Thu Trang Lê

Jean-Luc Froger
email: j-luc.froger@uca.fr

Dinh Ho

Tong Minh

Multiscale framework for rapid change analysis from SAR image time series: Case study of ood monitoring in the central coast regions of Vietnam

Keywords: Change analysis, Flood monitoring, Vietnam central coastal regions, SAR image time series, Sentinel-1, Tile-based change detection matrix

Recently, the frequency of natural and environmental disasters has increased signicantly, causing constant changes on the Earth's surface. Synthetic Aperture Radar (SAR) data have been proved to be useful for operational change monitoring tasks. The multiscale framework presented in this paper aims at detecting and analyzing changes using SAR image time series composed of large-size images. Spatio-temporal changes are initially detected at the subimage scale analysis stage to determine regions and image acquisition dates related to the change occurrence. Detailed changes are then identied at the pixel scale analysis stage between selected acquisitions at each recognized region. This framework was used for ood monitoring over a large area along the central coast of Vietnam (from Thua Thien Hue province to Quang Nam province). We exploited a Sentinel-1 image time series acquired during two rainy seasons and typhoon seasons in the Western Pacic (from September to December of the two years 2017 and 2018). The proposed framework detected ooded areas with a high overall accuracy of 90.4% and could analyze dierent types of changes that occurred in this time series, i.e. dirac, periodic, chaotic changes, and temporal stability.

Introduction

The Earth's surface is aected by dierent natural and environmental disasters, such as volcanic eruptions, earthquakes, tsunamis, oods, deforestations, forest res, etc.

These catastrophic events are also major threats to human life and the world economy [START_REF] Guo | Understanding global natural disasters and the role of earth observation[END_REF]. The development of Earth observation satellites has allowed the acquisition of a large amount of information about the Earth's surface. Synthetic Aperture Radar (SAR) images, with the independence of light and weather conditions, have been widely exploited as an ideal tool for studying natural hazards and environmental problems [START_REF] Hu | Detecting seasonal landslide movement within the cascade landslide complex (washington) using time-series sar imagery[END_REF], [START_REF] Mason | Automatic near real-time selection of ood water levels from high resolution synthetic aperture radar images for assimilation into hydraulic models: A case study[END_REF], [START_REF] Peltier | Assessing the reliability and consistency of InSAR and GNSS data for retrieving 3D-displacement rapid changes, the example of the 2015 Piton de la Fournaise eruptions[END_REF], [START_REF] Belenguer-Plomer | Burned area detection and mapping using sentinel-1 backscatter coecient and thermal anomalies[END_REF], [START_REF] Reiche | Improving near-real time deforestation monitoring in tropical dry forests by combining dense sentinel-1 time series with landsat and alos-2 palsar-2[END_REF]). SAR image time series (ITS) densely and regularly provided by new generation satellites (Sentinel-1, for instance) are often large datasets with images of high quality, i.e., high resolution and large coverage (large-size images), short repeat cycles (large number of images) and multipolarization (dual-pol/quad-pol data), that require robust algorithms for big data processing. The temporal evolution analysis of objects of interest from repetitive SAR acquisitions allows natural and environmental hazard characterization [START_REF] Atzori | Insar fullresolution analysis of the 2017-2018 m>6 earthquakes in mexico[END_REF], [START_REF] Alpers | Oil spill detection by imaging radars: Challenges and pitfalls[END_REF], [START_REF] Giustarini | Flood hazard mapping combining hydrodynamic modeling and multi annual remote sensing data[END_REF], [START_REF] Le | Multitemporal insar coherence change analysis: Application to volcanic eruption monitoring[END_REF]), damage assessment [START_REF] Monti-Guarnieri | Coherent change detection for multipass sar[END_REF], Le et al. (2019b)), post-disaster recovery monitoring [START_REF] Solari | Fast detection of ground motions on vulnerable elements using sentinel-1 insar data[END_REF], and also the improvement of near-real-time disaster forecasts (e.g., ood forecasts in [START_REF] Hostache | Near-real-time assimilation of SAR derived ood maps for improving ood forecasts[END_REF]).

In this context, the ecient exploitation of information provided by SAR ITS for change and deformation detection and monitoring is, therefore, an attractive issue. Indeed, diverse approaches to change detection and analysis using SAR ITS have been proposed recently in the literature. Classical detectors based on log-ratio [START_REF] Rignot | Change detection techniques for ERS 1 SAR data[END_REF], statistical similarity measure [START_REF] Inglada | A new statistical similarity measure for change detection in multitemporal SAR images and its extension to multiscale change analysis[END_REF] developed Preprint submitted to Elsevier for change detection between a pair of SAR images have been extended to multitemporal SAR images for both single-and multi-polarization [START_REF] Lombardo | Maximum likelihood approach to the detection of changes between multitemporal SAR images[END_REF], [START_REF] Quin | MIMOSA: An automatic change detection method for SAR time series[END_REF], [START_REF] Conradsen | Determining the points of change in time series of polarimetric sar data[END_REF], Le et al. (2015a), Le et al. (2015b)). Several approaches provide a general view of the temporal variation over observed areas via a symmetric matrix containing change information between all possible image pairs in the time series. In [START_REF] Atto | Multidate divergence matrices for the analysis of SAR image time series[END_REF], a Multi-Date Divergence Matrix (MDDM) consisting of dissimilarity between wavelet and curvelet features of each image (or subimage) pair was built for the whole ITS to identify dates when changes occurred. Spatial changes were then detected between images of these dates. The framework proposed in [START_REF] Le | Adaptive multitemporal SAR image ltering based on the change detection matrix[END_REF] aims at building a Change Detection Matrix (CDM) for each pixel stack of the time series, exhaustively. Changes detected in the CDM are results of similarity tests between every two dates. The NORCAMA [START_REF] Su | NORCAMA: Change analysis in sar time series by likelihood ratio change matrix clustering[END_REF] computes a Change Criterion Matrix (CCM) using likelihood ratio test at each pixel position. Then changes detected in CCM are separated into some types by clustering and recognizing classication steps.

In this paper, we focus on the detection of changes induced by ood events. For ood detection and monitoring, in situ methods can obtain highly accurate assessments. However, they have limitations due to the high costs of implementation, the dependence on external factors, and the diculty in undertaking at a large scale [START_REF] Kussul | Flood Monitoring from SAR Data[END_REF].

Remote sensing based methods (i.e., airborne or spaceborne observations) can deal with large ooded areas for continuous monitoring [START_REF] Kuenzer | Flood mapping and ood dynamics of the mekong delta: Envisat-asar-wsm based time series analyses[END_REF]. Numerous studies on ood monitoring have used optical data [START_REF] Ahamed | A modis-based automated ood monitoring system for southeast asia[END_REF], [START_REF] Chignell | Multi-temporal independent component analysis and landsat 8 for delineating maximum extent of the 2013 colorado front range ood[END_REF], [START_REF] Wang | An ecient method for mapping ood extent in a coastal oodplain using landsat tm and dem data[END_REF], [START_REF] Ticehurst | The strengths and limitations in using the daily modis open water likelihood algorithm for identifying ood events[END_REF]) and/or SAR data [START_REF] Amarnath | An evaluation of ood inundation mapping from modis and alos satellites for pakistan[END_REF], [START_REF] Clement | Multi-temporal synthetic aperture radar ood mapping using change detection[END_REF], [START_REF] Boni | A prototype system for ood monitoring based on ood forecast combined with cosmo-skymed and sentinel-1 data[END_REF], [START_REF] Tsyganskaya | Detection of temporary ooded vegetation using sentinel-1 time series data[END_REF], [START_REF] Landuyt | Flood mapping based on synthetic aperture radar: An assessment of established approaches[END_REF]). The high cloud cover at ooded areas, particularly in rainy seasons, often obscures the ground observations from optical images. Thus, SAR images are the most suitable data for this task. Overall, among the main algorithms for water detection, such as backscatter based thresholding approaches [START_REF] Chini | A hierarchical split-based approach for parametric thresholding of sar images: Flood inundation as a test case[END_REF][START_REF] Manjusree | Optimization of threshold ranges for rapid ood inundation mapping by evaluating backscatter proles of high incidence angle sar images[END_REF], [START_REF] Mason | Detection of ooded urban areas in high resolution synthetic aperture radar images using double scattering[END_REF], [START_REF] Martinez | Mapping of ood dynamics and spatial distribution of vegetation in the amazon oodplain using multitemporal sar data[END_REF], [START_REF] Martinis | Towards operational near real-time ood detection using a split-based automatic thresholding procedure on high resolution terrasar-x data[END_REF]), classication based approaches (including pixel-based [START_REF] Chapman | Mapping regional inundation with spaceborne l-band sar[END_REF], Manjusree et al. ( 2012)), object-oriented [START_REF] Martinis | Unsupervised extraction of ood-induced backscatter changes in sar data using markov image modeling on irregular graphs[END_REF][START_REF] Evans | Using alos/palsar and radarsat-2 to map land cover and seasonal inundation in the brazilian pantanal[END_REF]), region growing [START_REF] Wan | Automatic extraction of ood inundation areas from sar images: a case study of jilin, china during the 2017 ood disaster[END_REF][START_REF] Marti-Cardona | Wetland inundation monitoring by the synergistic use of envisat/asar imagery and ancilliary spatial data[END_REF]), and fuzzy classication [START_REF] Martinis | A fully automated terrasar-x based ood service[END_REF], [START_REF] Twele | Sentinel-1-based ood mapping: a fully automated processing chain[END_REF])), change detection based approaches [START_REF] Giustarini | A change detection approach to ood mapping in urban areas using terrasar-x[END_REF], [START_REF] Schlaer | Flood detection from multi-temporal sar data using harmonic analysis and change detection[END_REF], [START_REF] Clement | Multi-temporal synthetic aperture radar ood mapping using change detection[END_REF]), the thresholding methods are the most widely used. Based on the backscatter signal, these methods separate a water surface (with low intensities/ amplitudes) from a non-water one (with high intensities/ amplitudes) by using a predened threshold value. On a scene composed of urban areas and suburbs, the threshold selection is more challenging. In the case of oods, in sparsely populated areas, the backscatter values decrease due to radar returning from water bodies. However, in densely built areas, radar backscatter values increase because of double bounce scattering between buildings and water surface. Therefore, these two areas should be discriminated by user analysis and two dierent threshold values should be selected. [START_REF] Chini | Sentinel-1 insar coherence to detect oodwater in urban areas: Houston and hurricane harvey as a test case[END_REF] addressed this issue by taking advantage of the InSAR coherence feature to detect oodwater in urban areas.

