia were rapidly observed after seizures, prior to the onset of neuronal damage [START_REF] Eyo | Neuronal Hyperactivity Recruits Microglial Processes via Neuronal NMDA Receptors and Microglial P2Y12 Receptors after Status Epilepticus[END_REF][START_REF] Avignone | Altered morphological dynamics of activated microglia after induction of status epilepticus[END_REF]. However, other studies have highlighted the wide variability of inflammatory responses observed in patients and animal epilepsy models [START_REF] Gasmi | Low grade inflammation in the epileptic hippocampus contrasts with explosive inflammation occurring in the acute phase following status epilepticus in rats: translation to patients with epilepsy[END_REF]. Some data suggest that microglia may be harmful in an epileptic context, probably owing to the release of pro-inflammatory cytokines, known to be pro-epileptogenic factors [START_REF] Vezzani | Interleukin-1β immunoreactivity and microglia are enhanced in the rat hippocampus by focal kainate application: Functional evidence for enhancement of electrographic seizures[END_REF][START_REF] Musto | The role of inflammation in the development of epilepsy[END_REF][START_REF] Andoh | Synaptic Pruning by Microglia in Epilepsy[END_REF]. Conversely, other studies suggest that microglia may play a beneficial role in epilepsy by decreasing the activity of neuronal networks (Li, X. Du, et al., 2012;[START_REF] Vinet | Neuroprotective function for ramified microglia in hippocampal excitotoxicity[END_REF][START_REF] Eyo | Neuronal Hyperactivity Recruits Microglial Processes via Neuronal NMDA Receptors and Microglial P2Y12 Receptors after Status Epilepticus[END_REF][START_REF] Badimon | Negative feedback control of neuronal activity by microglia[END_REF]. Data also suggest that the response of microglia to epileptic seizures is not uniform throughout the brain, with cells showing a wide range of phenotypes within the same individual [START_REF] Morin-Brureau | Microglial phenotypes in the human epileptic temporal lobe[END_REF]. Moreover, the levels of both pro-inflammatory and antiinflammatory cytokines are increased in microglia after epileptic seizures, supporting the notion that the response of these cells to seizures is not limited to classic M1-type pro-inflammatory activation, thus highlighting the complexity of this microglial response [START_REF] Benson | Complex alterations in microglial M1/M2 markers during the development of epilepsy in two mouse models[END_REF].

Whether microglia activity has an overall detrimental or beneficial role in epilepsy is still a largely open question with far-reaching therapeutic implications [START_REF] Lukens | Microglia and Neurodevelopmental Disorders[END_REF].

To address this issue and gain a better understanding of the role of microglia in epilepsy, we used a zebrafish model of Dravet syndrome (DS), a severe intractable epileptic syndrome in infants caused in 70-80% of cases by de novo mutations in the gene encoding the voltagegated sodium channel, SCN1A [START_REF] Claes | De Novo Mutations in the Sodium-Channel Gene SCN1A Cause Severe Myoclonic Epilepsy of Infancy[END_REF][START_REF] Dravet | Dravet syndrome history[END_REF]. We used this zebrafish epilepsy model to investigate the morphology and dynamic behavior of microglial cells in vivo, using the Tg[mpeg1:mCherryF] line, which enables imaging of microglial cells in living zebrafish embryos and larvae combined with neuronal Ca 2+ imaging, local field potential (LFP) recording and genetic microglia ablation.

We found that microglia were deeply remodeled in scn1Lab-deficient larvae, with morphology shifted toward the amoeboid and less branched phenotype, combined with a markedly increased mobility of their cell bodies and an increased number of Il1β-expressing cells. In addition, in microglia-depleted scn1Lab-deficient larvae, neuronal hyperactivation was significantly increased compared with their siblings with microglia, suggesting that activities of these cells lent neuroprotection to epileptic neurons and decreased their activity.

Results

Microglia morphological changes in scn1Lab-KD larvae

To increase our understanding of microglia phenotypes in epileptic brains in vivo, we first made use of the live imaging techniques available in zebrafish, especially real-time microglia imaging [START_REF] Hassan-Abdi | Neurons Expressing Pathological Tau Protein Trigger Dramatic Changes in Microglial Morphology and Dynamics[END_REF], combined with two zebrafish scn1Lab models of Dravet syndrome epilepsy, scn1Lab morphant [START_REF] Brenet | Defective Excitatory/Inhibitory Synaptic Balance and Increased Neuron Apoptosis in a Zebrafish Model of Dravet Syndrome[END_REF] and scn1Lab s552/s552 mutant larvae (Scott C Baraban, Dinday and Hortopan, 2013). Both models show recurrent spontaneous epileptiform seizures at 4 days post-fertilization (dpf), as visualized by LFP recording and Ca 2+ imaging and by the characteristic swirling behavior of the larvae (Scott C. Baraban, Dinday and Hortopan, 2013;[START_REF] Zhang | Pharmacological Characterization of an Antisense Knockdown Zebrafish Model of Dravet Syndrome: Inhibition of Epileptic Seizures by the Serotonin Agonist Fenfluramine[END_REF][START_REF] Brenet | Defective Excitatory/Inhibitory Synaptic Balance and Increased Neuron Apoptosis in a Zebrafish Model of Dravet Syndrome[END_REF]. Specifically, we mainly used scn1Lab morphants, referred to below as scn1Lab-KD individuals, and verified that the results were identical in scn1Lab s552/s552 mutants, displaying a complete loss of Scn1Lab function (Scott C. Baraban, Dinday and Hortopan, 2013;[START_REF] Brenet | Defective Excitatory/Inhibitory Synaptic Balance and Increased Neuron Apoptosis in a Zebrafish Model of Dravet Syndrome[END_REF]. To visualize microglial cells in vivo, we chose the transgenic Tg[mpeg1:mCherryF] line, which shows intense labeling of these cells [START_REF] Travnickova | Primitive macrophages control HSPC mobilization and definitive haematopoiesis[END_REF]. However, as this transgenic line expresses mCherryF in both microglia and macrophages, we verified that all the microglia imaged in this study also expressed the Tg[p2y12:P2Y12-GFP] transgene [START_REF] Sieger | Long-Range Ca2+ Waves Transmit Brain-Damage Signals to Microglia[END_REF], as P2RY12 is a specific marker of microglial cells [START_REF] Mildner | Ghosts in the shell: Identification of microglia in the human central nervous system by p2y12 receptor[END_REF].

