https://orcid.org/0000-0002-4778-1348
Figures
Abstract
Despite accumulating evidence that blood flow restriction (BFR) training promotes muscle hypertrophy and strength gain, the underlying neurophysiological mechanisms have rarely been explored. The primary goal of this study is to investigate characteristics of cerebral cortex activity during BFR training under different pressure intensities. 24 males participated in 30% 1RM squat exercise, changes in oxygenated hemoglobin concentration (HbO) in the primary motor cortex (M1), pre-motor cortex (PMC), supplementary motor area (SMA), and dorsolateral prefrontal cortex (DLPFC), were measured by fNIRS. The results showed that HbO increased from 0 mmHg (non-BFR) to 250 mmHg but dropped sharply under 350 mmHg pressure intensity. In addition, HbO and functional connectivity were higher in M1 and PMC-SMA than in DLPFC. Moreover, the significant interaction effect between pressure intensity and ROI for HbO revealed that the regulation of cerebral cortex during BFR training was more pronounced in M1 and PMC-SMA than in DLPFC. In conclusion, low-load resistance training with BFR triggers acute responses in the cerebral cortex, and moderate pressure intensity achieves optimal neural benefits in enhancing cortical activation. M1 and PMC-SMA play crucial roles during BFR training through activation and functional connectivity regulation.
Citation: Jia B, Lv C, Li D, Lv W (2024) Cerebral cortex activation and functional connectivity during low-load resistance training with blood flow restriction: An fNIRS study. PLoS ONE 19(5): e0303983. https://doi.org/10.1371/journal.pone.0303983
Editor: Jeremy P. Loenneke, University of Mississippi, UNITED STATES
Received: December 19, 2023; Accepted: May 3, 2024; Published: May 23, 2024
Copyright: © 2024 Jia et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The raw data of fNIRS, R code, and statistical results from JASP were unloaded in figshare (DOI:10.6084/m9.figshare.25560594).
Funding: This work was supported by the Scientific Research Center at Wuhan sports university, China under project number 2022J03.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Blood flow restriction (BFR) training typically involves using pneumatic cuffs placed around the limbs to limit blood inflow into the muscles during exercise [1]. It has been proven to promote muscle hypertrophy and strength gains among individuals with varying athletic abilities and low exercise loads [2–4]. While BFR has been combined with various types of exercise, research indicates that the most substantial muscular gains come with resistance training (RT) under 20%-40% of the 1 repetition maximum (1RM) or maximum voluntary contraction (MVC) [5]. With the increasing popularity of BFR in the training domain, many researchers have started to investigate its potential mechanisms, such as metabolic stress, cellular swelling, hormone regulation, and other mechanisms at the cellular and molecular levels [6–8]. Surprisingly, even though neural regulation has significantly contributed to muscle hypertrophy and strength gain [9–12], studies concerning BFR training in this area remain limited. However, there is already evidence suggesting the regulation of neural systems during BFR training. For instance, researchers have found that BFR training affected the electromyography signal, which supports the acute response of neuro-muscular [13,14]. In addition, studies have shown that muscle hypertrophy and strength gain can transfer from muscles exposed to BFR to muscles not exposed to BFR [15,16]. Moreover, Sugimoto et al.[17] found that combining BFR with walking enhanced participants’ performance in cognition tasks. However, studies employing electromyography have limitations when exploring the neural regulation process [18], transfer effect in muscle hypertrophy and strength gain, as well as cognition enhancement with BFR training only provide indirect evidence. To gain a deeper understanding of the neurophysiological mechanisms associated with BFR training, it is crucial to provide robust evidence for the characteristics of activity within the central nervous system (CNS), such as activation and functional connectivity (FC) of the cerebral cortex. Those indices not only serve as excellent windows for exploring the response pattern of the cerebral cortex, but have also been confirmed to undergo adaptive changes with resistance training[19].
Current evidence concerning the cortical response induced by BFR training is limited. A previous study by Morita et al. [20] reported increased activation in the prefrontal cortex during BFR training compared with non-BFR. Similarly, Brandner et al. [21] assessed changes in motor-evoked potentials (MEPs) by transcranial magnetic stimulation, revealing higher MEP amplitudes following BFR training. However, these studies suffered from a notably small sample size, and using MEPs to measure cortical excitability carries inherent limitations [22]. Additionally, the dose-response effect of pressure intensity, a key variable influencing the effectiveness of BFR training [2,5,23], on cortical activity has not been examined yet. As a result, the evidence supporting cortical regulation during BFR training remains weak and incomplete. Furthermore, there is a possibility that the increases in cortical activation from the prior study [20] are passive consequences of altered blood distribution and increased cerebral blood flow during BFR training, rather than active regulation of the CNS. However, this hypothesis has yet to be tested.