In general, almost all recent approaches addressing the Earth surface change detection and analysis using SAR ITS have focused on detecting either spatial changes [START_REF] Quin | MIMOSA: An automatic change detection method for SAR time series[END_REF] or temporal changes [START_REF] Colin-Koeniguer | Colored visualization of multitemporal SAR data for change detection: issues and methods[END_REF]. Few approaches have devoted eort to determine both spatial and temporal changes and the analysis of their nature. However, they might not be appropriate when dealing with images covering a large area, but where changes only occur in a small region [START_REF] Atto | Multidate divergence matrices for the analysis of SAR image time series[END_REF], or with a big dataset as they were tested on small ones (Le et al. (2015b), [START_REF] Su | NORCAMA: Change analysis in sar time series by likelihood ratio change matrix clustering[END_REF]). Therefore, a method is lacking that would exploit the information provided by a long SAR ITS composed of large-size images to identify spatio-temporal changes. In this study, we propose a framework that tackles this issue. The proposed approach is a multiscale processing strategy for a change analysis based on the CDM method (Le et al. (2015b)) suited to handle a long SAR ITS covering a large study area, which was briey introduced in [START_REF] Le | Change detection in multitemporal sar images using a strategy of multistage analysis[END_REF], Le et al. (2019a)). This work is applied to ood monitoring over a large area in the central coastal regions of Vietnam using a Sentinel-1 SAR ITS. Flooding is one of the major natural hazards in Vietnam that causes an annual huge loss of life and property. With more than 3200 km long of coastline and 70% of the population living in coastal regions [START_REF] Bangalore | Exposure to oods, climate change, and poverty in Vietnam[END_REF], Vietnam is usually at high risk of ooding due to tropical storms, in particular, at the central coast. The paper is organized as follows:

Section 2 describes the study area and the data used for ood monitoring. Section 3 is dedicated to the proposed multiscale framework. Section 4 presents the obtained results and discussion. Finally, conclusions and perspectives are given in section 5.

Case study and dataset

The coastal region in this study stretches three provinces: Thua Thien Hue, Da Nang and Quang Nam, between 14 o 57' N to 16 44' N and 107 o 00' E to 108 o 44' E (see Fig. 1). The tropical monsoon climate is found in this region. There are two distinct seasons, the dry season from January to August with high temperature, hot and humid climate, and the rainy season from September to December [START_REF] Matsumoto | Seasonal transition of summer rainy season over indochina and adjacent monsoon region[END_REF]. The rainy season coincides with the operation period of typhoons and tropical depressions in the Western Pacic, and the northeast monsoon. Therefore, the rainfall concentrates mainly in this season, accounting for 65% to 80% of the total annual precipitation of the region [START_REF] Bui | Forecast of coastal oods in the southern central region in the context of climate change and sea level rise[END_REF] Inherited and developed from SAR systems on ERS-1, 2 and Envisat, Sentinel-1 mission provides independent operational capability for medium-to high-resolution radar mapping of the Earth with enhanced temporal resolution, coverage, reliability for applications requiring long time series. The repeat cycle of each Sentinel-1 is 12 days (175 orbits per cycle), so that 6 days for both Sentinel-1A and B. Sentinel-1 SAR sensor operates at C-band frequency (5.405 GHz) and supports operation in single and dual polarizations.

Sentinel-1 uses the four observation modes: Stripmap (SM), Interferometric Wide swath (IW), Extra Wide Swath (EW), and Wave. Each mode can produce products at SAR Level-0, Level-1 SLC, Level-1 GRD, and Level-2 OCN. 3. Methods

Data preprocessing

The Sentinel-1 time series was preprocessed by several steps described in Fig. 2 before applying the proposed framework. We used the Sentinel Application Platform (SNAP), version 6.0, with Graph Processing Tool (GPT) for all preprocessing steps in this study.

First, we calibrated acquired SLC Sentinel-1 images so that the radar signal is converted into γ 0 . The radar backscatter γ 0 best expresses the actual area visible to the radar (in the plane perpendicular to the slant range plane) that can be retrieved [START_REF] Small | Flattening Gamma: Radiometric terrain correction for SAR imagery[END_REF].

Then we used the Sentinel-1 TOPS operators to implement the swath and polarization selection and the deburst of Sentinel-1 data, i.e., S-1 TOPS Split, S-1 TOPS Merge, and S-1 TOPS Deburst operators in this paper. Afterward, Sentinel-1 images were multilooked (e.g., four looks in range) to reduce speckle. In our work, no additional speckle ltering is needed to avoid smoothing change information of the time series that can aect the obtained change detection matrix. Finally, all images of the time series were coregistered to stack all pixels associated with the same position on the ground. Besides, we also extracted the subset of the ITS to t the study area and masked the sea region using the fractional land/water mask processing in SNAP.

Multiscale framework for change analysis

Let us consider a time series of N cocalibrated and coregistered SAR images sorted by acquisition date I = {I t } 1 t N , with I t the image at time t. The framework proposed for rapid analysis of changes from a long SAR ITS made up of large-size images includes two stages of analysis (Fig. 3):

i) subimage scale stage: change regions and image acquisitions of interest (i.e., image acquisition dates related to change events) are quickly detected thanks to CDM approach [START_REF] Le | Adaptive multitemporal SAR image ltering based on the change detection matrix[END_REF] at the subimage scale analysis instead of the pixel scale analysis in the original. ii) pixel scale stage: from regions and image acquisitions of interest extracted in the previous stage, detailed changes in these regions are determined.

Subimage scale stage

At this analysis stage, we split each large-size image of the time series into subimages, called tiles, and hence the whole time series is also split into tile stacks. Then we apply the multitemporal change analysis using the CDM approach to each tile stack. The quadtree data structure was rst introduced in [START_REF] Samet | The quadtree and related hierarchical data structures[END_REF] which refers to a hierarchical set of maximal blocks (tiles) that partition a region (an image). An image is iteratively divided into four equal-sized quadrants, subquadrants. The initial image to be decomposed is the root of the quadtree, and the quadrants (subquadrants) are nodes, including internal node with four children and leaf node with no children (see Fig. 4 and 5). At each decomposition level, we can test several predened criteria for each quadrant (e.g., criteria of homogeneity). If the quadrant meets the criteria, it will not be further decomposed, on the opposite, it will be subdivided into four new subquadrants.

This procedure nishes whenever every quadrant/ subquadrant meets the criteria or when the size of quadrants reaches the minimum value. The minimum size of quadrants is set such that statistical representativeness is guaranteed. In this paper, we used a variance threshold value (λ) which is the weighted variance of the whole image γ 0 . If the variance of a quadrant (an image tile) is greater or equal to λ, the quadrant will be split further. Therefore the image is split sparsely in relatively uniform areas and more intensively where variation is large. This test criterion was applied to each image of the time series to determine the homogeneity of tiles in the quadtree decomposition.

Let Q denote the quadtree decomposition operator, each image I t of the ITS is decomposed into homogeneous tiles of various sizes as follows:

I S(i,j,k) t = Q [S t (i, j, k)] (I t ) , (1)
where I S(i,j,k) t is an image tile of size P × P pixels (with P = 2 (m-k) , 2 m and 2 n -the maximum and minimum sizes of a tile, respectively) at decomposition level k (0 k n), S t (i, j, k) is the quadtree structure of the decomposed image I t , and (i, j) is the upper left coordinate of the tile.

The texture varies from one image to another in the time series making the quadtree • Change detection matrix at subimage scale

We apply the CDM approach to the split ITS for detecting and analyzing changes in each tile stack (i, j) to reduce the computational cost of the pixel-based CDM. A tile-based CDM (TCDM) version constructed by two steps is presented hereafter.

• Bi-date TCDM: For each tile stack (i, j), we calculate a similarity matrix by taking a similarity measure D between each pair of tiles of two dierent dates (t, ) as follows:

H (t, ) (i, j) = D I S(i,j,k) t , I S(i,j,k) 1 t, N
.

(2)

The bi-date TCDM M (a binary matrix) is then obtained by comparing values of similarity in H to a threshold T in order to determine changed (denoted as 1) and unchanged (denoted as 0) tiles in the tile stack:

M (t, ) (i, j) = H (t, ) (i, j) 0 ≶ 1 T .
(3)

• Multidate TCDM: From the bi-date TCDM, we redene changed and unchanged tiles using multidate information.

Ψ I S(i,j,k) t = I S(i,j,k) | M (t, ) (i, j) = 0 1 N . (4)
Similar to the previous step, we then compute the multidate TCDM M by applying the same similarity measure D and a threshold T to temporal neighborhoods of each pair of dates (t, ):

Ĥ(t, ) (i, j) = D Ψ I S(i,j,k) t , Ψ I S(i,j,k) 1 t, N , (5)
M(t, ) (i, j) = Ĥ(t, ) (i, j) 0 ≶ 1 T . (6)
The matrix obtained after two steps of temporal change analysis can expose changes occurred over time in each stack. We recall here the characteristic forms of certain types of change (Fig. 6) that can be observed from the appearance of TCDMs (see more details in [START_REF] Le | Change information extraction from synthetic aperture radar image time series[END_REF]). Two extreme cases are the temporal stability (Fig. ). We can also discover more complex change types from the TCDMs. The rampe change in Fig. 6 i and j reports a gradual one with increasing magnitude from date t k to date t or till the end of the time series (for example, changes related to cultivated activities, soil/coast erosion). The periodic change in Fig. 6 k and l also shows a gradual change, however, it occurs in a period from date t k to date t and repeats cyclically (e.g., seasonal change).

