# The interplay between sleep and neural respiratory drive in COPD: contribution of semi-automated analysis of long duration recordings

Léo Grassion, Maxime Patout, Nicolas Wattiez, Stefania Redolfi, Isabelle Arnulf, Thomas Similowski, Thomas Andrillon, Jésus Gonzalez-Bermejo

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Léo Grassion, Maxime Patout, Nicolas Wattiez, Stefania Redolfi, Isabelle Arnulf, et al.. The interplay between sleep and neural respiratory drive in COPD: contribution of semi-automated analysis of long duration recordings. NeuroImage, 2025, 320, pp.121445. 10.1016/j.neuroimage.2025.121445. hal-05264959

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# The interplay between sleep and neural respiratory drive in COPD: contribution of semi-automated analysis of long duration recordings

Léo Grassion<sup>a,b,c,●</sup>, Maxime Patout<sup>b,c,●</sup>, Nicolas Wattiez<sup>b,●</sup>, Stefania Redolfi<sup>d</sup>, Isabelle Arnulf<sup>c,e,f</sup>, Thomas Similowski<sup>b,g</sup>, Thomas Andrillon<sup>e,1</sup>, Jésus Gonzalez-Bermejo<sup>b,g,*</sup>

## ARTICLE INFO

### Keywords:

- COPD

- Sleep

- Neural respiratory drive

- EMG

## ABSTRACT

**Background:** Neural respiratory drive (NRD) is a clinically relevant biomarker in patients with chronic obstructive pulmonary disease (COPD). However, its analysis is challenging due to several technical considerations, including the need to obtain a stable recording over a short time period. However, a short recording duration may be inadequate to comprehensively record clinically relevant information, particularly during sleep, because NRD varies across sleep stages and over time. In the present study, we evaluated the outcomes of an automatic analysis of NRD data from electromyography (EMG) recordings of neck inspiratory muscles during sleep in COPD patients.

**Methods:** This retrospective post hoc cohort analysis was based on a previously published study. Respiratory cycles were determined based on inspiratory peaks. The area under the curve (AUC) and peak values of EMG signals from neck inspiratory muscles (EMG<sub>NIM</sub>) were extracted for each respiratory cycle and normalized using awake EMG<sub>NIM</sub>.

**Results:** Polysomnographic data from 26 patients and 9 healthy volunteers were analysed. Contrary to healthy volunteers, COPD patients show a clear EMG<sub>NIM</sub> activity time-locked to inspiration. The normalized EMG<sub>NIM</sub> AUC was lower during N2 (48 % [30–80], *p* < 0.001), N3 (47 % [27–68], *p* < 0.001), and REM (38 % [21–56], *p* < 0.001) sleep stages. Similar results were observed for EMG<sub>NIM</sub> peak. We found that the AUC and peak of EMG<sub>NIM</sub> in all sleep stages were negatively correlated with the total sleep time, sleep efficiency, and N3 sleep duration (min). Coherence analyses show that the EMG<sub>NIM</sub> correlate with EEG activity for COPD patients, even during sleep. This correlation is particularly strong for beta and gamma frequencies and Granger Causality suggests the relationship is driven by brain activity.

**Conclusions:** Automatic analysis of EMG signals is feasible and clinically useful for prolonged recording periods across various vigilance states. Respiratory adaptions to COPD are preserved during sleep and could be cortically-mediated.

## 1. Introduction

Ventilation is an automatic, homeostatic process modulated by central pattern generators located in the brainstem. These generators rely on multiple chemical or mechanical afferents (notably PaCO₂).

Furthermore, variations in ventilatory control can affect the activation of respiratory muscles, particularly in pathological conditions. The assessment of neural respiratory drive (NRD), which indicates the motor output of the central nervous system to respiratory muscles, holds clinical significance. In individuals with chronic obstructive pulmonary

<footnote><sup>a</sup> Service des Maladies Respiratoires, CHU de Haut-Lévèque, Pessac, France</footnote>

<footnote><sup>b</sup> UMRS1158 Neurophysiologie Respiratoire Expérimentale et Clinique, INSERM, Sorbonne Université, Paris, France</footnote>

<footnote><sup>c</sup> Assistance Publique-Hôpitaux de Paris, Hôpital Pitié-Salpetrière, Département R3S, Service de Pathologies du Sommeil, Paris, France</footnote>

<footnote><sup>d</sup> Dipartimento di Scienze Mediche e Sanità Pubblica, Università degli Studi di Cagliari, Cagliari, Italy</footnote>

<footnote><sup>e</sup> Institut du Cerveau - Paris Brain Institute - ICM, INSERM, CNRS, Paris, France</footnote>

<footnote><sup>f</sup> Sorbonne Université, Paris, France</footnote>

<footnote><sup>g</sup> Assistance Publique-Hôpitaux de Paris, Hôpital Pitié-Salpetrière, Département R3S, Paris, France</footnote>

<footnote>* Corresponding author at: Service des Maladies Respiratoire, CHU de Haut-lévèque, Pessac, France.
E-mail address: leo.grassion@chu-bordeaux.fr (J. Gonzalez-Bermejo).</footnote>

<footnote n="1">1 Co last authors.</footnote>
disease (COPD), an increased NRD is associated with dyspnoea (Jolley et al., 2015), increased risks of exacerbations and re-exacerbation (Suh et al., 2015), and poor long-term outcomes (Patout et al., 2019).

The activity of the central generators of respiratory patterns cannot be directly recorded; hence, respiratory muscle electromyography (EMG) is used to indirectly assess the NRD. Diaphragmatic EMG activity remains the gold-standard surrogate marker for NRD (Jolley et al., 2015). However, its acquisition requires the use of an oesophageal probe (Laveneziana et al., 2019), posing challenges for routine use. Consequently, numerous previous studies have evaluated the usefulness of the EMG activity of accessory respiratory muscles obtained using surface EMG (Hug et al., 2011; Patout et al., 2019; Redolfi et al., 2020; Suh et al., 2015).

The findings of a round table meeting that discussed the analysis and applications of respiratory surface EMG were recently reported (Jonkman et al., 2024). The authors highlight the need to obtain a stable recording over a brief period (approximately 30 min). However, NRD can vary considerably throughout the day and particularly during sleep (Domnik et al., 2021), making short recordings potentially inadequate for capturing comprehensive clinical information.

Monitoring the presence or intensity of NRD during sleep is of particular importance because of the way sleep affects respiratory control in healthy volunteers and COPD patients. Sleep can adversely affect respiration by inducing reduced muscle tone (Tabachnik et al., 1981), increased upper airway resistance (Wiegand et al., 1985), and decreased chemosensitivity (Berthon-Jones and Sullivan, 1982; Douglas et al., 1982). It also reduces NRD in healthy subjects, and in COPD patients (Luo et al., 2014).

