Premotor Function in Interpersonal Bimanual Coordination: Neural Responses to Varying Frequencies and Spatio-Temporal Relationships

Premotor Function in Interpersonal Bimanual Coordination: Neural Responses to Varying Frequencies and Spatio-Temporal Relationships | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Premotor Function in Interpersonal Bimanual Coordination: Neural Responses to Varying Frequencies and Spatio-Temporal Relationships Yanan Li, Ruoyu Niu, Lei Liu, Ying Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1821802/v2 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 Oct, 2023 Read the published version in Physiology & Behavior → Version 2 posted You are reading this latest preprint version Show more versions Abstract Background: Interpersonal movement coordination is an important aspect of daily life. Behavioral studies have found that rhythmic bimanual coordination of movement is mainly influenced by two factors, spatio-temporal relationship and frequency of movements. How these factors affect action coordination at the neural level needs further exploration. Methods: Participants were asked to perform symmetric or alternating hand movements under conditions of different spatio-temporal relationships (symmetric, alternating) and frequencies. A multi-channel, continuous wave, functional near-infrared spectral (fNIRS) imaging instrument was used to monitor hemodynamic activity while 16 pairs of volunteers performed the task. Results: Behaviorally, as indexed by phase locking value, movements were more stable in symmetric mode than in alternate mode. With increasing frequency, symmetric mode became more unstable; in contrast, alternating mode became more stable at higher frequencies, suggesting phase transition. Activation in brain regions of interest was much stronger in symmetric mode as compared with alternate mode. In alternate mode, but not symmetric mode, [HbO] varied with frequency. Conclusion: Interpersonal bimanual coordination involves activity in premotor areas (premotor cortex, supplementary motor area, and frontal eye fields). More oxygen is consumed in these regions in alternating mode than in symmetric mode. rhythmic movement movement coordination mechanism neural mechanism fNIRS inter-individual Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Interpersonal movement coordination is an indispensable aspect of daily life, and often entails a relationship between individuals trying to achieve a common goal. When people walk together and talk together, they show this kind of coordinated action. Well-trained athletes (e.g. synchronized divers, basketball players) show more refined coordination. Behavioral studies on intra- and inter-individual coordination have found that rhythmic bimanual coordination of body movements is mainly influenced by two factors: one is the spatio-temporal relationship (symmetric or alternating), and the other is frequency. Complex interactions also exist between these two factors. Alternate movement mode involves the alternating activity of homologous muscle groups; symmetric mode involves the simultaneous activity of homologous muscle groups, and is generally more accurate and more stable in performing actions(Swinnen, Jardin, Meulenbroek, Dounskaia, & Den Brandt, 1997 ). Behavioral performance of Parkinson's patients and related research findings using transcranial magnetic stimulation (TMS) have demonstrated the instability of alternate mode. Parkinson's patients often show more errors and variations in performing alternate movement(Quincy, Laurie, & Timothy, 2002 ). Alternate mode can be disrupted by TMS, but not symmetric mode(Chen et al., 2005 ). As frequency increases, the stability of both symmetric and alternating modes decreases. Increasing frequency eventually leads to a phase transition of alternate to symmetric mode, but phase transitions in the opposite direction rarely occur(Kelso, 1984 ). There are two major theories for explaining intra-individual movement coordination from a biological perspective. General motor program theory proposes that there is a common action plan for the control of both hands(R. A. Schmidt, 1975 ). Inter-manual crosstalk theory proposes that the two hands are governed by two different action plans, and crosstalk can occur between those one-handed action plans(Marteniuk & MacKenzie, 1980 ). From the perspective of action planning, these theories can partly explain the difference in stability between alternate and symmetric modes. Self-organization theory further employs a non-linear dynamic self-organizing system to model movement coordination in different modes(Haken, Kelso, & Bunz, 1985 ; Schöner & Kelso, 1988 ). Self-organization theory can be used to explain both intra- and inter-individual movement coordination. However, at the biological level it remains unclear whether inter-individual coordination uses a common action plan or crosstalk between individual action plans. Due to the cooperative nature of humans, two individuals can be considered as a single organism (Newtson, Hairfield, Bloomingdale, & Cutino, 1987 ). Although intra- and inter-individual movement coordination follow some common rules, there are still essential differences between them. For example, inter-individual coordination relies on the connection of two independent nervous systems through vision(R. C. Schmidt, Carello, & Turvey, 1990 ; R. C. Schmidt & Richardson, 2008a ). Thus the question arises as to how low-level (frequency) and high-level (spatiotemporal) factors interact to affect action coordination at the neural level. The answer may be fundamentally different from that of intra-individual coordination, which is controlled by a single nervous system. Functional imaging studies have shown that hand movement coordination in different modes (symmetric, alternating) involves the supplementary motor area (SMA). In particular, SMA is integral to maintaining alternating movements and phase transitions(Debaere, Wenderoth, Sunaert, Van Hecke, & Swinnen, 2003 ; Jäncke et al., 2000 ; Weerd et al., 2003 ). The unique requirements of linking two independent nervous systems through vision makes the mirror nervous system a key mechanism of inter-individual movement coordination(Niu, Yu, Li, & Liu, 2019 ). Various neuroimaging studies have localized mirror neurons to the regions (anterior frontal gyrus, anterior premotor cortex, posterior inferior parietal lobule) (Buccino et al., 2010 ; Fadiga, Fogassi, Pavesi, & Rizzolatti, 1995 ; Fogassi, 2011 ; Grèzes, Armony, Rowe, & Passingham, 2003 ; Iacoboni, Woods, & Brass, 1999 ; Tai, Scherfler, Brooks, Sawamoto, & Castiello, 2004 ). These regions have been associated with phase transitions under frequency pressure (Aramaki, Honda, Okada, & Sadato, 2006 ) Our hemodynamic analysis targeted frontal and parietal areas because previous studies implicated these regions in rhythmic movement coordination. Interpersonal interaction is often accompanied by head movement. Therefore, the present study used functional near-infrared spectroscopy (fNIRS) to track cortical activity because it is mobile and resistant to motion artifacts (Cui, Bryant, & Reiss, 2012 ; Ferrari & Quaresima, 2012 ; Scholkmann, Holper, Wolf, & Wolf, 2013 ). Pairs of subjects performed hand movements under conditions of different spatio-temporal relationships (symmetric, alternating) and different frequencies (1.8, 2.1, 2.4, 2.7, 3.0, and 3.3 Hz, based on previous studies)(Kelso, 1984 ; Kelso, Scholz, & Schoner, 1986 ). The present study adopts factor design to explore the neural basis of movement coordination under different spatio-temporal relationships and different frequencies, and their complicated interaction. Methods Participants Thirty-two volunteers (16 pairs, 9 pairs of males and 7 pairs of females; mean age 21.75 ± 2.11 years) participated in current study. All