• Change dynamics of image time series

Each TCDM contains change information corresponding to a tile stack. By taking into account all 1 elements of a TCDM representing changed tiles, we denote an index of change dynamics that measures the level of change in a tile stack as follows:

δ(i, j) = 2 N (N -1) N t=1 N =t+1 M(t, ) (i, j). (7)
The range of this index is a sequence starting at 0 that indicates the absolute stability, and nishing at 1 that presents the extreme instability of the considered tile stack.

The map of change dynamics of ITS allows us to extract regions of interest from various levels of change. For example, regions with high indexes δ often show regular changes. But some areas with low indexes δ may signify changes linked to a specic event. In this paper, we selected regions of interest manually after analyzing the obtained map of change dynamics and TCDMs. However, regions of interest can be identied automatically based on the index of change dynamics. We can select tiles having a specic δ value or having δ values within a certain range. The TCDMs in these extracted regions reveal acquisitions of interest associated with change events.

Pixel scale stage

After locating regions and image acquisitions of interest, we implement a pixel-wise analysis at these regions between two images acquired on determined dates (t, ) to obtain a change map CM (t, ) :

G (t, ) = L I S t , I S t = , (8)
CM (t, ) = G (t, ) 0 ≶ 1 T , (9)
where L measures the similarity degree G (t, ) at region I S between dates t and .

It is worth noting that similarity measures D and L in two stages of the proposed framework can be the same or dierent. The selection of a similarity measure and a threshold depends on the work scale, data used, for instance, the spatial resolution, the type of SAR data (i.e., single-, multi-polarization SAR, InSAR, etc.), the required accuracy, the cost and time constraints, etc. Several similarity measures for SAR data and automatic thresholding algorithms were reviewed in Le et al. (2015b). In this work, we used the Kullback-Leibler distance (KLD) between two Log-normal distributions (Lognormal KLD) as the similarity measure to identify the dierence of shapes of the local probability density functions(PDF) of images in the ITS [START_REF] Inglada | A new statistical similarity measure for change detection in multitemporal SAR images and its extension to multiscale change analysis[END_REF], and the minimum error thresholding (Kittler-Illingworth) method [START_REF] Kittler | Minimum error thresholding[END_REF] for the selection of threshold values in all stages of the framework. In Atto et al.

(2013), Log-normal KLD is computed as follows:

KLD Logn (I t , I ) = 1 2 (α It -α I ) 2 1 β 2 It + 1 β 2 I + 1 2 β 2 I β 2 It + β 2 It β 2 I -1, ( 10
)
with α the log-scale and β the shape parameters of probability distributions of the two SAR images (I t , I ) in the comparison.

Results and discussion

Thanks to the quadtree-based splitting step of the TCDM approach, we only processed 65,640 tile stacks instead of 125,829,120 initial pixel stacks. The TCDM computation time is about 2 hours on an Intel ® Xeon ® Silver 4110 CPU @ 2.1 GHz, 32 cores with 64 GBytes of RAM. By analyzing TCDMs and map of change dynamics obtained at the subimage scale, we can identify changed regions and image acquisitions of interest.

Then we can obtain a detailed change map for each detected region using KLD between selected image pairs at the pixel scale.

4.1. Change analysis at subimage scale

Analysis of change dynamics

The map of change dynamics in Fig. 7 gives a general view on the behavior and the trend of the variation over time of the observed area at the subimage (tile) scale. Each homogeneous tile of the map has a value that reects the level of change from 0 to 1.

We can see three regions along the coast marked in red rectangles with higher indexes of change dynamics (0.6 ≤ δ ≤ 0.8) than the others. They are the coasts of Thua Thien Hue province, Da Nang city, and Quang Nam province, respectively. Besides changes due to regular evolution, these areas were also aected by oods occurred during acquisition period. Some small areas marked in white ellipses are lakes and hydropower reservoirs with high indexes δ due to their water extent changes. Cultivated areas appear on the map with moderate indexes of change dynamics, from 0.3 to 0.5, since the progressive change of crops at these areas was observed during the same growth period of two years (i.e., from September to December of 2017 and 2018). Except for the masked sea area in the upper triangular portion of the map (δ = 0), it could be seen that mountainous areas are quite stable. These areas (almost located in the lower triangular portion of the map) have small δ values (0.1 ≤ δ ≤ 0.2). From the map of change dynamics, we selected three regions along the coast marked in red rectangles in Fig. 7, and also lake and reservoir areas for further analysis at the pixel scale.

Analysis of TCDMs

Fig. 8 shows the TCDMs and temporal proles of ooded tiles selected in three identied regions of interest. From TCDMs, we detected that in the acquisition period, the study area was aected by three oods observed on November 10, 2017, November 22, 2017, and December 11, 2018 (i.e., the fth, the sixth, and the eighteenth dates in the time series). On the TCDMs of ooded areas, dirac changed can be determined. The TCDMs reveal that some areas were impacted by only one of three oods (Fig. 8 a, b, c), but some areas were ooded two times (Fig. 8 d, e, f ), and even three times (Fig. 8 g) during two rainy seasons of 2017 and 2018. The backscatter mean values of tiles on ood dates are lower than those on other dates (Fig. 8 h, i, j, k, l, m, n). Therefore, the temporal proles can help us better understand the nature of change types detected by TCDMs. For example, TCDM in Fig. 9 f shows a dirac change detected on November 10, 2017 (the fth date of the time series) at a mountainous area which looks like a change caused by a ood. However, looking at its temporal prole (Fig. 9 l), the tile backscatter mean value on the fth date is higher than values on other dates. Thus, this change was not due to a ood but a landslide. Indeed, the typhoon Damrey on November 4, 2017 caused prolonged heavy rains that triggered landslides and ash oods in several mountainous regions in Thua Thien Hue and Quang Nam provinces in the study area. 2017(TaiNguyenMoiTruong, 2017)).

In addition to dirac change at ooded areas, we can also identify other types of change in the study area (see Fig. 9). At rice eld areas, periodic change (i.e., seasonal change according to crop evolution) can be observed (Fig. 9 a andc). In the case of ooding, we can see in Fig. 9 a and b that on the fth, the sixth, and the eighteenth dates of the time series, the rice eld area was also ooded like its surrounding areas. And Fig. 9 d shows the opposite case of a rice eld stack selected outside of the ooded areas. Aquaculture lagoons along the coast of Thua Thien Hue province (region 1) are covered by water almost all the time. The backscatter signal at this area changes from one date to another in the time series, therefore its TCDM shows a chaotic change (Fig. 9 e). During the three oods, water overowed the banks of many lagoons. Therefore, TCDMs observed at regions around these lagoons also present dirac changes as in Fig. 8. As marked in white ellipses in Fig. 7, the study area has many hydropower reservoirs (e.g., Huong

Dien, Binh Dien in Thua Thien Hue province, A Vuong, Song Tranh, etc. in Quang Nam province) and lakes (e.g., Khe Ngang, Ta Trach, etc. in Thua Thien Hue province, Hoi Khe, Chua Truoc Dong, etc. in Da Nang city, Khe Tan, Thach Ban, Phu Ninh, etc. in Quang Nam province) and the same phenomenon as the case of aquaculture lagoons can be observed (see Fig. 9 g and h). At almost all lakes and reservoirs, water overowed their banks during the observed oods. At rocky mountain areas, the TCDM in Fig. 9 i shows temporal stability in the tile stack, consistent with the map of change dynamics.

Thus, from information provided by the map of change dynamics and TCDMs, alongside the analysis of temporal proles of tile stacks at each specic region, it is possible to preliminarily identify areas aected by oods and acquisition dates of images related to these events. The TCDMs and map of change dynamics are helpful to determine changed regions and images involving these changes, in particular when dealing with a long SAR ITS covering a large area but without additional data sources to get more information about the study area.

Regions and image acquisitions of interest

The three regions along the coast marked in red rectangles, and several lakes and hydropower reservoirs in Fig. 7 were selected for the pixel scale analysis stage. The rst region is the coastal region of Thua Thien Hue province. The second one includes the coastal area of Da Nang city and a small part of Quang Nam province, and zones along two sides of Cam Le river of Da Nang city and Thu Bon river of Quang Nam province.

The last one is the rest of the coastal region of Quang Nam province.

As described above, TCDMs disclosed three ooding times observed by the acquired ITS of the study area, i.e., [START_REF] Nhandan | Thua thien -hue: Mua lon, nhieu noi chim trong bien nuoc[END_REF][START_REF] Lake | Rice eld not aected by oods; (c), (i) Aquaculture lagoon aected by three oods on November 10, 2017[END_REF]and December 11, 2018 regions were superimposed on the mean image of the time series and combined in an RGB color image. the inundation map provides a clear view of the frequently ooded areas of the observed region. We can further exploit these maps for the ood-damaged assessment and the ood vulnerability mapping.

