Interhemispheric fronto-parietal EEG alpha phase synchronization reflects inhibitory control during the Stroop task

Interhemispheric fronto-parietal EEG alpha phase synchronization reflects inhibitory control during the Stroop task | 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 Article Interhemispheric fronto-parietal EEG alpha phase synchronization reflects inhibitory control during the Stroop task Sangbin Yun, Byoung-Kyong Min This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8810043/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract The ability to inhibit irrelevant information and resist distraction is central to goal-directed behavior and constitutes a core function of human cognition. Although electroencephalographic (EEG) research has consistently implicated fronto-parietal alpha-band phase synchronization in top-down processing, a core mechanism of cognitive control, its neurodynamic contribution to inhibitory control remains underexplored. In this study, we examined whether large-scale EEG alpha-band synchronization within the fronto-parietal network reflects inhibitory control during a color-word Stroop task. Twenty-four participants completed congruent, neutral, and incongruent trials while EEG activity was recorded. Inter-site phase clustering (ISPC) was used to quantify alpha-band phase synchronization across bilateral frontocentral and parietocentral regions. Behaviorally, incongruent trials elicited significantly slower reaction times and reduced accuracy compared with congruent and neutral conditions, indicating increased conflict demands. Electrophysiological results revealed significantly enhanced alpha-band phase synchronization during incongruent trials, centered on the right frontocentral region, with stronger interhemispheric coupling between the right frontocentral and left parietocentral regions. Notably, the peak latency of this connectivity positively correlated with reaction times exclusively during incongruent trials, suggesting that the temporal dynamics of alpha synchronization are closely linked to behavioral performance during conflict processing. These findings indicate that interhemispheric fronto-parietal alpha phase synchronization reflects a neural mechanism underlying inhibitory control. Our results highlight the significance of EEG alpha-band synchronization and its temporal dynamics in coordinating large-scale brain networks supporting top-down cognitive control. Biological sciences/Neuroscience Biological sciences/Psychology Social science/Psychology EEG Inhibitory control Stroop task Alpha phase synchronization Interhemispheric fronto-parietal connectivity Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction A fundamental feature of human cognition is its capacity for goal-directed behavior, which is achieved by resolving competing inputs and prioritizing task-relevant information 1 . This capacity mainly depends on inhibitory control, the cognitive mechanism that suppresses distracting or prepotent responses in favor of goal-relevant processing 2 . Inhibitory control is crucial for context-sensitive decision-making, particularly when one must override automatic but inappropriate responses 3 , 4 . According to the conflict monitoring hypothesis, inhibitory control involves multiple stages, including conflict detection, suppression, and resolution 5 , 6 . These processes recruit prefrontal regions such as the anterior cingulate cortex (ACC), dorsolateral prefrontal cortex (dlPFC), and medial prefrontal cortex (mPFC), as demonstrated in electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) studies 7 – 10 . These patterns of brain activation have been observed across multiple inhibitory control paradigms, including the canonical Stroop task. Notably, the prefrontal regions are differentially involved in subprocesses of inhibitory control, and their dynamic interactions form an integrated inhibitory control network 11 – 13 . Within this network, top-down neural communication seems to play a pivotal role, optimizing goal-directed task execution through regulatory influence over sensory regions 14 , 15 . For instance, we recently demonstrated that EEG theta-band neuromodulation targeting synchronized interactions between the dorsal ACC (dACC) and left dlPFC enhanced behavioral performance by reducing reaction times during incongruent trials of the Stroop task 7 , 16 . Similarly, other studies have reported functional connectivity between the mPFC and ACC, further supporting the cooperative dynamics among prefrontal regions during inhibitory control 8 , 17 . It is noteworthy that top-down communication across distant brain regions is mediated by low-frequency oscillations, particularly in the theta and alpha bands 18 – 20 . This communication often manifests through phase synchronization between oscillatory activities across widespread cortical areas, a key electrophysiological marker of functional connectivity 21 . Among these rhythms, EEG theta-band phase synchronization between the dACC or mPFC and the dlPFC is well known to support conflict monitoring and resolution during inhibitory control, forming a highly interactive prefrontal network 10 , 17 . Although theta-band phase synchronization has been widely recognized as a key mechanism for top-down inhibitory control, alpha-band phase synchronization has been less frequently reported in this context. Notably, interregional EEG alpha phase synchronization has been shown to regulate cortical excitability and facilitate top-down modulation across distant cortical areas 18 , 19 , 22 – 24 . For instance, robust alpha-band coupling between frontal and occipital regions has been observed during working memory maintenance, especially when active manipulation of information is required 24 . Moreover, EEG alpha oscillations have been proposed to reflect internally driven top-down processes as indicated by findings that prestimulus alpha activity represents preparatory states influencing subsequent task performance 25 , 26 . Therefore, we hypothesized that EEG alpha-band synchronization reflects inhibitory control. In addition, alpha phase synchronization relates to cognitive control at the level of large-scale brain networks, with consistent associations to the fronto-parietal network 27 – 29 . Therefore, the present study investigated whether EEG alpha-band phase synchronization over the fronto-parietal region significantly reflects inhibitory control. Specifically, we examined its functional significance during the Stroop task by quantifying both global and local alpha phase synchronization. 2. Results 2.1. Behavioral data We observed significantly slower reaction times in the incongruent condition than in the congruent ( Z = 3.43, p < 0.001; incongruent, 737.82 ms; congruent, 669.53 ms) and neutral conditions ( Z = 4.01, p < 0.001; neutral, 629.16 ms), with no difference between the congruent and neutral conditions (( Z = 1.92, n.s. ; Fig. 1 A). As for task-performance accuracy, it was significantly lower in the incongruent condition than in the congruent ( Z = 2.43, p < 0.05; incongruent, 97.07%; congruent, 98.69%) and neutral conditions ( Z = 2.03, p < 0.05; neutral, 98.59%; Fig. 1 B). The comparison between the congruent and neutral conditions did not reach statistical significance ( Z = 0.25, n.s. ). 2.2. EEG alpha phase synchronization Figure 2 A shows topographic maps of the EEG alpha-band regional inter-site phase clustering (ISPC) across four regions of interests (ROIs) 30 . In the right frontocentral region, EEG alpha regional ISPC was significantly higher in the incongruent condition than in both the congruent ( Z = 2.09, p < 0.05; incongruent, 0.98; congruent, 0.18) and neutral conditions ( Z = 2.42, p < 0.05; neutral, − 0.12; Fig. 2 A). Meanwhile, in the right parietocentral region, regional ISPC was significantly higher in the incongruent condition compared with the congruent condition ( Z = 2.06, p < 0.05; incongruent, 0.73; congruent, − 0.27), but not relative to the neutral condition ( Z = 1.54, n.s. ). No significant differences in regional ISPC were observed in the left frontocentral or left parietocentral regions across task conditions. Subsequently, seed-based ISPC analysis revealed that the right frontocentral seed showed significantly stronger synchronization with the left parietocentral region during the incongruent condition compared to both the congruent ( Z = 2.16, p < 0.05; incongruent, 1.79; congruent, 0.39) and neutral conditions ( Z = 2.00, p < 0.05; neutral, − 0.07; Fig. 2 B). Seed ISPC between the right frontocentral and the right parietocentral regions was significantly higher in the incongruent condition relative to the neutral condition ( Z = 2.91, p < 0.01; incongruent, 1.31; neutral, − 0.52), but not relative to the congruent condition ( Z = 1.87, n.s. ). No significant differences were observed in seed ISPC between the right and left frontocentral regions. 