# Detecting emotion in individuals with disorders of consciousness: a pilot study of heart rate deceleration on affective auditory stimuli

Hugo Ardaillon, Anne Dubois, Kate-Mary Ndocko, Jérôme Etienne, Catherine Tallon-Baudry, Jacques Luauté

► To cite this version:

Hugo Ardaillon, Anne Dubois, Kate-Mary Ndocko, Jérôme Etienne, Catherine Tallon-Baudry, et al. Detecting emotion in individuals with disorders of consciousness: a pilot study of heart rate deceleration on affective auditory stimuli. Clinical Neurophysiology, 2025, 183, pp.2111469. 10.1016/j.clinph.2025.2111469. hal-05452770

HAL Id: hal-05452770

https://hal.science/hal-05452770v1

Submitted on 11 Jan 2026

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

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

Distributed under a Creative Commons CC BY 4.0 - Attribution - International License
# Detecting emotion in individuals with disorders of consciousness: a pilot study of heart rate deceleration on affective auditory stimuli

Hugo Ardaillon<sup>a,b,c,*</sup> <sup>□</sup>, Anne Dubois<sup>d</sup>, Kate-Mary Ndocko<sup>b</sup>, Jérôme Etienne<sup>b,e</sup>, Catherine Tallon-Baudry<sup>f,g □</sup>, Jacques Luauté<sup>a,b,c □</sup>

<figure><img src="image_2.png" /></figure>

## ARTICLE INFO

### Keywords:

Disorders of consciousness

Acquired brain injury

Emotion

Sounds

Heart rate

## ABSTRACT

**Objective:** To evaluate heart rate deceleration on affective auditory stimulations as an objective marker of purposeful affective behaviour, as used in the diagnosis of minimally conscious state (MCS) in brain-injured individuals with disorders of consciousness (DoC), awake but unable to communicate.

**Methods:** We recorded the heart rate of participants (14 healthy controls and 10 brain injured individuals, conscious or with DoC) on repeated exposure to a random sequence of three emotional sounds of neutral, positive, and negative valence, in an unconditioned manner (experiment 1) or in a trace-conditioning procedure (experiment 2), with successful trace-conditioning assumed to indicate consciousness.

**Results:** In experiment 1, heart rate deceleration after aversive acoustic stimulus was significantly higher in healthy subjects at the group level from the second to the seventh inter-beat interval, with limited sensitivity at the individual level. In experiment 2, there was no evidence of trace-conditioned learning in healthy subjects, precluding any extrapolation to brain-injured individuals.

**Conclusions:** Heart rate deceleration after aversive acoustic stimulus was significant in passive hearing but not in a trace-conditioning paradigm and was of limited sensitivity at the individual level.

**Significance:** Heart rate deceleration may constitute an objective marker of purposeful affective behaviour triggered by emotional sounds. The question of whether this response is a reflex or a marker of conscious access needs further study.

## 1. Introduction

Improvement in neurological intensive care in the second half of the 20th century has meant that individuals with severe brain injury are more likely to survive and awaken from a coma (Plum and Posner, 1972). A crucial task for clinicians is to distinguish individuals with no signs of awareness (in an unresponsive wakefulness syndrome (UWS), otherwise known as vegetative state (VS) (Jennett and Plum, 1972; Laureys et al., 2010)) from those showing inconsistent but reproducible purposeful behaviour, possibly including response to commands (Kondziella et al., 2020; Naccache, 2018), a condition known as the minimally conscious state (MCS) (Giacino et al., 2002). MCS is

associated with a better prognosis than UWS, in terms of survival, functional outcomes, and consciousness recovery (Luauté et al., 2010). Identifying individuals likely to regain self-consciousness therefore has important treatment and ethical implications, as this may affect end-of-life decisions (Kondziella et al., 2020).

The Coma Recovery Scale-Revised (CRS-R) was developed to characterise and monitor individuals with disorders of consciousness (DoC) awake but unable to communicate (Giacino et al., 2004). Subtle but potentially meaningful affective behaviours could be difficult to detect and hard to differentiate from reflexive responses. Giacino defined purposeful emotional behaviour (such as smiling or crying, triggered by relevant content but not by neutral stimuli), as one of the criteria for

<footnote><sup>a</sup> Department of Neuro-Rehabilitation, Hospices Civils de Lyon, Université Claude Bernard Lyon 1, Lyon, France</footnote>

<footnote><sup>b</sup> Université Claude Bernard Lyon 1, Université de Lyon, 43 boulevard du 11 Novembre, 191869622 Villeurbanne cedex, France</footnote>

<footnote><sup>c</sup> Centre de recherche en neuroscience de Lyon (CRNL), équipe TRAJECTOIRES, Inserm U1028, France</footnote>

<footnote><sup>d</sup> Clinique Gustav ZANDER, 10 promenade du sierroz, 73100 Aix les Bains, France</footnote>

<footnote><sup>e</sup> CIRI, Centre International de Recherche en Infectiologie, Université de Lyon, Inserm U1111, France</footnote>

<footnote><sup>f</sup> Laboratoire de Neurosciences Cognitives et Computationnelles, U960, Ecole Normale Supérieure, Université PSL (Paris Sciences et Lettres), France</footnote>

<footnote><sup>g</sup> Institut National de la Santé et de la Recherche Médicale (INSERM), Paris, France</footnote>

<footnote>* Corresponding author at: Service de Rééducation Post-Réanimation (SRPR), Hôpital Neurologique, 59 Boulevard Pinel, 69500 Bron, France.
E-mail address: hugo.ardaillon@chu-lyon.fr (H. Ardaillon).</footnote>
diagnosing MCS (Giacino et al., 2002) however absent from the CRS-R total score. On this basis, environmental awareness could be inferred from emotional behaviour. The CRS-R is the reference standard for the behavioural diagnosis of DoC, and in the hands of an experienced clinician, is a reliable and rapid tool to distinguish UWS from MCS (Kondziella et al., 2020), but does not include any item pertaining to emotions in its total score.

Various protocols have been used to study perceptive consciousness or cognitive processing of information or pain in individuals with DoC but to our knowledge, very few studies have focused on emotional experience itself. Emotional experience is nevertheless fundamental in building our subjective experience of consciousness. Consciousness and emotion are variously defined in the neuroscientific community. However, these two modes of subjective perception of our environment interact with cognition and are therefore perfectly suited to being studied together (Dehaene, 2011). The use of acoustic stimuli seems appropriate as they are readily and comfortably delivered, and have been used in many paradigms involving individuals with DoC (Beaurenaut et al., 2020; Luauté et al., 2018; Riganello et al., 2010).

Meanwhile, in addition to clinical assessments, imaging or electro-
physiological exams can provide information on cognitive or anatomical
functions, but inferring consciousness from the results of these exams is
debatable, since some complex cognitive tasks can be performed un-
consciously (for example blindsight (Sanders et al., 1974; Weiskrantz,
1990), implicit facial expression recognition (Whalen et al., 1998), im-
plicit semantic priming (Dehaene et al., 1998; Naccache and Dehaene,
2001)). Therefore, in the absence of a gold standard test to detect con-
sciousness (Kondziella et al., 2020), combining complex exams with a
simple method of gathering additional information on individuals'
appropriate emotional reactions at the bedside would be valuable.