participants were right-handed, with normal or corrected-to-normal vision, and no participant had a history of physical disability or mental illness. Participants provided informed written consent and were paid 100￥ ( $ 15) for their participation. The study followed ethical guidelines set forth by the Declaration of Helsinki and was approved by the local ethics committee at Shanghai University of Sport in China (tracking number 102772019RT007). Tasks and Procedures A modified version of an inter-individual movement coordination paradigm was adopted. Each participant sat on a stable chair close to his or her partner in front of the testing table with the designated shoulder in approximately 50° of flexion, 0° of abduction, and 90° of mediati rotation. The forearm was placed on the testing table, approximately 45° to the outer edge. The elbow was stabilized at approximately 120° of flexion, with the forearm in 0° of pronation, the wrist in 0° of extension and adduction. The designated hands — the left hand for the person seated on the left and the right hand for the person seated on the right – were placed in the center of the table beneath a high-speed camera fixed approximately 0.6 m above the table. Participants were asked to perform a cyclic pinching motion with the thumb and index finger, opening as widely as possible and closing to make fingertip contact as accurately as possible, and to consciously coordinate their movements in either symmetric or alternate phase modes relative to the other person’s movements. Fingers were always on a plane parallel to the desktop. The desired pace was indicated by a metronome pulse. Symmetric coordination was described to the participants as having their fingers at the same place in a cycle at the same time (the finger movements of both hands should always be mirror symmetrical). Alternate mode was described as having their fingers at opposite places in a cycle at the same time. The two modes were demonstrated by a research assistant beforehand, and some practice trials were conducted to familiarize participants with the task. The experiment used a randomized block design. All participant pairs were asked to coordinate their movements in 2 phase modes × 6 frequencies (4 blocks per phase mode per frequency, 48 blocks in total). The task conditions were randomly arranged. Each block lasted for 10 s and there was a 20-s rest between blocks. Behavior Data Acquisition One digital video camera (HDR-CX 700E; SONY; Japan) was used to record images (sampling rate: 50 frames per second, resolution: 1920×1080). The camera was fixed approximately 0.6 m above the table, facing the subjects’ hands. Fluorescent markers were placed on the tips of each subject’s index finger and thumb. Positions of the markers were recorded during the cyclic pinching tasks, and changes in distance between marker positions were calculated offline. A Hilbert transform was then applied to compute the instantaneous phase (φ), a value between –π and π. The difference between these values (Δφ) for each subject pair quantifies locking between the phases of interpersonal bimanual coordination. If signals rise and fall together or with a consistent lag, then Δφ will be stable across trials. If there is no relationship between the signals, then Δφ will be random across trials. Phase locking value (PLV) was calculated from Δφ and ranges from 0 to 1; PLV = 0 signifies purely random rise and fall, and PLV = 1 signifies that one signal perfectly follows the other(Marple, 1999 ; Oppenheim, Schafer, & Buck, 1999 ). A 2 (spatio-temporal relationship: symmetric vs. alternating) × 6 (frequency: 1.8, 2.1, 2.4, 2.7, 3.0, and 3.3 Hz) repeated measures analysis of variance was performed for the PLV of each subject pair. Hemodynamic Data Acquisition A multi-channel, continuous wave, fNIRS instrument (NIRScout; NIRx Medical Technologies LLC; Minneapolis, MN, United States) was used to monitor hemodynamic activity throughout the experiment, with a sampling rate of 7.81 Hz. The probes were arranged in accordance with the 10/20 international system(Jurcak, Tsuzuki, & Dan, 2007 ; Niu et al., 2019 ). Three optode probe sets (seven emitters and seven detectors, with 3-cm optode separation) were used. Fifteen channels were placed over the premotor and bilateral inferior parietal fields of the brain (Fig. 1 ). Hemodynamic Imaging Individual-level analysis Because oxygenated hemoglobin has a better signal-to-noise ratio than deoxygenated hemoglobin, only HbO data were used(Schaeffer, Yennu, Gandy, Fenghua, & Hanli, 2014 ). The HbO concentration ([HbO]) was analyzed using the HomER2 (MGH-Martinos Center for Biomedical Imaging; Boston, MA, United States)(Huppert, Diamond, Franceschini, & Boas, 2009 ) toolkit for MATLAB (MathWorks; Natick, MA, United States). First, the signal quality of individual channels was checked by means of a coefficient of variation. The exclusion value was set at 25%. Subsequently, data were subjected to baseline correction (0–2 s before trial onset). Low-frequency noise (such as head movement) was removed by a high-pass filter (cutoff frequency 0.01 Hz), and high-frequency noise and cardiovascular artifacts were removed by a low-pass filter (cutoff frequency 0.1 Hz). Optical data were converted into hemoglobin signals with units of mol/L in accordance with the modified Beer–Lambert Law(Cope et al., 1988 ). Group-level analysis To reduce signal variation, signals were averaged across a small number of channels overlying a cortical region of interest (ROI). These channels of interest corresponded to 3 ROIs. ROI-1 (channels 1 to 4) was located in the left inferior parietal lobe (lIPL), ROI-2 (channels 5 to 8) was located in the right inferior parietal lobe (rIPL), and ROI-3 (channels 9 to 15) was located in the premotor areas. Mean [HbO] for each ROI (averaged across channels) during the task period was calculated for each experimental condition and subjected to a 3 (ROI: 1, 2, 3) × 2 (spatio-temporal relationship: symmetric vs. alternating) × 6 (frequency: 1.8, 2.1, 2.4, 2.7, 3.0, 3.3 Hz) repeated measure analysis of variance (ANOVA) in SPSS 22.0 (IBM, New York, NY, United States). Post hoc analysis (least significant difference) was used to detect the source comparison for observed variance. Mean [HbO]s are reported with standard errors. Results Behavior There was a significant main effect of spatio-temporal relationship for the finger tapping movement (F 1,15 = 53.552, p < 0.001, partial η 2 = 0.781; Symmetric mode: 0.915 ± 0.011, Alternating mode: 0.753 ± 0.023) (Fig. 2 ). The spatio-temporal relationship × frequency interaction was also significant, F 5,75 = 7.259, p < 0.001, partial η 2 = 0.326. In symmetric mode, the mean PLV at 1.8 Hz was significantly greater than that at 3.3 Hz (p = 0.006), and the mean PLV at 2.1 Hz was significantly greater than that at 3.0 (p = 0.030) and 3.3 Hz (p = 0.005). In alternating mode, the mean PLV at 3.3 Hz was greater than at all other frequencies (3.3 vs 1.8 Hz: p = 0.010, 3.3 vs 2.1 Hz: p = 0.014, 3.3 vs 2.4 Hz: p < 0.001, 3.3 vs 2.7 Hz: p = 0.008, 3.3 vs 3.0 Hz: p = 0.040). In addition, the mean PLV at 2.7 Hz was significantly greater than that at 2.4 Hz (p = 0.049) (Fig. 3 , Table 1 ). Table 1 Detailed PLV for each experimental condition (Mean ± Standard Error) 1.8 Hz 2.1 Hz 2.4 Hz 2.7 Hz 3.0 Hz 3.3 Hz Symmetric 0.923 ± 0.012 0.927 ± 0.011 0.916 ± 0.013 0.918 ± 0.011 0.910 ± 0.013 0.898 ± 0.014 Alternating 0.736 ± 0.028 0.733 ± 0.029 0.748 ± 0.023 0.758 ± 0.021 0.760 ± 0.022 0.784 ± 0.019 Hemodynamics For ROI-3, there was a significant main effect of spatio-temporal relationship (F 1, 31 = 6.822, p = 0.014, partial η 2 = 0.180, Symmetric mode: 1.208 ± 0.233, Alternate mode: 0.704 ± 0.189), and a significant interaction effect of spatio-temporal relationship × frequency (F 5, 155 = 2.606, p = 0.027, partial η 2 = 