2.3. Brain-behavior correlation Spearman’s rank correlation analysis was performed between reaction time and the peak latency of right frontocentral–left parietocentral ISPC (corresponding to the most prominent reddish connections in Fig. 2 B). ISPC peak latency was defined as the time point corresponding to a local maximum in ISPC, falling approximately within the time window of 700–900 ms poststimulus. As shown in Fig. 3 B, a significant positive correlation was observed in the incongruent condition ( r = 0.44, p < 0.05), indicating that longer synchronization delays were associated with slower responses. In contrast, there were no significant correlations in the congruent ( r = 0.01, n.s. ) and neutral ( r = − 0.20, n.s. ) conditions. 3. Discussion During the Stroop task, we found a significant increase in EEG alpha phase synchronization during the incongruent condition, particularly on the right frontocentral region (Fig. 2 A). Seed-based connectivity analysis further revealed significantly pronounced phase synchronization between the right frontocentral and left parietocentral regions (Fig. 2 B). Notably, the peak latency of this interhemispheric connectivity positively correlated with reaction times (Fig. 3 B), highlighting the behavioral significance of alpha-band synchronization in inhibitory control. Together, these findings highlight the crucial role of alpha-phase synchronization, possibly reflecting a top-down mechanism during inhibitory control. Previous studies consistently demonstrated that frontal-driven long-range alpha connectivity has been associated not only with top-down cognitive control 18 , 24 , but also with multiple higher-order cognitive domains, including attention, working memory, and mental imagery 22 , 31 – 34 . During the Stroop task, the right frontocentral region was prominently involved in inhibitory control, potentially reflecting top-down processing mediated by long-range alpha phase synchronization. The seed connectivity analysis consequently revealed increased alpha-band coupling between the right frontocentral and left parietocentral regions during the incongruent condition. This finding indicated enhanced interhemispheric communication within the fronto-parietal network, reflecting the network’s involvement in inhibitory control processes (Fig. 2 B). Consistently, previous fMRI studies have identified the fronto-parietal network as a core system for cognitive control, facilitating task-related information processing through the flexible initiation and adjustment of control demands 35 – 37 . In addition, resting-state studies have reported substantial anatomical overlap between networks correlated with alpha phase synchronization and the fronto-parietal network, as well as a negative association between alpha phase synchronization and fronto-parietal network lesion volume 28 , 29 . Moreover, a positive correlation between ISPC peak latency and reaction times was observed in the incongruent condition (Fig. 3 B). This finding implies that earlier alpha phase synchronization is associated with faster conflict resolution. Given the close association between alpha phase synchronization and top-down processing 18 , 24 , earlier synchronization likely corresponds to more rapid and efficient engagement of top-down control. The latency of alpha phase synchronization may represent the temporal dynamics of network recruitment for top-down inhibitory control. The interhemispheric connectivity observed in this study may reflect cooperative engagement of lateralized frontal and parietal cortices with complementary contributions. The right frontocentral region includes frontal executive areas such as the right dlPFC, implicated in conflict-driven adaptive regulation, dynamic adjustment under conflict 38 , 39 , and nonverbal color processing involving color–label matching during inhibitory control tasks 40 . On the other hand, the left parietocentral region incorporates the left parietal cortex, which includes extended Wernicke’s area within the left perisylvian language network 41 . This area has been implicated in language association and semantic processing, particularly for multimodal integration 42 . Accordingly, the observed interhemispheric alpha phase synchronization between the right frontocentral and left parietocentral regions may reflect interhemispheric top-down regulations to suppress automatic semantic processing that is irrelevant to a task goal, while facilitating task-relevant color processing. In accordance with this interpretation, a previous study showing a similar connectivity pattern suggested that enhanced executive function was associated with increased functional connectivity between the right dlPFC and the left parietal cortex 43 . Nevertheless, the other contralateral combination—left frontocentral and right parietocentral—did not show statistical significance in alpha phase synchronization, suggesting functional asymmetry across brain regions in the fronto-parietal network. As the left frontocentral region has been implicated in attentional preparation for anticipated conflict 9 , 38 , 44 , the lack of connectivity in this region may reflect limited engagement of anticipatory control during our Stroop task. Although our previous non-invasive brain stimulation studies have associated left dlPFC activation with conflict resolution and improved Stroop performance 7 , 16 , this region may be more involved in attentional preparation for anticipated conflict, rather than resolving ongoing conflict 9 , 38 , 44 . For instance, the left dlPFC shows increased activity during task preparation when participants receive anticipatory instructions about upcoming task demands, such as whether to read the word or name the color of the stimuli during a Stroop task 9 . In our study, however, pseudorandom trial presentation without cueing probably reduced the need for such anticipatory mechanisms. This could have resulted in the lack of left dlPFC involvement, potentially explaining the absence of significant left frontocentral connectivity. Similarly, the absence of significant synchronization in the right parietocentral region may reflect the limited relevance of its established role in visuospatial attention 45 – 47 , which was likely not strongly engaged by the centrally presented stimuli in our Stroop task. The right parietal cortex is typically recruited during spatial attention-shifting tasks involving responses to lateralized stimuli 46 . As our paradigm did not require visual attentional shifting, reduced involvement of the right parietal cortex may account for the lack of significant right parietocentral effects. To sum up, the interhemispheric fronto-parietal alpha phase synchronization appears to reflect a neural mechanism of inhibitory control during the Stroop task. These results align with the established roles of the fronto-parietal network in cognitive control and with previous observations linking alpha phase synchronization to this network. Given the involvement of alpha phase synchronization in top-down modulation across higher-order cognitive functions, the observed large-scale alpha phase synchronization may represent the coordinated engagement of distributed fronto-parietal regions to implement inhibitory control 18 , 19 , 24 . Furthermore, the lateralized pattern of interhemispheric connectivity suggests task-specific top-down modulation optimized for the Stroop task, potentially facilitating non-verbal color processing while suppressing automatic semantic processing through communication between the right frontal and the left parietal regions. Finally, the positive correlation between peak latency of interhemispheric alpha phase synchronization and reaction times during the incongruent condition indicates that earlier alpha synchronization is associated with faster engagement of top-down control, thereby accelerating conflict resolution. Taken together, these findings suggest that both the strength and temporal dynamics of alpha-band synchronization within the frontoparietal network reflect the neural dynamics underlying inhibitory control during the Stroop task. 4. Materials and Methods 4.1. Participants Twenty-four healthy individuals (mean age, 23.67 ± 0.53; 11 females, all right-handed) participated in this study. All participants reported normal color vision, normal or corrected-to-normal visual acuity, and no history of neurological, psychiatric, or cognitive disorders. Written informed consent was obtained from each participant before participation, by procedures approved by the Korea University Institutional Review Board (No. KUIRB-2021-0209-06). We confirmed that all research was performed in accordance with relevant guidelines/regulations and the Declaration of Helsinki. Of the original 24 participants, two were subsequently excluded from further analyses due to insufficient data quality. 