Dorsolateral prefrontal cortex to ipsilateral primary motor cortex intercortical interactions during inhibitory control enhance response inhibition in open-skill athletes

doi:10.21203/rs.3.rs-4290600/v1

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Dorsolateral prefrontal cortex to ipsilateral primary motor cortex intercortical interactions during inhibitory control enhance response inhibition in open-skill athletes

https://doi.org/10.21203/rs.3.rs-4290600/v1

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published 17 Oct, 2024

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Numerous studies have reported that long-term sports training can affect inhibitory control and induce brain functional alterations. However, the influence of environmental dynamics in sports training on inter-cortical connectivity has not been well studied. In the current study, we used twin-coil transcranial magnetic stimulation to investigate the functional connectivity between dorsolateral prefrontal cortex (DLPFC) and ipsilateral primary motor cortex (M1) during proactive and reactive inhibition in participants with sports skills in dynamic environment (open-skill experts, n=20), stable environment (closed-skill experts, n=20), and no sports skills (controls, n=20). Using a modified stop signal task, proactive inhibition was measured by the response delay effect (RDE) and reactive inhibition was measured by the stop-signal reaction time (SSRT). During the task, inter-hemispheric DLPFC-M1 interactions and single pulse motor-evoked potentials (MEPs) were measured. Open-skill experts had larger RDE and shorter SSRT than non-athlete controls (p=0.024 and 0.028, respectively). Closed-skill and open-skill experts were faster than controls in choice reaction time (p=0.024 and <0.001). In early proactive inhibition, no significant neurophysiological results were found. In late proactive inhibition, open-skill experts had larger DLPFC-M1 facilitation compared with early reactive phase (p < 0.001 and p = 0.002) but not with baseline. In early reactive inhibition, open-skill experts had increased corticospinal excitability than baseline (ps<0.001). They also had more pronounced DLPFC-M1 inhibition than baseline (p=0.002). The correlational analysis showed the open-skill experts’ SSRTs were positively related to DLPFC-M1 inhibition in early reactive control (r=0.496, p=0.026). Our study suggests that DLPFC to ipsilateral M1 intercortical interactions during inhibitory control can enhance response inhibition in open-skill athletes. Such enhancement may be due to the combination of environmental complexity and physical fitness in long term training.

Biological sciences/Psychology

Biological sciences/Psychology/Human behaviour

Proficiency in motor skills is linked to enhanced response inhibition^1,2, a crucial executive function for suppressing superfluous ongoing actions³. Studies have demonstrated that athletes involved in dynamic sports environments exhibit superior response inhibition levels compared to those in self-paced disciplines^4,5. Animal studies have shown that physical activity conducted within intricate environments leads to augmented cortical thickness, enhanced neurogenesis, and heightened neurotransmission^6,7. Generally, open skills necessitate reactions within dynamically changing and externally paced contexts (e.g., badminton, tennis, football), while closed skills involve performance in more uniform, stable environments (e.g., swimming, track and field)^8,9. Exploring the neural underpinnings between highly experienced open-skill and closed-skill athletes could elucidate the mechanism underlying enhanced response inhibition associated with motor skill training with different environment complexity.

Effective response inhibition is achieved through a combination of reactive and proactive control mechanisms, highlighting the brain's dynamic capability to regulate behavior in anticipation of or in response to stimuli^10–13. Research have shown individuals engaged in open-skill sports exhibit superior reactive and proactive inhibitory control compared to their closed-skill counterparts^14,15. Yet, limited evidence has been found on the contribution of environmental interaction in motor skill training to brain plasticity¹⁵. Considering the dynamics in inhibitory control in open-skill training, it is necessary to employ a nuanced experimental approach that accounts for the temporal dynamics and spatial specificity of brain activity under different inhibitory control conditions. The modified stop signal task has emerged as a paramount paradigm for dissecting the nuances of reactive and proactive inhibitory control, offering an intricate view into the various underlying functions across a temporal spectrum, from advanced network configuration to modulate response tendency and the execution of the stopping process upon signal presentation^16,17.

