Dominique Henry

Patrick Pons

Hervé Aubert

Range-Doppler Imaging Approach for the Wireless Reading of Mechanical Sensors

Keywords: chipless sensor, mechanical sensor, radar imagery, range-Doppler, remote sensing, wireless sensor, zero-power sensor

We investigate in this paper the wireless interrogation of chipless and passive mechanical sensors using a range-Doppler imaging algorithm. As an example, a Crookes radiometer illuminated by a light of controlled illuminance is interrogated by using a 24GHz FM-CW radar. A remote detection algorithm is proposed to estimate the reading range and Doppler harmonics embedded in the electromagnetic signal backscattered by the radiometer. From these harmonics the accurate estimation of illuminance is achieved for long reading ranges of at least 3.5m in a cluttered environment.

Introduction

Wireless and chipless sensors are a relevant solution for remotely interrogating and sensing physical quantities, especially when batteries cannot be replaced easily, such as for structural health monitoring applications, or where using electronic devices is critical, for example in harsh environments. To achieve the wireless interrogation of such sensors, various technologies can be considered using Radio Frequency Identification (RFID) tags (see e.g., [START_REF] Nair | A humidity sensor for passive chipless RFID applications[END_REF]) or fully-passive sensing devices remotely interrogated by radar readers [START_REF] Henry | Radar Imaging Approach for Zero-Power Millimeter-Wave Wireless Sensors[END_REF]. Beside short interrogation range of typically few ten centimeters, RFID tags often require an increased coding capacity to be efficient [START_REF] Rance | RCS magnitude coding for chipless RFID based on depolarizing tag[END_REF]. Longer reading range can be achieved from using harmonic radars [START_REF] Palazzi | A novel compact harmonic RFID sensor in paper substrate based on a variable attenuator and nested antennas[END_REF], but this technology requires high power-consuming frequency transposition by using a nonlinear device in the tag. Other technologies include useful Surface Acoustic Wave tags for the remote sensing in extreme conditions [START_REF] Pereira Da Cunha | Wireless harsh environment SAW array system for power plant application[END_REF]. However, achieving reading range of more than few meters remains very challenging from using this well-documented technology. The authors of the present study investigated an alternative solution. It consists of using of millimeter-wave radar imagery technique for the long-range interrogation of chipless and passive (or zero-power ) sensors. This approach allows remotely identifying and reading sensors at a range of tenth of meters in cluttered environments [START_REF] Henry | Classification of Radar Echoes for Identification and Remote Reading of Chipless Millimeter-wave Sensors[END_REF]. However, it requires the use of delay lines for mitigating the electromagnetic clutter, and it needs the challenging design and fabrication of electromagnetic transducers operating in the millimeter-wave frequency band.

In this paper, we investigate the feasability of another solution for chipless sensors based on mechanical transduction. Such mechanical sensor consists of mobile sensing structures with no integrated circuit and no power supply. Sensors based on sub-wavelength displacement of matter [START_REF] Thai | Novel Design of a Highly Sensitive RF Strain Transducer for Passive and Remote Sensing in Two Dimensions[END_REF] or fluid [START_REF] Bouaziz | Novel Microfluidic Structures for Wireless Passive Temperature Telemetry Medical Systems Using Radar Interrogation Techniques in Ka-Band[END_REF] have been proposed as an alternative solution to the electromagnetic transduction. In this paper, using a range-Doppler imaging technique, we propose to use mechanical sensors with much larger mobile constitutive parts for the remote detection and wireless reading of physical quantities in stationary cluttered environments. The very first example of such sensor studied here is a Crookes radiometer with 4 pans, which rotate thanks to the so-called thermal diffusion effect [START_REF] Reynolds | The Dimensional Properties of Matter in the Gaseous State[END_REF]. This physical effect is still of scientific interest today [START_REF] Azadi | Demonstration of Atmospheric-Pressure Radiometer With Nanocardboard Vanes[END_REF]. A 24GHz Frequency-Modulated Continuous-Wave (FM-CW) radar interrogates the rotating structure of few centimeters size. We show that the range-Doppler frequencies derived from radar images allows remotely estimating the reading range of the Crookes radiometer and the rotation speed of the pans when illuminated by a light. From the remote measurement of this rotation speed, the illuminance is derived. Detection of rotating targets from radar imaging technique is generally used in aeronautics and defence applications [START_REF] Lim | Radar Cross Section Measurements of a Realistic Jet Engine Structure with Rotating Parts[END_REF]. We extend it to the long-range wireless interrogation of the Crookes mechanical sensor for the remote measurement of the illuminance in cluttered environments. Application to the long-range reading of other kind of chipless mechanical sensors could be investigated.

The paper is divided in two sections. The first section describes the measurement principle. The FM-CW signal backscattered by a stationary rotating target and its Doppler harmonics specifically generated by the metallic pans rotation are rigorously simulated for various incident linearly polarized plane wave. Next, a range-Doppler imaging algorithm is applied for estimating from a range-Doppler imaging algorithm both the radar-to-sensor distance and the rotation speed of the pans for multiple controlled light illuminances. Material and methods are exposed in the second section. The reading range as well as the illuminance of light are finally derived from measured radar data in an anechoic chamber and in a cluttered environment.

Measurement Principle

Basics on Signal Processing of FM-CW Radar Data

A FM-CW radar is used for remotely interrogating a stationary mechanical chipless sensor. The FM-CW radar transmits a frequency modulated signal, so called chirp, at the carrier frequency f c and with the modulation bandwidth B. The shape of the frequency modulation is here a saw-tooth signal with the up-ramp duration T up . Successive chirps are transmitted with the repetition time (or pulse repetition interval ) denoted by T rep (> T up ). Let α c = B Tup be the frequency sweep rate. The radar-to-sensor distance is denoted by R and the round trip duration τ of the electromagnetic signal backscattered by the sensor is given by τ = 2R c , where c designates the vacuum speed of light. From the mixing and filtering of the transmitted and received signals, the radar delivers the following signal s(t) at time t (see e.g., [START_REF] Meta | Range Non-linearities Correction in FMCW SAR[END_REF]):

s(t) = K.exp{2jπ(f c τ + α c tτ - 1 2 α c τ 2 )} + N (t) (1)
where K designates the signal magnitude and N (t) models eventual additive noise. The sampled version s(n) of this signal is then given by:

s(n) = K.exp{2jπ(f c τ + α c n N T up τ - 1 2 α c τ 2 )} + N (n) ( 2
)
where N is the number of signal samples during the period T up and the natural number n (0 ≤ n < N ) represents the so-called fast time index. Let σ(t) be the Radar Cross Section (RCS) of the mechanical chipless sensor at time t. We assume here that slow variations over time of the RCS occur during the radar up-ramp duration T up . The signal magnitude K defined in eq. ( 2) is then no more constant and varies slowly over time. Based on the linear simplified model, the magnitude K(t) of the signal s(t) delivered by the radar can be approximated as follows:

K(t) = K 0 P t G t σ(t)A ef f (4πR 2 ) 2 (3)
where P t is the transmitted power, G t is the gain of the (impedance matched) transmitting radar antenna, K 0 is a constant (which depends on the power gain of the radar receiver channel) and A ef f denotes the effective area of the radar receiving antenna. If the radar transmits M consecutive chirps, the sampled signal can be rewritten in the following matrix form:

S(m, n) = K(mT rep ).f (n) + N (m, n) (4)
where the natural number m (0 ≤ m < M ) represents the slow time index and, according to eq. ( 1),

f (n) = exp{2jπ(f c τ +α c n N T up τ -1 2 α c τ 2 )} with 0 ≤ n < N .
To estimate the reading range of the chipless mechanical sensor, the Discrete Fourier Transform (DFT) of size N is applied on the fast time dimension of S(m, n) as follows:

S DF T (m, n 2 ) = DF T {S(m, n)} (5)
where the natural number n 2 (0 ≤ n 2 < N 2 ) denotes the range index. As the theoretical depth resolution d is given by d = c 2B [START_REF] Piper | Receiver frequency resolution for range resolution in homodyne FMCW radar[END_REF], the predicted maximal readable range is then given by R max = N.d 2 . To derive the range-Doppler image from the radar measurement [START_REF] De Wit | Modified range-Doppler processing for FM-CW synthetic aperture radar[END_REF], the DFT of size M is applied to the slow time dimension of S DF T (m, n 2 ), that is:

S 2,DF T (m 2 , n 2 ) = DF T {S DF T (m, n 2 )} (6)
where the natural number m 2 (-M/2 ≤ m 2 < M/2) denotes the Doppler frequency index. The Doppler frequency resolution δ f is given by δ f = 1 M.Trep and the measurable Doppler frequency f m ranges from -1 2.Trep to 1 2.Trep .

Electromagnetic Simulation of the Rotating Mechanical Sensor

We simulate in this section the range-Doppler response of a mechanical sensor composed of four metallic pans that rotate around a stationary axis. This sensor is derived from the Crookes radiometer. Each pan is a square of size a=2cm separated from each other by the angle of π 2 around the z-axis. The azimuth angular speed is denoted by ω (=0.5 rad/s). The shortest distance b between two opposite pans is of 0.5cm. The rotating pans are located at the radar-to-sensor distance R of 4m. The radar transmits M =1024 chirps with the repetition time T rep =30ms. The carrier frequency f c of the chirp is of 24GHz and the modulation bandwidth B is of 1GHz. The theoretical depth resolution d is then of 15cm. The duration T up of the up-ramp is 50µs and the number of signal samples N is 256. N (t) is assumed to be a white Gaussian noise with the standard deviation of 0.1V and zero mean. The transmitted power P t is set to 100mW while G t =8dBi and A ef f =1m 2 .