As previously shown [START_REF] Peri | Live Imaging of Neuronal Degradation by Microglia Reveals a Role for v0-ATPase a1 in Phagosomal Fusion In Vivo[END_REF][START_REF] Hassan-Abdi | Neurons Expressing Pathological Tau Protein Trigger Dramatic Changes in Microglial Morphology and Dynamics[END_REF], in wild-type (WT) larvae, microglia displayed the characteristic branched morphology of "resting" microglia, with a relatively small cell body and several long and ramified processes (Figure 1A To further characterize the morphological differences in the two genetic contexts, we used the 3D image analysis software Imaris (Bitplane) to measure several microglial cell morphological parameters (sphericity, volume, surface area, branch number, branch length and ramification index). Data confirmed that microglia in scn1Lab-KD larvae displayed an increased sphericity (WT: 0.567 ± 0.006 vs. scn1Lab-KD: 0.644 ± 0.006; p < 0.0001) (Figure 1C), a decreased number of branches (WT: 6.3 ± 0.2 vs. scn1Lab-KD: 5.1 ± 0.2; p < 0.0001) (Figure 1D), a decreased total (WT: 115 ± 3 µm vs. scn1Lab-KD: 85 ± 3 µm; p < 0.0001) (Figure 1E) and mean processes length (control: 23.5 ± 0.4 µm vs. scn1Lab-KD: 19.6 ± 0.3 µm; p < 0.0001) (Figure 1F), and a decreased ramification index (WT: 1.94 ± 0.05 vs. scn1Lab-KD: 1.74 ± 0.05; p < 0.0001) (Figure 1G) compared with microglia in WT individuals. Sholl analysis, a method used to quantify both the extent and complexity of cell branches (Imaris software), further confirmed that microglia in scn1Lab-KD individuals had fewer and shorter branches than their WT siblings (Figure 1H). Taken together, our data show that microglia exhibited an amoeboid-like morphology reminiscent of that of M1-type activated macrophages in scn1Lab-KD larvae. To better evaluate the extent of microglia morphological differences in scn1Lab-KD and WT larvae, we performed a cluster analysis of these cells in the two genetic contexts based on the five morphological parameters showing significant changes as indicated above (sphericity, branch number, branch length, surface area, and volume) (see Materials and Methods). The first population comprised cells showing low sphericity, numerous elongated branches, and high surface area and volume, referred to below as "branched" microglia. The second comprised microglia displaying high sphericity, few short processes, and low surface area and volume, referred to below as "amoeboid" cells. The third comprised cells with intermediate characteristics, referred to below as "transitional" microglia (Figure 1J). Although the three populations of microglia were found in both contexts, their respective proportion varied considerably. In scn1Lab-KD larvae, we observed a significant increase in the percentage of "amoeboid" microglia (29.1 ± 1.2 vs. 10.4 ± 0.3%; p < 0.001) combined with a significant decrease in the percentage of "branched" microglia (10.2 ± 0.8 vs. 27.8 ± 1.5%; p < 0.01) (Figure 1K). These findings further prove that microglia display an M1-like reactive phenotype in scn1Lab-KD larvae.

Visual examination of the behavior of microglia in scn1Lab-KD and WT larvae provided a first indication that these cells displayed an increased mobility in the mutant context. To confirm this observation, we measured the speed of microglia cell bodies in the two genetic contexts.

In WT larvae, as previously described [START_REF] Hassan-Abdi | Neurons Expressing Pathological Tau Protein Trigger Dramatic Changes in Microglial Morphology and Dynamics[END_REF], microglia had several branches that were constantly extending and retracting, while their cell bodies remained almost

Increased microglia-mediated brain inflammation in scn1Lab-KD larvae

Because the microglia phenotypic changes observed in scn1Lab-KD larvae were strongly reminiscent of those of "activated" M1-type neuroinflammatory microglia observed in various neuronal disease situations, including epilepsy [START_REF] Morin-Brureau | Microglial phenotypes in the human epileptic temporal lobe[END_REF], we next evaluated the neuroinflammatory profile in the brain of scn1Lab-KD larvae. First, we used qRT-PCR to quantify the brain accumulation of transcripts encoding two key pro-inflammatory cytokines, interleukin-1β (Il1β) and interleukin-8 (Il8), and three anti-inflammatory cytokines, interleukin-10 (Il10), interleukin-4 (Il4) and transforming growth factor β-3 (Tgfβ3). Surprisingly, we did not observe any significant changes, neither in the expression of pro-inflammatory markers il1β (WT: 1.00 ± 0.22 vs. scn1Lab-KD: 1.33 ± 0.18; p > 0.05) and il8 (WT: 1.00 ± 0.09 vs. scn1Lab-KD: 0.93 ± 0.11; p > 0.05) (Figure 3A), nor in that of anti-inflammatory markers il10 (WT: 1.00 ± 0.21 vs. scn1Lab-KD: 0.767 ± 0.17; p > 0.05), il4 (WT: 1.00 ± 0.12 vs. scn1Lab-KD: 0.89 ± 0.19; p > 0.05) and tgfβ3 (WT: 1.00 ± 0.04 vs. scn1Lab-KD: 0.876 ± 0.08; p > 0.05) (Figure 3B). Therefore, although morphological changes seen in scn1Lab-KD larvae suggested proinflammatory microglial activation, no significant increase in the expression of pro-or antiinflammatory cytokines could be detected in the brain of these individuals. It may be that in this genetic model of epilepsy, levels of neuroinflammation are lower than those observed in rodent pharmacological models, as also suggested by the relatively small percentage of amoeboid microglia in scn1Lab-KD individuals (~30%). To analyze more precisely the microglial expression of Il1β in this genetic context, we generated scn1Lab-KD larvae also expressing the Tg[mpeg1:mCherryF] and Tg[il1β:GFP] transgenes [START_REF] Nguyen-Chi | Identification of polarized macrophage subsets in zebrafish[END_REF]. The results revealed a significant increase in the percentage of microglia expressing Il1β (WT: 14.6 ± 1.7 vs. scn1Lab-KD: 33.6 ± 1.7%; p < 0.0001) (Figure 3C-F). These results confirmed an increased number of activated microglial cells in scn1Lab-KD larvae, consistent with the increased number of "amoeboid" microglia previously characterized. All images were acquired using a Leica SP8 confocal microscope, equipped with a 20x/water objective with a numerical aperture of 0.75. All graphs represent mean ± sem. The p-values were calculated using a Student's t-test. n.s., not significant; ****, p<0.0001.

