Dynamic Regulation of Interoceptive Processing in Relaxation and Anxiety

Dynamic Regulation of Interoceptive Processing in Relaxation and Anxiety | 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 Dynamic Regulation of Interoceptive Processing in Relaxation and Anxiety Jianfeng Zhang, Yuqi Zhang, Jingyu Hua, Na Li, Georg Northoff, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7435608/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Interoceptive processing—the neural monitoring of bodily signals—functions through regulatory mechanisms that adapt bodily signal monitoring to different cognitive and environmental demands. However, temporal dynamics underlying these mechanisms and their modulation by anxiety remain incompletely characterized. Using magnetoencephalography (MEG), we examined heartbeat-evoked responses (HERs) across interoceptive and exteroceptive tasks under anxiety induction, with a relaxation condition included to control for anxiety-related effects. Under relaxation, interoceptive processing exhibited context-dependent modulation: HERs were attenuated during exteroceptive relative to interoceptive tasks, predominantly in sensorimotor cortical regions. This regulation manifested progressively across sequential heartbeats following a linear temporal trajectory, revealing dynamic evolution rather than instantaneous switching. Anxiety, in contrast, disrupted this adaptive dynamic pattern, abolishing such progressive context-dependent modulation by specifically impairing interoceptive suppression during exteroceptive task. Control analyses confirmed these effects were not attributable to cardiac artifacts or other physiological confounds. These findings establish a neurophysiological mechanism whereby anxiety impairs the dynamics of adaptive interoceptive regulation. That, in turn, results in inappropriate integration of bodily signals into cognitive processing and provides a neural basis for interoceptive hypervigilance observed in anxiety. Biological sciences/Neuroscience/Neuro–vascular interactions Biological sciences/Neuroscience/Emotion Biological sciences/Neuroscience/Cognitive neuroscience/Perception Biological sciences/Neuroscience/Stress and resilience Biological sciences/Psychology Interoception Heartbeat-evoked Response Anxiety Brain-body Interaction Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Interoceptive processing—the neural monitoring of bodily signals—functions through regulatory mechanisms that allocate neural resources between interoceptive and exteroceptive domains based on contextual demands (Barrett & Simmons, 2015 ; Khalsa et al., 2018 ; Kleckner et al., 2017 ; Nord & Garfinkel, 2022 ; Seth & Friston, 2016 ; Tumati et al., 2021 ). These mechanisms exhibit bidirectional flexibility: interoceptive signals can enhance exteroceptive perception when bodily information becomes relevant to environmental judgments (Park et al., 2014 ), while interoceptive processing can be attenuated during exteroceptive demands to prioritize environmental assessment (Garcia-Cordero et al., 2017 ; Petzschner et al., 2019 ; Villena-Gonzalez et al., 2017 ). This regulation — involving the coordination between interoceptive and exteroceptive demands — enables the brain to maintain physiological homeostasis while responding to environmental challenges, and its disruption appears in psychiatric disorders (de la Fuente et al., 2019 ; Lutz et al., 2019 ; Schulz et al., 2015 ; Yoris et al., 2017 ), particularly anxiety disorders (Pang et al., 2019 ; Paulus & Stein, 2010 ; Tumati et al., 2021 ). While evidence demonstrates that interoceptive regulation by the demands of interoceptive–exteroceptive trade-offs, the temporal dynamics underlying this regulatory process—specifically how it evolves across sequential physiological cycles rather than through instantaneous switching—remain incompletely characterized. How such temporal dynamics, in turn, contribute to the manifestation of anxiety remains unclear. Addressing this gap in our knowledge is the goal of our paper. Interoceptive regulation arises from interactions between bottom-up physiological signals and top-down cognitive mechanisms (Barrett & Simmons, 2015 ; Gu et al., 2013 ; Kleckner et al., 2017 ; Seth, 2013 ), with anxiety providing a window for studying disruptions to their regulatory balance (Harrison et al., 2021 ; Pang et al., 2019 ; Paulus & Stein, 2010 ; Tumati et al., 2021 ). (Cornwell et al., 2017 ; Richards et al., 2014 ) Anxiety appears to alter both autonomic activity and cognitive control systems (Grupe & Nitschke, 2013 ; Thayer et al., 2012 ). During anxiety states, sympathetic arousal increases interoceptive signal salience, and also affects prefrontal regulatory control, creating a neurophysiological environment where bodily signals may influence cognitive processing (Garfinkel & Critchley, 2016 ; Paulus & Stein, 2006 ). This effect likely impacts mechanisms of interoceptive-exteroceptive coordination. Evidence from anxiety disorders indicates patterns of interoceptive hypervigilance (Olatunji et al., 2007 ; Schmidt et al., 1997 ), alongside altered environmental threat monitoring (Cornwell et al., 2017 ; Richards et al., 2014 ), suggesting changes in attentional allocation between internal and external signals. Neurobiologically, these patterns correspond with regional differences across networks including anterior insula, anterior cingulate cortex, and sensorimotor cortex (Domschke et al., 2010 ; Paulus & Stein, 2006 ), supporting frameworks that consider anxiety as involving altered allostatic interoceptive regulation (Paulus & Stein, 2010 ; Tumati et al., 2021 ). While these patterns have been documented, the temporal dynamics through which anxiety affects the coordination between interoceptive and exteroceptive processing remain incompletely characterized (Critchley & Garfinkel, 2018 ; Tallon-Baudry et al., 2018 ). Experimental anxiety induction offers methodological advantages for investigating these dynamics, enabling examination of emotional regulatory mechanisms while avoiding factors such as medication effects and physiological adaptations present in clinical anxiety populations (Ochayi & Anyaehie, 2021 ; Robinson et al., 2013 ). The interoceptive regulation under intero–exteroceptive coordination demand requires neural markers capable of capturing fluctuations in interoceptive signal strength. Heartbeat-evoked responses (HERs) serve as a neural indicator of interoceptive processing intensity (Park & Blanke, 2019 ; Schandry & Montoya, 1996 ; Schandry et al., 1986 ), enabling researchers to examine how cardiac signal processing adapts across heartbeats during different task contexts. Studies demonstrate that HERs integrate cardiac information with ongoing cognitive functions, as evidenced by their ability to predict detection of exteroceptive stimuli like visual stimuli (Park et al., 2014 ), correlate with somatosensory perception accuracy (Al et al., 2020 ), and index self-related processing during mind-wandering (Babo-Rebelo, Richter, et al., 2016; Babo-Rebelo, Wolpert, et al., 2016 ). HER amplitudes also exhibit differentiation in frontal-central regions during tasks requiring interoceptive versus exteroceptive attention (Garcia-Cordero et al., 2017 ; Petzschner et al., 2019 ; Villena-Gonzalez et al., 2017 ). This differentiation indicates that HERs capture allocation of neural resources between processing domains, e.g., intero- and exteroceptive. Studies of HER patterns in anxiety disorders reveal altered differentiation between more interoceptive-focused eye-closed and rather exteroceptive-dominated eye-open conditions compared to healthy controls (Pang et al., 2019 ), suggesting a diminished capacity for interoceptive regulation under intero–extero coordination demand. Together, these findings suggest that HERs, may serve as a neurophysiological measure of interoceptive regulation, including how that is modulated by anxiety. Our study employs simultaneous MEG and physiological recordings to directly examine how experimentally induced anxiety affects the dynamic regulation of interoceptive processing. We use controlled interoceptive (heartbeat counting) and exteroceptive (tone counting) tasks under both anxiety and relaxation states to address two specific questions: Does interoceptive regulation, as indexed by dynamic HER, flexibly respond to changes in intero- and exteroceptive demand, and how is this flexibility affected by anxiety? Is there a changing pattern in the dynamic interaction of interoceptive regulation over time, and how is that modulated by anxiety? By examining HERs across multiple consecutive heartbeats, we can characterize the temporal dynamics of interoceptive regulation and identify precise patterns of disruption under anxiety. We consider two potential mechanisms through which anxiety might affect HER differentiation: either enhanced HER during interoceptive tasks, reflecting hypersensitivity to internal bodily signals, or disrupted HER suppression during exteroceptive demand, indicating failure to appropriately filter cardiac information during environmental monitoring. Based on evidence of reduced HER differentiation in anxiety disorders (Pang et al., 2019 ) and anxiety's documented effects on body hypervigilance (Esteve & Camacho, 2008 ; Olatunji et al., 2007 ; Schmidt et al., 1997 ), we predict that anxiety will disrupt normative HER differentiation, specifically through abnormal enhancement of HERs during exteroceptive demand. This approach provides a novel perspective on anxiety's neural mechanisms by directly examining how it alters the dynamic interoceptive regulation under intero–exteroceptive coordination demand, potentially explaining the characteristic hypervigilance observed in anxiety disorders. Methods Participants. Thirty-three right-handed Participants (20 females; mean age ± SD, 21.18 ± 2.24 years) were recruited for the experiment. Inclusion criteria required participants to have no history of heart disease, respiratory problems, eating disorders, mental health issues, or brain trauma. All participants provided informed consent and received monetary compensation for their involvement. The experimental procedures were approved by the Medical Ethics Committee of the Medical School at XX University. One participant was excluded from the MEG analysis due to exhibiting discrepant interoceptive performance between the two mood states (bias under relaxation: 0.322; bias under anxiety: 0.964). Tasks and procedures. Participants underwent two emotion induction sessions (anxiety and relaxation) in a counterbalanced order across two separate visits, spaced one week apart. In each session, participants completed a sequence consisting of an initial mood induction, a 6-minute resting-state scan, a second mood induction, and two runs of interoceptive and exteroceptive tasks (see Fig. 1 A). The study employed a validated multimodal mood induction protocol combining verbal and musical stimuli, an approach that has demonstrated robust effectiveness in inducing target moods (Hallford et al., 2019 ; Liu & Li, 2020 ; Nuevo et al., 2015 ). The verbal stimuli comprised 120 statements selected from established emotional databases: 60 anxiety-provoking and 60 calming statements with empirically validated valence and arousal ratings (Sinclair et al., 1997 ). In the anxiety induction phase, participants read 30 anxiety-provoking statements (e.g., "I'm haunted with thoughts about myself and how I come across to others") while listening to Ligeti's Requiem , a musical piece previously shown to reliably enhance anxiety induction (Barbieri et al., 2024 ; Wang et al., 2022 ). Participants indicated whether they had experienced similar situations to each statement to promote greater self-involvement. To enhance and maintain the induction effect, participants then spent 2 minutes focusing on anxiety-related thoughts and then reported them (Hallford et al., 2019 ; Sinclair et al., 1994 ). The relaxation phase followed an identical structure: participants read 30 calming statements (e.g., "Sometimes it feels good to get away from the noise by going to a park") while listening to Gabriel Fauré's Ballade for Piano and Orchestra (Hallford et al., 2019 ), then focused on and reported relaxation experiences for 2 minutes. To establish the tone rate for the normal condition in the exteroceptive task that would match each participant's individual heart rate, a 6-minute resting-state recording was conducted following the first mood induction. This procedure was essential for capturing participants' actual cardiac rhythm after mood manipulation, which would later serve as a parameter for the experimental setup in the exteroceptive task. During this resting-state period, participants were instructed to keep their eyes open, refrain from sleeping, avoid engaging in vivid thoughts, and abstain from counting their heartbeats. The pulse rates recorded during this interval were subsequently used to determine the precise tone rate in the exteroceptive task. The interoceptive task involved counting heartbeats, while the exteroceptive task involved counting tones (Wiebking et al., 2010 ; Wiebking et al., 2014 ; Wiebking & Northoff, 2015 ). Participants completed two runs, each consisting of 18 heart-counting trials and 18 tone-counting trials (9 trials at pulse rates (normal condition) and 9 trials at 80% of pulse rates (slow condition)) presented in random order. In each heart-counting trial, participants first viewed a fixation cross for 3–4 seconds, followed by a heart icon displayed at the center of the screen for 9–13 seconds. During this period, participants counted their heartbeats. Subsequently, they reported the number of counts (on a scale from 0 to 25) and their confidence level (on a scale from 0 to 6) using a slider. In tone-counting trials, a note icon appeared at the center of the screen, prompting participants to count the number of tones they heard. Tones were delivered through earphones with a duration of 200 ms and were jittered by ± 100 ms to avoid synchronization with heartbeats. The tone volume was adjusted to the minimum level participants could reliably hear before the experiment, to minimize the influence of sound-evoked neural oscillations on heartbeat-evoked responses. The use of two tone rates served to confirm that participants were counting tones rather than heartbeats during the exteroceptive task (see Fig. 1 B). Recordings. Continuous MEG data were acquired using a whole-head MEG system equipped with 102 magnetometers and 204 planar gradiometers (Elekta Neuromag TRIUX) at a sampling rate of 1000 Hz, with an online high-pass filter of 0.1 Hz and a low-pass filter set at 330 Hz. Data were recorded during both the resting-state and task periods. Simultaneously, electrocardiogram (ECG) and electrooculogram (EOG) data were collected to monitor cardiac and ocular activity (sampling rate 1000 Hz, filter 0.1–330 Hz). For ECG recordings, two electrodes were positioned around the left clavicle and right clavicle (Lead I configuration). For EOG recordings, two vertical electrodes were placed above and below the left eye, while two horizontal electrodes were positioned to the left and right of the eyes. A ground electrode was placed on the left forearm. Respiratory (RSP) and photoplethysmography (PPG) data were acquired using the BIOPAC MP160 data acquisition system (BIOPAC Systems Inc., Goleta, CA). RSP signals were recorded via an RSP100C amplifier configured with a sampling rate of 1000 Hz, an online band-pass filter ranging from 0.05 Hz to 10 