Inspiratory pre‐motor potentials during quiet breathing in ageing and chronic obstructive pulmonary disease

Abstract

Key points

• A cortical contribution to breathing, as indicated by a Bereitschaftspotential (BP) in averaged electroencephalographic signals, occurs in healthy individuals when external inspiratory loads are applied.

• Chronic obstructive pulmonary disease (COPD) is a condition where changes in the lung, chest wall and respiratory muscles produce an internal inspiratory load. These changes also occur in normal ageing, although to a lesser extent.

• In the present study, we determined whether BPs are present during quiet breathing and breathing with an external inspiratory load in COPD compared to age‐matched and young healthy controls.

• We demonstrated that increased age, rather than COPD, is associated with a cortical contribution to quiet breathing.

• A cortical contribution to inspiratory loading is associated with more severe dyspnoea (i.e. the sensation of breathlessness).

• We propose that cortical mechanisms may be engaged to defend ventilation in ageing with dyspnoea as a consequence.

Abstract

A cortical contribution to breathing is determined by the presence of a Bereitschaftspotential, a low amplitude negativity in the averaged electroencephalographic (EEG) signal, which begins ∼1 s before inspiration. It occurs in healthy individuals when external inspiratory loads to breathing are applied. In chronic obstructive pulmonary disease (COPD), changes in the lung, chest wall and respiratory muscles produce an internal inspiratory load. We hypothesized that there would be a cortical contribution to quiet breathing in COPD and that a cortical contribution to breathing with an inspiratory load would be linked to dyspnoea, a major symptom of COPD. EEG activity was analysed in 14 participants with COPD (aged 57–84 years), 16 healthy age‐matched (57–87 years) and 15 young (18–26 years) controls during quiet breathing and inspiratory loading. The presence of Bereitschaftspotentials, from ensemble averages of EEG epochs at Cz and FCz, were assessed by blinded assessors. Dyspnoea was rated using the Borg scale. The incidence of a cortical contribution to quiet breathing was significantly greater in participants with COPD (6/14) compared to the young (0/15) (P = 0.004) but not the age‐matched controls (6/16) (P = 0.765). A cortical contribution to inspiratory loading was associated with higher Borg ratings (P = 0.007), with no effect of group (P = 0.242). The data show that increased age, rather than COPD, is associated with a cortical contribution to quiet breathing. A cortical contribution to inspiratory loading is associated with more severe dyspnoea. We propose that cortical mechanisms may be engaged to defend ventilation with dyspnoea as a consequence.

Key points

• A cortical contribution to breathing, as indicated by a Bereitschaftspotential (BP) in averaged electroencephalographic signals, occurs in healthy individuals when external inspiratory loads are applied.

• Chronic obstructive pulmonary disease (COPD) is a condition where changes in the lung, chest wall and respiratory muscles produce an internal inspiratory load. These changes also occur in normal ageing, although to a lesser extent.

• In the present study, we determined whether BPs are present during quiet breathing and breathing with an external inspiratory load in COPD compared to age‐matched and young healthy controls.

• We demonstrated that increased age, rather than COPD, is associated with a cortical contribution to quiet breathing.

• A cortical contribution to inspiratory loading is associated with more severe dyspnoea (i.e. the sensation of breathlessness).

• We propose that cortical mechanisms may be engaged to defend ventilation in ageing with dyspnoea as a consequence.

Introduction

Chronic obstructive pulmonary disease (COPD) is a progressive respiratory disease that results most commonly from damage to the airways and lung parenchyma by chemical irritants such as tobacco smoke (Vogelmeier et al. 2017). The major symptoms of the disease are shortness of breath, or dyspnoea, and sputum production in cough, both of which are heightened during an acute exacerbation. COPD, which encompasses emphysema, chronic bronchitis and/or chronic asthma (Mirza & Benzo, 2017), is characterized by hyperinflation, airflow limitation and decreased alveolar gas exchange (McKenzie et al. 2009). Hyperinflation disadvantages the diaphragm in terms of its length–tension relationship and airflow limitation increases the resistive and elastic loads. With altered chest wall and lung mechanics, the diaphragm increases the proportion of type‐1 fibres (Stubbings et al. 2008) and requires a two‐ to three‐fold increased neural drive to maintain homeostasis in quiet breathing (De Troyer et al. 1997; Jolley et al. 2009). Thus, there is increased work of breathing, which is linked to dyspnoea. Theories on the mechanisms of dyspnoea centre around a disparity between motor cortical and/or medullary efferent commands and sensory afferent feedback (O'Donnell et al. 2007; Parshall et al. 2012), although none to date completely explain the various modalities of the dyspnoeic experience (Banzett et al. 2015).

Changes in the respiratory system, such as reductions in chest wall compliance (as a result of deterioration of the elastic, resistive and parenchymal properties of the lung) and reduced strength of the respiratory muscles that occur in COPD overlap with those in normal ageing (Lalley, 2013), although to a lesser extent. Indeed, COPD has been described as accelerated lung ageing (Ito & Barnes, 2009). Thus, common to both COPD and normal ageing is an increased internal inspiratory load, which will require additional neural drive to maintain ventilation, as observed for the diaphragm in both older (>50 years) and participants with COPD (Jolley et al. 2009).

The respiratory rhythm for quiet breathing is generated in the medulla (Feldman et al. 2013). In the presence of an increased inspiratory load, cortical areas, including the supplementary motor area, are assumed to be recruited to maintain ventilatory requirements (Raux et al. 2013b). A cortical contribution to movements can be determined by the presence of a Bereitschaftspotential (BP), comprising electroencephalographic (EEG) activity that precedes voluntary movement by ∼1 s (Papakostopoulos et al. 1974; Shibasaki & Hallett, 2006). For voluntary movements of the respiratory muscles (e.g. in voluntary self‐paced sniffs), BPs are present prior to the onset of inspiration (Macefield & Gandevia, 1991).

In healthy younger (<35 years) people, a cortical contribution to breathing as indicated by a BP has been observed during inspiratory threshold loading and also during incorrectly adjusted non‐invasive ventilation (Raux et al. 2007a, b ; Hudson et al. 2016). A cortical contribution to quiet breathing has been observed in obstructive sleep apnoea (OSA), congenital central hypoventilation syndrome and amyotrophic lateral sclerosis, in which there may be an internal inspiratory load, insufficient medullary central drive or respiratory muscle weakness, respectively (Launois et al. 2014; Tremoureux et al. 2014a; Georges et al. 2016). It is considered that a cortical contribution to quiet breathing may compensate for insufficient medullary drive to the respiratory muscles and an association between a higher incidence of cortical contribution and more severe ratings of dyspnoea has been observed (Raux et al. 2007b; Morawiec et al. 2015; Georges et al. 2016). The finding of a cortical contribution to breathing in OSA (Launois et al. 2014) is pertinent given the high rates of OSA in people with moderate‐severe COPD (Soler et al. 2015).