The observation of a peripheral response mediated by the autonomic nervous system, concomitant with the perception of an emotional stimulus, has been the subject of major literature since the founding works of William James. Heart rate is considered as an objective marker of emotion, such as hearing emotional sounds (Bradley and Lang, 2000), as well as skin conductance level (Luauté et al., 2018) or pupillary diameter. Heart rates are simple to record, but more complex to interpret. Heart rate variability can be assessed in DoC individuals (Riganello et al., 2010, 2019b) and was used to probe learning ability to noxious stimuli (Cortese et al., 2020), as a pain indicator (Riganello et al., 2019a), as a reaction to musical stimuli (Riganello et al., 2015), or to evidence residual cognitive processing of auditory stimuli (Raimondo et al., 2017). Heart rate deceleration seems to be a cardiac defence response mediated by the parasympathetic nervous system, leading to what is known as attentional bradycardia, the cardiac deceleration observed during attentional orientating tasks (Lacey and Lacey, 1978). The cardiac defence response is a dynamic sequence of reactions and consists of both attentional and motivational processes: first, a short-term deceleration that promotes cognitive and attentional processes (attentive freezing, related to increased sensory processing and orienting); and a second, longer-term acceleration to promote motivational processes (active mobilisation related to the fight-or-flight response, preparation for active defence), the magnitude of which increases with the proximity of the danger (Ramírez et al., 2010). Human screams and distress calls are effective danger signals to conspecifics and are relevant by evolutionary and social means (Arnal et al., 2015), with highly aversive acoustic properties (Anikin et al., 2018) that engage specific subcortical regions known to be involved in orientation to danger (Arnal et al., 2015). This frequency regime, known as the roughness regime, is expressed in all human warning signals and artificial alarms but is absent from common speech (Arnal et al., 2015). These acoustic properties are thought to confer a behavioural advantage by allowing the source of the scream to be located faster and more accurately. Human screams therefore seem likely eligible to elicit a cardiac defence response. Human screams have already been used in fear conditioning paradigms (Beaurenaut et al., 2020; Lau et al., 2011, 2008), inducing an increased

skin conductance (Ahrens et al., 2016), startle reflex (Glenn et al., 2012; Haddad et al., 2013) and increasing levels of reported anxiety (Den et al., 2015; Haddad et al., 2013) compared to other stimuli. The cardiac defence response to threats therefore involves attentional, cognitive, and emotional components, and thus seems a relevant measure of subjects' integrated subjective experience.

Hypothesising that an objective marker of emotional response con-
stitutes evidence of awareness of the environment, we recorded the
heart rates of participants (healthy controls and brain injured in-
dividuals) repeatedly exposed to a random sequence of three emotional
sounds (of neutral, positive, and negative valence) from an international
bank (Bradley and Lang, 2007), either passively (experiment 1) or with
trace-conditioning (Bekinschtein et al., 2009) (experiment 2). In passive
listening in healthy participants, we aimed to measure emotional reac-
tivity in response to exposure to sounds of different valence, with a
peripheral measure easy to acquire: heart rate. We expected to replicate
prior findings that valenced sounds would lead to heart rate changes. We
further examined whether the effect was strong enough to be reliably
detected at the single participant level. We then investigated heart rate
changes in individuals with DoC, both at the group level and at the in-
dividual level. Finally, we investigated whether learning of an aversive
acoustic stimulus by trace-conditioning could be evidenced by heart rate
deceleration to improve the diagnosis of consciousness.

## 2. Methods

This was a prospective, non-blinded, pilot, non-randomised inter-
ventional clinical trial conducted from October 15, 2018, until March
18, 2021, with parallel assignments. The study was approved by the
local ethics committee (Comité de Protection des Personnes Sud-
Méditerranée IV; approval number 2018-A02673-52). The study was
registered at ClinicalTrials.gov (NCT03706170). Participants provided
written informed consent.

2.1. Population

Control subjects were recruited among local students, researchers,
and hospital staff. Individuals with brain injury were recruited from the
physical medicine and rehabilitation department of the Hospices Civils
de Lyon, at least one month after brain injury (of traumatic, vascular, or
anoxic origin), with or without a DoC as diagnosed using the CRS-R
scale. The exclusion criteria were uncontrolled epilepsy, dysautono-
mia, unstable medical condition, pregnancy or breast-feeding, and
guardianship or conservatorship. The exclusion criteria for control
participants were neurological disorder, pregnancy or breast-feeding,
and guardianship or conservatorship.

## 2.2. Material

Heart rate was recorded by electrocardiogram or with a pulse ox-
imeter (Biopac Systems INC, USA) (Table 1). Electrocardiograms were
recorded at a sampling frequency of 2000 Hz with three non-polarisable
silver chloride electrodes positioned in the left (positive polarity, black
colour) and the right (positive polarity, white colour) infraclavicular
area, as well as in the left groin crease (ground electrode, red colour).
The pulse oximeter was placed on the distal phalanx of the ring finger (at
a sampling frequency of 2000 Hz, a low-pass filter of 3.0 Hz and a high-
pass filter of 0.5 Hz) and was connected to Biopac BioNomadix trans-
mitter boxes (ECG100C and PPG100C) attached to the wrist, connected
wirelessly to a Biopac MP150 receiver. The data were acquired using the
software AcqKnowledge (v.5.0, Biopac Systems INC, USA) which was
interfaced via TTL signal and a PCI Expresscard with the software
managing the sequences of sound stimuli (SMI Experimental Center;
SMI, Germany) to synchronise the individual data and the stimuli. The
sounds were played through DT-770 Pro over-ear headphones (Beyer-
dynamic, Germany) and measured using a decibel meter (Parkside,
<table><caption>**Table 1**
Participant characteristics.</caption><thead><tr><th>Identifier</th><th>Gender</th><th>Age (years)</th><th>Time since brain injury (months)</th><th>Cause of brain injury</th><th>Diagnosis according to CRS-R score</th><th>Total CRS-R score</th><th>Recording method for heart rate</th></tr></thead><tbody><tr><td colspan="8"><strong>Healthy subjects</strong></td></tr><tr><td>n = 14</td><td>6 M: 8 F</td><td>35 ± 14</td><td>—</td><td>—</td><td>—</td><td>—</td><td>PPG</td></tr><tr><td>H01</td><td>M</td><td>24</td><td>—</td><td>—</td><td>—</td><td>—</td><td>PPG</td></tr><tr><td>H02</td><td>F</td><td>24</td><td>—</td><td>—</td><td>—</td><td>—</td><td>PPG</td></tr><tr><td>H03</td><td>M</td><td>52</td><td>—</td><td>—</td><td>—</td><td>—</td><td>PPG</td></tr><tr><td>H04</td><td>F</td><td>28</td><td>—</td><td>—</td><td>—</td><td>—</td><td>PPG</td></tr><tr><td>H05</td><td>F</td><td>26</td><td>—</td><td>—</td><td>—</td><td>—</td><td>PPG</td></tr><tr><td>H06</td><td>F</td><td>28</td><td>—</td><td>—</td><td>—</td><td>—</td><td>PPG</td></tr><tr><td>H07</td><td>M</td><td>61</td><td>—</td><td>—</td><td>—</td><td>—</td><td>PPG</td></tr><tr><td>H08</td><td>F</td><td>24</td><td>—</td><td>—</td><td>—</td><td>—</td><td>PPG</td></tr><tr><td>H09</td><td>M</td><td>29</td><td>—</td><td>—</td><td>—</td><td>—</td><td>PPG</td></tr><tr><td>H10</td><td>F</td><td>25</td><td>—</td><td>—</td><td>—</td><td>—</td><td>PPG</td></tr><tr><td>H11</td><td>M</td><td>28</td><td>—</td><td>—</td><td>—</td><td>—</td><td>PPG</td></tr><tr><td>H12</td><td>F</td><td>50</td><td>—</td><td>—</td><td>—</td><td>—</td><td>PPG, ECG</td></tr><tr><td>H13</td><td>F</td><td>57</td><td>—</td><td>—</td><td>—</td><td>—</td><td>PPG, ECG</td></tr><tr><td>H14</td><td>M</td><td>28</td><td>—</td><td>—</td><td>—</td><td>—</td><td>PPG, ECG</td></tr><tr><td colspan="8"><strong>Brain injured patients</strong></td></tr><tr><td>n = 10</td><td>4 M: 6 F</td><td>34 ± 13</td><td>7.5 ± 6</td><td>—</td><td>—</td><td>14 ± 6</td><td>—</td></tr><tr><td>P01</td><td>M</td><td>45</td><td>6</td><td>Anoxia</td><td>VS</td><td>6</td><td>PPG</td></tr><tr><td>P02</td><td>F</td><td>51</td><td>22</td><td>Trauma</td><td>MCS</td><td>15</td><td>PPG</td></tr><tr><td>P03</td><td>F</td><td>49</td><td>11</td><td>Haemorrhagic stroke</td><td>VS</td><td>9</td><td>PPG</td></tr><tr><td>P04</td><td>M</td><td>18</td><td>6</td><td>Trauma</td><td>MCS</td><td>8</td><td>PPG, ECG</td></tr><tr><td>P05</td><td>F</td><td>41</td><td>9</td><td>Toxoplasmosis</td><td>eMCS</td><td>13</td><td>PPG, ECG</td></tr><tr><td>P06</td><td>F</td><td>42</td><td>3</td><td>Trauma</td><td>MCS</td><td>8</td><td>PPG, ECG</td></tr><tr><td>P07</td><td>M</td><td>32</td><td>3</td><td>Trauma</td><td>eMCS</td><td>21</td><td>PPG, ECG</td></tr><tr><td>P08</td><td>F</td><td>20</td><td>3</td><td>Trauma</td><td>eMCS</td><td>20</td><td>PPG, ECG</td></tr><tr><td>P09</td><td>F</td><td>26</td><td>6</td><td>Anoxia</td><td>MCS</td><td>15</td><td>PPG, ECG</td></tr><tr><td>P10</td><td>M</td><td>19</td><td>6</td><td>Trauma</td><td>eMCS</td><td>23</td><td>PPG, ECG</td></tr></tbody></table>

