# Impact of Facial Palsy and Reanimation Surgery on Emotion Recognition: A Magnetoencephalography Study of Early Face Processing: The M170 Component

Rémi Hervochon, Guillaume Dupuch, Maximilien Chaumon, Deborah Ziri, Claire Foirest, Laurent Hugueville, Christophe Gitton, Diane Picard, Frédéric Tankere, Nathalie George

<figure><img src="image_1.png" /></figure>

## To cite this version:

Rémi Hervochon, Guillaume Dupuch, Maximilien Chaumon, Deborah Ziri, Claire Foirest, et al. Impact of Facial Palsy and Reanimation Surgery on Emotion Recognition: A Magnetoencephalography Study of Early Face Processing: The M170 Component. Facial Plastic Surgery & Aesthetic Medicine, 2025, 27 (5), pp.439 - 446. 10.1177/26893614251369491. hal-05405460

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<figure><img src="image_2.png" /></figure>
# Impact of Facial Palsy and Reanimation Surgery on Emotion Recognition: A Magnetoencephalography Study of Early Face Processing - The M170 Component

Running Title: M170 Modulation by Facial Palsy and Rehabilitation

Rémi HERVOCHON<sup>1,2,3</sup>, MD, MSc, Guillaume DUPUCH<sup>1,2</sup>, MD, MSc, Maximilien

CHAUMON<sup>1</sup>, PhD, Deborah ZIRI<sup>1</sup>, PhD, Claire FOIREST<sup>1,2</sup>, MD, MSc, Laurent

HUGUEVILLE<sup>1</sup>, PhD, Christophe GITTON<sup>1</sup>, Diane PICARD<sup>2</sup>, SLP, PhD, Frédéric

TANKERE<sup>1,2</sup>, MD, PhD £, Nathalie GEORGE<sup>1</sup>, PhD £

1. Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, Inserm, CNRS, APHP, CENIR, Centre MEG-EEG and Experimental Neurosurgery team, Hôpital de la Pitié Salpêtrière, Paris, France.

2. AP-HP, Hôpital de la Pitié Salpêtrière, Oto-Rhino-Laryngology and Cervico-Facial Surgery Department, 75013 Paris, France

3. American Hospital of Paris, 55 boulevard du chateau, 92200 Neuilly sur Seine

£ contributed equally, co-last authors

**Corresponding author :**

Rémi HERVOCHON

Service d'ORL et Chirurgie Cervico-Faciale

Groupe Hospitalier Pitié-Salpêtrière

47-83 Boulevard de l'Hôpital

F - 75013 Paris

Email: remihervochon@gmail.com

Tel: +33 6 72 70 69 10

**Key Words :**

Facial palsy, magnetoencephalography, M170, early face processing, hypoglosso-facial anastomosis
# KEY POINTS

**Question:**

How does facial palsy (FP)—and its surgical reanimation—affect early brain processing when recognizing other's emotional expressions?

**Findings:**

Using magnetoencephalography (MEG), we found that patients with untreated FP showed delayed and heightened brain responses to faces compared to controls, indicating disrupted processing. Patients who underwent hypoglossal-facial nerve transfer had brain responses more similar to those of controls.

**Meaning:**

These results suggest that hypoglossal-facial nerve transfer can help normalize early brain processing of emotional faces. This supports the facial feedback hypothesis, the idea that being able to move your own face helps you recognize others' emotions.
# ABSTRACT

## Background

Facial palsy (FP) can impair nonverbal communication and patients often report difficulty recognizing emotional expressions in others.

## Objective

To compare early cortical processing of emotional facial expressions in healthy controls, FP patients, and patients who had undergone facial reanimation surgery using magnetoencephalography (MEG) and analyzing the M170 component, a face-sensitive neuromagnetic response

## Methods

Neuromagnetic responses were recorded with a whole-head 306-sensors MEG system. We studied M170 responses to *angry, happy, sad, and neutral* faces in 10 healthy controls, 5 untreated FP patients, 11 patients with hypoglossal-facial nerve transfer (HFNT), and 4 patients with temporalis muscle transfer.

## Results

Untreated FP patients had delayed M170 latencies (174.8±14.5ms) and increased amplitudes (430.2±214.2 fT) compared to controls (162.8±8.6ms and 306.0±185.0 fT). HFNT patients demonstrated reduced latencies (167.7±12.5ms) and amplitudes (285.5±125.2 fT) relative to FP patients, approximating control values. No significant hemispheric effects or emotion-specific modulation were observed.