Given the changes in respiratory mechanics and evidence for increased neural drive to the respiratory muscles in COPD, we hypothesized that participants with COPD would have a higher incidence of a cortical contribution during quiet breathing compared to healthy older and younger controls. Thus, we used EEG activity to detect the presence of BPs to indicate a cortical contribution to breathing. Furthermore, we examined the relationship between dyspnoea and respiratory‐related cortical activation using an inspiratory threshold load. We hypothesized that a cortical contribution to inspiratory threshold loading would be associated with higher ratings of dyspnoea. A preliminary version of some of the findings of the present study has been presented in abstract form (Nguyen et al. 2016).

Methods

Ethical approval

All participants gave informed written consent to take part in the present study. The study received ethical approval from the University of New South Wales Human Research Ethics Committee (HC16128) and was conducted in accordance with the guidelines of the Declaration of Helsinki (World Medical Association, 2013), except for registration in a database (clause 35).

Participants

Forty‐seven participants were recruited for the present study. Two were excluded from the analysis: one because of a concomitant neurological diagnosis that was identified after recordings were made and one because of a lack of a positive control condition in the study (see below). Fourteen participants with moderate‐severe COPD (aged 57–85 years; nine males), 16 healthy age‐matched adults (aged 57–87, years; 11 males) and 15 healthy young adults (aged 18–26 years; eight males) were included in the analysis. Table 1 shows the average anthropometric data for COPD, age‐matched and young control participants and Tables 2, 3, 4 show data for each participant. Exclusion criteria across all groups included neurological disorders and pulmonary malignancy. In addition, asthma was an exclusion criterion for the age‐matched and young control participants. Risk of OSA was assessed with the Berlin Sleep Questionnaire (Netzer et al. 1999) in the participants with COPD and age‐matched controls (Tables 1, 2, 3).

Table 1.

Group average anthropometric data

	Age (years)	Height (m)	Mass (kg)	BMI (kg m–2)	FEV1 (L)	FEV1 (% pred)	FVC (L)	FVC (% pred)	FEV1/FVC(%)	tc $P_{\mathrm{C}{\mathrm{O}}_2}$ (mmHg)	$S_{\mathrm{p}{\mathrm{O}}_2}$ (%)	Pack year	High OSA risk
COPD
Mean	70	1.71	70	24.1	1.41	49	2.53	66	55	42.4	95.6	32	6/14
± SD	± 9*	± 0.08	± 14	± 4.5	± 0.58 * †	± 18* †	± 0.73* †	± 16* †	± 11* †	± 5.7	± 2.6*	± 37* †	(43%)
Age‐matched
Mean	71	1.69	72	25.0	2.55	93	3.43	95	75	42.0	96.7	1.9	2/16
± SD	± 10*	± 0.10	± 11	± 2.5	± 0.64*	± 13	± 0.91*	± 13	± 6	± 4.5	± 1.5	± 2.9	(13%)
Young
Mean	23	1.71	66	22.2	3.54	90	4.47	99	80	43.2	97.7	0	–
± SD	± 2	± 0.08	± 12	± 2.8	± 0.86	± 11	± 1.03	± 16	± 9	± 3.0	± 0.9	± 0
Statistic and P value	H 2 = 29.5; P < 0.001	F 2 = 0.260; P = 0.772	F 2 = 0.714; P = 0.495	F 2 = 2.79; P = 0.073	F 2 = 32.9 P < 0.001	F 2 = 43.9 P < 0.001	F 2 = 16.8 P < 0.001	F 2 = 19.7 P < 0.001	F 2 = 31.7 P < 0.001	F 2 = 0.314 P = 0.732	H 2 = 9.13 P = 0.010	H 2 = 9.05 P = 0.003	P = 0.064

Includes age, height, mass, BMI, FEV₁ and %pred, FVC and %pred, FEV₁/FVC, resting tc $P_{\mathrm{C}{\mathrm{O}}_2}$, resting transcutaneous $S_{\mathrm{p}{\mathrm{O}}_2}$, number of pack years (product of number of years smoking and cigarette packs per day) and risk of OSA. For each group, the mean ± SD is shown. ^*Significant post hoc difference compared to young (P < 0.05). ^†Significant post hoc difference compared to age‐matched (P < 0.05).

Table 2.

Anthropometric data for participants with COPD

	Age (years)	Height (m)	Mass (kg)	BMI (kg m–2)	FEV1 (L)	FEV1 (%pred)	FVC (L)	FVC (%pred)	FEV1/FVC (%)	tc $P_{\mathrm{C}{\mathrm{O}}_2}$ (mmHg)	Hyper‐inflation	$S_{\mathrm{p}{\mathrm{O}}_2}$ (%)	Pack year	OSA risk	BP
COPD
1M	85	1.78	80	25.2	0.90	33	1.75	47	51	36.2	–	95.7	17	High	Present
2M	77	1.60	65	25.4	1.00	27	1.70	35	59	34.1	Y	92.6	40	Low	Absent
3M	75	1.69	81	28.4	1.41	56	2.49	70	57	42.1	Y	99.0	0	Low	Present
4M	74	1.78	96	30.3	2.63	74	3.80	83	69	43.8	N	96.7	110	High	Present
5M	69	1.80	70	21.6	1.65	49	3.10	70	53	42.6	N	97.0	15	Low	Present
6M	69	1.73	83	27.7	0.81	28	2.46	65	33	40.8	–	97.2	38	High	Absent
7M	65	1.82	72	21.7	0.85	24	2.30	49	37	50.8	–	93.2	120	Low	Absent
8M	57	1.73	68	22.7	2.45	70	3.95	67	62	40.1	–	98.1	3	Low	Absent
9M	57	1.70	67	23.2	1.20	36	2.75	64	44	43.0	–	94.7	30	Low	Absent
1F	80	1.68	58	20.5	1.53	70	2.57	98	60	35.4	–	96.7	0	High	Present
2F	75	1.64	38	14.1	0.98	50	2.03	78	48	45.6	Y	97.3	25	High	Absent
3F	65	1.52	62	26.8	0.95	47	1.42	55	67	39.1	N	89.1	40	High	Present
4F	65	1.67	84	30.1	1.75	70	2.60	81	67	45.2	Y	94.4	2	Low	Absent
5F	60	1.73	59	19.7	1.60	56	2.49	68	64	54.9	N	96.1	4*	Low	Absent
COPD
Mean	70	1.71	70	24.1	1.41	49	2.53	66	55	42.4	–	95.6	32
± SD	± 9	± 0.08	± 14	± 4.5	± 0.58	± 18	± 0.73	± 16	± 11	± 5.7	–	± 2.6	± 37