Microglia ablation increases neuronal activation in scn1Lab-KD larvae

The phenotypic changes and Il1β expression observed in microglia of scn1Lab-KD larvae strongly suggest that a small, albeit significant, proportion of microglia, approximately 30% of the cells, respond to neuronal seizures through M1-type pro-inflammatory activation. These results then raised the question of whether this microglial response globally mitigates or worsens subsequent activity of neuronal networks. As a first attempt to address this question, we generated scn1Lab-KD larvae expressing the Tg[HuC:GCaMP5G] transgene, a sensitive calcium bio-sensor [START_REF] Akerboom | Optimization of a GCaMP calcium indicator for neural activity imaging[END_REF], and entirely devoid of microglia following morpholino-mediated inactivation of the pU.1 gene, which encodes a factor essential for proper differentiation of macrophages and microglia [START_REF] Rhodes | Interplay of pu.1 and Gata1 determines myelo-erythroid progenitor cell fate in zebrafish[END_REF][START_REF] Peri | Live Imaging of Neuronal Degradation by Microglia Reveals a Role for v0-ATPase a1 in Phagosomal Fusion In Vivo[END_REF]. As previously described [START_REF] Peri | Live Imaging of Neuronal Degradation by Microglia Reveals a Role for v0-ATPase a1 in Phagosomal Fusion In Vivo[END_REF], the brain of larvae injected with PU.1 morpholinos are devoid of microglia, as shown by the absence of L-plastin immunostaining (Figure 4A2) compared with non-injected larvae (Figure 4A1). With these tools, we analyzed and compared neuronal activation in WT and scn1Lab-KD larvae, devoid or not of microglia, using calcium imaging and local field potential (LFP) recordings simultaneously.

As previously shown [START_REF] Brenet | Defective Excitatory/Inhibitory Synaptic Balance and Increased Neuron Apoptosis in a Zebrafish Model of Dravet Syndrome[END_REF][START_REF] Liu | Network properties revealed during multi-scale calcium imaging of seizure activity in Zebrafish[END_REF], when compared to WT individuals, scn1Lab-KD larvae display a markedly increased neuronal activity as indicated by the larger number of seizure-like events revealed by either calcium imaging (WT: 16.5 ± 1.0 vs. scn1Lab-KD: 32.2 ± 1.4, p < 0.0001) (Figures 4B,4D1) and (Supp. Video 5, https://urlz.fr/jlSX), (Supp. Video 6, https://urlz.fr/jlT1) and LFP recording (WT: 4.8 ± 1.2 vs. scn1Lab-KD: 28.2 ± 2.4, p < 0.0001) (Figures 4C,4E1). More importantly, microglia depletion increased the amplitude of neuronal events in both WT and scn1Lab-KD larvae (Figure 4D2, 4E2) and (Supp. Video 7, https://urlz.fr/jlTa), (Supp. Video 8, https://urlz.fr/jlTr), and exacerbated the number of epileptiform events in scn1Lab-KD individuals, suggesting that microglia displayed a neuroprotective activity that was able to mitigate, at least partially, neuronal hyperactivation. indicates a statistically significant difference between control and scn1Lab-KD larvae. $$, p < 0.01; $$$, p < 0.001; $$$$, p < 0.0001: indicates a statistically significant difference between larvae with or without microglial cells. p-values were determined using two-way ANOVA with Tukey post-tests.

Microglia ablation exacerbates seizure-like swimming behavior of scn1Lab-KD larvae

It has been shown that scn1Lab mutant and morphant exhibit spontaneous seizure-like swimming behavior consisting of whole-body convulsions and rapid undirected movements (Scott C. Baraban, Dinday and Hortopan, 2013;[START_REF] Brenet | Defective Excitatory/Inhibitory Synaptic Balance and Increased Neuron Apoptosis in a Zebrafish Model of Dravet Syndrome[END_REF]. Therefore, we questioned whether the locomotor activity of scn1Lab-KD larvae was dependent on microglia (Figure 5A).