Ages and times since injury are summarised as mean ± standard deviation. M = male, F = female, VS = vegetative state, MCS = minimally conscious state, eMCS = emerged from minimally conscious state, CRS-R = Coma-Recovery-Scale Revised, ECG = electrocardiogram, PPG = photoplethysmogram. Note that regardless of the total CRS-R score, a patient need only satisfy one of 11 criteria to be classified as MCS, i.e., a VS patient may have a higher score than a MCS patient if the latter meets one of the sufficient criteria.

Germany). We performed statistical analyses in the software R via RStudio (Comprehensive R Archive Network, CRAN).

### 2.2.1. Acoustic stimuli: emotional sounds and tones

Three sounds of negative, neutral and positive emotional valence were selected from the International Affective Digitized Sounds (IADS) database (Bradley and Lang, 2007; Yang et al., 2018): a woman's scream, the sound of a fan in operation, and a sample of samba music. These were played for 3000 ms at a non-painful amplitude (Beaurenaut et al., 2020). The structural integrity of the auditory system, from the periphery to the cortex, was verified to minimise confounding factors and reduce the risk of false negatives due to hearing impairment, either clinically (by assessing the presence of a startle or blink reflex and gaze orientation to noise) or through auditory evoked potentials, as commonly performed in prognostic evaluation (Barami, 2024). To avoid nociceptive bias, the sound volume was adjusted to below 75 dB before the start of experiments (Beaurenaut et al., 2020).

Two easily differentiated tones, a high-pitched tone (3322 Hz, A7 flat) and a low-pitched tone (1075 Hz, C6), each lasting 500 ms were also used. The conditioning stimulus was the high-pitched tone followed by the negative emotional sound (scream), while the low-pitched tone followed by silence was used as the control stimulus.

### 2.3. Experimental paradigm

Healthy subjects and individuals with DoC participated in two experiments, a preconditioning experiment (experiment 1) and a conditioning experiment (experiment 2). Experiment 1 lasted 30 min and Experiment 2 lasted about 1 h. Participants were informed they would hear various sounds, with no other instruction.

#### 2.3.1. Experiment 1

In experiment 1, cardiac responses to the stimuli were recorded without any conditioning. Baseline parameters were first recorded for 5 min with the participants at rest. The neutral, negative, and positive sounds and the high and low tones were presented 10 times each in a random fashion, with 10 s intervals between the end of one sound or tone and the beginning of the next (Fig. 1). Our hypothesis was that the aversive sound would provoke the greatest response in heart rate as suggested for highly arousing sounds by Bradley and Lang (2000) and more recently by Engelen et al. (2023), so only the responses to the aversive and neutral sounds were analysed.

#### 2.3.2. Experiment 2

A trace-conditioning paradigm was used for the second experiment, involving three different sequences (Fig. 2): an aversive sequence (Fig. 2A), a control sequence (Fig. 2B) and a test sequence (Fig. 2C). These were repeated in a random order, respectively 100, 50 and 50 times each, to avoid extinction of the conditioning.

The aversive sequence (Fig. 2A) consisted of the high-pitched tone (500 ms) followed by 500 ms and then by the aversive sound (the woman's scream, 3000 ms), followed by 10 s of silence before the next sequence. The control sequence (Fig. 2B) consisted of the low-pitched tone alone (500 ms) followed by 10 s of silence. The test sequence (Fig. 2C) consisted of the high-pitched tone alone (500 ms) to identify a possible conditioned response to the high-pitched tone. Successful conditioning with this paradigm would be evidenced by a detectable change in heart rate in response to the high-pitched tone when played by itself, different from the response to the control tone. At the end of the experiment, healthy subjects and individuals without disorders of consciousness were asked to complete questionnaires on the emotional
<figure><img src="image_3.png" /><figcaption>Fig. 1. Experimental paradigm of experiment 1: three emotional sounds and two distinguishable tones played in a random sequence, 10 times each: (A) A fan in operation (neutral valence), (B) a woman's scream (negative valence), (C) a sample of samba music (positive valence), (D) a high-pitched tone (3322 Hz), and (E) a low-pitched tone (1075 Hz).</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>Fig. 2. Experimental paradigm for experiment 2: three sound sequences presented in a pseudo-random fashion. (A) The conditioning stimulus played 100 times: a high-pitched tone (500 ms) followed by silence (500 ms) and then a woman's scream (3000 ms). (B) The control stimulus played 50 times: a low-pitched tone (500 ms) followed by 10 s of silence. (C) The sequence used to test for the presence of trace - conditioning, played 50 times: the high-pitched tone (500 ms) followed by 10 s of silence (and not by the woman's scream).</figcaption></figure>

valence of the three sounds (The Self-Assessment Manikin (SAM) assessment (Measuring emotion, 1994)) and their perception of the timing of the conditioning procedure.

## 2.4. Measurement and artefact rejection

Heart rate was recorded with a pulse oximeter (for preliminary data) or by oximeter and electrocardiogram (additional data), both approaches giving comparable results for rhythm analyses (Plews et al., 2017; Schäfer and Vagedes, 2013). The variable of interest was the inter-beat interval (IBI, in ms). The IBI corresponding to the beginning of the sound (trigger) was defined as the reference interval (IBI₀) with the following IBIs counted in sequence (IBI₁, IBI₂, etc.). The IBI were expressed as differences in milliseconds relative to the reference interval IBI₀: Δn, for the n'th IBI. Heart beats were detected automatically by peak detection in the oximetry data and by detection of the R wave in the electrocardiograms using the AcqKnowledge Biopac software. The timings of the sounds and heartbeats were exported from AcqKnowledge into Microsoft Excel, sorted using PowerQuery and then summarised as ranges, means and standard errors by beat number and sound/tone using a VBA macro. Correct detection was always confirmed by a visual analysis, and measurement sequence of which the first 11 beats after the triggering sound (IBI₀ to IBI₁₀) could not be detected were excluded. Outliers (beyond two standard errors of the mean) were identified and checked in the original recordings to determine the source of the problem (detection error, artefact, extrasystole, etc.) and the corresponding measurements were corrected or excluded from the analysis, as appropriate. Recording artefacts led to the exclusion of proportions of recordings (on average, 1.07 runs per subject). Then, the variation between inter-beat intervals, expressed as Δn = IBIₙ - IBI₀, were averaged separately for each sound/tone, such that the average values of Δn were obtained for the first 11 IBIs for each of the five sounds/tones.

## 2.5. Statistical analysis at the group level

Differences in heart rate responses to negative and neutral sounds were compared by two-way independent analysis of variance (ANOVA) for both groups. The two independent variables were the Time (after sound onset, measured in IBIs, with 11 levels correspond to the 11 IBIs, from 0 to 10) and the Valence of the sound (aversive or neutral in experiment 1, and test or control sequence in experiment 2) and the dependent variable was the mean value of Δn for each participant. The significance level was set at 5 %. The time window to detect differences between the two conditions was determined using multiple paired t-tests, at a significance level of 5 %, within the healthy group for each Δn.