Functional near-infrared spectroscopy (fNIRS) has been widely used to examine brain activity. It is well-suited for monitoring cortical response during exercise scenarios [24]. This technique enables us to evaluate characteristics of cerebral cortex activity throughout BFR training by examining the concentration changes of oxygenated hemoglobin (HbO) and deoxygenated hemoglobin (HHb). It’s important to mention that we chose HbO as the primary indicator for assessing cortical activation and functional connectivity in this study. This is due to the HbO’s superior signal-to-noise ratio, reliability, heightened sensitivity to cortical blood flow changes, and more significant contribution to overall oxygen signal compared with HHb [25–28]. Additionally, the ROIs we focused on in this research include primary motor cortex (M1), pre-motor cortex (PMC), supplementary motor area (SMA), and dorsolateral prefrontal cortex (DLPFC). These regions not only play significant roles in motor planning and execution [29–31] but are also crucial in facilitating the induction of CNS adaptations resulting from exercise [10,32].
Therefore, the primary purpose of this study is to provide stable and comprehensive evidence about the cortical response during BFR training. Specifically, we intend to combine the 30% 1RM squat exercise with BFR under different pressure intensities (150 mmHg, 250 mmHg, 350 mmHg, and 0 mmHg or non-BFR as a control condition) to investigate the cortical activation and FC in M1, PMC-SMA, DLPFC via fNIRS. The first hypothesis is that pressure intensity affects cortical response. Furthermore, we infer activation and FC strength would enhance with pressure intensity to improve muscle force output [19, 33,34]. This is because the increasing metabolic stress with BFR restricts the capacity of muscle [6,34], and the CNS can compensate for the muscle force loss under BFR by enhancing the recruitment of motor units, and improving the frequency of neural impulse discharge[35], resulting in higher activation. Additionally, considering the changes in cortical HbO during BFR training may result from altered blood distribution and increased cerebral blood flow [36,37], rather than active regulation of the CNS, we propose the second hypothesis that the influence of pressure intensity on cortical activation is moderated by the regions of interest (ROI). This is based on the assumption that if the ROIs in our study play an active role in the regulation of CNS during BFR training, we will detect an interaction effect between pressure intensity and ROIs regarding cortical activation. Conversely, if changes in cortical activation result from variations in cerebral blood flow during BFR training, the regulatory effects of pressure intensity across different ROIs should be consistent.
Methods
Participants
This study enrolled 24 male participants (age, 20.08 ± 0.93 years; height, 179 ± 5 cm; weight, 73.63 ± 10.53 kg; 1RM, 133.33 ± 15.37 kg). The sample size determination was based on prior-power analysis in G*Power and MPower [38,39]. More details about the prior power analysis can be found in the S1 Appendix. To reduce the potential risk of injury during resistance training (RT), all participants had at least 1 year of squat training experience (3.52 ± 1.25 years). Exclusion criteria included neurological or psychological disorders (Depression, Autism, Mania, Schizophrenia, Epilepsy, Stroke, etc.), the use of medications affecting the CNS, consumption of caffeine or alcohol within 24 hours before the experiment, acute or chronic exercise-related injuries, as well as cardiovascular diseases. The recruitment period started on June 10, 2023, and ended on November 30, 2023. Written informed consent was obtained from all participants before the study, and ethical approval for this experiment was granted by the Ethics Committee of Wuhan Sports University (Approval No. 2023050).
Experiment material and task
The tools utilized in this study included a squat rack, a near-infrared imaging system (NIRx-sport2, NIRx Medizintechnik GmbH, Berlin, Germany), and pneumatic cuffs with a width of 7cm (B-Strong, USA). The task was conducted with E-prime 2.0 (Psychology Software Tools, Pittsburgh, USA). All experiments took place within a laboratory isolated from external light and noise. Specifically, participants were initially presented with a cue indicating the preparation for a squat. Subsequently, participants performed squats following the cues displayed on the screen. After completing each squat, participants unloaded the barbell and maintained a static standing posture during an interval until the cue for the following squat preparation appeared, as illustrated in Fig 1.
The individuals in this photograph have given written informed consent (as outlined in the PLOS consent form) to publish these case details.