The RCS of the rotating structure has been rigorously simulated by using the Finite Element Method solver HFSS. In this simulation, the time-harmonic vertically polarized (V-polarized) incident plane wave at 24GHz is generated in the elevation plane θ=0 • for an azimuth angle ranging from 0 to 2π with a resolution of 1 • . Two configurations are simulated:

• In the first configuration, the four pans are tangential to the V-polarized incident electric field, as illustrated in Fig. 2.2. The simulated RCS is displayed in Fig. 2.2 between 0 and 2π in the elevation plane. As expected the RCS pattern has a π 2 -rotational symmetry. The half-power beamwidth of the main lobes at φ ≡ 0[ π 2 ] is of 3 • and we observe two high amplitude sidelobes. These lobes are apparent when two opposite pans are normal to the wavenumber direction of the incident electric field. The half-power beamwidth of the main lobes at φ ≡ π 2 [ π 2 ] is of 22 • and are generated by the dihedral corners formed by two tangential metallic pans;

• In the second configuration, the four pans are inclined by the angle ∆θ=5 • to the incident electric field polarization, as shown in Fig. 2.2. However, the axis of rotation is still tangential to the incident electric field. The simulated RCS is displayed in Fig. 2.2. Compared with the first configuration, the π 2 -rotational symmetry of the RCS pattern is replaced by the In order to simulate the rotation of the four metallic pans, the computed RCS patterns presented above are sampled into M =1024 samples with angular resolution ∆ω ≡ mωT rep [2π]. The rotational frequency of modes are given by f k = kω 2π , where f 1 = ω 2π is the fundamental rotational frequency of the pans. The range-Doppler signal is then computed according to the equations reported in Section 2.1. In Fig. 2 are displayed the range-Doppler signal at the range index m 2 =27, which corresponds approximately to the sensor location. Results are given when four pans are tangential to the incident electric field (first configuration) and when the four pans are inclined by 5 • to the electric field polarization (second configuration). The frequency resolution of these spectra is δ f =3.2E-2Hz. In the first configuration, the two lower frequency peaks are f 4 = 2ω π =0.31Hz and f 8 = 4ω π =0.62Hz. Because of the π 2 -rotational symmetry of the pans, f 1 and f 2 do not correspond to peaks in the simulated spectra. However the highest peak is f 8 (magnitude of -32.5dB) due to the π 8 angle between each main lobes. In the second configuration, the first frequency peak is f 2 = ω π =0.18Hz with the magnitude of -42.8dB because of the π-rotational symmetry of the rotating structure. Moreover, the magnitude of the f 8 peak decreases (-37.0dB) and the peak at frequency f 4 is very low (-54.3dB). In both cases, peaks at frequency f 6 are not easily distinguishable. Therefore, the mechanical sensor generates specific Doppler harmonics that directly depend on the inclination angle ∆θ.

Range-Doppler Algorithm

We propose algorithm to estimate the range of the interrogated mechanical sensor presented in Section 2.2, and to remotely determine the Doppler harmonics. The algorithm is decomposed into the following 8 steps (see Fig. 3):

(i) Raw data are first recorded. The signal s(t) given in eq. ( 2) is the average of successive mixed signals that are sampled during four up-ramps of duration T up =50µs. This average is performed with the repetition time T rep =30ms for M =2000 successive signals s(t);

(ii) and (iii) The matrix S(m, n) defined in eq. ( 4) is built and the singular value decomposition is performed on matrix S along the fast time axis. The 0Hz-frequency peak generated by the stationary target is removed by replacing the first singular value σ 1 of the matrix Σ by zero. Removing the 0Hz-frequency peak might be necessary when computing the mean Doppler echo level (see step (v));

(iv) As described in eq. ( 5) and ( 6), the range-Doppler transform is applied on the matrix S ′ (m, n). Positive range indexes n 2 ≥ 0 are selected. The theoretical depth resolution is d=15cm and the frequency resolution is δ f =3.2E-2 Hz;

(v) For each range index n 2 , a peak detection algorithm is applied to positive Doppler frequencies (0 ≤ m 2 ). The minimal frequency distance between two peaks is δ min =0.1Hz. The minimal threshold level for detecting peaks is ∆ th =⟨S 2,DF T (n 2 )⟩ + 10dB, where ⟨S 2,DF T (n 2 )⟩ is the mean value of S 2,DF T at the range index n 2 for positive indexes 0 ≤ m 2 < M 2 . Because the 0Hz-frequency peak is removed in step (iii ), it has no influence on the computed mean value; (vi) We select harmonics among the detected peaks for each range index n 2 and we define a selection criterion G(f p,n2 ) representing the number of harmonics of the frequency f p,n2 at the range index n 2 ;

(vii) We assume that the Doppler spectrum obtained at the estimated radarto-sensor distance contains the maximal number of detected harmonics. The radar-to-sensor distance is finally determined at the range where the maximal number of harmonics has been detected;

(viii) Once the radar-to-sensor distance is estimated, harmonics in the signal 3 Material and Method

Experimental Setup

The schematic of the experimental setup is displayed in Fig. 4a. The radar used in the experiment is a 24GHz FM-CW radar manufactured by Silicon Radar (SiRad Easy, see [START_REF]Evalkits -Silicon Radar Wiki[END_REF]). Parameters of the transmitted signal are similar to those described in Section 2.2. The maximal input power is 6dBm (around 4W) and transmitting and receiving antennas are V-polarized patch antenna arrays. The power gain of the radar receiver amplifier is set to 56dB. The passive mechanical sensor is a Crookes radiometer illuminated by a light source.

A second variable and identical light source, called here the reference light, illuminates the probe of a luxmeter (model CA1110 ). The distance between the probe of the luxmeter and the reference light is 5cm. The 2x2cm 2 pans of the Crookes radiometer are inclined by few degrees as described in Section 2.2. The sensor is interrogated by the radar for two different scenarios: (i) the first measurement is performed in an anechoic chamber and the radarto-sensor distance R is set to 2.1m;

(ii) the second measurement is performed in the room of our Laboratory and the radar-to-sensor distance R is set to 3.5m, as depicted in Fig. 4b. Measurements are finally performed for various controlled light illuminances. For each measurement, minimal, maximal and averaged illuminances are recorded.

Measurement Results

Measurements are performed for the two scenarii and the range-Doppler imaging algorithm described in Section 2.3 is applied on the measured radar data. In order to refine the estimation of the radar-to-sensor distance R, the matrix S(m, n) is averaged with a window of varying size n w (from n w =1 to n w =10) along the slow time axis. The final estimated radar-to-sensor distance is derived from the highest occurrence among 10 estimations. In Fig. 5 is displayed the typical range-Doppler image obtained after applying the step (iv) the algorithm. In measurement, the Crookes radiometer is illuminated with an illuminance of about 1500lx in the room of our Laboratory. Black to white colors represent low (-80dB) to high (-20dB) echo levels. Blue crosses are frequency peaks detected after applying the step (v) of the algorithm. Green crosses are harmonics detected among all the frequency peaks (step (vi) of the algorithm). Red crosses are the three first harmonics f 2 , f 4 and f 6 detected after estimating the distance of the Crookes radiometer (step (vii) of the algorithm). For both scenarii, the distance R is estimated with a precision of ±2d. The performance of the detection depends on the harmonics selection described in step (vi) as well as the signal-to-noise ratio of range-Doppler images.

After estimating the radar-to-sensor distance, we apply the step (viii ) of the algorithm on the Doppler spectrum S 2,DF T (m 2 , n sensor ). Harmonics are measured and plotted in Fig. 6 for various illuminances obtained in the anechoic chamber and in the room of our Laboratory. Black, blue and green markers represent respectively the first, second and third detected harmonics. Up and down triangles are minimal and maximal measured illuminances, whereas crosses are averaged measured illuminances. As expected, the frequency of the detected harmonics increases as the illuminance increases. We note that most of the frequencies are found to be f 2 , f 4 and f 6 since the pans of the Crookes radiometer do not have a perfect π 2 -rotational symmetry (see Section 2.2). A linear model based on the measurements of the first detected harmonic f 2 in the anechoic chamber as a function of the averaged illuminance is now proposed (see black dashed line in Fig. 6a). We obtain the correlation coefficient R 2 =0.973, the sensitivity α=10807lx/Hz and the intercept β=-1013lx. We extend the validity of the linear model for different harmonics, as follows:

I k = 2αf k k + β (7)
where I k denotes the illuminance and k=1,2,... is the rank of the corresponding harmonic. The linear model is plotted in Fig. 6 for harmonics f 4 (blue dashed line), f 6 (green dashed line) and f 8 (red dashed line). Most of the detected harmonics match to the linear model for the two scenarii. We note that some harmonics expected to follow the f 6 model correspond in fact to the f 8 model, because f 6 peaks might have not been detected (see explanations in Section 2.2). Resolution of the measurement depends on the resolution frequency δ f =3.2E-2Hz. If we consider the linear model of eq. ( 7), the illuminance is given with a resolution δI k = 2αδ f k . For harmonics f 2 , f 4 and f 6 , we have respectively δI 2 =346lx, δI 4 =172lx and δI 6 =116lx. We observe a small offset between linear models and measurements in Fig. 6b. It is explained by variations of the distance between the reference light and the probe of the luxmeter. Measurements of the irradiance (in W/m 2 ) instead of illuminance (in lx) may remove this offset. We also observe that the gap between minimal and maximal values (down and up triangles) of the illuminance increases with the averaged illuminance. This effect is caused by the variability of the used light source. Anyway, these primarily measurement results are very encouraging. They pave the way to the design of new class of wireless passive sensor solutions for the remote sensing in cluttered stationnary environments based on the mechanical transduction principle.

Conclusion

In this paper, we showed that the long-range interrogation of mechanical sensors embedded in cluttered environments is possible from using a range-Doppler imaging algorithm. As they use mechanical transduction, such sensors do not require electronic chip and/or power supply, and do not need the challenging design and fabrication of electromagnetic transducers. We estimated wirelessly the reading range and the rotation speed of centimeter-size pans at radar-tosensor distance of 3.5m in a cluttered environment. The proposed range-Doppler imaging technique may be applied to remotely measure other physical quantities, such as pressure of temperature, in cluttered stationary environments by taking advantage of the mechanical transduction principle.

Figure 1 :

 1 Figure 1: Schematic of the mechanical chipless sensor composed of 4 rotating metallic pans (a) tangential or (c) inclined to the incident electric field polarization (a=2cm and b=0.5cm). Simulated RCS of (b) tangential or (d) inclined 4 metallic pans for the incident V-polarized electric field at 24GHz in the elevation plane θ=0 • . π-rotational symmetry. Magnitudes of two of the first main lobes (θ=0 and θ=π) decrease by 2.5dB due to the 5 • inclination of the normal pans.

Figure 2 :

 2 Figure 2: Simulated Doppler spectrum of the rotating metallic pans at the range index m 2 =27 and angular speed ω=0.5 rad/s for the 4 rotating metallic pans: (a) first configuration and (b) second configuration with the inclination angle of 5 • .

Figure 3 :

 3 Figure 3: The different steps of the proposed range-Doppler imaging algorithm. S 2,DF T (m 2 , n sensor ) are derived. The detected harmonics f p,nsensor are used to determine the illuminance of the light that illuminates the mechanical sensor.