Includes age, height, mass, BMI, FEV₁ and %pred, FVC and % pred, indication of hyperinflation based on recent lung volume assessment (residual volume ≥ 120% predicted) or imaging data, resting tc $P_{\mathrm{C}{\mathrm{O}}_2}$, resting transcutaneous $S_{\mathrm{p}{\mathrm{O}}_2}$, number of pack years (product of number of years smoking and cigarette packs per day), and risk of OSA. M, male; F, female; Y, yes; N, no. The mean ± SD for numerical data is shown. The presence or absence of a BP during quiet breathing is indicated in the final column. ^*Cannabis use reported, thus probably underestimating true tobacco exposure.

Table 3.

Anthropometric data for age‐matched control participants

	Age (years)	Height (m)	Mass (kg)	BMI (kg m–2)	FEV1 (L)	FEV1 (%pred)	FVC (L)	FVC (%pred)	FEV1/FVC (%)	tc $P_{\mathrm{C}{\mathrm{O}}_2}$(mmHg)	$S_{\mathrm{p}{\mathrm{O}}_2}$ (%)	Pack year	OSA risk	BP in QB
Age‐matched
1M	87	1.62	75	28.6	1.55	71	2.20	75	70	38.0	93.7	20	Low	Present
2M	84	1.62	71	27.1	2.55	114	3.2	106	80	40.6	94.7	0	Low	Present
3M	82	1.83	80	23.9	2.65	88	3.9	95	68	39.7	97.4	0	Low	Absent
4M	77	1.7	69	23.9	2.85	106	4.05	113	70	49.5	94.6	6	Low	Present
5M	77	1.7	70	24.2	2.65	99	3.75	105	71	39.6	98.1	8	Low	Absent
6M	74	1.85	82	24.0	3.7	110	4.55	101	81	49.1	97.1	0	High	Absent
7M	69	1.74	88	29.1	3.25	105	4.35	107	75	43.3	97.7	0	High	Absent
8M	68	1.78	73	23.0	2.95	90	3.65	84	81	43.2	98.7	4	Low	Absent
9M	63	1.78	72	22.7	2.95	85	4.85	107	61	40	95.7	0	Low	Absent
10M	59	1.73	70	23.4	3.25	95	4.35	99	75	49.2	98.3	0	Low	Absent
11M	57	1.77	94	30.0	2.95	81	3.8	81	78	46.3	95.8	0	Low	Absent
1F	78	1.52	57	24.7	1.75	96	2.35	105	74	39.8	96	0	Low	Present
2F	76	1.57	62	25.2	1.95	103	2.35	96	83	34.5	98.4	0	Low	Present
3F	64	1.65	70	25.7	1.75	71	2.3	73	76	38.8	96	0	Low	Present
4F	64	1.59	52	20.6	2.1	92	2.75	95	76	38.4	97.4	0	Low	Absent
5F	58	1.6	61	23.8	1.9	77	2.5	80	76	41.8	96.8	5	Low	Absent
Age‐matched
Mean	71	1.69	72	25.0	2.55	93	3.43	95	75	42.0	96.7	3
± SD	± 10	± 0.10	± 11	± 2.5	± 0.64	± 13	± 0.91	± 13	± 6	± 4.5	± 1.5	± 5.3

Includes age, height, mass, BMI, FEV₁ and %pred, FVC and %pred, resting tc $P_{\mathrm{C}{\mathrm{O}}_2}$, resting transcutaneous $S_{\mathrm{p}{\mathrm{O}}_2}$, number of pack years (product of number of years smoking and cigarette packs per day) and risk of OSA. M, male; F, female. The mean ± SD for numerical data is shown. The presence or absence of a BP during quiet breathing is indicated in the final column.

Table 4.

Anthropometric data for healthy young control participants

	Age (years)	Height (m)	Mass (kg)	BMI (kg m–2)	FEV1 (L)	FEV1 (%pred)	FVC (L)	FVC (%pred)	FEV1/FVC (%)	tc $P_{\mathrm{C}{\mathrm{O}}_2}$ (mmHg)	$S_{\mathrm{p}{\mathrm{O}}_2}$ (%)	Pack year	BP in QB
Young
1M	25	1.85	85	24.8	4.85	96	5.65	93	86	45.5	97.1	0	Absent
2M	23	1.82	74	22.3	4.7	99	5.15	91	91	46.9	98.1	0	Absent
3M	21	1.75	72	23.5	4.05	97	4.8	98	84	44.3	96.7	0	Absent
4M	26	1.73	68	22.7	3.45	93	4.05	93	85	42.4	96	0	Absent
5M	23	1.78	82	25.9	4.4	94	6.05	109	73	40	97.8	0	Absent
6M	23	1.76	70	22.6	3.95	95	5.1	106	77	48.2	98.4	0	Absent
7M	22	1.73	63	21.0	3.65	87	5.5	109	66	44.5	98.3	0	Absent
8M	18	1.78	68	21.5	4	88	4.6	86	87	44.2	98.5	0	Absent
1F	23	1.7	65	22.5	3	83	4.2	100	71	39.5	97.9	0	Absent
2F	21	1.68	55	19.5	2.25	71	2.6	74	87	41.2	98.1	0	Absent
3F	25	1.54	44	18.6	2.35	91	2.7	93	87	41.7	98.3	0	Absent
4F	26	1.64	61	19.0	2.65	69	3.3	73	80	37.8	98.2	0	Absent
5F	22	1.71	85	29.1	4.2	98	5.05	100	83	45.8	96	0	Absent
6F	22	1.63	52	19.6	2.45	83	3.9	118	63	45.6	98.6	0	Absent
7F	22	1.6	52	20.3	3.1	109	4.35	138	71	41.1	97.8	0	Absent
Young
Mean	23	1.71	66	22.2	3.54	90	4.47	99	80	43.2	97.7	0
± SD	± 2	± 0.08	± 12	± 2.8	± 0.86	± 11	± 1.03	± 16	± 9	± 3.0	± 0.9	(0)

Includes age, height, mass, BMI, FEV₁ and %pred, FVC and %pred, resting tc $P_{\mathrm{C}{\mathrm{O}}_2}$, resting transcutaneous $S_{\mathrm{p}{\mathrm{O}}_2}$, number of pack years (product of number of years smoking and cigarette packs per day). M, male; F, female. The mean ± SD for numerical data is shown. The presence or absence of a BP during quiet breathing is indicated in the final column.