As previously shown, scn1Lab-KD larvae swam longer and over longer distances than WT (WT: 212 ± 11 vs. scn1Lab-KD: 355 ± 11 mm, p < 0.0001), reflecting their neuronal hyperactivation (Figures 5F,5G,5H). Interestingly, in scn1Lab-KD larvae devoid of microglia, we observed a markedly increased locomotor activity compared with that observed in scn1Lab-KD individuals not microglia-depleted (scn1Lab-KD: 355 ± 11 vs. microglia-depleted scn1Lab-KD: 423 ± 12 mm, p < 0.0001) (Figures 5B-D), suggesting that microglia depletion worsened the locomotor behavior of scn1Lab-KD larvae thus confirming that neuronal activation was exacerbated in scn1Lab-KD larvae lacking microglia compared to that observed in scn1Lab-KD individuals.

Discussion

A close relationship between epileptic seizures and microglia-mediated brain inflammation was established long ago [START_REF] Vezzani | Functional role of proinflammatory and anti-inflammatory cytokines in seizures[END_REF]. However, the precise response of microglia to epileptic seizures and its consequences on subsequent neuronal network function have been scantly studied so far, especially in in vivo models. Furthermore, it has not been known whether seizure-induced microglia activities reduce or exacerbate neuronal network activity [START_REF] Lukens | Microglia and Neurodevelopmental Disorders[END_REF]. This knowledge gap has several causes, one being that microglia cannot be imaged in vivo through the skull in rodents and most other animal species.

Transparent windows must be cut, involving delicate surgery and risks of brain trauma that can influence the state of microglia, which are highly sensitive to disturbances in brain homeostasis [START_REF] He | RNA sequencing analysis reveals quiescent microglia isolation methods from postnatal mouse brains and limitations of BV2 cells[END_REF]. Zebrafish larvae offer a helpful alternative model because they are transparent, enabling easy imaging of microglial cells in real-time, in a living brain and in its physiological environment [START_REF] Peri | Live Imaging of Neuronal Degradation by Microglia Reveals a Role for v0-ATPase a1 in Phagosomal Fusion In Vivo[END_REF].

Another difficulty has been that most studies investigating microglia activities in rodent epilepsy models have been conducted using pharmacologically-induced seizures. Those chemicals can then cause extended status epilepticus-like activity, usually lasting several hours [START_REF] Ravizza | Innate and adaptive immunity during epileptogenesis and spontaneous seizures: Evidence from experimental models and human temporal lobe epilepsy[END_REF][START_REF] Avignone | Altered morphological dynamics of activated microglia after induction of status epilepticus[END_REF][START_REF] Vezzani | Neuroinflammatory pathways as treatment targets and biomarkers in epilepsy[END_REF][START_REF] Maupu | Diisopropylfluorophosphate-induced status epilepticus drives complex glial cell phenotypes in adult male mice[END_REF], and evoke much stronger brain inflammation than that observed in genetic models, and probably human patients [START_REF] Gasmi | Low grade inflammation in the epileptic hippocampus contrasts with explosive inflammation occurring in the acute phase following status epilepticus in rats: translation to patients with epilepsy[END_REF][START_REF] Maupu | Diisopropylfluorophosphate-induced status epilepticus drives complex glial cell phenotypes in adult male mice[END_REF][START_REF] Somkhit | Microglia Remodelling and Neuroinflammation Parallel Neuronal Hyperactivation Following Acute Organophosphate Poisoning[END_REF]. This difficulty can be overcome by using a genetic epilepsy model. We report here what is, to our knowledge, the first study of the response of microglial cells in vivo in such a model.

We chose an established zebrafish model of Dravet syndrome (DS), a severe epileptic encephalopathy caused by a deficit in the activity of inhibitory interneurons, most commonly caused by de novo loss-of-function mutations in the SCN1A gene encoding the voltage-gated sodium channel NaV1.1. This zebrafish DS model is based on the loss-of-function of one of the two zebrafish orthologues of the SCN1A gene, scn1Lab. Mutants and morphants scn1Lab larvae display recurrent epileptiform seizures from 3 dpf onward, which we and others had previously characterized (Scott C. Baraban, Dinday and Hortopan, 2013;[START_REF] Brenet | Defective Excitatory/Inhibitory Synaptic Balance and Increased Neuron Apoptosis in a Zebrafish Model of Dravet Syndrome[END_REF].

In particular, we previously showed that loss of scn1Lab function induced a shift in the excitatory/inhibitory balance, with a decreased number of GABAergic synapses and an increased number of glutamatergic ones, combined with an increased number of apoptotic neurons [START_REF] Brenet | Defective Excitatory/Inhibitory Synaptic Balance and Increased Neuron Apoptosis in a Zebrafish Model of Dravet Syndrome[END_REF].