Figure 4 :

 4 Figure 4: (a) Schematic of the experimental setup. (b) Second scenario where the Crookes radiometer is at a distance of 3.5m from the radar in a room of our Laboratory.

Figure 5 :

 5 Figure 5: Range-Doppler image obtained when the Crookes radiometer is illuminated by a light source of illuminance about 1500lx in a room of our Laboratory at 3.5m. Final detected harmonics are represented by the red crosses.

Figure 6 :

 6 Figure 6: Measured illuminance as a function of the frequency of detected harmonics (a) in anechoic chamber with a Crookes radiometer located at 2.1m from the radar and (b) in room of our Laboratory with a Crookes radiometer located at 3.5m from the radar.


Basma El Hamzaoui

Maureen Laroche

Philippe Parola
email: philippe.parola@univ-amu.fr

Prof Parola Philippe

Vitrome

Detection of Bartonella spp. in Cimex lectularius by MALDI-TOF MS

Keywords: Cimex lectularius, Bartonella quintana, Bartonella henselae, MALDI-TOF MS

Bed bugs are small hematophagous insects. They are found in temperate and tropical climates around the world. Their vectorial capacity for several pathogens, including Bartonella spp., has been suspected. An experimental study of artificial infection of Cimex lectularius with Bartonella quintana and Bartonella henselae bacteria was developed to evaluate the ability of MALDI-TOF MS to simultaneously identify bed bugs and their infectious status. This experimental study confirmed the ability of MALDI-TOF MS to identify bed bugs. In addition, it was able to differentiate between control bed bugs, bed bugs infected with Bartonella quintana and bed bugs infected with Bartonella henselae, with an identification percentage above 90%.

Introduction

Bed bugs are brown, flat parasitic arthropods belonging to the Cimicidae family. Males and females are strictly hematophagous and can live for 12 months without feeding [START_REF] Delaunay | Bedbugs and infectious diseases[END_REF]. The two cosmopolite species, Cimex lectularius and Cimex hemipterus, are most often found among humans [START_REF] Roberson | Bed Bugs: They Still are Among Us!![END_REF]. Because of their hematophagous habits and intimate association with humans, bed bugs have long been suspected in disease transmission, but their vectorial capacity has rarely been studied [START_REF] Delaunay | Bedbugs and infectious diseases[END_REF].

Recently, bed bugs have increased significantly in frequency and in geographical distribution.

An increasing number of infestations have been reported in Europe and America, causing several clinical and psychological problems [START_REF] Delaunay | Bedbugs and infectious diseases[END_REF][START_REF] Gbakima | High prevalence of bedbugs Cimex hemipterus and Cimex lectularis in camps for internally displaced persons in Freetown, Sierra Leone: a pilot humanitarian investigation[END_REF][START_REF] Paul | Is infestation with the common bedbug increasing?[END_REF].

Bartonella spp. are fastidious, aerobic, Gram-negative bacteria. Bartonella quintana is the agent of trench fever, known to be transmitted by the human body louse (Pediculus humanus humanus) [START_REF] Leulmi | Competence of Cimex lectularius Bed Bugs for the Transmission of Bartonella quintana, the Agent of Trench Fever[END_REF]. Bartonella henselae is the agent of cat-scratch disease, shown to be transmitted by fleas [START_REF] Chomel | Experimental transmission of Bartonella henselae by the cat flea[END_REF]. Recently, B. quintana DNA was detected in C. hemipterus (tropical bed bugs) in Rwanda [START_REF] Angelakis | Bartonella quintana in Cimex hemipterus, Rwanda[END_REF]. Subsequently, C. lectularius was shown to be a potential vector of this bacterium using an experimental study of C. lectularius bed bug infection with B. quintana [START_REF] Leulmi | Competence of Cimex lectularius Bed Bugs for the Transmission of Bartonella quintana, the Agent of Trench Fever[END_REF].

The monitoring and identification of bed bugs, as well as the detection of associated pathogens, are crucial steps for the surveillance of diseases potentially transmitted by these arthropods. Currently, bed bugs are identified primarily by observing morphological characteristics. However, the number of entomologists specialized in taxonomy is very small, and the distinction between C. lectularius and C. hemipterus cannot be made by a nonspecialist [START_REF] Cuisance | Current status of medical and veterinary entomology in France: endangered discipline or promising science?[END_REF]. As an alternative, molecular tools have been used to improve identification and compensate for the limitations of morphological identification [START_REF] Yssouf | Emerging tools for identification of arthropod vectors[END_REF].

In recent years, matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) has emerged as an efficient tool for arthropod identification [START_REF] Yssouf | Emerging tools for identification of arthropod vectors[END_REF] in mosquitoes [START_REF] Nebbak | Standardization of sample homogenization for mosquito identification using an innovative proteomic tool based on protein profiling[END_REF][START_REF] Tandina | Use of MALDI-TOF MS and culturomics to identify mosquitoes and their midgut microbiota[END_REF][START_REF] Tandina | Using MALDI-TOF MS to identify mosquitoes collected in Mali and their blood meals[END_REF], fleas [START_REF] Yssouf | Identification of flea species using MALDI-TOF/MS[END_REF] and ticks [START_REF] Yssouf | Identification of tick species and disseminate pathogen using hemolymph by MALDI-TOF MS[END_REF]. More recently, MALDI-TOF MS has been successfully applied to differentiate ticks infected or not with Borrelia crocidurae [START_REF] Fotso Fotso | MALDI-TOF mass spectrometry detection of pathogens in vectors: the Borrelia crocidurae/Ornithodoros sonrai paradigm[END_REF] or Rickettsia spp. [START_REF] Yssouf | Detection of Rickettsia spp in ticks by MALDI-TOF MS[END_REF], fleas infected or not with B. quintana and B. henselae [START_REF] Hamzaoui | Detection of Bartonella spp. in fleas by MALDI-TOF MS[END_REF] and mosquitoes infected or not with Plasmodium berghei [START_REF] Laroche | MALDI-TOF MS as an innovative tool for detection of Plasmodium parasites in Anopheles mosquitoes[END_REF].

In this work, we set up an experimental study of bed bug infection with B. quintana and B. henselae. We ranked the bed bug groups according to their infectious status through molecular methods, and we evaluated the ability of MALDI-TOF to differentiate the different groups and different pathogens.

Materials and methods

Ethical statement

Human blood was obtained from the "Etablissement Français du Sang" (EFS, French Blood Establishment) accredited by the Institutional Animal Care of IHU Méditerranée Infection.

Bed bug rearing

Bed bugs (C. lectularius) obtained from the Cimex store (London, England, cimexstore.co.uk) were maintained under standard laboratory rearing conditions, as previously described [START_REF] Leulmi | Competence of Cimex lectularius Bed Bugs for the Transmission of Bartonella quintana, the Agent of Trench Fever[END_REF].

The breeding containers were placed in the laboratory incubator at 60% humidity and 22°C, and the same conditions were used for adults and larvae [START_REF] Leulmi | Competence of Cimex lectularius Bed Bugs for the Transmission of Bartonella quintana, the Agent of Trench Fever[END_REF]. The bed bugs were fed once a week using a Hemotek artificial feeder machine (Hemotek 5W1; Discovery Workshops, Accrington, UK). The device was covered by an artificial membrane of Parafilm M (Sigma-Aldrich, Saint-Louis, Missouri, USA) and filled with 2 mL of blood [START_REF] Sheele | Ivermectin causes Cimex lectularius (bedbug) morbidity and mortality[END_REF].

Bartonella quintana and Bartonella henselae strain

All experiments with B. quintana and B. henselae were conducted inside a biosafety cabinet in a Level 2 Biosafety Room (BSL2). The two bacterial strains (strain B. quintana Oklahoma ATCC 49793, strain B. henselae Marseille) were cultured on Columbia sheep blood agar plates (5%, BioMérieux, Marcy l'Etoile, France) at 37°C in an atmosphere enriched with 5% CO2 [START_REF] Yssouf | Identification of flea species using MALDI-TOF/MS[END_REF]. After 10 days of culture, the bacteria were collected and transferred into a tube containing 400 µL of phosphate buffered saline (PBS), pH 7.2 (PBS, BioMérieux, Marcy l'Etoile, France). Two hundred microliters of each bacterial suspension mixed with two mL of human blood were used for artificial infection of bed bugs. An aliquot of two mL of human whole blood containing 200 µL of PBS was used to feed the control group [START_REF] Yssouf | Identification of tick species and disseminate pathogen using hemolymph by MALDI-TOF MS[END_REF][START_REF] Hamzaoui | Detection of Bartonella spp. in fleas by MALDI-TOF MS[END_REF]. Spectra from both strains were also obtained using MALDI-TOF MS. One colony was directly deposited on a MALDI-TOF target plate (Bruker Daltonik), and 4 spots were made for each bacterium. Each spot was covered with 2 µL of matrix solution (saturated solution of α-cyano-4-hydroxycinnamic acid in 50% acetonitrile, and 2.5% trifluoracetic-acid) and the matrix-sample was crystallized by air-drying at room temperature for 5 minutes, as previously described (X). Measurements were performed with an Autoflex II mass spectrometer (Bruker Daltonik) equipped with a 337-nm nitrogen laser. Spectra were recorded in the positive linear mode (delay, 170 ns; ion source 1 voltage, 20 kV; ion source 2 voltage, 18.5 kV; lens voltage, 7 kV; mass range, 2-20 kDa) [START_REF] Seng | Ongoing revolution in bacteriology: routine identification of bacteria by matrix-assisted laser desorption ionization time-of-flight mass spectrometry[END_REF].

Artificial infection of bed bugs

Three groups were created, including a group containing 100 C. lectularius exposed to B. quintana, a group containing 100 C. lectularius exposed to B. henselae and a control group containing 50 C. lectularius.

Each bed bug group was fed with three successive human blood meals (every 48 hours).

Control bed bugs were fed with Bartonella-free blood mixed with 200 µL of PBS and exposed bed bugs were fed with two mL of blood infected with 200 µL of bacterial suspension of B. henselae and B. quintana [START_REF] Leulmi | Competence of Cimex lectularius Bed Bugs for the Transmission of Bartonella quintana, the Agent of Trench Fever[END_REF][START_REF] Hamzaoui | Detection of Bartonella spp. in fleas by MALDI-TOF MS[END_REF] Two hundred microliters of infected blood were tested using qPCR, to ensure the presence of B. quintana and B. henselae. One hundred microliters of each inoculum were cultured on sheep blood agar to ensure bacterial viability and, at the same time, calculate the concentration by making a serial dilution of culture from 10 -1 to 10 10 [START_REF] Leulmi | Competence of Cimex lectularius Bed Bugs for the Transmission of Bartonella quintana, the Agent of Trench Fever[END_REF][START_REF] Kernif | Acquisition and excretion of Bartonella quintana by the cat flea, Ctenocephalides felis felis[END_REF].