Yet, sleep is necessary (Franken et al., 2009), which poses a challenge for individuals with COPD. Recently, we demonstrated the presence of an adaptive mechanism in certain COPD patients, sustaining accessory muscle EMG activity continuously throughout the night (Redolfi et al., 2020). However, this protective response is associated with a decline in sleep quality (Redolfi et al., 2020). There is thus a bidirectional relationship between sleep and neural respiratory drive (NRD) in COPD patients: Sleep stages influence NRD, and variations in NRD seem to influence sleep. In our previous work, we implemented a visual and dichotomous classification of NRD, which fails to capture inter-individual variability and poses challenges for the replication of this work. Moreover, a visual assessment is not scalable, limiting its application to long sleep recordings. Finally, a dichotomous definition of the NRD does not allow to explore its underlying mechanisms. To address these challenges, we devised an automated quantitative analysis of peak values of EMG signals from neck inspiratory muscles (EMG<sub>NIM</sub>) over an extended period, i.e. the entire night. We leveraged this continuous approach to explore the bi-directional relationship between NRD and sleep: how sleep impacts NRD within individuals and how variations of NRD predicts sleep metrics across individuals. Last, we explore the directional relationship between muscular and neural activity. This study outlines this innovative approach to the evaluation of NRD and evaluates its clinical relevance.

## 2. Methods

Two separate analysis were conducted in two sets of participants.

### 2.1. Protocols (Design, participants, ethics, data recording, and measurements)

#### 2.1.1. Analysis 1

**2.1.1.1. Design.** The first analysis was a retrospective post hoc cohort analysis based on a previously published study (Redolfi et al., 2020).

**2.1.1.2. Participants.** Over a period of 14 months, we prospectively

considered for participation in the source study all patients with 1) a diagnosis of COPD, classified as GOLD C or GOLD D according to the Global Initiative for Obstructive Lung Disease classification; 2) having experienced a severe exacerbation (defined as an exacerbation requiring hospital admission); and 3) had been hospitalized in the Pulmonary Rehabilitation Unit at the Pitié-Salpêtrière Hospital.

Exclusion criteria were: 1) body mass index >35 kg/m2; 2) other concomitant respiratory diseases or continuous positive airway pressure to treat sleep apnea; 3) chronic use of non-invasive ventilation (NIV) before the exacerbation episode; 4) rehabilitation program not completed; 5) major behavioral or psychiatric problems.

All patients were contacted by phone for six months after the sleep study to determine whether a severe exacerbation requiring hospitalization had occurred.

**2.1.1.3. Ethics.** The source study was conducted in accordance with the Declaration of Helsinki (Berry et al., 2012). The study protocol was approved by the Ethics Committee of the French Respiratory Society (approval no CEPRO 2016-033). The study participants provided written informed consent.

**2.1.1.4. Data recording.** All participants underwent a full-night video polysomnography (PSG) with transcutaneous CO2 monitoring (PtcCO2) and breathing monitoring through a pressure signal derived from a nasal cannula (Pnas). This was performed in the Sleep Disorders Unit of the Pitié-Salpêtrière Hospital. EEG signals were recorded using 3 electroencephalogram (EEG) leads (Fp1/A2, C3/A2, O1/A2) positioned according to the international EEG 10-20 system. Recordings were amplified, and the signals were digitised at 256 Hz (Compumedics, Australia). Respiratory muscle electromyographic signals (EMG) — including right and left neck inspiratory muscles (NIM) (De Troyer et al., 1994), were recorded alongside the polysomnographic signals using surface silver chloride monopolar electrodes taped to the skin over the muscle mass of interest, with a 3 cm inter-electrode distance. The recording quality was assessed by visually examining the enhancement of EMG activity during deep inspirations. All recordings were made during spontaneous ventilation.

Out of the twenty-nine COPD patients recruited in the source study, data for 2 could not be retrieved due to a storage issue on the server. Additionally, one participant was excluded from the analysis due to a technical problem with the nasal pressure signal, rendering analysis impossible. We thus analysed a total of 26 COPD patients.

#### 2.1.2. Analysis 2 (Respisleep study)

**2.1.2.1. Design.** The Respisleep study is an experimental polysomnography protocol conducted in 10 healthy individuals.

**2.1.2.2. Participants.** Exclusion criteria included a history of chronic respiratory disorders, a history of neurological disorders, severe anxiety disorders, current treatment for any chronic disease, use of analgesics or psychotropic substances, and, for women, declaring themselves as pregnant.

**2.1.2.3. Ethics.** The study was approved by the French " Comité de Protection des Personnes Île-de-France X " (registration number: 2021-A02977-34), and participants provided written informed consent.

**2.1.2.4. Data recording.** All Respisleep participants underwent a full-night video polysomnography (PSG) sessions with transcutaneous CO2 monitoring (PtcCO2) and breathing monitoring using a pressure signal derived from a nasal cannula (nasal pressure signal (Pnas)). This was done in the Sleep Disorders Unit of the Pitié-Salpêtrière Hospital. EEG signals were recorded using a system of 64 active electrodes (Neuroscan, Compumedics, Australia) positioned according to the international EEG
10–20 system, including two electrodes on the mastoids (which served as references). Impedance was regularly monitored and kept below 5 kΩ. Recordings were amplified, and the signals were digitised at 256 Hz (Compumedics, Australia). EMG signals were recorded as described for analysis 1 (see above).

Out of the 10 healthy subjects recruited, we excluded from the analyses 1 subject for whom we did not have EMG data. We thus analysed a total of 9 healthy participants.

## 2.2. 2.2 Objectives

The first objective is to compare the Neural Respiratory Drive (NRD) obtained through a semi-automated analysis of NRD during sleep in healthy subjects and in COPD patients.

The second objective is to investigate how NRD is affected by different sleep stages within COPD patients

The third objective is to evaluate correlations between EMG activity of inspiratory respiratory muscles (reflecting NRD) and sleep macro and microstructure across COPD patients

## 2.3. Signal processing and data analysis

All analyses were performed in Matlab (Mathworks, version 2021b) and R Studio (Posit Software, v.2023.12.1). For the analysis of physiological signals, we used the Fieldtrip toolbox complemented with custom algorithms.

### 2.3.1. Respiratory signal

#### 2.3.1.1. Nasal pressure (Pnas).
To quantify the NRD, EMG data were time-locked to the inspiration peaks. To do so, we devised an automated algorithm to identify individual respiratory cycle based on the Pnas. First, the Pnas was bandpass filtered between 0.05 and 1 Hz using a 4th order two-pass Butterworth filter.