Research indicates that the dorsolateral prefrontal cortex (DLPFC) and the primary motor cortex (M1) play crucial roles within the hyperdirect pathway of the inhibitory control network^18,19. Moreover, GABA-receptor-mediated pathways in M1 were recruited in proactive and reactive inhibitory control20. Functionally connected with M1, premotor dorsal area (PMd)²¹ and the posterior parietal cortex²² have been found to modulate M1 via intracortical inhibitory networks. Therefore, DLPFC to M1 modulation via GABA-receptor-mediated pathways^23–25 provides a potential mechanism influencing M1 activity through intracortical inhibitory networks, exerting regulatory control over the motor commands essential for initiating stops. Delving into the dynamic interplay between the DLPFC and M1 in open skilled and closed skilled athletes while doing inhibitory control task can shed light on the association between environment interaction in skill training and the upstream to downstream modulation in the inhibitory control framework.

The dynamic relationship between the primary motor cortex (M1) and its associated cortical areas can be intricately analyzed using twin-coil transcranial magnetic stimulation (tcTMS) techniques^{21–24,26,27}. This approach involves administering a conditioning TMS pulse to a specific cortical area, followed shortly by a test pulse directed at the hand knob region of M1. The interconnectivity between the dorsolateral prefrontal cortex (DLPFC) and M1 is quantified by comparing the electromyography (EMG) response induced by the test pulse when delivered alone versus when preceded by the conditioning pulse. Beyond mapping out the functional pathways, tcTMS offers precise temporal resolution, enabling the exploration of the neurobiological foundations of cognitive functions. The differential EMG response to the twin pulses may reflect changes in motor or cognitive states at the time of TMS application. Hence, variations in the excitability of the DLPFC-M1 pathway preceding the completion of inhibitory control tasks suggest that the DLPFC communicates crucial information to M1 during the process of inhibitory control.

This study is designed to explore the connectivity between the dorsolateral prefrontal cortex (DLPFC) and the primary motor cortex (M1) among athletes proficient in open-skill and closed-skill sports, as well as non-athlete controls, during tasks requiring proactive and reactive inhibitory control. This investigation aims to uncover neural mechanisms that may explain how dynamic environments influence inhibitory control capabilities. The athletes selected for this study are recognized experts in their respective sports, distinguished by their high-level competitive performance and extensive training experience. Previous research²⁸ has established a correlation between time spent engaging in open-skill sports and enhanced inhibitory control performance, indicating that motor skill training can influence inhibitory mechanisms. In our study, we employ a nuanced stop signal task, incorporating both 'Never Stop' (NS) and 'Maybe Stop' (MS) conditions, to assess proactive and reactive inhibition among experts in open motor skills (e.g., table tennis players), closed motor skills (e.g., swimmers), and control participants without athletic backgrounds. The DLPFC-M1 functional interplay is evaluated across stages of early and late proactive inhibition, as well as early reactive inhibition. We hypothesize that (1) athletes with expertise in open-skill sports will demonstrate superior proactive and reactive inhibition compared to their closed-skill counterparts and non-athlete controls, and (2) that these open-skill experts will exhibit enhanced DLPFC-M1 connectivity during both proactive and reactive inhibition tasks.

Behavioral results

To test proactive control across groups, we conducted a one-way ANOVA on RDE RT. A main effect of group was found (F(2, 57) = 3.863, p = 0.027). Post-hoc comparison indicated that table tennis players had significantly higher RDE RT than non-athlete controls (p = 0.024). Comparisons between swimmers and the other two groups were not significant (Fig. 1A).

In the NS condition, a main effect of group (F(2, 57) = 9.101, p < 0.001) was found in Go RT (Fig. 1B). Table tennis players had shorter response time than controls (p < 0.001). The same result was found in swimmers (p = 0.024). No significant result was found between the two groups of athletes.

There were no significant group effects in NS condition Go ACC, MS condition Go RT (Fig. 1C), and MS condition Go ACC.

[Insert Fig. 1 here]

To test reactive control across groups, we conducted a one-way ANOVA on SSRT in the MS condition with group as independent variable. We found a statistically significant between-group effect in SSRT (F(2, 57) = 4.037, p = 0.023). Table tennis players were significantly faster at stopping than non-athlete controls (p = 0.028) as revealed by Bonferroni post-hoc tests (Fig. 2A). No significant result was found in Stop ACC (Fig. 2B).

[Insert Fig. 2 here]

M1 corticospinal excitability

Go Trials

Analysis of the motor evoked potentials (MEPs) induced by test stimulation (TS) during Go trials revealed no significant differences across timepoints or between groups in both the 'Never Stop' (NS) Condition (Fig. 3A) and the 'Maybe Stop' (MS) Condition (Fig. 3B). This indicates that TS-induced MEP responses were consistent across these conditions and groups.