4.2. Procedure The color–word Stroop task was used to engage inhibitory control. In this task, participants respond to a physical feature (font color) while suppressing interference from an automatically processed dimension (word meaning), thereby engaging inhibitory control. Participants completed a total of five consecutive sessions, each lasting approximately 9 minutes. Behavioral and EEG data were simultaneously recorded while participants performed the Stroop task. Participants were instructed to judge whether the color of a word was green or red, irrespective of its semantic content (Fig. 4 ). The task conditions included a congruent condition, where the meaning and color of color words ("Red" and "Green") matched; an incongruent condition, where the meaning and color did not match; and a neutral condition, where the word was replaced by non-meaningful characters ("XXX"). One session consisted of 135 trials, with 45 trials per condition. Each trial lasted 4 s, consisting of fixation 1.5 s, stimulus presentation 1.5 s, and feedback 1 s. Immediately after the feedback, the fixation cross ("+") appeared, indicating the next trial. The stimuli were presented randomly using E-Prime software (E-Prime 3.0 Professional, Psychology Software Tools, USA). The visual angle of the items subtended 5°. Participants were instructed to respond as quickly as possible to the stimuli by pressing a button with either their left or right index finger. The response hands were counterbalanced across participants. 4.3. EEG data acquisition Data were collected in a sound-attenuated chamber using 64 Ag/AgCl actiCap electrodes placed according to the international 10–10 system and a BrainAmp DC amplifier (Brain Products, Germany) with a sampling rate of 500 Hz. The reference electrode was placed on the nose tip, and the ground electrode was positioned at AFz. Eye movement activity was recorded using an electrooculogram (EOG) electrode placed below the left eye. Vertical and horizontal electro-ocular activity was estimated from two pairs of electrodes positioned vertically and horizontally relative to both eyes (i.e., Fp1 and EOG for the vertical EOG, F7 and F8 for the horizontal EOG). These signals were used to identify and remove artifacts caused by eye movements. Electrode impedance was maintained below 25 kΩ before data collection. 4.4. Data preprocessing Behavioral performance was assessed using reaction times and accuracy. To ensure data reliability, trials within the 95% confidence interval of the gamma distribution fitted to the reaction times of correct trials were included in further analysis 16 , 48 . EEG data were preprocessed using Brain Vision Analyzer 2 (Brain Products, Germany). The data were bandpass filtered between 0.5 Hz and 250 Hz. Ocular artifacts, such as eye blinks and saccades, were corrected using Independent Component Analysis (ICA) with the restricted Infomax algorithm 49 . This procedure was applied to the entire continuous data set, following which components representing ocular artifacts were identified and removed. To allow for a thorough analysis of low-frequency oscillations, epochs were extracted with a duration of 1.2 s, spanning from − 200 ms to 1000 ms relative to stimulus onset. After segmentation, artifact rejection was performed automatically using ± 100 µV amplitude and 50 µV/ms gradient thresholds. 4.5. Functional connectivity analysis To investigate phase synchronization during the Stroop task, ISPC was calculated using the Python package 50 . Phase information was extracted via time-frequency decomposition using a 7-cycle Morlet wavelet convolution across 1–50 Hz (0.5 Hz steps). Phase synchronization was then quantified using ISPC 30 . The following formulas illustrate the wavelet transformation and ISPC calculation: $$\:\begin{array}{c}{W}_{X}^{\varPsi\:}\left(a,\:b\right)=\int\:{\varPsi\:}^{*}\left(\frac{t-a}{b}\right)X\left(t\right)dt\#\left(1\right)\end{array}$$ $$\:\begin{array}{c}{ISPC}_{t,f}=\left|{N}^{-1}{\sum\:}_{n=1}^{N}{e}^{i\left({\varphi\:}_{jn}-{\varphi\:}_{kn}\right)}\right|\#\left(2\right)\end{array}$$ In the formulas, \(\:{W}_{X}^{{\Psi\:}}\left(a,b\right)\) is the wavelet coefficient at time \(\:a\) and scale \(\:b\) , \(\:X\left(t\right)\) is the EEG signal, and \(\:{\text{Ψ}}^{\text{*}}\) is the complex conjugate of the Morlet wavelet. In Eq. (2), \(\:N\) is the number of trials, and \(\:{{\upvarphi\:}}_{jn}\) and \(\:{{\upvarphi\:}}_{kn}\) are the phase angles at electrodes \(\:j\) and \(\:k\) on trial \(\:n\) . ISPC values range from 0 to 1, where 0 indicates no phase synchronization and 1 indicates perfect phase synchronization between electrodes. Subsequently, \(\:ISP{C}_{t,f}\) values were baseline-normalized to a 200 ms prestimulus period to quantify task-relevant phase synchronization 51 . Letting \(\:{\mu\:}\) and \(\:{\sigma\:}\) denote the mean and standard deviation of ISPC within the baseline window, the normalized phase synchronization was formulated as: $$\:\begin{array}{c}\begin{array}{c}\:{ISPC}^{-}=\left(ISPC-\mu\:\right)/\sigma\:\end{array}\#\left(3\right)\end{array}$$ To identify regions exhibiting globally enhanced synchronization, we first calculated regional connectivity by averaging ISPC values between each seed electrode and all others, referred to hereafter as regional ISPC (Fig. 5 ). Regional connectivity, which has been analyzed for detecting localized hypersynchrony in epilepsy, is well-suited for mapping spatially structured synchronization in scalp EEG 52 . The time window for the ISPC analyses was selected based on the centroid of individual ISPC peak times averaged across the electrodes. The calculated regional ISPC was then averaged over the alpha band (8–13 Hz) within the time window from 700 ms to 900 ms poststimulus, where most peak ISPC latencies were observed. The time window spanned approximately two cycles of the alpha band’s central frequency. Regional ISPC was calculated for all electrodes to generate topographical maps. Based on the topographical distribution, four ROIs were defined, each consisting of six electrodes: left frontocentral (F1, F3, F5, FC1, FC3, and FC5), right frontocentral (F2, F4, F6, FC2, FC4, and FC6), left parietocentral (P1, P3, P5, CP1, CP3, and CP5), and right parietocentral regions (P2, P4, P6, CP2, CP4, and CP6). Regional ISPC values were averaged within each cluster for statistical comparisons across task conditions. To further examine the connectivity patterns of regions showing significant task-related effects, we conducted a seed-based connectivity analysis, referred to as seed ISPC. To obtain seed ISPC values, ISPC values were calculated between a seed ROI and all other electrodes and averaged across the seed electrodes. The resulting ISPC matrices were collapsed over the same time-frequency windows used for regional ISPC, which were used for topographical mapping and comparisons of ROI-to-ROI connectivity across conditions. While both regional and seed ISPC use the same ISPC-based calculation, the former provides a global map of connectivity from each electrode, whereas the latter focuses on connectivity patterns originating from an ROI. This complementary approach enables us to identify broad patterns of functional connectivity as well as to focus on specific regions implicated in task-related processes. 4.6. Statistics Normality of behavioral data was evaluated with the Shapiro-Wilk test, which showed that the data violated the assumption of normality. Therefore, the non-parametric Wilcoxon signed-rank test was applied for behavioral and connectivity analyses. To further examine brain-behavior relationships, Spearman’s rank correlation analysis was conducted between behavioral data and connectivity measures. Declarations Additional information Correspondence and requests for materials should be addressed to B.-K.M. ( [email protected] ). Funding This work was supported by the New Faculty Startup Fund from Seoul National University and the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (grant number RS-2025-00513128 to B.