Hz, and a gain setting of 10. Data were collected using a TSD201 transducer respiration belt, which was securely positioned around the participant’s chest. PPG signals were captured with a PPG100C-MRI amplifier, set to a sampling rate of 1000 Hz, an online band-pass filter spanning 0.05 Hz to 10 Hz, and a gain setting of 100. The TSD200 pulse transducer was attached to the fourth finger of the right hand to avoid interference with button-pressing responses. MEG data preprocessing. External noise was removed using temporal signal space separation (TSSS) implemented in MaxFilter (Taulu & Simola, 2006 ). All preprocessing steps were performed using the FieldTrip toolbox ( https://www.fieldtriptoolbox.org/ ) (Oostenveld et al., 2011 ). The MEG data were first resampled to 250 Hz, then underwent offline filtering between 1 and 35 Hz, and were finally detrended. ICA was conducted with the runica algorithm in the FieldTrip, using PCA to reduce the data to 40 components before decomposition. For artifact detection, each independent component was individually correlated with vertical and horizontal electrooculogram (EOG) signals. These EOG signals were low-pass filtered at 5 Hz before being used for correlation analysis. Components with a Fisher’s z-transformed correlation coefficient exceeding 3 standard deviations of the distribution of correlation coefficients across all components were identified as artifacts and rejected. For each subject, between 0 and 2 components were removed for vertical and horizontal EOG artifacts respectively. The ECG-related ICA components were not removed, as some of the ECG artifacts identified by ICA may contain HER signal components, which could influence subsequent analyses (Buot et al., 2021 ). To assess potential differences in cardiac activity between interoceptive and exteroceptive conditions, statistical analyses were conducted on ECG amplitude, heart rate, and heart rate variability parameters as control measures. Heartbeat-evoked responses (HERs). R peaks in ECG lead I signals were identified using the ft_artifact_zvalue function from FieldTrip after applying a 25 Hz low-pass filter. Only R peaks occurring during the designated heartbeat-counting or tone-counting periods were selected for analysis. To ensure data quality and reliability, we calculated the intervals between consecutive R peaks (RR intervals) and implemented stringent exclusion criteria: intervals exceeding 3 standard deviations from the mean RR interval or shorter than 400 ms were removed from further analysis. For the remaining valid RR intervals, T peaks were identified as the maximum amplitude points within the window spanning from 200 ms after an R peak to 200 ms before the subsequent R peak. The accuracy of T peak detection was verified through visual inspection by trained researchers. Heartbeat-evoked responses were subsequently derived from magnetometer data time-locked to these validated T peaks (Azzalini et al., 2021 ; Babo-Rebelo et al., 2019 ; Babo-Rebelo, Richter, et al., 2016; Park et al., 2014 ). We focused our analysis on neural responses within the 50–300 ms window following the T wave (Azzalini et al., 2021 ), as this time window is minimally affected by cardiac artifacts (Dirlich et al., 1997 ). Anatomical MR acquisition and preprocessing. High-resolution T1-weighted anatomical images were acquired using a 3T Siemens PRISMA scanner with a magnetization-prepared rapid gradient-echo (MPRAGE) sequence (repetition time = 2300 ms, echo time = 2.26 ms, flip angle = 8°, field of view = 256 mm, voxel size = 1 mm³ isotropic). Cortical surfaces were reconstructed using the standard recon-all pipeline ( https://surfer.nmr.mgh.harvard.edu/ ) (Fischl, 2012 ). The results were visually inspected and used for computing head models. Source reconstruction. Source reconstruction was performed using Brainstorm software ( https://neuroimage.usc.edu/brainstorm/ ) (Tadel et al., 2011 ). Anatomical constraints were provided by individual cortical surface models from FreeSurfer. Head models were computed using the overlapping spheres method. For the MEG data, magnetometers and gradiometers were used as sensor configurations. Source localization was performed using the dynamic statistical parametric mapping (dSPM) algorithm (minimum-norm imaging; signal-to-noise ratio of 3; depth weighting of 0.5). A noise model was applied without the use of empty-room recordings. The reconstructed sources were localized in cortical surface space from FreeSurfer. These source maps were then projected to the cortical surface for group-level analysis. A 6-mm Gaussian kernel was applied, and the absolute values of the source maps were taken. Statistical analysis. Statistical analyses were conducted at the sensor level using magnetometer data to examine HER differences between interoceptive and exteroceptive tasks. Analyses were implemented in MATLAB using the FieldTrip. To address multiple comparisons, we employed a non-parametric cluster-based permutation approach(Maris & Oostenveld, 2007 ). Initially, paired-sample t-tests were performed at each sensor and time point within a 50–300 ms post-T peak window. Clusters were formed by spatially and temporally adjacent data points that exceeded a cluster-defining threshold of p < 0.05 (uncorrected), with a minimum of two neighboring sensors required. The statistical significance of identified clusters was assessed by randomly permuting condition labels 10,000 times to generate a null distribution of the maximum cluster-level test statistic. Clusters were considered significant if their Monte Carlo p-value was below 0.05. To identify cortical sources contributing to the observed differences in HERs between conditions, the averaged HER data were projected onto source space and cortical currents were reconstructed. For each vertex in the source space, we averaged the signal values across the time window that had shown significant between-condition differences at the sensor level (as determined by the cluster-based permutation t-test). To identify brain regions that made primary contributions to the observed effects, vertex-wise statistical comparisons of absolute dipole current values were conducted using two-tailed t-tests. Significant brain regions were defined as clusters of contiguous spatially adjacent vertices (> 50) with t-values exceeding an uncorrected threshold of p < 0.005. Control analysis of ECG and respiration. To clarify the physiological changes associated with interoceptive and exteroceptive processing, physiological metrics were computed. After low-pass filtering the ECG signal at 25 Hz, the signal was time-locked to the T peak (set as the 0 ms time point) and averaged across trials. Additionally, the ECG amplitude between 50–300 ms post-T peak was statistically analyzed to control for potential condition-specific differences in ECG signals that might confound the heart-rate evoked responses (HER). RR intervals were averaged within each condition, and the mean heart rate was calculated by dividing the average RR interval by one minute. The standard deviation of RR intervals was computed as an index of heart rate variability (HRV). To examine respiratory patterns, data from a 9-second window starting from the onset of either the interoceptive or exteroceptive task (time 0) were extracted after applying a 1 Hz low-pass filter to the respiratory signal. After detrending and mean removal, the data were converted to z-scores. The averaged z-scores were then used to obtain the mean respiratory pattern over time. Additionally, the findpeaks function in MATLAB was used to identify respiratory peaks. The time interval between two consecutive peaks was calculated to determine the respiratory intervals. After averaging the extracted respiratory intervals, the data were transformed to compute the mean respiratory rate. The lowest point between two peaks was identified as the respiratory trough. The time intervals between the peaks and troughs were then used to determine the durations of inhalation and exhalation, respectively, from which the inhalation-to-exhalation ratio was calculated. Results Mood induction effects. To establish the validity of our experimental manipulation, we first assessed whether our anxiety induction protocol successfully created differential mood states. Participants rated their anxiety levels on a 0–6 scale prior to the experiment and after each stage. At baseline (e.g., before the respective induction procedure), no significant difference in anxiety levels was observed between the anxiety induction session and the relaxation maintenance session ( t (31) = 0.724, p = 0.475). Following the initial mood induction procedure, anxiety ratings diverged significantly between conditions (first induction: t (31) = 6.542, p < 0.001) and remained different throughout all subsequent experimental phases (post-resting state: t (31) = 3.514, p = 0.001; second induction: t (31) = 9.213, p < 0.001; first run: t (31) = 5.999, p < 0.001; second run: t (31) = 3.473, p = 0.002) (see Fig. 2 A). Objective behavioral and physiological measures provided further validation of our manipulation. In the interoceptive task, heartbeat estimation bias was calculated as (reported heartbeats - actual heartbeats)/actual heartbeats. We observed diminished accuracy (reduced bias) in the heartbeat counting during the anxiety condition ( t (31) = 2.282, p = 0.030) when compared to the relaxation condition (see Fig. 2 B). During exteroceptive task, we observed that the heart rate differentially responded to tone rates during the anxiety condition (mood state × tone rate interaction: F (1,31) = 8.516, p = 0.006). This effect appeared exclusively during anxiety ( p = 0.001) but not during relaxation ( p = 0.354). These findings confirm the success of our mood manipulation and establish a reliable foundation for interpreting subsequent neural analyses. Anxiety Disrupts Neural Differentiation of HERs under intero–exteroceptive coordination demand. To determine whether anxiety affects the normal neural differentiation between interoceptive and exteroceptive demands, we examined HER patterns during interoceptive versus exteroceptive tasks across mood states. We applied cluster-based permutation t-tests at the sensor level to compare interoceptive versus exteroceptive conditions during relaxation. This analysis revealed significantly differing clusters during the 128–228 ms after T peaks in the left prefrontal area of the topographic map (Monte Carlo cluster level, p = 0.026) (see Fig. 3 A and 3 B). To investigate how anxiety impacts this HER differentiation, we averaged the data within the identified cluster under both mood states and compared their differences. This analysis revealed that anxiety significantly reduced the HER differences between interoceptive and exteroceptive demands when compared to the relaxation state ( t (31) = 3.358, p = 0.002) (see Fig. 3 C and 3 D). To localize the neural origins of these effects, we projected the data into source space and performed a t-test on the time-averaged data during the 128–228 ms interval after T peaks. Source localization revealed two significant regions primarily in left and right sensorimotor cortex (lSMC & rSMC) (uncorrected p 50) (see Fig. 3 F). The amplitude in these regions was greater during interoceptive tasks than exteroceptive tasks (see Fig. 3 E). Importantly, we identified significant regions only in the relaxation state, with no significant findings observed during anxiety (see Fig. 3 G and 3 H). These findings demonstrate that anxiety abolishes the ‘normal’ neural differentiation between interoceptive and exteroceptive demands in sensorimotor regions critical for both bodily sensation and environmental interaction. Temporal Dynamics Reveal Progressive Differentiation Pattern. To track how HER differentiation under intero–extero coordination demand evolved over time, we averaged HERs separately for each heartbeat position within the trial sequence (e.g., first heartbeat across all trials, second heartbeat, etc.). Our t-test analysis revealed significant HER differences during relaxation at the 3rd ( t (31) = 2.330, p = 0.048, FDR corrected), 5th ( t (31) = 2.837, p = 0.018, FDR corrected), 6th ( t (31) = 3.360, p = 0.018, FDR corrected), 8th ( t (31) = 2.834, p = 0.018, FDR corrected), and 9th heartbeats ( t (31) = 2.881, p = 0.018, FDR corrected) (see the left panel of Fig. 4 A). In contrast, no significant HER differences were observed at any heartbeat order during anxiety ( ps > 0.05, FDR corrected) (see the left panel of Fig. 4 B). To quantify these temporal dynamics, we performed a linear fit analysis on the progressive HER differences between interoceptive and exteroceptive demands, extracting the slope and intercept parameters. During relaxation, the slope was significantly greater than zero ( t (31) = 2.202, p = 0.035), while the intercept was approximately zero ( t (31) = 0.938, p = 0.355) (see the middle and right panels of Fig. 4 A). Under anxiety, both slope ( t (31) = 0.754, p = 0.457) and intercept ( t (31) = -0.289, p = 0.775) parameters approximated zero (see the middle and right panels of Fig. 4 B). These findings reveal that interoceptive regulation under intero–extero coordination demand involves a gradual, progressive dynamic change with neural modulation that evolves over multiple cardiac cycles rather than an instantaneous state transition, and that anxiety fundamentally disrupts this dynamic regulatory process. Anxiety Specifically Affects the HER Dynamics in Exteroceptive task. To determine whether anxiety differentially modulates the temporal dynamics of HERs during interoceptive versus exteroceptive tasks, we performed linear fit analyses on heartbeat-by-heartbeat HER changes separately for each task condition. We then compared the slope and intercept parameters between mood states. During interoceptive task, we found no significant differences in either slope ( t (31) = -0.554, p = 0.584) or intercept ( t (31) = 1.581, p = 0.124) between relaxation and anxiety (see Fig. 4 C). In contrast, during exteroceptive task, we observed a significant difference in slope between mood states ( t (31) = -2.395, p = 0.023), while no difference in intercept was identified ( t (31) = 0.496, p = 0.623) (see Fig. 4 D). Specifically, the slope was negative during relaxation and positive during anxiety, indicating a condition-dependent change in the direction of the effect. These findings demonstrate that anxiety selectively disrupts normal HER suppression dynamics during exteroceptive tasks, causing inappropriate enhancement of cardiac signal processing during environmental monitoring while leaving interoceptive processing largely intact. Control Analyses. To verify these temporal patterns reflected genuine neural modulation rather than task practice effects, we analyzed the average HERs for each trial and performed linear fits across trial sequences. Regardless of mood state, the slopes were approximately zero (relaxation: t (31) = -1.121, p = 0.271; anxiety: t (31) = 0.196, p = 0.846). The intercept during relaxation was significantly greater than zero ( t (31) = 4.004, p < 0.001), whereas the intercept during anxiety approximated zero ( t (31) = 0.585, p = 0.562). This suggests that HER differentiation is dynamically coordinated based on immediate demands, representing a flexible neural mechanism rather than continuous optimization brought about by practice. To rule out potential physiological confounds in our neural findings, we conducted comprehensive control analyses of cardiac and