## Conclusion

These findings suggest that facial reanimation via HFNT may promote partial normalization of cortical processing of facial emotions, reflecting adaptive neuroplasticity. This study supports the facial feedback hypothesis, whereby the ability to express facial emotions contributes to effective emotion recognition.
# 1. INTRODUCTION

Facial palsy (FP) results in loss of voluntary and emotional motor control of the facial musculature, due to damage to the facial nerve. FP significantly impairs emotional communication and social interaction, as facial expressions are a primary nonverbal channel of emotional exchange. Patients with FP often report diminished emotional recognition abilities due to a disruption of sensorimotor integration and emotional contagion processes¹⁻⁴.

Two surgical approaches are commonly used for facial reanimation in cases of irreversible FP: hypoglossal-facial nerve transfer (HFNT) and temporalis muscle transfer (TMT). HFNT involves connecting the hypoglossal nerve to the facial nerve⁵,⁶. This procedure aims to provide dynamic reinnervation of the whole hemiface and induce cortical reorganization due to the re-routing of motor commands through a non-native pathway⁷⁻¹⁰. TMT, by contrast, consists of mechanically transposing the temporalis muscle to the peribuccal area allowing smile production¹¹,¹². Cortical reorganization is necessary to smile with this masticatory muscle¹³,¹⁴. Both operations require post-operative rehabilitation to optimize motor control and integrate the new facial dynamics into daily expression.

Early face processing involves rapid, automatic brain mechanisms that detect and recognize faces within a few hundred milliseconds, essential for interpreting identity and emotions. It is indexed by the early, pre-conscious N170 in EEG and its magnetic counterpart, the M170 in Magnetoencephalography (MEG). MEG is a functional imaging modality that measures magnetic fields generated by neural activity, providing excellent temporal resolution. Peaking around 170ms, the M170 arises from fusiform and occipito-temporal regions and reflects early structural encoding of faces. The amplitude of M170 has been shown to vary across experimental manipulations and pathological conditions¹⁵⁻¹⁷. M170 responses are larger for faces compared to non-face stimuli, reflecting category-specific
sensitivity<sup>18</sup>. Individuals with autism exhibit longer N170 latencies in response to faces than typical individuals do but comparable latencies to objects<sup>19</sup>. Saavedra et al. showed that the N170 had reduced amplitude in patients with cognitive impairment which could indicate perceptual impairment in configural face-encoding processing<sup>22</sup>. The amplitude of N170 is also modulated by structural factors, for example glasses or the appearance of an emotional face instead of a neutral face<sup>23,24</sup>. Recent studies showed significantly higher amplitudes of N170 response to masked faces compared to unmasked faces<sup>25,26</sup>. Together, these results support the view that M170 amplitude reflects both structural sensitivity and interindividual variability in face perception.

The simulationist model of emotion perception proposes that recognizing others’ facial expressions involves internally simulating the observed emotion, partly through the activation of the corresponding motor representations<sup>27</sup>. According to the facial feedback hypothesis, the ability to produce facial expressions helps support accurate perception of emotional faces.

Although the M170 component has been extensively studied in healthy individuals and in various neurological and psychiatric conditions, to our knowledge, no previous research has specifically examined the M170 in patients with FP or after surgical rehabilitation of facial motor function.

This study examines M170 responses to emotional faces using MEG in 1/ healthy controls, 2/ untreated, non-congenital, unilateral complete flaccid FP (no synkinesis), 3/ patients after HFNT and 4/ after TMT. We compare M170 amplitude and latency across groups. We hypothesize that patients with FP will show altered M170 responses compared to healthy controls, and that facial reanimation surgery will partially normalize these early cortical responses.
## 2. METHODS

### 2.1. Participants

All patients with FP benefited from rehabilitation led by three speech therapists specialized exclusively in FP. Surgery was considered only for those who showed no motor recovery at 9 months, defined as complete, flaccid palsy without synkinesis. All surgical patients underwent specific postoperative rehabilitation with these same therapists, consisting of weekly sessions for one month followed by monthly sessions. In addition, all patients were instructed to perform daily self-rehabilitation exercises at home.