Accuracy assessment

We can observe a relatively high proportion of changed (ooded) pixels at each selected region, suggesting that the proposed TCDM works correctly. Detected inundation areas are located in low-lying land regions. For a quantitative assessment of obtained change detection results, we used comparison samples extracted based on the ood map on November 7, 2017, derived from MODIS and Sentinel-1 data over Thua Thien Hue province (see Fig. 11 b) to compare to the ood map obtained by the proposed framework on November 10, 2017 (the rst ood). We adopted the following evaluation metrics to measure the limited errors, i.e., False Alarm (FA), Missed Alarm (MA), and Overall

Error (OE). We also used the Critical Success Index (CSI) [START_REF] Schaefer | The critical success index as an indicator of warning skill[END_REF]

where T P, F P, T N, F N are true positive, false positive, true negative, and false negative, respectively, in the confusion matrix (Table 2).

Among the total of 13891 comparison samples, the change map has 6405 correct and 166 false changed pixels, while 1165 changed pixels are misclassied into unchanged class.

Figure 1 :

 1 Figure 1: Location of the study area (© University of Texas Libraries (left), and Thua Thien Hue Department of Tourism (right)).

  From such a dataset, we can observe not only the abrupt and cyclical changes caused by oods but also seasonal changes related to aquaculture and agriculture areas. Besides, for the assessment of change detection results, we used a ood map of Thua Thien Hue province regarding the typhoon Damrey in 2017 provided by[START_REF] Brakenridge | Dfo ood event 4533[END_REF], Dartmouth Flood Observatory at the University of Colorado, from MODIS and Sentinel-1 data (see Fig.11 a).

Figure 2 :

 2 Figure 2: Preprocessing chain for Sentinel-1 image time series.

Figure 3 :

 3 Figure 3: Flowchart of the multiscale change analysis for SAR ITS.

Figure 4 :

 4 Figure 4: A quadtree with four levels of decomposition.

  structures of images dierent. In order to split the ITS {I t } 1 t N into tile stacks, we took the intersection of all separate quadtree structures and restructured it with respect to maximum and minimum sizes of tiles as the nal quadtree structure of the time series S = N t=1 S t . A tile stack of an ITS split by quadtree decomposition is denoted by {I S(i,j,k) t } 1 t N .

Figure 5 :

 5 Figure 5: SAR image split into tiles based on the quadtree decomposition. (a) Sentinel-1 SAR image of Thang Binh district, Quang Nam province, Vietnam, acquired on September 11, 2017, (b) Quadtreebased tiles of the image in Fig. 5a, (c) Final quadtree-based tiles of the time series.

  6 a and b) and the chaotic change (Fig.6 c and d). The former shows that no changes occurred in the stack, and the latter often describes a rapid surface evolution (e.g., glacier displacement). Two typical change types that can be detected by TCDMs are the dirac (Fig.6 e and f) and the step changes (Fig.6 g and h). The dirac change occurs on only one date in the time series and expresses the behavior of the radiometric temporal signal like a dirac pulse (for instance, a ood event observed on only one date (image)). The step change arises from date t k and lasts for a certain period or till the end of the time series. This type of change presents an abrupt change (e.g., changes due to natural disasters like volcano eruption, earthquake, etc.

Fig. 9 f

 9 Fig. 9 f, l show the TCDM and temporal prole of a tile selected at Tra My commune, Bac Tra My district, Quang Nam province, where a landslide occurred on November 5,

  Fig.10shows the inundation maps of three oods during two rainy seasons in 2017 and 2018 of Thua Thien Hue province, Da Nang city, Quang Nam province. We can simultaneously observe the eects of all three oods and identify the frequently ooded areas on these maps. The red, green and blue colors indicate ooded areas induced by the rst, the second and the third oods. The yellow, magenta and cyan colors express areas aected by two oods: the rst and the second, the rst and the third, and the second and the third, respectively. And white color points out areas aected by all three oods. Specically, in Thua Thien Hue province, all three oods aected coastal areas, such as Quang Dien, Phu Vang, Phu Loc, Phong Dien, Huong Tra, Huong Thuy districts, and Hue city. In particular, in low-lying regions of Quang Dien, Phong Dien, Huong Tra, and Huong Thuy districts, the damage to cultivation and aquaculture areas was severe.In Da Nang city, the rst ood had a minor eect on this city, mainly on one side of Cam Le River. The second and the third aected Cam Le (both sides of the river), some parts of Ngu Hanh Son, Hoa Vang and Hai Chau districts. Like Thua Thien Hue, Quang Nam is also one of the most frequently ooded provinces of Vietnam. We can see that the rst ood aected both sides of the Thu Bon river (i.e., Dien Ban, Dai Loc, Duy Xuyen districts), and the coastal regions of Hoi An city, Thang Binh, Phu Ninh and Tam Ky districts. The second one had a smaller impact, mainly in Thang Binh and Phu Ninh districts. And the third one had the most widespread inuence of the three oods, in Dien Ban, Duy Xuyen, Hoi An, Thang Binh, Phu Ninh, Tam ky districts. Overall,

F

  to assess the obtained binary change map. CSI combines the FA and MA into one score and does not consider the nonooded fraction within the test-site. This index ranges from [0, 1], the higher values indicating the better performance of the change detection operator. These metrics are calculated as follows:

  these stacks for change analysis at the subimage scale. The proposed TCDM approach allows the fast and correct detection of regions and image acquisitions of interest (i.e., changed areas and acquisition dates of images involving change events). Then we derived detailed change maps of detected regions between image acquisitions of interest at the pixel scale. In order to better interpret, the obtained change maps were combined in an RGB color image. The proposed framework was successfully applied to monitor inundation areas along the coastal regions of Thua Thien Hue, Da Nang and Quang Nam provinces of Vietnam by using a SAR ITS composed of 19 ascending Sentinel-1 images. Obtained TCDMs were capable of identifying dierent kinds of change in this time series (i.e., dirac change, periodic change, chaotic change, and temporal stability). The results indicate that during two rainy seasons from September to December of the two years 2017 and 2018, the coastal regions from Thua Thien Hue to Quang Nam suered three oods observed by images acquired on November 10 and 22, 2017, and on December 11, 2018. Most of the coastal districts of these provinces were aected, some of them were seriously ooded, such as Phong Dien, Huong Tra, Phu Vang, etc., of Thua Thien Hue, Hoa Vang, Cam le of Da Nang, and Dien Ban, Dai Loc, Duy Xuyen, Thang Binh, Phu Ninh, etc., of Quang Nam. The established inundation map has the potential to be exploited in further tasks like ood-damaged assessment, and spatial prediction of ood-susceptible areas.For further applications of the proposed framework, when using SAR data of dierent sensors covering dierent regions, an appropriate similarity measure should be used to t the size of split tiles. With sensors of low or medium resolution, we can use a similarity measure between tiles containing a large number of samples to detect changes in the study area (e.g., the KLD in this paper). With high resolution ones, a similarity measure between tiles of small neighborhoods (like the coecient of variation) can be used to preserve the details of high resolution data. The TCDM analysis shows that many kinds of change can be detected by TCDMs. Change detection results of the proposed framework are expected to be classied into dierent types of landcover change based on TCDMs, and improved by using global terrain data.

Figure 6 :

 6 Figure 6: Six types of changes can be observed by TCDMs. Left: temporal proles; Right: TCDMs' appearances. (a), (b) Temporal stability; (c), (d) Chaotic change; (e), (f) Dirac change; (g), (h) Step change; (i), (j) Rampe change; (k), (l) Periodic change.

Figure 7 :

 7 Figure 7: Map of change dynamics showing the frequency of all kinds of changes occurred in the study area. (1), (2), (3): three regions of interest; (a), (b), (c), (d), (e), (f), (g): locations of observed tiles in Fig. 8; (+): location of observed tile in Fig. 9 f.

Figure 8 :

 8 Figure 8: TCDMs and temporal proles at regions aected by oods. Left: TCDMs; Right: temporal proles of tile stacks. (a), (h) Flood on November 10, 2017; (b), (i) Flood on November 22, 2017; (c), (j) Flood on December 11, 2018; (d), (k) Floods on November 10, 2017 and November 22, 2017; (e), (l) Floods on November 10, 2017 and December 11, 2018; (f), (m) Floods on November 22, 2017 and December 11, 2018; (g), (n) Floods on November 10, 2017, November 22, 2017 and December 11, 2018.

Figure 9 :

 9 Figure 9: TCDMs and temporal proles at several specic regions. Left: TCDMs; Right: temporal proles of tile stacks. (a), (g) Rice eld aected by three oods on November 10, 2017, November 22, 2017 and December 11, 2018; (b), (h) Rice eld not aected by oods; (c), (i) Aquaculture lagoon aected by three oods on November 10, 2017, November 22, 2017 and December 11, 2018; (d), (j) Lake; (e), (k) Rocky mountain area with almost no change; (f), (l) A dirac change not caused by a ood.

Figure 10 :

 10 Figure 10: Inundation map of the study area in two rainy seasons, from September to December of 2017 and 2018 (opened in Google Earth).

Figure 11 :

 11 Figure 11: Accuracy assessment of ood detection. (a) Flood map of Thua Thien Hue province on November 7, 2017, regarding the typhoon Damrey, © Brakenridge and Kettner (2017) (Red: ooding mapped from NASA MODIS and Copernicus Sentinel-1 data, Gray: previously-mapped ooding, Blue: reference normal water extent); (b) Flood map of Thua Thien Hue province on November 10, 2017 produced by the proposed framework from Sentinel-1 data and comparison samples manually extracted from the map in Fig 11 a (White: ooded areas, Blue: ooded samples, Maroon: non-ooded samples, Green: false alarms, Red: missed alarms).