respiratory parameters. To evaluate potential ECG signal influence on HERs, we compared ECG amplitudes between interoceptive and exteroceptive tasks. No significant differences were observed ( ps > 0.05), confirming that the observed HER differences between interoceptive and exteroceptive demands were unlikely attributable to cardiac artifacts (see Fig. 5 A). Additionally, across both relaxation and anxiety states, we found no significant differences in heart rate and heart rate variability between interoceptive and exteroceptive processing ( ps > 0.05) (see Fig. 5 B). To examine potential temporal confounds, we investigated whether physiological signals exhibited specific patterns within the time windows of interoceptive and exteroceptive tasks. No distinct interbeat interval patterns or respiration patterns were observed during either task condition ( ps > 0.05) (see Fig. 5 C and 5 D). These control analyses confirm that our HER findings reflect genuine neural differentiation between intero- and exteroceptive demands and their disruption by anxiety, rather than artifacts of peripheral physiology or other systematic confounds. Discussion In this study, we demonstrated that anxiety modulates heartbeat-evoked responses (HER) differently during interoceptive and exteroceptive demands. During the relaxation state, we observed distinctive HER differences in bilateral sensorimotor cortex between interoceptive and exteroceptive tasks within the 128–228 ms post-T peak window. In contrast, this HER differentiation was no longer visible under anxiety. Notably, applying a temporal or dynamic analysis, the HER difference between interoceptive and exteroceptive demands showed a linear increase across heartbeat sequences during relaxation, with significant effects, suggesting a gradual rather than abrupt regulatory transition. This progressive pattern was abolished under anxiety where no such dynamic adaptative HER changes were observed. Further analysis revealed that anxiety specifically affected HER patterns during exteroceptive tasks, showing altered temporal dynamics with a slope more similar to that observed during interoceptive tasks, compared to the relaxation state. These findings suggest that anxiety disrupts the normal suppression of cardiac signal processing during exteroceptive demands, potentially reflecting impaired regulatory mechanisms that allow inappropriate integration of bodily signals into environmental information processing. Neural Mechanisms Underlying Interoceptive-Exteroceptive Coordination and Its Dynamics. Understanding interoceptive-exteroceptive coordination has emerged as crucial for cognitive neuroscience from both basic cognition and clinical perspectives. From a cognitive standpoint, interoceptive processing contributes to mood awareness, decision-making, and self-regulation by providing information about internal bodily states relative to the external task demands (Azzalini et al., 2019 ; Seth, 2013 ; Tallon-Baudry, 2023 ). From a clinical perspective, disrupted interoceptive-exteroceptive coordination represents a transdiagnostic feature across psychiatric disorders, particularly anxiety and depression, where altered bodily signal processing contributes to symptom maintenance and treatment resistance (Khalsa et al., 2018 ; Nord & Garfinkel, 2022 ; Paulus & Stein, 2010 ). Empirical investigations of interoceptive-exteroceptive interactions have yielded diverse findings that suggest multiple potential mechanisms underlying these interactions (Engelen et al., 2023 ). Current evidence points to at least two possible modes of interaction that may characterize interoceptive-exteroceptive coordination. Some studies suggest facilitative integration, where interoceptive signals may enhance exteroceptive perception when bodily information becomes contextually relevant (Galvez-Pol et al., 2020 ; Park et al., 2014 ; Ronchi et al., 2017 ). Other research indicates possible competitive suppression, where interoceptive and exteroceptive processing may compete for limited neural resources, with mutual inhibition potentially optimizing performance based on task demands (Al et al., 2020 ; Al et al., 2021 ). Somatosensory perception studies provide direct evidence that prestimulus heartbeat-evoked potential (HEP) amplitudes negatively correlate with somatosensory detection, reflecting a more conservative detection criterion, while higher HEP amplitudes are followed by decreases in both early and late somatosensory-evoked potential components (Al et al., 2020 ). Recent steady-state visual evoked potential research demonstrates dynamic resource trade-offs, showing that visual stimuli coupled with stronger cardiac signals exhibit decreased neural processing during systole and increased processing during diastole, with frequent cardiac-visual coupling leading to larger HEPs but smaller visual N2 components (Ren et al., 2024 ). Attention classification studies reveal that heartbeat-evoked responses and brain dynamics can distinguish interoceptive from exteroceptive attention with 85% accuracy, with exteroceptive attention causing overall flattening of power spectral density and reduced neural complexity (Flo et al., 2024 ). Beyond identifying these potential interaction modes, recent researches have increasingly focused on the temporal dynamics underlying these interoceptive-exteroceptive trade-offs. Attentional modulation studies demonstrate that HEP amplitudes are significantly higher during interoceptive compared to exteroceptive attention within specific temporal windows (524–620 ms after R-peak), providing direct evidence for dynamic attentional gating of cardiac signal processing (Petzschner et al., 2019 ). Bidirectional interaction research reveals that external stimuli can modulate heartbeat-evoked responses for heartbeats occurring 200–1200 ms after auditory stimulus presentation, while prestimulus HERs can bias perceptual judgments, with these effects mediated by distinct neural pathways showing different spatial and temporal patterns (Zhang et al., 2023 ). Respiratory-cardiac coupling investigations demonstrate that attention to cardiac sensations enhances heartbeat-evoked potentials specifically during exhalation phases, revealing intricate cardio-respiratory temporal dynamics in interoceptive processing (Zaccaro et al., 2024 ). Our investigation advances understanding of interoceptive-exteroceptive coordination by revealing the temporal architecture underlying regulatory mechanisms across sequential cardiac cycles. Specifically, we demonstrated that neural differentiation between interoceptive and exteroceptive demands emerges through progressive modulation rather than instantaneous switching, providing novel evidence for the dynamic, time-dependent nature of sensory integration. The observed 128–228 ms post-T peak temporal window and linear evolution across heartbeat sequences establish critical parameters for future investigations of interoceptive regulation. Furthermore, our findings that anxiety selectively disrupts the normal suppression of interoceptive signal processing during exteroceptive tasks—while preserving interoceptive processing itself—illuminate the mechanistic specificity of mood influences on sensory coordination, contributing essential insights into how affective states modulate the fundamental balance between internal and external attention streams in cognitive neuroscience. Heartbeat-Evoked Responses as Neural Pattern of Dynamic Interoceptive-Exteroceptive Coordination and Its Progressive Dynamic Changes. Heartbeat-evoked responses represent a fundamental neurophysiological marker of interoceptive processing, reflecting the brain's cortical representation of ascending cardiac signals. Previous investigations have revealed systematic differences in HER characteristics during intero-exteroceptive coordination demands. Early attention studies demonstrated that directing focus toward heartbeat sensations enhances HER amplitude in central and parietal regions within the 350–550 ms post-R-wave time window, while distraction toward external auditory stimuli reduces these responses (Montoya et al., 1993 ). More recent research has refined these temporal parameters, showing that interoceptive attention specifically enhances HER amplitude during 524–620 ms after the R-peak compared to exteroceptive attention (Petzschner et al., 2019 ). We observed differential HER amplitudes between interoceptive and exteroceptive tasks during the 128–228 ms window following T peaks, with significantly attenuated responses during exteroceptive relative to interoceptive processing. These findings establish HER as a dynamic neural marker, with the approximately 128–420 ms period following the T peak emerging as a critical time window for the balance between internal monitoring and external environmental processing. Building upon this foundation, our investigation revealed novel spatial characteristics of HER modulation that advance understanding of interoceptive-exteroceptive coordination. Source localization revealed this regulatory effect was primarily mediated by bilateral sensorimotor cortical regions, consistent with their established role in integrating bodily interoceptive signals with exteorepctive environmental monitoring demands (Al et al., 2020 ). The sensorimotor cortex not only receives but can also amplify or attenuate cardiac information through its modulatory effects on HER amplitude, representing a critical site of interaction between interoceptive signals and somatic sensory input (Pollatos et al., 2016 ). The sensorimotor cortex has traditionally been viewed as effector-specific, corresponding to the classic homuncular representation (Penfield & Rasmussen, 1950 ). However, recent precision functional mapping studies have revealed another important system within primary motor cortex—the Somato-Cognitive Action Network (SCAN)—that interdigitates with the classic effector-specific regions (Gordon et al., 2023 ). This network establishes extensive functional connections with internal organs, the insula which processes interoceptive signals, and the cingulate-opercular network involved in action control and physiological regulation, playing a crucial role in maintaining internal balance (allostasis) (Gordon et al., 2023 ). The functional significance of SCAN becomes evident in its integrative role: as the sensorimotor cortex processes both interoceptive signals and somatic sensory input, it represents a critical site of interaction between internal and external sensory streams. A study using transcranial magnetic stimulation showed that suppressing activity in the somatosensory cortex led to reduced interoceptive accuracy and lower HEP amplitude, suggesting its dynamic flexibility in regulating internal bodily signals (Pollatos et al., 2016 ). This regulatory mechanism aligns with predictive coding models, providing strong evidence that SCAN enables the brain to predictively evaluate the physiological demands of planned actions and implement anticipatory control over bodily states through top-down mechanisms (Gordon et al., 2023 ), thereby optimizing the synergy between action execution and physiological regulation. This integrated perspective helps explain how the brain coordinates complex whole-body actions while maintaining physiological homeostasis. Critically, our findings revealed that HER regulation exhibits progressive temporal dynamics rather than instantaneous switching between interoceptive and exteroceptive. This progressive pattern demonstrates that interoceptive-exteroceptive coordination involves a gradual, dynamic neural modulation that unfolds over multiple cardiac cycles, aligning strongly with homeostatic theory's emphasis on maintaining stability while allowing adaptive flexibility (McEwen & Wingfield, 2003 ; Sterling, 1988 ; Sterling, 2012 ). The pattern we observed—where HER shows no significant differences between interoceptive and exteroceptive tasks initially but gradually differentiates— exemplifies the dual characteristics of stability and adaptability in bodily internal states. This slowly evolving neural response ensures that the internal environment does not fluctuate dramatically in response to external stimuli while allowing the system to gradually adjust to current task demands. This balance between maintaining stability and supporting dynamic adaptability provides a neural foundation for organisms to maintain core functions while flexibly responding to environmental changes, consistent with allostatic regulation frameworks proposed by Sterling ( 1988 ) and elaborated by McEwen and Wingfield ( 2003 ). From a predictive coding perspective, this progressive change in HER reflects the brain's continuous updating process of prediction models for internal signals (Barrett & Simmons, 2015 ; Gu et al., 2013 ; Seth, 2013 ). As consecutive heartbeats accumulate, the brain constantly adjusts its precision weighting of cardiac signals, optimizes prediction errors, and gradually forms task-relevant internal representations. This evidence accumulation mechanism across multiple cardiac cycles closely matches the dynamic equilibrium between top-down predictions and bottom-up error signals emphasized in predictive coding theory (Barrett & Simmons, 2015 ; Friston, 2010 ; Seth, 2013 ). The progressive evolution of HER differences in sensorimotor cortex we observed likely represents the neural manifestation of prediction models being incrementally adjusted and optimized, further supporting Barrett and Simmons ( 2015 ) view that the brain utilizes iterative processing to continuously update and refine its predictive models of internal bodily states. This finding advances our understanding of interoceptive processing as a dynamic, time-dependent phenomenon rather than a static cognitive process. Understanding Dynamic Interoceptive Regulation Through Anxiety. Understanding anxiety provides valuable insights into the dynamic regulation of interoceptive processing, as these conditions involve disruptions in the brain's capacity to flexibly coordinate between monitoring internal bodily states and attending to external environmental demands (Paulus & Stein, 2010 ). Empirical research has identified specific neural mechanisms underlying these regulatory disruptions. Recent neurocardiac synchronization research has demonstrated that anxiety disrupts the temporal coordination between brain activity and cardiac cycles, creating what has been described as "neuronal noise" that reduces certainty about bodily states (Tumati et al., 2021 ). At the level of primary visceroceptive regions such as the posterior insula and somatosensory cortex, reduced phase-locking with cardiac activity creates uncertainty about incoming interoceptive signals, leading to mismatches between bottom-up sensory information and top-down predictive models (Tumati et al., 2021 ). Building upon this theoretical and empirical foundation, our investigation directly examined how anxiety affects the interoceptive temporal dynamics under interoceptive-exteroceptive coordination across sequential cardiac cycles. Recent neuroimaging studies have demonstrated that anxiety affects the regulation of brain-body communication(Paulus & Stein, 2006 ), and consistent with these investigations, our findings revealed that anxiety disrupts the flexible modulation of brain-heart interactions in response to varying interoceptive-exteroceptive demands. Importantly, this disruption was specifically observed during exteroceptive rather than interoceptive processing, where anxious individuals