Spirometry

Measurements of forced expiratory volume in 1 s (FEV₁) and forced vital capacity (FVC) were made before commencing the study with a hand‐held spirometer (One Flow FVC Memo; Clement Clarke, Harlow, UK). Predicted values for FEV₁ and FVC were determined using the methods of the ERS Global Lung Function Initiative 2012 (Quanjer et al. 2012). Table 1 shows the spirometry data and smoking history in terms of pack years for each participant group. Individual data are provided in Tables 2, 3, 4.

Borg rating

Participants were asked to rate their current effort to breathe using the modified Borg scale (Burdon et al. 1982), which is a reliable scale for the assessment of dyspnoea (Kendrick et al. 2000).

Experimental set‐up

The experiments took place in a quiet, soundproof room with the temperature and lighting adjusted to the participant's comfort. Participants were seated in a comfortable chair with full support to the back, arms, neck and head. During the experiment, participants watched a nature documentary. Participants were instructed to minimize eye and body movements during the recording periods. Experimenters in an adjacent room communicated with participants via a video monitor and microphone. The protocol comprised four experimental conditions: voluntary finger movement, quiet breathing, voluntary self‐paced sniffs and inspiratory threshold loading (see below).

Neurophysiological measurements

Electroencephalography

EEG activity was recorded from an EEG cap with 32 surface electrodes (Acticap; BrainProducts, Gilching, Germany) and placed in accordance with the International 10/20 system. Surface electrodes on each earlobe served as references (A1 and A2). Online, recordings were referenced to A2 with a ground at AFz. Electrooculographic activity (EOG) was monitored with an electrode placed under the right eye to detect eye movement and eye blinks. Impedance was kept below 2 kΩ at all times. The recordings were amplified and filtered at 0.1–500 Hz and sampled at 500 Hz (BrainAmp, Brain Products). EEG signals were time‐locked to a simultaneous digital trigger pulse that was generated from each threshold crossing of inspiratory nasal pressure or finger electromyographic activity. Nasal pressure and finger electromyographic (EMG) signals were also recorded as auxiliary channels in the EEG amplifier.

Electromyography

EMG signals was recorded from the first dorsal interosseous muscle on the right hand. EMG signals were amplified (1000×), band‐pass filtered (16 Hz to 1 kHz), notch filtered (50 Hz) and sampled at 2 kHz (CED 1902 amplifiers, CED 1401plus, Spike2, version 7.12; Cambridge Electronic Design, Cambridge, UK).

Respiratory measurements

Nasal pressure indicating airflow was monitored using a nasal cannula placed under the nostrils and attached to a differential pressure transducer (DP45‐18; Validyne, Northridge, CA, USA). During inspiratory loading, the participant breathed through a mouthpiece when wearing a noseclip. The mouthpiece was connected to a two‐way valve (2700 series; Hans Rudolph Inc., Shawnee Mission, KS, USA), with a pneumotachograph (3700 series; Hans Rudolph Inc.) and an inspiratory threshold muscle trainer (POWERbreathe Internation, Southam, UK) attached to the inspiratory port. During the loading condition, participants signalled their effort to breathe every 2 min using the Borg scale by turning a dial to indicate the level of respiratory effort with direct feedback of the Borg level descriptors. Breaks of 5 min in duration were given between conditions. All respiratory and Borg rating signals were digitized and sampled at 1 kHz (CED 1401plus, Spike2, version 7.12; Cambridge Electronic Design, UK).

Blood gas measurements

Blood gas levels (transcutaneous carbon dioxide tension; tc $P_{\mathrm{C}{\mathrm{O}}_2}$; and resting transcutaneous oxygen saturation; $S_{\mathrm{p}{\mathrm{O}}_2}$) were estimated using a transcutaneous sensor (Sentec Digital Monitoring System; Sentec, Therwil, Switzerland) attached to the forehead above the right eyebrow. Tables 1, 2, 3, 4 show the blood gas data during the quiet breathing condition.

Protocol

Participants performed four conditions.

1. Voluntary self‐paced finger movement: the right arm was comfortably placed on the armrest of the chair and participants were instructed to perform self‐paced abductions with the right index finger at a rate of once every 5–10 s. Participants were instructed to adjust their effort, if necessary. This condition was designed as a non‐respiratory positive control where detection of a BP was expected.

2. Quiet breathing: participants were told they were performing a ‘non‐finger movement’ condition (so as to divert attention away from their breathing). Finger movements were requested occasionally throughout the recording period (every 5 min), although these EEG epochs were discarded during offline analysis.

3. Voluntary self‐paced sniffs: participants were instructed to perform a brisk sniff at their own pace when thinking about expanding the abdomen with minimal movement in the upper chest and shoulders, so as to reduce any potential movement artefact on the EEG signals. Each sniff had to be performed at the start of a breath and had to produce a peak nasal pressure around twice the magnitude the tidal volumes of their quiet breaths. Participants were instructed to adjust their effort when necessary over the microphone. This condition was designed as a respiratory positive control where detection of a BP was expected.

4. Inspiratory threshold loading (ITL): participants performed a maximal inspiratory pressure (MIP) manoeuvre, at end‐expiratory lung volume, using a hand‐held portable device (MicroRPM Pressure Meter; CareFusion, Yorba Linda, CA, USA). The inspiratory threshold load was subsequently set at 5–10% of the participant's MIP (5–15 cmH₂O). Participants were instructed to breathe at a comfortable pace. Participants were given a dial to rate their breathing effort on the Borg scale every 2 min. The condition would be stopped at the participant's request or with a Borg rating of more than 5 (severe). No participant gave a rating larger than 5.

Data analysis

EEG recordings were divided into epochs of 3.5 s in duration (2.5 s before and 1 s after), with the onset of inspiration determined from the onset of inspiratory nasal pressure or mouth pressure for quiet breathing and sniffs or ITL conditions, respectively (or onset of finger muscle electromyographic activity for the voluntary finger movement). A minimum of 250 epochs was recorded for quiet breathing and 120 epochs were recorded for all other conditions. Individual EEG epochs were inspected offline to ensure that the markers were positioned correctly and to reject those epochs that had artefacts, including any EOG activity or large deviations from baseline (i.e. with head movement). For each participant and condition, all remaining epochs were ensemble averaged, as well as averages of the odd‐ and even‐numbered epochs, for BP assessment. The channels of interest were Cz, FCz (all tasks) and C3 (finger movement only). For each participant and condition, a set of signals (ensemble, odd and even averages overlaid for each of the three channels) with a vertical marker indicating the onset of inspiratory pressure or muscle electromyographic activity was de‐identified for blinded analysis.