Ten controls (7 men, 39.6±14.0 years), 5 patients with total FP without any surgical rehabilitation (5 men, 47.0±11.1 years, 4 before HFNT, 1 before TMT), 11 HFNT patients (7 men, 44.8±12.2 years; including the 4 patients seen with PF before HFNT) and 4 TMT patients (1 man, 50.3±12.8 years; including the patient seen with PF before TMT) were included. All patients underwent surgery in the Oto-Rhino-Laryngology department in Pitié-Salpêtrière Hospital in Paris, France. Patients who had undergone successful surgeries and who met the inclusion and exclusion criteria were offered to participate in the study. All subjects were right-handed. Exclusion criteria were: being under 18 years old, presenting a progressive neurological or psychiatric disease, taking drug with known action on the central nervous system, diplegia, botulinum toxin injection in the past 12 months, and native FP. Post-operative MEG recordings were performed at least six months after surgery.

### 2.2. Apparatus and data acquisition

All participants underwent MEG recordings at the *Centre de Neuroimagerie de Recherche (CENIR)* of the Paris Brain Institute.
During MEG, participants were seated in a dimly lit, magnetically shielded room, with a screen display placed at a distance of 85 cm. Instructions were delivered on it.

Neuromagnetic fields were recorded with a 306-channels whole-head Elekta Neuromag® Triux MEG system. Magnetic signals were recorded continuously with a sampling rate of 1 kHz and a low-pass filter set at 330Hz.

Four head position indicator (HPI) coils were affixed to the subject's head. The positions of the HPI coils and of multiple points on the scalp were digitized with a magnetic digitizer (Polhemus-3D-Fastrack system). These positions were recorded with respect to a head coordinate frame defined by three anatomical landmarks: nasion, left and right tragi. These points were later used to coregister the coordinate systems of the head and the MEG sensors. For eye movements and blinks, standard electrooculograms (EOG) were recorded by disposable bipolar electrodes. The electrocardiogram (EKG) was monitored. The recording also included electromyographic (EMG) electrodes placed on the face for the purpose of another study<sup>13,28</sup>. These were not analyzed. The EKG, EOG, and MEG signals were recorded simultaneously.

**2.3. Procedure**

The subject was seated in the MEG system. Participants performed an emotion
recognition task during MEG recording. Facial stimuli were selected from the Karolinska
Directed Emotional Faces database<sup>29</sup>. The set included 320 images displaying four primary
emotional expressions : 80 *neutral*, 80 *joy*, 80 *sadness*, and 80 *anger*—commonly recognized
as universal and culturally unambiguous<sup>30</sup>. All images were preprocessed to equalize
luminance histograms using the SHINE toolbox<sup>31</sup>. The task was organized into 5 stimulation
blocks, each containing between 16 and 20 static images per emotion, presented in a
randomized order. Each trial began with a fixation cross lasting 900 milliseconds, followed by
stimulus presentation for 800 milliseconds, and an inter-trial interval of 2 seconds. Each block lasted around 4 minutes, and the full task took approximately 30 minutes to complete.

Between blocks, brief breaks were provided to verify and adjust the head position. A five-minute resting state recording was performed.

**2.4. Data processing**

Denoising of the raw MEG data was done with *Maxfilter* (version 2.2.10; Elekta AB, Stockholm, Sweden) using temporal signal space separation (tSSS) to remove interferences from magnetic fields originating outside of a sphere delimited by the sensor helmet.

Subsequent data processing and analysis were performed using *Brainstorm* (version of 07/15/2017, http://neuroimage.usc.edu/brainstorm)³². Segments containing artifacts were manually identified and marked as "BAD". Ocular and cardiac artifacts were detected and removed using Signal-Space Projection (SSP). If ocular or cardiac artifacts were rare, Independent Component Analysis (ICA) was used instead.

Epochs of interest were extracted in a time window ranging from -200ms to +800ms relative to stimulus onset. Baseline subtraction was performed with the window [-200 ; -100] ms.

