A cognitive task, deep breathing, and static stretching reduce variability of motor evoked potentials during subsequent transcranial magnetic stimulation (2025)

Abstract

Background:

Motor evoked potentials (MEPs) induced via transcranial magnetic stimulation (TMS) demonstrate trial-to-trial variability limiting detection and interpretation of changes in corticomotor excitability. This study examined whether performing a cognitive task, voluntary breathing, or static stretching before TMS could reduce MEP variability.

Methods:

20 healthy young adults performed no-task, a cognitive task (Stroop test), deep breathing, and static stretching before TMS in a randomized order. MEPs were collected in the non-dominant tibialis anterior muscle at 130% active motor threshold. Variability of MEP amplitude was quantified as coefficient of variation (CV).

Results:

MEP CV was greater after no-task (25.4 ± 7.0) than after cognitive task (23.3 ± 7.2; p<0.05), deep breathing (20.1 ± 6.3; p<0.001), and static stretching (20.9 ± 6.0; p=0.004). MEP CV was greater after cognitive task than after deep breathing (p=0.007) and static stretching (p=0.01). There was no effect of condition on MEP amplitude.

Conclusions:

Performing brief cognitive, voluntary breathing, and stretching tasks before TMS can reduce MEP variability with no effect on MEP amplitude in the tibialis anterior of healthy, young adults. Similar tasks could be incorporated into research and clinical settings to improve detection of changes, normative data, and clinical predictions.

Keywords: Transcranial magnetic stimulation, motor evoked potentials, variability, deep breathing, static stretching

Graphical Abstract

1. Introduction

Transcranial magnetic stimulation (TMS) is a non-invasive brain stimulation tool to study the functional integrity of corticomotor and intracortical neuronal circuits (Rossini and Rossi, 2007). The output from the motor cortex elicited by TMS is objectively measured using electromyography (EMG) and characterized as the motor evoked potential (MEP). The MEP is broadly considered to be a neurophysiological marker of corticomotor excitability (Bestmann and Krakauer, 2015) and a potential prognostic tool for motor recovery. However, it is well known that MEPs have considerable trial-to-trial variability (Wassermann, 2002). Relative variability of MEP amplitude, often quantified via coefficient of variation (CV, standard deviation relative to the mean), has been reported to range from 22% to 112% for various upper limb muscles (Darling et al., 2006; Ellaway et al., 1998; Hordacre et al., 2017; Kiers et al., 1993), and 7–92% for various lower limb muscles (Cacchio et al., 2009; Charalambous et al., 2018; O’Leary et al., 2015; Tallent et al., 2012; Temesi et al., 2017), with differences between studies likely arising from a variety of factors. This variability can make it difficult to determine actual corticomotor excitability and detect changes in excitability in response to an intervention, experimental manipulation, recovery, or aging. Consequently, MEP variability can hamper the ability to identify interventions that elicit changes in corticomotor excitability.

To strengthen the rigor, reproducibility, and interpretation of single session and longitudinal TMS data, it is important to generate innovative solutions to minimize trial-to-trial or within session MEP variability. It has been recommended (Chipchase et al., 2012) that investigators control for several factors that may contribute to MEP variability (including participant, methodological, and analysis factors). Less research has focused on neurophysiological factors that contribute to MEP variability and whether specific actions can be taken during testing sessions to reduce MEP variability. Previous work has shown that spontaneous oscillations in brain state (Goetz et al., 2014; Janssens and Sack, 2021; Silvanto et al., 2008) and neuronal excitability at the spinal and/or cortical level (Ellaway et al., 1998; Kiers et al., 1993; Rosler et al., 2008) likely contribute to MEP variability. Hence, actions during testing sessions that stabilize these sources of variability may reduce trial-to-trial MEP variability.

Because of the complexity and duration of TMS sessions (often >2 hours), participants can experience changes in alertness and attention throughout a session (and corresponding changes in brain oscillatory state and neuronal excitability), which influence MEP amplitude (Noreika et al., 2020). Cognitive tasks that stimulate alertness or arousal performed before application of TMS may reduce large variations in brain oscillatory state and reduce MEP variability. Similarly, deep breathing can promote a synchronized, slow cortical rhythm that is less variable (Noble and Hochman, 2019). Finally, stretching may increase the level of afferent feedback to the cortex, helping stabilize cortical neuronal excitability (Opplert et al., 2020).