Determination of the presence of a BP

The following rules were given to two blinded assessors to determine the presence of a BP:

1. For each channel, indicate, using Yes/No, if there is a negative slope between −2000 ms and 0 ms.

   1. The slope can begin anywhere between −2000 ms and −500 ms, ± 100 ms
   2. The slope can be interrupted by short regions of plateaus or positive slopes if the overall slope is negative.

2. If Yes to 1, indicate overall, using Yes/No, whether a BP is present across channels. A BP must be present in at least one channel (see below).

3. If Yes to 1 and 2, indicate the latency in each channel for which a BP has been identified.

The presence of a BP overall for each condition was determined as a BP in either Cz or C3 for the finger movement and as a BP in either Cz or FCz (i.e. midline electrodes) for the three respiratory conditions. One participant who did not show a BP in either of the positive control conditions (finger movement or sniff, see above) was excluded from the analysis. The level of inter‐observer agreement between the two blinded examiners was determined using the Cohen's κ coefficient statistic which, in the present study, was 0.91 and is similar to that of Dubois et al. (2016) and Morawiec et al. (2015) (0.85 and 0.87, respectively). Therefore, these guidelines may be applicable to the analysis of any respiratory‐related cortical activity.

Statistical analysis

All statistical analyses were performed with SigmaStat, version 4.0 (Systat Software Inc., Chicago, IL, USA). Data were organized into contingency tables with the conditions ‘BP present’ or ‘BP absent’ and the three groups (COPD, age‐matched controls and young controls). Three‐by‐two chi‐squared tests were used to determine whether the incidence of the occurrence of BPs differed between the three groups in quiet breathing and the inspiratory loading condition. Two‐by‐two chi‐squared tests were used to compare incidences between two groups at a time in quiet breathing and inspiratory threshold loading, as well as to compare the incidences of high/low risk OSA and the presence of a BP in the COPD and age‐matched control groups. In addition, for quiet breathing, a multiple logistic regression was performed to determine the odds of a BP using the predictor variables ‘group’ and ‘age’. The group categories were ‘COPD’ and ‘non‐COPD’, in which the latter group included the young and age‐matched control participants. To account for the age range across our study (18–87 years), the age of each participant was converted using the function ‘age – mean age’ for the regression model.

A one‐way ANOVA was performed to compare age, height, mass, body mass index (BMI), FEV₁, FEV₁ (% pred), FVC, FVC (% pred), FEV₁/FVC (%), tc $P_{\mathrm{C}{\mathrm{O}}_2}$, $S_{\mathrm{p}{\mathrm{O}}_2}$ (%) and pack years between COPD, age‐matched and young control groups. Normality of data was determined using a Shapiro–Wilk test. Where the test of normality failed, a one‐way ANOVA on ranks was performed. Tukey's tests were performed for post hoc analysis. Differences between groups are given in Table 1. F statistics are given for one‐way ANOVA analyses and H statistics are given for one‐way ANOVA on ranks analyses.

A two‐way ANOVA was performed to compare age, tc $P_{\mathrm{C}{\mathrm{O}}_2}$, FEV₁ (% pred) and MIP between COPD and age‐matched control groups for quiet breathing (no young control participants had a BP in this condition) and between COPD, age‐matched and young control groups for inspiratory threshold loading with the presence of a BP (Yes/No). A two‐way ANOVA was also performed to compare average mouth pressure between groups for the inspiratory threshold loading condition. Tukey's tests were performed for post hoc analysis. For quiet breathing, Borg scores failed the Shapiro–Wilk test of normality, even after transformation, and so a non‐parametric ANOVA with bootstrapping was performed. A Mann–Whitney rank sum test was used to compare OSA risk between participants with and without a BP during quite breathing. Data are presented as the mean ± SD throughout but, where distributions were not normal, data are presented as the median and interquartile range, as indicated. P < 0.05 was considered statistically significant. Exact P values are reported.

Results

Fifteen participants with COPD, 17 age‐matched control and 15 young control participants were recruited for the present study. One COPD participant was excluded from the analysis because their initial inclusion was inconsistent with our exclusion criteria. One age‐matched participant did not have a BP evident in either of the positive control conditions (see Methods) and was excluded from further analysis. Thus, BPs were assessed for 45 participants for finger movement, quiet breathing and brisk self‐paced sniffs, although only 40 participants completed the inspiratory threshold loading condition (five were unwilling as a result of time constraints or anticipated discomfort). The presence of BPs was determined by two blinded assessors whose level of agreement was 0.91 (see Methods).

BPs in quiet breathing

Example BPs during quiet breathing from a participant in each group are shown in Fig. 1. As shown in Fig. 2 A, there was a significant difference in the incidence of BPs between the three groups in quiet breathing (P = 0.016). The incidence was significantly greater in COPD (6/14) compared to healthy young controls (0/15) (P = 0.004), although it was not significantly greater compared to age‐matched controls (6/16) (P = 0.765). However, the incidence of BPs in quiet breathing was also significantly greater in age‐matched controls compared to healthy young controls (P = 0.008). These results suggest that the difference between groups in the incidence of BPs is because of age rather than COPD.

Figure 1. Examples of the ensemble‐averaged EEG activity recorded from one participant from each group for all conditions.

Channels used for the confirmation of a BP in a condition are shown for each condition. Averages for odd (red) and even (blue) numbered trials are shown overlaid with the ensemble (black) averages of EEG activity. Dotted vertical lines indicate the onset of finger electromyographic activity or inspiratory pressure. The horizontal scale represents time and is consistent throughout. The vertical scale for finger electromyographic activity is shown for the finger movement condition. The vertical scale for inspiratory pressure is consistent for the three subsequent conditions. The vertical scale for EEG activity is consistent for all of the conditions. ^*Absence of a BP (in the young participant during quiet breathing and the age‐matched participant during inspiratory threshold loading). Details regarding participants 6M, 3F and 5M are provided in Tables 2, 3, 4.

Figure 2. Incidence of BPs for each group during quiet breathing and when breathing through an inspiratory threshold load.

A, in quiet breathing, the incidence of BPs was significantly different between COPD and young control groups, as well as between age‐matched and young control groups but not between COPD and age‐matched control groups (see Results). B, in the inspiratory threshold loading condition, the incidence of BPs was not significantly different between groups. ^*Significantly higher incidence in both the COPD and age‐matched control groups compared to the young control group.