-K.M.). The authors declare no competing interests. Author Contribution Sangbin Yun: Investigation, Formal analysis, Visualization, Writing - Original draft. Byoung-Kyong Min: Conceptualization, Methodology, Investigation, Formal analysis, Writing - Original draft, Reviewing and Editing, Supervision, Project administration, Funding acquisition. All authors reviewed the manuscript. 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Perception's shadow: long-distance synchronization of human brain activity. Nature 397 , 430–433 (1999). Ricci, L. et al. Measuring the effects of first antiepileptic medication in Temporal Lobe Epilepsy: Predictive value of quantitative-EEG analysis. Clin. Neurophysiol. 132 , 25–35 (2021). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 10 May, 2026 Reviewers agreed at journal 10 Mar, 2026 Reviewers invited by journal 23 Feb, 2026 Editor assigned by journal 13 Feb, 2026 Editor invited by journal 13 Feb, 2026 Submission checks completed at journal 11 Feb, 2026 First submitted to journal 11 Feb, 2026 You are reading this latest preprint version 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. 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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-8810043","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":598276651,"identity":"434f511b-6926-4fa3-a3a0-1ccb17a56741","order_by":0,"name":"Sangbin Yun","email":"","orcid":"","institution":"Korea University","correspondingAuthor":false,"prefix":"","firstName":"Sangbin","middleName":"","lastName":"Yun","suffix":""},{"id":598276654,"identity":"0cc2afac-80c3-4d2b-8d03-52bb3b4df3dc","order_by":1,"name":"Byoung-Kyong Min","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsElEQVRIiWNgGAWjYDACZhA6YAPjJhCtJY0ULWBdBw6ToEW3nfcAc8GZ8/bmEgmMH34wpOUT1GJ2mC+BecaN24k7ZyQwS/Yw5Fg2ENbCY8DM8+F2gsGNBAZpBoYKAyJsAWs5Zw/UwvybBC03DjBuuJHABrQlhzgth2ecSU7ccOZhm2WPQRoRWs6fMXxccMzO3uB48uEbPyqSCWsBgQMQirGBgYE4DaNgFIyCUTAKCAEAQtQ4nrNVCVsAAAAASUVORK5CYII=","orcid":"","institution":"Seoul National University","correspondingAuthor":true,"prefix":"","firstName":"Byoung-Kyong","middleName":"","lastName":"Min","suffix":""}],"badges":[],"createdAt":"2026-02-06 18:23:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8810043/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8810043/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104166923,"identity":"e770b76a-a2b7-4b29-9cdf-b1ff79401aee","added_by":"auto","created_at":"2026-03-08 14:20:16","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":111762,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBehavioral results during the Stroop task.\u003c/strong\u003e (A) Reaction times and (B) performance accuracy for the congruent (blue), neutral (green), and incongruent (red) conditions. Box plots display the interquartile range (IQR), spanning from the 25\u003csup\u003eth\u003c/sup\u003e to the 75\u003csup\u003eth\u003c/sup\u003e percentile. Horizontal lines inside boxes indicate median values, and whiskers extend to 1.5 × IQR. Color dots represent individual scores. Asterisks indicate statistical significance (∗\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, ∗∗∗\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8810043/v1/c1bfd0d00484a321aec19509.png"},{"id":104166920,"identity":"3938d740-ee4b-4eb6-b547-8cfa2e6d2c58","added_by":"auto","created_at":"2026-03-08 14:20:16","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":885384,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTopographical maps of EEG alpha-band ISPC across task conditions.\u003c/strong\u003e (A) In regional ISPC topographical maps, four ROIs are outlined in white boxes. In the incongruent condition, the purple box highlights a region showing a significantly higher ISPC within the 700–900 ms poststimulus interval relative to other conditions (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05), and the remaining three white-outlined ROIs did not show significant differences across task conditions. Topographical maps are displayed from a vertex view, with the nose oriented toward the top of the image. (B) In seed ISPC topographical maps, purple boxes indicate a seed ROI used for seed connectivity analysis. Color dots indicate electrodes belonging to the other three ROIs, and lines indicate ISPC values calculated between each electrode and the seed ROI. Dot and line colors reflect normalized ISPC values, with warmer colors indicating stronger phase synchronization.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8810043/v1/318ecd8675d023641af2fa99.jpeg"},{"id":104166922,"identity":"2581146d-5b75-44d7-9664-56674a25f257","added_by":"auto","created_at":"2026-03-08 14:20:16","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":439878,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe right frontocentral–left parietocentral ISPC time course and its correlation with reaction times.\u003c/strong\u003e (A) Line plots show the time course of EEG alpha-band ISPC between the right frontocentral and left parietocentral regions for the congruent (blue), neutral (green), and incongruent (red) conditions. Error bands indicate ±SEM (standard error of the mean). A vertical dashed line indicates stimulus onset (0 s), and a horizontal dashed line indicates zero ISPC value. (B) Spearman correlations between ISPC peak latency and reaction time are shown for the congruent (blue), neutral (green), and incongruent (red) conditions. Scatter plots show individual participants’ score, and dashed lines represent regression lines for each condition. The correlation reached statistical significance in the time window of 700–900 ms poststimulus for the incongruent condition (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8810043/v1/696e88a4ded4c66ed6a35bc1.jpeg"},{"id":104403828,"identity":"ec246a31-89fc-4255-91f3-84845828bfb6","added_by":"auto","created_at":"2026-03-11 12:19:09","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":43456,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTrial sequence in the Stroop task.\u003c/strong\u003e Each trial began with a fixation cross, followed by the presentation of a color-word stimulus and immediate feedback. Examples of congruent, neutral, and incongruent trials are shown sequentially.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8810043/v1/004f6cd2acd55649201e3ed4.png"},{"id":104166919,"identity":"e9327217-0b41-4486-b3e3-4ebc32466d2c","added_by":"auto","created_at":"2026-03-08 14:20:16","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":135894,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSchematic illustration of regional ISPC calculation. \u003c/strong\u003eThe left panel shows time-frequency matrices of ISPCs between all electrode pairs. The right panel maps the resulting regional ISPC values onto the EEG montage, highlighting four ROIs outlined in black boxes. A blue dotted rectangular box and arrow illustrate the process of calculating regional ISPC by averaging the ISPC values between a specific electrode (e.g. F3) and all other electrodes, and projecting the resulting value onto the electrode of the EEG montage. The formula below defines the regional ISPC of the electrode c averaged over specified time T and frequency F ranges, where t and f represent individual elements within those ranges. C denotes the full set of electrodes, and c' refers to any other electrode in the set. Vertical bars |·| indicate the total number of elements in each set.