failed to maintain appropriate levels of cardiac signal processing and showed an ascending trend across successive heartbeat cycles. This pattern aligns with theoretical frameworks proposing that anxiety involves altered balance between internal and external processing demands (Pang et al., 2019 ), while providing novel evidence for the beat-to-beat dynamics of this regulatory process. In contrast to the exteroceptive task, we found that anxiety did not significantly affect brain-heart interactions during explicit interoceptive task. This differential pattern aligns with previous behavioral research, particularly a meta-analysis by Adams et al. ( 2022 ) that examined the relationship between anxiety and interoceptive accuracy across multiple studies. The absence of anxiety effects during interoceptive tasks may reflect successful top-down modulation from explicit task demands, potentially counteracting anxiety-related alterations in visceral processing. By demonstrating that anxiety disrupts the progressive, time-dependent modulation of heartbeat-evoked responses during different task contexts, our findings provide evidence for the importance of temporal dynamics in understanding both normal regulation and its disruption in psychopathology. The specific pattern we observed—where anxiety selectively impairs the normal suppression of cardiac signal processing during exteroceptive tasks while preserving interoceptive processing itself—reveals the mechanistic specificity through which anxiety disrupts dynamic regulatory flexibility. These findings advance our understanding by demonstrating that anxiety does not simply involve global alterations in interoceptive sensitivity, but rather represents a specific failure in the context-dependent modulation of brain-heart interactions, providing insights into both the mechanisms underlying normal dynamic regulation and potential targets for therapeutic intervention. These findings have important therapeutic implications. Interoceptive tasks, such as heartbeat counting, which explicitly direct attention to bodily signals, form a fundamental component of mindfulness and meditation practices (Farb et al., 2013 ). The therapeutic efficacy of these approaches may therefore stem from their ability to enhance cognitive engagement and strengthen brain-heart interactions, thereby regulating anxiety-related influences on visceral processing. 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External awareness and GABA--a multimodal imaging study combining fMRI and [18F]flumazenil-PET. Hum Brain Mapp , 35 (1), 173-184. https://doi.org/10.1002/hbm.22166 Wiebking, C., & Northoff, G. (2015). Neural activity during interoceptive awareness and its associations with alexithymia-An fMRI study in major depressive disorder and non-psychiatric controls [Original Research]. Front Psychol , 6 , 589. https://doi.org/10.3389/fpsyg.2015.00589 Yoris, A., Garcia, A. M., Traiber, L., Santamaria-Garcia, H., Martorell, M., Alifano, F., Kichic, R., Moser, J. S., Cetkovich, M., Manes, F., Ibanez, A., & Sedeno, L. (2017). The inner world of overactive monitoring: neural markers of interoception in obsessive-compulsive disorder. Psychol Med , 47 (11), 1957-1970. https://doi.org/10.1017/S0033291717000368 Zaccaro, A., Della Penna, F., Mussini, E., Parrotta, E., Perrucci, M. G., Costantini, M., & Ferri, F. (2024). Attention to cardiac sensations enhances the heartbeat-evoked potential during exhalation. Iscience , 27 (4), 109586. https://doi.org/10.1016/j.isci.2024.109586 Zhang, Y., Zhang, J., Xie, M., Ding, N., Zhang, Y., & Qin, P. (2023). Dual interaction between heartbeat-evoked responses and stimuli. Neuroimage , 266 , 119817. https://doi.org/10.1016/j.neuroimage.2022.119817 Additional Declarations There is NO Competing Interest. Cite Share Download PDF Status: Under Review Version 1 posted 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-7435608","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":506316331,"identity":"d5a23486-4f10-4dd3-b4c2-642acb154367","order_by":0,"name":"Jianfeng 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1","display":"","copyAsset":false,"role":"figure","size":109294,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExperimental design and task procedure. \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eA,\u003c/strong\u003e\u003c/em\u003e Experimental procedure. Participants underwent two emotion induction sessions (anxiety and relaxation) in a counterbalanced order across two separate visits, spaced one week apart. Each session consisted of an initial induction followed by a six-minute resting-state recording, and a second induction followed by task runs (~12 minutes each). \u003cem\u003e\u003cstrong\u003eB,\u003c/strong\u003e\u003c/em\u003eTrial time course. Each trial began with a fixation period (3-4 seconds), followed by a counting phase (9-13 seconds) during which participants either counted their heartbeats (interoceptive task, INT) or tones delivered via headphones (exteroceptive task, EXT). Participants then reported their counted quantity on a numerical scale (1-25) and indicated their confidence level (0-6). Each experimental run comprised 18 interoceptive trials and 18 exteroceptive trials, yielding 36 trials per run. INT, interoceptive task; EXT, exteroceptive task.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7435608/v1/1a3d41f2bf7b27aa27da2976.png"},{"id":93137327,"identity":"86db036c-20fe-41dc-a6cc-5e0a1d983ffa","added_by":"auto","created_at":"2025-10-09 12:24:07","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":76975,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEffects of emotional induction on subjective ratings and task performance. \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eA,\u003c/strong\u003e\u003c/em\u003eSubjective anxiety ratings across experimental phases. Anxiety induction (red line) elicited significantly elevated anxiety ratings compared to relaxation condition (blue line), with this differential effect persisting throughout all experimental phases. \u003cem\u003e\u003cstrong\u003eB,\u003c/strong\u003e\u003c/em\u003e Interoceptive task performance quantified by heartbeat estimation bias [calculated as (reported heartbeats – actual heartbeats)/actual heartbeats]. Participants in the anxiety condition exhibited diminished accuracy (reduced bias) in heartbeat counting relative to the relaxation condition (\u003cem\u003et\u003c/em\u003e(31) = 2.282, \u003cem\u003ep\u003c/em\u003e = 0.030). INT, interoceptive task; EXT, exteroceptive task. *\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003ep\u003c/em\u003e\u0026lt; 0.01, ***\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, n.s., not significant.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7435608/v1/3e50555cc34554fea0ce5489.png"},{"id":93137333,"identity":"b7bf136a-b414-4b92-a131-361618f65ec3","added_by":"auto","created_at":"2025-10-09 12:24:08","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":191098,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNeural differentiation between interoceptive and exteroceptive tasks at sensor and source levels. \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eA,\u003c/strong\u003e\u003c/em\u003eTemporal dynamics of HERs (±SEM) for INT versus EXT under relaxation condition within the significant cluster identified in \u003cem\u003eB\u003c/em\u003e. Significant differences occurred between128-228 ms after cardiac T-peaks (Monte Carlo cluster level, \u003cem\u003ep\u003c/em\u003e= 0.026). \u003cem\u003e\u003cstrong\u003eB,\u003c/strong\u003e\u003c/em\u003e Topographical distribution of significant heartbeat-evoked response (HER) differences between INT and EXT under the relaxation condition. \u003cem\u003e\u003cstrong\u003eC,\u003c/strong\u003e\u003c/em\u003e Temporal dynamics of HERs (±SEM) for INT versus EXT under anxiety condition within the same cluster as \u003cem\u003eB\u003c/em\u003e. \u003cem\u003e\u003cstrong\u003eD,\u003c/strong\u003e\u003c/em\u003eQuantified mean HER differences (INT-EXT) within the cluster from \u003cem\u003eB\u003c/em\u003eduring the 128-228 ms interval after cardiac T-peaks, comparing relaxation and anxiety conditions. \u003cem\u003e\u003cstrong\u003eE,\u003c/strong\u003e\u003c/em\u003e Temporal evolution of source-reconstructed HER amplitudes (±SEM) in the significant brain regions identified in \u003cem\u003eF\u003c/em\u003efor INT versus EXT under relaxation condition. \u003cem\u003e\u003cstrong\u003eF,\u003c/strong\u003e\u003c/em\u003e Source-level analysis showing cortical regions primarily contributing to HER differences between INT and EXT in the relaxation condition, with significant clusters located within the sensorimotor area (SMA) (threshold: minimum 50 adjacent vertices with uncorrected \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.005). \u003cem\u003e\u003cstrong\u003eG,\u003c/strong\u003e\u003c/em\u003e Temporal evolution of source-reconstructed HER amplitudes (±SEM) in the regions in \u003cem\u003eF\u003c/em\u003efor INT versus EXT under anxiety condition.\u003cem\u003e \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eH,\u003c/strong\u003e\u003c/em\u003e Quantified mean amplitude differences (INT-EXT) in the identified source regions in \u003cem\u003eF\u003c/em\u003eduring 128-228 ms interval after cardiac T-peaks, comparing relaxation and anxiety conditions. Error bars represent standard error of the mean. HER, heartbeat-evoked response. INT, interoceptive task; EXT, exteroceptive task. *\u003cem\u003ep\u003c/em\u003e\u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7435608/v1/7f34a455de27e17476fc9854.png"},{"id":93137352,"identity":"4cc1ab9b-f238-4f09-9066-63a7b0d73028","added_by":"auto","created_at":"2025-10-09 12:24:09","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":204914,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDynamic changes in heartbeat-evoked responses (HERs): Comparing tasks (INT vs. EXT) and emotional states (relaxation vs. anxiety). \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eA,\u003c/strong\u003e\u003c/em\u003e Heartbeat-by-heartbeat dynamics of HER differences (INT-EXT) during relaxation condition. The left panel demonstrates systematic changes across heartbeat sequences within trials, with the black dashed line representing the linear regression fit. Slope values show a significant positive trend (\u003cem\u003et\u003c/em\u003e(31) = 2.202, p = 0.035) (middle), while intercept values are non-significant (right). \u003cem\u003e\u003cstrong\u003eB,\u003c/strong\u003e\u003c/em\u003eHeartbeat-by-heartbeat dynamics of HER differences (INT-EXT) during anxiety condition. The slope did not differ significantly from zero (\u003cem\u003et\u003c/em\u003e(31) = 0.754, \u003cem\u003ep\u003c/em\u003e = 0.457) (middle), indicating no systematic change across the heartbeat sequence. \u003cem\u003e\u003cstrong\u003eC,\u003c/strong\u003e\u003c/em\u003eHER dynamics in the INT comparing emotional states: relaxation (blue) versus anxiety (red). The top panel demonstrates systematic changes across heartbeat sequences within trials. Slope values (\u003cem\u003et\u003c/em\u003e(31) = -0.554, p = 0.584) and intercept values (\u003cem\u003et\u003c/em\u003e(31) = 1.581, p = 0.124) are non-significant (lower left and right), indicating similar changes in both emotional conditions. \u003cem\u003e\u003cstrong\u003eD,\u003c/strong\u003e\u003c/em\u003eHER dynamics in the EXT comparing emotional states: relaxation (purple) versus anxiety (orange). Slope values show a significant difference between emotional conditions (\u003cem\u003et\u003c/em\u003e(31) = -2.395, \u003cem\u003ep\u003c/em\u003e = 0.023) (lower left), with anxiety eliciting a more positive slope than relaxation. Error bars represent standard error of the mean. HER, heartbeat-evoked response. INT, interoceptive task; EXT, exteroceptive task. *p \u0026lt; 0.05, ***p \u0026lt; 0.001, n.s., not significant.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7435608/v1/c50f029b6fd176b711d72026.png"},{"id":93137913,"identity":"e1854135-143c-4176-a9dd-b18513f660d4","added_by":"auto","created_at":"2025-10-09 12:32:08","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":244509,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePhysiological measurements remain stable across interoceptive and exteroceptive tasks under different emotional states. \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eA,\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e \u003c/em\u003eTime course of the average ECG signal (±SEM) during relaxation (top) and anxiety (bottom) conditions for both INT (blue/red) and EXT (purple/orange), recorded from vertical lead II. Signals were averaged by aligning to the T-peak (time 0). \u003cem\u003e\u003cstrong\u003eB,\u003c/strong\u003e\u003c/em\u003e Comparative analysis of mean heart rate (bpm, left panels) and heart rate variability (Standard Deviation of RR Intervals, SDRR, right panels) between INT and EXT during relaxation (top) and anxiety (bottom) conditions. No statistically significant differences were observed between task conditions. \u003cem\u003e\u003cstrong\u003eC,\u003c/strong\u003e\u003c/em\u003e Dynamic changes in RR intervals across heartbeat sequences within trials during relaxation (top) and anxiety (bottom) conditions, showing no significant differences between tasks under the two emotional states. \u003cem\u003e\u003cstrong\u003eD,\u003c/strong\u003e\u003c/em\u003e Respiratory amplitude fluctuations over time during relaxation (top) and anxiety (bottom) conditions. Time 0 represents the onset of the heart or musical note icon. No significant differences were observed between tasks under the two emotional states. Error bars represent standard error of the mean. INT, interoceptive task; EXT, exteroceptive task. RR interval, time between two successive R-waves in the ECG. n.s., no significant.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7435608/v1/e5ea8a6a95a32ab660f7f9e5.png"},{"id":93138697,"identity":"b7958555-45ba-43b5-9239-826ee3451b32","added_by":"auto","created_at":"2025-10-09 12:40:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1452187,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7435608/v1/deda1c73-693b-42f6-9a7d-9519b018acc2.