Several studies have evaluated the effects of breathing and stretching tasks on MEP amplitude. Voluntary breathing (fast inspiration and expiration) during TMS increases MEP amplitude (Li and Rymer, 2011; Ozaki and Kurata, 2015; Shirakawa et al., 2015), but no studies have looked at the effect of brief deep breathing on subsequent TMS. Static or dynamic stretching of the ankle plantarflexors has been reported to increase (Budini et al., 2018; Opplert et al., 2020) or have no effect (Budini et al., 2017; Budini et al., 2019; Pulverenti et al., 2019; Pulverenti et al., 2020) on subsequent [or concurrent; Guissard et al. (2001)] MEP amplitude in the stretched muscles. Only one study that has evaluated MEP amplitude after stretch of a different muscle; static stretch of the ankle plantarflexors did not influence MEP amplitude in the tibialis anterior (Budini et al., 2018). We are not aware of any studies that have evaluated the effects of cognitive tasks on MEP amplitude or variability or of breathing or stretching tasks on subsequent MEP variability.

The objective of this study was to determine whether performing brief tasks before TMS can reduce trial-to-trial MEP variability without influencing MEP amplitude. We chose to investigate the effects of a cognitive task, deep breathing, and static stretching because these tasks can be performed simply from a seated position without additional equipment, have been used in prior literature, and theoretically could impact MEP variability. Furthermore, in the current study, these tasks were performed without involving the target muscle and prior to TMS to decrease potential impact on MEP amplitude, which can obscure interpretation of the effects of an intervention on corticomotor excitability. We hypothesized that a cognitive task, voluntary breathing, and static stretching would all decrease subsequent trial-to-trial MEP variability as compared to a no-task condition.

2. Results

20 participants completed the study (mean age 25.95 ± 3.93 years,12 females and 16 right-leg dominant). Briefly, participants performed four tasks (no task, cognitive task [Stroop], deep breathing, and static stretching) before TMS with 3-minute washout between tasks. TMS MEP amplitude and variability (CV) were measured after each condition and compared with a one-way ANOVA and post-hoc t-tests. MEP amplitude, MEP CV, and F-NRS values are shown in Table 1. The rANOVA for MEP CV (Fig. 1) revealed a significant main effect of task (F(3,17)=9.5, p=0.001). Post hoc pairwise comparisons showed that MEP CV following no-task was greater than following the cognitive condition (mean difference: 2.1 ± 4.4, 95% CI: −0.8, 5.0; t(19)=2.1, p<0.05, d=0.5), the deep breathing condition (mean difference: 5.3 ± 4.5, 95% CI: 2.3, 8.3; t(19)=5.2, p<0.001, d=1.2) and the static stretching condition (mean difference: 4.5 ± 5.0, 95% CI: 1.2, 7.7; t(19)=4.0, p=0.004, d=0.9). MEP CV following the cognitive condition was greater than following the deep breathing condition (mean difference: 3.2 ± 3.8, 95% CI: 0.7, 5.7; t(19)=3.8, p=0.007, d=0.8) and the static stretching condition (mean difference: 2.4 ± 3.7, 95% CI: −0.05, 4.9; t(19)=2.9, p=0.01, d=0.6). There was no significant difference in MEP CV following the breathing condition than following the stretching condition (mean difference: −0.8 ± 3.8, 95% CI: −3.4, 1.7; t(19)=1.0, p=0.35, d=0.2). The rANOVA for MEP amplitude found no significant effect of condition (F(3,17)=0.7, p=0.57). On average, the F-NRS score was not different from the beginning to the end of the session (1.9 ± 2.3 to 2.6 ± 2.5, mean difference: 0.75, 95% CI: −0.2, 1.7; Z=−1.6, p=0.10). There were no differences in F-NRS score between any condition (Z≥−1.04, p≥0.30).

Table 1:

Motor evoked potentials (MEP) and fatigue severity values.

Condition	MEP amplitude (mV)	MEP amplitude CV (%)	F-NRS

No-task	0.71 ± 0.27	25.4 ± 7.0	2.6 ± 2.4
Cognitive	0.73 ± 0.32	23.3 ± 7.2	2.5 ± 2.3
Breathing	0.73 ± 0.33	20.1 ± 6.3	2.3 ± 2.3
Stretching	0.71 ± 0.30	20.9 ± 6.0	2.3 ± 2.2

Open in a new tab

Displayed are the MEP amplitude, MEP coefficient of variation (CV), and fatigue numerical rating scale (F-NRS) for all four conditions.