The formal experiment was divided into 4 blocks, each consisting of 20 trials, with 2–3 minutes of rest inserted between blocks. BFR intensity was controlled using pneumatic cuffs at 4 pressure intensities (0 mmHg, 150 mmHg, 250 mmHg, and 350 mmHg) across these 4 blocks. The cuffs were positioned at one-third of the participants’ upper thighs bilaterally during each block, with no occlusion during the rest periods between blocks, as depicted in Fig 2. Blocks were presented in a pseudo-random order to minimize the potential impact of block order on experimental outcomes. To determine the external resistance of squat in BFR training, all participants underwent a 1RM test 1–2 days before the experiment. The test started with a warm-up, followed by the squat test with the initial weight set at 70% of the participant’s self-estimated 1RM. After the completion of the 1RM squat test, participants engaged in static stretching for 3–5 minutes.
fNIRS recording
The NIRx-Sport2 (continuous wave) with wavelengths 760 and 850 nm was used to record changes in cortical HbO at a sampling rate of 10.2 Hz. This system has 8 light sources and 7 detectors, which form 22 channels (as illustrated in Fig 3). These channels mainly covered M1, PMC-SMA, and DLPFC. The placement of the light sources and detectors was determined by fNIRS optodes’ Location Decider [40]. The brain atlas referred to the Brodmann Brain Regions and the coordinates for the light sources and detectors were based on the 10–10 international system.
BrainnetViewer visualized the channel layout with the smoothed Colin brain template [41]. The coordinates of the nodes corresponded to the positions of the light sources (red) and detectors (blue), while the edges represented the 22 channels. The term ‘specificity’ refers to the representativeness of each channel for its corresponding brain area based on its anatomical location.
Data analysis and statistics
The fNIRS data were processed using HOMER3 [42]. The data processing workflow is illustrated in Fig 4, and more details can be found in the S2 Appendix. The HbOmean and HbOmax were extracted from temporal changes of HbO during BFR training under different conditions for further statistics. The FC index represents Pearson’s correlation of HbO between channels from 2s to 15s during the trial in our task, which accounts for the delay in hemodynamic response [43]. The Pearson’s r values were then translated to Fisher Z (Z = 0.5 * ln((1+r)/(1-r)). Afterward, the fNIRS data underwent pre-processing in R (https://www.r-project.org/) for visualization and were then input into JASP (https://jasp-stats.org/, version 0.16.4) for statistical inference. The raw data of fNIRS, R code, and statistical results from JASP were unloaded in figshare (DOI:10.6084/m9.figshare.25560594).
Repeated measures analysis of variance (RMANOVA) was employed to test our hypotheses. The significance level was set at 0.05. For both main effects and interaction, we provided the F-value, p-value, and effect size partial eta square (). In cases where sphericity was violated, Greenhouse-Geisser-corrected statistics were reported. Multiple comparisons were conducted using a paired-sample t-test. Statistical information, including t-value, p-value, and effect size Cohen’d with its 95 confidence interval (95%CI), was provided. It is important to note that due to missing data for some participants in specific experimental conditions, multivariate imputation was performed using the MICE package [44] to maintain data balance and predetermined statistical power of this 2 within-factors (pressure intensity and ROI) repeated measures design. Details regarding the imputation of specific variables will be shown in the Result section.
Results
HbOmean under different pressure intensities during BFR training
RMANOVA for HbOmean was conducted with a data imputation rate of 1.36% (15/1104). This analysis revealed significant main effects of pressure intensity (F(3,66) = 9.55, = 0.3, p<0.001) and ROI (F(2,44) = 12.59,
= 0.36, p<0.001). Moreover, a significant interaction effect between pressure intensity and ROI was detected (F(6,132) = 2.32,
= 0.1, p = 0.04). Subsequently, a simple main effects analysis demonstrated that the regulatory effect of pressure intensity on HbOmean was more pronounced in M1 (F(3,66) = 9.67,
= 0.31, p<0.01) and PMC-SMA (F(3,66) = 9.00,
= 0.29, p<0.01) compared to DLPFC (F(3,66) = 2.24,
= 0.09, p = 0.09). Between condition comparisons were illustrated in Fig 5, more detailed statistical results can be found in Tables 1–3.
The error bars represent mean ± 95% CI. * p < .05, ** p < .01, *** p < .001.