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Dynamic Regulation of Interoceptive Processing in Relaxation and Anxiety","fulltext":[{"header":"Introduction","content":"\u003cp\u003eInteroceptive processing\u0026mdash;the neural monitoring of bodily signals\u0026mdash;functions through regulatory mechanisms that allocate neural resources between interoceptive and exteroceptive domains based on contextual demands (Barrett \u0026amp; Simmons, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Khalsa et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Kleckner et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Nord \u0026amp; Garfinkel, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Seth \u0026amp; Friston, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Tumati et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These mechanisms exhibit bidirectional flexibility: interoceptive signals can enhance exteroceptive perception when bodily information becomes relevant to environmental judgments (Park et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), while interoceptive processing can be attenuated during exteroceptive demands to prioritize environmental assessment (Garcia-Cordero et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Petzschner et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Villena-Gonzalez et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This regulation \u0026mdash; involving the coordination between interoceptive and exteroceptive demands \u0026mdash; enables the brain to maintain physiological homeostasis while responding to environmental challenges, and its disruption appears in psychiatric disorders (de la Fuente et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Lutz et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Schulz et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Yoris et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), particularly anxiety disorders (Pang et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Paulus \u0026amp; Stein, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Tumati et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). While evidence demonstrates that interoceptive regulation by the demands of interoceptive\u0026ndash;exteroceptive trade-offs, the temporal dynamics underlying this regulatory process\u0026mdash;specifically how it evolves across sequential physiological cycles rather than through instantaneous switching\u0026mdash;remain incompletely characterized. How such temporal dynamics, in turn, contribute to the manifestation of anxiety remains unclear. Addressing this gap in our knowledge is the goal of our paper.\u003c/p\u003e\u003cp\u003eInteroceptive regulation arises from interactions between bottom-up physiological signals and top-down cognitive mechanisms (Barrett \u0026amp; Simmons, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Gu et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Kleckner et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Seth, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), with anxiety providing a window for studying disruptions to their regulatory balance (Harrison et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Pang et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Paulus \u0026amp; Stein, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Tumati et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). (Cornwell et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Richards et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) Anxiety appears to alter both autonomic activity and cognitive control systems (Grupe \u0026amp; Nitschke, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Thayer et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). During anxiety states, sympathetic arousal increases interoceptive signal salience, and also affects prefrontal regulatory control, creating a neurophysiological environment where bodily signals may influence cognitive processing (Garfinkel \u0026amp; Critchley, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Paulus \u0026amp; Stein, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). This effect likely impacts mechanisms of interoceptive-exteroceptive coordination. Evidence from anxiety disorders indicates patterns of interoceptive hypervigilance (Olatunji et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Schmidt et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e1997\u003c/span\u003e), alongside altered environmental threat monitoring (Cornwell et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Richards et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), suggesting changes in attentional allocation between internal and external signals. Neurobiologically, these patterns correspond with regional differences across networks including anterior insula, anterior cingulate cortex, and sensorimotor cortex (Domschke et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Paulus \u0026amp; Stein, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), supporting frameworks that consider anxiety as involving altered allostatic interoceptive regulation (Paulus \u0026amp; Stein, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Tumati et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). While these patterns have been documented, the temporal dynamics through which anxiety affects the coordination between interoceptive and exteroceptive processing remain incompletely characterized (Critchley \u0026amp; Garfinkel, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Tallon-Baudry et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Experimental anxiety induction offers methodological advantages for investigating these dynamics, enabling examination of emotional regulatory mechanisms while avoiding factors such as medication effects and physiological adaptations present in clinical anxiety populations (Ochayi \u0026amp; Anyaehie, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Robinson et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe interoceptive regulation under intero\u0026ndash;exteroceptive coordination demand requires neural markers capable of capturing fluctuations in interoceptive signal strength. Heartbeat-evoked responses (HERs) serve as a neural indicator of interoceptive processing intensity (Park \u0026amp; Blanke, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Schandry \u0026amp; Montoya, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Schandry et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e1986\u003c/span\u003e), enabling researchers to examine how cardiac signal processing adapts across heartbeats during different task contexts. Studies demonstrate that HERs integrate cardiac information with ongoing cognitive functions, as evidenced by their ability to predict detection of exteroceptive stimuli like visual stimuli (Park et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), correlate with somatosensory perception accuracy (Al et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and index self-related processing during mind-wandering (Babo-Rebelo, Richter, et al., 2016; Babo-Rebelo, Wolpert, et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). HER amplitudes also exhibit differentiation in frontal-central regions during tasks requiring interoceptive versus exteroceptive attention (Garcia-Cordero et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Petzschner et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Villena-Gonzalez et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This differentiation indicates that HERs capture allocation of neural resources between processing domains, e.g., intero- and exteroceptive. Studies of HER patterns in anxiety disorders reveal altered differentiation between more interoceptive-focused eye-closed and rather exteroceptive-dominated eye-open conditions compared to healthy controls (Pang et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), suggesting a diminished capacity for interoceptive regulation under intero\u0026ndash;extero coordination demand. Together, these findings suggest that HERs, may serve as a neurophysiological measure of interoceptive regulation, including how that is modulated by anxiety.\u003c/p\u003e\u003cp\u003eOur study employs simultaneous MEG and physiological recordings to directly examine how experimentally induced anxiety affects the dynamic regulation of interoceptive processing. We use controlled interoceptive (heartbeat counting) and exteroceptive (tone counting) tasks under both anxiety and relaxation states to address two specific questions: Does interoceptive regulation, as indexed by dynamic HER, flexibly respond to changes in intero- and exteroceptive demand, and how is this flexibility affected by anxiety? Is there a changing pattern in the dynamic interaction of interoceptive regulation over time, and how is that modulated by anxiety?\u003c/p\u003e\u003cp\u003eBy examining HERs across multiple consecutive heartbeats, we can characterize the temporal dynamics of interoceptive regulation and identify precise patterns of disruption under anxiety. We consider two potential mechanisms through which anxiety might affect HER differentiation: either enhanced HER during interoceptive tasks, reflecting hypersensitivity to internal bodily signals, or disrupted HER suppression during exteroceptive demand, indicating failure to appropriately filter cardiac information during environmental monitoring. Based on evidence of reduced HER differentiation in anxiety disorders (Pang et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and anxiety's documented effects on body hypervigilance (Esteve \u0026amp; Camacho, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Olatunji et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Schmidt et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e1997\u003c/span\u003e), we predict that anxiety will disrupt normative HER differentiation, specifically through abnormal enhancement of HERs during exteroceptive demand. This approach provides a novel perspective on anxiety's neural mechanisms by directly examining how it alters the dynamic interoceptive regulation under intero\u0026ndash;exteroceptive coordination demand, potentially explaining the characteristic hypervigilance observed in anxiety disorders.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cem\u003eParticipants.\u003c/em\u003e Thirty-three right-handed Participants (20 females; mean age\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, 21.18\u0026thinsp;\u0026plusmn;\u0026thinsp;2.24 years) were recruited for the experiment. Inclusion criteria required participants to have no history of heart disease, respiratory problems, eating disorders, mental health issues, or brain trauma. All participants provided informed consent and received monetary compensation for their involvement. The experimental procedures were approved by the Medical Ethics Committee of the Medical School at XX University. One participant was excluded from the MEG analysis due to exhibiting discrepant interoceptive performance between the two mood states (bias under relaxation: 0.322; bias under anxiety: 0.964).\u003c/p\u003e\u003cp\u003e\u003cem\u003eTasks and procedures.\u003c/em\u003e Participants underwent two emotion induction sessions (anxiety and relaxation) in a counterbalanced order across two separate visits, spaced one week apart. In each session, participants completed a sequence consisting of an initial mood induction, a 6-minute resting-state scan, a second mood induction, and two runs of interoceptive and exteroceptive tasks (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA).\u003c/p\u003e\u003cp\u003eThe study employed a validated multimodal mood induction protocol combining verbal and musical stimuli, an approach that has demonstrated robust effectiveness in inducing target moods (Hallford et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Liu \u0026amp; Li, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Nuevo et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The verbal stimuli comprised 120 statements selected from established emotional databases: 60 anxiety-provoking and 60 calming statements with empirically validated valence and arousal ratings (Sinclair et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). In the anxiety induction phase, participants read 30 anxiety-provoking statements (e.g., \"I'm haunted with thoughts about myself and how I come across to others\") while listening to \u003cem\u003eLigeti's Requiem\u003c/em\u003e, a musical piece previously shown to reliably enhance anxiety induction (Barbieri et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Participants indicated whether they had experienced similar situations to each statement to promote greater self-involvement. To enhance and maintain the induction effect, participants then spent 2 minutes focusing on anxiety-related thoughts and then reported them (Hallford et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Sinclair et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). The relaxation phase followed an identical structure: participants read 30 calming statements (e.g., \"Sometimes it feels good to get away from the noise by going to a park\") while listening to \u003cem\u003eGabriel Faur\u0026eacute;'s Ballade for Piano and Orchestra\u003c/em\u003e (Hallford et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), then focused on and reported relaxation experiences for 2 minutes.\u003c/p\u003e\u003cp\u003e To establish the tone rate for the normal condition in the exteroceptive task that would match each participant's individual heart rate, a 6-minute resting-state recording was conducted following the first mood induction. This procedure was essential for capturing participants' actual cardiac rhythm after mood manipulation, which would later serve as a parameter for the experimental setup in the exteroceptive task. During this resting-state period, participants were instructed to keep their eyes open, refrain from sleeping, avoid engaging in vivid thoughts, and abstain from counting their heartbeats. The pulse rates recorded during this interval were subsequently used to determine the precise tone rate in the exteroceptive task.\u003c/p\u003e\u003cp\u003eThe interoceptive task involved counting heartbeats, while the exteroceptive task involved counting tones (Wiebking et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Wiebking et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Wiebking \u0026amp; Northoff, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Participants completed two runs, each consisting of 18 heart-counting trials and 18 tone-counting trials (9 trials at pulse rates (normal condition) and 9 trials at 80% of pulse rates (slow condition)) presented in random order. In each heart-counting trial, participants first viewed a fixation cross for 3\u0026ndash;4 seconds, followed by a heart icon displayed at the center of the screen for 9\u0026ndash;13 seconds. During this period, participants counted their heartbeats. Subsequently, they reported the number of counts (on a scale from 0 to 25) and their confidence level (on a scale from 0 to 6) using a slider. In tone-counting trials, a note icon appeared at the center of the screen, prompting participants to count the number of tones they heard. Tones were delivered through earphones with a duration of 200 ms and were jittered by \u0026plusmn;\u0026thinsp;100 ms to avoid synchronization with heartbeats. The tone volume was adjusted to the minimum level participants could reliably hear before the experiment, to minimize the influence of sound-evoked neural oscillations on heartbeat-evoked responses. The use of two tone rates served to confirm that participants were counting tones rather than heartbeats during the exteroceptive task (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eRecordings.\u003c/em\u003e Continuous MEG data were acquired using a whole-head MEG system equipped with 102 magnetometers and 204 planar gradiometers (Elekta Neuromag TRIUX) at a sampling rate of 1000 Hz, with an online high-pass filter of 0.1 Hz and a low-pass filter set at 330 Hz. Data were recorded during both the resting-state and task periods. Simultaneously, electrocardiogram (ECG) and electrooculogram (EOG) data were collected to monitor cardiac and ocular activity (sampling rate 1000 Hz, filter 0.1\u0026ndash;330 Hz). For ECG recordings, two electrodes were positioned around the left clavicle and right clavicle (Lead I configuration). For EOG recordings, two vertical electrodes were placed above and below the left eye, while two horizontal electrodes were positioned to the left and right of the eyes. A ground electrode was placed on the left forearm.