Magnetic Event-Related Fields (ERFs) and corresponding topographies were studied within the same time window [-200ms; +800ms] in order to identify the M170 component (Figure 1). Its peak latency and amplitude corresponding to each emotional expression condition were manually identified and measured on the occipito-temporal sensor where the M170 maximum was seen.
<figure><img src="image_3.png" /><figcaption>**Figure 1:** M170 component. Top: amplitude (fT). Bottom: Magnetic fields topography.</figcaption></figure>

**2.5. Statistical analysis**

The statistical analyses were performed using the R software (R version 4.5.0; R core team 2025)³³. We analyzed the M170 latency and amplitude using linear mixed-effects models and ANOVA of the models with Satterthwaite's method to compute degrees of freedom³⁴. For both latency and amplitude, the model included two within-subject fixed-effect factors: “Emotion” (four levels: anger, joy, neutral, sadness), and “Hemisphere” (two levels: right, left); and one between-subject fixed-effect factor: “Group” (four levels: controls, FP, HFNT, and TMT). The interaction between emotion and group was also included. Random effects across subjects comprised a random intercept and random slopes for Emotion and for Hemisphere. The linear mixed model accounted for the fact that the 5 subjects of the FP group were also seen post-operatively (four in the HFNT group and one in the TMT group). Post-hoc, planed, pairwise comparisons of between-group differences were performed with the *emmeans* package³⁵, testing for the two following contrasts: controls versus FP patients, FP patients versus patients post-HFNT. We did not include the contrast between FP patients and
patients post-TMT because of the too small number of patients post-TMT. The multivariate t
method ("mvt") was applied to adjust for multiple comparisons, controlling the family-wise
error rate by accounting for the joint distribution of the test statistics. We performed
additional equivalence test in the form of two one-sided tests (TOST)³⁶ between HFNT and
control groups.

**2.6. Ethics**

The study was approved by the Ethical Committee of Paris IDF VI (CPP-Ouest-6,
reference: CPP975-HPS2).
### 3. RESULTS

The interval between surgery and postoperative MEG was 46.8±25.9 months in the HFNT group and 43.2±33.3 months in the TMT group.

The linear mixed-effects model ANOVAs revealed significant effects of the Group on both the latency and the amplitude of the M170 component (F(3,51.75)=3.94, *p*=0.013, and F(3,53.50)=13.7, *p*<10<sup>-6</sup>, respectively). There was not any other significant effect or interaction (Tables 1a,1b). We performed post-hoc, planned comparisons to analyze further the effects of Group on M170 latency and amplitude.

<table><caption>**Table 1:** Type III Analysis of Variance tables for the linear mixed-effects models on M170 peak latency (a) and amplitude (b). Significance codes: 0 ‘***’, 0.001 ‘**’, 0.01 ‘*’, 0.05 ‘.’, 0.1 ‘.’. Sum of square (Sum Sq), Mean Square (Mean Sq), Numerator Degrees of freedom (NumDF), Denominator Degrees od Freedom (DenDF), F value, *p*-value for F (Pr(>F))</caption><thead><tr><th></th><th>Sum Sq</th><th>Mean Sq</th><th>NumDF / DenDF</th><th>F value</th><th>Pr(&gt;F)</th></tr></thead><tbody><tr><td>Group (G)</td><td>328.71</td><td>109.570</td><td>3 / 51.745</td><td>3.9442</td><td>0.01312 *</td></tr><tr><td>Emotion (E)</td><td>50.62</td><td>16.873</td><td>3 / 32.479</td><td>0.6074</td><td>0.61501</td></tr><tr><td>Hemisphere</td><td>79.66</td><td>79.659</td><td>1 / 21.900</td><td>2.8675</td><td>0.10456</td></tr><tr><td>G x E</td><td>118.91</td><td>13.212</td><td>9 / 59.189</td><td>0.4756</td><td>0.88526</td></tr></tbody></table><table><thead><tr><th></th><th>Sum Sq</th><th>Mean Sq</th><th>NumDF / DenDF</th><th>F value</th><th>Pr(&gt;F)</th></tr></thead><tbody><tr><td>Group (G)</td><td>113189</td><td>37730</td><td>3 / 53.503</td><td>13.6827</td><td>9.672e-07 ***</td></tr><tr><td>Emotion (E)</td><td>3349</td><td>1116</td><td>3 / 93.508</td><td>0.4048</td><td>0.7499</td></tr><tr><td>Hemisphere</td><td>1159</td><td>1159</td><td>1 / 23.375</td><td>0.4202</td><td>0.5232</td></tr><tr><td>G x E</td><td>17056</td><td>1895</td><td>9 / 156.690</td><td>0.6873</td><td>0.7197</td></tr></tbody></table>