We then computed the 1st derivative of the signal and identified the negative and positive zero-crossing in this new time series. These

negative and positive zero-crossings respectively correspond to local minima and maxima in the original signal. We selected the local maxima as they correspond to the inspiration peaks.

Respiratory peaks occurring during events that could potentially disrupt EMG signals (such as apnoea, hypopnea, arousal, and movement) were excluded from the analysis. These events were identified visually by experts during the sleep scoring. A median of 11 % (range: 4–21 %) of the detected peaks were discarded in COPD patients. No peaks were discarded in healthy participants (median: 0 %; range: 0–0 %). We then defined the ventilatory period as a 4 s time window starting 2 s before the inspiratory peak and ending 2 s after the peak (Fig. 1).

Respiratory events were defined according to current recommendations. Apneic events were defined by $a \ge 90$ % drop in oronasal thermal flow lasting at least 10 s. Hypopneic events were defined by $a \ge 30$ % drop of pre-event baseline using Pnas lasting at least 10 s associated with $a \ge 3$ % oxygen desaturation from pre-event baseline or an arousal (Berry et al., 2012).

#### 2.3.1.2. Abdominal and thoracic belt. 
Abdominal and thoracic belt signals were bandpass filtered between 0.05 and 1 Hz using a 4th order two-pass Butterworth filter. Each continuous signal was divided into 4s-long epochs corresponding time-locked to inspiration peaks (2 s before and 2 s after the inspiratory peaks as defined above). We computed the average signal across all time-locked epochs (event-related potential, ERP) for each sleep stage (wakefulness, NREM1, NREM2, NREM3 and REM). We also performed a baseline correction ([-2, -1.5]s) to remove slow drifts in the signal and improve the comparison between subjects or sleep stages.

#### 2.3.1.3. Abdominal and thoracic belt signals.
Abdominal and thoracic belt signals were bandpass filtered between 0.05 and 1 Hz using a 4th order two-pass Butterworth filter. Each continuous signal was divided into 4s-long epochs corresponding to each ventilatory period (2 s before and 2 s after the inspiratory peak as defined above). Average signals were computed by averaging all epochs (time-locked to the peak of inspiration) for each sleep stage. We performed a baseline correction

<figure><img src="image_2.png" /><figcaption>**Fig. 1.** Representative plot of raw data over a 20-s period. The first line represents the nasal pressure, with the red dot corresponding to the inspiratory peak detected by the algorithm. The dotted box represents the limits of the ventilation period (2 s before and 2 s after inspiratory peak). The second line represents the EMG signal of the neck inspiratory muscle (EMG<sub>NIM</sub>), and the third line depicts the EMG<sub>NIM</sub> RMS.</figcaption></figure>

Note: The signal of the first line is the flow rate recorded with a nasal pressure cannula
([−2, −1.5] s) in order to be able to compare subjects with each other.

### 2.3.2. EMG

Raw EMG data were band-pass filtered between 40 and 120 Hz using a 4th-order two-pass Butterworth filter. Then we visually inspected the left and right EMGNIM (NIM: neck inspiratory muscles) across the entire recording. By default, we chose the right EMGNIM, but in case of strong visual artefacts on the right EMGNIM, we switched to the left EMGNIM if the signal was less artefacted.

The root mean square (RMS) of the EMGNIM data were used to obtain the continuous EMGNIM envelope using the following formula:

$$RMS = \sqrt{\frac{1}{n} \sum_{i} x_{i}^{2}}$$

The continuous EMGNIM envelope was then epoched in 4s-long segments time-locked to the same inspiration peaks as described above (2 s before and 2 s after the inspiratory peaks) (Fig. 1). This 4 s window was chosen to minimize the overlap with other parts of the respiratory cycles or other cycles.

To address our first objective, we averaged the EMGNIM envelope across all epochs for each sleep stage. EMG segments with an absolute amplitude exceeding a specific threshold (1 mV) were considered artefactual and excluded from further analyses. This threshold was determined through a visual inspection of the EMGNIM data. As there is a great deal of inter-individual variability in the EMGNIM signal (due in particular to variations in positioning and underlying adipose tissue), we performed a baseline correction ([-2, −1.5] s) to better compare sleep stages and participants.

To address our second objective, for each of the EMGNIM envelope epoch, we calculated the envelope's area under the curve (AUC) and peak (Fig. 1). To mitigate the intersubject variability and focus on variations across different sleep stages and wakefulness, we normalized the EMGNIM envelope for each epoch. To do so, we divided the EMGNIM AUC and peak by the corresponding average values calculated across wake epochs (we selected wake epochs before or after sleep onset during which the patient was reclined according to video recordings; periods when the patient was sitting or standing on the video footage were excluded).

### 2.3.3. EEG

Polysomnographic recordings were analysed using standard techniques (Berry et al., 2017).

EEG signals were preprocessed as follows. The EEG signal was bandpass filtered between 0.1 and 80 Hz using a 4th order two-pass Butterworth filters, notch-filtered at 50 Hz and all electrodes were referenced to the mastoid. The continuous EEG was divided into 4s-long epochs centered on the inspiration peaks (2 s before and 2 s after the inspiratory peak as defined above).

We also estimated the EEG power on continuous 30 s windows to match the sleep scoring. We applied a multi-taper time-frequency transformation on the EEG signals using Hanning windows. We extracted the power on 1 s windows with a 50 % overlap between windows, leading to 60 time-bins per 30 s window (temporal resolution: 0.5 s). The power was computed between 2 Hz and 80 Hz, following a logarithmic scale (log10) but removing the frequency bin overlapping with the 50 Hz line noise band, leading to 24 frequency bins.

### 2.3.4. Coherence between EEG and inspiratory neck muscle EMG

We extracted the EMG envelope on continuous segments of 30 s aligned with the sleep scoring. The EMG envelope was down-sampled to a resolution of 2 Hz as for the EEG power. We computed the coherence between the EMG envelope and EEG power for each frequency bin, each channel and each 30 s epoch. We then averaged the CMC coherence by sleep stage.

To estimate the chance-level CMC coherence, we repeated this

procedure after permuting the epochs for the EMG envelope. By doing so, we cancelled any functional relationship between the EMG and EEG. The CMC coherence computed following this permutation represents the baseline level of coherence obtained by chance and we compared the actual CMC coherence to this baseline level across sleep stages, channels and frequency bins. We focused on the C3 electrode for statistical analyses.

### 2.3.5. Granger causality

The CMC coherence quantifies the correlation between two signals but does not inform on the directionality of this relationship. To further quantify the influence of muscular respiratory activity on brain dynamics and of brain dynamics on muscular respiratory activity, we leveraged Granger Causality (GC). To determine if a timeseries X could cause a timeseries Y, GC tests whether the past values of X can predict the present value of Y.