0.078). Further analysis revealed that the mean [HbO] in symmetric mode was significantly greater than that of alternating mode at frequencies of 2.4 Hz (p = 0.036), 2.7 Hz (p = 0.010) and 3.3 Hz (p = 0.013), but not at the other frequencies (Fig. 4 ). In alternate mode, the mean [HbO] at 1.8 Hz was significantly higher than that at 2.4 Hz (p = 0.008), 2.7 Hz (p = 0.032) and 3.0 Hz (p = 0.049), and the mean [HbO] at 2.1 Hz was also significantly higher than that at 2.4 Hz (p = 0.041) (Fig. 5 , Table 2 ). Table 2 Detailed [HbO] for each experimental condition (Mean ± Standard Error, × 10 − 7 mol/L) 1.8 Hz 2.1 Hz 2.4 Hz 2.7 Hz 3.0 Hz 3.3 Hz Symmetric 0.750 ± 0.364 1.165 ± 0.398 1.068 ± 0.363 1.546 ± 0.294 1.209 ± 0.316 1.511 ± 0.334 Alternating 1.399 ± 0.346 1.161 ± 0.436 0.050 ± 0.328 0.349 ± 0.410 0.600 ± 0.280 0.662 ± 0.307 Discussion The current study explored the relationship between important parameters of coordination complexity (spatio-temporal relationship and frequency) and performance of interpersonal bimanual coordination at both the behavioral and neural levels. A modified inter-individual bimanual coordination paradigm was adopted and the neural mechanisms mediating inter-individual movement coordination were explored using fNIRS. Behaviorally, PLV in symmetric mode was significantly greater than that in alternating mode at all frequencies, indicating that performance was more stable in symmetric mode. Symmetric mode is widely reported to be more accurate and stable in the execution of actions as compared to alternating mode(Swinnen et al., 1997 ). Considering the fNIRS data, in ROI-3 the alternating mode showed lower [HbO] than symmetric mode, which reflects a higher degree of oxygen consumption. This may represent the neural mechanism underlying differences in behavioral stability between symmetric and alternating modes. There was also a behavioral effect of frequency in both modes. For symmetric mode, higher frequencies led to less stable behavioral performance with the index of PLV; for alternate mode, the opposite was true. The more stable coordination observed in alternating mode at high frequencies may correspond to a phase transition from alternating mode to symmetric (Haken et al., 1985 ; Kelso, 1984 ; R. C. Schmidt et al., 1990 ; R. C. Schmidt & Richardson, 2008b ). This hypothesis is further bolstered by the present fNIRS data showing that mean [HbO]s at 2.4, 2.7, and 3.0 Hz were all significantly lower than those at 1.8 Hz, and the mean [HbO] at 2.4 Hz was significantly lower than at 2.1 Hz. Previously observed phase transitions were also found in the range of 2.41–2.88 Hz (Kelso et al., 1986 ). Therefore, the differences we found in ROI-3 (premotor areas) may correspond to the neural basis of behavioral phase transition. The mean [HbO] in symmetric mode was significantly higher than that in alternating mode in ROI-3 at frequencies of 2.4, 2.7, and 3.3 Hz. This ROI includes premotor cortex, SMA, and frontal eye fields(Talairach & Tournoux, 1988 ). Using event-related functional magnetic resonance imaging (fMRI), Norihiro et al. found that activation of posterior SMA was significantly stronger during parallel movements than during mirror sequential movements, suggesting that posterior SMA is related to the bimanual coordination of finger movements(Norihiro & Yoshiharu, 1997 ). Aramaki and colleagues demonstrated that SMA is activated when participants reach a frequency limit during involuntary phase transitions between alternate and symmetric modes(Aramaki et al., 2006 ).Subsequently, Kennerley and colleagues proposed that the pre-SMA may be an important brain hub for maintaining alternating movements(Kennerley & S., 2003). In an fNIRS study Wilson and colleagues demonstrated that the SMA, and especially the pre-SMA, is critical for programming and maintaining intra-individual bimanual coordination in alternate mode(Wilson, Kurz, & Arpin, 2014 ). These results are all in line with those of the present study. What’s more, compared to intra-individual coordination, inter-individual coordination requires observation of other people’s actions to identify their intentions and imitate their actions. Thus, inter-individual coordination may involve engagement of mirror neurons(R. C. Schmidt, Fitzpatrick, Caron, & Mergeche, 2011 ), which have been localized to the anterior inferior frontal gyrus and premotor cortex, and posterior inferior parietal lobule(Buccino et al., 2010 ; Fadiga et al., 1995 ; Fogassi, 2011 ; Grèzes et al., 2003 ; Iacoboni et al., 1999 ; Tai et al., 2004 ). A previous study by our research group found that the inferior parietal lobule plays an important role in inter-individual leg-swinging coordination(Niu et al., 2019 ). Differences between coordination modes in the current study were observed at another important sector of mirror neurons, premotor cortex. As mentioned, inter-individual movement coordination involves activity in SMA and premotor cortex. There is also evidence that these regions are involved in other processes related to motor control. The premotor cortex is more activated when learning new sequences, and the SMA is more activated when practicing old sequences(Jenkins, Brooks, Nixon, Frackowiak, & Passingham, 1994 ). Information from multiple sources guides movement coordination(Gazzaniga, Ivry, & Mangun, 2011 ). SMA likely plays an important role in internal motor control, while premotor cortex is important to coordinating movement under the guidance of interpersonal external sensory information. Interpersonal coordination relies on the link between two independent nervous systems through vision(R. C. Schmidt et al., 1990 ; R. C. Schmidt & Richardson, 2008a ). Frontal eye fields may also play an important role in the connection of interpersonal coordination. Frontal eye fields control eyeball movement, especially gaze-following behavior, therefore enabling automatic detection and tracking of salient features(Paus, 1996 ). Therefore, common action plan or crosstalk between individual action plans may be related to the activity of these premotor areas.. In summary, interpersonal bimanual coordination involves activity in the premotor areas (premotor cortex, SMA, and frontal eye fields). More oxygen is consumed in these regions in alternating mode as compared to symmetric mode. These regions are also related to the phase transition from alternating mode to symmetric mode at higher frequencies. Declarations Ethical Approval The study followed ethical guidelines set forth by the Declaration of Helsinki and was approved by the local ethics committee at Shanghai University of Sport in China (tracking number 102772019RT007). Competing interests Conflict of interest All the authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript. Funding This study was supported by grants from Humanities and Social Sciences Youth Foundation, Ministry of Education of the People's Republic of China (Grant 21YJC890019). Author contributions Ying Liu conceptualization, methodology, data curation, writing-original draft preparation, writing-review and editing, supervision. Yanan Li conceptualization, methodology, formal analysis, data curation, writing-original draft preparation, writing-review and editing. Ruoyu Niu conceptualization, methodology, data curation.Lei Liu formal investigation , data curation. Availability of data and materials All data generated or analyzed during this study is available and can be provided if required. 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D., Reinke, K., Ryan, L., Mcisaac, T., Perschler, P., Schnyer, D.,.. . Gmitro, A. (2003). Cortical mechanisms for acquisition and performance of bimanual motor sequences. Neuroimage, 19 (4), 1405–1416. Wilson, T. W., Kurz, M. J., & Arpin, D. J. (2014). Functional specialization within the supplementary motor area: a fNIRS study of bimanual coordination. Neuroimage, 85 , 445–450. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 01 Oct, 2023 Read the published version in Physiology & Behavior → Version 2 posted You are reading this latest preprint version Show more versions Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1821802","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":137886830,"identity":"74c6c5c7-9e52-4fdc-93b6-0287e03466e1","order_by":0,"name":"Yanan Li","email":"","orcid":"","institution":"Shanghai University of Sport","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yanan","middleName":"","lastName":"Li","suffix":""},{"id":137886831,"identity":"7414b753-6352-4402-91e3-a6ad18764f86","order_by":1,"name":"Ruoyu Niu","email":"","orcid":"","institution":"Shanghai University of Sport","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ruoyu","middleName":"","lastName":"Niu","suffix":""},{"id":137886832,"identity":"45939591-8cc7-4348-97d6-ef3a8cb0a7ce","order_by":2,"name":"Lei Liu","email":"","orcid":"","institution":"Shanghai University of Sport","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Lei","middleName":"","lastName":"Liu","suffix":""},{"id":137886833,"identity":"6741006a-1dd6-4397-865b-37175af7068d","order_by":3,"name":"Ying Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAs0lEQVRIiWNgGAWjYPACCx4G9sbGBx9I0CLBw8BzuNlwBilagCi9TZqDGLUGN7JTN/MwSMgY3HzYIM3AYCen20BQS+6220AtPAa3ExuMCxiSjc0OkKIleQbDgcRtxGu5ebDhMA9pWm4wNjYTpUXyzNttN+cAtUieSWxmnGFAhF/4juduu/GGwcae7/jx5z8+VNjJEdSiAFTAxPsP7k4CykFAvoGBgfEHEQpHwSgYBaNgBAMABbREJ3vvUQQAAAAASUVORK5CYII=","orcid":"","institution":"Shanghai University of Sport","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2022-07-04 02:14:10","currentVersionCode":2,"declarations":"","doi":"10.21203/rs.3.rs-1821802/v2","doiUrl":"https://doi.org/10.21203/rs.3.rs-1821802/v2","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1016/j.physbeh.2023.114303","type":"published","date":"2023-10-01T16:08:46+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":26772281,"identity":"f485bf21-da57-4940-83e0-b66b993a2144","added_by":"auto","created_at":"2022-09-21 15:49:49","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":156457,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial profile of functional near-infrared spectral imaging (fNIRS) probes. Red circles indicate the 7 optical sources, green circles indicate the 7 detectors, and black numbers (1–15) indicate fNIRS channels. The optical sources and detectors were positioned according to the international 10/20 standard positions\u003c/p\u003e","description":"","filename":"FIGURE1.png","url":"https://assets-eu.researchsquare.com/files/rs-1821802/v2/d834a19755f8687047dc6f7b.png"},{"id":26772279,"identity":"e46246fb-3b3d-42d3-873d-e4c439ce7530","added_by":"auto","created_at":"2022-09-21 15:49:49","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":33030,"visible":true,"origin":"","legend":"\u003cp\u003eMean PLV averaged across subjects. Error bar: standard error.\u003c/p\u003e","description":"","filename":"FIGURE2.png","url":"https://assets-eu.researchsquare.com/files/rs-1821802/v2/e3d3148cca1706b89123102d.png"},{"id":26772691,"identity":"929c3c10-ed4e-409c-be85-b7b10d58005e","added_by":"auto","created_at":"2022-09-21 15:54:49","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":34398,"visible":true,"origin":"","legend":"\u003cp\u003eBar plot of Mean PLV. *, p \u0026lt;.05. **, p \u0026lt;.01. Error bar: standard error.\u003c/p\u003e","description":"","filename":"FIGURE3.png","url":"https://assets-eu.researchsquare.com/files/rs-1821802/v2/a7577220521680e1a2a0f526.png"},{"id":26772282,"identity":"0f0dba1f-874f-4e6a-b247-4016ca9f491a","added_by":"auto","created_at":"2022-09-21 15:49:49","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":34708,"visible":true,"origin":"","legend":"\u003cp\u003eOxygenated hemoglobin concentration in ROI-3 (premotor areas), averaged across subjects and time. Error bar: standard error. *, p \u0026lt;.05.\u003c/p\u003e","description":"","filename":"FIGURE4.png","url":"https://assets-eu.researchsquare.com/files/rs-1821802/v2/67f10dad5858dafaed7dfee8.png"},{"id":26772692,"identity":"aa9cba74-163c-4ee7-9a3c-7017fbd320ca","added_by":"auto","created_at":"2022-09-21 15:54:49","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":40792,"visible":true,"origin":"","legend":"\u003cp\u003eSimple effect analysis.*, p \u0026lt;.05. Error bar: standard error.\u003c/p\u003e","description":"","filename":"FIGURE5.png","url":"https://assets-eu.researchsquare.com/files/rs-1821802/v2/bbd7c664c49716b19c97dede.png"},{"id":52777302,"identity":"563d2bb9-1149-41b5-9b67-29c3c0300246","added_by":"auto","created_at":"2024-03-15 16:08:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":539123,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1821802/v2/71487b39-ab2e-4925-82b1-845ab29e61f1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Premotor Function in Interpersonal Bimanual Coordination: Neural Responses to Varying Frequencies and Spatio-Temporal Relationships","fulltext":[{"header":"Introduction","content":"\u003cp\u003eInterpersonal movement coordination is an indispensable aspect of daily life, and often entails a relationship between individuals trying to achieve a common goal. When people walk together and talk together, they show this kind of coordinated action. Well-trained athletes (e.g. synchronized divers, basketball players) show more refined coordination.\u003c/p\u003e \u003cp\u003eBehavioral studies on intra- and inter-individual coordination have found that rhythmic bimanual coordination of body movements is mainly influenced by two factors: one is the spatio-temporal relationship (symmetric or alternating), and the other is frequency. Complex interactions also exist between these two factors. Alternate movement mode involves the alternating activity of homologous muscle groups; symmetric mode involves the simultaneous activity of homologous muscle groups, and is generally more accurate and more stable in performing actions(Swinnen, Jardin, Meulenbroek, Dounskaia, \u0026amp; Den Brandt, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). Behavioral performance of Parkinson's patients and related research findings using transcranial magnetic stimulation (TMS) have demonstrated the instability of alternate mode. Parkinson's patients often show more errors and variations in performing alternate movement(Quincy, Laurie, \u0026amp; Timothy, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Alternate mode can be disrupted by TMS, but not symmetric mode(Chen et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). As frequency increases, the stability of both symmetric and alternating modes decreases. Increasing frequency eventually leads to a phase transition of alternate to symmetric mode, but phase transitions in the opposite direction rarely occur(Kelso, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1984\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThere are two major theories for explaining intra-individual movement coordination from a biological perspective. General motor program theory proposes that there is a common action plan for the control of both hands(R. A. Schmidt, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e1975\u003c/span\u003e). Inter-manual crosstalk theory proposes that the two hands are governed by two different action plans, and crosstalk can occur between those one-handed action plans(Marteniuk \u0026amp; MacKenzie, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1980\u003c/span\u003e). From the perspective of action planning, these theories can partly explain the difference in stability between alternate and symmetric modes. Self-organization theory further employs a non-linear dynamic self-organizing system to model movement coordination in different modes(Haken, Kelso, \u0026amp; Bunz, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1985\u003c/span\u003e; Sch\u0026ouml;ner \u0026amp; Kelso, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1988\u003c/span\u003e). Self-organization theory can be used to explain both intra- and inter-individual movement coordination. However, at the biological level it remains unclear whether inter-individual coordination uses a common action plan or crosstalk between individual action plans.