A multiple logistic regression was performed using Age, Group and the Group × Age interaction as factors. There was no significant effect of the interaction (P = 0.92) and the subsequent analysis was performed without the interaction, giving the model:

$$\mathrm{Odds}\mathrm{BP}=0.0311\times 1.904^{\mathrm{Group}}\times 1.179^{\mathrm{Age}}$$

Odds BP is the ratio of the probability of the presence of a BP vs. that of the absence of a BP with group and age as the predictor variables in the model. Details of the model are given in Table 5, which shows that age is statistically significant (odds ratio = 1.176, P = 0.009) but group is not (odds ratio = 2.055, P = 0.43) with respect to determining the presence of a BP in quiet breathing. Table 5 also shows the slope coefficients, standard errors and confidence intervals for each predictor variable.

Table 5.

Details of the multiple logistic regression

	Coefficient	Standard error	Odds ratio	5% CI (lower)	95% CI (upper)	P value
Constant	−3.472	1.352	0.0311	0.00220	0.439	0.010*
Group	0.644	0.919	1.904	0.314	11.529	0.484
Age	0.165	0.0622	1.179	1.044	1.332	0.008*

Includes coefficient, standard error, odds ratio, lower and upper 5% confidence intervals (CI) and P values for each factor in the model: constant, group and age. Inputs for the age for each participant are given as the difference between the age and the mean age of all the participants (see Methods). ^*Significant effect of age on the probability of a BP being present in quiet breathing.

Further to the chi‐squared tests and the multiple logistic regression, the two‐way ANOVA between COPD and age‐matched control groups also suggest age as the determinant of a BP during quiet breathing (Fig. 3 A). The young control group was excluded from this analysis because no participant in this group had a BP during quiet breathing. Age was not significantly different between COPD (70 ± 9 years) and age‐matched control (71 ± 10 years) groups (P = 0.445). Across COPD and age‐matched control participants, there was a significant difference in age between those with (76 ± 7 years) and without (66 ± 8 years) a BP during quiet breathing (P = 0.003). There was no significant interaction between group and the presence of a BP during quiet breathing (P = 0.811). This supports the previous analysis on incidence between groups that showed that age, and not COPD, is the major factor in determining the presence of a BP during quiet breathing.

Figure 3. A comparison of age, CO₂, FEV1 (% pred) and Borg between those with a BP and those without during quiet breathing (QB) within the COPD and age‐matched control (AMC) groups.

Data shown are values for individual participants (open circles) and the mean ± SD (filled circles) for those participants with a BP present (on left) and BP absent (on right). QB Borg is given by median and interquartile range. Comparisons are shown for (A) Age, (B) tc $P_{\mathrm{C}{\mathrm{O}}_2}$, (C) FEV1 (% pred) and (D) Borg rating at the end of the quiet breathing condition (QB). ^*Significant difference between factors.

Figure 3 B shows no significant difference in tc $P_{\mathrm{C}{\mathrm{O}}_2}$ during quiet breathing between COPD patient (42.4 ± 5.7 mmHg) and age‐matched control (42.0 ± 4.5 mmHg) groups (P = 0.803). Across COPD and age‐matched control participants, there was no significant difference between those with (40.1 ± 4.3 mmHg) and without (43.6 ± 5.0 mmHg) a BP during quiet breathing (P = 0.056), nor was there a significant interaction between group and the presence of a BP (P = 0.668). In the COPD group, an exploratory test between CO₂ retainers (tc $P_{\mathrm{C}{\mathrm{O}}_2}$ > 45 mmHg) and non‐CO₂ retainers (tc $P_{\mathrm{C}{\mathrm{O}}_2}$ < 45 mmHg) showed a significant difference in the incidence of a BP during quiet breathing (0/4 and 6/10, respectively) (P = 0.040).

Spirometry results with respect to FEV₁ and FVC for all COPD, age‐matched and young control participants are given in Tables 2, 3, 4. Figure 3 C shows a significant difference in FEV₁ (percentage predicted; %pred) between COPD (49 ± 18%) and age‐matched control (93 ± 13%) groups (P < 0.001). Across COPD and age‐matched control participants, there was no significant difference in FEV₁ (%pred) between those with (74 ± 26%) and without (71 ± 28%) a BP during quiet breathing (P = 0.468). There was no significant interaction between group and the presence of a BP during quiet breathing (P = 0.422).

Figure 3 D shows a significant difference in Borg ratings at the end of quiet breathing between COPD [1.5 (1–2.75) points] and age‐matched control [0 (0–0.5) points] groups (P < 0.001). Across COPD and age‐matched control participants, there was no significant difference between those with [1.0 (0.4–1.0) points] and without [0.75 (0–1.75) points] a BP during quiet breathing (P = 0.368). There was no significant interaction between group and the presence of a BP during quiet breathing (P = 0.200).

In addition, MIP did not differ between COPD and age‐matched control groups (P = 0.421) or between those with (88.8 ± 27.5 cmH₂O) and without (89.3 ± 18.9 cmH₂O) a BP during quiet breathing (P = 0.967). There was also no interaction (P = 0.838). There was a higher proportion of male participants in the COPD and age‐matched control groups, although the incidence of BPs in male participants was similar in both groups (P = 0.423). This suggests that there was also no effect of sex and that age appears to be the main factor associated with the presence of a BP during quiet breathing.

BPs in inspiratory threshold loading

Figure 2 B shows the incidence of BPs during breathing via an inspiratory threshold load: COPD (4/9), age‐matched controls (7/16) and healthy young controls (8/15). There was no effect of group on the incidence of BPs in inspiratory threshold loading across the three groups (P = 0.848). For the young control group, there was an increase in the incidence of a BP between quiet breathing and inspiratory threshold loading (P < 0.001), although this did not occur for COPD participants (P = 0.916) or age‐matched controls (P = 0.719). A comparison of the latencies and amplitudes of the BPs between quiet breathing and inspiratory threshold loading is provided in Table 6.

Table 6.

Latencies and amplitudes of BPs

	Quiet breathing			Inspiratory threshold loading
	COPD	Age‐matched	Young	COPD	Age‐matched	Young
	(n = 6)	(n = 6)	(n = 0)	(n = 4)	(n = 7)	(n = 8)
Latency (s)	1.3	1.3	NA	1.1	1.2	1.4
	(0.8–1.9)	(1.0–1.7)		(0.7–1.6)	(1.0–1.6)	(1.2–1.5)
Amplitude (μV)	8.6	2.3	NA	8.1	3.3	5.4
	(4.9–10.7)	(1.9–3.7)		(6.2–8.9)	(2.8–4.5)	(4.4–7.2)

Shown for COPD, age‐matched and young control groups for the quiet breathing and inspiratory threshold loading conditions. Latency is given in seconds (s) and amplitude is given in microvolts (μV). Values are medians with interquartile range (in parentheses) for n participants. NA, not available.