The mean±SD of M170 latencies were 162.8±9.9ms in controls, 174.8±14.6ms in FP group, 167.7±13.0ms HFNT group, and 181.7±11.8ms in TMT group. The corresponding estimated marginal means (EMMs), as derived from the linear mixed-effects model are presented in Table 2. Post-hoc, planned comparisons showed a suggestive difference between control and FP groups (*p*=0.061) and a significant difference between FP and HFNT groups
(p=0.02). We performed an additional equivalence test (TOST) on the M170 latency between VS and HFNT groups, with boundary for equivalence set at ±20ms; this equivalence test was significant (t(160.917)=8.463, p<0.001, mean difference = -4.98ms). Thus, the M170 component appeared delayed in patients with FP compared to controls and its latency was shortened after HFNT surgery, reaching a value close to controls (Figure 2). In contrast, on average, M170 latency seemed to increase after TMT surgery.

<figure><img src="image_4.png" /><figcaption>**Figure 2** : Latencies of M170 component (ms). The figure represents the individual data, with their median and quartiles as box plots. For each subject, the data from both right and left hemispheres are presented (two data points per subject in each boxplot).</figcaption></figure>

(£) : suggestive difference, ( * ) : significant difference

The mean±SD of M170 amplitudes were 306.0±186.0fT in controls, 430.2±210.2fT in FP patients, 285.5±125.8fT in patients after HFNT, and 434.9±293.9fT in patients after TMT.

The corresponding EMMs derived from the linear mixed-effects model are presented in Table
2. Post-hoc, planned comparisons showed no significant difference between controls and FP patients, but the FP versus HFNT group difference was significant (p=0.04). We performed an additional equivalence test (TOST) on the M170 amplitude between VS and HFNT groups using a boundary for equivalence of ±20fT; this equivalence test was not significant (p=0.51, mean difference = 20.5fT). Thus, the M170 component appeared with a higher amplitude in patients with FP compared to controls and its amplitude decreased after HFNT surgery, yet it did not reach equivalent value to controls (Figure 3). In contrast, on average, the M170 amplitude seemed unchanged after TMT surgery.

<table><caption>**Table 2:** Estimated marginal means (EMM) of M170 peak latency (in ms) and amplitude (in fT) for each group and emotion, as estimated from the linear mixed-effects models of the data. For each group (in rows) and each emotion (in columns), we report EMM ± SE. The last column (“Total”) is the EMM± SE for each group, across emotions.</caption><thead><tr><th>Group</th><th>Anger</th><th>Joy</th><th>Neutral</th><th>Sadness</th><th>Total</th></tr></thead><tbody><tr><td>Controls</td><td>162.2 ± 7 ms<br>306.3 ± 118.8 fT</td><td>161.8 ± 7.6 ms<br>305.9 ± 112 fT</td><td>163.3 ± 7.8 ms<br>299.2 ± 118.2 fT</td><td>163.3 ± 8.4 ms<br>315 ± 122 fT</td><td><strong>162.6 ± 7.2 ms</strong><br><strong>306.6 ± 115.8 fT</strong></td></tr><tr><td>FP</td><td>175 ± 6.6 ms<br>320.4 ± 102.6 fT</td><td>172.9 ± 7.2 ms<br>321.5 ± 97.4 fT</td><td>172.6 ± 7.2 ms<br>291.7 ± 101.8 fT</td><td>173.8 ± 7.8 ms<br>294.8 ± 104.8 fT</td><td><strong>173.6 ± 6.2 ms</strong><br><strong>307.1 ± 96.2 fT</strong></td></tr><tr><td>After<br>HFNT</td><td>170.5 ± 6 ms<br>269.9 ± 98.6 fT</td><td>171.5 ± 6.6<br>282.8 ± 93 fT</td><td>168.7 ± 6.6 ms<br>279.3 ± 97.8 fT</td><td>170 ± 7.2 ms<br>274.3 ± 101 fT</td><td><strong>170.2 ± 6 ms</strong><br><strong>276.6 ± 95.4 fT</strong></td></tr><tr><td>After<br>TMT</td><td>175.8 ± 7.6 ms<br>478 ± 109.8 fT</td><td>176.7 ± 8.2 ms<br>437.9 ± 104.2 fT</td><td>173.5 ± 8.2 ms<br>457.6 ± 109.2 fT</td><td>175.1 ± 9.2 ms<br>459 ± 112.4 fT</td><td><strong>175.3 ± 7.2 ms</strong><br><strong>458.1 ± 103.2 fT</strong></td></tr></tbody></table>
<figure><img src="image_5.png" /><figcaption>**Figure 3:** Amplitudes of M170 component (fT). The figure represents the individual data, with their median and quartiles as box plots. For each subject, the data from both right and left hemispheres are presented (two data points per subject in each boxplot).</figcaption></figure>

( * ): significant difference
4. DISCUSSION

We investigated the impact of FP and surgical intervention on the cerebral response to
facial expressions of emotions, focusing on the M170 component.