Table 1 :

 1 Description of Sentinel-1 data.

	Specications	Sentinel-1 data
	Operator	European Space Agency (ESA)
	Satellite	Sentinel-1A
	Launched date	April 03, 2014
	Satellite orbit	Ascending, track 55
	Repeat cycle	12 days
	Imaging frequency	C-band at 5.4 GHz
	Imaging mode	IW
	Data product	SLC Level-1
	Resolution	3.5 m×22 m (range×azimuth)
	Pixel spacing	2.3 m×14.1 m (range×azimuth)
	Polarization	VV
	Swath	IW1, IW2
	Number of images	19 images
	Acquisition dates	17/09/11; 17/09/23; 17/10/05;

(YY/MM/DD) 17/10/29; 17/11/10; 17/11/22; 17/12/04; 17/12/16; 17/12/28; 18/09/06; 18/09/18; 18/09/30; 18/10/12; 18/10/24; 18/11/05; 18/11/17; 18/11/29; 18/12/11; 18/12/23

The time series used in this study composed of 19 IW Level-1 Single-Look Complex (SLC) Sentinel-1 images were acquired from the ascending track 55 with a repeat cycle of 12 days, in two periods: from

September 11, 2017, to December 28, 2017, and from September 06, 2018, to December 23, 2018

. Table

1

summarizes the principal parameters of the dataset used.

  To build temporal neighborhoods for each tile

I

S(i,j,k) t , unchanged tiles corresponding to date t, having the same statistical properties as I S(i,j,k) t with respect to D and T in the similarity test (3), are aggregated as follows:

  Accordingly, limited errors are 9.6% of OE, 2.5% of FA, and 15,4% of MA. The obtained change detection result has overall accuracy of 90.4% and the CSI of 82.8%. We can see that the ood detection results of the two maps in Fig 11 are quite consistent, with few false alarms. The comparison map reports the ooding state on November 7, 2017,

	whilst the ood map produced in this paper describes the state on November 10, 2017,
	resulting in changes in the ood delineation between the two maps that might explain
	false alarms.

Table 2 :

 2 Confusion matrix of change detection result.In this study, we have developed a methodology for a multiscale change analysis from a long SAR image time series composed of large-size image. We proposed to use a quadtree decomposition to split the ITS into tile stacks, and then apply the CDM approach to

	Class		Validation samples	Total
			Changed Unchanged
	Change map	Changed	6405	166	6571
		Unchanged 1165	6155	7320
	Total		7570	6321	13891
	5. Conclusions

Acknowledgment

This research was nanced by the French government IDEX-ISITE initiative 16-IDEX-0001 (CAP 20-25).

(the fth, sixth and eighteenth dates in the time series). Firstly, on November 4, 2017, Damrey -one of the strongest storms hit Vietnam in 2017, caused widespread damage from Thua Thien Hue to Binh Thuan provinces and the Central Highlands. Heavy rain brought by the typhoon the following days caused the rst ood. According to the Commanding Committee for Natural Disaster Prevention and Control and Search and Rescue of Thua Thien Hue province, the precipitation from 19:00 November 3, 2017 to 13:00 November 5, 2017 was about 500 to 600 mm at mountainous area. The precipitation was higher than that in some areas, such as 769 mm at Khe Tre, 840 mm at A Luoi, and up to 1,855 mm at Bach Ma (nhandan, 2017). Then, typhoon Kirogi weakened to a tropical depression struck central and southern Vietnam on November 19, 2017. Heavy rain caused the second ood in many parts of this region. In Thua Thien Hue, from 7:00 November 19, 2017 to 19:00 November 20, 2017, precipitation recorded at some stations was: 290 mm at Ho Truoi, 357 mm at Ta Luong, 246 mm at A Luoi, 400 mm at Bach Ma (PCTTTKCN, 2017). Finally, downpours from December 7 to December 10, 2018 induced the third ood in the study site. For example, at some rainfall stations in Hue on December 10, 2018, the precipitation was 513 mm at Phong Binh, 484 mm at Bach Ma, 256 mm at Binh Thanh, 255 mm at Ho Truoi (dantri, 2018). To determine areas aected by each ood in detail, we have chosen the three following image pairs for the calculation of KLD at pixel scale: images acquired on October 29, 2017 (before the rst ood) and November 10, 2017 (during the rst ood); on October 29, 2017 and[START_REF] Lake | Rice eld not aected by oods; (c), (i) Aquaculture lagoon aected by three oods on November 10, 2017[END_REF] (before and during the second ood, respectively); and the pair of images acquired on November 29, 2018 and[START_REF] Lake | Rice eld not aected by oods; (c), (i) Aquaculture lagoon aected by three oods on November 10, 2017[END_REF] (before and during the third ood).

Inundation maps at pixel scale

The KLD was calculated between three selected pairs of γ 0 calibrated amplitude images acquired before and during each ood to obtain the similarity maps. The KI


Vianney Poinsignon

Romain Colle

El Khalil

Indira Asmar

Denis Mendez-David

Abd El David

Ait Kader

Kenneth Tayeb

Florence Chappell

Hugo Gressier

Bruno Herrero

Fève

Khalil El Asmar

Indira Mendez-David

Denis J David

Abd El

Kader Ait Tayeb

Kenneth Chappell

Florence Gressier

Hugo Herrero

Bruno Fève

Laurent Becquemont

Emmanuelle Corruble

Céline Verstuyft
email: celine.verstuyft@aphp.fr

Céline Pr

Verstuyft

The GG genotype of the serotonin 4 receptor genetic polymorphism, rs1345697, is associated with lower remission rates after antidepressant treatment: Findings from the METADAP cohort

Keywords: Pharmacogenetics, HTR4 polymorphism, major depressive episode, Antidepressant, Remission

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

Introduction

Major depressive disorder (MDD) is the main cause of disability worldwide [START_REF] Jaffe | The humanistic and economic burden of treatment-resistant depression in Europe: a cross-sectional study[END_REF], affecting around 350 million individuals. However, antidepressants, the main treatment, have insufficient efficacy in the treatment of major depressive episodes (MDE) in both short [START_REF] Trivedi | Evaluation of outcomes with citalopram for depression using measurement-based care in STAR*D: implications for clinical practice[END_REF] and long term (Rush, et al., 2006). Among factors involved in treatment efficacy, genetic variants affecting drug-metabolizing enzymes such as CYP2C19 or CYP2D6 are known to interfere with treatment response, especially when taking selective serotonin reuptake inhibitors (SSRI) or serotonin-noradrenaline reuptake inhibitors (SNRI) [START_REF] Caudle | Standardizing terms for clinical pharmacogenetic test results: Consensus terms from the Clinical Pharmacogenetics Implementation Consortium (CPIC)[END_REF][START_REF] Hicks | Clinical Pharmacogenetics Implementation Consortium (CPIC) Guideline for CYP2D6 and CYP2C19 Genotypes and Dosing of Selective Serotonin Reuptake Inhibitors[END_REF][START_REF] Molden | CYP2D6 Reduced Function Variants and Genotype/Phenotype Translations of CYP2D6 Intermediate Metabolizers: Implications for Personalized Drug Dosing in Psychiatry[END_REF][START_REF] Quaranta | Pharmacogénétique des médicaments antidépresseurs : état des connaissances et des pratiques -recommandations du Réseau national de pharmacogénétique (RNPGx)[END_REF]. In addition, it would be useful to identify biological factors, including biomarkers that predict antidepressant treatment efficacy [START_REF] Ozomaro | Personalized medicine in psychiatry: problems and promises[END_REF] and, particularly, remission, which is the optimal treatment outcome [START_REF] Zimmerman | How Should Remission From Depression Be Defined? The Depressed Patient's Perspective[END_REF]. Recently, a predictive algorithm for SSRI clinical outcomes in MDD was developed using a multiple omics research strategy that identified genes such as TSPAN5, ERICH3, DEFB1, and AHR [START_REF] Nguyen | Selective Serotonin Reuptake Inhibitor Pharmaco-Omics: Mechanisms and Prediction[END_REF]. Among novel genes to evaluate, the serotonin type 4 receptors (HTR4) may be candidates. Indeed, the HTR4, metabotropic Gs-protein coupled receptors, are expressed in brain areas associated with stress and emotion, including the amygdala, the nucleus accumbens, and the hippocampus [START_REF] Agrawal | Role of serotonin 4 receptor in the growth of hippocampal neurons during the embryonic development in mice[END_REF][START_REF] Bockaert | 5-HT(4) receptors: history, molecular pharmacology and brain functions[END_REF][START_REF] Vidal | Serotonin 5-HT4 Receptors: A New Strategy for Developing Fast Act[END_REF]. Recent findings have indicated that targeting the serotonin 4 (5-HT4R) receptors may constitute a new way to treat anxiety and depression [START_REF] Faye | Rapid Anxiolytic Effects of RS67333, a Serotonin Type 4 Receptor Agonist, and Diazepam, a Benzodiazepine, Are Mediated by Projections From the Prefrontal Cortex to the Dorsal Raphe Nucleus[END_REF][START_REF] Mendez-David | Rapid Anxiolytic Effects of a 5-HT4 Receptor Agonist Are Mediated by a Neurogenesis-Independent Mechanism[END_REF][START_REF] Samuels | Serotonin 1A and Serotonin 4 Receptors: Essential Mediators of the Neurogenic and Behavioral Actions of Antidepressants[END_REF]. Preclinical and clinical data support a role of HTR4 in depression and anxiety. HTR4 knockout mice show increased depressive-like and anxiety-like behaviours [START_REF] Amigó | The absence of 5-HT4 receptors modulates depressionand anxiety-like responses and influences the response of fluoxetine in olfactory bulbectomised mice: Adaptive changes in hippocampal neuroplasticity markers and 5-HT1A autoreceptor[END_REF], and the depressive behavior of anhedonia in rats is associated with a downregulation of HTR4 in the hippocampus [START_REF] Bai | Anhedonia was associated with the dysregulation of hippocampal HTR4 and microRNA Let-7a in rats[END_REF]. Two clinical studies assessed the role of HTR4 in mood disorders. One Japanese case-control study reported polymorphisms in the splice variant region of the HTR4 gene associated with unipolar depression [START_REF] Ohtsuki | Association between serotonin 4 receptor gene polymorphisms and bipolar disorder in Japanese case-control samples and the NIMH Genetics Initiative Bipolar Pedigrees[END_REF]. The second study, a Spanish post-mortem case-control study, showed a higher HTR4 density in the frontal cortex and caudate nucleus of depressed violent suicide victims in comparison to control subjects with no history of psychiatric disease [START_REF] Rosel | Altered 5-HT2A and 5-HT4 postsynaptic receptors and their intracellular signalling systems IP3 and cAMP in brains from depressed violent suicide victims[END_REF]. However, such postmortem studies are not able to assess whether the association is related to depression or suicide. In addition, decreased striatal 5-HT4 receptor binding is associated with risk for familial depression [START_REF] Madsen | Familial risk for major depression is associated with lower striatal 5-HT₄ receptor binding[END_REF]. Within the hippocampus in humans, HTR4 levels were inversely correlated with cognitive function in memory test performance [START_REF] Haahr | The 5-HT4 receptor levels in hippocampus correlates inversely with memory test performance in humans[END_REF]. Furthermore, preclinical findings demonstrate a putative benefit of HTR4 signaling modulation in treating anxiety and/or depressive symptoms, with a shorter onset of action when using HTR4s agonists [START_REF] Compan | Feeding disorders in 5-HT4 receptor knockout mice][END_REF][START_REF] Compan | Attenuated response to stress and novelty and hypersensitivity to seizures in 5-HT4 receptor knock-out mice[END_REF][START_REF] Faye | Rapid Anxiolytic Effects of RS67333, a Serotonin Type 4 Receptor Agonist, and Diazepam, a Benzodiazepine, Are Mediated by Projections From the Prefrontal Cortex to the Dorsal Raphe Nucleus[END_REF][START_REF] Lucas | Serotonin receptors, type 4: a new hope?[END_REF][START_REF] Lucas | Serotonin4 (5-HT4) Receptor Agonists Are Putative Antidepressants with a Rapid Onset of Action[END_REF][START_REF] Mendez-David | Rapid Anxiolytic Effects of a 5-HT4 Receptor Agonist Are Mediated by a Neurogenesis-Independent Mechanism[END_REF][START_REF] Pascual-Brazo | Modulation of neuroplasticity pathways and antidepressant-like behavioural responses following the short-term (3 and 7 days) administration of the 5-HT4 receptor agonist RS67333[END_REF].