We used the Multivariate Granger Causality (MVGC) Toolbox for Matlab (Barnett and Seth, 2014). On the one hand, we considered the EMG envelope extracted over 30s epochs. On the other hand, we considered the EEG time-frequency decomposition extracted from the same 30s epochs.

Before computed GC, we fitted a Vector Auto-Regression (VAR) for the EEG and EMG data. This model had a maximal order of 20 and a maximum auto-covariance lag of 1000. The model order to use was selected with the AIC criterion. We computed F-statistics to estimate the GC from muscle-to-brain and from brain-to-muscle.

To compute GC, all epochs from a given sleep or wake stage were pulled together and we estimated the GC across all these epochs. We thus obtained two GC values (brain-to-muscle and muscle-to-brain) for each frequency bin, channel and sleep stage. We also computed GC following the permutation of EMG epochs compared to EEG epochs, to obtain baseline values of GC for each frequency bin, channel and sleep stage. We focused on the C3 electrode for statistical analyses.

### 2.3.6. Photoplethysmography

For each ventilator period, we extracted the mean heart rate (HR)

## 2.4. Statistical analysis

Statistics were calculated using MATLAB (MathWorks Inc.), RStudio version 4.2.1 (R Foundation for Statistical Computing, Vienna, Austria), and GraphPad PRISM 5 (GraphPad Software, San Diego, CA, USA).

To correct for multiple comparisons across time samples (EMG, thoracic and abdominal belts signals), we used a cluster permutation approach (Maris and Oostenveld, 2007). A parametric Student t-test was applied to each sample and across individuals of the same group (one EMG, thoracic or abdominal belts signals value per subject). We first identified clusters of continuous samples crossing a specific threshold (cluster alpha < 0.05). For each of these clusters, we computed the cluster statistics: the sum of the t-values of all the samples within the cluster. Then, we compared these real cluster statistics with the maximum cluster statistics of 1000 random permutations. These permutations were obtained by multiplying randomly the signal of each individual average by 1 or −1. The comparison between real and permuted cluster statistics allowed us to compute a nonparametric p-value (Monte-Carlo p-value). We report in the text and figures only the clusters with a Monte-Carlo p-value below 0.05.

Correlations between variables were evaluated using Spearman's correlation analysis. False discovery rate correction was applied for multiple comparisons. Differences among continuous variables were examined using non parametric Mann-Withney tests, Friedman's non-parametric matched-pairs tests, followed by Dunn's post-hoc tests and among categorical variables with non-parametric Fisher tests to assess differences between groups.

Time to next severe exacerbation were performed using the log rank method. Patients being divided into 2 groups based on median EMG data
(normalized EMG<sub>NIM</sub> peak and AUC). As in the source study, subjects without REM sleep were assigned to the most severe group.”

CMC Coherence and GC were analysed with linear mixed-effect models with a random effect of subject. We examine the main effect and interaction for Group and Frequency. These main effects and interactions were quantified using the anova function from the Statistics and Machine Learning Toolbox for Matlab.

# 3. Results

## 3.1. Study participants

Of the 29 COPD patients initially recorded, PSG data for 2 patients could not be retrieved. Additionally, one participant was excluded from the analysis due to a technical problem with the nasal pressure signal, rendering any analysis impossible.

The clinical characteristics of the study population are summarized in Table 1. In summary, the median age was 71 years, and 58 % of the participants were male. Patients exhibited severe obstruction, with a median predicted forced expiratory volume in 1 s (FEV1 %) of 38 (27–46), corresponding to GOLD stage E. The median BODE index was 6 (Hug et al., 2011; Jonkman et al., 2024; Redolfi et al., 2020). Oxygen therapy was utilized by 23 % of patients, whereas 31 % required non-invasive ventilation at discharged.

Table 2 summarizes the sleep-related characteristics of the patients.

<table><caption>**Table 1**
Clinical characteristics of the study population.</caption><thead><tr><th></th><th>COPD</th></tr></thead><tbody><tr><td>n</td><td>26</td></tr><tr><td>Age, y</td><td>71 [64–74]</td></tr><tr><td>Sex, m, (%(n))</td><td>58 (15)</td></tr><tr><td>Body mass index, kg/m<sup>2</sup></td><td>24 [21–29]</td></tr><tr><td>≥ 30 kg/m<sup>2</sup></td><td>19(5)</td></tr><tr><td>&lt; 21 kg/m<sup>2</sup></td><td>27 (7)</td></tr><tr><td>Smoking status</td><td></td></tr><tr><td>Current smoker (%(n))</td><td>31 (8)</td></tr><tr><td>Smoking pack-years</td><td>63 [45–80]</td></tr><tr><td>Respiratory characteristics</td><td></td></tr><tr><td>PaO<sub>2</sub>*, mmHg</td><td>69 [64–75]</td></tr><tr><td>PaCO<sub>2</sub>*, mmHg</td><td>44 [38–47]</td></tr><tr><td>FEV1 % predicted</td><td>38 [27–46]</td></tr><tr><td>TLC % predicted</td><td>124 [108–133]</td></tr><tr><td>RC % predicted</td><td>186 [141–232]</td></tr><tr><td>RC/TLC</td><td>62 [51–65]</td></tr><tr><td>IC/TLC</td><td>26 [17–30]</td></tr><tr><td>Maximal inspiratory pressure % predicted</td><td>91 [60–104]</td></tr><tr><td>K<sub>co</sub></td><td>34 [23–49]</td></tr><tr><td>6-min walk test, m</td><td>240 [178–324]</td></tr><tr><td>6-min walk test improvement, m</td><td>63 [48–105]</td></tr><tr><td>MRC dyspnoea scale, 0–4</td><td>3 [2–4]</td></tr><tr><td>BODE index, 0–10</td><td>6 [5–7]</td></tr><tr><td>Exacerbations in the previous year</td><td>3 [1–3]</td></tr><tr><td>Treatments</td><td></td></tr><tr><td>Beta agonists (%(n))</td><td>96 (25)</td></tr><tr><td>Muscarinic antagonists (%(n))</td><td>88 (23)</td></tr><tr><td>Inhaled glucocorticoids (%(n))</td><td>65 (17)</td></tr><tr><td>Oxygen therapy (%(n))</td><td>23 (6)</td></tr><tr><td>Sedative/hypnotics (%(n))</td><td>31(8)</td></tr><tr><td>Antidepressants (%(n))</td><td>31(8)</td></tr><tr><td>Discharged on nocturnal NIV (%(n))</td><td>3 1(8)</td></tr></tbody></table>

6-min walk test improvement was the difference between the first 6-min walk test at the beginning of rehabilitation and the last test at the end of the rehabilitation.