HbOmax under different pressure intensities during BFR training
RMANOVA for HbOmax was also conducted with a data imputation rate of 1.36% (15/1104). This analysis revealed significant main effects of pressure intensity (F(3,66) = 10.22, = 0.2, p<0.001) and ROI (F(2,44) = 19.34,
= 0.47, p<0.001). Moreover, a significant interaction effect between pressure intensity and ROI was also detected (F(6,132) = 3.4,
= 0.13, p = 0.03). Subsequently, a simple main effects analysis demonstrated that the regulatory effect of pressure intensity on HbOmax was more pronounced in M1 (F(3,66) = 13.56,
= 0.38, p<0.001) and PMC-SMA (F(3,66) = 10.16,
= 0.32, p<0.001) compared to DLPFC (F(3,66) = 1.34,
= 0.06, p = 0.27). Between condition comparisons were illustrated in Fig 6, more detailed statistical results can be found in Tables 4–6.
FC under different pressure intensities during BFR training
The FC between fNIRS channels during BFR training was presented in Fig 7. RMANOVA for FC was conducted with a data imputation rate of 3.99% (88/2208). The main effect of ROI was significant (F(5,110) = 16.99, = 0.43, p<0.01, between condition comparisons are illustrated in Fig 8, more detailed statistical results can be found in Table 7. Meanwhile, the main effect of pressure intensity did not reach statistical significance (F(3,66) = 2.29,
= 0.09, p = 0.11), as well as the interaction effect between pressure intensity and ROI (F(15,330) = 0.67,
= 0.03, p = 0.67).
The chord diagrams were generated based on paired t-tests comparing FC under 0 mmHg with other pressure intensities. Channels with dashed lines indicate p < 0.05.
Discussion
This research confirms the occurrence of cortical regulation during BFR training. To our knowledge, this study was the first to investigate the cortical activation and FC pattern during BFR training under different pressure intensities. Initially, our results demonstrated that the pressure intensity during BFR training affects cortical activation. Specifically, we found an increase in HbO from 0 mmHg to 250 mmHg, which is consistent with previous research [20]. This phenomenon can be interpreted as a compensatory response by the CNS to improve muscle force. It occurs as a result of heightened metabolic stress during BFR training, which includes decreased oxygen saturation and the accumulation of metabolic waste products like blood lactate, carbon dioxide, and hydrogen ions, which limit muscle capacity [6,45]. In this context, the CNS enhances muscle force output by recruiting large motor units and a higher neural impulse firing rate [35,46]. Consequently, there is an elevation in cortical HbO, ensuring cerebral energy supply and subsequently leading to increased cortical activity during BFR training. Furthermore, this heightened activity has been consistently associated with elevated force output [34,47,48].
Interestingly, the increase of HbO we observed during BFR training is similar to the results obtained from RT with heavier loads [34]. This pattern of cortical activity reflects a shared mechanism in regulating muscle force output by the CNS. Furthermore, it elucidates why low-load RT with BFR can induce muscle hypertrophy, and strength gains comparable to moderate or high-load RT from the perspective of cortical response. However, we noted that the overall changes in HbO levels under different pressure intensities were lower than training with various external loads. This can be attributed to the reduction in cerebral blood flow caused by BFR, which may limit the CNS’s capacity to enhance muscle force output fully. This inference is also consistent with findings indicating that the increase in muscle strength after BFR with low-load RT was lower compared to high-load RT alone [49,50].
This study also found that the relationship between pressure intensity and cortical activation is not linear. Specifically, HbO declined sharply during 350 mmHg BFR training after an increase from 0 mmHg to 250 mmHg. We attribute this result to a significant reduction in cerebral blood flow induced by the high occlusion pressure. Interestingly, this non-linear pattern has also been observed in BFR studies using EMG as an index of neural response [13,14]. Considering the strong correlation between cortical activation and EMG signals [47,51], these consistent findings collectively suggest the dose-response effect between cortical activation and pressure intensity during BFR training. In addition, it is worth noting that the decrease of cortical oxygenation has also been observed in high-load exercise [52,33], and some researchers suggested this cerebral hypoxia-like phenomena may induce beneficial adaptive response of the CNS [53,54]. However, whether it supports training benefits such as muscle hypertrophy and strength gain is still under debate. Furthermore, considering the vulnerability and importance of the brain, the potential risks of cerebral hypoxia induced by high occlusion pressure need to be considered cautiously.
More importantly, the significant interaction effect in our study indicating the regulation of cortical activation by pressure intensity is moderated by ROI. Specifically, the impact of pressure intensity on HbO changes was more pronounced in M1 and PMC-SMA compared to DLPFC. This finding suggests that M1 and PMC-SMA play more critical roles during BFR training. Moreover, this result also supports the active role of cerebral cortex regulation during BFR training under different pressure intensities, and the main effect of pressure intensity on HbO changes is not solely a by-product of systemic blood flow distribution variation caused by BFR [55].