Patients with untreated FP exhibited on average both increased amplitude and delayed latency of the M170 compared to healthy controls, suggesting disrupted and less efficient neural processing of facial stimuli. Interestingly, patients who underwent HFNT showed M170 characteristics more similar to those of healthy controls, with reduced amplitude and shorter latency relative to the FP group. This suggests a partial normalization of cortical face processing following HFNT, suggesting adaptive neuroplasticity. In contrast, flaccid FP lacks any neural input, and TMT provides only a muscular reconstruction without re-establishing functional neural connectivity.

Due to the small sample size and high variability, data from the TMT group should be
interpreted with caution. These patients showed the highest M170 amplitude and longest
latency, possibly reflecting inefficient or compensatory cortical activation. This may be
explained by the purely muscular nature of TMT, which bypasses the facial nerve, and its
restriction to the lower face, leaving upper facial regions unrecovered.

Emotion recognition impairment in FP patients have been explored by various authors³⁷. Storbeck et al. found no deficits in emotion recognition among FP patients but reported significantly longer reaction times². Picard et al. assessed that FP patients exhibited less accurate emotion perception, which was correlated with the duration of FP¹. Kheirkhah et al. suggested that the ability to perform facial expressions is necessary to have normal emotional processing and experience of emotions³⁸. Our study shows a neurophysiological alteration in early face processing in FP. It is also well established that patients with FP have a higher prevalence of depressive symptoms³⁹. Moreover, emotion recognition deficits are
commonly observed in depression⁴⁰,⁴¹. This suggests there may be an indirect link between FP, depression, and impaired emotion recognition. It remains unclear whether depression leads to poorer emotion recognition in FP, or whether difficulties in recognizing emotions contribute to depressive symptoms in these patients.

Our study and most of prior research used static stimuli to support emotion recognition. This differs from dynamic stimuli, which better reflect real-life interactions. FP patients struggle not only with spatial mimicry (e.g., asymmetrical smiles) but also with the timing of facial expressions (e.g., synchronicity between the two sides of the face).

## 5. CONCLUSION

The present findings suggest that HFNT may partially normalize early face cortical processing, reflecting adaptive neuroplasticity. Understanding these mechanisms may inform future rehabilitation strategies targeting both motor and perceptual components.
Acknowledgments

Denis SCHWARTZ: methodology, software. Portions of this article were previously
published on the bioRxiv preprint server and can be found at
https://doi.org/10.1101/2025.05.13.652175.

Authorship confirmation/contribution statement

Conceptualization: FT, NG, RH

Data curation: RH, MC, DZ, CF, GD, LH, CG

Formal Analysis: MC, NG, RH, DZ, CF, GD

Methodology: FT, NG, RH, LH, CG

Software: MC, LH, CG

Supervision: NG, FT,

Writing – original draft: RH, MC, DZ, CF, NG, FT, GD

Writing – review & editing: RH, MC, DZ, CF, NG, FT, GD

Authors' disclosure

The authors have neither commercial association nor financial disclosure. The author(s)
declared no potential conflicts of interest with respect to the research, authorship, and/or
publication of this article

**Financial Disclosure Statement :**

This research has received funding from the “Fondation pour la Recherche Médicale” and
from the “Fondation des Gueules Cassées”. It was performed on a platform of the France
Life Imaging network partly funded by the grant “ANR-11-INBS-0006” and by the program
"Investissements d'avenir" ANR-10-IAIHU-06. The funding bodies had no role in the study design, the data collection, analysis or interpretation, or the article writing.

**Ethical considerations:** The Ethical Committee of Paris Idf VI approved the study (CPP Ouest 6-CPP975-HPS2).

**Consent to participate:** Written, approved by the IRB

**Consent for publication:** Informed consent for publication was provided by the participant
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