The mean Borg ratings for each group in whom a BP was present and in whom a BP was absent during inspiratory loading are shown in Fig. 4. The two‐way ANOVA showed an increase of 42% (0.94 points) in the mean Borg ratings during the inspiratory loading as a main effect across all participants where a BP was present (n = 19) and where a BP was absent (n = 21) (P = 0.007) (Fig. 4 D). However, there was no effect of group (P = 0.242) and there was no interaction between group and the presence or absence of a BP (P = 0.268).

Figure 4. Borg ratings where a BP was either present or absent during inspiratory threshold loading.

A, COPD. B, Age‐matched. C, Young. D, Combined (n = 36). Combined includes the data for young, COPD and age‐matched participants for which the two‐way ANOVA was performed. Data shown are values for individual participants (open circles) and the mean ± SD (filled circles) for those participants with a BP present (left) and BP absent (right). ^*Significant difference in the combined group.

Average mouth pressure reached during inspiratory threshold loading was not significantly different between COPD (8.1 ± 2.5 cmH₂O), age‐matched control (8.6 ± 3.6 cmH₂O) and young control (8.1 ± 1.9 cmH₂O) groups (P = 0.874). Across all participants, there was also no significant difference between those with (8.5 ± 2.6 cmH₂O) and without (8.1 ± 2.9 cmH₂O) a BP (P = 0.332). There was also no significant interaction between group and the presence of a BP (P = 0.192).

OSA risk

The Berlin Sleep Questionnaire was used to assess the risk of OSA in our COPD and age‐matched control groups. Six participants with COPD and two age‐matched control participants showed a high risk of undiagnosed OSA, although there was no significant difference between the incidence of a high risk of OSA in the COPD group compared to that in the age‐matched control group (P = 0.061). There was also no relationship between a high risk of OSA and the presence of a BP during quiet breathing (P = 0.525). No age‐matched control participant with a BP during quiet breathing was at high risk of OSA. Four of the six COPD participants with a BP during quiet breathing were at high risk of OSA.

Discussion

The novel finding of the present study is that a cortical contribution to inspiration in quiet breathing, as indicated by the presence of a BP, occurred more frequently in participants with COPD than in healthy young participants. Furthermore, ageing rather than COPD, appears to be the primary factor associated with this cortical contribution. In addition, across all participants during inspiratory loading, a cortical contribution to breathing was linked to subjective reports of more intense dyspnoea.

BPs in quiet breathing

The present study confirmed that BPs preceding inspiration are absent during quiet breathing in healthy young participants (see Introduction), as well as present in ∼40% of participants within both the COPD and age‐matched control groups. Although the automatic drive generated by the medulla (Duffin, 2004; Feldman et al. 2013) underlies quiet breathing, cortical contributions to central respiratory drive may be engaged to overcome a respiratory load–capacity imbalance (Georges et al. 2016). Increased cortical excitability during inspiratory loading has been documented with transcranial magnetic stimulation (Locher et al. 2006) and is detectable using functional magnetic resonance imaging (Raux et al. 2013a).

Some respiratory disorders have a respiratory load‐capacity imbalance, which may result in a cortical contribution to breathing. A cortical contribution to quiet breathing occurs during wakefulness in OSA (Launois et al. 2014) where upper airway abnormalities are considered to impose an intrinsic inspiratory load, although this prior study did not fully address the effect of age. In central congenital hypoventilation syndrome, where there is a deficiency in brainstem generation of automatic breathing, there is a cortical contribution to quiet breathing (Tremoureux et al. 2014a). Here, a cortical drive is present in patients with central congenital hypoventilation syndrome, presumably to compensate for their deficient automatic drive. Finally, in amyotrophic lateral sclerosis, a cortical contribution to quiet breathing was observed and was associated with significantly larger phasic activity in the scalene muscles (Georges et al. 2016).

Our finding that there is a cortical contribution to breathing associated with ageing is consistent with the concept that changes to the respiratory system in normal ageing overlap with changes in COPD (MacNee et al. 2014; Mercado et al. 2015). For example, calcification of rib cartilages and changes in the chest wall geometry contribute to a decrease in chest wall compliance (Hirata et al. 2015). Degeneration of elastic fibres in the lung reduces the static recoil pressure and contributes to ‘senile’ hyperinflation (Suki & Bartolák‐Suki, 2015). Respiratory muscle fibres decrease in number, function at a shorter disadvantageous length, convert from type II to weaker fatigue‐resistant type I fibres and undergo progressive denervation (Biolo et al. 2014). An increase in neural drive to the respiratory muscles in people over 50 years old (Jolley et al. 2009) may reflect compensation for these mechanical changes. Although COPD has been described as an accelerated form of lung ageing (Ito & Barnes, 2009), the changes in normal ageing are less severe than those in COPD. However, we found no apparent effect of measures of COPD severity such as FEV₁, CO₂, dyspnoea or inspiratory muscle strength on the incidence of BPs in quiet breathing, nor was there an obvious link between hyperinflation and the incidence of BPs in the patients with COPD for whom data were available (Table 2). Thus, we speculate that a cortical contribution during quiet breathing in wakefulness occurs because there are neural deficits in the automatic control of breathing, namely age‐related degeneration of preBötzinger neurons that generate the respiratory rhythm (McKay et al. 2005; Wellman et al. 2007).

Dyspnoea as a consequence of the cortical drive to breathe

In the present study, we found that a cortical contribution to inspiration during inspiratory threshold loading is associated with more severe dyspnoea across all participants. However, it was more pronounced in the young control group. The differences in mean Borg ratings for the inspiratory threshold loading condition, although statistically significant, are numerically small. They represent a difference in ratings in descriptors from between ‘slight’ and ‘mild’ to between ‘mild’ and ‘somewhat severe’. Dyspnoea is an experience with sensory and affective components (Laviolette & Laveneziana, 2014; Banzett et al. 2015; Booth & Lansing, 2016) (see Introduction). In the present study, participants were asked to rate their ‘effort to breathe’, which corresponds with the work/effort sensory component of dyspnoea. The work/effort modality is assumed to result from an imbalance between the outgoing voluntary motor command (detected by a corollary discharge) and the output of the respiratory muscles (detected by respiratory muscle afferents). The supplementary motor area (SMA) is involved in the prediction of the sensory consequences of movement (Nachev et al. 2008; Makoshi et al. 2011) and also receives inputs from respiratory muscles (Davenport et al. 1985; Davenport & Vovk, 2009). Therefore, the SMA may be involved in driving the work/effort modality of dyspnoea.