For the FC index, the main effect of pressure intensity was not significant. Firstly, we attribute this to the already high connectivity during 0 mmHg or non-BFR condition, which may have limited the improvement of FC during BFR training. Secondly, the effect size in prior-power analysis was underestimated due to the limited research on FC during BFR training, which led to an insufficient sample size to detect statistically significant results. However, the raw data indicate FC in most fNIRS channels had been strengthened during BFR training, and a non-linear pattern was also observed among different pressure intensities. combining our FC results with previous findings that suggest the positive correlation between muscle force output and FC strength [19], we suggest enhancing FC is a cost-effective strategy for the CNS to increase force output during BFR training with limited muscle capacity under low pressure. Nevertheless, as the metabolic stress within muscles rises with high pressures, the CNS must employ more efficient ways like enhancement of cortical activation to increase force output, just like the significant increase of HbO we found from 150 mmHg to 250 mmHg. This inference also aligns with a fundamental brain characteristic, which involves the delicate balance between cost and efficiency [56].
Lastly, the higher HbO increase and stronger FC observed in M1 and PMC-SMA suggest their greater involvement compared with DLPFC during BFR training. This is consistent with previous findings highlighting the importance of PMC-SMA in spontaneous and sequential movements [57,58], as well as the dominant role of M1 in regulating muscle output parameters such as direction, speed, and magnitude [51,59,60]. Moreover, given the positive relationship between DLPFC activation and cognitive load during motor task [61], we attribute the lower levels of HbO and FC in DLPFC to the participant’s long-term training experience (3.52±1.25 years), which may render the squat movement more automated [62], thereby reducing the cognitive load and DLPFC activation during BFR training.
This study has three limitations. Firstly, our primary focus was on the acute responses of the cerebral cortex during BFR training under different pressure intensities. The effects of long-term BFR training on the cerebral cortex were out of our radar. Secondly, the fNIRS technology used in this study for monitoring brain activity is limited to the cortical surface and cannot detect activity in sub-cortical brain tissue. Lastly, many variables affect the outcome of BFR training, such as subjects’ athletic ability, method of compression, material of the compression band, etc. Future researchers could focus on these variables and combine longitudinal study design with other brain imaging technology, like fMRI, to investigate the effects of long-term BFR training on the structure and function of the brain.
Conclusion
Low-load RT with BFR under different pressure intensities elicits acute responses in the cerebral cortex, and moderate pressure intensity optimally enhances cortical activation. The M1 and PMC-SMA play crucial roles during BFR training through the regulation of activation and FC. In summary, this study provides evidence for the occurrence of regulation of CNS during low-load RT with BFR.
References
- 1. Sato Y. The history and future of KAATSU Training. Int J KAATSU Train Res. 2005;1: 1–5.
- 2. Scott BR, Loenneke JP, Slattery KM, Dascombe BJ. Exercise with Blood Flow Restriction: An Updated Evidence-Based Approach for Enhanced Muscular Development. Sports Med. 2015;45: 313–325. pmid:25430600
- 3. Hughes L, Paton B, Rosenblatt B, Gissane C, Patterson SD. Blood flow restriction training in clinical musculoskeletal rehabilitation: a systematic review and meta-analysis. Br J Sports Med. 2017;51: 1003–1011. pmid:28259850
- 4. Hassanlou FP, Vakili J, Nikokheslat SD. A New Exercise Training Methods for Untrained Middle-Age Males: Comparison of Effectiveness Resistance Training with Blood Restriction Cuffs vs Traditional Resistance Training. 2020;4: 1–10.
- 5. Patterson SD. Blood Flow Restriction Exercise: Considerations of Methodology, Application, and Safety. Front Physiol. 2019;10.