\u003c/p\u003e \u003cp\u003eDue to the cooperative nature of humans, two individuals can be considered as a single organism (Newtson, Hairfield, Bloomingdale, \u0026amp; Cutino, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1987\u003c/span\u003e). Although intra- and inter-individual movement coordination follow some common rules, there are still essential differences between them. For example, inter-individual coordination relies on the connection of two independent nervous systems through vision(R. C. Schmidt, Carello, \u0026amp; Turvey, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; R. C. Schmidt \u0026amp; Richardson, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2008a\u003c/span\u003e). Thus the question arises as to how low-level (frequency) and high-level (spatiotemporal) factors interact to affect action coordination at the neural level. The answer may be fundamentally different from that of intra-individual coordination, which is controlled by a single nervous system.\u003c/p\u003e \u003cp\u003eFunctional imaging studies have shown that hand movement coordination in different modes (symmetric, alternating) involves the supplementary motor area (SMA). In particular, SMA is integral to maintaining alternating movements and phase transitions(Debaere, Wenderoth, Sunaert, Van Hecke, \u0026amp; Swinnen, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; J\u0026auml;ncke et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Weerd et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). The unique requirements of linking two independent nervous systems through vision makes the mirror nervous system a key mechanism of inter-individual movement coordination(Niu, Yu, Li, \u0026amp; Liu, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Various neuroimaging studies have localized mirror neurons to the regions (anterior frontal gyrus, anterior premotor cortex, posterior inferior parietal lobule) (Buccino et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Fadiga, Fogassi, Pavesi, \u0026amp; Rizzolatti, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Fogassi, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Gr\u0026egrave;zes, Armony, Rowe, \u0026amp; Passingham, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Iacoboni, Woods, \u0026amp; Brass, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Tai, Scherfler, Brooks, Sawamoto, \u0026amp; Castiello, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). These regions have been associated with phase transitions under frequency pressure (Aramaki, Honda, Okada, \u0026amp; Sadato, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) Our hemodynamic analysis targeted frontal and parietal areas because previous studies implicated these regions in rhythmic movement coordination.\u003c/p\u003e \u003cp\u003eInterpersonal interaction is often accompanied by head movement. Therefore, the present study used functional near-infrared spectroscopy (fNIRS) to track cortical activity because it is mobile and resistant to motion artifacts (Cui, Bryant, \u0026amp; Reiss, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Ferrari \u0026amp; Quaresima, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Scholkmann, Holper, Wolf, \u0026amp; Wolf, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Pairs of subjects performed hand movements under conditions of different spatio-temporal relationships (symmetric, alternating) and different frequencies (1.8, 2.1, 2.4, 2.7, 3.0, and 3.3 Hz, based on previous studies)(Kelso, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1984\u003c/span\u003e; Kelso, Scholz, \u0026amp; Schoner, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1986\u003c/span\u003e). The present study adopts factor design to explore the neural basis of movement coordination under different spatio-temporal relationships and different frequencies, and their complicated interaction.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eThirty-two volunteers (16 pairs, 9 pairs of males and 7 pairs of females; mean age 21.75\u0026thinsp;\u0026plusmn;\u0026thinsp;2.11 years) participated in current study. All participants were right-handed, with normal or corrected-to-normal vision, and no participant had a history of physical disability or mental illness. Participants provided informed written consent and were paid 100￥ (\u003cspan\u003e$\u003c/span\u003e15) for their participation. The study followed ethical guidelines set forth by the Declaration of Helsinki and was approved by the local ethics committee at Shanghai University of Sport in China (tracking number 102772019RT007).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eTasks and Procedures\u003c/h2\u003e \u003cp\u003eA modified version of an inter-individual movement coordination paradigm was adopted. Each participant sat on a stable chair close to his or her partner in front of the testing table with the designated shoulder in approximately 50\u0026deg; of flexion, 0\u0026deg; of abduction, and 90\u0026deg; of mediati rotation. The forearm was placed on the testing table, approximately 45\u0026deg; to the outer edge. The elbow was stabilized at approximately 120\u0026deg; of flexion, with the forearm in 0\u0026deg; of pronation, the wrist in 0\u0026deg; of extension and adduction. The designated hands \u0026mdash; the left hand for the person seated on the left and the right hand for the person seated on the right \u0026ndash; were placed in the center of the table beneath a high-speed camera fixed approximately 0.6 m above the table. Participants were asked to perform a cyclic pinching motion with the thumb and index finger, opening as widely as possible and closing to make fingertip contact as accurately as possible, and to consciously coordinate their movements in either symmetric or alternate phase modes relative to the other person\u0026rsquo;s movements. Fingers were always on a plane parallel to the desktop. The desired pace was indicated by a metronome pulse. Symmetric coordination was described to the participants as having their fingers at the same place in a cycle at the same time (the finger movements of both hands should always be mirror symmetrical). Alternate mode was described as having their fingers at opposite places in a cycle at the same time. The two modes were demonstrated by a research assistant beforehand, and some practice trials were conducted to familiarize participants with the task.\u003c/p\u003e \u003cp\u003eThe experiment used a randomized block design. All participant pairs were asked to coordinate their movements in 2 phase modes \u0026times; 6 frequencies (4 blocks per phase mode per frequency, 48 blocks in total). The task conditions were randomly arranged. Each block lasted for 10 s and there was a 20-s rest between blocks.