Dyspnoea correlates better with inspiratory neural drive (as assessed by a diaphragmatic EMG activity) than ventilation parameters during incremental exercise in moderate–severe COPD as a result of the neuroventilatory uncoupling that results from an intrinsic inspiratory load and hyperinflation (Jolley et al. 2015; Jolley & Moxham, 2016). However, prior to the present study, it was not known whether the inspiratory neural drive associated with exertional dyspnoea is the result of a cortical drive, a medullary drive, or both. The results of the present study show an association between a cortical contribution to inspiration during threshold loading and dyspnoea, in particular mainly the work/effort dimension of dyspnoea, because we did not separately assess breathing discomfort, a key component of dyspnoea (Parshall et al. 2012). The SMA may therefore be implicated in both inspiratory‐related cortical activity and the sensation of dyspnoea in response to an inspiratory load.

Consideration of the effect of concomitant OSA

A potential confounder for the present study is that many people with moderate‐severe COPD also have OSA (Soler et al. 2015). People with COPD and OSA tend to be older than isolated OSA patients (O'Brien & Whitman, 2005; Zamarrón et al. 2008). One study has shown a higher incidence of BPs in quiet breathing in wakefulness in participants with OSA (grouped across mild, moderate and severe OSA) (Launois et al. 2014). In the severe OSA group, BMI was similar in those with and without a BP in quiet breathing (Launois et al. 2014). However, because those with a BP were, on average, 13 years older than those without a BP, the higher incidence of BPs in the severe OSA group may have been confounded by age. Our data suggest that age is a significant contributor to the presence of a BP in quiet breathing and, although we do not have polysomnography tests for OSA in our participants, our OSA risk data, which is a good predictor of OSA (Sharma et al., 2011), suggests that, similar to COPD, OSA did not play a part in BP incidence.

Assessment of BPs

Standardization of the assessment of BPs is necessary to ensure confidence when comparing incidences in BPs. To date, no formal guidelines on the assessment of BPs have been published. BPs do not typically have latencies shorter than 500 ms or longer than 2000 ms, although factors such as preparatory state, force and speed can alter their latency and magnitude (Lang, 2003; Shibasaki & Hallett, 2006). In the present study, we have attempted to standardize the assessment of the BPs between our two observers. One BP was reported as having a latency less than 500 ms and three were reported as having a latency greater than 2000 ms, although the latencies were within 100 ms of the range allowed by the rules (see Methods). BPs with small amplitudes of ∼1 μV are difficult to detect, in which case confidence is increased by the averages of odd‐ and even‐numbered trials.

The average latency of BPs in the young control group in the present study is consistent with those obtained in most previous studies for the voluntary self‐paced sniff (1.2 s in the present study vs. range 0.8–1.7 s) (Macefield & Gandevia, 1991; Raux et al. 2007b, 2010; Jutand et al. 2012; Jeran et al. 2013; Tremoureux et al. 2014b; Dubois et al. 2016; Hudson et al. 2016) and inspiratory threshold loading (1.3 s in the present study vs. range 0.6–1.6 s) (Raux et al. 2007b, 2010; Tremoureux et al. 2014a; Hudson et al. 2016) conditions. However, the average amplitude of BPs for voluntary self‐paced sniffs in the young control group is higher in the present study study than in previous studies (12.1 μV in the present study vs. 3.9–9.9 μV in other studies) (Macefield & Gandevia, 1991; Raux et al. 2007b, 2010; Jeran et al. 2013; Tremoureux et al. 2014b; Dubois et al. 2016; Hudson et al. 2016). The higher variability of the amplitude of the BP suggests that it may be more sensitive to the force or rate of the respiratory task than the BP latency measures.

BPs are not expected in healthy young people during unloaded quiet breathing, although most studies report a low incidence (16%; 15 of 91 healthy controls, as derived from several studies) (Macefield & Gandevia, 1991; Raux et al. 2007a, b , 2010; Tremoureux et al. 2014a; Morawiec et al. 2015; Dubois et al. 2016; Hudson et al. 2016). The presence of occasional BPs has been attributed to experimental factors such as the presence of respiratory apparatus and external cues about breathing. In the present study, no participants in the young group had a BP during quiet breathing, which may reflect minimization of these factors.

Conclusions

A cortical contribution to quiet breathing was linked to ageing rather than to COPD. This may be a compensatory response to an age‐related deficit in the medullary drive to breathe but if so, this response does not appear to be strongly correlated with changes in intrinsic inspiratory load that are greater in COPD than ageing. Furthermore, a cortical contribution to breathing via an extrinsic inspiratory load was associated with more severe dyspnoea ratings across all participants. Thus, cortical activation during breathing may contribute in part to the sense of work/effort aspect of dyspnoea and compensate for age‐related neural deficits that result in insufficient medullary neural drive.

Additional information

Competing interests

DJE has a Cooperative Research Centre Project Grant that is co‐supported by the Australian Government and an industry partner (Oventus Medical) and he receives personal fees as a consultant for Bayer, both of which are outside the submitted work.

Author contributions

This work was carried out at Neuroscience Research Australia, Sydney, Australia. DATN, SCG, JEB and ALH contributed to study conception and design. DATN, CLB‐R, RAM, JEB and ALH collected the data. DATN analysed the data and drafted the manuscript. All authors interpreted the analysed data and critically revised the manuscript. All authors approved the final version of the manuscript submitted for publication and all persons who qualify for authorship are listed.

Funding

This work was funded by a National Health and Medical Research Council (NHMRC) Project Grant (no. 1138920). Additionally, DATN is supported by an Australian Research Training Program Scholarship; EJK, SCG and JEB are supported by NHMRC Research Fellowships; and ALH is supported by a Lung Foundation Australia/Boehringer Ingelheim COPD Research Fellowship.

Biography

David Nguyen studied at the University of New South Wales, graduating with a Bachelor of Medical Science (First Class Honours) before commencing his PhD at Neuroscience Research Australia in 2017. David's research foci are changes in the neural control of breathing in chronic obstructive pulmonary disease, spinal cord injury and healthy ageing, using the techniques of electroencephalography and electromyography. His work will help clarify the common and unique mechanisms which underpin respiratory motor impairment in healthy ageing and disease.