Stop Trials

However, the analysis of Stop trials unveiled a significant interaction between timepoint and group (F(5.676, 161.778) = 2.511, p = 0.026, with Greenhouse-Geisser correction applied). A simple effects test revealed that within the table tennis (TT) group, MEPs at timepoints T5 and T6 were significantly elevated compared to baseline levels (p < 0.001 for both T5 and T6), as depicted in Fig. 3C. Moreover, these MEP enhancements at T5 and T6 were notably more pronounced than those observed at T4, exclusively within the TT group (p = 0.002 for both T5 and T6), underscoring a distinct temporal pattern of M1 excitability modulation in response to stopping cues (Fig. 3C).

DLPFC-M1 connectivity

Early proactive phase

Investigations into the DLPFC-M1 functional connectivity revealed no significant interaction effects or main effects within the 'Never Stop' (NS) Condition (Fig. 3D) or during early proactive inhibition within the 'Maybe Stop' (MS) Condition (Fig. 3E). This indicates that the neural connectivity between DLPFC and M1 remained consistent across these specific phases of the task.

Late proactive and early reactive inhibition phases

However, a notable shift was observed in the analysis of late proactive inhibition and the onset of early reactive inhibition. The ratios of paired-pulse to single-pulse MEPs uncovered a statistically significant interaction effect (F(5.426, 154.628) = 2.891, p = 0.013, with Greenhouse-Geisser correction applied). Detailed simple effect analyses pinpointed that, within the table tennis (TT) group, the MEP ratio at timepoint T5 was significantly diminished compared to the baseline level (p = 0.002) (Fig. 3F). The MEP ratio at T4 was significantly larger than later early reactive inhibition phase (p < 0.001 for T5 and p = 0.002 for T6) (Fig. 3F), suggesting a specific temporal modulation of DLPFC-M1 connectivity in response to inhibitory signals.

Further comparative insights were gleaned from inter-group analyses. At T4, the MEP ratio in the TT group was significantly elevated compared to that of the swimmers (SW) group (p = 0.019) (Fig. 3F). Additionally, at T5, the MEP ratio in the TT group was significantly lower than that in the non-athlete (CON) group (p = 0.044) (Fig. 3F), highlighting differential dynamics in neural connectivity across the groups during critical moments of inhibitory control.

[Insert Fig. 3 here]

Correlation between DLPFC-M1 connectivity and behavioral performance

Within the cohort of table tennis players, a significant correlation emerged between the DLPFC-M1 motor evoked potential (MEP) ratio at timepoint T5_MS and the Stop-Signal Reaction Time (SSRT) (r = 0.496, p = 0.026) (Fig. 4A). This finding indicates a meaningful relationship in this group, where variations in neural connectivity at a crucial moment of inhibitory control (T5_MS) are associated with differences in the speed of behavioral response inhibition.

Conversely, no significant correlations were detected between the DLPFC-M1 MEP ratios at any examined timepoints and measures of behavioral performance within the groups of swimmers or non-athlete controls (Fig. 4B and 4C). This suggests that the observed neural-behavioral linkage may be specific to individuals with extensive training in dynamic, open-skill sports like table tennis, highlighting a unique aspect of their neural adaptation to inhibitory control demands.

[Insert Fig. 4 here]

In this study, we leveraged the expertise of motor skill practitioners to explore how cognitive demands in motor skill learning influence the frontal motor neural circuitry during dual inhibitory control tasks. Our findings highlight a distinctive pattern of neural adaptation in open-skill experts. Specifically, we observed an inhibition of the DLPFC-M1 circuit during early reactive control stages when compared to baseline levels. In addition, this inhibition was pronounced when comparing open-skill experts to non-athlete controls, a relationship that was significantly correlated with superior reactive control performance. Furthermore, during late proactive control phases, the DLPFC-M1 circuitry of open-skill experts showed facilitation when contrasted with later reactive control timepoints, although this facilitation did not extend when compared to baseline conditions. Behavioral results also demonstrated enhanced proactive control abilities in open-skill experts relative to non-athlete controls.

