Sniffing-related motor cortical potential: topography and possible generators

Combined head accelerometry and EEG improves the detection of respiratory-related cortical activity during inspiratory loading in healthy participants

Mechanical ventilation is a highly utilized life-saving tool, particularly in the current era. The use of EEG in a brain-ventilator interface (BVI) to detect respiratory discomfort (due to sub-optimal ventilator settings) would improve treatment in mechanically ventilated patients. This concept has been realized via development of an EEG covariance-based classifier that detects respiratory-related cortical activity associated with respiratory discomfort. The aim of this study was to determine if head movement, detected by an accelerometer, can detect and/or improve the detection of respiratory-related cortical activity compared to EEG alone. In 25 healthy participants, EEG and acceleration of the head were recorded during loaded and quiet breathing in the seated and lying postures. Detection of respiratory-related cortical activity using an EEG covariance-based classifier was improved by inclusion of data from an Accelerometer-based classifier, i.e. classifier 'Fusion'. In addition, 'smoothed' data over 50s, rather than one 5 s window of EEG/Accelerometer signals, improved detection. Waveform averages of EEG and head acceleration showed the incidence of pre-inspiratory potentials did not differ between loaded and quiet breathing, but head movement was greater in loaded breathing. This study confirms that compared to event-related analysis with >5 min of signal acquisition, an EEG-based classifier is a clinically valuable tool with rapid processing, detection times, and accuracy. Data smoothing would introduce a small delay (<1 min) but improves detection results. As head acceleration improved detection compared to EEG alone, the number of EEG signals required to detect respiratory discomfort with future BVIs could be reduced if head acceleration is included.

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

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.

A simple method for EEG guided transcranial electrical stimulation without models

OBJECTIVE

There is longstanding interest in using EEG measurements to inform transcranial Electrical Stimulation (tES) but adoption is lacking because users need a simple and adaptable recipe. The conventional approach is to use anatomical head-models for both source localization (the EEG inverse problem) and current flow modeling (the tES forward model), but this approach is computationally demanding, requires an anatomical MRI, and strict assumptions about the target brain regions. We evaluate techniques whereby tES dose is derived from EEG without the need for an anatomical head model, target assumptions, difficult case-by-case conjecture, or many stimulation electrodes.

APPROACH

We developed a simple two-step approach to EEG-guided tES that based on the topography of the EEG: (1) selects locations to be used for stimulation; (2) determines current applied to each electrode. Each step is performed based solely on the EEG with no need for head models or source localization. Cortical dipoles represent idealized brain targets. EEG-guided tES strategies are verified using a finite element method simulation of the EEG generated by a dipole, oriented either tangential or radial to the scalp surface, and then simulating the tES-generated electric field produced by each model-free technique. These model-free approaches are compared to a 'gold standard' numerically optimized dose of tES that assumes perfect understanding of the dipole location and head anatomy. We vary the number of electrodes from a few to over three hundred, with focality or intensity as optimization criterion.

MAIN RESULTS

Model-free approaches evaluated include (1) voltage-to-voltage, (2) voltage-to-current; (3) Laplacian; and two Ad-Hoc techniques (4) dipole sink-to-sink; and (5) sink to concentric. Our results demonstrate that simple ad hoc approaches can achieve reasonable targeting for the case of a cortical dipole, remarkably with only 2-8 electrodes and no need for a model of the head.

SIGNIFICANCE

Our approach is verified directly only for a theoretically localized source, but may be potentially applied to an arbitrary EEG topography. For its simplicity and linearity, our recipe for model-free EEG guided tES lends itself to broad adoption and can be applied to static (tDCS), time-variant (e.g., tACS, tRNS, tPCS), or closed-loop tES.

Electroencephalographic detection of respiratory-related cortical activity in humans: from event-related approaches to continuous connectivity evaluation

The presence of a respiratory-related cortical activity during tidal breathing is abnormal and a hallmark of respiratory difficulties, but its detection requires superior discrimination and temporal resolution. The aim of this study was to validate a computational method using EEG covariance (or connectivity) matrices to detect a change in brain activity related to breathing. In 17 healthy subjects, EEG was recorded during resting unloaded breathing (RB), voluntary sniffs, and breathing against an inspiratory threshold load (ITL). EEG were analyzed by the specially developed covariance-based classifier, event-related potentials, and time-frequency (T-F) distributions. Nine subjects repeated the protocol. The classifier could accurately detect ITL and sniffs compared with the reference period of RB. For ITL, EEG-based detection was superior to airflow-based detection (P < 0.05). A coincident improvement in EEG-airflow correlation in ITL compared with RB (P < 0.05) confirmed that EEG detection relates to breathing. Premotor potential incidence was significantly higher before inspiration in sniffs and ITL compared with RB (P < 0.05), but T-F distributions revealed a significant difference between sniffs and RB only (P < 0.05). Intraclass correlation values ranged from poor (-0.2) to excellent (1.0). Thus, as for conventional event-related potential analysis, the covariance-based classifier can accurately predict a change in brain state related to a change in respiratory state, and given its capacity for near "real-time" detection, it is suitable to monitor the respiratory state in respiratory and critically ill patients in the development of a brain-ventilator interface.