BODE = body mass index, airflow obstruction, dyspnoea, and exercise; FEV1 % = predicted forced expiratory volume in 1 s; IC = inspiratory capacity; K<sub>CO</sub> = carbon monoxide transfer coefficient MRC = Medical Research Council; NIM = neck inspiratory muscles; NIV = noninvasive ventilation; PaCO<sub>2</sub> = partial pressure of carbon dioxide; PaO<sub>2</sub> = arterial oxygen pressure; RC = respiratory capacity; RV = residual volume; TLC = total lung capacity. Data are presented as median (25th–75th percentile) and % (n).

<table><caption>**Table 2**
Sleep-related characteristics of the both study populations.</caption><thead><tr><th></th><th>COPD patient</th><th>Healthy participants</th><th>p</th></tr></thead><tbody><tr><td>Total sleep time, min</td><td>345 [300–416]</td><td>424 [398–483]</td><td><strong>0.019</strong></td></tr><tr><td>Sleep efficacy, %</td><td>69 [56–79]</td><td>91 [89–96]</td><td><strong>0.007</strong></td></tr><tr><td>Latency to sleep onset, min</td><td>15.3 [5.4–33.3]</td><td>23 [7–42]</td><td>0.361</td></tr><tr><td>Latency to REM sleep, min</td><td>89 [58–129]</td><td>160 [78–187]</td><td>0.330</td></tr><tr><td>N1–2 sleep, % total</td><td>54 [46–64]</td><td>49 [44–52]</td><td>0.181</td></tr><tr><td>N3 sleep, % total</td><td>26 [17–35]</td><td>28 [19–36]</td><td>0.778</td></tr><tr><td>N3 sleep, min</td><td>86 [63–115]</td><td>101 [92–148]</td><td>0.105</td></tr><tr><td>REM sleep, %</td><td>20 [13–27]</td><td>21 [17–24]</td><td>0.552</td></tr><tr><td>Arousal index, n/h</td><td>7 [4–16]</td><td>0 [0–0]</td><td><strong>0.022</strong></td></tr><tr><td>Apnoea-hypopnea index, n/h</td><td>1.3 [0–11.4]</td><td>0 [0–0]</td><td>0.048</td></tr><tr><td>Apnoea-hypopnea index &gt; 15</td><td>15 (4)</td><td>0 (0)</td><td>0.165</td></tr><tr><td>Mean SpO<sub>2</sub>, %</td><td>90 [87–92]</td><td>97 [96–97]</td><td><strong>&lt;0.001</strong></td></tr><tr><td>Mean PTcCO<sub>2</sub></td><td>46 [42–52]</td><td>46 [44–47]</td><td>0.966</td></tr><tr><td>Epworth Sleepiness Scale</td><td>4 [2–6]</td><td>—</td><td>—</td></tr></tbody></table>

N = sleep stage; PTcCO<sub>2</sub> = transcutaneous carbon dioxide; SpO<sub>2</sub> = oxygen saturation as measured by pulse oximetry.

Data are shown as median (25th–75th percentile) and % (n) n(%) unless otherwise specified. Sleep efficacy: total sleep time/time in bed.

P-values were computed by contrasting the COPD and healthy volunteers' group using Mann-Whitney tests.

*Three patients had no REM sleep. All patients were monitored during spontaneous breathing in room air.

The median sleep duration was 345 [300–416] min (median [25th, 75th] percentiles) in COPD patient and 424 [398–483] min in healthy participants (p = 0.019). Sleep architecture in COPD patients appeared normal, with a median of 86 min of N3 sleep and 20 % of median REM sleep (101 min in N3 Sleep and 21 % of median REM sleep in healthy participants p = 0.105 and p = 0.552 respectively). However, sleep efficacy was low in the COPD group (69 %(56–79) vs 91 %(89–96), p = 0.007).

Of the 10 healthy participants, the EMG signal in 1 participant was too artefacted to be included in the analysis. Of the 9 patients remaining, 5 (56 %) were women and 4 (44 %) men, and the median age was 27 years [25–29].

## 3.2. Respiratory signal analysis

Following the removal of respiratory events we identified a median of 8100 inspiratory peaks (5554–9327) for each patient in the COPD group and 9892 inspiratory peaks (8939–10,629) for each individual in the control group.

To avoid the spurious detection of inspiratory peaks, we examined the distribution of distances between consecutive peaks in COPD patients (Fig. 2). This distributions of peak-to-peak distances across all COPD patients and for each sleep stage are shown in Supplemental Figure 1A-F. In all stages, we observed a bimodal distribution: the first mode exhibited an average interval of approximately 1 s, whereas the second mode had an average interval of around 2.5 s. Peaks detected with intervals of around 1 s are too short since respiratory cycles typically last for >2 s (respiratory rate > 30 cycles/min) and the associated peaks are likely associated with artifacts or abnormal respiratory cycles. We removed peaks with peak-to-peak intervals < 2 s from our analyses to prevent an inaccurate estimation of the NRD. This resulted in a median rejection rate of 9.2 % (5–14.4 %) across COPD patients. We replicated this approach in healthy subjects, resulting in a median rejection rate of 7 % (7–9 %).

## 3.3. Analysis of EMG signals

Fig. 2 shows the average normalized EMG<sub>NIM</sub> envelope for each sleep stage in healthy participant and in COPD patients. Across all sleep stage and wake, in COPD patients, we can see an EMG activation time locked to breathing and aligned with the inspiration (i.e., preceding the
<figure><img src="image_3.png" /><figcaption>**Fig. 2.** Average EMG signal time locked to inspiratory peak in COPD and healthy participants in a Wake, b N2, c N3 and d REM sleep. The timing of statistical differences (cluster permutation, *p* < 0.05) between both groups are represented by the shaded grey area. Coloured shaded areas show the standard-error of the mean (SEM) across participants.</figcaption></figure>

inspiration peak). This modulation is absent in healthy participants (Statistical analysis are reported in Table 3). After extracting the AUC and peak of the EMG modulation in COPD patients (see Methods), we quantified the delay between the EMG peak and the inspiratory peak. Across all sleep stages, EMG bursts consistently preceded peak inspiration (wakefulness: −0.11 s [−0.22 to −0.08], N2: −0.29 s [−0.39 to −0.21], N3: −0.32 s [−0.45 to −0.21], REM: −0.24 s [−0.32 to −0.12]), which reinforces the interpretation of this increase as reflecting the respiratory effort.