\u003c/p\u003e\u003cp\u003eRespiratory (RSP) and photoplethysmography (PPG) data were acquired using the BIOPAC MP160 data acquisition system (BIOPAC Systems Inc., Goleta, CA). RSP signals were recorded via an RSP100C amplifier configured with a sampling rate of 1000 Hz, an online band-pass filter ranging from 0.05 Hz to 10 Hz, and a gain setting of 10. Data were collected using a TSD201 transducer respiration belt, which was securely positioned around the participant\u0026rsquo;s chest. PPG signals were captured with a PPG100C-MRI amplifier, set to a sampling rate of 1000 Hz, an online band-pass filter spanning 0.05 Hz to 10 Hz, and a gain setting of 100. The TSD200 pulse transducer was attached to the fourth finger of the right hand to avoid interference with button-pressing responses.\u003c/p\u003e\u003cp\u003e\u003cem\u003eMEG data preprocessing.\u003c/em\u003e External noise was removed using temporal signal space separation (TSSS) implemented in MaxFilter (Taulu \u0026amp; Simola, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). All preprocessing steps were performed using the FieldTrip toolbox (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.fieldtriptoolbox.org/\u003c/span\u003e\u003cspan address=\"https://www.fieldtriptoolbox.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (Oostenveld et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The MEG data were first resampled to 250 Hz, then underwent offline filtering between 1 and 35 Hz, and were finally detrended. ICA was conducted with the runica algorithm in the FieldTrip, using PCA to reduce the data to 40 components before decomposition. For artifact detection, each independent component was individually correlated with vertical and horizontal electrooculogram (EOG) signals. These EOG signals were low-pass filtered at 5 Hz before being used for correlation analysis. Components with a Fisher\u0026rsquo;s z-transformed correlation coefficient exceeding 3 standard deviations of the distribution of correlation coefficients across all components were identified as artifacts and rejected. For each subject, between 0 and 2 components were removed for vertical and horizontal EOG artifacts respectively. The ECG-related ICA components were not removed, as some of the ECG artifacts identified by ICA may contain HER signal components, which could influence subsequent analyses (Buot et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). To assess potential differences in cardiac activity between interoceptive and exteroceptive conditions, statistical analyses were conducted on ECG amplitude, heart rate, and heart rate variability parameters as control measures.\u003c/p\u003e\u003cp\u003e\u003cem\u003eHeartbeat-evoked responses (HERs).\u003c/em\u003e R peaks in ECG lead I signals were identified using the ft_artifact_zvalue function from FieldTrip after applying a 25 Hz low-pass filter. Only R peaks occurring during the designated heartbeat-counting or tone-counting periods were selected for analysis. To ensure data quality and reliability, we calculated the intervals between consecutive R peaks (RR intervals) and implemented stringent exclusion criteria: intervals exceeding 3 standard deviations from the mean RR interval or shorter than 400 ms were removed from further analysis. For the remaining valid RR intervals, T peaks were identified as the maximum amplitude points within the window spanning from 200 ms after an R peak to 200 ms before the subsequent R peak. The accuracy of T peak detection was verified through visual inspection by trained researchers. Heartbeat-evoked responses were subsequently derived from magnetometer data time-locked to these validated T peaks (Azzalini et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Babo-Rebelo et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Babo-Rebelo, Richter, et al., 2016; Park et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). We focused our analysis on neural responses within the 50\u0026ndash;300 ms window following the T wave (Azzalini et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), as this time window is minimally affected by cardiac artifacts (Dirlich et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1997\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cem\u003eAnatomical MR acquisition and preprocessing.\u003c/em\u003e High-resolution T1-weighted anatomical images were acquired using a 3T Siemens PRISMA scanner with a magnetization-prepared rapid gradient-echo (MPRAGE) sequence (repetition time\u0026thinsp;=\u0026thinsp;2300 ms, echo time\u0026thinsp;=\u0026thinsp;2.26 ms, flip angle\u0026thinsp;=\u0026thinsp;8\u0026deg;, field of view\u0026thinsp;=\u0026thinsp;256 mm, voxel size\u0026thinsp;=\u0026thinsp;1 mm\u0026sup3; isotropic). Cortical surfaces were reconstructed using the standard recon-all pipeline (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://surfer.nmr.mgh.harvard.edu/\u003c/span\u003e\u003cspan address=\"https://surfer.nmr.mgh.harvard.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (Fischl, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The results were visually inspected and used for computing head models.\u003c/p\u003e\u003cp\u003e\u003cem\u003eSource reconstruction.\u003c/em\u003e Source reconstruction was performed using Brainstorm software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://neuroimage.usc.edu/brainstorm/\u003c/span\u003e\u003cspan address=\"https://neuroimage.usc.edu/brainstorm/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (Tadel et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Anatomical constraints were provided by individual cortical surface models from FreeSurfer. Head models were computed using the overlapping spheres method. For the MEG data, magnetometers and gradiometers were used as sensor configurations. Source localization was performed using the dynamic statistical parametric mapping (dSPM) algorithm (minimum-norm imaging; signal-to-noise ratio of 3; depth weighting of 0.5). A noise model was applied without the use of empty-room recordings. The reconstructed sources were localized in cortical surface space from FreeSurfer. These source maps were then projected to the cortical surface for group-level analysis. A 6-mm Gaussian kernel was applied, and the absolute values of the source maps were taken.\u003c/p\u003e\u003cp\u003e\u003cem\u003eStatistical analysis.\u003c/em\u003e Statistical analyses were conducted at the sensor level using magnetometer data to examine HER differences between interoceptive and exteroceptive tasks. Analyses were implemented in MATLAB using the FieldTrip. To address multiple comparisons, we employed a non-parametric cluster-based permutation approach(Maris \u0026amp; Oostenveld, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Initially, paired-sample t-tests were performed at each sensor and time point within a 50\u0026ndash;300 ms post-T peak window. Clusters were formed by spatially and temporally adjacent data points that exceeded a cluster-defining threshold of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (uncorrected), with a minimum of two neighboring sensors required. The statistical significance of identified clusters was assessed by randomly permuting condition labels 10,000 times to generate a null distribution of the maximum cluster-level test statistic. Clusters were considered significant if their Monte Carlo p-value was below 0.05. To identify cortical sources contributing to the observed differences in HERs between conditions, the averaged HER data were projected onto source space and cortical currents were reconstructed. For each vertex in the source space, we averaged the signal values across the time window that had shown significant between-condition differences at the sensor level (as determined by the cluster-based permutation t-test).\u003c/p\u003e\u003cp\u003eTo identify brain regions that made primary contributions to the observed effects, vertex-wise statistical comparisons of absolute dipole current values were conducted using two-tailed t-tests. Significant brain regions were defined as clusters of contiguous spatially adjacent vertices (\u0026gt;\u0026thinsp;50) with t-values exceeding an uncorrected threshold of p\u0026thinsp;\u0026lt;\u0026thinsp;0.005.\u003c/p\u003e\u003cp\u003e\u003cem\u003eControl analysis of ECG and respiration.\u003c/em\u003e To clarify the physiological changes associated with interoceptive and exteroceptive processing, physiological metrics were computed.\u003c/p\u003e\u003cp\u003eAfter low-pass filtering the ECG signal at 25 Hz, the signal was time-locked to the T peak (set as the 0 ms time point) and averaged across trials. Additionally, the ECG amplitude between 50\u0026ndash;300 ms post-T peak was statistically analyzed to control for potential condition-specific differences in ECG signals that might confound the heart-rate evoked responses (HER). RR intervals were averaged within each condition, and the mean heart rate was calculated by dividing the average RR interval by one minute. The standard deviation of RR intervals was computed as an index of heart rate variability (HRV).\u003c/p\u003e\u003cp\u003eTo examine respiratory patterns, data from a 9-second window starting from the onset of either the interoceptive or exteroceptive task (time 0) were extracted after applying a 1 Hz low-pass filter to the respiratory signal. After detrending and mean removal, the data were converted to z-scores. The averaged z-scores were then used to obtain the mean respiratory pattern over time. Additionally, the findpeaks function in MATLAB was used to identify respiratory peaks. The time interval between two consecutive peaks was calculated to determine the respiratory intervals. After averaging the extracted respiratory intervals, the data were transformed to compute the mean respiratory rate. The lowest point between two peaks was identified as the respiratory trough. The time intervals between the peaks and troughs were then used to determine the durations of inhalation and exhalation, respectively, from which the inhalation-to-exhalation ratio was calculated.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cem\u003eMood induction effects.\u003c/em\u003e To establish the validity of our experimental manipulation, we first assessed whether our anxiety induction protocol successfully created differential mood states. Participants rated their anxiety levels on a 0\u0026ndash;6 scale prior to the experiment and after each stage. At baseline (e.g., before the respective induction procedure), no significant difference in anxiety levels was observed between the anxiety induction session and the relaxation maintenance session (\u003cem\u003et\u003c/em\u003e(31)\u0026thinsp;=\u0026thinsp;0.724, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.475). Following the initial mood induction procedure, anxiety ratings diverged significantly between conditions (first induction: \u003cem\u003et\u003c/em\u003e(31)\u0026thinsp;=\u0026thinsp;6.542, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and remained different throughout all subsequent experimental phases (post-resting state: \u003cem\u003et\u003c/em\u003e(31)\u0026thinsp;=\u0026thinsp;3.514, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001; second induction: \u003cem\u003et\u003c/em\u003e(31)\u0026thinsp;=\u0026thinsp;9.213, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; first run: \u003cem\u003et\u003c/em\u003e(31)\u0026thinsp;=\u0026thinsp;5.999, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; second run: \u003cem\u003et\u003c/em\u003e(31)\u0026thinsp;=\u0026thinsp;3.473, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002) (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA).\u003c/p\u003e\u003cp\u003eObjective behavioral and physiological measures provided further validation of our manipulation. In the interoceptive task, heartbeat estimation bias was calculated as (reported heartbeats - actual heartbeats)/actual heartbeats. We observed diminished accuracy (reduced bias) in the heartbeat counting during the anxiety condition (\u003cem\u003et\u003c/em\u003e(31) = 2.282, p = 0.030) when compared to the relaxation condition (see Fig. \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). During exteroceptive task, we observed that the heart rate differentially responded to tone rates during the anxiety condition (mood state \u0026times; tone rate interaction: \u003cem\u003eF\u003c/em\u003e(1,31) = 8.516, \u003cem\u003ep\u003c/em\u003e = 0.006). This effect appeared exclusively during anxiety (\u003cem\u003ep\u003c/em\u003e = 0.001) but not during relaxation (\u003cem\u003ep\u003c/em\u003e = 0.354). These findings confirm the success of our mood manipulation and establish a reliable foundation for interpreting subsequent neural analyses.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eAnxiety Disrupts Neural Differentiation of HERs under intero\u0026ndash;exteroceptive coordination demand.\u003c/em\u003e To determine whether anxiety affects the normal neural differentiation between interoceptive and exteroceptive demands, we examined HER patterns during interoceptive versus exteroceptive tasks across mood states. We applied cluster-based permutation t-tests at the sensor level to compare interoceptive versus exteroceptive conditions during relaxation. This analysis revealed significantly differing clusters during the 128\u0026ndash;228 ms after T peaks in the left prefrontal area of the topographic map (Monte Carlo cluster level, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.026) (see Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). To investigate how anxiety impacts this HER differentiation, we averaged the data within the identified cluster under both mood states and compared their differences. This analysis revealed that anxiety significantly reduced the HER differences between interoceptive and exteroceptive demands when compared to the relaxation state (\u003cem\u003et\u003c/em\u003e(31)\u0026thinsp;=\u0026thinsp;3.358, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002) (see Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD).\u003c/p\u003e\u003cp\u003eTo localize the neural origins of these effects, we projected the data into source space and performed a t-test on the time-averaged data during the 128\u0026ndash;228 ms interval after T peaks. Source localization revealed two significant regions primarily in left and right sensorimotor cortex (lSMC \u0026amp; rSMC) (uncorrected \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.005, adjacent vertices\u0026thinsp;\u0026gt;\u0026thinsp;50) (see Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF). The amplitude in these regions was greater during interoceptive tasks than exteroceptive tasks (see Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE). Importantly, we identified significant regions only in the relaxation state, with no significant findings observed during anxiety (see Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eH). These findings demonstrate that anxiety abolishes the \u0026lsquo;normal\u0026rsquo; neural differentiation between interoceptive and exteroceptive demands in sensorimotor regions critical for both bodily sensation and environmental interaction.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eTemporal Dynamics Reveal Progressive Differentiation Pattern.