In humans, two completed, but not yet published, clinical studies (ClinicalTrials.gov) were conducted to evaluate the effects of an HTR4 agonist, prucalopride, on emotional processing and neural activity (NCT03572790) and on emotional processing and non-emotional cognition (NCT03863366). To the best of our knowledge, only a genome-wide association study (GWAS) [START_REF] Uher | Genome-Wide Pharmacogenetics of Antidepressant Response in the GENDEP Project[END_REF] in 706 European patients from the GENDEP project (Genomebased therapeutic drugs for depression) assessed antidepressant treatment response and HTR4 genetic polymorphisms. Patients from this GWAS were treated for 12 weeks either by escitalopram (n=394) or nortriptyline (n=312), and the HTR4 rs1345697 genetic polymorphism was the strongest associated marker in the HTR4 gene for the nortriptyline group, although it did not reach statistical significance and was not assessed after long-term treatment. To date, and to our knowledge, there are no elements allowing us to specify the implication of this intronic genetic polymorphism on the functionality of the receptor.

Patients treated for MDD may receive many different classes of antidepressants. Antidepressants act by modulating serotonin and/or other monoamines, such as noradrenaline and dopamine, in the synaptic cleft in different ways. None of them are known to interact with HTR4, as determined by their mechanism of action (monoamine oxidase inhibition, serotonin transporter (SERT) and/or norepinephrine transporter (NET) blockade) and by their lack of affinity for this specific serotonin receptor subtype as defined in the NIMH Psychoactive Drug Screening Program Ki database [START_REF] Roth | The Multiplicity of Serotonin Receptors: Uselessly Diverse Molecules or an Embarrassment of Riches? The Neuroscientist 6(4)[END_REF]. Moreover, whatever the antidepressant strategies used, including electroconvulsive therapy, the main pharmacological effect is a marked increase in serotonergic transmission [START_REF] Blier | Serotonin and beyond: therapeutics for major depression[END_REF].

Thus, our study aimed to assess the association of the HTR4 (rs1345697) polymorphism with depressive symptoms improvement after 6 months of antidepressant treatment, using Hamilton Depression Rating Scale-17 (HDRS) score reduction and remission as the main outcomes, after 6 months of antidepressant treatment in predominantly European patients with a current MDE.

Material and Methods

Patients and design

The study sample comprised patients from the METADAP (Do Antidepressants Induce Metabolic Syndromes) cohort (ClinicalTrials.gov NCT00526383) [START_REF] Corruble | Treating major depressive episodes with antidepressants can induce or worsen metabolic syndrome: results of the METADAP cohort[END_REF]. Over a four-year period, from November 2009 to March 2013, this 6-month prospective, real-world treatment, multi-center study was conducted in 6 psychiatric care settings in France. Six hundred and twenty-four in-or out-patients with a major depressive episode (MDE), requiring the beginning of antidepressant treatment, as a monotherapy, were enrolled. Among them, 492 provided a DNA sample for genotyping.

Eligible patients were aged 18-65 years and had a current MDE diagnosis in the context of MDD and a minimum depression score on the 17-item Hamilton Depression Rating Scale (HDRS) of 18, corresponding to moderate and severe depression [START_REF] Zimmerman | Severity classification on the Hamilton Depression Rating Scale[END_REF].

Patients were not included if they presented with psychotic symptoms, bipolar, psychotic or eating disorders, current substance abuse or dependence, unstable medical conditions, organic brain syndromes, pregnancy, or were breast-feeding. Patients with a history of antipsychotic or mood stabilizer treatment the month before inclusion, and/or for four months or more during the year preceding inclusion, were not included.

The choice of antidepressant drug and its dose was left to the psychiatrist's discretion. Polytherapy with other antidepressants, antipsychotics, or mood stabilizers was not allowed. To treat symptoms like insomnia or anxiety, benzodiazepines were tolerated at the minimum effective dose and for the minimum duration.

The study followed international ethics standards and was approved by the local Ethics Committee. All participants provided a written informed consent.

The patients were clinically evaluated by their trained psychiatrist at baseline (M0) and one month (M1), three months (M3), and six months (M6) later. Patients' ethnicities were determined according to the place of birth and the ethnicity of the two parents of each patient.

Depressive symptoms improvement after antidepressant treatment

The Hamilton Depression Rating Scale-17 [START_REF] Hamilton | A rating scale for depression[END_REF] score was rated by trained clinicians at baseline, and 1 (M1), 3 (M3), and 6 (M6) months after the beginning of antidepressant treatment to assess depressive symptoms improvement. To minimize interrater reliability and reliability over time, all recruiting psychiatrists were trained by the same senior psychiatrist with two independent rating sessions. Antidepressant response was defined by a decrease in the total HDRS score of at least 50% from baseline to follow-up. A total HDRS score ≤7 defined remission [START_REF] Rush | Report by the ACNP Task Force on response and remission in major depressive disorder[END_REF]. The Clinical Global Impression Efficacy Index (CGI-E), rated with a symmetrical (4x4) matrix of drug effect and side effects, is a ratio of current therapeutic benefit to severity of sides effects [START_REF] Guy | ECDEU Assessment Manual for Psychopharmacology[END_REF].

Genotyping

At baseline, 5 mL of whole blood was collected. Genomic DNA was extracted from circulating blood leukocytes using Puregene Blood Kits (Gentra Systems) according to the manufacturer's protocol (Qiagen) and cryopreserved at -20°C. The HTR4 genetic polymorphism (rs1345697) was genotyped using TaqMan allelic discrimination with the ABI Prisms 7900HT Sequence Detection System (Life Technologies) as previously described [START_REF] Coulbault | Environmental and genetic factors associated with morphine response in the postoperative period[END_REF][START_REF] Taranu | The Catechol-O-methyltransferase Val(108/158)Met Genetic Polymorphism cannot be Recommended as a Biomarker for the Prediction of Venlafaxine Efficacy in Patients Treated in Psychiatric Settings[END_REF]. The sequence of interest was amplified using the following forward primer: 5′-TGCTATGTATTCATATGGGAAGCAG-3′ and reverse primer 5′-TCACAACCACTTTTATCCCACTAACTAC-3′. Allele A (reference allele) was detected using a 5′-GAGTTCAATTTTGAACATGT-3′VIC-fluorescent probe. Variant allele (G) was detected using the 5′-GTTCAATTTTGGACATGTTA-3′FAM-fluorescent probe. In addition, the HTR4 rs1345697 polymorphism (NM_001040173.2(HTR4):c.27-12413A>G) is located in an intronic region of the HTR4 gene. Genotypic analyses were run blind to clinical evaluations.

Statistical Analysis

Statistical analyses were performed using Stata v.13, and all tests were two-tailed. An alpha level of 5% was considered as statistically significant. Our initial hypothesis was that the rs1345697 genetic polymorphism might have an impact on antidepressant treatment response, without prejudging its direction.

Hardy-Weinberg equilibrium was assessed using the chi-square test.

The HTR4 genetic polymorphism (rs1345697) was the independent variable, and GG carriers were compared to allele A carriers (AA and AG patients). Bivariate analyses were performed to compare socio demographic and baseline MDD clinical characteristics (sex, age, weight, marital status, educational level, smoking status, recurrent MDD, previous antidepressant treatment, antidepressant class, HDRS scores) using chi-square tests for categorical variables and independent t-tests for continuous variables. All patients were analyzed even though marital or smoking status was missing for some patients.