A cognitive task, deep breathing, and static stretching reduce variability of motor evoked potentials during subsequent transcranial magnetic stimulation (2)

3. Discussion

TMS provides important information about corticomotor excitability, but variability in TMS MEPs makes interpretation difficult. Previous studies have shown that spontaneous oscillations in brain state (Goetz et al., 2014; Janssens and Sack, 2021; Silvanto et al., 2008) and neuronal excitability at the spinal and/or cortical level (Ellaway et al., 1998; Kiers et al., 1993; Rosler et al., 2008) likely contribute to MEP variability. The objective of this study was to determine whether performing brief tasks prior to TMS (cognitive task, deep breathing, and static stretching) can reduce trial-to-trial MEP variability in a healthy, young population. These tasks were chosen because they have the potential reduce variability of brain state and neuronal excitability and can be performed from a seated position without additional equipment.

We found that MEP variability was lesser following all three conditions than following the no-task condition, and deep breathing and static stretching led to a greater reduction in MEP variability than the cognitive task. Deep breathing has been shown to promote a synchronized, slow cortical rhythm (Noble and Hochman, 2019), which may reduce fluctuations in the excitability of descending motor pathways stimulated by TMS. Static stretching may increase the level of afferent feedback from muscle spindle and joint receptors to the cortex, helping stabilize cortical neuronal excitability (Opplert et al., 2020). Importantly, we did not find an effect of any of these tasks on MEP amplitude. These findings suggest that engaging in brief (~1 min) goal-directed tasks prior to the application of TMS can lead to reduced MEP variability without influencing MEP amplitude and obscuring interpretation.

Although no studies have evaluated the effect of any of these tasks on MEP variability, previous work has shown that fast inspiration and expiration during TMS increases MEP amplitude (Li and Rymer, 2011; Ozaki and Kurata, 2015; Shirakawa et al., 2015). Unlike the current work, these previous studies used a different breathing task and assessed MEP amplitude during the task. Previous work studying MEP amplitude and stretching have had ambivalent results with some showing that static or dynamic stretching increases subsequent MEP amplitude (Budini et al., 2018; Opplert et al., 2020) and other showing no effect (Budini et al., 2017; Budini et al., 2019; Pulverenti et al., 2019; Pulverenti et al., 2020). Unlike the current study, these studies all evaluated MEP amplitude in the same muscle that was stretched (ankle plantarflexors). Consistent with the current study, Budini et al. (2018) found that stretching one muscle (ankle plantarflexor) does not influence subsequent MEP amplitude in another muscle (tibialis anterior).

Our findings have important implications for studying corticomotor excitability in research and clinical settings. In research settings, incorporating brief tasks (i.e., deep breathing and static stretching) prior to TMS may reduce MEP trial-to-trial variability without influencing MEP amplitude. Consequently, this strategy could aid in the ability to detect changes in corticomotor excitability in response to an intervention, experimental manipulation, recovery, or aging. In clinical settings, MEPs have been used in a variety of populations (including healthy populations and populations with neurodegenerative disorders) to provide insight into the integrity of the corticospinal tract, generate normative data, aid in diagnosis, and yield prediction of clinical outcomes (Rossini and Rossi, 2007; Rossini et al., 2007; Vucic and Kiernan, 2017). Implementation of brief tasks such as deep breathing and static stretching prior to TMS may provide more precise estimates of norms within different populations, improve the ability to detect meaningful associations between TMS and clinical outcomes, and improve the use of TMS as a predictive tool. We found that these brief pre-TMS tasks do not induce fatigue and are easy to administer, which should make it easy to implement in the clinic.

Some limitations of this study may limit interpretation and generalizability. As an initial foray into these cognitive, voluntary breathing, and stretching tasks before TMS, this study was not designed to provide detailed insight into the mechanisms of action. Additionally, variations in the methodology (specific task, intensity/difficulty, duration, etc.) and enhanced TMS methodology (testing stimulus response curves in multiple muscles) may yield different results. This study was performed in healthy, young adults, so the results may not generalize to older or special populations.