DNA extraction and qPCR detection of B. quintana and B. henselae in bed bugs

From the third day post infection, 10 bed bugs were collected every 48 hours in each group.

Each bed bug was washed with 70% ethanol, rinsed twice with distilled water and dried with paper. Half of the abdomen was reserved for the detection of B. quintana and B. henselae by qPCR and the head was stored at -20°C for MALDI-TOF MS tests [START_REF] Leulmi | Competence of Cimex lectularius Bed Bugs for the Transmission of Bartonella quintana, the Agent of Trench Fever[END_REF][START_REF] Hamzaoui | Detection of Bartonella spp. in fleas by MALDI-TOF MS[END_REF].

The bed bug samples reserved for molecular biology were incubated overnight at 56°C in 180 µL of G2 buffer (Qiagen, Hilden, Germany) (30 mM Tris-Cl, 30 mMEDTA; 5% Tween 20, 0.5% Triton X-100; 800 mMGuHCl) and 20 µL of proteinase K (Qiagen, Hilden, Germany) (activity of 600 mAU/mL solution or 40 mAU/mg of protein). DNA extraction was performed in the automatic extractor EZ1 (Qiagen, Hilden, Germany). The presence of B. quintana and B. henselae DNA in these bed bugs was assessed by B. quintana and B. henselae-specific real-time PCRs targeting the yopP3 gene [START_REF] Angelakis | Altitude-dependent Bartonella quintana genotype C in head lice, Ethiopia[END_REF] and pap31 gene [START_REF] Zeaiter | Genomic variation of Bartonella henselae strains detected in lymph nodes of patients with cat scratch disease[END_REF], respectively. The presence of Bartonella spp. was tested individually in each bed bug.

Detection of Bartonella spp. in Cimex lectularius by MALDI-TOF MS

Samples reserved for MALDI-TOF MS (bed bug head) were washed a second time with HPLC-grade water and acetonitrile (Fluka, Buchs, Switzerland). They were then crushed using the automated grinding device TissueLyser (Qiagen, Hilden, Germany) 3 times for 90 seconds at a frequency of 30 Hertz, with a pinch of glass powder (Sigma, Lyon, France) and a mix of 20 µL of 70% formic acid (v/v) and 20 µL of 50% acetonitrile (v/v) (Fluka, Buchs, Switzerland).

After centrifugation at 200 g for 1minute, 1 µL of supernatant of each sample was deposited in quadruplicate on the MALDI-TOF plate and covered by 1 µL of CHCA matrix solution composed of saturated α-cyano-4-hydroxycynnamic acid (CHCA) (Sigma, Lyon, France), 50% (v/v) acetonitrile (Fluka, Buchs, Switzerland), 2.5% trifluoroacetic acid (v/v) (Aldrich, Dorset, UK) and HPLC-grade water. The target was then placed in the Microflex LT 171 MALDI-TOF mass spectrometer (Bruker Daltonics) for analysis [START_REF] Yssouf | Emerging tools for identification of arthropod vectors[END_REF][START_REF] Hamzaoui | Detection of Bartonella spp. in fleas by MALDI-TOF MS[END_REF]

MALDI-TOF MS parameters

Protein mass profiles were generated using a Microflex LT MALDI-TOF (Bruker Daltonics, Germany) mass spectrometer, with detection in positive ion linear mode at a laser frequency of 50 Hz in a mass range 2-20 kDa. The acceleration voltage was set to 20 kV, and the time of extraction delay was 200 ns. Each spectrum corresponds to the ions obtained from 240 laser shots fired in six regions of the same deposit on the ground plate and acquired automatically using the function of the Flex Control AutoXecute V.2.4 software (Bruker Daltonics).

The spectra were visualized with Flex analysis v.3.3 software and exported to ClinProTools2.2 and MALDI Biotyper v.3.0 (Bruker Daltonics, Germany) for data processing. To obtain usable results, we evaluated the reproducibility of each group using the ClinProTools 2.2 software.

Bed bug spectra were chosen based on their reproducibility and intensity for all MS analyses, including database creation. Our database was upgraded with spectra of 4 to 11 specimens of non-infected bed bugs or bed bugs infected with B. quintana or B. henselae.

Blind tests

Blind test analysis was used to evaluate the ability of MALDI-TOF MS to distinguish the three groups. After the evaluation of the quality and the reproducibility of the spectra, a dendrogram was established for each group to evaluate the homogeneity and a high quality

MALDI-TOF MS biomarker mass set

To evaluate the reproducibility of the MS profiles, the spectra of the three groups (bed bugs infected with B. quintana, bed bugs infected with B. henselae and bed bug controls) were imported into the ClinProTools 2.2 software. For the detection of the discriminant peaks associated with infection status, we compared the average spectrum of the three categories of bed bugs. The software was used to generate a peak list for each group in the mass range of 2 to 20 kDa and to identify the discriminant peaks. The parameters of the ClinProTools 2.2 software for the generation of the spectra were as follows: resolution of 800; noise threshold of 2.50; maximum peak of 800 ppm and correspondence with calibration peaks of 20%. For the calculation of the peak, the peak was made on single spectra, with a signal-to-noise threshold of 2.50 and an aggregation of 800 ppm. The spectra were then analyzed with the genetic algorithm (GA) model, which provided a list of discriminant peaks. Manual inspection and peak validation by the operator gave a "recognition capability" (RC) value with the highest "cross-validation" (CV) value.

Results

Acquisition of Bartonella quintana and Bartonella henselae by bed bugs

The inoculum used for artificial infection with B. quintana contained 3 x 10 8 CFU/mL bacteria and the one used for artificial infection with B. henselae contained 2 x 10 7 CFU/mL bacteria.

Three days after the last infective meal, ten bed bugs from each group were subsequently collected every two days. At the end of the collections, we had a total of 80

MALDI-TOF MS for detection of B. quintana and B. henselae in bed bugs

The spectra sorted by Flex Control AutoXecute V.2.4 software were analyzed by ClinProTools 2.2 software to check the reproducibility of each category (Figure 1). This analysis showed the presence of some peaks only in bed bugs infected with B. quintana and other peaks present only in bed bugs infected with B. henselae but absent in controls (Figure 2). The ClinProTools 2.2 software also calculated and created the principal component analysis (PCA) of the three groups of bed bugs. It compared the group of infected bed bugs with B. quintana and the control group (Figure 3.A), as well as the group of infected bed bugs with B. henselae with the control group (Figure 3.B).

To better identify discriminating peaks between the infected bed bugs and the control bed bugs, we used the genetic algorithm tool of the ClinProTools 2.2 software. We compared the spectra of 62 bed bugs infected with B. quintana and 34 spectra of control bed bugs. Similarly with the second pathogen, we compared 61 specimens of infected bed bugs with B. henselae and 34 control bed bugs.

The genetic algorithm model and operator peak picking generated 10 discriminating peaks between bed bug controls and bed bugs infected with B. henselae (Table 1), with a recognition capability (RC) and cross-validation (CV) of 100% for both, and nine discriminating peaks between bed bug controls and bed bugs infected with B. quintana (Table 2), with a recognition capability (RC) of 100% and cross-validation (CV) of 98.53 % (Figure 4,5).

The last step of the MALDI TOF analysis was the blind test of the spectra of bed bugs against our arthropod database, previously upgraded with the reference spectra of each bed bug category (infected with B. quintana, infected with B. henselae, and controls). Bed bug species identification was correct for all reproducible spectra. Regarding the infection status, 3).

Discussion

This work presents a preliminary study assessing the ability of MALDI-TOF MS to differentiate between non-infected bedbugs and bedbugs infected with two different species of Bartonella using a previously validated study of artificial infection of C. lectularius with Bartonella spp. [START_REF] Leulmi | Competence of Cimex lectularius Bed Bugs for the Transmission of Bartonella quintana, the Agent of Trench Fever[END_REF]. The protocol used was developed by comparing the reproducibility and quality of spectra obtained from different laboratory bed bug body parts. It was later validated on field bed bugs from very different geographical areas (urban area in south of France, rural village in Senegal) (Laroche et al., personal communication). It proved its effectiveness on infected bed bugs and controls with 88.5% of all the spectra analyzed included in the study, based on quality and reproducibility. These results are encouraging, since these spectra allowed an identification rate of bed bugs infected by B. quintana and B. henselae above 90%.

In the case of slight heterogeneity within the same group, the more reference spectra one has, the more accurate the blind test analyses become. For this proof of concept step, it is necessary to keep only the best spectra to assess the performance of the tool in a blind test analysis. A validated database following a successful blind test analysis with high quality spectra means that each of these spectra can be used eventually as a reference to encompass all the future diversity encountered.

Two groups of bed bugs were fed blood infected with B. henselae or B. quintana separately; the qPCR showed the acquisition of both bacteria by bed bugs with a 100% infection rate, in accordance with what was previously reported [START_REF] Leulmi | Competence of Cimex lectularius Bed Bugs for the Transmission of Bartonella quintana, the Agent of Trench Fever[END_REF].

After its success in microbiology, MALDI-TOF MS has emerged in the field of entomology and identification of arthropods. To increase the efficiency and shorten the time-to-result of MALDI-TOF MS analyses, it is necessary to standardize the sample preparation protocol, which also makes it possible to reproduce the tests in different research laboratories [START_REF] Cuisance | Current status of medical and veterinary entomology in France: endangered discipline or promising science?[END_REF][START_REF] Yssouf | Detection of Rickettsia spp in ticks by MALDI-TOF MS[END_REF]. This standardization encompasses several parameters; the arthropod body part used, sample washing steps and lastly, speed and frequency of grinding. These parameters vary according to the characteristics of each arthropod, requiring the development of a sample preparation protocol for MALDI-TOF MS specific to each arthropod [START_REF] Nebbak | Standardization of sample homogenization for mosquito identification using an innovative proteomic tool based on protein profiling[END_REF][START_REF] Nebbak | Comparative analysis of storage conditions and homogenization methods for tick and flea species for identification by MALDI-TOF MS[END_REF].