These insights contribute to a deeper understanding of the neural mechanisms underpinning the cognitive benefits associated with motor skill learning. The distinct neural responses observed in open-skill experts, especially the inhibited DLPFC-M1 connectivity during early reactive control and its facilitation during late proactive control, reflect a complex interplay between neural circuitry and cognitive demands specific to the nature of the motor skills being practiced. This suggests that the type of motor skill learned (open vs. closed skill) can have significant implications for the development of cognitive control strategies, potentially mediated through specific neural adaptations.

Open-skill experts, as anticipated, demonstrated a more pronounced response delay effect and exhibited quicker responses to stop signals than novices, signifying enhanced proactive and reactive control. This aligns with previous findings suggesting that participants with open-skill training possess superior motor reprogramming and anticipation abilities^9,14,15,28. Our study suggests that these enhanced capabilities may stem from improved top-down control over complex tasks, as evidenced by increased reaction times in the Maybe Stop (MS) condition. Furthermore, with quicker visual reaction times in the Never Stop (NS) condition, open-skill experts efficiently allocated cognitive resources, enabling them to swiftly inhibit actions in more demanding situations.

Conversely, closed-skill experts displayed comparable levels of proactive and reactive control to novices, suggesting that chronic closed-skill training may offer limited benefits for inhibitory control, supporting previous large-sample study²⁹. Notably, our investigation utilized the stop-signal task to examine the dual model of cognitive control in response inhibition, a novel approach not widely reported in the literature. Despite this, closed-skill experts demonstrated faster choice reaction times compared to control groups. This finding was anticipated, considering that motor experiences enhance sensory-cognitive skills specific to their sports domain³⁰, such as swimmers' rapid response to starting signals and table tennis players' quick inhibition of inappropriate movements.

Interestingly, no significant difference in inhibitory control was observed between open-skill and closed-skill experts. A meta-analysis study has confirmed a superior open-skill related inhibition benefit over closed-skill³¹. However, both response inhibition and interference inhibition were included in the meta-analysis. It is possible that the contribution of environmental complexity in motor skill training to response inhibition control was limited. Moreover, inhibitory control was found to be enhanced in the existence of open-skill training and physical fitness^4,32. These findings collectively suggest that the combination of high cognitive demand training and aerobic fitness is crucial for improving inhibitory control. Future research should employ more sophisticated designs that vary both cognitive and cardiovascular demands to further explore this relationship.

The intricate processes of response inhibition, comprising both reactive and proactive control, are delineated by their unique temporal dynamics^11,33,34. Our study segmented the inhibition timeline into early proactive (T1, T2, T3), late proactive (T4), and early reactive (T5, T6) phases to examine these dynamics.

During the early proactive phase, no significant differences were observed in M1 corticospinal excitability between the groups or across timepoints. This finding aligns with Jahfari et al³⁵, who noted stable MEP amplitudes shortly after the Go signal in a choice reaction task, suggesting a uniform engagement across the groups during this phase. Similarly, DLPFC-M1 circuit activity remained consistent across groups, echoing the mixed results of DLPFC activation in stop-signal tasks reported in fMRI studies due to their broader time windows^36,37. Our focused examination within 100 ms post-Go signal mirrors the limitations of fMRI in capturing these early neural responses. The result was consistent with event-related potentials (ERP) and magnetoencephalography (MEG) studies which also failed to find early component in frontal cortex during stop signal task^38,39. Regardless of motor skill experiences, DLPFC may not be involved in early proactive inhibition through inhibitory or facilitatory circuit influencing M1.

In late proactive inhibition, we observed a trend of facilitation from DLPFC to M1 (p = 0.071) only in open-skill experts, while corticospinal excitability was stable compared with baseline in late proactive control. Open-skill athletes have been found to have higher neural proficiency in proactive control of motor preparation in the frontal cortices, which may be involved in motor preparation⁴⁰. They have also been reported to have a unique task dependent potentials compared with closed-skill experts¹⁴ or non-professional players⁴¹. Our study suggested the temporal and spatial elements of the mechanism underlying higher neural proficiency in proactive control related to open skill experiences. That is, long-term open skill training may increase the DLPFC downstream influence on excitatory glutamatergic interneurons and N-methyl-D-aspartate (NMDA) receptors⁴² in late proactive control.