A mixed-effects linear regression model was used to compare HDRS scores, while mixedeffects logistic regression models were used to assess response and remission rates over time, considering each time point of evaluation. These mixed-effects models were adjusted for age, sex, ethnicity. These variables were chosen a priori as potential confounders, regardless of the possible differences between genotype groups at baseline.

Post hoc analyses were performed in the event of a significant genotype effect. Linear and logistic regressions were carried out at M1, M3, and M6, with HDRS and remission as explicative variables and the HTR4 genetic polymorphism (rs1345697: GG versus A-carriers) as the independent variable. The same potential cofounders used in mixed-effects linear regression models, among demographic and clinical variables (i.e., age, sex, ethnicity) were controlled in these multivariate analyses.

Chi-square tests were used to compare frequency tables between genotype groups and SSRI/SNRI use. Mixed-effects models for HDRS scores, response, and remission rates over time, were also used to examine the interaction between genotype groups and SSRI/SNRI use.

In addition, mixed-effects models for HDRS scores, response, and remission rates over time were performed among SSRI and SNRI treated patients.

Moreover, to assess potential differences in adverse events between genotype groups, adverse events severity, rated with the CGI-E after 3 and 6 months of antidepressant treatment, were compared using chi-square tests.

The association between baseline characteristics used as covariates in the mixed-effects models and post hoc analyses and the dropout rates were assessed using chi-square tests for categorical variables (sex, ethnicity) and independent t-tests for continuous variables (age, HDRS). The proportion of study completers among HTR4 genetic polymorphism groups (rs1345697: GG versus A-carriers) was compared using Pearson chi-square tests.

Results

Sample characteristics

Among the 624 patients included in the METADAP cohort, 492 had a DNA sample available for genotyping. The 492 patients were mainly women (69%, n=337) with a mean age of 45.4±13.3 years and a high educational level (defined as having at least a university-level degree) in 48% (n=236). At baseline, their mean HDRS score was 24.8±4.9. Seventy-five percent (n=367) of patients had received a previous antidepressant treatment and 73% (n=359) had a recurrent MDE, determined by a clinical exam. At baseline, most of the patients (89%, n=437) required hospitalization. During the study, most of the patients were discharged, with 47% (n=173), 13% (n=35), and 5% (n=11) hospitalized after one, three, and six months, respectively. After discharge, some patients were re-hospitalized when their psychiatrist deemed it necessary. During the study, they were mainly treated (Table II.) with SSRIs (including paroxetine, escitalopram, and citalopram) and SNRIs (mostly venlafaxine) in 43% (n=208) and 40% (n=193) of cases, respectively. Six percent (n=31) of patients were treated with tricyclic antidepressants (mainly clomipramine) and 9% (n=46) with other antidepressants (mainly mirtazapine). The classes of antidepressants used were not significantly different between the genotype subgroups (i.e., AA-AG versus GG).

The Pearson chi-square test showed no significant difference in SSRI/SNRI use (p=0.316) or in adverse events severity after 3 (p=0.083) and 6 months (p=0.924) between genotype groups.

Mixed-effects models showed no interaction between genotypes groups and SNRI/SSRI use for HDRS scores (p=0.586), response rates (p=0.593), and remission rates (p=0.517) over time.

Their socio-demographic and clinical characteristics according to rs1345697 genotype groups are presented in Table I. At baseline, allele A-carriers (AA and AG) and homozygous variant genotype (GG) groups did not differ for socio-demographic, MDD clinical characteristics, antidepressant drug class, and HDRS scores.

One hundred and thirty-five (27%) were homozygous wild type (AA), 243 (49%) were heterozygous (AG), and 114 (23%) were homozygous variant (GG). The genotype distribution showed no significant deviation (p=0.6923) from Hardy-Weinberg equilibrium. Ninety percent (n=447) of the studied samples were European (defined as two European parents according to a self-report). In our sample, the observed allelic frequencies were 52% for the A allele and 48% for the G allele. The allelic frequencies in the general population of the genome Aggregation Database (gnomAD) [START_REF] Karczewski | The mutational constraint spectrum quantified from variation in 141,456 humans[END_REF] are 51% (A allele) and 48% (G allele). In Non-Finnish Europeans, these frequencies are 52% and 48% for A and G alleles, respectively. The allelic frequencies in our whole sample, comprising 90% Europeans, were thus comparable to those in Europeans of gnomAD. Since the minor allele is G, GG homozygotes were compared to carriers of the A allele.

Depressive symptoms improvement after antidepressant treatment

The dropout rates were 24%, 45%, and 58% after 1 month, 3 months, and 6 months, respectively. The main reasons for dropout were an antidepressant change (36%), the use of unauthorized drugs (7%), or a loss to follow-up (51%). Thus, HDRS scores were available for 374, 271, and 207 patients after 1, 3, and 6 months, respectively. Dropout rates were not statistically associated with the baseline characteristics used as covariates in the statistical analyses (age: p=0.4663; HDRS score: p= 0.1118; sex: p=0.209; ethnicity: p=0.284. The number of completers among the HTR4 genetic polymorphism groups (rs1345697: GG versus A-carriers) was not statistically different (p=0.966).

Overall results do not change when the models are unadjusted, but the standard error is smaller with adjusted covariates.

Lower HDRS score improvement in the homozygous variant (GG) carriers (M0=25.0±4.8; M1=14.7±6.7; M3=14.3±7.5; M6=13.0±9.3) as compared to the allele A carriers (M0=24.8±4.9; M1=14.4±7.1; M3=11.4±7.1; M6=9.7±7.3) was observed in a linear mixed-effects model (coefficient=1.52; confident interval (CI) 95% [0.37-2.67]; p=0.010) (figure 1, Table III). In a multiple linear regression post-hoc analysis, homozygous GG carriers had significantly higher HDRS scores after 3 (p=0.0062) and 6 (p=0.0097) months of antidepressant treatment. They were not significantly different at baseline and after 1 month of antidepressant treatment.

Lower remission rates (figure 2A) in the GG carriers [M1: 16% (n=13), M3: 19% (n=12), and M6: 37% (n=17)] as compared to allele A carriers [M1: 21% (n=61), M3: 32% (n=68), and M6: 47% (n=75)] were observed with a logistic mixed-effects model (OR=2.0; CI95% [1.0-4.1]; (p=0.05) (Table III). No specific sub-time effects were identified (M1: OR=0.72; CI95% [0.36-1.37], p=0.343; M3: OR =2.08; CI95% [0.93-4.76]; p=0.078), and M6: OR=1.79; CI95% [0.80-4.00]; p=0.16).

Response rates at M1, M3, and M6, respectively, were 46% (n=37), 46% (n=29), and 54% (n=25) among the homozygous variant (GG) carriers and 40% (n=115), 60% (n=125), and 71% (n=114) among the allele A carriers subgroup (figure 2B.). The mixed-effects model showed no statistically significant association between the HTR4 rs1345697 polymorphism and the response rates over time (Table III.).

In the subgroups analysis, the mixed-effects model showed no statistically significant association between the HTR4 rs1345697 polymorphism and HDRS, response and remission rates over time for SSRI treated patients. (Table III).

As in the whole sample, the mixed-effects models showed lower HDRS score improvement (Coefficient=1.8, CI95% [0.08-3.52], p=004) and lower remission rates (OR=2.7, CI95% [1.0-7.1], p=0.04) in the GG carriers and no statistically significant association for the response rates in the subgroup of SNRI treated patients (Table III.).

Discussion

We show here for the first time the association between the HTR4 rs1345697 polymorphism and depressive symptoms improvement and remission after antidepressant treatment. As seen with the mixed-effects model, GG carriers had significantly lower HDRS scores improvement and lower remission rates than allele A carriers. As measured with the HDRS scores, the magnitude of the effect might appear modest, however these encouraging results are worth further exploration and replication with multiple omics research strategies, such as those described by [START_REF] Nguyen | Selective Serotonin Reuptake Inhibitor Pharmaco-Omics: Mechanisms and Prediction[END_REF]. In addition, based on our clinical experience with patients, a 3.7-points decrease in HDRS score begins to be clinically significant. Interestingly, the effect size of the association is important since the GG carriers had two-fold lower remission rates than allele A carriers.

To our knowledge, this is the first study to explore the association between the HTR4 rs1345697 genetic polymorphism and the response after antidepressant treatment in a large cohort of mostly European MDE patients. Our initial hypothesis was based on a GWAS [START_REF] Uher | Genome-Wide Pharmacogenetics of Antidepressant Response in the GENDEP Project[END_REF] of the European GENDEP population, in which, among more than 500,000 genetic variants, this genetic polymorphism was reported as the strongest marker among 41 HTR4 genetic polymorphisms detected associated with nortriptyline response, although it did not reach genome-wide statistical significance and was not significantly associated with symptoms improvement and remission over 12 weeks. Indeed, our post hoc analyses indicated that a difference in HDRS improvement between genotypes was not observed in the earlier assessment one month after treatment, but only after 3 and 6 months of treatment.