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8810043/v1/a6a9b9a5720fd26d84cbf91c.png"},{"id":104409370,"identity":"62530c2d-fe83-40ca-8867-0d3a29371363","added_by":"auto","created_at":"2026-03-11 12:44:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2341367,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8810043/v1/5ef3a69f-e416-4519-af22-42fc0665724f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Interhemispheric fronto-parietal EEG alpha phase synchronization reflects inhibitory control during the Stroop task","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eA fundamental feature of human cognition is its capacity for goal-directed behavior, which is achieved by resolving competing inputs and prioritizing task-relevant information \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. This capacity mainly depends on inhibitory control, the cognitive mechanism that suppresses distracting or prepotent responses in favor of goal-relevant processing \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Inhibitory control is crucial for context-sensitive decision-making, particularly when one must override automatic but inappropriate responses \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. According to the conflict monitoring hypothesis, inhibitory control involves multiple stages, including conflict detection, suppression, and resolution \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. These processes recruit prefrontal regions such as the anterior cingulate cortex (ACC), dorsolateral prefrontal cortex (dlPFC), and medial prefrontal cortex (mPFC), as demonstrated in electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) studies \u003csup\u003e\u003cspan additionalcitationids=\"CR8 CR9\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThese patterns of brain activation have been observed across multiple inhibitory control paradigms, including the canonical Stroop task. Notably, the prefrontal regions are differentially involved in subprocesses of inhibitory control, and their dynamic interactions form an integrated inhibitory control network \u003csup\u003e\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Within this network, top-down neural communication seems to play a pivotal role, optimizing goal-directed task execution through regulatory influence over sensory regions \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. For instance, we recently demonstrated that EEG theta-band neuromodulation targeting synchronized interactions between the dorsal ACC (dACC) and left dlPFC enhanced behavioral performance by reducing reaction times during incongruent trials of the Stroop task \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Similarly, other studies have reported functional connectivity between the mPFC and ACC, further supporting the cooperative dynamics among prefrontal regions during inhibitory control \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIt is noteworthy that top-down communication across distant brain regions is mediated by low-frequency oscillations, particularly in the theta and alpha bands \u003csup\u003e\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. This communication often manifests through phase synchronization between oscillatory activities across widespread cortical areas, a key electrophysiological marker of functional connectivity \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Among these rhythms, EEG theta-band phase synchronization between the dACC or mPFC and the dlPFC is well known to support conflict monitoring and resolution during inhibitory control, forming a highly interactive prefrontal network \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Although theta-band phase synchronization has been widely recognized as a key mechanism for top-down inhibitory control, alpha-band phase synchronization has been less frequently reported in this context.\u003c/p\u003e \u003cp\u003eNotably, interregional EEG alpha phase synchronization has been shown to regulate cortical excitability and facilitate top-down modulation across distant cortical areas \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. For instance, robust alpha-band coupling between frontal and occipital regions has been observed during working memory maintenance, especially when active manipulation of information is required \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Moreover, EEG alpha oscillations have been proposed to reflect internally driven top-down processes as indicated by findings that prestimulus alpha activity represents preparatory states influencing subsequent task performance \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Therefore, we hypothesized that EEG alpha-band synchronization reflects inhibitory control. In addition, alpha phase synchronization relates to cognitive control at the level of large-scale brain networks, with consistent associations to the fronto-parietal network \u003csup\u003e\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTherefore, the present study investigated whether EEG alpha-band phase synchronization over the fronto-parietal region significantly reflects inhibitory control. Specifically, we examined its functional significance during the Stroop task by quantifying both global and local alpha phase synchronization.\u003c/p\u003e"},{"header":"2. Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e\u003cem\u003e2.1. Behavioral data\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eWe observed significantly slower reaction times in the incongruent condition than in the congruent (\u003cem\u003eZ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.43, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; incongruent, 737.82 ms; congruent, 669.53 ms) and neutral conditions (\u003cem\u003eZ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.01, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; neutral, 629.16 ms), with no difference between the congruent and neutral conditions ((\u003cem\u003eZ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.92, \u003cem\u003en.s.\u003c/em\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). As for task-performance accuracy, it was significantly lower in the incongruent condition than in the congruent (\u003cem\u003eZ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.43, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; incongruent, 97.07%; congruent, 98.69%) and neutral conditions (\u003cem\u003eZ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.03, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; neutral, 98.59%; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). The comparison between the congruent and neutral conditions did not reach statistical significance (\u003cem\u003eZ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.25, \u003cem\u003en.s.\u003c/em\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e\u003cem\u003e2.2. EEG alpha phase synchronization\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA shows topographic maps of the EEG alpha-band regional inter-site phase clustering (ISPC) across four regions of interests (ROIs) \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. In the right frontocentral region, EEG alpha regional ISPC was significantly higher in the incongruent condition than in both the congruent (\u003cem\u003eZ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.09, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; incongruent, 0.98; congruent, 0.18) and neutral conditions (\u003cem\u003eZ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.42, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; neutral, \u0026minus;\u0026thinsp;0.12; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Meanwhile, in the right parietocentral region, regional ISPC was significantly higher in the incongruent condition compared with the congruent condition (\u003cem\u003eZ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.06, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; incongruent, 0.73; congruent, \u0026minus;\u0026thinsp;0.27), but not relative to the neutral condition (\u003cem\u003eZ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.54, \u003cem\u003en.s.\u003c/em\u003e). No significant differences in regional ISPC were observed in the left frontocentral or left parietocentral regions across task conditions.\u003c/p\u003e \u003cp\u003eSubsequently, seed-based ISPC analysis revealed that the right frontocentral seed showed significantly stronger synchronization with the left parietocentral region during the incongruent condition compared to both the congruent (\u003cem\u003eZ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.16, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; incongruent, 1.79; congruent, 0.39) and neutral conditions (\u003cem\u003eZ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.00, \u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.05; neutral, \u0026minus;\u0026thinsp;0.07; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Seed ISPC between the right frontocentral and the right parietocentral regions was significantly higher in the incongruent condition relative to the neutral condition (\u003cem\u003eZ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.91, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01; incongruent, 1.31; neutral, \u0026minus;\u0026thinsp;0.52), but not relative to the congruent condition (\u003cem\u003eZ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.87, \u003cem\u003en.s.\u003c/em\u003e). No significant differences were observed in seed ISPC between the right and left frontocentral regions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e\u003cem\u003e2.3. Brain-behavior correlation\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eSpearman\u0026rsquo;s rank correlation analysis was performed between reaction time and the peak latency of right frontocentral\u0026ndash;left parietocentral ISPC (corresponding to the most prominent reddish connections in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). ISPC peak latency was defined as the time point corresponding to a local maximum in ISPC, falling approximately within the time window of 700\u0026ndash;900 ms poststimulus. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, a significant positive correlation was observed in the incongruent condition (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.44, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating that longer synchronization delays were associated with slower responses. In contrast, there were no significant correlations in the congruent (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01, \u003cem\u003en.s.\u003c/em\u003e) and neutral (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.20, \u003cem\u003en.s.\u003c/em\u003e) conditions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Discussion","content":"\u003cp\u003eDuring the Stroop task, we found a significant increase in EEG alpha phase synchronization during the incongruent condition, particularly on the right frontocentral region (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Seed-based connectivity analysis further revealed significantly pronounced phase synchronization between the right frontocentral and left parietocentral regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Notably, the peak latency of this interhemispheric connectivity positively correlated with reaction times (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB), highlighting the behavioral significance of alpha-band synchronization in inhibitory control. Together, these findings highlight the crucial role of alpha-phase synchronization, possibly reflecting a top-down mechanism during inhibitory control. Previous studies consistently demonstrated that frontal-driven long-range alpha connectivity has been associated not only with top-down cognitive control \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, but also with multiple higher-order cognitive domains, including attention, working memory, and mental imagery \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan additionalcitationids=\"CR32 CR33\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDuring the Stroop task, the right frontocentral region was prominently involved in inhibitory control, potentially reflecting top-down processing mediated by long-range alpha phase synchronization. The seed connectivity analysis consequently revealed increased alpha-band coupling between the right frontocentral and left parietocentral regions during the incongruent condition. This finding indicated enhanced interhemispheric communication within the fronto-parietal network, reflecting the network\u0026rsquo;s involvement in inhibitory control processes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Consistently, previous fMRI studies have identified the fronto-parietal network as a core system for cognitive control, facilitating task-related information processing through the flexible initiation and adjustment of control demands \u003csup\u003e\u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. In addition, resting-state studies have reported substantial anatomical overlap between networks correlated with alpha phase synchronization and the fronto-parietal network, as well as a negative association between alpha phase synchronization and fronto-parietal network lesion volume \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMoreover, a positive correlation between ISPC peak latency and reaction times was observed in the incongruent condition (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). This finding implies that earlier alpha phase synchronization is associated with faster conflict resolution. Given the close association between alpha phase synchronization and top-down processing \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, earlier synchronization likely corresponds to more rapid and efficient engagement of top-down control. The latency of alpha phase synchronization may represent the temporal dynamics of network recruitment for top-down inhibitory control. The interhemispheric connectivity observed in this study may reflect cooperative engagement of lateralized frontal and parietal cortices with complementary contributions. The right frontocentral region includes frontal executive areas such as the right dlPFC, implicated in conflict-driven adaptive regulation, dynamic adjustment under conflict \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e, and nonverbal color processing involving color\u0026ndash;label matching during inhibitory control tasks \u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. On the other hand, the left parietocentral region incorporates the left parietal cortex, which includes extended Wernicke\u0026rsquo;s area within the left perisylvian language network \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. This area has been implicated in language association and semantic processing, particularly for multimodal integration \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. Accordingly, the observed interhemispheric alpha phase synchronization between the right frontocentral and left parietocentral regions may reflect interhemispheric top-down regulations to suppress automatic semantic processing that is irrelevant to a task goal, while facilitating task-relevant color processing. In accordance with this interpretation, a previous study showing a similar connectivity pattern suggested that enhanced executive function was associated with increased functional connectivity between the right dlPFC and the left parietal cortex \u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eNevertheless, the other contralateral combination\u0026mdash;left frontocentral and right parietocentral\u0026mdash;did not show statistical significance in alpha phase synchronization, suggesting functional asymmetry across brain regions in the fronto-parietal network. As the left frontocentral region has been implicated in attentional preparation for anticipated conflict \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e, the lack of connectivity in this region may reflect limited engagement of anticipatory control during our Stroop task. Although our previous non-invasive brain stimulation studies have associated left dlPFC activation with conflict resolution and improved Stroop performance \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, this region may be more involved in attentional preparation for anticipated conflict, rather than resolving ongoing conflict \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. For instance, the left dlPFC shows increased activity during task preparation when participants receive anticipatory instructions about upcoming task demands, such as whether to read the word or name the color of the stimuli during a Stroop task \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. In our study, however, pseudorandom trial presentation without cueing probably reduced the need for such anticipatory mechanisms. This could have resulted in the lack of left dlPFC involvement, potentially explaining the absence of significant left frontocentral connectivity. Similarly, the absence of significant synchronization in the right parietocentral region may reflect the limited relevance of its established role in visuospatial attention \u003csup\u003e\u003cspan additionalcitationids=\"CR46\" citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e, which was likely not strongly engaged by the centrally presented stimuli in our Stroop task. The right parietal cortex is typically recruited during spatial attention-shifting tasks involving responses to lateralized stimuli \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. As our paradigm did not require visual attentional shifting, reduced involvement of the right parietal cortex may account for the lack of significant right parietocentral effects.