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eBehavior Data Acquisition\u003c/h2\u003e \u003cp\u003eOne digital video camera (HDR-CX 700E; SONY; Japan) was used to record images (sampling rate: 50 frames per second, resolution: 1920\u0026times;1080). The camera was fixed approximately 0.6 m above the table, facing the subjects\u0026rsquo; hands. Fluorescent markers were placed on the tips of each subject\u0026rsquo;s index finger and thumb. Positions of the markers were recorded during the cyclic pinching tasks, and changes in distance between marker positions were calculated offline. A Hilbert transform was then applied to compute the instantaneous phase (φ), a value between \u0026ndash;π and π. The difference between these values (Δφ) for each subject pair quantifies locking between the phases of interpersonal bimanual coordination. If signals rise and fall together or with a consistent lag, then Δφ will be stable across trials. If there is no relationship between the signals, then Δφ will be random across trials. Phase locking value (PLV) was calculated from Δφ and ranges from 0 to 1; PLV\u0026thinsp;=\u0026thinsp;0 signifies purely random rise and fall, and PLV\u0026thinsp;=\u0026thinsp;1 signifies that one signal perfectly follows the other(Marple, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Oppenheim, Schafer, \u0026amp; Buck, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). A 2 (spatio-temporal relationship: symmetric vs. alternating) \u0026times; 6 (frequency: 1.8, 2.1, 2.4, 2.7, 3.0, and 3.3 Hz) repeated measures analysis of variance was performed for the PLV of each subject pair.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eHemodynamic Data Acquisition\u003c/h2\u003e \u003cp\u003eA multi-channel, continuous wave, fNIRS instrument (NIRScout; NIRx Medical Technologies LLC; Minneapolis, MN, United States) was used to monitor hemodynamic activity throughout the experiment, with a sampling rate of 7.81 Hz. The probes were arranged in accordance with the 10/20 international system(Jurcak, Tsuzuki, \u0026amp; Dan, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Niu et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Three optode probe sets (seven emitters and seven detectors, with 3-cm optode separation) were used. Fifteen channels were placed over the premotor and bilateral inferior parietal fields of the brain (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eHemodynamic Imaging\u003c/h2\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003eIndividual-level analysis\u003c/h2\u003e \u003cp\u003eBecause oxygenated hemoglobin has a better signal-to-noise ratio than deoxygenated hemoglobin, only HbO data were used(Schaeffer, Yennu, Gandy, Fenghua, \u0026amp; Hanli, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The HbO concentration ([HbO]) was analyzed using the HomER2 (MGH-Martinos Center for Biomedical Imaging; Boston, MA, United States)(Huppert, Diamond, Franceschini, \u0026amp; Boas, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) toolkit for MATLAB (MathWorks; Natick, MA, United States). First, the signal quality of individual channels was checked by means of a coefficient of variation. The exclusion value was set at 25%. Subsequently, data were subjected to baseline correction (0\u0026ndash;2 s before trial onset). Low-frequency noise (such as head movement) was removed by a high-pass filter (cutoff frequency 0.01 Hz), and high-frequency noise and cardiovascular artifacts were removed by a low-pass filter (cutoff frequency 0.1 Hz). Optical data were converted into hemoglobin signals with units of mol/L in accordance with the modified Beer\u0026ndash;Lambert Law(Cope et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1988\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003eGroup-level analysis\u003c/h2\u003e \u003cp\u003eTo reduce signal variation, signals were averaged across a small number of channels overlying a cortical region of interest (ROI). These channels of interest corresponded to 3 ROIs. ROI-1 (channels 1 to 4) was located in the left inferior parietal lobe (lIPL), ROI-2 (channels 5 to 8) was located in the right inferior parietal lobe (rIPL), and ROI-3 (channels 9 to 15) was located in the premotor areas. Mean [HbO] for each ROI (averaged across channels) during the task period was calculated for each experimental condition and subjected to a 3 (ROI: 1, 2, 3) \u0026times; 2 (spatio-temporal relationship: symmetric vs. alternating) \u0026times; 6 (frequency: 1.8, 2.1, 2.4, 2.7, 3.0, 3.3 Hz) repeated measure analysis of variance (ANOVA) in SPSS 22.0 (IBM, New York, NY, United States). Post hoc analysis (least significant difference) was used to detect the source comparison for observed variance. Mean [HbO]s are reported with standard errors.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eBehavior\u003c/h2\u003e \u003cp\u003eThere was a significant main effect of spatio-temporal relationship for the finger tapping movement (F\u003csub\u003e1,15\u003c/sub\u003e = 53.552, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, partial η\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.781; Symmetric mode: 0.915\u0026thinsp;\u0026plusmn;\u0026thinsp;0.011, Alternating mode: 0.753\u0026thinsp;\u0026plusmn;\u0026thinsp;0.023) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The spatio-temporal relationship \u0026times; frequency interaction was also significant, F\u003csub\u003e5,75\u003c/sub\u003e = 7.259, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, partial η\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.326. In symmetric mode, the mean PLV at 1.8 Hz was significantly greater than that at 3.3 Hz (p\u0026thinsp;=\u0026thinsp;0.006), and the mean PLV at 2.1 Hz was significantly greater than that at 3.0 (p\u0026thinsp;=\u0026thinsp;0.030) and 3.3 Hz (p\u0026thinsp;=\u0026thinsp;0.005). In alternating mode, the mean PLV at 3.3 Hz was greater than at all other frequencies (3.3 vs 1.8 Hz: p\u0026thinsp;=\u0026thinsp;0.010, 3.3 vs 2.1 Hz: p\u0026thinsp;=\u0026thinsp;0.014, 3.3 vs 2.4 Hz: p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, 3.3 vs 2.7 Hz: p\u0026thinsp;=\u0026thinsp;0.008, 3.3 vs 3.0 Hz: p\u0026thinsp;=\u0026thinsp;0.040). In addition, the mean PLV at 2.7 Hz was significantly greater than that at 2.4 Hz (p\u0026thinsp;=\u0026thinsp;0.049) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDetailed PLV for each experimental condition (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;Standard Error)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.8 Hz\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.1 Hz\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.4 Hz\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.7 Hz\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.0 Hz\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.3 Hz\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSymmetric\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.923\u0026thinsp;\u0026plusmn;\u0026thinsp;0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.927\u0026thinsp;\u0026plusmn;\u0026thinsp;0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.916\u0026thinsp;\u0026plusmn;\u0026thinsp;0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.918\u0026thinsp;\u0026plusmn;\u0026thinsp;0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e0.910\u0026thinsp;\u0026plusmn;\u0026thinsp;0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.898\u0026thinsp;\u0026plusmn;\u0026thinsp;0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlternating\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.736\u0026thinsp;\u0026plusmn;\u0026thinsp;0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.733\u0026thinsp;\u0026plusmn;\u0026thinsp;0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.748\u0026thinsp;\u0026plusmn;\u0026thinsp;0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.758\u0026thinsp;\u0026plusmn;\u0026thinsp;0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e0.760\u0026thinsp;\u0026plusmn;\u0026thinsp;0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.784\u0026thinsp;\u0026plusmn;\u0026thinsp;0.