During this work, we followed the standardized protocol implemented in our laboratory for bed bug analyses (Laroche et al., personal communication). This protocol has proven its effectiveness on infected bugs and controls, with a good percentage of reproducibility (88.5%) on all spectra analyzed. In this study, qPCR allowed proper classification of the three groups of bedbugs according to their infectious status. As demonstrated in previous work, bed bug heads provide reproducible spectra. Therefore, 50 specimens only were selected as controls. The database was upgraded with reference spectra of each category. The blind test showed a percentage of identification ranging from 90.16% to 91% for all three groups.

Regarding the identification of incorrect Bartonella species, we have reported in previous studies that the proteins of the infected arthropod itself could play a role in the differentiation of the two groups (infected/non-infected). Indeed, in Plasmodium-infected mosquitoes, a lower number of peaks and a decreased intensity of others was found compared to control mosquitoes. The role of down-regulated mosquito proteins, involved in the immune response, can be hypothesized [START_REF] Laroche | MALDI-TOF MS as an innovative tool for detection of Plasmodium parasites in Anopheles mosquitoes[END_REF]. In the mass spectrometry approach used in this study, we could not assess the exact origin of each peak. We can hypothesize that the immune response may play a role in the identification of Bartonella spp. in bed bugs as well. Assuming that the proteins regulated are the same for the two bacteria, we would have common peaks associated with bed bug proteins, and specific ones for each bacterium.

In a previous work, we evaluated the ability of MALDI-TOF MS to distinguish between fleas infected or not by B. quintana and B. henselae, and then we noticed the presence of a third group, the group of fleas exposed to the bacteria, but negative in qPCR. This category was well-differentiated from the others by MALDI-TOF MS. It had been suggested that the separation of an exposed group might be explained by the presence of the proteins of the immune response [START_REF] Hamzaoui | Detection of Bartonella spp. in fleas by MALDI-TOF MS[END_REF]. In the current study, we did not have an exposed uninfected group identified by MALDI-TOF, since the rate of infection was 100%. The few errors that have been noticed regarding the status of infection might be also linked with the immune response in bed bugs, where the immune response is more important in some samples or weak or even absent in others. The listed discriminating peaks are those which allow distinction of the two groups with the lowest number of peaks. Several other peaks are actually discriminative between the two groups and are not reported. This can explain why no common discriminating peaks are found in the list presented in this study.

Compared to other arthropod spectra, bed bug samples demonstrate significant intra-group heterogeneity. It is therefore impossible to have a relevant identification with 2 or 3 reference spectra, as was the case for highly reproducible Australian mosquito spectra, for example [START_REF] Lawrence | Accurate identification of Australian mosquitoes using protein profiling[END_REF]. An upgrade of the database with a high number of bed bug reference spectra should be a satisfactory approach to avoid misidentifications.

In conclusion, after a preliminary work on fleas and the present study on bed bugs, MALDI-TOF MS appears to be a promising tool for differentiating arthropods infected or not with Bartonella, and further complementary studies are needed to better characterize the differential proteome of infected and non-infected arthropods. Perspectives include testing this tool in the field during entomological studies. Since the MALDI-TOF MS device is bought for microbiology perspectives, its use in entomology does not require additional cost and might be very useful, given the resurgence of bed bugs.

Tables

  (Laroche et al., personal communication).

  spectra within each cluster, and high quality and reproducible MALDI-TOF MS spectra from the head of 4 C. lectularius controls, 5 C. lectularius infected with B. henselae and 11 C. lectularius infected with B. quintana were introduced into our in-house arthropod database (available on request) (Species of this database are reported in Supplementary data 1). Then, a total of 34 spectra of C. lectularius controls, 61 spectra of C. lectularius infected with B. henselae and 62 spectra of C. lectularius infected with B. quintana were queried against the upgraded database.

  identification was correct for 56/62 C. lectularius infected with B. quintana (90.32 %), but 6 of these 62 B. quintana-infected bed bugs were identified either as C. lectularius controls (n=3) or C. lectularius infected with Borrelia recurrentis (n=3). The identification was correct for 55/61 C. lectularius infected with B. henselae (90.16 %), but 6 of these 61 B. henselae-infected bed bugs were identified either as C. lectularius controls (n=4) or C. lectularius infected with B. quintana (n=2). Finally, the identification was correct for 31/34 C. lectularius controls, but 3 of these 34 C. lectularius controls were identified either as C. lectularius infected with B. quintana (n=2) or C. lectularius infected with B. recurrentis (n=1) (Table

Figure legends:Figure 1 :

 1 Figure legends:

Figure 2 :

 2 Figure 2: The gel view tool of three categories of bed bugs. The circles frame some specific

Figure 3 :

 3 Figure 3: A: bed bug head MS spectra from infected Cimex lectularius by Bartonella

  bed bugs exposed to B. quintana, 80 bed bugs exposed to B. henselae and 40 bed bug controls. Quantitative PCR results demonstrated the presence of B. quintana in all exposed bed bugs, with cycle threshold (Ct) values ranging from 16.19 to 27.69, and the presence of B. henselae in all exposed bed bugs (100% infection rate), with cycle threshold (Ct) values ranging from 19.13 to 24.46. All control fleas tested negative.

Table 1 :

 1 Discriminant peaks between bed bug controls and bed bugs infected with Bartonella henselae detected by the genetic algorithm model analyzed with ClinProTools software.

	Comparison between Cimex lectularius
	infected with Bartonella henselae and
		controls
	Number	Controls	Infected
	of	Mass (m/z)	Mass (m/z)
	peaks	(Da)	(Da)
	1	Absent	4567
	2	Absent	5513
	3	5999	Absent
	4	Absent	7332

Table 2 :

 2 Discriminant peaks between bed bug controls and bed bugs infected with Bartonella quintana detected by the genetic algorithm model analyzed with ClinProTools software.

	.

Table 3 :

 3 Results of the blind test analysis, after update of the MALDI-TOF MS database, analyzed by Biotyper Software.

Acknowledgements

We thank Nathalie Duclos (IHU Méditerannée infection, Marseille) for providing us the Bartonella quintana and Bartonella henselae strains.

The study was funded by the Méditerranée Infection foundation and the National Research Agency under the program "Investissements d'avenir", reference ANR-10-IAHU-03.

Bartonella henselae (green dots) and Cimex lectularius controls (red dots) were compared by Principal Component Analysis.

Supplementary data

Supplementary data 1: List of all arthropod species included in our in-lab database

Competing interests:

The authors declare that they have no conflicts of interest.


Jenna Maire
email: jenna.maire@u-bordeaux.fr

Cédric Galéra

Solenne Roux
email: solenne.roux@u-bordeaux.fr.

Stéphanie Bioulac
email: stephanie.bioulac@chu-bordeaux.fr

Manuel Bouvard
email: bouvard.manuel@wanadoo.fr.

Grégory Michel
email: gregory.michel@u-bordeaux.fr.

Cédric Galera
email: cedric.galera@u-bordeaux.fr

Facial emotion recognition in children with or without Attention Deficit/Hyperactivity Disorder: Impact of comorbidity

Keywords:

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

organization of scientific meetings, has participated in meetings and workshops with industrial partners (Shire) and, in the past (before 2014), was also the principal investigator in clinical trials for Shire and Lilly.

Résumé

Objectif : Cette étude a pour premier but d'évaluer les capacités de reconnaissance des émotions faciales chez des enfants atteints de Trouble Déficit de l'Attention Hyperactivité en les comparant à celles d'enfants au développement typique. Dans un second temps, l'objectif est de tester l'hypothèse qu'un déficit dans la reconnaissance des émotions faciales peut être exacerbé par la sévérité des symptômes comorbides externalisés (l'oppositionalité), internalisés (l'anxiété) et émotionnels (la labilité émotionnelle). Méthode : Quarante enfants de 7 à 11 ans diagnostiqués avec un Trouble Déficit de l'Attention Hyperactivité ont pris part à cette étude ainsi que 40 enfants du même âge non atteints de ce trouble. Pour évaluer la reconnaissance des émotions faciales, un test sur ordinateur a été construit à partir des images contenues dans les Pictures of Facial Affect. Ce test a été construit en trois parties : une phase d'entrainement, une phase de test (reconnaissance des émotions faciales) et une phase contrôle (reconnaissance de formes géométriques). Des données concernant la symptomatologie du Trouble Déficit de l'Attention Hyperactivité, externalisée, internalisée et émotionnelle ainsi que la sévérité de celles-ci ont été recueillies par questionnaires remplis les parents (Conners' Revised Parent Rating Scale). Résultats : De manière catégorielle (analyses de variance), les enfants atteints de Trouble Déficit de l'Attention Hyperactivité ont, de manière significative, moins de réponses correctes sur la tâche de reconnaissance des émotions faciales, alors qu'ils ont autant de réponses correctes sur la tâche de reconnaissance de formes géométriques. La reconnaissance de l'émotion de tristesse apparaît comme la plus déficitaire chez les enfants atteints de ce trouble. De manière dimensionnelle (analyses de régression linéaire), les symptômes du Trouble Déficit de l'Attention Hyperactivité semblent avoir peu de lien avec les capacités de reconnaissance des émotions faciales. En revanche, les symptômes oppositionnels apparaissent comme associés à des difficultés de reconnaissance des émotions faciales particulièrement celle de la tristesse et celle de la surprise. Conclusion : Les capacités de reconnaissance des émotions faciales semblent entravées chez les enfants atteints du Trouble Déficit de l'Attention Hyperactivité. Ces difficultés peuvent relever d'un handicap ou d'un retard dans le développement des processus émotionnels durant l'enfance. De plus, le Trouble Oppositionnel avec Provocation semble apparaître comme un facteur de risque de difficultés exacerbées dans la reconnaissance des émotions faciales.

Introduction

Attention Deficit Hyperactivity Disorder (ADHD) is a complex neurodevelopmental disorder. Most children with ADHD experience social difficulties, tend to have fewer friends and are prone to be rejected by peers [START_REF] Nijmeijer | Attention-deficit/hyperactivity disorder and social dysfunctioning[END_REF]. The possibility of a general deficit in social cognition in ADHD [START_REF] Uekermann | Social cognition in attention-deficit hyperactivity disorder (ADHD)[END_REF] has been proposed to explain the difficulty in successfully interacting with others. Interestingly, it has been hypothesized that this deficit is due to specific difficulties in the ability to recognize emotions from facial expressions [START_REF] Aspan | Emotion recognition pattern in adolescent boys with Attention-Deficit/Hyperactivity Disorder[END_REF][START_REF] Kats-Gold | The role of simple emotion recognition skills among school aged boys at risk of ADHD[END_REF][START_REF] Pelc | Recognition of emotional facial expressions in Attention-Deficit Hyperactivity Disorder[END_REF].