## 2.6. At the individual level

The largest continuous period with significant differences in IBIs between the two conditions was chosen as a time window. All corresponding IBIs were thus averaged for each individual (called μΔscream and μΔfan for experiment 1, μΔtestsequence and μΔcontrolsequence for experiment 2), yielding a single value per run for each individual. Multiple unpaired Student's t-tests were performed of the difference between μΔscream and μΔfan for experiment 1 (μΔtestsequence and μΔcontrolsequence for experiment 2) for each individual under each condition to determine whether differences would be detectable at the individual level. The difference was assumed to be positive (μΔscream - μΔfan > 0) based on the report that highly arousing and aversive sounds have a greater heart deceleration effect than other sounds (Bradley and Lang, 2000), so one-tailed tests were used. Bonferroni corrections for multiple comparisons were performed. The size of the effect for each individual was quantified using Cohen's d, the difference between μΔscream and μΔfan divided by the pooled standard deviation of the data for the two conditions:

$$d = \frac{\mu_{\Delta scream} - \mu_{\Delta fan}}{\text{pooledSD}}$$
$$ \text{with pooled standard deviation} = \sqrt{\frac{(n_{scream} - 1)s_{scream}^2 + (n_{fan} - 1)s_{fan}^2}{n_{scream} + n_{fan} - 2}} $$

Cohen's d is typically interpreted as indicating a very weak effect at 0.01, a weak effect at 0.20, a moderate effect at 0.50, a strong effect at 0.80 and a very strong effect at 1.20 (Sawilowsky, 2009). This measure provides a single value for each individual.

# 3. Results

## 3.1. Population characteristics

For experiment 1, 14 healthy subjects (six men, mean age ± SD, 35 ± 14 years) and ten brain-injured individuals were included (four men, mean age ± SD, 34 ± 13 years), including six individuals with DoC. For experiment 2, eleven healthy subjects and 3 brain-injured individuals were included. Their characteristics are summarised in Table 1.

## 3.2. Heart rate

### 3.2.1. Experiment 1

#### 3.2.1.1. At the group level.
In healthy subjects (N = 14, Fig. 3A), heart rate was affected by Time ($F_{2,45,31.8} = 3.74, p = 0.027$), with a significant deceleration of the heart after sound presentation, and by sound valence ($F_{1,13} = 10.18, p = 0.007$), with more pronounced deceleration after the aversive sound, with no significant interaction between the IBI and valence factors ($F_{2,78,36.18} = 2.1, p = 0.12$). Healthy participants' heart rate slowed down on hearing any type of sound, with IBIs increasing on average by 20.4 ± 2.35 ms over baseline for the aversive sound and by 1.11 ± 1.80 ms for the neutral sound. Averaged over all 10 IBIs, the aversive sound was associated with a greater decrease in heart rate (difference between mean decreases, 19.27 ms).

Among healthy subjects, the differences to the responses to aversive and neutral sounds were significant for IBIs 2, 3, 4, 5, 6, 7 and 10 (Fig. 3A; for IBI 2, mean difference, 13.76 ms, $t_{13} = 3.45, p = 0.004$; for IBI 3, 24.23 ms, $t_{13} = 4.19, p = 0.001$; for IBI 4, 23.26 ms, $t_{13} = 2.25, p = 0.025$; for IBI 5, 26.13 ms, $t_{13} = 2.77, p = 0.016$; for IBI 6, 22.00 ms, $t_{13} = 2.21, p = 0.046$; for IBI 7, 22.98 ms, $t_{13} = 2.29, p = 0.04$; for IBI 10, 18.99 ms, $t_{13} = 2.68, p = 0.019$). The average difference over continuous intervals IBI 2 to 7 for each condition was therefore chosen (i.e.: $\mu_{\Delta scream}$ and $\mu_{\Delta fan}$) for the analysis at the individual level.

In brain-injured individuals (N = 10, Fig. 3B), heart rate did not change over Time ($F_{10,90} = 0.75, p = 0.68$) and was not affected by sound valence ($F_{1,9} = 0.065, p = 0.80$), with no significant interaction between the IBI and sound valence factors ($F_{10,90} = 0.62, p = 0.79$). The mean $\Delta_n$ value over all runs was 9.64 ± 1.27 ms for the aversive sound and 6.36 ± 0.82 ms for the neutral sound, and the differences between these values was not significant (the difference between mean decreases, 3.28 ms). The difference in the responses to the two conditions was

<figure><img src="image_5.png" /></figure>

Descriptors for magnitudes of Cohen's d, according to Cohen and Sawilowsky:
Very small: 0.01; Small: 0.20; Medium: 0.50; Large: 0.80; Very large: 1.20; Huge: 2.0.

<caption>**Fig. 3.** (A) and (B): Variations in the inter-beat intervals (IBIs) of (A) healthy subjects and (B) brain-injured patients in response to hearing a woman's scream (negative valence, red line) or a fan (neutral valence, grey line). IBI = inter-beat interval; error bars represent the standard error of the mean. IBI lengths were measured as differences relative to the length of $IBI_0$, the IBI synchronous with the triggering sound. Statistically significant results (paired t-test, $p < 0.05$) are marked with an asterisk (*) (C) and (D): Effect size (Cohen's d) of the differences in average responses to the scream and fan sounds for (C) healthy subjects and (D) brain-injured patients. Statistically significant results (unpaired t-test, $p < 0.05$) are marked with an asterisk (*). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</caption>
negligible for all IBIs (all *p* > 0.05).

Statistical comparison between the positive and neutral sound showed no significant differences in IBI at the group level in either healthy subjects ($F_{1,13} = 0.683, p = 0.423$) or brain-injured individuals ($F_{1,9} = 0.344, p = 0.572$), and the difference in the responses to the two conditions was negligible for all IBIs (all *p* > 0.05) at any individual IBI timepoint.

### 3.2.1.2. At the individual level.
In healthy subjects (N = 14), the average difference over IBIs 2-7 differed significantly between the aversive and neutral condition in three individuals only: subject H04 (mean difference, 54.8 ms, Cohen's *d* = 1.42, magnitude large, $t_{12.5} = 3.02, p = 0.010$), subject H06 (mean difference, 39.63 ms, *d* = 1.03, magnitude large, $t_{16.5} = 2.41, p = 0.028$) and subject H01 (mean difference, 89.41 ms, *d* = 1.02, magnitude large, $t_{15.61} = 2.31, p = 0.035$). In brain-injured individuals (N = 10), the average difference over intervals 2 to 7 differed significantly between the aversive and neutral condition for one individual: subject P03 (mean difference, 16.08 ms, *d* = 1.31, magnitude large, $t_{14,76} = 2.89, p = 0.011$). The corresponding effect size was very large for subject H04 and subject P03 (Cohen's *d* > 1.2; Fig. 3C and D) and large for subjects H06 and H01 (Cohen's *d* > 0.8; Fig. 3C).

### 3.2.2. Experiment 2

#### 3.2.2.1. At the group level.
Among healthy subjects (N = 11, Fig. 4A),

heart rate deceleration was not significantly associated with the IBI factor ($F_{1,66,16.60} = 0.75, p = 0.46$) or with the sound valence factor ($F_{1,10} = 0.033, p = 0.86$), with no significant interaction between the two factors ($F_{2,62,26.23} = 0.482, p = 0.67$). There were no significant differences between the test and control sequences for any of the IBIs (*p* > 0.99 for all paired t-tests). Among brain-injured individuals likewise (N = 3, Fig. 4B) heart rate deceleration was not significantly associated with the IBI factor ($F_{10,20} = 0.50, p = 0.87$) or with the valence factor ($F_{1,2} = 1.86, p = 0.31$) with no significant interaction between the two factors ($F_{10,20} = 0.98, p = 0.49$). None of the differences between responses to the test and control sequences were significant (paired t-tests *p* > 0.05 for all IBIs).

#### 3.2.2.2. At the individual level.
As in experiment 1, the effect size of the difference in mean heart rate between the control and test conditions was quantified for each individual using Cohen's *d* statistic and evaluated with significance tests.