As far as we know, the HTR4 rs1345697 genetic polymorphism has never been studied in such a naturalistic study, especially in patients mainly treated with SSRIs and SNRIs (mainly citalopram and venlafaxine). Our results support what was highlighted by Uher et al. [START_REF] Uher | Genome-Wide Pharmacogenetics of Antidepressant Response in the GENDEP Project[END_REF] as the effect of HTR4 rs1345697 genetic polymorphism on HDRS score improvement and remission is different between SSRI (mainly citalopram) and SNRI treated patient groups. Indeed, in their study comparing a group of patients treated either by escitalopram (n=394) or nortriptyline (n=312), the HTR4 rs1345697 was identified as a potential marker associated with response in the nortriptyline group. As a matter of fact, despite not being an SNRI, nortriptyline acts by inhibiting both serotonin and norepinephrine reuptake. Given the lack of affinity of antidepressants for HTR4s as defined in the NIMH Psychoactive Drug Screening Program Ki database [START_REF] Roth | The Multiplicity of Serotonin Receptors: Uselessly Diverse Molecules or an Embarrassment of Riches? The Neuroscientist 6(4)[END_REF] and the fact that the main pharmacological effect, regardless of the class of antidepressant treatment used, is a marked increase in serotonergic transmission [START_REF] Blier | Serotonin and beyond: therapeutics for major depression[END_REF], the reasons for this difference remain unknown. We can hypothesize that the difference might not be due to serotonin but to an indirect mechanism. To our best knowledge no interaction between HTR4 and norepinephrine has been described. However, an interaction between HTR4 and dopamine levels exists [START_REF] Guo | Activation and Blockade of Serotonin-4 Receptors in the Lateral Habenula Produce Antidepressant Effects in the Hemiparkinsonian Rat[END_REF][START_REF] Parga | Serotonin decreases generation of dopaminergic neurons from mesencephalic precursors via serotonin type 7 and type 4 receptors[END_REF]. Moreover, this difference might be due to efficacy and safety profiles that are different from one class of antidepressants to another, rather than to a questionable influence on the HTR4.

The HTR4 rs1345697 is an intronic genetic polymorphism and could not explain a modification of the HTR4 protein. We could hypothesize that this polymorphism is in linkage disequilibrium with another polymorphism in the 5'-flanking region, or an exonic region, of the HTR4 gene, that could affect transcription. Indeed, several single-nucleotide polymorphisms (SNP) have been described in the gene but, until now, the 5'-flanking region sequence was not available because the GWAS GENDEP analyzed only some exonic SNPs. It is important to note that in pharmacogenetics, many others intronic genetic polymorphisms are considered relevant with a phenotypic response (e.g., CYP2C19*17 and citalopram [START_REF] Sim | A common novel CYP2C19 gene variant causes ultrarapid drug metabolism relevant for the drug response to proton pump inhibitors and antidepressants[END_REF], CYP3A5*3 and tacrolimus, or CYP2D6*4 and antidepressants (Ingelman-Sundberg and Sim, 2010)). Thus, an extensive analysis of the 5'-flanking region and a functional characterization study of the protein would greatly help to understand the implication of the HTR4 rs1345697 in remission.

If HTR4 exhibits a wide distribution [for review [START_REF] Vidal | Serotonin 5-HT4 Receptors: A New Strategy for Developing Fast Act[END_REF]], it is mainly located in limbic areas, including the hippocampus. Serotonin, whose levels are increased by antidepressants, binds to the HTR4 G protein-coupled receptor, which stimulates the cAMP-PKA signaling pathway, resulting in the expression of genes involved in neuroplasticity. All signaling cascades reported to be affected by HTR4 stimulation are involved in remodeling of neuronal morphology and activity-dependent structural plasticity [for review [START_REF] Vidal | Serotonin 5-HT4 Receptors: A New Strategy for Developing Fast Act[END_REF]]. Moreover, we previously showed that HTR4 contributes to the effects of SSRI on adult hippocampal neurogenesis, including in the maturation of newborn neurons [START_REF] Mendez-David | Rapid Anxiolytic Effects of a 5-HT4 Receptor Agonist Are Mediated by a Neurogenesis-Independent Mechanism[END_REF]. We could assume that HTR4 does not participate in the initial phases of the antidepressant response, but helps to consolidate the response present after one month of antidepressant treatment through its role in cerebral plasticity. HTR4 could therefore contribute to the overall remission by giving a boost to avoid relapse. Recent data suggested that HTR4 agonists are also effective prophylactics against stress [START_REF] Chen | Prophylactic efficacy of 5-HT4R agonists against stress[END_REF]. An altered plasticity might explain the lack of symptoms improvement after the first month of antidepressant treatment in GG carriers. Indeed, onset of action of antidepressant treatment takes time and its effects on outcome, as seen here, appear after 3 or 6 months. In addition, promising results are emerging from studies of HTR4 agonists used in humans to treat depression and cognitive impairment (for review [START_REF] Murphy | Translating the promise of 5HT4 receptor agonists for the treatment of depression[END_REF]. Among the latter, the cognitive effects of prucalopride have been characterized [START_REF] Murphy | A role for 5-HT4 receptors in human learning and memory[END_REF], suggesting a role for HTR4 agonists as an adjunct to SSRIs to improve cognitive function.

Our study has several strong points. Firstly, it was a naturalistic study conducted in everyday psychiatric care settings. Such a real-life setting provides good insights into patients' outcomes. Secondly, it had a long-term follow-up (6 months), with multiple evaluations at baseline, 1, 3, and 6 months. Thirdly, it was conducted in a large homogeneous sample in terms of diagnosis: MDD with a current MDE requiring an antidepressant drug treatment. Most patients were female, which is typical of the reality of MDD.

However, our study has several limitations. Firstly, the treatment was left to the psychiatrist's discretion according to his clinical assessment in a non-randomized, naturalistic, open way. However, this method allows for the assessment of rs1345697 in "real life" conditions. Secondly, many patients dropped out prematurely, with an attrition rate of 44% and 57% at 3 and 6 months of treatment, respectively. Nonetheless, these rates are close to those observed in other naturalistic cohorts like STAR*D [START_REF] Trivedi | Evaluation of outcomes with citalopram for depression using measurement-based care in STAR*D: implications for clinical practice[END_REF]. Furthermore, we used mixedeffects models, which are robust in the case of attrition. As a matter of fact, using mixed-effects models, we analyzed our data in a long format. Therefore, a patient with missing data at a specific visit/time-point would not result in a complete loss of information for that patient, and an average estimation could still be calculated based on the remaining non-missing data points. Thirdly, our study did not include a specific assessment for anxiety disorders and cognition. Fourthly, anxiolytic treatment was allowed, but not exhaustively reported and did not allow for taking this variable into account for statistical analyses. However, these treatments were used primarily during the first month of antidepressant treatment; their mandatory prescription duration is limited to a maximum of 12 weeks. Thus, although this variable should not impact the results after 3 and 6 months, it may still be a confounding factor. In addition, other potential confounders, such as family psychiatric history, known to contribute to MDD liability, were not available in our study. Lastly, as discussed above, the intronic position of the rs1345697 polymorphism, and the lack of data regarding its influence on receptor function, is a limitation. This could suggest that another polymorphism in this region is also correlated with response and remission in people with MDD.

In conclusion, this study explored for the first time the association of the HTR4 genetic polymorphism (rs1345697) and remission after antidepressant treatment in a large cohort of predominantly European depressed patients. In our sample, this polymorphism was associated with antidepressant remission, with ancestral allele carriers presenting with greater remission over time. Additional studies remain to be undertaken to determine if rs1345697 plays a role in HTR4 structure and to characterize its functional aspects. Beside such functional studies, a study comparing two groups, differing according to HTR4 rs1345697 genetic polymorphism, randomized to either nortriptyline or escitalopram, would provide more clarity regarding the relevance of the polymorphism to treatment outcome. This might help to understand the underlying mechanisms involved in treatment response and improve outcome in MDD patients.

Contributors

VP drafted the manuscript with input from all authors. EC, LB and CV designed the study. RC, AAT, FG, BF and EC included patients in the study. CV analyzed samples and oversaw the biobank storage. KEA conducted the statistical analysis of the data. All authors approved the final version of the manuscript. Iproniazid 137.5 +/-62.5 n=4 150 +/-66.7 n=3

Figures legends

Figure 1 .

 1 Figure 1. HTR4 (rs1345697) genetic polymorphism affects Hamilton Depression Rating Scale-17 (HDRS-17) score evolution after antidepressant treatment [at baseline (M0), 1 (M1), 3 (M3), and 6 (M6) months (**p<0.01 GG versus AA-AG genotype)].

Figure 2 :

 2 Figure 2: Remission (2A) and response (2B) rates after antidepressant treatment at 1 (M1), 3 (M3), and 6 (M6) months: AA-AG versus GG genotypes of the HTR4 (rs1345697) genetic polymorphism.

Acknowledgments

We want to thank all staff members for their involvement with the patients and Allison Cardoso for the genotyping.

Funding

The study received funds from the Programme Hospitalier de Recherche Clinique National (PHRC) of the French Ministry of Health (AOM06022). The METADAP biobank is stored in the Centre de Ressources Biologiques (CRB) Paris SUD (BRIF: BB-0033-00089).

Declaration of conflicting Interests

Laurent Becquemont: investigator for Antisense Therapeutics, Alnylam Pharmaceuticals, PregLem SA, Ionis Pharmaceuticals, Novartis, Auris Medical, MedDay Pharma, Gilead Sciences, Actelion. Received consulting fees from Sanofi, Pfizer and Servier; lecture fees from Genzyme and Pfizer. Bruno Fève received lecture fees from Sanofi, Amgen, Lilly, Novonordisk and MSD, and grants from MSD and Viiv Healthcare for his laboratory. Denis J. David serves as a consultant for Lundbeck, Inc., and receives compensation from Lundbeck. Indira Mendez-David, Denis J. David are named on nonprovisional patent applications for the prophylactic use of RS67333 against stress-related psychiatric disorders, Céline Verstuyft serves as a consultant and receives compensation from Galapagos and Novartis. Abd El Kader Ait Tayeb, Kenneth Chappell, Romain Colle, Emmanuelle Corruble, Khalil El-Asmar, Florence Gressier, Hugo Herrero and Vianney Poinsignon have no conflict of interest to disclose.

Ethical standards

The authors assert that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2008. No smoking 49 (243) 37 ( 184) 12 ( 59)

Ceased smoking 38 (186) 29 ( 141) 9 (45)

Active smoking SSRI 43 (208) 34 ( 165) 9 (43)

SNRI 40 (193) 30 ( 145