Previous studies have shown that children and adolescents with ADHD perform worse on a facial emotion recognition task than typically developing controls do. They are less accurate in identifying facial affect [START_REF] Aspan | Emotion recognition pattern in adolescent boys with Attention-Deficit/Hyperactivity Disorder[END_REF][START_REF] Kats-Gold | The role of simple emotion recognition skills among school aged boys at risk of ADHD[END_REF][START_REF] Pelc | Recognition of emotional facial expressions in Attention-Deficit Hyperactivity Disorder[END_REF][START_REF] Boakes | Facial affect interpretation in boys with Attention Deficit/Hyperactivity Disorder[END_REF][START_REF] Cadesky | Beyond words: How do children with ADHD and/or conduct problems process nonverbal information about affect?[END_REF][START_REF] Corbett | Processing affective stimuli in children with Attention-Deficit Hyperactivity Disorder[END_REF][START_REF] Fonseca | Emotion understanding in children with ADHD[END_REF][START_REF] Demopoulos | A Comparison of social cognitive profiles in children with Autism Spectrum Disorders and Attention-Deficit/Hyperactivity Disorder: A matter of quantitative but not qualitative difference?[END_REF][START_REF] Singh | Recognition of facial expressions of emotion by children with attention-deficit hyperactivity disorder[END_REF][START_REF] Sinzig | Do hyperactivity, impulsivity and inattention have an impact on the ability of facial affect recognition in children with autism and ADHD?[END_REF][START_REF] Sjöwall | Multiple deficits in ADHD: Executive dysfunction, delay aversion, reaction time variability, and emotional deficits[END_REF][START_REF] Williams | Misinterpreting emotional expressions in Attention-Deficit/Hyperactivity Disorder: Evidence for a neural marker and stimulant effects[END_REF][START_REF] Yuill | Selective difficulty in recognising facial expressions of emotion in boys with ADHD[END_REF] and slower to recognize them [START_REF] Kats-Gold | The role of simple emotion recognition skills among school aged boys at risk of ADHD[END_REF]. They exhibit a flawed performance when presented with a variety of facial emotions as represented in static pictures [START_REF] Aspan | Emotion recognition pattern in adolescent boys with Attention-Deficit/Hyperactivity Disorder[END_REF][START_REF] Kats-Gold | The role of simple emotion recognition skills among school aged boys at risk of ADHD[END_REF][START_REF] Pelc | Recognition of emotional facial expressions in Attention-Deficit Hyperactivity Disorder[END_REF][START_REF] Boakes | Facial affect interpretation in boys with Attention Deficit/Hyperactivity Disorder[END_REF][START_REF] Cadesky | Beyond words: How do children with ADHD and/or conduct problems process nonverbal information about affect?[END_REF][START_REF] Corbett | Processing affective stimuli in children with Attention-Deficit Hyperactivity Disorder[END_REF][START_REF] Demopoulos | A Comparison of social cognitive profiles in children with Autism Spectrum Disorders and Attention-Deficit/Hyperactivity Disorder: A matter of quantitative but not qualitative difference?[END_REF][START_REF] Sinzig | Do hyperactivity, impulsivity and inattention have an impact on the ability of facial affect recognition in children with autism and ADHD?[END_REF][START_REF] Sjöwall | Multiple deficits in ADHD: Executive dysfunction, delay aversion, reaction time variability, and emotional deficits[END_REF][START_REF] Williams | Misinterpreting emotional expressions in Attention-Deficit/Hyperactivity Disorder: Evidence for a neural marker and stimulant effects[END_REF], by dynamic presentation or cartoon images [START_REF] Boakes | Facial affect interpretation in boys with Attention Deficit/Hyperactivity Disorder[END_REF], by pictures or videos in context [START_REF] Corbett | Processing affective stimuli in children with Attention-Deficit Hyperactivity Disorder[END_REF][START_REF] Sinzig | Do hyperactivity, impulsivity and inattention have an impact on the ability of facial affect recognition in children with autism and ADHD?[END_REF] and when requested to match facial emotions with corresponding situations [START_REF] Singh | Recognition of facial expressions of emotion by children with attention-deficit hyperactivity disorder[END_REF][START_REF] Yuill | Selective difficulty in recognising facial expressions of emotion in boys with ADHD[END_REF]. More specifically, children with ADHD performed worse than typically developing children in recognizing negative emotions such as anger, fear, sadness and disgust [START_REF] Aspan | Emotion recognition pattern in adolescent boys with Attention-Deficit/Hyperactivity Disorder[END_REF][START_REF] Pelc | Recognition of emotional facial expressions in Attention-Deficit Hyperactivity Disorder[END_REF][START_REF] Boakes | Facial affect interpretation in boys with Attention Deficit/Hyperactivity Disorder[END_REF][START_REF] Williams | Misinterpreting emotional expressions in Attention-Deficit/Hyperactivity Disorder: Evidence for a neural marker and stimulant effects[END_REF]. However, other studies failed to find any differences in emotion recognition between children and/or adolescents with ADHD and controls [START_REF] Guyer | Specificity of facial expression labeling deficits in childhood psychopathology[END_REF][START_REF] Schwenck | Emotion recognition in children and adolescents with attention-deficit/hyperactivity disorder (ADHD)[END_REF].

Beyond the classical symptomatology of inattention, hyperactivity and impulsivity, comorbid features are frequently associated with the disorder. Both externalizing (such as Oppositional Defiant Disorder, ODD) and internalizing (such as anxiety) disorders are commonly observed in ADHD children [START_REF] Lecendreux | Prevalence of Attention Deficit Hyperactivity Disorder and associated features among children in France[END_REF][START_REF] Ralston | ADORE -Attention-Deficit Hyperactivity Disorder Observational Research in Europe[END_REF]. Moreover, children with ADHD and comorbid externalizing disorders are even more socially impaired than those with 'pure' ADHD: in parents' and teachers' reports, they were rated as having fewer social skills and in peer sociometric nominations, they were less accepted/liked and more rejected/disliked (for review, see [START_REF] Becker | Co-occurring mental health problems and peer functioning among youth with Attention-Deficit/Hyperactivity Disorder: A Review and recommendations for future research[END_REF]). Additionally, research on emotional lability has shown this clinical feature to be frequent in children with ADHD [START_REF] Sobanski | Emotional lability in children and adolescents with attention deficit/hyperactivity disorder (ADHD): Clinical correlates and familial prevalence[END_REF]. This complex picture of behavioral and/or emotional symptoms means that the profiles of children suffering from ADHD are heterogeneous and exacerbates the impairment associated with the core symptoms of ADHD.

While the majority of studies suggest that children with ADHD exhibit a facial emotion recognition deficit, their conclusions are limited because comorbidity has not often been taken into account to date. Overall comorbid ODD in ADHD children seems to play a role in facial emotion recognition deficit [START_REF] Downs | Emotional understanding, cooperation, and social behavior in highfunctioning children with autism[END_REF] and a significant association was found between opposition and anger recognition in children with ADHD [START_REF] Tehrani-Doost | Is Emotion recognition related to core symptoms of childhood ADHD? J Can Acad Child[END_REF][START_REF] Noordermeer | Neurocognitive deficits in Attention-Deficit/Hyperactivity Disorder with and without comorbid Oppositional Defiant Disorder[END_REF]. In addition to externalizing symptoms, internalizing symptoms are also thought to affect the perception of emotion. Correlations between anxiety and/or depression scores and impairment in the recognition of some negative emotions have been observed in ADHD patients [START_REF] Boakes | Facial affect interpretation in boys with Attention Deficit/Hyperactivity Disorder[END_REF][START_REF] Williams | Misinterpreting emotional expressions in Attention-Deficit/Hyperactivity Disorder: Evidence for a neural marker and stimulant effects[END_REF]. Moreover, in adults with ADHD, the intensity of their emotional responses (e.g. self-report of how intensely they experience affects) has been related to an emotion recognition deficit [START_REF] Rapport | Experienced emotion and affect recognition in adult attention-deficit hyperactivity disorder[END_REF]. However, this hypothesis has not been tested in a children sample with ADHD.

On the whole, studies have reported a facial emotion recognition deficit in ADHD but the influence of comorbid features has received little attention. This study assesses the facial emotion recognition of children with ADHD by taking into account simultaneously the contribution of clinical features frequently associated with ADHD such as externalizing (e.g. opposition), internalizing (e.g. anxiety) and emotional (e.g. emotional lability) symptoms. We hypothesized that children with ADHD would be more impaired than children without ADHD in emotion recognition abilities and this deficit would be partially explained by comorbid features.

Method

Participants Eighty children aged 7 to 11 years old participated in the study. Forty children with ADHD were recruited in an outpatient clinic specialized in ADHD diagnosis. Forty typically developing children matched the ADHD group in age and gender.

The clinical group: The criterion for inclusion in the ADHD group was having a DSM-IV diagnosis of ADHD given by a child psychiatrist. The diagnosis was confirmed by the administration of the Schedule for Affective Disorders and Schizophrenia for School-Age Children-Present and Lifetime Version (KIDDIE-SADS-PL [START_REF] Kaufman | Schedule for Affective Disorders and Schizophrenia for School-Age Children-Present and Lifetime Version (K-SADS-PL): Initial reliability and validity data[END_REF]). Exclusion criteria were Intellectual Quotient < 70, history or evidence of neurological disorders or impairment such as epilepsy or concussion, pervasive developmental disorder, psychosis, manic episode and non-corrected visual deficit. If children were taking methylphenidate, they had to discontinue it at least 24 hours prior to the experiment (in the final sample, 14 children were receiving such treatment).

The control group:

The inclusion criteria for the control group were: absence of present diagnosis or history of ADHD as confirmed by the KIDDIE-SADS-PL and any elevated T-score (>60) on the Cognitive Problem, Hyperactivity and ADHD Index in Conners' Revised Parent Rating Scale (CPRS-R [START_REF] Conners | The Revised Conners' Parent Rating Scale (CPRS-R): Factor structure, reliability, and criterion validity[END_REF]). Exclusion criteria were history or evidence of intellectual disability, neurological disorders or impairment such as epilepsy or concussion, pervasive developmental disorder, psychosis, manic episode and non-corrected visual deficit.