Among healthy subjects (N = 11, Fig. 4C), the average difference over intervals 2 to 7 differed significantly between the test and control sequences for just one healthy subject H08 with a moderate negative effect size (mean difference, 15.2 ms, *d* = -0.66, $t_{75.2} = -3.02, p = 0.034$). Among brain-injured individuals (N = 3, Fig. 4D), none of the differences in responses were significant at the individual level (*p* > 0.05 for all t-tests).

## Experiment 2

### Group Level:

#### Heart rate during trace-conditioning paradigm

<figure><img src="image_6.png" /></figure>

### Individual Level:

#### Effect size of selected time window (Test condition – Control condition, from IBI 2 to 7)

<figure><img src="image_7.png" /></figure>

Descriptors for magnitudes of Cohen's *d*, according to Cohen and Sawloawsky: Very small: 0.01; Small: 0.20; Medium: 0.50; Large: 0.80; Very large: 1.20; Huge: 2.0.

**Fig. 4.** (A) and (B): Variations in the inter-beat intervals (IBIs) of healthy subjects (A) and brain-injured patients (B) in response to the test stimulus (yellow line) or the control stimulus (green line). IBI = inter-beat interval, error bars represent the standard error of the mean. IBI lengths were measured as differences relative to the length of $IBI_0$, the IBI synchronous with the triggering sound. (C) and (D): Effect size (Cohen's *d*) of the differences in average responses to the test and control stimuli. Statistically significant results (unpaired *t*-test, *p* < 0.05) are marked with an asterisk (*). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
## 4. Discussion

The aim of this study was to determine whether hearing an aversive sound is associated with a specific cardiac response in brain-injured individuals, to facilitate diagnosis of consciousness in individuals with DoC. We used a conditioning paradigm to assess for a learning ability supposedly mediated by consciousness. Subjects (healthy controls and brain injured individuals) were therefore repeatedly exposed to neutral, positive, and negative valence sounds from an international database (Bradley and Lang, 2007) and two distinctive tones, either in an unconditioned manner (experiment 1) or with trace-conditioning (Bekinschtein et al., 2009) (experiment 2) and the corresponding variations in heart rate were monitored. In experiment 1, the aversive sound was associated with significantly a greater decrease in heart rate than the neutral sound from the second to the seventh IBI in healthy subjects but not in brain-injured individuals. At the individual level, this difference was significant in 21 % of healthy subjects (3/14) and in 14 % of brain-injured individuals (1/7). In experiment 2, there was no evidence of trace-conditioning either in brain-injured individuals or healthy subjects.

The main result of our study (+23.64 ms heart rate deceleration on hearing an aversive rather than a neutral sound) is consistent with Bradley and Lang's (Bradley and Lang, 2000) findings of a -2.17 bpm (equivalent to +27 ms deceleration) difference in the responses to a female scream (aversive sound) and a cough (neutral sound). As in our study (Fig. 3A), Bradley and Lang also observed a triphasic heart rate response to emotional sounds at the group level, regardless of the valence of the sound (Bradley and Lang, 2000): an initial deceleration followed by an acceleration, and finally a second deceleration. This difference was also found at the individual level but was only significant in a small proportion of subjects (Fig. 3C and D). Only one of the seven brain-injured individuals in our study (Subject P03, classified as MCS) showed a significant increase in cardiac deceleration in response to the aversive sound (Fig. 3D). At the time of the study, this individual had a CRS-R scale score of 9/23 to 14/23 and was classified as MCS (with eye-following, localisation of nociceptive stimulus). The presence of an emotional response could therefore have a prognostic relevance. Moreover, a dysregulation of the autonomic nervous system after severe brain injury is frequent after a coma, for example with paroxysmal sympathetic hyperactivity (Meyfroidt et al., 2017). In our study, subject P03's cardiac deceleration was one of the largest among all participants, and we hypothesised that cardiac hypersensitivity to emotional stimulus might also reflect a disinhibited cardiac response in brain-injured individuals with overt consciousness, in a similar manner than abolition of habituation of auditory startle reflex (Hermann et al., 2020). Although our primary hypothesis focused on heart rate deceleration in response to aversive sounds, we also recorded responses to a positively valenced stimulus (samba music), used as a supplementary, emotionally non-threatening control. No significant differences between the positive and neutral sounds were observed, and these findings are consistent with previous literature, which reports stronger cardiac deceleration only in response to aversive stimuli compared to pleasant or neutral ones (Bradley and Lang, 2000). Crucially, the heart rate deceleration observed in response to aversive auditory stimuli in experiment 1 should not be interpreted as evidence of conscious processing. Rather, this response reflects the functional integrity of central autonomic pathways involved in the processing of emotionally salient stimuli. Although such autonomic modulation might occur independently of conscious awareness, its presence indicates preserved sensory-affective integration and brain-body coupling. These findings underscore the importance of assessing emotional reactivity in individuals with DoC, not only as a proxy for awareness, but also as a complementary dimension of residual brain function that may inform clinical evaluation alongside behavioural and neurophysiological markers of consciousness.

For experiment 2, the absence of a significant response could be due to the small size of the study (as addressed above) or to the limitations of

the study paradigm. The length of the trace interval (500 ms) between the neutral stimulus (high-pitched or low-pitched tone) and the conditioning stimulus (an aversive sound) (Fig. 2) was based on Bekinschtein et al.'s trace-conditioning paradigm (Bekinschtein et al., 2009; Clark et al., 2002), which involved electromyography. Heart rates are indeed more challenging to measure. Contrary to the continuous measure produced by electromyography, heart rate is the average of a series of discrete measurements, meaning that any response can only be assessed after at least one IBI (around 750 ms) making a rapidly changing phenomenon difficult to detect. While spectral analysis is a widely recognised approach to heart rate variability (HRV), it represents only one category among several. Time-domain methods, which analyse the variability of inter-beat intervals over time, are equally established and offer valuable insights into autonomic dynamics. In the present study, the analysis focused on beat-to-beat variability relative to a pre-stimulus reference IBI (IBIo), which is a valid temporal HRV measure. This method captures short-term fluctuations in cardiac rhythm and is particularly suited to contexts where temporal dynamics are of primary interest. While spectral HRV analysis may offer additional precision and has been used in prior studies involving patients with disorders of consciousness (Riganello et al., 2019a, 2018, 2010), it requires high-quality, artefact-free recordings. Due to signal quality limitations in some early inclusions, spectral analysis was not feasible in our dataset. Future studies may benefit from incorporating spectral HRV measures, particularly when investigating the autonomic effects of emotional auditory stimuli (Juan et al., 2016; Quintana et al., 2012). Moreover, we measured in our study frequency heart rate variability, while spectral heart rate variability (HRV) assessment is reportedly more precise, as used in several studies of DoC individuals but requires better recording quality. Artefacts in recordings limited our ability to perform spectral HRV in the first inclusions. It may be beneficial in future studies of the effects of emotional sounds on heart rate to measure spectral rather than frequency variability. Whether the trace-conditioning paradigm requires awareness has recently been questioned, with the observation of successful trace-conditioning during coma (Juan et al., 2016) and deep sleep (Arzi et al., 2012; Züst et al., 2019). For the interpretation of our results, however, the question is moot because no response was detected using this paradigm even in healthy subjects who were aware of the condition process. Nonetheless, the presence of a trace-conditioned response, if observed, may still indicate preserved neurological pathways and learning capacity, which are relevant to assessing the level of brain function in individuals with DoC. Similarly, the preservation of an unconscious learning ability, such as the habituation to startle reflex, is relevant to distinguish MCS from UWS (Hermann et al., 2020).