\u003c/p\u003e \u003cp\u003eTo sum up, the interhemispheric fronto-parietal alpha phase synchronization appears to reflect a neural mechanism of inhibitory control during the Stroop task. These results align with the established roles of the fronto-parietal network in cognitive control and with previous observations linking alpha phase synchronization to this network. Given the involvement of alpha phase synchronization in top-down modulation across higher-order cognitive functions, the observed large-scale alpha phase synchronization may represent the coordinated engagement of distributed fronto-parietal regions to implement inhibitory control \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Furthermore, the lateralized pattern of interhemispheric connectivity suggests task-specific top-down modulation optimized for the Stroop task, potentially facilitating non-verbal color processing while suppressing automatic semantic processing through communication between the right frontal and the left parietal regions. Finally, the positive correlation between peak latency of interhemispheric alpha phase synchronization and reaction times during the incongruent condition indicates that earlier alpha synchronization is associated with faster engagement of top-down control, thereby accelerating conflict resolution. Taken together, these findings suggest that both the strength and temporal dynamics of alpha-band synchronization within the frontoparietal network reflect the neural dynamics underlying inhibitory control during the Stroop task.\u003c/p\u003e"},{"header":"4. Materials and Methods","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e\u003cem\u003e4.1. Participants\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eTwenty-four healthy individuals (mean age, 23.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53; 11 females, all right-handed) participated in this study. All participants reported normal color vision, normal or corrected-to-normal visual acuity, and no history of neurological, psychiatric, or cognitive disorders. Written informed consent was obtained from each participant before participation, by procedures approved by the Korea University Institutional Review Board (No. KUIRB-2021-0209-06). We confirmed that all research was performed in accordance with relevant guidelines/regulations and the Declaration of Helsinki. Of the original 24 participants, two were subsequently excluded from further analyses due to insufficient data quality.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e\u003cem\u003e4.2. Procedure\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eThe color\u0026ndash;word Stroop task was used to engage inhibitory control. In this task, participants respond to a physical feature (font color) while suppressing interference from an automatically processed dimension (word meaning), thereby engaging inhibitory control. Participants completed a total of five consecutive sessions, each lasting approximately 9 minutes. Behavioral and EEG data were simultaneously recorded while participants performed the Stroop task. Participants were instructed to judge whether the color of a word was green or red, irrespective of its semantic content (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The task conditions included a congruent condition, where the meaning and color of color words (\"Red\" and \"Green\") matched; an incongruent condition, where the meaning and color did not match; and a neutral condition, where the word was replaced by non-meaningful characters (\"XXX\"). One session consisted of 135 trials, with 45 trials per condition. Each trial lasted 4 s, consisting of fixation 1.5 s, stimulus presentation 1.5 s, and feedback 1 s. Immediately after the feedback, the fixation cross (\"+\") appeared, indicating the next trial. The stimuli were presented randomly using E-Prime software (E-Prime 3.0 Professional, Psychology Software Tools, USA). The visual angle of the items subtended 5\u0026deg;. Participants were instructed to respond as quickly as possible to the stimuli by pressing a button with either their left or right index finger. The response hands were counterbalanced across participants.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e\u003cem\u003e4.3. EEG data acquisition\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eData were collected in a sound-attenuated chamber using 64 Ag/AgCl actiCap electrodes placed according to the international 10\u0026ndash;10 system and a BrainAmp DC amplifier (Brain Products, Germany) with a sampling rate of 500 Hz. The reference electrode was placed on the nose tip, and the ground electrode was positioned at AFz. Eye movement activity was recorded using an electrooculogram (EOG) electrode placed below the left eye. Vertical and horizontal electro-ocular activity was estimated from two pairs of electrodes positioned vertically and horizontally relative to both eyes (i.e., Fp1 and EOG for the vertical EOG, F7 and F8 for the horizontal EOG). These signals were used to identify and remove artifacts caused by eye movements. Electrode impedance was maintained below 25 kΩ before data collection.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e\u003cem\u003e4.4. Data preprocessing\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eBehavioral performance was assessed using reaction times and accuracy. To ensure data reliability, trials within the 95% confidence interval of the gamma distribution fitted to the reaction times of correct trials were included in further analysis \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. EEG data were preprocessed using Brain Vision Analyzer 2 (Brain Products, Germany). The data were bandpass filtered between 0.5 Hz and 250 Hz. Ocular artifacts, such as eye blinks and saccades, were corrected using Independent Component Analysis (ICA) with the restricted Infomax algorithm \u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. This procedure was applied to the entire continuous data set, following which components representing ocular artifacts were identified and removed. To allow for a thorough analysis of low-frequency oscillations, epochs were extracted with a duration of 1.2 s, spanning from \u0026minus;\u0026thinsp;200 ms to 1000 ms relative to stimulus onset. After segmentation, artifact rejection was performed automatically using\u0026thinsp;\u0026plusmn;\u0026thinsp;100 \u0026micro;V amplitude and 50 \u0026micro;V/ms gradient thresholds.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e\u003cem\u003e4.5. Functional connectivity analysis\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eTo investigate phase synchronization during the Stroop task, ISPC was calculated using the Python package \u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. Phase information was extracted via time-frequency decomposition using a 7-cycle Morlet wavelet convolution across 1\u0026ndash;50 Hz (0.5 Hz steps). Phase synchronization was then quantified using ISPC \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. The following formulas illustrate the wavelet transformation and ISPC calculation:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\begin{array}{c}{W}_{X}^{\\varPsi\\:}\\left(a,\\:b\\right)=\\int\\:{\\varPsi\\:}^{*}\\left(\\frac{t-a}{b}\\right)X\\left(t\\right)dt\\#\\left(1\\right)\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:\\begin{array}{c}{ISPC}_{t,f}=\\left|{N}^{-1}{\\sum\\:}_{n=1}^{N}{e}^{i\\left({\\varphi\\:}_{jn}-{\\varphi\\:}_{kn}\\right)}\\right|\\#\\left(2\\right)\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn the formulas, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{W}_{X}^{{\\Psi\\:}}\\left(a,b\\right)\\)\u003c/span\u003e\u003c/span\u003e is the wavelet coefficient at time \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:a\\)\u003c/span\u003e\u003c/span\u003e and scale \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:b\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:X\\left(t\\right)\\)\u003c/span\u003e\u003c/span\u003e is the EEG signal, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{\u0026Psi;}}^{\\text{*}}\\)\u003c/span\u003e\u003c/span\u003e is the complex conjugate of the Morlet wavelet. In Eq.