### 3.4. Impact of sleep on NRD within COPD patients

The EMGNIM AUC exhibited significant differences across sleep stages (*p* < 0.001), with lower values observed in N2 (39 % [24–59 %], *p* < 0.001), N3 (30 % [21–47 %], *p* < 0.001), and REM sleep (16 % [10–24 %], *p* < 0.001) compared to wakefulness (100 % [100–100 %]). Notably, the EMGNIM AUC in N2 and N3 was higher than in REM sleep (*p* < 0.001 and *p* < 0.05 respectively) (Fig. 3a). »

EMGNIM peak values across sleep stages are depicted in Fig. 3b, with lower peak values seen in N2 (56 % [32–70 %], *p* < 0.005), N3 (45 % [30–68 %], *p* < 0.005), and REM sleep (22 % [15–32 %], *p* < 0.001). Notably, the EMGNIM peak was also lower in REM sleep compared to N2 and N3 (*p* < 0.001 and *p* < 0.05, respectively).

In order to assess the impact of sleep on ventilatory mechanics beyond the NRD, we analysed variations in thoracic and abdominal

band signals as in different sleep stages.

The averaged signals are presented for the thoracic and abdominal bands in supplemental Figure 2 and 3 respectively. No significant differences were found between the different sleep stages using cluster permutation approach.

We also evaluated the impact of sleep on the photoplethysmogram data in COPD patients. Data are shown in supplemental figure 4. There is a reduction in heart beat (HB) between wake and all sleep stages (median HB during Wake 75 [71–85] b/min, compared to 73 [66–81] b/min during N2, *p* < 0.001; 72 [67–84] b/min, *p* < 0.001 during N3 and 70 [66–76] b/min, *p* < 0.001 during REM sleep). There was no difference between sleep stages and there no correlation between NRD and HB.

### 3.5. Impact of NRD on sleep in COPD patients

#### 3.5.1. Macrostructure

Fig. 4 shows the correlation between the surrogate markers of NRD (EMG<sub>NIM</sub> AUC and peak) and sleep data. EMG<sub>NIM</sub> AUC and peak in N2 showed a moderate but significant negative correlation with total sleep time, sleep efficacy and duration of N3 (in minutes) (−0.17, *p* = 0.007; −0.32, *p* = 0.002 and −0.38, *p* = 0.040 for AUC and −0.06, *p* = 0.020; −0.25, *p* = 0.006 and −0.38, *p* = 0.027 for peak respectively). In N3, EMG<sub>NIM</sub> AUC showed a weak but significant negative correlation with total sleep time, and sleep efficacy (−0.16, *p* = 0.040 and −0.23, *p* = 0.018) and the EMG<sub>NIM</sub> peak showed a similar negative correlation with sleep efficacy (−0.12, *p* = 0.038). Overall, our markers of NRD index the inter-individual differences in sleep disturbances observed in the COPD population.

#### 3.5.2. Coherence between EEG and inspiratory neck muscle EMG (CMC) and granger causality (GC)

CMC captures the correlation between fluctuation of the EMG<sub>NIM</sub> envelope and the power of EEG in different frequency bands. Results are shown in Fig. 5 (a-d). For all sleep stages, we found significant interactions between the group (COPD vs. controls) and frequency (*p* < 0.0001, linear mixed-effect models) indicating that coherence is

<caption>**Table 3**</caption>

Cluster's timing of statistical difference between COPD patients and healthy participants of EMG signal time locked to inspiratory peak.

<table><thead><tr><th></th><th>Time</th><th>p</th></tr></thead><tbody><tr><td>Wake</td><td>-1625 to 1656 ms</td><td><0.001</td></tr><tr><td>N2</td><td>-1183 to 1245 ms</td><td><0.001</td></tr><tr><td>N3</td><td>-1105 to 843 ms</td><td><0.001</td></tr><tr><td>REM</td><td>-1609 to 1003 ms</td><td>0.020</td></tr></tbody></table>

Cluster peaks timing measured from the peak of inspiration defined by the nasal pressure signal.
<figure><img src="image_4.png" /><figcaption>**Fig. 3.** Normalized EMG<sub>NIM</sub> AUC (a) and peak (b) across different sleep stages. Data are normalized to activity during wakefulness. Values are presented as median. Error bars show the interquartile range. Stars (*) denote significant differences at *p* < 0.05.</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>**Fig. 4.** Correlation between the EMG<sub>NIM</sub> AUC and peak across different sleep stages and sleep parameters. Sleep efficacy: total sleep time/time in bed. Data are presented using a colorimetric scale based on the Spearman correlation coefficient between different variables (orange indicates a positive correlation and blue indicates a negative correlation). Stars (*) denote significant Spearman correlations (*p* < 0.05) with a correction for multiple comparisons (false discovery rate).</figcaption></figure>

modulated both by frequency and the pathology. Cortico-muscular coherence is modulated by the sleep stages and is greater during wakefulness than during different sleep stages. When examining coherence across frequencies, we observed an increase in coherence in low-frequency (Delta [0.5–4 Hz]) and fast-frequency (Beta [12–30 Hz] and Gamma [>30 Hz]). Turning toward group differences, we observed a difference between COPD patients and healthy subjects in fast frequencies (Beta [12–30 Hz] and Gamma [>30 Hz]), in Wake, N2, N3, and REM sleep.

In order to evaluate the direction of the interaction, we performed a Granger causality analysis in both COPD and healthy participants (Figure b-i). Across all sleep stages and wakefulness, we obtained higher GC values from the EEG (brain) to EMG (muscle) than EMG to EEG. As for coherence, we fitted linear mixed effect models by sleep stage and direction of information flow (brain to muscle or muscle to brain) with the group and frequency bin as fixed effects and subject identity as a random effect. When examining the muscle-to-brain direction, we found no effect of group or interaction between group and frequency in NREM2, NREM3 and REM (*p* > 0.05). We found only an interaction between group and frequency in wakefulness (*p* < 0.001). This indicates that the prediction of EEG activity with EMG is not impacted by the clinical group in sleep but is during wakefulness. This contrasts with the brain-to-muscle direction for which we found an interaction between group and frequency in wakefulness and all sleep stages (*p* < 0.001). Thus, the predictive power of the EEG on EMG depends on the pathology and frequency in both wakefulness and sleep. This flow of information is particularly high in the higher frequency range (beta and gamma), especially for COPD patients, which could illustrate efferent signals from motor cortices to respiratory muscles reflecting the NRD.

### 3.6. Impact of NRD during sleep on survival

As in the source study demonstrated we wanted to evaluate the impact of a high NRD during REM on exacerbation free survival. To do so, we grouped patient according to the median of the EMG<sub>NIM</sub> AUC or peak during REM sleep (as in the source study, patient without REM sleep were considered in the high NRD during REM). Exacerbation free survival curves are represented in Fig. 6. COPD patients with a low NRD during REM sleep (AUC or peak) had a lowest 6-month risk of rehospitalization for a severe COPD exacerbation (*p* = 0.008 and *p* < 0.001 respectively).