The use of generic sounds from an international database (Bradley and Lang, 2007) improves reproducibility compared with personalised sounds, which are more meaningful for subjects (a favourite song (Luauté et al., 2018), the individual's own first name (Di et al., 2007; Staffen et al., 2006), etc.) but are difficult to assess and reproduce. Bradley and Lang found that discrimination between valences was only possible for highly arousing sounds, with aversive sounds associated with greater initial deceleration than neutral sounds (-1.36 bpm and -0.44 bpm respectively), but there was no such difference between responses to pleasant and neutral sounds (Bradley and Lang, 2000). A highly arousing sound being considered most likely to provoke a distinguishable cardiac response, a female scream therefore seemed an appropriate choice for our study. Autonomic nervous system responses to acoustic stimuli have been shown to vary with the complexity and dynamics of music, even in patients with DoC. Riganello et al. (2015) demonstrated that musical pieces differing in formal complexity and general dynamics induced distinct patterns of heart rate variability parameters in VS/UWS patients, with higher complexity correlating with reduced autonomic reactivity. These findings suggest that the internal structure of auditory stimuli, beyond their emotional valence, plays a critical role in modulating autonomic responses and could inform the design of stimulation protocols for consciousness assessment.
Recent work has shown that auditory stimulation not only elicits traditional event-related potentials (ERP) but also modulates heartbeat-evoked responses (HER), reflecting brain-heart interactions associated with perceptual consciousness. In particular, *Candia-Rivera et al.* (2023) demonstrated that the processing of auditory irregularities in the local-global paradigm induced distinct HER in MCS compared to UWS patients, suggesting that HER may serve as an independent marker of conscious access (*Candia-Rivera et al.*, 2023). This complements traditional behavioural and electrophysiological assessments and supports the relevance of examining both direct auditory responses and visceral processing in disorders of consciousness.

A major limitation of this study is its small size. Large cohorts of DoC individuals are indeed difficult to assemble in single centre studies given the rarity of the condition and the often rapid improvement of symptoms. The inclusion of individuals without disorders of consciousness among brain-injured participants, although reducing the sample's homogeneity, was designed to provide a comparative reference group. Given the limited sample size and heterogeneity, the generalisability of the findings is reduced, which supports the exploratory nature of the present study. A significant effect with a moderate effect size was nonetheless detected between the aversive and the neutral sound for healthy participants, the difference of around 20 ms between heart rate responses corresponding to roughly two beats per minute. Increasing the number of trials would have increased the statistical power and thus the likelihood of detecting this relatively small difference. However, it would have increased the length of the experiments and would have made it difficult to perform in individuals with DoC who have a variable arousal. The interval between trial runs of 10 s was sufficient at the group level to ensure heart rates returned to baseline (Fig. 3A), but a longer interval would have been beneficial in reducing the likelihood of interfering with the next trial in some individuals with a longer return to baseline. Moreover, since the heart rate after sound stimulation was compared to the IBI synchronous with the sound, a more reliable baseline reference for each run could have been obtained by using the mean of several IBIs over a longer inter-run interval.

Baseline heart rate could also be influenced by autonomic dysfunctions such as paroxysmal sympathetic hyperactivity (Meyfroidt et al., 2017), or medication such as betablockers, which are frequently used in that context.

Regarding medication, in our cohort, four subjects were under beta-blocker treatment during the study: P03 received atenolol 100 mg/day, while P07, P09, and P10 received propranolol at daily doses of 100 mg, 120 mg, and 80 mg, respectively. Remarkably, the subject P03 showed a difference of HR between aversive and neutral condition. Hence, the presence of betablockers alone does not exclude the possibility of a heart rate modulation induced by an emotional stimulation.

Regarding the baseline heart rate, the mean heart rates across the whole trial (±SD) were: 76.57 ± 9.86 bpm for the aversive sound, 76.42 ± 8.88 bpm for the positive sound, and 76.06 ± 9.53 bpm for the neutral sound. A repeated-measures ANOVA showed no significant differences in mean heart rate between aversive and neutral conditions ($F_{1,13} = 0.31, p = 0.58$). These findings suggest that the observed beat-by-beat differences in inter-beat intervals are not attributable to baseline HRV or general heart rate shifts, but rather reflect condition-specific modulations. This supports the validity of our time-domain HRV approach in capturing subtle autonomic responses to emotional auditory stimuli.

To address the identified limitations, future research should adopt a multimodal approach combining not only cardiac recordings but also complementary neurophysiological measures such as EEG, heartbeat-evoked responses (HER), or galvanic skin response, to improve the specificity and interpretability of autonomic markers. Given the potential for autonomic dysregulation in brain-injured individuals, a baseline characterisation of autonomic function should be first assessed and systematically included. Early inclusion in the subacute phase may improve homogeneity and data quality, and multicentre collaboration will be crucial to reach adequate sample sizes and stratify patients more effectively across diagnostic categories.

In addition to technical difficulties in detecting physiological responses, the clinical interpretation of our results is limited by conceptual pitfalls. In healthy subjects, emotional experience (affect) can be confirmed by verbal or non-verbal reports. In individuals unable to report, this approach, however, poses a conceptual problem at the heart of current debates in the study of emotion and consciousness (Adolphs, 2010). Indeed, consciousness is defined by the existence of an inner mental life and subjective experience from a first-point perspective, and is not necessarily expressed by behaviour (Block, 2005; Tsuchiya and Adolphs, 2007). Furthermore, the role of consciousness in emotion is still debated, since there is no consensus definition of what an emotion is (Adolphs, 2010). Classically, the *emotional feeling* (Damasio, 1999) (i.e., the conscious experience of emotion) is distinguished from the *emotional state* (Lambie and Marcel, 2002) (i.e., the behavioural, physiological and cognitive consequences of emotion). These two features can be observed separately: *emotional behaviour* without *emotional feeling* in pathological laughter or crying (Parvizi et al., 2001) or in the unconscious amygdala response to subliminal fear signals in fMRI (Vuilleumier et al., 2004). Their time ordering and interactions of these aspects of emotion are debated. Some authors argue that emotion is a specific response of the body to an external stimulus identifiable by the individual: 'I am sad, therefore I cry' (Ekman, n.d.; Adolphs, 2010). According to this interpretation, consciousness plays a central role in emotion. For other authors, emotion is simply an attempt to interpret non-specific bodily reactions (Barrett and Satpute, 2019), making *affect* as a property of consciousness, distinct from *emotion*. If emotions are not reactions to the environment but rather predictions pre-corrected by sensory signals, these predictive signals may act as post-hoc explanations for the causes of the sensations (Barrett and Satpute, 2019) (e.g. 'I am crying, but why? I must be sad'). Separating these two interpretations is difficult with the current state of the art. The two models may be complementary, and the mechanisms may be interdependent and both contribute at different points in time (Tsuchiya and Adolphs, 2007). An additional argument for the co-dependency of consciousness and emotion is the partial overlap between the two functions: conscious access seems important in emotion, and the brain structures involved in emotion (including the brainstem, basal ganglia and medial cortex) partially overlap with those regulating levels of consciousness (Tsuchiya and Adolphs, 2007). Studying the physiological responses of organs to specific emotional stimuli therefore seems a relevant approach to better understand their relationship with consciousness and their sensitivity in DoC individuals. A better understanding of cardiac response to affective sounds in DoC individuals, either passively or with conditioning would be helpful to determine whether and to what extent this simple and accessible measure can improve the detection of emotional experience and behavioural phenotyping in brain-injured individuals. Given the ethical stakes in assessing consciousness in brain-injured individuals, physiological markers such as heart rate deceleration should be considered as complementary indicators, interpreted with caution and always integrated within a multimodal diagnostic framework. The misinterpretation of physiological signs as definitive proof of emotional experience or awareness may lead to premature or inappropriate clinical decisions. Therefore, any observed response must be contextualised, replicated, and corroborated by converging evidence. Moreover, just like nociceptive perception which could be assessed independently of subjective feeling of pain or suffering, to detect an emotion independently from cues of awareness is still relevant to guide the care, prognosis, and ethical support of individuals with DoC.

## 5. Contributions

J.L. and A.D. conceptualized the study. Methodology was developed by H.A., A.D. and C.T.-B. Data collection was carried out by A.D., H.A. Data analysis was performed by H.A., C.T.-B., K.-M.N., and A.D. All authors contributed to writing the original draft and participated in
reviewing and editing the manuscript. Supervision, project administration and funding acquisition was secured by J.L. and C.T.-B.

Financial support provided by the Fondation des Gueules Cassées (France). This work was supported by FrontCog grant number ANR-17-EURE-0017. The authors declare they have no competing interests. The proofreading of this article was supported by *Bibliothèque Scientifique de l'Internat de Lyon* and the *Hospices Civils de Lyon* (HCL). Study registration: ClinicalTrial.gov NCT03706170.

## Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## References

Adolphs, R., 2010. Emotion. Curr. Biol. 20, R549-R552. https://doi.org/10.1016/j.cub.2010.05.046.

Ahrens, L.M., Pauli, P., Reif, A., Mühlberger, A., Langs, G., Aalderink, T., et al., 2016. Fear conditioning and stimulus generalization in patients with social anxiety disorder. J. Anxiety Disord. 44, 36-46. https://doi.org/10.1016/j.janxdis.2016.10.003.

Anikin, A., Bååth, R., Persson, T., 2018. Human non-linguistic vocal repertoire: call types and their meaning. J. Nonverbal Behav. 42, 53-80. https://doi.org/10.1007/s10919-017-0267-y.

Arnal, L.H., Flinker, A., Kleinschmidt, A., Giraud, A.-L., Poeppel, D., 2015. Human screams occupy a privileged niche in the communication soundscape. Curr. Biol. 25, 2051-2056. https://doi.org/10.1016/j.cub.2015.06.043.

Arzi, A., Shedlesky, L., Ben-Shaul, M., Nasser, K., Oksenberg, A., Hairston, I.S., et al., 2012. Humans can learn new information during sleep. Nat. Neurosci. 15, 1460-1465. https://doi.org/10.1038/nn.3193.

Barami, K., 2024. Confounding factors impacting the Glasgow coma score: a literature review. Neurol. Res. 46, 479-486. https://doi.org/10.1080/01616412.2024.2329860.

Barrett, L.F., Satpache, A.B., 2019. Historical pitfalls and new directions in the neuroscience of emotion. Neurosci. Lett. 693, 9-18. https://doi.org/10.1016/j.neulet.2017.07.045.

Beaurenaut, M., Tokarski, E., Dezecache, G., Grèzes, J., 2020. The ‘Threat of Scream’ paradigm: a tool for studying sustained physiological and subjective anxiety. Sci. Rep. 10, 12496. https://doi.org/10.1038/s41598-020-68889-0.

Bekinschtein, T.A., Shalom, D.E., Forcato, C., Herrera, M., Coleman, M.R., Manes, F.F., et al., 2009. Classical conditioning in the vegetative and minimally conscious state. Nat. Neurosci. 12, 1343-1349. https://doi.org/10.1038/nn.2391.

Block, N., 2005. Two neural correlates of consciousness. Trends Cogn. Sci. 9, 46-52. https://doi.org/10.1016/j.tics.2004.12.006.

Bradley MM, Lang PJ. The International Affective Digitized Sounds (IADS-2): Affective ratings of sounds and instruction manual. University of Florida, Gainesville, FL, Tech Rep B-3 2007.

Bradley, M.M., Lang, P.J., 2000. Affective reactions to acoustic stimuli. Psychophysiology 37, 204-215.

Candia-Rivera, D., Raimondo, F., Pérez, P., Naccache, L., Tallon-Baudry, C., Sitt, J.D., 2023. Conscious processing of global and local auditory irregularities causes differentiated heartbeat-evoked responses. Elife 12, e75352. https://doi.org/10.7554/eLife.75352.

Clark, R.E., Manns, J.R., Squire, L.R., 2002. Classical conditioning, awareness, and brain systems. Trends Cogn. Sci. 6, 524-531. https://doi.org/10.1016/S1364-6613(02)02041-7.

Cortese, D., Riganello, F., Arcuri, F., Lucca, L., Tonin, P., Schnakers, C., et al., 2020. The trace conditional learning of the noxious stimulus in UWS patients and its prognostic value in a GSR and HRV entropy study. Front. Hum. Neurosci. 14, 97. https://doi.org/10.3389/fnhum.2020.00097.

Damasio, A.R., 1999. Sentiment même de soi (Le: Corps, émotions, conscience. Odile Jacob.

Dehaene S. *Définitions et premiers paradoxes* 2011. https://www.college-de-france.fr/site/stanislas-dehaene/course-2011-01-04-09h30.htm (accessed November 29, 2020).

Dehaene S, Naccache L, Le Clec'H G, Koechlin E, Mueller M, Dehaene-Lambertz G, et al. Imaging unconscious semantic priming. Nature 1998;395:597-600. 10.1038/26967.

Den, M.L., Graham, B.M., Newall, C., Richardson, R., 2015. Teens that fear screams: a comparison of fear conditioning, extinction, and reinstatement in adolescents and adults. Dev. Psychobiol. 57, 818-832. https://doi.org/10.1002/dev.21330.

Di, H.B., Yu, S.M., Weng, X.C., Laureys, S., Yu, D., Li, J.Q., et al., 2007. Cerebral response to patient's own name in the vegetative and minimally conscious states. Neurology 68, 895-899. https://doi.org/10.1212/01.wnl.0000258544.79024.d0.

Ekman P. *Telling Lies: Clues to Deceit* in the Marketplace, Politics, and Marriage n.d.: 368.

Engelen, T., Buot, A., Grèzes, J., Tallon-Baudry, C., 2023. Whose emotion is it? Perspective matters to understand brain-body interactions in emotions. Neuroimage 268, 119867. https://doi.org/10.1016/j.neuroimage.2023.119867.

Giacino, J.T., Ashwal, S., Childs, N., Cranford, R., Jennett, B., Katz, D.I., et al., 2002. The minimally conscious state: definition and diagnostic criteria. Neurology 58, 349-353. https://doi.org/10.1212/wnl.58.3.349.

Giacino, J.T., Kalmar, K., Whyte, J., 2004. The JFK Coma Recovery Scale-revised: Measurement characteristics and diagnostic utility 11No commercial party having a direct financial interest in the results of the research supporting this article has or will confer a benefit upon the authors or upon any organization with which the authors are associated. Arch. Phys. Med. Rehabil. 85, 2020-2029. https://doi.org/10.1016/j.apmr.2004.02.033.

Glenn, C.R., Lieberman, L., Hajcak, G., 2012. Comparing electric shock and a fearful screaming face as unconditioned stimuli for fear learning. Int. J. Psychophysiol. 86, 214-219. https://doi.org/10.1016/j.ijpsycho.2012.09.006.

Haddad, A.D.M., Xu, M., Raeder, S., Lau, J.Y.F., 2013. Measuring the role of conditioning and stimulus generalisation in common fears and worries. Cogn. Emot. 27, 914-922. https://doi.org/10.1080/02699931.2012.747428.

Hermann, B., Salah, A.B., Perlbarg, V., Valente, M., Pyatigorskaya, N., Habert, M.-O., et al., 2020. Habituation of auditory startle reflex is a new sign of minimally conscious state. Brain 143, 2154-2172. https://doi.org/10.1093/brain/awa159.

Jennett, B., Plum, F., 1972. Persistent vegetative state after brain damage. a syndrome in search of a name. Lancet 1, 734-737. https://doi.org/10.1016/s0140-6736(72)90242-5.

Juan, E., Nguepno Nguissi, N.A., Tzovara, A., Viceic, D., Rusca, M., Oddo, M., et al., 2016. Evidence of trace conditioning in comatose patients revealed by the reactivation of EEG responses to alerting sounds. Neuroimage 141, 530-541. https://doi.org/10.1016/j.neuroimage.2016.07.039.

Kondziella, D., Bender, A., Diserens, K., van Erp, W., Estraneo, A., Formisano, R., et al., 2020. European Academy of Neurology guideline on the diagnosis of coma and other disorders of consciousness. Eur. J. Neurol. 27, 741-756. https://doi.org/10.1111/ene.14151.

Lacey, B.C., Lacey, J.I., 1978. Two-way communication between the heart and the brain. Significance of time within the cardiac cycle. Am. Psychol. 33, 99-113. https://doi.org/10.1037/0003-066x.33.2.99.

Lambie, J.A., Marcel, A.J., 2002. Consciousness and the varieties of emotion experience: a theoretical framework. Psychol. Rev. 109, 219-259. https://doi.org/10.1037/0033-295x.109.2.219.