\u0026nbsp;(2), \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:N\\)\u003c/span\u003e\u003c/span\u003e is the number of trials, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{{\\upvarphi\\:}}_{jn}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{{\\upvarphi\\:}}_{kn}\\)\u003c/span\u003e\u003c/span\u003e are the phase angles at electrodes \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:j\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:k\\)\u003c/span\u003e\u003c/span\u003e on trial \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:n\\)\u003c/span\u003e\u003c/span\u003e. ISPC values range from 0 to 1, where 0 indicates no phase synchronization and 1 indicates perfect phase synchronization between electrodes. Subsequently, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:ISP{C}_{t,f}\\)\u003c/span\u003e\u003c/span\u003e values were baseline-normalized to a 200 ms prestimulus period to quantify task-relevant phase synchronization \u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. Letting \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\mu\\:}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sigma\\:}\\)\u003c/span\u003e\u003c/span\u003e denote the mean and standard deviation of ISPC within the baseline window, the normalized phase synchronization was formulated as:\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:\\begin{array}{c}\\begin{array}{c}\\:{ISPC}^{-}=\\left(ISPC-\\mu\\:\\right)/\\sigma\\:\\end{array}\\#\\left(3\\right)\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eTo identify regions exhibiting globally enhanced synchronization, we first calculated regional connectivity by averaging ISPC values between each seed electrode and all others, referred to hereafter as regional ISPC (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Regional connectivity, which has been analyzed for detecting localized hypersynchrony in epilepsy, is well-suited for mapping spatially structured synchronization in scalp EEG \u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. The time window for the ISPC analyses was selected based on the centroid of individual ISPC peak times averaged across the electrodes. The calculated regional ISPC was then averaged over the alpha band (8\u0026ndash;13 Hz) within the time window from 700 ms to 900 ms poststimulus, where most peak ISPC latencies were observed. The time window spanned approximately two cycles of the alpha band\u0026rsquo;s central frequency.\u003c/p\u003e \u003cp\u003eRegional ISPC was calculated for all electrodes to generate topographical maps. Based on the topographical distribution, four ROIs were defined, each consisting of six electrodes: left frontocentral (F1, F3, F5, FC1, FC3, and FC5), right frontocentral (F2, F4, F6, FC2, FC4, and FC6), left parietocentral (P1, P3, P5, CP1, CP3, and CP5), and right parietocentral regions (P2, P4, P6, CP2, CP4, and CP6). Regional ISPC values were averaged within each cluster for statistical comparisons across task conditions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo further examine the connectivity patterns of regions showing significant task-related effects, we conducted a seed-based connectivity analysis, referred to as seed ISPC. To obtain seed ISPC values, ISPC values were calculated between a seed ROI and all other electrodes and averaged across the seed electrodes. The resulting ISPC matrices were collapsed over the same time-frequency windows used for regional ISPC, which were used for topographical mapping and comparisons of ROI-to-ROI connectivity across conditions. While both regional and seed ISPC use the same ISPC-based calculation, the former provides a global map of connectivity from each electrode, whereas the latter focuses on connectivity patterns originating from an ROI. This complementary approach enables us to identify broad patterns of functional connectivity as well as to focus on specific regions implicated in task-related processes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e\u003cem\u003e4.6. Statistics\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eNormality of behavioral data was evaluated with the Shapiro-Wilk test, which showed that the data violated the assumption of normality. Therefore, the non-parametric Wilcoxon signed-rank test was applied for behavioral and connectivity analyses. To further examine brain-behavior relationships, Spearman\u0026rsquo;s rank correlation analysis was conducted between behavioral data and connectivity measures.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eAdditional information\u003c/h2\u003e \u003cp\u003eCorrespondence and requests for materials should be addressed to B.-K.M. ([email protected]).\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by the New Faculty Startup Fund from Seoul National University and the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (grant number RS-2025-00513128 to B.-K.M.). The authors declare no competing interests.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eSangbin Yun: Investigation, Formal analysis, Visualization, Writing - Original draft. Byoung-Kyong Min: Conceptualization, Methodology, Investigation, Formal analysis, Writing - Original draft, Reviewing and Editing, Supervision, Project administration, Funding acquisition. All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data are not shared in a public repository owing to the privacy rights of the human subjects. The data and analytical tools used in the current study are available from the corresponding author upon request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMirabella, G. Should I stay or should I go? Conceptual underpinnings of goal-directed actions. \u003cem\u003eFront. Syst. 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Neurophysiol.\u003c/em\u003e \u003cb\u003e132\u003c/b\u003e, 25\u0026ndash;35 (2021).\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":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"EEG, Inhibitory control, Stroop task, Alpha phase synchronization, Interhemispheric fronto-parietal connectivity","lastPublishedDoi":"10.21203/rs.3.rs-8810043/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8810043/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe ability to inhibit irrelevant information and resist distraction is central to goal-directed behavior and constitutes a core function of human cognition. Although electroencephalographic (EEG) research has consistently implicated fronto-parietal alpha-band phase synchronization in top-down processing, a core mechanism of cognitive control, its neurodynamic contribution to inhibitory control remains underexplored. In this study, we examined whether large-scale EEG alpha-band synchronization within the fronto-parietal network reflects inhibitory control during a color-word Stroop task. Twenty-four participants completed congruent, neutral, and incongruent trials while EEG activity was recorded. Inter-site phase clustering (ISPC) was used to quantify alpha-band phase synchronization across bilateral frontocentral and parietocentral regions. Behaviorally, incongruent trials elicited significantly slower reaction times and reduced accuracy compared with congruent and neutral conditions, indicating increased conflict demands. Electrophysiological results revealed significantly enhanced alpha-band phase synchronization during incongruent trials, centered on the right frontocentral region, with stronger interhemispheric coupling between the right frontocentral and left parietocentral regions. Notably, the peak latency of this connectivity positively correlated with reaction times exclusively during incongruent trials, suggesting that the temporal dynamics of alpha synchronization are closely linked to behavioral performance during conflict processing. These findings indicate that interhemispheric fronto-parietal alpha phase synchronization reflects a neural mechanism underlying inhibitory control. Our results highlight the significance of EEG alpha-band synchronization and its temporal dynamics in coordinating large-scale brain networks supporting top-down cognitive control.\u003c/p\u003e","manuscriptTitle":"Interhemispheric fronto-parietal EEG alpha phase synchronization reflects inhibitory control during the Stroop task","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-08 14:20:11","doi":"10.21203/rs.3.rs-8810043/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-10T15:27:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"285158814863149589323684109388813269726","date":"2026-03-10T15:21:12+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-23T13:53:39+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-13T18:56:44+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-13T18:21:12+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-11T17:39:24+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-02-11T17:27:29+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0b49f3bc-313e-4f8a-aae3-0c3695629527","owner":[],"postedDate":"March 8th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-10T15:27:06+00:00","index":78,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":63669878,"name":"Biological sciences/Neuroscience"},{"id":63669879,"name":"Biological sciences/Psychology"},{"id":63669880,"name":"Social science/Psychology"}],"tags":[],"updatedAt":"2026-03-08T14:20:11+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-08 14:20:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8810043","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8810043","identity":"rs-8810043","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}