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eHemodynamics\u003c/h2\u003e \u003cp\u003eFor ROI-3, there was a significant main effect of spatio-temporal relationship (F\u003csub\u003e1, 31\u003c/sub\u003e = 6.822, p\u0026thinsp;=\u0026thinsp;0.014, partial η\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.180, Symmetric mode: 1.208\u0026thinsp;\u0026plusmn;\u0026thinsp;0.233, Alternate mode: 0.704\u0026thinsp;\u0026plusmn;\u0026thinsp;0.189), and a significant interaction effect of spatio-temporal relationship \u0026times; frequency (F\u003csub\u003e5, 155\u003c/sub\u003e = 2.606, p\u0026thinsp;=\u0026thinsp;0.027, partial η\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.078). Further analysis revealed that the mean [HbO] in symmetric mode was significantly greater than that of alternating mode at frequencies of 2.4 Hz (p\u0026thinsp;=\u0026thinsp;0.036), 2.7 Hz (p\u0026thinsp;=\u0026thinsp;0.010) and 3.3 Hz (p\u0026thinsp;=\u0026thinsp;0.013), but not at the other frequencies (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In alternate mode, the mean [HbO] at 1.8 Hz was significantly higher than that at 2.4 Hz (p\u0026thinsp;=\u0026thinsp;0.008), 2.7 Hz (p\u0026thinsp;=\u0026thinsp;0.032) and 3.0 Hz (p\u0026thinsp;=\u0026thinsp;0.049), and the mean [HbO] at 2.1 Hz was also significantly higher than that at 2.4 Hz (p\u0026thinsp;=\u0026thinsp;0.041) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDetailed [HbO] for each experimental condition (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;Standard Error, \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e mol/L)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.8 Hz\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.1 Hz\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.4 Hz\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.7 Hz\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.0 Hz\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.3 Hz\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSymmetric\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.750\u0026thinsp;\u0026plusmn;\u0026thinsp;0.364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.165\u0026thinsp;\u0026plusmn;\u0026thinsp;0.398\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e1.068\u0026thinsp;\u0026plusmn;\u0026thinsp;0.363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e1.546\u0026thinsp;\u0026plusmn;\u0026thinsp;0.294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e1.209\u0026thinsp;\u0026plusmn;\u0026thinsp;0.316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e1.511\u0026thinsp;\u0026plusmn;\u0026thinsp;0.334\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlternating\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.399\u0026thinsp;\u0026plusmn;\u0026thinsp;0.346\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.161\u0026thinsp;\u0026plusmn;\u0026thinsp;0.436\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.050\u0026thinsp;\u0026plusmn;\u0026thinsp;0.328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.349\u0026thinsp;\u0026plusmn;\u0026thinsp;0.410\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e0.600\u0026thinsp;\u0026plusmn;\u0026thinsp;0.280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.662\u0026thinsp;\u0026plusmn;\u0026thinsp;0.307\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe current study explored the relationship between important parameters of coordination complexity (spatio-temporal relationship and frequency) and performance of interpersonal bimanual coordination at both the behavioral and neural levels. A modified inter-individual bimanual coordination paradigm was adopted and the neural mechanisms mediating inter-individual movement coordination were explored using fNIRS.\u003c/p\u003e \u003cp\u003eBehaviorally, PLV in symmetric mode was significantly greater than that in alternating mode at all frequencies, indicating that performance was more stable in symmetric mode. Symmetric mode is widely reported to be more accurate and stable in the execution of actions as compared to alternating mode(Swinnen et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). Considering the fNIRS data, in ROI-3 the alternating mode showed lower [HbO] than symmetric mode, which reflects a higher degree of oxygen consumption. This may represent the neural mechanism underlying differences in behavioral stability between symmetric and alternating modes.\u003c/p\u003e \u003cp\u003eThere was also a behavioral effect of frequency in both modes. For symmetric mode, higher frequencies led to less stable behavioral performance with the index of PLV; for alternate mode, the opposite was true. The more stable coordination observed in alternating mode at high frequencies may correspond to a phase transition from alternating mode to symmetric (Haken et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1985\u003c/span\u003e; Kelso, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1984\u003c/span\u003e; R. C. Schmidt et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; R. C. Schmidt \u0026amp; Richardson, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2008b\u003c/span\u003e). This hypothesis is further bolstered by the present fNIRS data showing that mean [HbO]s at 2.4, 2.7, and 3.0 Hz were all significantly lower than those at 1.8 Hz, and the mean [HbO] at 2.4 Hz was significantly lower than at 2.1 Hz. Previously observed phase transitions were also found in the range of 2.41\u0026ndash;2.88 Hz (Kelso et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1986\u003c/span\u003e). Therefore, the differences we found in ROI-3 (premotor areas) may correspond to the neural basis of behavioral phase transition.\u003c/p\u003e \u003cp\u003eThe mean [HbO] in symmetric mode was significantly higher than that in alternating mode in ROI-3 at frequencies of 2.4, 2.7, and 3.3 Hz. This ROI includes premotor cortex, SMA, and frontal eye fields(Talairach \u0026amp; Tournoux, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1988\u003c/span\u003e). Using event-related functional magnetic resonance imaging (fMRI), Norihiro et al. found that activation of posterior SMA was significantly stronger during parallel movements than during mirror sequential movements, suggesting that posterior SMA is related to the bimanual coordination of finger movements(Norihiro \u0026amp; Yoshiharu, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). Aramaki and colleagues demonstrated that SMA is activated when participants reach a frequency limit during involuntary phase transitions between alternate and symmetric modes(Aramaki et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).Subsequently, Kennerley and colleagues proposed that the pre-SMA may be an important brain hub for maintaining alternating movements(Kennerley \u0026amp; S., 2003). In an fNIRS study Wilson and colleagues demonstrated that the SMA, and especially the pre-SMA, is critical for programming and maintaining intra-individual bimanual coordination in alternate mode(Wilson, Kurz, \u0026amp; Arpin, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). These results are all in line with those of the present study. What\u0026rsquo;s more, compared to intra-individual coordination, inter-individual coordination requires observation of other people\u0026rsquo;s actions to identify their intentions and imitate their actions. Thus, inter-individual coordination may involve engagement of mirror neurons(R. C. Schmidt, Fitzpatrick, Caron, \u0026amp; Mergeche, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), which have been localized to the anterior inferior frontal gyrus and premotor cortex, and posterior inferior parietal lobule(Buccino et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Fadiga et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Fogassi, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Gr\u0026egrave;zes et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Iacoboni et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Tai et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). A previous study by our research group found that the inferior parietal lobule plays an important role in inter-individual leg-swinging coordination(Niu et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Differences between coordination modes in the current study were observed at another important sector of mirror neurons, premotor cortex.