3.1. Conclusions

In this study, we found that brief cognitive, deep breathing, and static stretching tasks performed prior to TMS in healthy, young adults, reduced MEP variability with no effect on MEP amplitude in the TA. These or similar tasks may be incorporated into research studies the to improve ability to detect changes in corticomotor excitability in response to an intervention, experimental manipulation, recovery, or aging. Furthermore, in the clinic using these tasks prior to TMS may yield more precise normative data and improve the use of TMS as a predictive tool. Future work should elucidate the mechanisms of action. For example, EEG and peripheral stimulation could be used to determine how brief cognitive, deep breathing, and static stretching tasks influence brain state and spinal excitability and potentially reduce MEP variability. Moreover, future work could evaluate the duration of the effect of these tasks on MEP variability.

4. Experimental procedure

A convenience sample of 20 healthy participants (8 males, 12 females) were recruited for this study conducted in the Brain Plasticity Laboratory at the University of Illinois at Chicago. Individuals aged 18–35 years (young adults) with a Mini Mental State Examination score of ≥24 were recruited. Exclusion criteria included contraindications to TMS (previous adverse reaction to TMS, unexplained or recurring headaches, concussion within the last 6 months, use of medication that alter cortical excitability) and any chronic musculoskeletal, neurological, or cardiovascular conditions. All participants provided written informed consent. This study was approved by the Institutional Review Board at the University of Illinois at Chicago and adhered to the Declaration of Helsinki.

4.1. Study procedure

The study was performed in a single day and consisted of four condition blocks with the following sequence (Fig. 2):

3-minute washout rest period. A washout period was performed prior to the first task because the rest period could influence MEP variability, and we sought to standardize procedure between the four conditions. We chose 3-minutes to try to balance the duration of the session with the potential for each task to impact the results of subsequent tasks.
1 minute task performance, followed by 25 TMS Pulses. We selected 1-minute as the duration of each task because this was comparable to previous work (Budini et al., 2018), and we sought a task that was brief enough to likely not induce changes in MEP amplitude.
1 minute task performance, followed by 25 TMS pulses. We decided to repeat each task in a second block because we were unsure of how long the effect on MEP variability would persist. This decision also yielded double the MEPs.

The only break between conditions was the washout. The task performed during each block was randomly selected for each participant, without replacement (i.e., each participant performed every condition one time in a random order with no repetitions), from the following conditions:

No-task: participants were instructed to sit quietly. This was designed to mimic rest periods typically provided during TMS studies between trains of TMS pulses.
Cognitive: participants performed a modified version of the Stroop test, a neuropsychological test used to assess a person’s ability to inhibit cognitive interference (Stroop, 1935). Words that spelled a color were presented to participants in colored fonts different from what the word implied. Participants were instructed to name the color of the word not what the word spelled.
Deep breathing: participants were instructed to take self-paced, slow deep inspirations followed by passive long expirations.
Static stretching: participants performed horizontal shoulder abduction, triceps, forearm flexor, and forearm extensor stretches (5 second hold).

These conditions were chosen because they could be performed from a seated position with minimal disruption of experimental setup. Severity of perceptions of fatigue was assessed after every experimental block using the fatigue numerical rating scale (F-NRS), an 11-point unidimensional numerical scale where 0 represents no fatigue and 10 represents the worst possible fatigue (Schwartz et al., 2002).

4.2. TMS

Participants were seated comfortably in a chair with the knee and ankle joints at 90°. The non-dominant foot (foot preferred to kick a ball was considered the dominant leg) was placed on a wooden board with a cushioned metal clamp across the dorsum of the foot to restrict motion. EMG data were collected with the Delsys Bagnoli System (Natick, MA, USA; frequency: 2000 Hz, amplification: 1000x, band pass filter: 20–450 Hz) from the tibialis anterior (TA) using surface electrodes on the muscle belly and a reference electrode on the C7 spinous process. EMG data were sampled with Spike2 software (Cambridge Electronic Design, Cambridge, UK). Maximum voluntary isometric contraction (MVIC) of each TA was determine as the best of three trials.

