Reply from Geoffrey L. Hartley and Stephen S. Cheung
Bibliographic record
Abstract
We thank Dr Gandevia for his thorough review of our article ‘Corticospinal excitability is associated with hypocapnia but not changes in cerebral blood flow’ (Hartley et al. 2016). In his Letter, Dr Gandevia (2016) has highlighted some aspects of our methods and statistics, which we are happy to discuss in further detail. The goal of our work was to isolate the influence of reductions in cerebral blood flow, with and without reductions in , on motor cortex excitability. This research could provide mechanistic insight into neuromuscular responses associated with altered cerebral blood flow (CBF) during exercise. Specifically, the regulation of CBF is highly dependent on numerous interacting variables (Ainslie & Duffin, 2009), and consequently the cerebrovascular response to a similar magnitude of exogenous stress may be significantly different across individuals. Furthermore, change in in itself is a potent regulator of both CBF and neuromuscular function. Therefore, a combination of end-tidal forcing and cyclooxygenase inhibition via indomethacin was used to examine the influence of changes in CBF, independent of , on neuromuscular function. We have performed additional analyses to address some of Dr Gandevia's concerns regarding the measurement of voluntary activation. Firstly, interclass correlation (ICC) analyses indicates a high degree of reliability (ICC = 0.91, 95%CI: 0.73, 0.98) between baseline voluntary activation measurements and suggests that our methods and apparatus were operating properly. Secondly, baseline resting twitch responses to TMS were estimated (see Todd et al. (2004) for details) as 5.50 ± 2.4 N m through linear extrapolation of submaximal twitch responses. The mean correlation coefficient for twitch extrapolation across all participants and conditions was r2 = 0.90 ± 0.07 and ranged between r2 = 0.74 and r2 = 0.99. When examined in the context of recent works by Todd et al. (2016), we feel that our methods adhere to many of the suggested guidelines (e.g. individualized TMS intensity, MEP amplitude > 80% Mmax) and therefore represent valid measurements of voluntary activation. In response to the hypocapnic (10 mmHg below eucapnia) experimental intervention, two participants produced voluntary activation levels of 47% and 30.5%, respectively; however, voluntary activation in response to this experimental intervention in the remaining eight participants did not fall below 50%. In line with the rationale of this study, we believe that this unique response may be due in part to individual differences in cerebrovascular CO2 reactivity and, consequently, significant reductions in CBF in response to the hypocapnia condition. Our data suggest a relationship between reductions in CBF and voluntary activation, which corroborates the work of Ross et al. (2012); however, further research is required to confirm this association. Furthermore, controlled hyperventilation during the hypocapnia condition is associated with significant increases in perception of effort and discomfort, factors that are known to influence voluntary activation (Berchicci et al. 2013; Lampropoulou & Nowicky, 2014). The observation of reduced voluntary activation in the absence of significant changes in maximal voluntary torque production is not novel. In response to interventions of similar physiological stress (i.e. thermal hyperpnoea and hypoxia-mediated hyperventilation), previous works of Ross et al. (2012) and Goodall et al. (2012) report significant reductions in cortical voluntary activation without changes in torque production. Therefore, we proposed that other factors, in addition to central mechanisms, regulate torque production in response to hypocapnic stress. Dr Gandevia takes exception to the twitch characteristics of the flexor carpi radialis evoked by magnetic stimulation of the motor cortex. Specifically, Dr Gandevia compares the evoked twitches presented in Fig. 2 of our article to the work of Lee et al. (2008) and suggests that the rise times are ‘extraordinarily long’. Rather than being influenced by possible compliance issues related to the experimental apparatus, a more plausible explanation is due to differences in shoulder and elbow position. The apparatus used by Lee et al. (2008) positions the participant at a smaller angle of elbow flexion, compared to a position of relative elbow extension while using our apparatus. Therefore, the increased muscle length of the flexor carpi radialis (with origin on the medial epicondyle of the humerus) induced by this posture is likely to account for the longer twitch rise times. We remain unconvinced that issues related to our statistical approach and interpretation cloud the conclusions of our work. We share Dr Gandevia's concerns regarding limitations associated with the statistical convention of using arbitrary P values (i.e. P < 0.05) to identify meaningful effects. Alternatively – as stated in the statistical analysis section – our data were interpreted based on effect size calculations and classifications outlined by Cohen (1988). Moreover, we believe that the linear mixed model methods employed in the analysis of our data provide appropriate and robust analysis of repeated measures data, especially when examining experimental conditions presented in a fixed sequence. Specifically, one advantage of this analysis technique is the prevention of false-positive associations due to inherent structures found within the condition factor. Furthermore, specification of a random intercepts model allows the examination of participants as a random factor. Although we feel strongly that this statistical approach is most appropriate given the experimental design and provides the most objective interpretation of the data, we recognize the limitation of substantial uncertainty in the effect size estimates due to the size of the confidence intervals. Therefore, the relationship between changes in CBF and voluntary activation warrant further investigation. None declared.
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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 | 0.005 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.023 | 0.044 |
| Insufficient payload (model declined to judge) | 0.006 | 0.009 |
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".