\u003c/em\u003e To track how HER differentiation under intero\u0026ndash;extero coordination demand evolved over time, we averaged HERs separately for each heartbeat position within the trial sequence (e.g., first heartbeat across all trials, second heartbeat, etc.). Our t-test analysis revealed significant HER differences during relaxation at the 3rd (\u003cem\u003et\u003c/em\u003e(31)\u0026thinsp;=\u0026thinsp;2.330, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.048, FDR corrected), 5th (\u003cem\u003et\u003c/em\u003e(31)\u0026thinsp;=\u0026thinsp;2.837, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018, FDR corrected), 6th (\u003cem\u003et\u003c/em\u003e(31)\u0026thinsp;=\u0026thinsp;3.360, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018, FDR corrected), 8th (\u003cem\u003et\u003c/em\u003e(31)\u0026thinsp;=\u0026thinsp;2.834, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018, FDR corrected), and 9th heartbeats (\u003cem\u003et\u003c/em\u003e(31)\u0026thinsp;=\u0026thinsp;2.881, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018, FDR corrected) (see the left panel of Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). In contrast, no significant HER differences were observed at any heartbeat order during anxiety (\u003cem\u003eps\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05, FDR corrected) (see the left panel of Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003eTo quantify these temporal dynamics, we performed a linear fit analysis on the progressive HER differences between interoceptive and exteroceptive demands, extracting the slope and intercept parameters. During relaxation, the slope was significantly greater than zero (\u003cem\u003et\u003c/em\u003e(31)\u0026thinsp;=\u0026thinsp;2.202, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.035), while the intercept was approximately zero (\u003cem\u003et\u003c/em\u003e(31)\u0026thinsp;=\u0026thinsp;0.938, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.355) (see the middle and right panels of Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Under anxiety, both slope (\u003cem\u003et\u003c/em\u003e(31)\u0026thinsp;=\u0026thinsp;0.754, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.457) and intercept (\u003cem\u003et\u003c/em\u003e(31) = -0.289, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.775) parameters approximated zero (see the middle and right panels of Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). These findings reveal that interoceptive regulation under intero\u0026ndash;extero coordination demand involves a gradual, progressive dynamic change with neural modulation that evolves over multiple cardiac cycles rather than an instantaneous state transition, and that anxiety fundamentally disrupts this dynamic regulatory process.\u003c/p\u003e\u003cp\u003e\u003cem\u003eAnxiety Specifically Affects the HER Dynamics in Exteroceptive task.\u003c/em\u003e To determine whether anxiety differentially modulates the temporal dynamics of HERs during interoceptive versus exteroceptive tasks, we performed linear fit analyses on heartbeat-by-heartbeat HER changes separately for each task condition. We then compared the slope and intercept parameters between mood states. During interoceptive task, we found no significant differences in either slope (\u003cem\u003et\u003c/em\u003e(31) = -0.554, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.584) or intercept (\u003cem\u003et\u003c/em\u003e(31)\u0026thinsp;=\u0026thinsp;1.581, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.124) between relaxation and anxiety (see Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). In contrast, during exteroceptive task, we observed a significant difference in slope between mood states (\u003cem\u003et\u003c/em\u003e(31) = -2.395, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.023), while no difference in intercept was identified (\u003cem\u003et\u003c/em\u003e(31)\u0026thinsp;=\u0026thinsp;0.496, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.623) (see Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). Specifically, the slope was negative during relaxation and positive during anxiety, indicating a condition-dependent change in the direction of the effect. These findings demonstrate that anxiety selectively disrupts normal HER suppression dynamics during exteroceptive tasks, causing inappropriate enhancement of cardiac signal processing during environmental monitoring while leaving interoceptive processing largely intact.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eControl Analyses.\u003c/em\u003e To verify these temporal patterns reflected genuine neural modulation rather than task practice effects, we analyzed the average HERs for each trial and performed linear fits across trial sequences. Regardless of mood state, the slopes were approximately zero (relaxation: \u003cem\u003et\u003c/em\u003e(31) = -1.121, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.271; anxiety: \u003cem\u003et\u003c/em\u003e(31)\u0026thinsp;=\u0026thinsp;0.196, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.846). The intercept during relaxation was significantly greater than zero (\u003cem\u003et\u003c/em\u003e(31)\u0026thinsp;=\u0026thinsp;4.004, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), whereas the intercept during anxiety approximated zero (\u003cem\u003et\u003c/em\u003e(31)\u0026thinsp;=\u0026thinsp;0.585, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.562). This suggests that HER differentiation is dynamically coordinated based on immediate demands, representing a flexible neural mechanism rather than continuous optimization brought about by practice.\u003c/p\u003e\u003cp\u003eTo rule out potential physiological confounds in our neural findings, we conducted comprehensive control analyses of cardiac and respiratory parameters. To evaluate potential ECG signal influence on HERs, we compared ECG amplitudes between interoceptive and exteroceptive tasks. No significant differences were observed (\u003cem\u003eps\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), confirming that the observed HER differences between interoceptive and exteroceptive demands were unlikely attributable to cardiac artifacts (see Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA).\u003c/p\u003e\u003cp\u003eAdditionally, across both relaxation and anxiety states, we found no significant differences in heart rate and heart rate variability between interoceptive and exteroceptive processing (\u003cem\u003eps\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (see Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). To examine potential temporal confounds, we investigated whether physiological signals exhibited specific patterns within the time windows of interoceptive and exteroceptive tasks. No distinct interbeat interval patterns or respiration patterns were observed during either task condition (\u003cem\u003eps\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (see Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). These control analyses confirm that our HER findings reflect genuine neural differentiation between intero- and exteroceptive demands and their disruption by anxiety, rather than artifacts of peripheral physiology or other systematic confounds.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we demonstrated that anxiety modulates heartbeat-evoked responses (HER) differently during interoceptive and exteroceptive demands. During the relaxation state, we observed distinctive HER differences in bilateral sensorimotor cortex between interoceptive and exteroceptive tasks within the 128\u0026ndash;228 ms post-T peak window. In contrast, this HER differentiation was no longer visible under anxiety. Notably, applying a temporal or dynamic analysis, the HER difference between interoceptive and exteroceptive demands showed a linear increase across heartbeat sequences during relaxation, with significant effects, suggesting a gradual rather than abrupt regulatory transition. This progressive pattern was abolished under anxiety where no such dynamic adaptative HER changes were observed. Further analysis revealed that anxiety specifically affected HER patterns during exteroceptive tasks, showing altered temporal dynamics with a slope more similar to that observed during interoceptive tasks, compared to the relaxation state. These findings suggest that anxiety disrupts the normal suppression of cardiac signal processing during exteroceptive demands, potentially reflecting impaired regulatory mechanisms that allow inappropriate integration of bodily signals into environmental information processing.\u003c/p\u003e\u003cp\u003e\u003cem\u003eNeural Mechanisms Underlying Interoceptive-Exteroceptive Coordination and Its Dynamics.\u003c/em\u003e Understanding interoceptive-exteroceptive coordination has emerged as crucial for cognitive neuroscience from both basic cognition and clinical perspectives. From a cognitive standpoint, interoceptive processing contributes to mood awareness, decision-making, and self-regulation by providing information about internal bodily states relative to the external task demands (Azzalini et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Seth, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Tallon-Baudry, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). From a clinical perspective, disrupted interoceptive-exteroceptive coordination represents a transdiagnostic feature across psychiatric disorders, particularly anxiety and depression, where altered bodily signal processing contributes to symptom maintenance and treatment resistance (Khalsa et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Nord \u0026amp; Garfinkel, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Paulus \u0026amp; Stein, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eEmpirical investigations of interoceptive-exteroceptive interactions have yielded diverse findings that suggest multiple potential mechanisms underlying these interactions (Engelen et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Current evidence points to at least two possible modes of interaction that may characterize interoceptive-exteroceptive coordination. Some studies suggest facilitative integration, where interoceptive signals may enhance exteroceptive perception when bodily information becomes contextually relevant (Galvez-Pol et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Park et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Ronchi et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Other research indicates possible competitive suppression, where interoceptive and exteroceptive processing may compete for limited neural resources, with mutual inhibition potentially optimizing performance based on task demands (Al et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Al et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Somatosensory perception studies provide direct evidence that prestimulus heartbeat-evoked potential (HEP) amplitudes negatively correlate with somatosensory detection, reflecting a more conservative detection criterion, while higher HEP amplitudes are followed by decreases in both early and late somatosensory-evoked potential components (Al et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Recent steady-state visual evoked potential research demonstrates dynamic resource trade-offs, showing that visual stimuli coupled with stronger cardiac signals exhibit decreased neural processing during systole and increased processing during diastole, with frequent cardiac-visual coupling leading to larger HEPs but smaller visual N2 components (Ren et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Attention classification studies reveal that heartbeat-evoked responses and brain dynamics can distinguish interoceptive from exteroceptive attention with 85% accuracy, with exteroceptive attention causing overall flattening of power spectral density and reduced neural complexity (Flo et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBeyond identifying these potential interaction modes, recent researches have increasingly focused on the temporal dynamics underlying these interoceptive-exteroceptive trade-offs. Attentional modulation studies demonstrate that HEP amplitudes are significantly higher during interoceptive compared to exteroceptive attention within specific temporal windows (524\u0026ndash;620 ms after R-peak), providing direct evidence for dynamic attentional gating of cardiac signal processing (Petzschner et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Bidirectional interaction research reveals that external stimuli can modulate heartbeat-evoked responses for heartbeats occurring 200\u0026ndash;1200 ms after auditory stimulus presentation, while prestimulus HERs can bias perceptual judgments, with these effects mediated by distinct neural pathways showing different spatial and temporal patterns (Zhang et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Respiratory-cardiac coupling investigations demonstrate that attention to cardiac sensations enhances heartbeat-evoked potentials specifically during exhalation phases, revealing intricate cardio-respiratory temporal dynamics in interoceptive processing (Zaccaro et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOur investigation advances understanding of interoceptive-exteroceptive coordination by revealing the temporal architecture underlying regulatory mechanisms across sequential cardiac cycles. Specifically, we demonstrated that neural differentiation between interoceptive and exteroceptive demands emerges through progressive modulation rather than instantaneous switching, providing novel evidence for the dynamic, time-dependent nature of sensory integration. The observed 128\u0026ndash;228 ms post-T peak temporal window and linear evolution across heartbeat sequences establish critical parameters for future investigations of interoceptive regulation. Furthermore, our findings that anxiety selectively disrupts the normal suppression of interoceptive signal processing during exteroceptive tasks\u0026mdash;while preserving interoceptive processing itself\u0026mdash;illuminate the mechanistic specificity of mood influences on sensory coordination, contributing essential insights into how affective states modulate the fundamental balance between internal and external attention streams in cognitive neuroscience.\u003c/p\u003e\u003cp\u003e\u003cem\u003eHeartbeat-Evoked Responses as Neural Pattern of Dynamic Interoceptive-Exteroceptive Coordination and Its Progressive Dynamic Changes.\u003c/em\u003e Heartbeat-evoked responses represent a fundamental neurophysiological marker of interoceptive processing, reflecting the brain's cortical representation of ascending cardiac signals. Previous investigations have revealed systematic differences in HER characteristics during intero-exteroceptive coordination demands. Early attention studies demonstrated that directing focus toward heartbeat sensations enhances HER amplitude in central and parietal regions within the 350\u0026ndash;550 ms post-R-wave time window, while distraction toward external auditory stimuli reduces these responses (Montoya et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1993\u003c/span\u003e). More recent research has refined these temporal parameters, showing that interoceptive attention specifically enhances HER amplitude during 524\u0026ndash;620 ms after the R-peak compared to exteroceptive attention (Petzschner et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). We observed differential HER amplitudes between interoceptive and exteroceptive tasks during the 128\u0026ndash;228 ms window following T peaks, with significantly attenuated responses during exteroceptive relative to interoceptive processing. These findings establish HER as a dynamic neural marker, with the approximately 128\u0026ndash;420 ms period following the T peak emerging as a critical time window for the balance between internal monitoring and external environmental processing.