In close agreement with findings in both preclinical animal models and human patients [START_REF] Altmann | A systems-level analysis highlights microglial activation as a modifying factor in common epilepsies[END_REF], microglia displayed significant morphological differences in scn1Lab-KD larvae compared to their wild-type siblings. In the mutant context, we observed a decreased number of "branched" microglia and an increased percentage of microglia showing an amoeboid morphology. These "amoeboid" microglia probably correspond to the previously described M1-type pro-inflammatory "activated" cells observed after brain injuries or in various neuronal disease situations including epilepsy [START_REF] Morin-Brureau | Microglial phenotypes in the human epileptic temporal lobe[END_REF], but also following intoxication by neurotoxic compounds, such as organophosphates [START_REF] Nakajima | Microglia: Activation and their significance in the central nervous system[END_REF][START_REF] Somkhit | Microglia Remodelling and Neuroinflammation Parallel Neuronal Hyperactivation Following Acute Organophosphate Poisoning[END_REF]. Interestingly, although the disease was associated with an increased number of microglia with an "activated" phenotype, the proportion of "transitional" microglia was similar in the two genetic contexts, and a small percentage of "resting" microglia was still observed in scn1Lab-KD individuals. These observations highlight the complexity of the microglial response in epilepsy and, more importantly, are evidence that different populations of microglial cells are present in epileptic brains, each likely playing a distinct role in the pathophysiology of the disease. Although increased expression of pro-inflammatory mediators has been observed both in patients with different forms of epilepsy and in various animal models [START_REF] Leal | Brain expression of inflammatory mediators in Mesial Temporal Lobe Epilepsy patients[END_REF][START_REF] Morin-Brureau | Microglial phenotypes in the human epileptic temporal lobe[END_REF], other studies have shown that anti-inflammatory cytokines are also increased in the epileptic brain [START_REF] Benson | Complex alterations in microglial M1/M2 markers during the development of epilepsy in two mouse models[END_REF]. All evidence of a complex response of microglia to epileptic seizures. Surprisingly, and in contrast with reports in most rodent epilepsy models, qRT-PCR analysis found no significant overexpression of pro-inflammatory cytokine-encoding genes, il1β and il8, in the brain of scn1Lab-KD individuals. This result is probably due to the moderate severity of the seizures in these larvae compared with those induced by pharmacological agents, such as PTZ, kainate, or DFP [START_REF] Maupu | Diisopropylfluorophosphate-induced status epilepticus drives complex glial cell phenotypes in adult male mice[END_REF][START_REF] Somkhit | Microglia Remodelling and Neuroinflammation Parallel Neuronal Hyperactivation Following Acute Organophosphate Poisoning[END_REF], whatever the model species. However, using the transgenic Tg[il1β:GFP] line, which enables Il1β-expressing cells to be visualized [START_REF] Nguyen-Chi | Identification of polarized macrophage subsets in zebrafish[END_REF], we observed a significant increase in the percentage of microglia expressing Il1β in scn1Lab-KD individuals compared with their WT siblings. This observation, which is in close agreement with our cluster analysis, further confirmed not only the increased number of M1-like "activated" microglia in scn1Lab-KD larvae, but also the existence of at least two distinct microglia populations, with and without expression of Il1β. However, the increased expression of il1β was probably too weak to be detected by qRT-PCR analysis of brain RNAs, highlighting the differences between genetic and pharmacological epilepsy models again [START_REF] Maupu | Diisopropylfluorophosphate-induced status epilepticus drives complex glial cell phenotypes in adult male mice[END_REF][START_REF] Somkhit | Microglia Remodelling and Neuroinflammation Parallel Neuronal Hyperactivation Following Acute Organophosphate Poisoning[END_REF]. It is also of note that studies of microgliamediated neuroinflammation in Scn1A +/-mice yielded widely varying results. In some studies, significant activation of microglial cells was observed in the prefrontal cortex and dentate gyrus, combined with an increased expression of the Il1β and Tnfα genes [START_REF] Satta | Neuropathological Characterization of a Dravet Syndrome Knock-In Mouse Model Useful for Investigating Cannabinoid Treatments[END_REF], while in other studies, no microglia activation was detected in the hippocampus of Scn1A +/-mice associated with a lack of over-expression of pro-inflammatory mediators [START_REF] Salgueiro-Pereira | A two-hit story: Seizures and genetic mutation interaction sets phenotype severity in SCN1A epilepsies[END_REF]. Furthermore, [START_REF] Benson | Complex alterations in microglial M1/M2 markers during the development of epilepsy in two mouse models[END_REF] showed that whereas microglia expressed both pro-and anti-inflammatory mediators in the mouse pilocarpine model, only pro-inflammatory mediators were overexpressed in microglia of mice exposed to kainate [START_REF] Benson | Complex alterations in microglial M1/M2 markers during the development of epilepsy in two mouse models[END_REF]. Complex alterations in microglial M1/M2 markers during the development of epilepsy in two mouse models highlight the wide variety of microglial responses to seizures.

Genetic depletion of microglia in scn1Lab-KD larvae exacerbated the hyper-activity of neuronal networks, as shown by the increased number of spontaneous epileptiform seizures and worsening of the characteristic swirling swimming behavior. These results thus suggest that microglia played a protective role in epileptic brains and mitigated neuronal activity. Like ours, other studies have concluded that microglia play a beneficial role in epilepsy through mitigation of neuronal activity and modulation of synaptic plasticity [START_REF] Mirrione | Microglial ablation and lipopolysaccharide preconditioning affects pilocarpine-induced seizures in mice[END_REF][START_REF] Li | Reciprocal Regulation between Resting Microglial Dynamics and Neuronal Activity In Vivo[END_REF][START_REF] Badimon | Negative feedback control of neuronal activity by microglia[END_REF]. In addition, [START_REF] Mirrione | Microglial ablation and lipopolysaccharide preconditioning affects pilocarpine-induced seizures in mice[END_REF] showed that total microglia ablation in adult mice led to an increased sensitivity to the pro-convulsant substance pilocarpine [START_REF] Mirrione | Microglial ablation and lipopolysaccharide preconditioning affects pilocarpine-induced seizures in mice[END_REF], further evidence that microglia display neuroprotective functions in an epileptic context. However, preventing microglia proliferation using the selective CSF1-R inhibitor GW2580 during the late stage of the epileptogenesis (but not the early stage) decreased spontaneous seizures in the kainate mouse model [START_REF] Di Nunzio | Microglia proliferation plays distinct roles in acquired epilepsy depending on disease stages[END_REF]. This work extends our understanding of the role of microglia in epilepsy. The principal added value of our study is that it is the first investigation of the response of microglial cells in vivo in a genetic epilepsy model. We show that epileptiform seizures rapidly induce microglia reprogramming characterized by significant changes in cell shape and dynamic behavior, and increased expression of pro-inflammatory cytokine Il-1b. However, this reprogramming is partial and concerns only part of the microglia, suggesting that the response of these cells to seizures is complex and not limited to M1-type activation. Importantly, we also show that scn1Lab-KD larvae entirely devoid of microglia presented an increased number of seizures, with greater intensity, suggesting that microglia activity lends neuroprotection to epileptic neurons. Future research will be needed to investigate how microglial cells modulate neuronal activity. Indeed, modulating microglial cells activity may offer a novel therapeutic strategy of great potential interest by reducing neuronal hyperactivity in epilepsies.