During early and late proactive inhibitory control, DLPFC-M1 circuit was found unchanged in all subjects (Fig. 3D and 3E). However, the DLPFC is reported to inhibit ipsilateral M1 at an ISI of 10 ms24 at rest. This finding indicates a context-dependent intra-cortical connectivity between DLPFC and M126. When a single TMS pulse is delivered, there is descending activity from the motor cortex from the corticospinal tract⁴³. The earliest of these “waves” is referred to as a direct wave (D-wave). Subsequent waves are referred to as indirect waves (I-waves). The relative distribution of D- and I-waves is related to a variety of factors, including the intensity of TMS and the direction of the induced current due to the coil orientation⁴⁴. In the current study, the DLPFC coil was oriented in the anterior-posterior direction in which later I-waves, termed as I3-waves were preferentially activated^43,45. It is likely that inhibitory post-synaptic potentials causing I-waves were context-dependent and were not sensitive to inhibitory control task.

In early reactive inhibition, M1 corticospinal excitability increased 50 to 100 ms after the onset of stop signal compared to baseline and T4. This observation uniquely found in open-skill experts suggests heightened training related firing activity of pyramidal neurons targeting spinal motoneurons, consequently leading to a reduction in the excitability threshold of the pyramidal tract which facilitates the execution of motor commands⁴⁶. Using normal participants as subjects, Wildenberg⁴⁷ observed an increasing trend of MEP amplitude evoked by single-pulse TMS from 45 ms to 134 ms after the stop signal onset. In contrast with the experimental protocol, our study used multiple go signal choices which may delay the onset of corticospinal responses. Open-skill training may shorten the corticospinal responses to a stop signal with further evidence investigating longer period after the stop signal onset.

Our findings suggest that the superior reactive inhibition observed in open-skill experts may be attributed to the interaction between the prefrontal cortex and M1, as reflected by longer stopping time associated with weaker DLPFC to M1 inhibition. Gamma-aminobutyric acid (GABA) inhibition in hippocampus during retrieval activity has been confirmed to be a key link in a fronto-hippocampal inhibitory control pathway underlying thought suppression. Open-skill training may increase the concentration of GABA type A (GABA_A) receptors in M1⁴⁸ or build stronger connectivity between frontal and motor cortex supporting response inhibition⁴⁹. Future study could investigate the potential mechanism by measuring the concentration of the receptors and using paired-pulse TMS to study inhibitory or excitatory circuit within M1.

A limitation of the present study is the reverse causal inference. That is, people with this type of DLPFC-M1 connectivity and associated inhibitory control advantages are better suited to open-skill training in the first place. Future study could use longitudinal design or cross-sectional design with more diverse training experience to avoid such flaw. Another direction future study is to extend the observation time after stop signal and distinguish successful inhibitions and unsuccessful commission errors in the analysis. Though the current study focused on an earlier phase in reactive inhibitory control, whether a response was committed could potentially influence the corticospinal excitability.

In summary, the present study extended the previous works by investigating the frontal and motor cortex functional connectivity during dual inhibitory controls in skilled athletes. Stronger DLPFC-M1 intercortical facilitation and inhibition during stop-signal task were found in table tennis players who are trained to appropriately anticipate and stop in complex environment. By capturing the temporal dynamics of corticospinal and intercortical mechanisms, the results extend our knowledge in several ways. First, long-term open-skill training may increase the DLPFC downstream influence on excitatory glutamatergic interneurons in late proactive control and GABA_A receptors in early reactive control. Second, the temporal neural dynamics in M1 response to DLPFC influence may explain the stronger response inhibitory control after long-term open-skill training. Third, environmental complexity and physical fitness together may reform the inter-cortical downstream interaction to allow for the enhanced performance in response inhibition.

Subjects

Twenty elite table tennis players (12 females, mean age = 18.85 ± 1.04 years), 20 elite swimmers (9 females, mean age = 19.20 ± 1.54 years) and 20 non-athlete controls (13 females, mean age = 20.35 ± 1.92) participated in the study. All groups were matched by age and education (mean years of education: 12.62 ± 1.75). All subjects were right-handed according to the Edinburgh handedness inventory⁵⁰ and had normal or corrected-to-normal vision. Elite table tennis players were recruited from Shanghai University of Sports and elite swimmers were recruited from Shanghai University of Sports and Tongji University. All athletes were Chinese national first-level and were matched in years of experience (see Table 1). Non-athletes received no training in any specific sports and did not exercise regularly. The recruitment criteria also excluded table tennis players who had closed-skill sports training experience and swimmers who had open-skill sports training experience. No participants were trained in any specific skills that required high finger dexterity. No participants reported having a history of neurological problems or cardiovascular diseases, nor were any taking any medications that affect cognitive functions. The participants’ written consents were obtained according to the Declaration of Helsinki after approval by the ethical committee of Shanghai University of Sports. Subjects were asked to visit the lab twice. On their first visit, a demographic questionnaire including personal information, sports skill type, years of training, hours of training per week, and other skills required finger dexterity was collected. The VO_2max was obtained by using the Queen's College step test^51,52. Experiment 1 and 2 were counterbalanced in their first and second visits.