Single pulse TMS was applied with a 110 mm double cone coil in the posterior–anterior orientation using a Magstim 200 stimulator (Magstim, Dyfed, Wales, UK). The hotspot or the optimal position to elicit consistent MEPs was determined according to previously established techniques (Sivaramakrishnan et al., 2016). Participants were provided visual feedback of muscle activity and were required to maintain a tonic contraction corresponding to approximately 15% MVIC during EMG recordings. The active motor threshold (AMT) for each participant was determined by identifying the stimulus intensity resulting in identifiable MEPs (≥0.1 mV) in four out of eight trials. MEPs were subsequently elicited during each experimental block at a suprathreshold TMS intensity of 130% AMT (least variability at this intensity in the TA muscle) (Sivaramakrishnan and Madhavan, 2020) with an inter-stimulus interval of 4 seconds (Awiszus, 2003).

4.3. Data analyses

MEPs were quantified using peak-to-peak amplitude. A MEP window was established for each stimulus by finding the onset and offset latencies (Madhavan and Stinear, 2010). Onset latency (ms) was calculated as the time from the TMS trigger to when EMG exceeded 125% of the mean background EMG for at least 5 ms. Offset latency was calculated as the time from the TMS trigger to when EMG dropped back below 125% of the mean background EMG for at least 5 ms. Amplitude (mV) was calculated as the voltage difference between the maximum positive and negative peaks. The coefficient of variation (CV) of MEP amplitude was calculated as: $\frac{standard deviation}{mean} \times 100 %$ . Lesser CV reflects lesser MEP variability. Mean and CV were calculated for each block of 25 MEPs and then averaged across the two blocks within each condition.

4.4. Statistical analyses

Data was analyzed using jamovi (Version 1.2) with significance level set at p<0.05 (jamovi, 2020). To confirm normality of data, the Shapiro-Wilk test was performed. To test for between condition differences in MEP amplitude and MEP CV, one-way repeated measures analysis of variance (rANOVA) were performed with four levels (task conditions). Post-hoc pairwise comparisons were performed with paired samples t-tests, with no correction for multiple comparisons. Cohen’s d was calculated to infer effect sizes and interpreted as small (d=0.2), medium (d=0.5) and large (d=0.8). The F-NRS data, which was not normally distributed, was compared using the Wilcoxon Signed Rank test.

Supplementary Material

Highlights

NIHMS1859846-supplement-Highlights.docx^{(13.4KB, docx)}

Acknowledgements

We thank the members of the Brain Plasticity Lab for their work involving the recruitment of participants and data collection.

Funding

This work was partly supported by the National Institutes of Health [R01HD075777].

Abbreviations

AMT: active motor threshold
CV: coefficient of variation
EMG: electromyography
F-NRS: fatigue numerical rating scale
MEP: motor evoked potential
MVIC: maximum voluntary isometric contraction
rANOVA: repeated measures analysis of variance
TA: tibialis anterior
TMS: transcranial magnetic stimulation

Footnotes

Declaration of interest

None.

Data statement

Deidentified data that underlie study results will be shared by the corresponding author upon reasonable request from qualified investigators immediately following publication.