Materials and Methods

Transgenic lines and fish maintenance

Zebrafish were maintained at 26.5°C in 14 h light and 10 h dark cycles. Embryos were collected by natural spawning and raised at 28.5°C in E3 medium. Developmental stages were determined as hours post-fertilization (hpf) or days post-fertilization (dpf) as previously described. 0.003% 1-phenyl-2-thiourea (PTU) was added at 1 dpf to avoid pigmentation. The scn1Lab s552 line has been previously described [START_REF] Schoonheim | Optogenetic localization and genetic perturbation of saccade-generating neurons in Zebrafish[END_REF] and was generously provided by the laboratory of Dr. Herwig Baier. Calcium imaging was performed using Tg[Huc: GCaMP5G] ( [START_REF] Akerboom | Optimization of a GCaMP calcium indicator for neural activity imaging[END_REF]. Microglia were visualized using Tg

Morpholino knockdown

The following morpholinos, obtained from Gene Tools (Philomath, Oregon, USA), were used in this study: 5'-CTGAGCAGCCATATTGACATCCTGC-3' was used at 0.62 mM to block the scn1Lab zebrafish mRNA transcription; 5'-GATATACTGATACTCCATTGGTGGT-3' was used at 0.88 mM to block the PU.1 zebrafish mRNA transcription. These morpholinos were injected with 0.03 mM rhodamine B dextran and 0.1 mM KCl into embryos at stage 1-2 cells.

Microglia morphology

4 dpf Tg[mpeg1:mCherryF] larvae were paralyzed using 300 µM pancuronium bromide (Sigma Aldrich, Saint-Louis, Missouri, USA) and agar-immobilized in the center of a 35 mm glassbottom dish. A 100 µm stack of the larval living optic tectum was acquired at 1024 × 1024-pixel resolution using a Leica SP8 laser scanning confocal microscope equipped with a 20×/0.75 multi-immersion objective. Images were processed using AutoQuant X3 (Media cybernetic, Rockville, Maryland, USA) and Fiji ImageJ (NIH) software. Microglial cells' surface area, volume and sphericity were quantified using Imaris MeasurementPro (Oxford Instruments, Abingdon, UK). The number and length of microglial processes and their Sholl analysis were determined using Imaris Filament Tracer (Oxford Instruments, Abingdon, UK).

Clustering of microglial cells

Cluster analysis was performed using sphericity (S), number of processes (NP), total processes length (TL), surface area (A), and volume (V) of microglial cells. Microglial cells with at least three of the following features: sphericity less than 0.5, number of processes greater than 7, total processes length greater than 140 µm, and a surface area greater than 2400 µm² were classified as "branched" microglia. Conversely, microglial cells with at least three of the following features: sphericity greater than 0.7, number of processes less than 3, total processes length less than 60 µm, and surface area less than 1500 µm² were classified as "amoeboid" microglia. Microglial cells with fewer than three "branched" or "amoeboid" characteristics were classified as "transitional" microglia.

RNA isolation and quantitative RT-PCR

For RNA isolation, whole larvae or dissected brains were homogenized using a syringe equipped with a 26G needle (10 brains per sample) using the RNA XS Plus kit (Macherey Nagel, Düren, Germany). Following RNA quantification with a Nanodrop 2000 (ThermoScientific, Waltham, Massachusetts, USA) and RNA integrity assessment using denaturing gel electrophoresis, total RNAs (1 µg) were reverse transcribed into cDNAs using the iScript cDNA Synthesis Kit (Bio-Rad, Germany) and qPCRs were performed using iQ SYBR Green Supermix (Bio-Rad, Germany). Samples were run in triplicate and expression levels of the studied genes were normalized to that of the tbp gene. The primers (Eurofins Genomics, Ebersberg, Germany) used are listed in Supplementary Table 1.

Microglial dynamics

4 dpf pancuronium bromide-paralyzed Tg[mpeg1:mCherryF] larvae were agar-immobilized in the center of a 35 mm glass-bottom dish. A 42 µm stack of the larval living half optic tectum was acquired at 1024 × 512-pixel resolution every 40 s for 1 h using a Leica SP8 laser scanning confocal microscope equipped with 40×/1.1 water objective (Leica, Wetzlar, Germany). Videos were processed using ImageJ software. Microglial cell body displacement, distance traveled between the first and the last frame, and microglial cell body speed displacement were quantified using Imaris MeasurementPro.

Calcium imaging

For quantification, 4 dpf Tg[Huc:GCaMP5G] larvae were paralyzed using pancuronium bromide, agar-immobilized in the center of a recording chamber, and placed under a Leica SP8 laser scanning confocal microscope equipped with a 20×/0.75 multi-immersion objective.

A single focal plane of the optic tectum was captured at a high frequency for 40 minutes. The mean grey value of each frame was measured using ImageJ software, and fluorescence variation (ΔF/F0) was calculated using Excel (Microsoft, Redmond, Washington, USA) by subtracting the mean fluorescence intensity of all frames from the fluorescence intensity of a frame of interest, then normalizing this difference by the mean fluorescence intensity of all frames. The fluorescence drift during recording was corrected by subtracting the background intensity of the surrounding frame. Calcium events were defined as any fluorescence increase greater than 0.04 ΔF/F0.

For movie illustration (Supp. Video 5, 6, 7, 8), larvae were paralyzed and immobilized as described above. Calcium transient in a single focal plane of the optic tectum of larvae were captured for 5 min, at 5 Hz, using a Yokogawa CSU-X1 spinning disk confocal microscope (Yokogawa, Tokyo, Japan) equipped with a LD LCI Plan-Apochromat 25x/0.8 DIC Imm Korr (UV) objective (Zeiss, Oberkochen, Germany).