Table 1

Demographics of participants
	Open-skill experts	Closed-skill experts	Controls
	(table tennis players)	(swimmers)	(non-athlete)
Number	20	20	20
Sex(male/female)	8/12	11/9	7/13
Age(years)	18.85 ± 1.04	19.20 ± 1.54	20.35 ± 1.92
Education (years)	12.42 ± 1.32	12.17 ± 1.53	13.44 ± 1.89
Experience (years)	13.10 ± 1.25	12.80 ± 1.61	N/A
VO_2max(M ± SD ml • kg^–1 • min^–1)	62.85 ± 3.07	64.49 ± 7.31	43.22 ± 5.35
1mV (% of maximal stimulator output)	51.70 ± 5.46	55.40 ± 3.65	52.30 ± 4.89
RMT (% of maximal stimulator output)	42.60 ± 4.68	45.90 ± 3.61	43.80 ± 4.21
Values are shown in mean ± standard variation.

[Insert Table 1 here]

Stimuli and task design

After the presentation of stimulus-response mappings, the participants were given four practice blocks (two NS Conditions and two MS Conditions) of 48 trials to become familiarized with the experimental structure and the response keys. During this period, accuracy feedback was given following each response. An accuracy rate of 90% was required before entering the formal experiment. The formal NS and MS Conditions behavioral tasks were completed in a pseudorandom order while applying TMS pulses. The instructions for each condition were provided before each block.

Experiment 1

During the experiment, participants sat 70 cm from a 21” LED monitor with a refresh rate of 60 Hz. The stimuli and paradigm were modified from Bender et al⁵³. Before the task, participants were shown an instruction screen with three coloured circles. Each coloured circle corresponded to a response number (“4,” “6”, or “8”). The participant was provided time to become familiarized with the targets and their correct responses before the experiment.

For each trial, they were asked to rest their index finger on the center “5” key and to focus on a fixation point. At 200ms, one of the three colored circles randomly appeared in the center of the screen. The participants were instructed to manually respond by pressing the corresponding number as quickly and accurately as possible. After each trial, they were instructed to return their right index finger to the starting position. To minimize the effect of color, the color of the circles was randomly selected from a list of eight colors: red (RGB 237 32 36), dark green (RGB 10 130 65), dark blue (RGB 44 71 151), light green (RGB 109 205 119), light blue (RGB 79 188 220), brown (RGB 167 106 48), pink (RGB 255 57 255), and yellow (RGB 255 235 30)⁵³. The experiment was generated and conducted in MATLAB (The MathWorks (2021a), Natick, MA).

In the NS condition, a fixation point appeared in the center of the screen for 200–600 ms, followed by a colored circle for 200 ms. The participants had to respond within 1800 ms after seeing the colored circle (Fig. 5A). They continuously responded to the stimuli for a total of four blocks with 48 trials in each block. In the MS condition, 1/4 of the trials were “Go” trials and 3/4 were “Stop” trials. The participants were asked to respond as quickly and accurately as possible to the relevant stimuli only in “Go” trials. In “Stop” trials, an auditory stop-signal (750Hz, 200ms) was presented shortly after the onset of the Go stimulus. Upon hearing the stop-signal, participants must withhold their response (Fig. 5B). The time between the go-signal and stop-signal (i.e., stop-signal delay, SSD) was initially set at 200 ms and continuously adjusted with an adaptive staircase procedure to keep response accuracy to 50% on stop-signal trials. Specifically, the SSD was decreased by 50 ms when the participant responded in a stop-signal trial and increased by 50 ms when inhibition was successful. The upper limit of the SSD was 700 ms. The participants completed four blocks of MS condition with each block containing 36 Go trials and 12 Stop trials. A short break was provided between each block.

To index proactive inhibition, we used response delay effect (RDE), which was obtained by subtracting the mean Go response time (RT) in the MS Condition from the mean Go RT in the NS Condition⁵⁴. To index reactive inhibition, we used stop-signal reaction time (SSRT), which was calculated by subtracting mean SSD from the mean RT.