Measures

ADHD and comorbidity were diagnosed with the KIDDIE-SADS-PL [START_REF] Kaufman | Schedule for Affective Disorders and Schizophrenia for School-Age Children-Present and Lifetime Version (K-SADS-PL): Initial reliability and validity data[END_REF], a semi-structured clinical interview, with a trained interviewer and at least one parent. To include children in the ADHD group, diagnoses from the KIDDIE-SADS had to be confirmed by a child psychiatrist.

The CPRS-R is a parent-report measure of ADHD symptoms and associated features. It consists of 80 items measuring 7 features: opposition, cognitive problems/inattention, hyperactivity, anxiety/shyness, perfectionism, social problems and psychosomatic; and indexes: ADHD, CGI (with CGI-Restlessness and CGI-Emotional Lability) and DSM-IV. T-scores were calculated depending on age and gender [START_REF] Conners | Conners' Rating Scales -Revised[END_REF].

The facial emotion recognition task

The facial emotion recognition task was created with the model of the MultiMorph© [START_REF] Robin | Apport du Multimorph à l'étude des processus de reconnaissance émotionnelle faciale (REF). Exemple de la personnalité borderline à l'adolescence[END_REF][START_REF] Robin | Decreased sensitivity to facial emotions in adolescents with Borderline Personality Disorder[END_REF] and contained three parts completed by the child in this order: facial emotion recognition practice, the facial emotion recognition task, and a control task. The first part, which all children did once, allows them to familiarize themselves with the procedure. The control task consisted of a simple task of recognizing geometric forms in order to compare the results with those of the facial emotion recognition task. It detects whether a potential deficit on the facial emotion recognition task can be assigned to a methodological bias (errors because of answering too fast, not understanding instructions, being distracted, etc.) or to a specific impairment in the facial emotion recognition process.

For the first two parts, the pictures used were adults' faces expressing fear, sadness, disgust, surprise, joy, anger and neutral expression from the Pictures of Facial Affect [START_REF] Ekman | Pictures of Facial Affect[END_REF]. In order to detect discrete deficits, morphs from the Multimorph© were added by blending each facial emotion with a neutral expression. The 6 emotions were presented at full (original pictures) and half-(morphing) intensity. During the practice, two faces expressing the six emotions at both half-and full-intensity and a neutral expression were presented representing 14 trials. In the facial emotion recognition task, 4 faces each expressing the six emotions at both half-and full-intensity and a neutral expression were presented in the same way representing 52 trials. The control task included pictures of 6 geometric forms (circle, square, triangle, star, cross, arrow) and one black screen (no form) representing 52 trials.

The facial emotion recognition task was designed to limit as much as possible the influence of inattention (pictures were presented with no time limit for answering) and impulsivity (no speed requirement in instructions, opportunity to correct the answers up to 8 times). Moreover, because children with ADHD might have difficulties with long tasks, the task was designed to last less than 10 minutes.

Instructions (available from corresponding author) were adapted from Yuill & Lyon's study [START_REF] Yuill | Selective difficulty in recognising facial expressions of emotion in boys with ADHD[END_REF], which showed that children with ADHD perform as well as control children provided the experimenter explains the test and regulates the rapidity of the child's answer.

Children were asked to indicate their answers on a keyboard showing the seven choices as soon as the emotion or the form was recognized. Tags with emotion and geometric form labels were stuck on the keyboard buttons. There was no feedback on the child's answers in order to assess their facial emotion recognition abilities and not their learning abilities. All task details are available from corresponding authors.

Procedure

All children and their parents received information about the study, its aim and their rights. A consent form was signed by at least one parent before planning a date for the experiment. A link to a WebInterface, LimeSurvey [START_REF] Schmitz | LimeSurvey: An Open Source survey tool[END_REF], was sent by e-mail with a code created for each child, thereby allowing parents to fill in the CPRS-R. Demographic information was obtained from parents prior to testing. During the experiment, children were placed in front of the computer for the facial emotion recognition task in an office with as few distractors as possible. Instructions were written on screen and read by the examiner to the child.

All procedures performed in this study involving human participants were in accordance with the ethical standards of the institutional and national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. The procedure was submitted by the regional ethics committee (Comité de Protection des Personnes Sud-Ouest et Outre Mer III) and approved by the national committee on informatics rights (Commission Nationale de l'Informatique et des Libertés).

Statistical analysis

All statistical analyses were performed with R software [START_REF] Core | [END_REF]. Means, standard deviations, frequencies and percentages were calculated in order to describe the sociodemographic, clinical and facial emotion recognition variables of the sample. Group matching was tested with the chi-squared and Mann Whitney U-test as appropriate.

A first mixed ANOVA was performed with group (ADHD-control) as between-factor, task (emotion recognition-geometric form recognition) as within-subject factor and number of correct answers as dependent variable. A second mixed ANOVA was performed with group (ADHD-control) as between-factor, emotion type (fear-sadness-disgust-anger-surprise-joy-neutral) and intensity of emotion (half-low) as within-subject factor and number of correct answers as dependent variable. After each mixed ANOVA, contrast analysis was used to identify where the difference lay. Then, a Pearson correlation was performed to test whether the severity of ADHD symptoms could be correlated with the severity of the emotion recognition deficit in the ADHD group.

To test the hypothesis that ADHD and comorbid symptoms are associated with an emotion recognition deficit, multiple linear regression models with backward selection were built with the following factors: age, gender, inattention, hyperactivity, opposition, anxiety and emotional lability scores. Dependent variables were mean number of correct answers on the facial emotion recognition task and scores by emotion.

Results

Five children in the ADHD group could not complete the task because of a computer hitch (n=3), too much agitation (n=1) and perseverations (n=1). One child in the ADHD group and 3 children in the control group were excluded from the analysis because too many data were missing.

The final sample included 34 children in the ADHD group and 37 children in the control group. There was no group difference regarding age, U=647.5, p=.83, and gender, χ²(1)=0.25, p=.62. In terms of schooling, the ADHD group had repeated a year significantly more often, χ²(1)=6.42, p=.01 and they had significantly higher rates of learning disorders, χ²(1)=8.9, p<.01. They had significantly higher CPRS-R scores (all p<.001). Group statistics are shown in Table 1.

(Table 1 about here)

Children with ADHD treated with methylphenidate did not differ from children with ADHD not treated with methylphenidate in terms of inattention severity symptoms, U=119.5, p=.48, hyperactivity, U=100, p=.17, anxiety, U=115.5, p=.40, opposition, U=176, p=.21, and emotional lability, U=148, p=.79.

Group effect for facial emotion recognition scores

The first mixed ANOVA with group and task as factors revealed that group was a significant factor for number of correct answers, F(1, 69)=5.41, p<.05, η²=.045 (small effect size). The interaction between group and task was non-significant, F(1,69)=3.51, p=.07 but trend. Contrast analysis revealed that children with ADHD had significantly lower scores on the emotion recognition task, p<.01, but not in the geometric form recognition task, p=.10.

The second mixed ANOVA with group, emotion type and intensity of emotion as factors revealed that group became non-significant, F(1,69)=1.25, p=.27 when these factors were taken into account. The interaction between group and emotion was significant, F(6,414)=3.26, p<.01, η²=.026 (small effect size). However, the interaction between group and intensity was not, F(1,69)=.07, p=.8, as was the interaction between group, emotion and intensity, F(6,414)=1.42, p=.22.

Contrast analysis revealed that children with ADHD had significantly lower scores only for full-intensity sadness recognition, p<.05. Lastly, the correlation between ADHD total score and global number of correct answers on the emotion recognition task was non-significant : r=.06, p=.72 in the ADHD group. Facial emotion recognition means, standard deviations and contrast analysis results are summarized in Table 2.

(Table 2 about here)

Type of errors in ADHD group

Considering type of errors in children with ADHD, all half-intensity emotions were mistaken for neutral expression, in 26% of total errors for fear and up to 79% for sadness.

Surprise was mainly mistaken for fear in 15%-54% of errors and fear for surprise in 44%-46%. Joy was also mainly mistaken for surprise in 31%-71% of errors. Disgust was misidentified as anger in 37%-62% of errors and anger as disgust in 11%-40%. Sadness, especially at full-intensity, was mistaken with negative emotions in 60% of errors, but not with one in particular. Percentages of errors are shown in Table 3.

(Table 3 about here)

Relationship between facial emotion recognition deficit and comorbid features

Multiple regressions are shown in Table 4. Only variables from the most parsimonious model according to the Akaike Information Criterion (AIC) were included in the results. While previous results showed a significant group effect, the severity of hyperactivity expressed as a dimensional score was no longer predictive of facial emotion recognition deficit.

Discussion

Children with ADHD performed worse than normally developing children on the facial emotion recognition task. Globally, they had lower scores than control children in recognizing sadness. Inattention symptoms were slightly associated with anger recognition deficit whereas oppositional symptoms were associated with global recognition deficit, especially sadness and surprise recognition deficits. Hyperactivity, anxiety and emotional lability remained non-significant.

Facial emotion recognition deficit in children with ADHD

Children with ADHD performed worse than typically developing children on the facial emotion recognition task. The small effect sizes on facial emotion recognition deficits suggest a small but potentially significant clinical relevance. Moreover, as the correlation between ADHD severity score and emotion recognition score was non-significant, the intensity of emotion recognition deficit seemed not to vary with severity of ADHD symptoms.

However, there is no consistency to date in research about whether only the recognition of some emotions type is impaired in children with ADHD or if the deficit is more global. Some studies found a facial emotion recognition deficit only for negative emotions [START_REF] Aspan | Emotion recognition pattern in adolescent boys with Attention-Deficit/Hyperactivity Disorder[END_REF][START_REF] Pelc | Recognition of emotional facial expressions in Attention-Deficit Hyperactivity Disorder[END_REF][START_REF] Boakes | Facial affect interpretation in boys with Attention Deficit/Hyperactivity Disorder[END_REF][START_REF] Williams | Misinterpreting emotional expressions in Attention-Deficit/Hyperactivity Disorder: Evidence for a neural marker and stimulant effects[END_REF] while others did for all emotions [START_REF] Cadesky | Beyond words: How do children with ADHD and/or conduct problems process nonverbal information about affect?[END_REF][START_REF] Yuill | Selective difficulty in recognising facial expressions of emotion in boys with ADHD[END_REF]. Methodological differences across studies may result in varying the task difficulty and may therefore lead to differences in the number of correct responses to each emotion presented. Joy, anger and sadness expressions have often been used in facial emotion recognition tasks but other expressions of emotion have received less attention. Moreover, neutral expression has rarely been included as a choice of answer. As a result, comparing the sadness recognition deficit we found with deficits in previous works is complex. However, a global emotion recognition deficit might be assumed as all scores were lower in the ADHD group.