\u003c/p\u003e\u003cp\u003eBuilding upon this foundation, our investigation revealed novel spatial characteristics of HER modulation that advance understanding of interoceptive-exteroceptive coordination. Source localization revealed this regulatory effect was primarily mediated by bilateral sensorimotor cortical regions, consistent with their established role in integrating bodily interoceptive signals with exteorepctive environmental monitoring demands (Al et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The sensorimotor cortex not only receives but can also amplify or attenuate cardiac information through its modulatory effects on HER amplitude, representing a critical site of interaction between interoceptive signals and somatic sensory input (Pollatos et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe sensorimotor cortex has traditionally been viewed as effector-specific, corresponding to the classic homuncular representation (Penfield \u0026amp; Rasmussen, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e1950\u003c/span\u003e). However, recent precision functional mapping studies have revealed another important system within primary motor cortex\u0026mdash;the Somato-Cognitive Action Network (SCAN)\u0026mdash;that interdigitates with the classic effector-specific regions (Gordon et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This network establishes extensive functional connections with internal organs, the insula which processes interoceptive signals, and the cingulate-opercular network involved in action control and physiological regulation, playing a crucial role in maintaining internal balance (allostasis) (Gordon et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The functional significance of SCAN becomes evident in its integrative role: as the sensorimotor cortex processes both interoceptive signals and somatic sensory input, it represents a critical site of interaction between internal and external sensory streams. A study using transcranial magnetic stimulation showed that suppressing activity in the somatosensory cortex led to reduced interoceptive accuracy and lower HEP amplitude, suggesting its dynamic flexibility in regulating internal bodily signals (Pollatos et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). This regulatory mechanism aligns with predictive coding models, providing strong evidence that SCAN enables the brain to predictively evaluate the physiological demands of planned actions and implement anticipatory control over bodily states through top-down mechanisms (Gordon et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), thereby optimizing the synergy between action execution and physiological regulation. This integrated perspective helps explain how the brain coordinates complex whole-body actions while maintaining physiological homeostasis.\u003c/p\u003e\u003cp\u003eCritically, our findings revealed that HER regulation exhibits progressive temporal dynamics rather than instantaneous switching between interoceptive and exteroceptive. This progressive pattern demonstrates that interoceptive-exteroceptive coordination involves a gradual, dynamic neural modulation that unfolds over multiple cardiac cycles, aligning strongly with homeostatic theory's emphasis on maintaining stability while allowing adaptive flexibility (McEwen \u0026amp; Wingfield, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Sterling, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e1988\u003c/span\u003e; Sterling, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The pattern we observed\u0026mdash;where HER shows no significant differences between interoceptive and exteroceptive tasks initially but gradually differentiates\u0026mdash; exemplifies the dual characteristics of stability and adaptability in bodily internal states. This slowly evolving neural response ensures that the internal environment does not fluctuate dramatically in response to external stimuli while allowing the system to gradually adjust to current task demands. This balance between maintaining stability and supporting dynamic adaptability provides a neural foundation for organisms to maintain core functions while flexibly responding to environmental changes, consistent with allostatic regulation frameworks proposed by Sterling (\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e1988\u003c/span\u003e) and elaborated by McEwen and Wingfield (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2003\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFrom a predictive coding perspective, this progressive change in HER reflects the brain's continuous updating process of prediction models for internal signals (Barrett \u0026amp; Simmons, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Gu et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Seth, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). As consecutive heartbeats accumulate, the brain constantly adjusts its precision weighting of cardiac signals, optimizes prediction errors, and gradually forms task-relevant internal representations. This evidence accumulation mechanism across multiple cardiac cycles closely matches the dynamic equilibrium between top-down predictions and bottom-up error signals emphasized in predictive coding theory (Barrett \u0026amp; Simmons, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Friston, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Seth, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The progressive evolution of HER differences in sensorimotor cortex we observed likely represents the neural manifestation of prediction models being incrementally adjusted and optimized, further supporting Barrett and Simmons (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) view that the brain utilizes iterative processing to continuously update and refine its predictive models of internal bodily states. This finding advances our understanding of interoceptive processing as a dynamic, time-dependent phenomenon rather than a static cognitive process.\u003c/p\u003e\u003cp\u003e\u003cem\u003eUnderstanding Dynamic Interoceptive Regulation Through Anxiety.\u003c/em\u003e Understanding anxiety provides valuable insights into the dynamic regulation of interoceptive processing, as these conditions involve disruptions in the brain's capacity to flexibly coordinate between monitoring internal bodily states and attending to external environmental demands (Paulus \u0026amp; Stein, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Empirical research has identified specific neural mechanisms underlying these regulatory disruptions. Recent neurocardiac synchronization research has demonstrated that anxiety disrupts the temporal coordination between brain activity and cardiac cycles, creating what has been described as \"neuronal noise\" that reduces certainty about bodily states (Tumati et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). At the level of primary visceroceptive regions such as the posterior insula and somatosensory cortex, reduced phase-locking with cardiac activity creates uncertainty about incoming interoceptive signals, leading to mismatches between bottom-up sensory information and top-down predictive models (Tumati et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBuilding upon this theoretical and empirical foundation, our investigation directly examined how anxiety affects the interoceptive temporal dynamics under interoceptive-exteroceptive coordination across sequential cardiac cycles. Recent neuroimaging studies have demonstrated that anxiety affects the regulation of brain-body communication(Paulus \u0026amp; Stein, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), and consistent with these investigations, our findings revealed that anxiety disrupts the flexible modulation of brain-heart interactions in response to varying interoceptive-exteroceptive demands. Importantly, this disruption was specifically observed during exteroceptive rather than interoceptive processing, where anxious individuals failed to maintain appropriate levels of cardiac signal processing and showed an ascending trend across successive heartbeat cycles. This pattern aligns with theoretical frameworks proposing that anxiety involves altered balance between internal and external processing demands (Pang et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), while providing novel evidence for the beat-to-beat dynamics of this regulatory process.\u003c/p\u003e\u003cp\u003eIn contrast to the exteroceptive task, we found that anxiety did not significantly affect brain-heart interactions during explicit interoceptive task. This differential pattern aligns with previous behavioral research, particularly a meta-analysis by Adams et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) that examined the relationship between anxiety and interoceptive accuracy across multiple studies. The absence of anxiety effects during interoceptive tasks may reflect successful top-down modulation from explicit task demands, potentially counteracting anxiety-related alterations in visceral processing.\u003c/p\u003e\u003cp\u003eBy demonstrating that anxiety disrupts the progressive, time-dependent modulation of heartbeat-evoked responses during different task contexts, our findings provide evidence for the importance of temporal dynamics in understanding both normal regulation and its disruption in psychopathology. The specific pattern we observed\u0026mdash;where anxiety selectively impairs the normal suppression of cardiac signal processing during exteroceptive tasks while preserving interoceptive processing itself\u0026mdash;reveals the mechanistic specificity through which anxiety disrupts dynamic regulatory flexibility. These findings advance our understanding by demonstrating that anxiety does not simply involve global alterations in interoceptive sensitivity, but rather represents a specific failure in the context-dependent modulation of brain-heart interactions, providing insights into both the mechanisms underlying normal dynamic regulation and potential targets for therapeutic intervention. These findings have important therapeutic implications. Interoceptive tasks, such as heartbeat counting, which explicitly direct attention to bodily signals, form a fundamental component of mindfulness and meditation practices (Farb et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The therapeutic efficacy of these approaches may therefore stem from their ability to enhance cognitive engagement and strengthen brain-heart interactions, thereby regulating anxiety-related influences on visceral processing. This mechanistic interpretation is supported by emerging evidence demonstrating the clinical effectiveness of interoception-based interventions in anxiety management (Price \u0026amp; Hooven, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Methodologically, our results highlight the importance of analyzing physiological responses across multiple cardiac cycles rather than averaging across all heartbeats. The typical approach of collapsing HERs across all heartbeats would have obscured the temporal evolution we observed, potentially missing crucial aspects of interoceptive processing.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAdams, K. L., Edwards, A., Peart, C., Ellett, L., Mendes, I., Bird, G., \u0026amp; Murphy, J. (2022). 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The inner world of overactive monitoring: neural markers of interoception in obsessive-compulsive disorder. \u003cem\u003ePsychol Med\u003c/em\u003e,\u003cem\u003e 47\u003c/em\u003e(11), 1957-1970. https://doi.org/10.1017/S0033291717000368\u003c/li\u003e\n\u003cli\u003eZaccaro, A., Della Penna, F., Mussini, E., Parrotta, E., Perrucci, M. G., Costantini, M., \u0026amp; Ferri, F. (2024). Attention to cardiac sensations enhances the heartbeat-evoked potential during exhalation. \u003cem\u003eIscience\u003c/em\u003e,\u003cem\u003e 27\u003c/em\u003e(4), 109586. https://doi.org/10.1016/j.isci.2024.109586\u003c/li\u003e\n\u003cli\u003eZhang, Y., Zhang, J., Xie, M., Ding, N., Zhang, Y., \u0026amp; Qin, P. (2023). Dual interaction between heartbeat-evoked responses and stimuli. \u003cem\u003eNeuroimage\u003c/em\u003e,\u003cem\u003e 266\u003c/em\u003e, 119817. https://doi.org/10.1016/j.neuroimage.2022.119817 \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Interoception, Heartbeat-evoked Response, Anxiety, Brain-body Interaction","lastPublishedDoi":"10.21203/rs.3.rs-7435608/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7435608/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eInteroceptive processing\u0026mdash;the neural monitoring of bodily signals\u0026mdash;functions through regulatory mechanisms that adapt bodily signal monitoring to different cognitive and environmental demands. However, temporal dynamics underlying these mechanisms and their modulation by anxiety remain incompletely characterized. Using magnetoencephalography (MEG), we examined heartbeat-evoked responses (HERs) across interoceptive and exteroceptive tasks under anxiety induction, with a relaxation condition included to control for anxiety-related effects. Under relaxation, interoceptive processing exhibited context-dependent modulation: HERs were attenuated during exteroceptive relative to interoceptive tasks, predominantly in sensorimotor cortical regions. This regulation manifested progressively across sequential heartbeats following a linear temporal trajectory, revealing dynamic evolution rather than instantaneous switching. Anxiety, in contrast, disrupted this adaptive dynamic pattern, abolishing such progressive context-dependent modulation by specifically impairing interoceptive suppression during exteroceptive task. Control analyses confirmed these effects were not attributable to cardiac artifacts or other physiological confounds. These findings establish a neurophysiological mechanism whereby anxiety impairs the dynamics of adaptive interoceptive regulation. That, in turn, results in inappropriate integration of bodily signals into cognitive processing and provides a neural basis for interoceptive hypervigilance observed in anxiety.\u003c/p\u003e","manuscriptTitle":"Dynamic Regulation of Interoceptive Processing in Relaxation and Anxiety","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-09 12:23:56","doi":"10.21203/rs.3.rs-7435608/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"0e7846cc-3bec-4cb7-955d-7bfa646d3564","owner":[],"postedDate":"October 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":53778576,"name":"Biological sciences/Neuroscience/Neuro\u0026#x2013;vascular interactions"},{"id":53778577,"name":"Biological sciences/Neuroscience/Emotion"},{"id":53778578,"name":"Biological sciences/Neuroscience/Cognitive neuroscience/Perception"},{"id":53778579,"name":"Biological sciences/Neuroscience/Stress and resilience"},{"id":53778580,"name":"Biological sciences/Psychology"}],"tags":[],"updatedAt":"2025-12-31T10:55:44+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-09 12:23:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7435608","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7435608","identity":"rs-7435608","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}