Experiment 2 (Transcranial magnetic stimulation setup)

In experiment 2, we recorded surface electromyography (EMG) from the right first-dorsal-interosseous muscle (FDI) via Ag/AgCl electrodes in a belly-tendon montage. The signal was amplified (1000×), band-pass filtered (2 Hz–2.5 kHz, Intronix Technologies Corporation Model 2024F, Bolton, ON, Canada), digitized at 5 kHz by an analog-to-digital interface (Micro1401, Cambridge Electronics Design, Cambridge, UK) and stored in a computer for offline analysis using SIGNAL (6.0) software (Cambridge Electronic Devices, Cambridge, UK).

Two figure-of-eight coils (40 mm Alpha Branding Iron, Magstim) connected to two single-pulse monophasic stimulators (Magstim Co., Whitland, Dyfeld, UK) were used to investigate DLPFC-M1 connectivity within the left hemisphere. With this experimental design, the influence of DLPFC on M1 was quantified by measuring the extent DLPFC stimulation changed the ipsilateral M1 excitability. The hotspot of left M1 was defined as the scalp location that induced the largest peak-to-peak MEP from the right FDI.

The conditioning stimulation (CS) intensity on DLPFC was set at 110% of resting motor threshold (RMT) over left M1 hotspot. RMT was defined as the lowest intensity that produced an MEP of > 50 µV in five out of 10 trials in the relaxed target muscle with the TMS-coil over the left M1. The CS coil handle was oriented forward at 45° from the mid-sagittal line to induce a anterior-to-posterior current²⁵. The CS was set at 110% RMT was based on previous finding²⁴ showing that 110% RMT over the DLPFC had peak inhibitory effects on M1 at an ISI of 10 ms. Other studies have also confirmed that a suprathreshold conditioning pulse can elicit functional interactions between the frontal lobe and M1²⁶.

The test stimulation (TS) intensity on M1 was set to evoke a 1mV motor evoked potential (MEP) at rest²³. The TS coil was placed at 5 cm anterior to the left M1 hotspot. The TS coil handle was oriented backward at 45° from the mid-sagittal line to induce a posterior-anterior current.

DLPFC-M1 functional connectivity was observed in the early and late stage of proactive inhibition and the beginning of reactive inhibition. EMG at the end of reactive inhibition was not measured because the TMS triggered muscle twitch can influence the inhibitory performance.

In the NS Condition, we measured the dynamics of the DLPFC-M1 connectivity across time. There were four pulse timepoints before and after the target onset (-50, 0, 50, and 100 ms) (Fig. 1A). At each timepoint, eight paired-pulses (TS followed CS after 10 ms) and four single-pulses (TS alone) were applied in each block. A total of 32 paired-pulses and 16 single-pulses at each timepoint were collected for analysis. The NS condition served as a control condition for the Go trials in MS condition.

In the MS Condition, Stop trials and Go trials were set up differently. In 2/3 Go trials, TMS pulses were applied before and after the stop-signal onset (-50, 0, 50, and 100 ms) (Fig. 1B). Baseline was measured at -50 ms while the early stage of proactive inhibition was measured at 0(T1_MS), 50(T2_MS), and 100(T3_MS)ms from the onset of the Go signal. For the rest of 1/3 Go trials, no TMS was applied. At each timepoint, four paired-pulses (TS followed CS after 10 ms) and two single-pulses (TS alone) were applied in one block, yielding 40 paired-pulses and 20 single-pulses at each timepoint were collected for analysis. In the Stop trials, TMS pulses were applied − 50 ms before the onset of the go signal and before and after the stop signal (-50, 50, and 100 ms) (Fig. 1B). Baseline was measured − 50 ms before Go signal represents baseline, late stage of proactive inhibition was measured at -50 ms(T4_MS) before the stop signal represents late stage of proactive inhibition, and early stage of reactive inhibition was measured at 50 (T5_MS) and 100 (T6_MS) ms after the stop signal. The stimulation times were selected based on⁵⁵, in which they gave TMS pulses as late as 200 ms after stop signal onset. In each MS block, there were 12 Stop trials which contained two paired-pulses and one single-pulse at each timepoint. A total of 20 paired-pulses and 10 single-pulses were collected for analysis. Note that the baseline was collected separately for Stop trials and Go trials.