- 6. Loenneke JP, Fahs CA, Wilson JM, Bemben MG. Blood flow restriction: The metabolite/volume threshold theory. Med Hypotheses. 2011;77: 748–752. pmid:21840132
- 7. Pearson SJ, Hussain SR. A review on the mechanisms of blood-flow restriction resistance training-induced muscle hypertrophy. Sports Med Auckl NZ. 2015;45: 187–200. pmid:25249278
- 8. Davids CJ, Roberts LA, Bjørnsen T, Peake JM, Coombes JS, Raastad T. Where Does Blood Flow Restriction Fit in the Toolbox of Athletic Development? A Narrative Review of the Proposed Mechanisms and Potential Applications. Sports Med. 2023 [cited 20 Sep 2023]. pmid:37578669
- 9. Aagaard P. Training-induced changes in neural function. Exerc Sport Sci Rev. 2003;31: 61–67. pmid:12715968
- 10. Gabriel DA, Kamen G, Frost G. Neural adaptations to resistive exercise: mechanisms and recommendations for training practices. Sports Med Auckl NZ. 2006;36: 133–149. pmid:16464122
- 11. Pearcey GEP, Alizedah S, Power KE, Button DC. Chronic resistance training: is it time to rethink the time course of neural contributions to strength gain? Eur J Appl Physiol. 2021;121: 2413–2422. pmid:34052876
- 12. Alix-Fages C, Del Vecchio A, Baz-Valle E, Santos-Concejero J, Balsalobre-Fernández C. The role of the neural stimulus in regulating skeletal muscle hypertrophy. Eur J Appl Physiol. 2022;122: 1111–1128. pmid:35138447
- 13. Yasuda T, Brechue WF, Fujita T, Sato Y, Abe T. Muscle Activation During Low-Intensity Muscle Contractions With Varying Levels of External Limb Compression. J Sports Sci Med. 2008;7: 467–474. pmid:24149952
- 14. Counts BR, Dankel SJ, Barnett BE, Kim D, Mouser JG, Allen KM, et al. Influence of relative blood flow restriction pressure on muscle activation and muscle adaptation: Relative BFR Pressure. Muscle Nerve. 2016;53: 438–445. pmid:26137897
- 15. Yasuda T, Fujita S, Ogasawara R, Sato Y, Abe T. Effects of low-intensity bench press training with restricted arm muscle blood flow on chest muscle hypertrophy: a pilot study. Clin Physiol Funct Imaging. 2010;30: 338–343. pmid:20618358
- 16. Wong V, Spitz RW, Song JS, Yamada Y, Kataoka R, Hammert WB, et al. Blood flow restriction augments the cross-education effect of isometric handgrip training. Eur J Appl Physiol. 2024. pmid:38168713
- 17. Sugimoto T, Suga T, Tomoo K, Dora K, Mok E, Tsukamoto H, et al. Blood Flow Restriction Improves Executive Function after Walking. Med Sci Sports Exerc. 2021;53: 131–138. pmid:32694372
- 18. Vigotsky AD, Halperin I, Lehman GJ, Trajano GS, Vieira TM. Interpreting Signal Amplitudes in Surface Electromyography Studies in Sport and Rehabilitation Sciences. Front Physiol. 2017;8: 985. pmid:29354060
- 19. Andrushko JW, Gould LA, Renshaw DW, Ekstrand C, Hortobágyi T, Borowsky R, et al. High Force Unimanual Handgrip Contractions Increase Ipsilateral Sensorimotor Activation and Functional Connectivity. Neuroscience. 2021;452: 111–125. pmid:33197497
- 20. Morita T, Fukuda T, Kikuchi H, Ikeda K, Yumoto M, Sato Y. Effects of blood flow restriction on cerebral blood flow during a single arm-curl resistance exercise. Int J KAATSU Train Res. 2010;6: 9–12.