Local field potential recording

Local field potential recording was performed as described in [START_REF] Brenet | Defective Excitatory/Inhibitory Synaptic Balance and Increased Neuron Apoptosis in a Zebrafish Model of Dravet Syndrome[END_REF] Briefly, 4 dpf larvae were paralyzed using pancuronium bromide and agar-immobilized in the center of a recording chamber. A glass electrode filled with artificial cerebrospinal fluid was placed in the right neuropil of the optic tectum. Neuronal activity was recorded for 1 h in current clamp mode at 10 µs sampling interval, amplified with a digital gain of 10, and filtered through a 0.1 Hz high-pass filter and 1 kHz low-pass filter. Recordings were analyzed using Clampfit software (Molecular Devices, San Jose, California, USA). Every downward membrane potential variation under -0.3 mV amplitude and lasting more than 100 ms was defined as an event.

Locomotor activity

Larval locomotor activity was assessed using an infrared automated tracking device (Zebrabox), controlled by ZebraLab software (ViewPoint, Lyon, France). First, 4 dpf larvae were individually spread in a 96-well plate in 200 µL E3 medium. The plate was then placed inside the Zebrabox recording chamber in the dark for 30 minutes of habituation after which larvae were tracked for 30 min divided into one-minute integration time. The animal color was set to dark and detection sensitivity to 12 for tracking. Besides the total distance swum by the larvae, we also recorded the number of times that the larvae were immobile, in low-speed, medium-speed, and high-speed movements, together with the duration of the events. The inactive speed threshold was set at 4 mm and the high-speed threshold at 8 mm. Each experiment replicates comprised at least 16 larvae per condition and was completed at least three times.

Data analysis

Data were analyzed and plotted using R-studio 1.2.5. (Posit, Boston, Massachusetts, USA).

Data are presented as mean ± sem. Statistical analysis of Figures 1 and 2 was performed

  https://urlz.fr/iPxZ). By contrast, in scn1Lab-KD larvae, microglia exhibited a more rounded morphology, with a larger cell body combined with fewer and shorter branches (Figure 1B; 1B1-3 and Supp. Fig. 1B3 (Video 2): https://urlz.fr/iPxP).

Figure 1 :

 1 Figure 1: Microglial morphology in the scn1Lab model. (A-B) Dorsal view of 4 dpf Tg[mpeg1:mCherryF] control (A1) and scn1Lab-KD (B2) larvae optic tectum showing microglial cell population, as well as 3D reconstruction of control (A2-A3) and scn1Lab-KD (B2-B3) microglia. (C-G) Quantification of microglial cells' sphericity (C), number of processes (D), total processes length (E), mean processes length (F) and ramification index (G) in 4 dpf scn1Lab-KD larvae (N = 11; n = 239) compared to sibling controls (N = 11; n = 228). (H) Sholl analysis of control (grey) and scn1Lab-KD (blue) microglia. (I) Diagram of the head of a zebrafish larva with the area of interest (the periventricular stratum) framed in red. (J) Cluster analysis of the microglial population of 4 dpf scn1Lab-KD (N = 11; n = 239) and sibling control (N = 11; n = 228) based on sphericity (S), number of processes (NP), total processes length (TL), area (A), and volume (V), leading to three microglial populations. (K) Repartition of the control (white) and scn1Lab-KD (blue) microglial population in the three clusters produced before. All images were acquired with SP8 Leica scanning laser confocal equipped with 20x/multi-immersion 0.75 objective. All data are represented as the mean ± sem. p-values were determined using Mann-Whitney or unpaired Student t-test depending on the normal distribution of the values. n.s., non-significant; **, p < 0.01; ***, p < 0.001; ****, p < 0.0001. Scale bars: 50 µm (A1, B1); 10 µm (A2, A3, B2, B3).

  immobile (Figure 2A1-A2, 2C1, 2F1) (Supp. Video 3, https://urlz.fr/iOIz). By contrast, cell bodies of scn1Lab-KD microglia were much more mobile (Figure 2B1-B2, 2C2, 2F2) (Supp. Video 4, https://urlz.fr/iOIA).

Figure 2 :

 2 Figure 2: Microglial dynamics in the scn1Lab model. (A) Confocal time-lapse showing microglial dynamic in a 4 dpf Tg[mpeg1:mCherryF] control living larval brain. (B) Confocal time-lapse showing microglial dynamic in a 4 dpf Tg[mpeg1:mCherryF] scn1Lab-KD living larval brain. (C) Merged image of two-time points highlighting the increase in microglial soma displacement in scn1Lab-KD larvae (C2) compared to sibling control (C1) (0': cyan, 36': magenta, merge: white). (D-E) Quantification of microglial cell body displacement (D) and mean displacement speed (E) over 60' recording in 4 dpf scn1Lab-KD larvae (N = 6; n = 55) compared to sibling controls (N = 4; n = 46). (F) Microglial cell tracking over 60' in 4 dpf control (F1) and scn1Lab-KD (F2) living larval brain. Scale bar 20 µm in all images. N = number of larvae and n = number of microglial cells. All images were acquired with a SP8 Leica laser scanning confocal equipped with a 40x/water 1.1 objective. All data are represented as the mean ± sem. p-values were determined using Mann-Whitney test. ****, p < 0.0001.