5. References

Awiszus F, 2003. TMS and threshold hunting. Suppl Clin Neurophysiol. 56, 13–23. [DOI] [PubMed] [Google Scholar]
Bestmann S, Krakauer JW, 2015. The uses and interpretations of the motor-evoked potential for understanding behaviour. Exp Brain Res. 233, 679–89. [DOI] [PubMed] [Google Scholar]
Budini F, Gallasch E, Christova M, Rafolt D, Rauscher AB, Tilp M, 2017. One minute static stretch of plantar flexors transiently increases H reflex excitability and exerts no effect on corticospinal pathways. Exp Physiol. 102, 901–910. [DOI] [PubMed] [Google Scholar]
Budini F, Christova M, Gallasch E, Kressnik P, Rafolt D, Tilp M, 2018. Transient Increase in Cortical Excitability Following Static Stretching of Plantar Flexor Muscles. Front Physiol. 9, 530. [DOI] [PMC free article] [PubMed] [Google Scholar]
Budini F, Kemper D, Christova M, Gallasch E, Rafolt D, Tilp M, 2019. Five minutes static stretching influences neural responses at spinal level in the background of unchanged corticospinal excitability. J Musculoskelet Neuronal Interact. 19, 30–37. [PMC free article] [PubMed] [Google Scholar]
Cacchio A, Cimini N, Alosi P, Santilli V, Marrelli A, 2009. Reliability of transcranial magnetic stimulation-related measurements of tibialis anterior muscle in healthy subjects. Clin Neurophysiol. 120, 414–9. [DOI] [PubMed] [Google Scholar]
Charalambous CC, Dean JC, Adkins DL, Hanlon CA, Bowden MG, 2018. Characterizing the corticomotor connectivity of the bilateral ankle muscles during rest and isometric contraction in healthy adults. J Electromyogr Kinesiol. 41, 9–18. [DOI] [PMC free article] [PubMed] [Google Scholar]
Chipchase L, Schabrun S, Cohen L, Hodges P, Ridding M, Rothwell J, Taylor J, Ziemann U, 2012. A checklist for assessing the methodological quality of studies using transcranial magnetic stimulation to study the motor system: an international consensus study. Clin Neurophysiol. 123, 1698–704. [DOI] [PMC free article] [PubMed] [Google Scholar]
Darling WG, Wolf SL, Butler AJ, 2006. Variability of motor potentials evoked by transcranial magnetic stimulation depends on muscle activation. Exp Brain Res. 174, 376–85. [DOI] [PMC free article] [PubMed] [Google Scholar]
Ellaway PH, Davey NJ, Maskill DW, Rawlinson SR, Lewis HS, Anissimova NP, 1998. Variability in the amplitude of skeletal muscle responses to magnetic stimulation of the motor cortex in man. Electroencephalogr Clin Neurophysiol. 109, 104–13. [DOI] [PubMed] [Google Scholar]
Goetz SM, Luber B, Lisanby SH, Peterchev AV, 2014. A novel model incorporating two variability sources for describing motor evoked potentials. Brain Stimul. 7, 541–52. [DOI] [PMC free article] [PubMed] [Google Scholar]
Guissard N, Duchateau J, Hainaut K, 2001. Mechanisms of decreased motoneurone excitation during passive muscle stretching. Exp Brain Res. 137, 163–9. [DOI] [PubMed] [Google Scholar]
Hordacre B, Goldsworthy MR, Vallence AM, Darvishi S, Moezzi B, Hamada M, Rothwell JC, Ridding MC, 2017. Variability in neural excitability and plasticity induction in the human cortex: A brain stimulation study. Brain Stimul. 10, 588–595. [DOI] [PubMed] [Google Scholar]
jamovi, 2020. The jamovi project: jamovi (Version 1.2). Vol., ed.êds. [Google Scholar]
Janssens SEW, Sack AT, 2021. Spontaneous Fluctuations in Oscillatory Brain State Cause Differences in Transcranial Magnetic Stimulation Effects Within and Between Individuals. Front Hum Neurosci. 15, 802244. [DOI] [PMC free article] [PubMed] [Google Scholar]
Kiers L, Cros D, Chiappa KH, Fang J, 1993. Variability of motor potentials evoked by transcranial magnetic stimulation. Electroencephalogr Clin Neurophysiol. 89, 415–23. [DOI] [PubMed] [Google Scholar]
Li S, Rymer WZ, 2011. Voluntary breathing influences corticospinal excitability of nonrespiratory finger muscles. J Neurophysiol. 105, 512–21. [DOI] [PMC free article] [PubMed] [Google Scholar]
Madhavan S, Stinear JW, 2010. Focal and bi-directional modulation of lower limb motor cortex using anodal transcranial direct current stimulation. Brain Stimul. 3, 42. [DOI] [PMC free article] [PubMed] [Google Scholar]
Noble DJ, Hochman S, 2019. Hypothesis: Pulmonary Afferent Activity Patterns During Slow, Deep Breathing Contribute to the Neural Induction of Physiological Relaxation. Front Physiol. 10, 1176. [DOI] [PMC free article] [PubMed] [Google Scholar]
