Modulation of chest wall intermuscular coherence: effects of lung volume excursion and transcranial direct current stimulation
Bibliographic record
Abstract
Chest wall muscle recruitment varies as a function of the breathing task performed. However, the cortical control of the chest wall muscles during different breathing tasks is not known. We studied chest wall intermuscular coherence during various task-related lung volume excursions in 10 healthy adults (34 ± 15 yr; 2 men, 8 women) and determined if transcranial direct current stimulation (tDCS) could modulate chest wall intermuscular coherence during these tasks. Simultaneous assessment of regional intercostal and oblique electromyographic activity was measured while participants performed standardized tidal breathing, speech, maximum phonation, and vital capacity tasks. Lung volume and chest wall kinematics were determined using variable inductance plethysmography. We found that chest wall area of intermuscular coherence was greater during tidal and speech breathing compared with phonation and vital capacity (all P < 0.05) and between tidal breathing compared with speech breathing (P < 0.05). Anodal tDCS increased chest wall area of intermuscular coherence from 0.04 ± 0.09 prestimulation to 0.18 ± 0.19 poststimulation for vital capacity (P < 0.05). Sham tDCS and cathodal tDCS had no effect on coherence during lung volume excursions. Chest wall kinematics were not affected by tDCS. Our findings indicate that lung volume excursions about the midrange of vital capacity elicit a greater area of chest wall intermuscular coherence compared with lung volume excursions spanning the entire range of vital capacity in healthy adults. Our findings also demonstrate that brief tDCS may modulate the cortical control of the chest wall muscles in a stimulation- and lung volume excursion task-dependent manner but does not affect chest wall kinematics in healthy adults.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".