Lau, J.Y., Britton, J.C., Nelson, E.E., Angold, A., Ernst, M., Goldwin, M., et al., 2011. Distinct neural signatures of threat learning in adolescents and adults. PNAS 108, 4500-4505. https://doi.org/10.1073/pnas.1005494108.

Lau, J.Y.F., Lissek, S., Nelson, E.E., Lee, Y., Roberson-Nay, R., Poeth, K., et al., 2008. Fear conditioning in adolescents with anxiety disorders: results from a novel experimental paradigm. J. Am. Acad. Child Adolesc. Psychiatry 47, 94-102. https://doi.org/10.1097/chi.0b01e31815a5f01.

Laureys, S., Celesia, G.G., Cohadon, F., Lavrijsen, J., León-Carrión, J., Sannita, W.G., et al., 2010. Unresponsive wakefulness syndrome: a new name for the vegetative state or apallic syndrome. BMC Med. 8, 68. https://doi.org/10.1186/1741-7015-8-68.

Luauté, J., Dubois, A., Heine, L., Guironnet, C., Juliat, A., Gaveau, V., et al., 2018. Electrodermal reactivity to emotional stimuli in healthy subjects and patients with disorders of consciousness. Ann. Phys. Rehabil. Med. 61, 401-406. https://doi.org/10.1016/j.rehab.2018.04.007.

Luauté, J., Maucort-Boulch, D., Tell, L., Quelard, F., Sarraf, T., Iwaz, J., et al., 2010. Long-term outcomes of chronic minimally conscious and vegetative states. Neurology 75, 246-252. https://doi.org/10.1212/WNL.0b013e3181e8e8df.

Measuring emotion: The self-assessment manikin and the semantic differential. J. Behav. Ther. Exp. Psychiatry 1994;25:49-59. 10.1016/0005-7916(94)90063-9.

Meyfroidt, G., Baguley, L.J., Menon, D.K., 2017. Paroxysmal sympathetic hyperactivity: the storm after acute brain injury. Lancet Neurol. 16, 721-729. https://doi.org/10.1016/S1474-4422(17)30259-4.

Naccache, L., 2018. Minimally conscious state or cortically mediated state? Brain 141, 949-960. https://doi.org/10.1093/brain/awx324.

Naccache, L., Dehaene, S., 2001. Unconscious semantic priming extends to novel unseen stimuli. Cognition 80, 215-229. https://doi.org/10.1016/S0010-0277(00)00139-6.

Parvizi, J., Anderson, S.W., Martin, C.O., Damasio, H., Damasio, A.R., 2001. Pathological laughter and crying: a link to the cerebellum. Brain 124, 1708-1719. https://doi.org/10.1093/brain/124.9.1708.

Plews, D.J., Scott, B., Altini, M., Wood, M., Kilding, A.E., Laursen, P.B., 2017. Comparison of heart-rate-variability recording with smartphone photoplethysmography, polar H7 chest strap, and electrocardiography. Int. J. Sports Physiology. Perform. 12, 1324-1328. https://doi.org/10.1123/ijspp.2016-0668.

Plum, F., Posner, J.B., 1972. The diagnosis of stupor and coma. Contemp. Neurol. Ser. 10, 1-286.

Quintana, D.S., Guastella, A.J., Outhred, T., Hickie, I.B., Kemp, A.H., 2012. Heart rate variability is associated with emotion recognition: direct evidence for a relationship between the autonomic nervous system and social cognition. Int. J. Psychophysiol. 86, 168-172. https://doi.org/10.1016/j.ijpsycho.2012.08.012.

Raimondo, F., Rohaut, B., Demertzi, A., Valente, M., Engemann, D.A., Salti, M., et al., 2017. Brain-heart interactions reveal consciousness in noncommunicating patients. Ann. Neurol. 82, 578-591. https://doi.org/10.1002/ana.25045.

Ramírez, I., Guerra, P., Muñoz, M.A., Perakakis, P., Anllo-Vento, L., Vila, J., 2010. The dynamics of cardiac defense: from attention to action. Psychophysiology 47, 879-887. https://doi.org/10.1111/j.1469-8986.2010.01008.x.

Riganello, F., Candelieri, A., Quintieri, M., Conforti, D., Dolce, G., 2010. Heart rate variability: an index of brain processing in vegetative state? An artificial intelligence, data mining study. Clin. Neurophysiol. 121, 2024-2034. https://doi.org/10.1016/j.clinph.2010.05.010.
Riganello, F., Chatelle, C., Schnakers, C., Laureys, S., 2019a. Heart rate variability as an indicator of nociceptive pain in disorders of consciousness? J. Pain Symptom Manage. 57, 47–56. https://doi.org/10.1016/j.jpainsymman.2018.09.016.

Riganello, F., Cortese, M.D., Arcuri, F., Quintieri, M., Dolce, G., 2015. How can music influence the autonomic nervous system response in patients with severe disorder of consciousness? Front. Neurosci. 9, 461. https://doi.org/10.3389/fnins.2015.00461.

Riganello, F., Larroque, S.K., Bahri, M.A., Heine, L., Martial, C., Carrière, M., et al., 2018. A heartbeat away from consciousness: heart rate variability entropy can discriminate disorders of consciousness and is correlated with resting-state fMRI brain connectivity of the central autonomic network. Front. Neurol. 9, 769. https://doi.org/10.3389/fneur.2018.00769.

Riganello, F., Larroque, S.K., Di Perri, C., Prada, V., Sannita, W.G., Laureys, S., 2019b. Measures of CNS-autonomic interaction and responsiveness in disorder of consciousness. Front. Neurosci. 13, 530. https://doi.org/10.3389/fnins.2019.00530.

Sanders, M.D., ElizabethK, W., Marshall, J., Wieskrantz, L., 1974. "BLINDSIGHT": vision in a field defect. Lancet 303, 707–708. https://doi.org/10.1016/S0140-6736(74) 92907-9.

Sawilowsky, S.S., 2009. New effect size rules of thumb. J. Mod. App. Stat. Meth. 8 (597–9). https://doi.org/10.22237/jmasm/1257035100.

Schäfer, A., Vagedes, J., 2013. How accurate is pulse rate variability as an estimate of heart rate variability?: A review on studies comparing photoplethysmographic technology with an electrocardiogram. Int. J. Cardiol. 166, 15–29. https://doi.org/10.1016/j.ijcard.2012.03.119.

Staffen, W., Kronbichler, M., Aichhorn, M., Mair, A., Ladurner, G., 2006. Selective brain activity in response to one's own name in the persistent vegetative state. J. Neurol. Neurosurg. Psychiatry 77, 1383–1384. https://doi.org/10.1136/jnnp.2006.095166.

Tsuchiya, N., Adolphs, R., 2007. Emotion and consciousness. Trends Cogn. Sci. (Regul. Ed.) 11, 158–167. https://doi.org/10.1016/j.tics.2007.01.005.

Vuilleumier, P., Richardson, M.P., Armony, J.L., Driver, J., Dolan, R.J., 2004. Distant influences of amygdala lesion on visual cortical activation during emotional face processing. Nat. Neurosci. 7, 1271–1278. https://doi.org/10.1038/nn1341.

Weiskrantz, L., 1990. A Case Study and Implications. Oxford University Press, Oxford, 10.1093/acprof:oso/9780198521921.001.0001.

Whalen, P.J., Rauch, S.L., Etcoff, N.L., McInerney, S.C., Lee, M.B., Jenike, M.A., 1998. Masked presentations of emotional facial expressions modulate amygdala activity without explicit knowledge. J. Neurosci. 18, 411–418. https://doi.org/10.1523/JNEUROSCI.18-01-00411.1998.

Yang, W., Makita, K., Nakao, T., Kanayama, N., Machizawa, M.G., Sasaoka, T., et al., 2018. Affective auditory stimulus database: an expanded version of the International Affective Digitized sounds (IADS-E). Behav. Res. 50, 1415–1429. https://doi.org/10.3758/s13428-018-1027-6.

Züst, M.A., Ruch, S., Wiest, R., Henke, K., 2019. Implicit vocabulary learning during sleep is bound to slow-wave peaks. Curr. Biol. 29, 541–553.e7. https://doi.org/10.1016/j.cub.2018.12.038.