\u003c/p\u003e \u003cp\u003eAs mentioned, inter-individual movement coordination involves activity in SMA and premotor cortex. There is also evidence that these regions are involved in other processes related to motor control. The premotor cortex is more activated when learning new sequences, and the SMA is more activated when practicing old sequences(Jenkins, Brooks, Nixon, Frackowiak, \u0026amp; Passingham, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). Information from multiple sources guides movement coordination(Gazzaniga, Ivry, \u0026amp; Mangun, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). SMA likely plays an important role in internal motor control, while premotor cortex is important to coordinating movement under the guidance of interpersonal external sensory information. Interpersonal coordination relies on the link between two independent nervous systems through vision(R. C. Schmidt et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; R. C. Schmidt \u0026amp; Richardson, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2008a\u003c/span\u003e). Frontal eye fields may also play an important role in the connection of interpersonal coordination. Frontal eye fields control eyeball movement, especially gaze-following behavior, therefore enabling automatic detection and tracking of salient features(Paus, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). Therefore, common action plan or crosstalk between individual action plans may be related to the activity of these premotor areas..\u003c/p\u003e \u003cp\u003eIn summary, interpersonal bimanual coordination involves activity in the premotor areas (premotor cortex, SMA, and frontal eye fields). More oxygen is consumed in these regions in alternating mode as compared to symmetric mode. These regions are also related to the phase transition from alternating mode to symmetric mode at higher frequencies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Approval\u0026nbsp;\u003c/strong\u003eThe study followed ethical guidelines set forth by the Declaration of Helsinki and was approved by the local ethics committee at Shanghai University of Sport in China (tracking number 102772019RT007).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003eConflict of interest All the authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003eThis study was supported by grants from Humanities and Social Sciences Youth Foundation, Ministry of Education of the People\u0026apos;s Republic of China (Grant 21YJC890019).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e Ying\u0026nbsp;Liu\u0026nbsp;conceptualization, methodology, data curation, writing-original draft preparation, writing-review and editing, supervision. Yanan Li conceptualization, methodology, formal analysis, data curation, writing-original draft preparation, writing-review and editing. Ruoyu Niu conceptualization, methodology, data curation.Lei Liu formal investigation , data curation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e All data generated or analyzed during this study is available and can be provided if required.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAramaki, Y., Honda, M., Okada, T., \u0026amp; Sadato, N. (2006). Neural Correlates of the Spontaneous Phase Transition during Bimanual Coordination. Cerebral Cortex, \u003cem\u003e16\u003c/em\u003e(9), 1338\u0026ndash;1348.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBuccino, G., Binkofski, F., Fink, G. R., Fadiga, L., Fogassi, L., Gallese, V., Freund, H. J. (2010). 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Current Biology Cb, \u003cem\u003e14\u003c/em\u003e(2), 117\u0026ndash;120.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTalairach, J. J., \u0026amp; Tournoux, P. (1988). \u003cem\u003eCoplanar stereotaxic atlas of the human brain\u003c/em\u003e: Georg Thieme Verlag.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeerd, P. D., Reinke, K., Ryan, L., Mcisaac, T., Perschler, P., Schnyer, D.,.. . Gmitro, A. (2003). Cortical mechanisms for acquisition and performance of bimanual motor sequences. Neuroimage, \u003cem\u003e19\u003c/em\u003e(4), 1405\u0026ndash;1416.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWilson, T. W., Kurz, M. J., \u0026amp; Arpin, D. J. (2014). Functional specialization within the supplementary motor area: a fNIRS study of bimanual coordination. Neuroimage, \u003cem\u003e85\u003c/em\u003e, 445\u0026ndash;450.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"rhythmic movement, movement coordination mechanism, neural mechanism, fNIRS, inter-individual","lastPublishedDoi":"10.21203/rs.3.rs-1821802/v2","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1821802/v2","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Interpersonal movement coordination is an important aspect of daily life. Behavioral studies have found that rhythmic bimanual coordination of movement is mainly influenced by two factors, spatio-temporal relationship and frequency of movements. How these factors affect action coordination at the neural level needs further exploration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e Participants were asked to perform symmetric or alternating hand movements under conditions of different spatio-temporal relationships (symmetric, alternating) and frequencies. A multi-channel, continuous wave, functional near-infrared spectral (fNIRS) imaging instrument was used to monitor hemodynamic activity while 16 pairs of volunteers performed the task.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Behaviorally, as indexed by phase locking value, movements were more stable in symmetric mode than in alternate mode. With increasing frequency, symmetric mode became more unstable; in contrast, alternating mode became more stable at higher frequencies, suggesting phase transition. Activation in brain regions of interest was much stronger in symmetric mode as compared with alternate mode. In alternate mode, but not symmetric mode, [HbO] varied with frequency.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e Interpersonal bimanual coordination involves activity in premotor areas (premotor cortex, supplementary motor area, and frontal eye fields). More oxygen is consumed in these regions in alternating mode than in symmetric mode.\u003c/p\u003e","manuscriptTitle":"Premotor Function in Interpersonal Bimanual Coordination: Neural Responses to Varying Frequencies and Spatio-Temporal Relationships","msid":"","msnumber":"","nonDraftVersions":[{"code":2,"date":"2022-09-21 15:49:47","doi":"10.21203/rs.3.rs-1821802/v2","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}},{"code":1,"date":"2022-07-12 17:27:50","doi":"10.21203/rs.3.rs-1821802/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"270ca005-fbb6-4843-a026-17d8285cd45f","owner":[],"postedDate":"September 21st, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-03-15T16:08:46+00:00","versionOfRecord":{"articleIdentity":"rs-1821802","link":"https://doi.org/10.1016/j.physbeh.2023.114303","journal":{"identity":"physiology-and-behavior","isVorOnly":true,"title":"Physiology \u0026 Behavior"},"publishedOn":"2023-10-01 16:08:46","publishedOnDateReadable":"October 1st, 2023"},"versionCreatedAt":"2022-09-21 15:49:47","video":"","vorDoi":"10.1016/j.physbeh.2023.114303","vorDoiUrl":"https://doi.org/10.1016/j.physbeh.2023.114303","workflowStages":[]},"version":"v2","identity":"rs-1821802","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1821802","identity":"rs-1821802","version":["v2"]},"buildId":"FbvkV6FR0MCFSLy54lSbu","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}