- 21. Brandner CR, Kidgell DJ, Warmington SA. Unilateral bicep curl hemodynamics: Low-pressure continuous vs high-pressure intermittent blood flow restriction: Acute hemodynamic responses to BFR exercise. Scand J Med Sci Sports. 2015;25: 770–777. pmid:25055880
- 22. Bestmann S, Krakauer JW. The uses and interpretations of the motor-evoked potential for understanding behaviour. Exp Brain Res. 2015;233: 679–689. pmid:25563496
- 23. Spitz RW, Wong V, Bell ZW, Viana RB, Chatakondi RN, Abe T, et al. Blood Flow Restricted Exercise and Discomfort: A Review. J Strength Cond Res. 2022;36: 871–879. pmid:32058360
- 24. Leff DR, Orihuela-Espina F, Elwell CE, Athanasiou T, Delpy DT, Darzi AW, et al. Assessment of the cerebral cortex during motor task behaviours in adults: A systematic review of functional near infrared spectroscopy (fNIRS) studies. NeuroImage. 2011;54: 2922–2936. pmid:21029781
- 25. Strangman G, Culver JP, Thompson JH, Boas DA. A quantitative comparison of simultaneous BOLD fMRI and NIRS recordings during functional brain activation. NeuroImage. 2002;17: 719–731. pmid:12377147
- 26. Hoshi Y. Functional near-infrared optical imaging: utility and limitations in human brain mapping. Psychophysiology. 2003;40: 511–520. pmid:14570159
- 27. Plichta MM, Herrmann MJ, Baehne CG, Ehlis A-C, Richter MM, Pauli P, et al. Event-related functional near-infrared spectroscopy (fNIRS): are the measurements reliable? NeuroImage. 2006;31: 116–124. pmid:16446104
- 28. Gagnon L, Yücel MA, Dehaes M, Cooper RJ, Perdue KL, Selb J, et al. Quantification of the cortical contribution to the NIRS signal over the motor cortex using concurrent NIRS-fMRI measurements. NeuroImage. 2012;59: 3933–3940. pmid:22036999
- 29. Chouinard PA, Paus T. The primary motor and premotor areas of the human cerebral cortex. Neurosci Rev J Bringing Neurobiol Neurol Psychiatry. 2006;12: 143–152. pmid:16514011
- 30. Nachev P, Kennard C, Husain M. Functional role of the supplementary and pre-supplementary motor areas. Nat Rev Neurosci. 2008;9: 856–869. pmid:18843271
- 31. Gordon EM, Chauvin RJ, Van AN, Rajesh A, Nielsen A, Newbold DJ, et al. A somato-cognitive action network alternates with effector regions in motor cortex. Nature. 2023. pmid:37076628
- 32. Glover IS, Baker SN. Cortical, Corticospinal, and Reticulospinal Contributions to Strength Training. J Neurosci Off J Soc Neurosci. 2020;40: 5820–5832. pmid:32601242
- 33. Rasmussen P, Rasmussen P, Rasmussen P, Nielsen J, Nielsen JJ, Nielsen J, et al. Reduced muscle activation during exercise related to brain oxygenation and metabolism in humans. J Physiol. 2010;588: 1985–1995. pmid:20403976
- 34. Kenville R, Maudrich T, Carius D, Ragert P. Hemodynamic Response Alterations in Sensorimotor Areas as a Function of Barbell Load Levels during Squatting: An fNIRS Study. Front Hum Neurosci. 2017;11: 241. pmid:28555098
- 35. Del Vecchio A, Casolo A, Negro F, Scorcelletti M, Bazzucchi I, Enoka R, et al. The increase in muscle force after 4 weeks of strength training is mediated by adaptations in motor unit recruitment and rate coding. J Physiol. 2019;597: 1873–1887. pmid:30727028
- 36. Takano H, Morita T, Iida H, Asada K, Kato M, Uno K, et al. Hemodynamic and hormonal responses to a short-term low-intensity resistance exercise with the reduction of muscle blood flow. Eur J Appl Physiol. 2005;95: 65–73. pmid:15959798
- 37. Spranger MD, Krishnan AC, Levy PD, O’Leary DS, Smith SA. Blood flow restriction training and the exercise pressor reflex: a call for concern. Am J Physiol-Heart Circ Physiol. 2015;309: H1440–H1452. pmid:26342064
- 38. Faul F, Erdfelder E, Buchner A, Lang A-G. Statistical power analyses using G*Power 3.1: tests for correlation and regression analyses. Behav Res Methods. 2009;41: 1149–1160. pmid:19897823
- 39. Campbell JID, Thompson VA. MorePower 6.0 for ANOVA with relational confidence intervals and Bayesian analysis. Behav Res Methods. 2012;44: 1255–1265. pmid:22437511
- 40. Zimeo Morais GA, Balardin JB, Sato JR. fNIRS Optodes’ Location Decider (fOLD): a toolbox for probe arrangement guided by brain regions-of-interest. Sci Rep. 2018;8: 3341. pmid:29463928
- 41. Xia M, Wang J, He Y. BrainNet Viewer: a network visualization tool for human brain connectomics. PloS One. 2013;8: e68910. pmid:23861951
- 42. Huppert TJ, Diamond SG, Franceschini MA, Boas DA. HomER: a review of time-series analysis methods for near-infrared spectroscopy of the brain. Appl Opt. 2009;48: D280. pmid:19340120
- 43. Zhao W, Liu Q, Zhang X, Song X, Zhang Z, Qing P, et al. Differential responses in the mirror neuron system during imitation of individual emotional facial expressions and association with autistic traits. NeuroImage. 2023;277: 120263. pmid:37399932
- 44. Buuren S van, Groothuis-Oudshoorn K. mice: Multivariate Imputation by Chained Equations in R. J Stat Softw. 2011;45.