## 4. Discussion

The automation of EMG analysis in COPD patients has already been proposed. For example Devot et al. (Devot et al., 2014) compared an automated algorithm with visual detection and demonstrated that automated analysis achieved an acceptable level of accuracy in patients with acute exacerbations of COPD (Barnett and Seth, 2014). However, to our knowledge, our study is the first to perform an automated analysis of the EMG of accessory muscles allowing the precise quantification of NRD during sleep in COPD patients and at scale. Additionally, our work provides the first description of a method enabling analysis over an extended period. Nonetheless, it is important to note that the current pipeline is not fully automated since it builds upon the visual sleep scoring and identification of respiratory events. These steps are usually not automated in clinical settings although there are now many automated solutions achieving human-level performance. We rather focused on the automation of the EMG analysis. Our findings underscore the feasibility of conducting an automatic analysis of the EMG signal (serving as a surrogate of NRD) over an extended recording period (> 8 h). Furthermore, we show the clinical relevance of this novel method in COPD patients, with correlations with sleep macrostructure in line with previous studies (Redolfi et al., 2020) but also CMC and survival.

When interpreting our findings, several technical considerations warrant attention. Firstly, our study utilized cervical inspiratory
<figure><img src="image_6.png" /><figcaption>Fig. 5. (a to d) Cortico muscular coherence between COPD patients (colored line) and healthy participants (grey line) across sleep stages. (e to h) Granger causality from Muscle to Brain in wake (e), N2 (f), N3 (g) and REM sleep (h) and from Brain to Muscle in wake (i), N2 (j), N3 (k), and REM sleep (l) in COPD patients (colored line) and healthy participants (grey line).</figcaption></figure>

accessory muscle recording, with electrodes positioned to capture EMG activity of the sternomastoid (Jonkman et al., 2024). It is probable that we also detected EMG activity from the scalene muscles (De Troyer et al., 1994). While this does not impact the validity of our results, most previous studies analysing NRD in COPD patients have focused on parasternal muscle activity (D'Cruz et al., 2021; Murphy et al., 2011; Patout et al., 2019). However, we focused on recordings from the sternomastoid to minimize potential interference from cardiac artifacts and arm movements. Next, we defined the peak of inspiration as the nadir of each ventilatory cycle. While the onset of inspiration is often considered more relevant (Hug et al., 2011; Roesthuis et al., 2020), the automatic detection of the inspiration onset is more challenging.

Furthermore, to minimize the detection of spurious respiratory peaks, we discarded peaks with a peak-to-peak interval cutoff below 2 s. This decision was based on the distribution of peak-to-peak intervals and may not prove optimal for other datasets. Additionally, it excludes inspiratory peaks associated with a respiratory rate exceeding 30 breaths per min whereas such accelerations in respiratory rate are possible in COPD patients. Further investigations of these short cycles could help dissociate genuine spurious detections from short COPD-related respiratory cycles.

We also compared our results in COPD patients with those obtained in younger healthy individuals. It is important to emphasize that the observed differences cannot be attributed solely to COPD, as age represents a significant confounding factor. However, as this was an

exploratory and validation study, the two groups were not intended to represent a conventional case-control design. Instead, they were deliberately chosen to maximize physiological contrast. The primary objective was to demonstrate the feasibility of the technique and to confirm that the observed signals were not artifacts by showing clear differences in a physiologically distinct population. To specifically isolate the effects of respiratory disease, future studies should include comparisons with age- and sex-matched healthy controls.

In our study, we also normalized EMG activity using wake EMG levels. This step was implemented to remove large variations in the EMG tonus, which could be related to variations in the quality of the EMG recordings. The normalization to a reference period, such as wakefulness, has been utilized previously. For instance, in intensive care settings, EMG activity during recordings in patients ventilated with increasing pressure has been expressed as a percentage of activation relative to the initial ventilation period with the lowest pressure (Roesthuis et al., 2020). We selected this approach because our aim was to assess the impact of sleep on NRD, thus making the use of wakefulness as a reference period relevant. This approach differs from methods employed in previous studies, where the normalization was based on the maximum EMG activity obtained during a sniff test (D'Cruz et al., 2021a; D'Cruz et al., 2021b; Patout et al., 2019). Importantly, this normalization requires the implementation of a dedicated test and is not adapted to retrospective analyses.

First of all, it is important to note that our results are consistent with
<figure><img src="image_7.png" /><figcaption>**Fig. 6.** Kaplan-Meyer plot for the time from the sleep study to the next severe exacerbation during the 6-month follow-up according to a AUC and b Peak EMGNIM. The red line represents patient with a high NRD (AUC or Peak EMGNIM above the median) and the blue curve represents patient with a low NRD (AUC or Peak EMGNIM above the median).

Stars (*) denote significant differences at *p* < 0.01</figcaption></figure>

physiological principles and the known impact of sleep on neural respiratory drive (NRD) (Morrell et al., 1985). In particular, REM sleep is associated with a reduction in NRD, a finding that is well reflected in our results using the present methodology.

Our findings align with two other studies investigating the effect of sleep on NRD in COPD, which similarly reported reductions in NRD during non-REM (NREM) and REM sleep. In particular, Luo et al. (Luo et al., 2014) observed a 31 % decrease in NREM sleep (N2 and N3) and a 49 % decrease in REM sleep. Similarly, Domnik et al. (Domnik et al., 2022) reported a 43 % decrease in NREM sleep and a 38 % decrease in REM sleep. The results of these previous studies are similar to our results for REM sleep, with a median decrease of 55 %. However, we found a higher decrease during NREM sleep (54 % in N2 and 62 % in N3). This difference may be attributed to several factors. First, these previous studies used diaphragmatic EMG data, which may be less susceptible to sleep-related variations in muscular activity. In addition, these studies focused on short recordings (15 min for Luo et al. and 15 breaths for Domnik et al.), possibly selecting times when EMG activity was most prominent. Second, they normalized the RMSmax value to the value obtained from maximal voluntary inspiratory manoeuvres (Luo et al., 2014).

Moreover, the previous studies performed diaphragmatic EMG using an oesophageal probe, which is not performed routinely in clinical practice. Conversely, our method constitutes a less invasive approach for the analysis of EMG activity without any restrictions on recording duration and is therefore more adapted to the standard practice of sleep clinics. With regard to sleep, our method enables a more accurate assessment of NRD taking into account the impact of sleep and time on ventilatory mechanics (Domnik et al., 2021).