Considering the heating of the machine and participants’ fatigue, TMS pulses were applied on all Stop trials to have enough trials for analysis of each condition. The participants completed 10 blocks of MS condition task, which lasted for about 50 mins. To minimize the influence of pre TMS onset on trial expectation, the 10 MS conditions tasks and four NS conditions were randomized.

Trials with incorrect responses pre-contraction in the target muscle (EMG amplitude in 100 ms before the TMS pulse > 2.5 × EMG amplitude 800–1000 ms before the TMS pulse) or RTs less than 80 ms were excluded from further analyses.

2.3 Behavioral measurements

To avoid the interference of late TMS-induced involuntary movement to response error, only behavioral measurements in Experiment 1 were analyzed. In the NS condition, the RT and accuracy were collected. In the MS condition, Go reaction time, Go accuracy (ACC), SSD, Stop ACC and SSRT were collected. To index proactive control, we used RDE as the difference between the mean Go RT in the MS Condition from the mean Go RT in the NS Condition⁵⁴. To test the difference between groups (table tennis players, swimmers and controls) on the behavioral results, one-way ANOVAs were used. For proactive control, RDE, MS Condition Go RT and NS Condition Go RT were compared. For reactive control, SSRT and Stop ACC in MS Condition were compared.

2.4 Data analysis

To rule out the contribution of aerobic fitness to inhibitory control, a one-way ANOVA was used to compare VO_2max in between groups.

TS-induced MEP and DLPFC-M1 MEP ratios were studied. The DLPFC-M1 connectivity was measured by the paired-pulse (CS/TS) to single-pulse (TS) MEP ratios at each timepoint. Two-way mixed repeated-measures ANOVAs were used to test the group differences on M1 corticospinal excitability and DLPFC-M1 connectivity. Group was set as the between-subject factor and time was set as the within-subject factor. Single-pulse (TS) induced MEPs and paired-pulse/single-pulse MEP ratios were compared. For proactive control, four timepoints in Go trials in NS and MS Conditions were examined. For reactive control, four timepoints in Stop trials in MS Condition were examined. Note that we did not distinguish successful inhibition trials and unsuccessful commission error trials because an early time window of stopping process was studied. Bonferroni correction for multiple comparisons were used for post-hoc analyses. In the linear models, sphericity was tested with Mauchly’s test. If the Mauchly’s test was significant (p < 0.05), the Greenhouse-Geisser correction was used.

Pearson’s correlations were used to examine behavioral and electrophysiological data.

Data availability

Data is provided within the supplementary information files. Other datasets used and analyzed during the current study available from the corresponding author on reasonable request.

Fundings

The project was funded by National Natural Science Foundation of China (11932013), China Postdoctoral Science Foundation (2023M731249), Knowledge Innovation Program of Wuhan-Shuguang Project (2023020201020385), the Fundamental Research Funds for the Central Universities (CCNU23XJ037), the Outstanding Clinical Discipline Project of Shanghai Pudong (PWYgy2021-11).

Competing interests (mandatory)

The authors declare no competing interests.

Acknowledgements (optional)

The author would like to thank Yuyu Song, Jing Xia, Xiaoxiao Zhang, Linqi Wang, Zhen Wang, Jianing Wei for providing help in data collection. The author also appreciated technical assistance from Xiangming Li (Shanghai Suiti Technology Co., Ltd) and Jiazheng Zhang. Special thanks goes to Stephanie Tran for proofreading the paper.

Author contributions

Y.W., N.C. and J.Z. conceived the experiment(s). Y.W., Y.L., Q.R. and X.X. conducted the experiment(s), and Y.W. performed statistical analysis and figure generation. All authors reviewed the manuscript.

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No competing interests reported.

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Dorsolateral prefrontal cortex to ipsilateral primary motor cortex intercortical interactions during inhibitory control enhance response inhibition in open-skill athletes

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Abstract

Figures

Introduction

Results

Behavioral results

M1 corticospinal excitability

Go Trials

Stop Trials

DLPFC-M1 connectivity

Early proactive phase

Late proactive and early reactive inhibition phases

Correlation between DLPFC-M1 connectivity and behavioral performance

Discussion

Methods

Subjects

Stimuli and task design

Experiment 1

Experiment 2 (Transcranial magnetic stimulation setup)

2.3 Behavioral measurements

2.4 Data analysis

Declarations

References

Additional Declarations

Supplementary Files

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