- 45. Takarada Y, Takazawa H, Sato Y, Takebayashi S, Tanaka Y, Ishii N. Effects of resistance exercise combined with moderate vascular occlusion on muscular function in humans. J Appl Physiol. 2000;88: 2097–2106. pmid:10846023
- 46. Sale DG. Neural adaptation to resistance training. Med Sci Sports Exerc. 1988;20: S135–145. pmid:3057313
- 47. Dai T, Liu J, Sahgal V, Brown R, Yue G. Relationship between muscle output and functional MRI-measured brain activation. Exp Brain Res. 2001;140: 290–300. pmid:11681304
- 48. Van Duinen H, Renken R, Maurits NM, Zijdewind I. Relation between muscle and brain activity during isometric contractions of the first dorsal interosseus muscle. Hum Brain Mapp. 2008;29: 281–299. pmid:17394210
- 49. Lixandrão ME, Ugrinowitsch C, Berton R, Vechin FC, Conceição MS, Damas F, et al. Magnitude of Muscle Strength and Mass Adaptations Between High-Load Resistance Training Versus Low-Load Resistance Training Associated with Blood-Flow Restriction: A Systematic Review and Meta-Analysis. Sports Med. 2018;48: 361–378. pmid:29043659
- 50. Teixeira EL, Painelli V de S, Schoenfeld BJ, Silva-Batista C, Longo AR, Aihara AY, et al. Perceptual and Neuromuscular Responses Adapt Similarly Between High-Load Resistance Training and Low-Load Resistance Training With Blood Flow Restriction. J Strength Cond Res. 2022;36: 2410–2416. pmid:33306591
- 51. Shibuya K, Kuboyama N, Tanaka J. Changes in ipsilateral motor cortex activity during a unilateral isometric finger task are dependent on the muscle contraction force. Physiol Meas. 2014;35: 417–428. pmid:24521545
- 52. Subudhi AW, Miramon BR, Granger ME, Roach RC. Frontal and motor cortex oxygenation during maximal exercise in normoxia and hypoxia. J Appl Physiol Bethesda Md 1985. 2009;106: 1153–1158. pmid:19150853
- 53. Neumann JT, Thompson JW, Raval AP, Cohan CH, Koronowski KB, Perez-Pinzon MA. Increased BDNF protein expression after ischemic or PKC epsilon preconditioning promotes electrophysiologic changes that lead to neuroprotection. J Cereb Blood Flow Metab Off J Int Soc Cereb Blood Flow Metab. 2015;35: 121–130. pmid:25370861
- 54. Rogers RS, Wang H, Durham TJ, Stefely JA, Owiti NA, Markhard AL, et al. Hypoxia extends lifespan and neurological function in a mouse model of aging. PLOS Biol. 2023;21: e3002117. pmid:37220109
- 55. Joyner MJ, Casey DP. Regulation of increased blood flow (hyperemia) to muscles during exercise: a hierarchy of competing physiological needs. Physiol Rev. 2015;95: 549–601. pmid:25834232
- 56. Bullmore E, Sporns O. The economy of brain network organization. Nat Rev Neurosci. 2012;13: 336–349. pmid:22498897
- 57. Hertrich I, Dietrich S, Ackermann H. The role of the supplementary motor area for speech and language processing. Neurosci Biobehav Rev. 2016;68: 602–610. pmid:27343998
- 58. Cannon JJ, Patel AD. How Beat Perception Co-opts Motor Neurophysiology. Trends Cogn Sci. 2021;25: 137–150. pmid:33353800
- 59. Chouinard PA, Paus T. The Primary Motor and Premotor Areas of the Human Cerebral Cortex. The Neuroscientist. 2006;12: 143–152. pmid:16514011
- 60. Kuhtz-Buschbeck JP, Gilster R, Wolff S, Ulmer S, Siebner H, Jansen O. Brain activity is similar during precision and power gripping with light force: An fMRI study. NeuroImage. 2008;40: 1469–1481. pmid:18316207
- 61. Jeon H-A, Friederici AD. Degree of automaticity and the prefrontal cortex. Trends Cogn Sci. 2015;19: 244–250. pmid:25843542
- 62. Haith AM, Krakauer JW. The multiple effects of practice: skill, habit and reduced cognitive load. Curr Opin Behav Sci. 2018;20: 196–201. pmid:30944847