Beyond the technical aspect, our results demonstrate the clinical

relevance of these analyses. Firstly, we confirmed the survival results observed in the source study showing that the automation of our procedure was not at the cost of clinical relevance. Our study also shows how a continuous analysis of the MEG signal allows CMC and GC during wakefulness and sleep in COPD patients. These analyses describe the interplay between brain and muscular activity during respiratory cycles and suggests that COPD patients are characterised by a maintenance of a cortical involvement in respiration even during sleep. This adaptation could facilitate breathing through the maintenance of the NRD during sleep, but could come at the price of a lesser sleep quality. To our knowledge, this is the first study to analyze cortico-muscular coherence of inspiratory muscles during wakefulness and sleep. The increased coherence in the fast frequency band, possibly driven by the EEG, as evidenced by Granger's causality analysis, open many questions. Is it a protective system, but one that degrades sleep quality? Is it evidence of a persistent cortical involvement in ventilation during sleep?

In conclusion, peak and AUC values as surrogates for NRD both revealed clinical relevance, and we found no discrepancy between the two measures. This suggests that our method of AUC and peak EMG analysis is suitable NRD respiratory evaluation. It demonstrates the feasibility of overnight NRD analysis during various sleep stages, providing new avenues for the automatic analysis of polysomnography and having the potential to optimize treatments such as non-invasive ventilation.

## Funding

None.

## CRediT authorship contribution statement

**Léo Grassion:** Writing – original draft, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. **Maxime Patout:** Writing – review & editing, Visualization, Validation, Supervision, Formal analysis, Conceptualization. **Nicolas Wattiez:** Writing – review & editing, Visualization, Software, Formal analysis. **Stefania Redolfi:** Writing – review & editing, Validation, Data curation, Conceptualization. **Isabelle Arnulf:** Writing – review & editing, Validation, Supervision, Conceptualization. **Thomas Similowski:** Writing – review & editing, Visualization, Validation, Supervision. **Thomas Andrillon:** Writing – review & editing, Validation, Methodology, Formal analysis. **Jésus Gonzalez-Bermejo:** Writing – review & editing, Visualization, Validation, Supervision, Methodology, Formal analysis, Conceptualization.

## Declaration of competing interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:

Leo Grassion reports a relationship with AADAIRC that includes: funding grants. Leo Grassion reports a relationship with AVAD that includes: funding grants. Leo Grassion reports a relationship with Asten that includes: speaking and lecture fees and travel reimbursement. Leo Grassion reports a relationship with Air Liquide Medical System that includes: consulting or advisory and speaking and lecture fees. Leo Grassion reports a relationship with Sanofi Genzyme that includes: speaking and lecture fees. Leo Grassion reports a relationship with SOS Oxygene that includes: speaking and lecture fees and travel reimbursement. Leo Grassion reports a relationship with AstraZeneca that includes: speaking and lecture fees. Leo Grassion reports a relationship with ASV that includes: speaking and lecture fees. Leo Grassion reports a relationship with Boehringer that includes: speaking and lecture fees. Leo Grassion reports a relationship with Vivisol that includes: board membership. Leo Grassion reports a relationship with ResMed that includes: consulting or advisory and speaking and lecture fees. Leo Grassion reports a relationship with Alize Sante that includes: travel
reimbursement. Maxime Patout reports a relationship with Fisher and Paykel that includes: funding grants and speaking and lecture fees. Maxime Patout reports a relationship with ResMed that includes: consulting or advisory, funding grants, and speaking and lecture fees. Maxime Patout reports a relationship with Asten that includes: consulting or advisory, funding grants, and travel reimbursement. Maxime Patout reports a relationship with Philips Respironics that includes: consulting or advisory and speaking and lecture fees. Maxime Patout reports a relationship with GSK that includes: consulting or advisory. Maxime Patout reports a relationship with Air Liquide Medical Systems that includes: consulting or advisory. Maxime Patout reports a relationship with SOS Oxygene that includes: speaking and lecture fees. Maxime Patout reports a relationship with Chiesi that includes: speaking and lecture fees. Maxime Patout reports a relationship with Lowenstein that includes: speaking and lecture fees. Maxime Patout reports a relationship with Bastide that includes: speaking and lecture fees. Maxime Patout reports a relationship with Elivie that includes: speaking and lecture fees. Maxime Patout reports a relationship with VitalAire France that includes: travel reimbursement. Maxime Patout reports a relationship with Kernel Biomedical that includes: equity or stocks. Thomas Andrillon reports a relationship with Agence national pour la recherche that includes: funding grants. Thomas Andrillon reports a relationship with European Research Council that includes: funding grants. Thomas Andrillon reports a relationship with MSCA DN Lullabyte that includes: funding grants. Thomas Andrillon reports a relationship with Fondation Recherche Alzheimer that includes: funding grants. Thomas Andrillon reports a relationship with Australian Research Council that includes: funding grants. Thomas Andrillon reports a relationship with Medical Research Council that includes: funding grants. Thomas Andrillon reports a relationship with Fondation Vinci Autoroute that includes: funding grants. Thomas Andrillon reports a relationship with Marie d'Ivry that includes: consulting or advisory. Thomas Andrillon reports a relationship with Swiss institute in Rome that includes: travel reimbursement. Stefania Redolfi reports a relationship with Sigvaris SAS that includes: consulting or advisory. Stefania Redolfi reports a relationship with Philips Respironics that includes: speaking and lecture fees. Stefania Redolfi reports a relationship with Vivisol that includes: travel reimbursement. Stefania Redolfi reports a relationship with Medicaid that includes: travel reimbursement. Thomas Similowski reports a relationship with AstraZeneca that includes: consulting or advisory. Thomas Similowski reports a relationship with Chiesi that includes: consulting or advisory. Thomas Similowski reports a relationship with KPL Consulting that includes: consulting or advisory. Thomas Similowski reports a relationship with Lungpacer that includes: consulting or advisory. Thomas Similowski reports a relationship with OSO-AI that includes: consulting or advisory. Thomas Similowski reports a relationship with Chiesi that includes: speaking and lecture fees. Thomas Similowski reports a relationship with VitalAire France that includes: speaking and lecture fees. Thomas Similowski reports a relationship with Austral Dx that includes: equity or stocks. Thomas Similowski reports a relationship with Hephai that includes: equity or stocks. Jesus Gonzalez-Bermejo reports a relationship with Breas that includes: consulting or advisory and funding grants. Jesus Gonzalez-Bermejo reports a relationship with Lowenstein that includes: consulting or advisory. Jesus Gonzalez-Bermejo reports a relationship with Srett that includes: consulting or advisory. Jesus Gonzalez-Bermejo reports a relationship with ASV that includes: speaking and lecture fees. Thomas Similowski has patent pending to WO2008006963A3. Thomas Similowski has patent pending to WO2012004534A1. Thomas Similowski has patent pending to WO2013164462A1. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Supplementary materials

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.neuroimage.2025.121445.

## Data availability

Data will be made available on request.

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