Figure 3 :

 3 Figure 3: Study of the inflammatory profile of scn1Lab-KD larvae. (A) Quantification of the expression of genes encoding pro-inflammatory cytokines, Il1β and Il8, relative to that of the tbp gene, in the brain of control (white) and scn1Lab-KD (blue) larvae, aged 4 dpf. (B) Quantification of the expression of the genes encoding the anti-inflammatory cytokines, Il10, Il4, and Tgfβ3, relative to that of the tbp gene, in the brain of control (white) and scn1Lab-KD (blue) larvae aged 4 dpf. (C-D) Dorsal view of the periventricular stratum of control (C) and scn1Lab-KD (D) 4 dpf larvae, showing microglial cells (C1, D1), Il1β protein expression (C2, D2), as well as the superposition of the two signals (C3, D3). (E) Diagram of the head of a zebrafish larva with the area of interest (the periventricular stratum) framed in red. (F) Quantification of the percentage of microglial cells expressing the cytokine Il1β in control larvae (N = 19) and scn1Lab-KD (N = 16). Scale: 10 µm. N = number of larvae analyzed.

Figure 4 :

 4 Figure 4: Consequences of microglial genetic depletion on neuronal activity in the scn1Lab model. (A) Schematic of the experimental setup. (B) 20 min representative calcium traces of 4 dpf control (N = 11), scn1Lab-KD (N = 16), control without microglia (N = 9) and scn1Lab-KD without microglia (N = 9) larvae (from top to bottom). (C) 20 min representative LFP traces of 4 dpf control (N = 5), scn1Lab-KD (N = 5), control without microglia (N = 5) and scn1Lab-KD without microglia (N = 5) larvae (from top to bottom). Scale bars of calcium traces: 0.05 ΔF/F0 and 1 min; of LFP traces: 0.5 mV and 1 min. (D) Quantification of calcium events, fluorescence increase greater than 0.04 ΔF/F0, frequency (D1), and amplitude (D2) during 1-hour recording in 4 dpf control (white) and scn1Lab-KD (blue) larvae. (E) Quantification of LFP events, downwards depolarization greater than 0.3 mV and lasting more than 100 ms, frequency (E1), and amplitude (E2) during 1-hour recording in 4 dpf control (white) and scn1Lab-KD (blue) larvae. N = number of larvae. All data are represented as the mean ± sem. ****, p < 0.0001:

Figure 5 :

 5 Figure 5: Consequences of genetic ablation of microglia on the locomotor activity of scn1Lab-KD larvae. (A) Diagram describing the experimental protocol. (B) Plots of distances traveled by control larvae (N = 148), scn1Lab-KD (N = 184), controls without microglia (N = 45) and scn1Lab-KD without microglia (N = 288) (from right to left). The swimming speed is represented according to the following color code: black represents a low speed, green a medium speed, and red a high speed. (C) Quantification of the total distance traveled by 4 dpf control (white) and scn1Lab-KD (blue) larvae during the 30 minutes recording. (D) Quantification of the time spent by 4 dpf control (white) and scn1Lab-KD (blue) larvae in a motionless position (D1), at low (D2), medium (D3), and high swimming speed (D4). N = number of larvae. All graphs represent the average ± sem. The p-values were calculated by a two-way ANOVA followed by a Tukey posttest. ***, p < 0,001; ****, p < 0,0001: indicates a statistical difference between genotypes. $$$, p < 0,001; $$$$, p < 0,0001: indicate significant differences depending on the presence or absence of microglia.

  [mpeg1: mCherryF] (Ellett et al., 2011) and cytokine Il1b expression using Tg[il1b: GFP] (Nguyen-Chi et al., 2015). All the animal experiments described were conducted at the French National Institute of Health and Medical Research (INSERM), UMR 1141, Paris, in compliance with European Union guidelines for the handling of laboratory animals (http://ec.europa.eu/environment/chemicals/lab_animals/home_en.htm). They were approved by the Direction Départementale de la Protection des Populations de Paris and the French Animal Ethics Committee under reference No. 2012-15/676-0069.

Acknowledgments:

We thank Francesca Peri (University of Zurich, Switzerland) for providing us with the Tg(p2y12:P2Y12-GFP) transgenic line and George Lutfalla (University of Montpellier, France) for providing us with the Tg(il1b:GFP). We also thank Christiane Romain and Olivier Bar (INSERM UMR 1141) for their technical assistance.

Funding: This work was supported by the Institut National de la Santé et de la Recherche Médicale (INSERM), and the National Centre for Scientific Research (CNRS). Funding sources had no involvement in the study design, collection, analysis, or interpretation of data, nor in the decision to publish.

using the Mann-Whitney test, if data did not follow a normal distribution, or Student's unpaired t-test with or without Welch's correction depending on the variance difference. Two-way ANOVA with Tukey post-test was used to analyze data in Figures 3, 4, and5.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Author

Conflicts of Interest:

The authors declare no conflict of interest.

Supplementary figures

Supplementary video 1: 3D reconstruction of a representative microglia from a control brain's larvae (Figure 1A). 3D images were generated using Imaris software (Biplane Inc., Version 9.5.0). (Link: https://urlz.fr/iPxZ).

Supplementary video 2: 3D reconstruction of a representative microglia from a scn1Lab-KD brain's larvae (Figure 1B). 3D images were generated using Imaris software (Biplane Inc., Version 9.5.0). (Link: https://urlz.fr/iPxP).

Supplementary video 3: Time-lapse of 4 dpf control larvae dorsal view showing microglial displacement and processes dynamic (Figure 2A). Interval between frames: 39 s. Video played at x6. Microglial tracking displacement was performed using Imaris software (Biplane Inc., Version 9.5.0). (Link: https://urlz.fr/iOIz).

Supplementary video 4: Time-lapse of 4 dpf scn1Lab larvae dorsal view showing microglial displacement and processes dynamic (Figure 2B). Interval between frames: 39 s. Video played at x6. Microglial tracking displacement was performed using Imaris software (Biplane Inc., Version 9.5.0). (Link: https://urlz.fr/iOIA).

Supplementary

Supplementary table il-1β