Facial emotion recognition deficit in children with ADHD: an emotional developmental delay?

Analysis of mistakes is underused in research despite the fact that it allows the severity of the facial emotion recognition deficit to be determined. Patterns of mistakes in children with ADHD were not aberrant: expressions of an emotion were not recognized or were confused with another emotion having the same valence and/or the same expression characteristics (eyes wide open, lips pressed/showing teeth, wrinkled nose, etc.). Indeed, at half-intensity, all emotions except surprise were not recognized as they were misidentified as neutral expression. At full-intensity, sadness, disgust and anger were confused with negative emotions while joy was mostly confused with surprise, which can be considered equally as a positive or a negative emotion. In fact, sadness was misidentified as fear and disgust, anger as disgust and disgust for anger, all negative emotions. There was a particular confusing association between fear and surprise presumably because of a similarity in facial expression (eyes and mouth wide open).

Facial emotion recognition deficits in children with ADHD may be due to difficulties in precisely labeling the expression of an emotion, whereas identifying the global valence appears to be a less demanding task. This deficit can occur at a perceptual state and/or later. For example, it might result from an incomplete extraction of information on the face to precisely recognize the emotion and/or from a cognitive-verbal impairment to precisely understand and label the emotion. Moreover, accurate recognition of joy and sadness appears as early as 5-6 years of age in the development of facial emotion recognition in children, while accurate recognition of disgust appears later around 12 years old [START_REF] Durand | The development of facial emotion recognition: The role of configural information[END_REF]. However, facial emotion recognition still develops from 8 to 11 years of age [START_REF] Mancini | Facial expressions of emotions: recognition accuracy and affective reactions during late childhood[END_REF]. Since ADHD is a neurodevelopmental disorder, emotional development might be delayed. It might also result from atypical developmental trajectories of emotion recognition in children with ADHD compared with children without ADHD.

Comorbid opposition as a severity factor for facial emotion recognition deficit

Unlike the results of [START_REF] Yuill | Selective difficulty in recognising facial expressions of emotion in boys with ADHD[END_REF] [START_REF] Yuill | Selective difficulty in recognising facial expressions of emotion in boys with ADHD[END_REF] and [START_REF] Noordermeer | Neurocognitive deficits in Attention-Deficit/Hyperactivity Disorder with and without comorbid Oppositional Defiant Disorder[END_REF] [START_REF] Noordermeer | Neurocognitive deficits in Attention-Deficit/Hyperactivity Disorder with and without comorbid Oppositional Defiant Disorder[END_REF] who did not find any connection between ODD and facial emotion recognition accuracy, this study found that sadness and surprise recognition were increased by oppositional symptoms. This means that oppositional symptoms as a dimensional feature could adversely impact facial emotion recognition abilities.

The development in facial emotion recognition abilities is closely linked to the child's environment (parents, peers, teachers). ODD often leads to brusque interactions with parents, teachers and peers who find it difficult to put up with the child's irritability, defiance and/or vindictiveness [START_REF] Burke | Reciprocal relationships between parenting behavior and disruptive psychopathology from childhood through adolescence[END_REF][START_REF] Harvey | The interplay among preschool child and family factors and the development of ODD symptoms[END_REF]. The frequency and intensity of these expressions of negative emotions when children with ODD interact with others could make them insensitive to such emotions (half-intensity sadness was mainly misidentified with neutral expression in this study) or lead them into misinterpreting negative emotions because of their frequent co-occurrence (full-intensity sadness was mainly misidentified with disgust, fear and anger). This is all the more the case in children with ADHD comorbid with ODD as ADHD also leads to brusque interactions with others (for review, see [START_REF] Becker | Co-occurring mental health problems and peer functioning among youth with Attention-Deficit/Hyperactivity Disorder: A Review and recommendations for future research[END_REF]).

This study may also fill a gap in the literature about whether the emotional features of children with ADHD impact their facial emotion recognition abilities or not. Anxiety and emotional lability were not related to facial emotion recognition deficit in ADHD children. With regard to anxiety, research has emphasized that anxious children were impaired in facial emotion recognition and/or had an attentional bias toward some emotions (for review, see [START_REF] Collin | Facial emotion recognition in child psychiatry: A systematic review[END_REF]). It can be hypothesized that a specific type of anxiety such as social, performance or separation anxiety might impact facial emotion recognition but not anxiety as a whole feature. To our knowledge, this has not yet been investigated.

Opposition and emotional lability are overlapping concepts. It was surprising that opposition accounted for a facial emotion recognition deficit in children with ADHD whereas emotional lability did not. However, ODD has been deconstructed in three dimensions: 'hurtful', 'headstrong' and 'irritable' [START_REF] Stringaris | Three dimensions of oppositionality in youth[END_REF]. 'Hurtful' and 'Headstrong' may be more related to facial emotion recognition difficulties because they generate conflicts in relationships more frequently. Dedicated investigations are needed to further explore facial emotion recognition in children with ADHD with or without ODD in comparison with ODD only.

This study has some limitations. Firstly, the small sample size limited the power of the study. This might have resulted in undetected effects especially regarding the impact of comorbid features on facial recognition deficit. Secondly, the ADHD group included children treated by methylphenidate. As methylphenidate seems to have an impact of facial emotion recognition abilities [START_REF] Williams | Misinterpreting emotional expressions in Attention-Deficit/Hyperactivity Disorder: Evidence for a neural marker and stimulant effects[END_REF][START_REF] Beyer Von Morgenstern | Improvement of facial affect recognition in children and adolescents with attention-deficit/hyperactivity disorder under methylphenidate[END_REF], some children treated for months by methylphenidate in this study may have experienced an increase in facial emotion recognition even after discontinuing their treatment 24 hours prior to the experiment. This could have led to underestimating the strength of the association between ADHD and facial emotion recognition deficit. Then, children with ADHD performed similarly to typically developing children on the control task. The difference between the two groups was not significant in contrast analysis but the interaction between group and task was also non-significant. This could have led to overestimating the facial emotion recognition in children with ADHD as they finally might experience more difficulties in completing the overall task. Last, parental reporting of inattention symptoms does not necessarily provide a measure of sustained attention that could potentially impact the facial emotion recognition task. Moreover, the inattention score had a low variance in the ADHD group as it is a core symptom of ADHD (SD=7.31). It might have had a limited impact in the regression models.

These emotional aspects of ADHD should be highlighted in research and practice. As social impairment in children with ADHD has been linked with impairment in social cognition, facial emotion recognition deficit could represent a mediator of this association. The development of facial emotion recognition should be sustained in children with ADHD, especially comorbid with ODD, in order to help them to label emotions, as is already undertaken in some cognitive remediation programs.

To conclude, the present findings suggest that children with ADHD have an impaired development in facial emotion recognition especially when an opposition dimension is present. Their deficit in sadness recognition seems to relate globally to ODD. However, anxiety and emotional lability do not seem to interfere in facial emotion recognition. As ODD is a frequent comorbid condition with ADHD and because it may contribute to social problems in children with ADHD, the deficit in facial emotion recognition should be addressed in interventions.

  Inattention was only predictive of lower anger recognition score, β=-.05, p<.05. Opposition predicted lower global facial emotion recognition scores, β=-.31, p<.01, lower sadness recognition scores, β=-.05, p<.01, and lower surprise recognition, β=-.04, p<.01. The main factor explaining facial emotion recognition scores was age predicting a higher global score, β=1.89, p<.001, higher disgust recognition, β=.23, p<.05, joy recognition, β=.29, p<.05, surprise recognition, β=.3, p<.01 and neutral recognition, β=.3, p<.01, scores.

(Table

4

about here)

Table 1 .

 1 Clinical and demographic features of the sample

	ADHD (n=34)	Control (n=37)	p-value
	n(%) or M±SD	n(%) or M±SD

Table 2 .

 2 Means, standard deviations and group comparison on correct answers to facial

	emotion recognition test
	ADHD (n=34)	Control (n=37)	Contrasts
		M±SD	M±SD	p-values
	Practice	7.88 ± 2.56	8.49 ± 2.01	-
	Emotion recognition 27.09 ± 7.65	30.57 ± 5.73	p=.03*
	Control task	50.15 ± 2	50.86 ± 1.42	p=.10
	Half fear	1.74 ± 1.02	1.76 ± 1.12	p=.43
	Full fear	2.32 ± 1.34	2.76 ± 1.04	p=.99
	Half sadness	.94 ± 1.04	1.7 ± 1.1	p=.81
	Full sadness	1.76 ± 1.26	2.49 ± 1.12	p=.02*
	Half disgust	.79 ± .77	1.27 ± .96	p=.50
	Full disgust	2.12 ± 1.34	1.89 ± 1.1	p=.91
	Half anger	1.09 ± .9	1.51 ± 1.19	p=.79
	Full anger	2.68 ± 1.15	2.89 ± 1.2	p=.17
	Half surprise	1.32 ± 1.3	1.57 ± 1.12	p=.99
	Full surprise	2.47 ± 1.6	2.68 ± 1.23	p=.82
	Half joy	3.12 ± 1.17	3.46 ± .8	p=.95
	Full joy	3.44 ± 1.02	3.86 ± .42	p=.08 †
	Neutral	3.29 ± 1.19	2.73 ± 1.47	p=.99

Mots clés : Trouble Déficitaire de l'Attention Hyperactivité -reconnaissance des émotionsexpressions faciales -comorbidité -opposition

† p<.10; * p<.05; **p<.01; ***p<.001

Acknowledgements

Thanks to Marion Robin (Department of Adolescent and Young Adult Psychiatry, Institut Mutualiste Montsouris, Paris, France) and Julie Salla (Department "Health and achievement in the young", Center for Research "Bordeaux Population Health", INSERM U1219, Bordeaux, France) for their respective contributions to the method of this study. We also thank Anne Gaïffas and Anne-Laure Thoumy (Child and Adolescent Psychiatry Department, Charles Perrens Hospital, Bordeaux, France) for participant recruitment.

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. No financial interest or benefits have arisen from direct applications of this research. Jenna Maire and Grégory Michel declare that they have no conflict of interest. In the past (before June 2012), Cédric Galera received support from the industry to attend scientific congresses. Stéphanie Bioulac received financial support for the organization of scientific meetings. Manuel Bouvard has received financial support for the