Noreika V, Kamke MR, Canales-Johnson A, Chennu S, Bekinschtein TA, Mattingley JB, 2020. Alertness fluctuations when performing a task modulate cortical evoked responses to transcranial magnetic stimulation. Neuroimage. 223, 117305. [DOI] [PMC free article] [PubMed] [Google Scholar]
O’Leary TJ, Morris MG, Collett J, Howells K, 2015. Reliability of single and paired-pulse transcranial magnetic stimulation in the vastus lateralis muscle. Muscle Nerve. 52, 605–15. [DOI] [PubMed] [Google Scholar]
Opplert J, Paizis C, Papitsa A, Blazevich AJ, Cometti C, Babault N, 2020. Static stretch and dynamic muscle activity induce acute similar increase in corticospinal excitability. PLoS One. 15, e0230388. [DOI] [PMC free article] [PubMed] [Google Scholar]
Ozaki I, Kurata K, 2015. The effects of voluntary control of respiration on the excitability of the primary motor hand area, evaluated by end-tidal CO2 monitoring. Clin Neurophysiol. 126, 2162–9. [DOI] [PubMed] [Google Scholar]
Pulverenti TS, Trajano GS, Kirk BJC, Blazevich AJ, 2019. The loss of muscle force production after muscle stretching is not accompanied by altered corticospinal excitability. Eur J Appl Physiol. 119, 2287–2299. [DOI] [PubMed] [Google Scholar]
Pulverenti TS, Trajano GS, Walsh A, Kirk BJC, Blazevich AJ, 2020. Lack of cortical or Ia-afferent spinal pathway involvement in muscle force loss after passive static stretching. J Neurophysiol. 123, 1896–1906. [DOI] [PubMed] [Google Scholar]
Rosler KM, Roth DM, Magistris MR, 2008. Trial-to-trial size variability of motor-evoked potentials. A study using the triple stimulation technique. Exp Brain Res. 187, 51–9. [DOI] [PubMed] [Google Scholar]
Rossini PM, Rossi S, 2007. Transcranial magnetic stimulation: diagnostic, therapeutic, and research potential. Neurology. 68, 484–8. [DOI] [PubMed] [Google Scholar]
Rossini PM, Rossi S, Babiloni C, Polich J, 2007. Clinical neurophysiology of aging brain: from normal aging to neurodegeneration. Prog Neurobiol. 83, 375–400. [DOI] [PubMed] [Google Scholar]
Schwartz AL, Meek PM, Nail LM, Fargo J, Lundquist M, Donofrio M, Grainger M, Throckmorton T, Mateo M, 2002. Measurement of fatigue. determining minimally important clinical differences. J Clin Epidemiol. 55, 239–44. [DOI] [PubMed] [Google Scholar]
Shirakawa K, Yunoki T, Afroundeh R, Lian CS, Matsuura R, Ohtsuka Y, Yano T, 2015. Voluntary breathing increases corticospinal excitability of lower limb muscle during isometric contraction. Respir Physiol Neurobiol. 217, 40–5. [DOI] [PubMed] [Google Scholar]
Silvanto J, Muggleton N, Walsh V, 2008. State-dependency in brain stimulation studies of perception and cognition. Trends Cogn Sci. 12, 447–54. [DOI] [PubMed] [Google Scholar]
Sivaramakrishnan A, Tahara-Eckl L, Madhavan S, 2016. Spatial localization and distribution of the TMS-related ‘hotspot’ of the tibialis anterior muscle representation in the healthy and post-stroke motor cortex. Neurosci Lett. 627, 30–5. [DOI] [PMC free article] [PubMed] [Google Scholar]
Sivaramakrishnan A, Madhavan S, 2020. Stimulus intensity affects variability of motor evoked responses of the non-Paretic, but not paretic tibialis anterior muscle in stroke. Brain Sci. 10. [DOI] [PMC free article] [PubMed] [Google Scholar]
Stroop J, 1935. Studies of interference in serial verbal reactions. Journal of Experimental Psychology: General. 121, 15–23. [Google Scholar]
Tallent J, Goodall S, Hortobagyi T, St Clair Gibson A, French DN, Howatson G, 2012. Repeatability of corticospinal and spinal measures during lengthening and shortening contractions in the human tibialis anterior muscle. PLoS One. 7, e35930. [DOI] [PMC free article] [PubMed] [Google Scholar]
Temesi J, Ly SN, Millet GY, 2017. Reliability of single- and paired-pulse transcranial magnetic stimulation for the assessment of knee extensor muscle function. J Neurol Sci. 375, 442–449. [DOI] [PubMed] [Google Scholar]
Vucic S, Kiernan MC, 2017. Transcranial Magnetic Stimulation for the Assessment of Neurodegenerative Disease. Neurotherapeutics. 14, 91–106. [DOI] [PMC free article] [PubMed] [Google Scholar]
Wassermann EM, 2002. Variation in the response to transcranial magnetic brain stimulation in the general population. Clin Neurophysiol. 113, 1165–71. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials