Breath-Modulated Biofeedback Loops for Enhancing Autonomic Regulation and Executive Functions through fNIRS and ECG

Breath-Modulated Biofeedback Loops for Enhancing Autonomic Regulation and Executive Functions through fNIRS and ECG | 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 Breath-Modulated Biofeedback Loops for Enhancing Autonomic Regulation and Executive Functions through fNIRS and ECG Shyama Shah, Gregory Lewis This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7255999/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Breathing is increasingly recognized as a dynamic regulator of autonomic and cognitive systems, yet its integrated impact on cardiac vagal tone, prefrontal hemodynamics, and executive functions remains poorly understood. Here, we introduce a multimodal biofeedback paradigm—combining electrocardiography (ECG), functional near‑infrared spectroscopy (fNIRS), and behavioral cognitive assays—to test how two contrasting yogic breathing techniques acutely modulate the “breath–ANS–PFC–EF” loop in healthy adults (n = 20). Participants performed either Right‑Nostril Yogic Breathing (RNYB; 4-6 breaths min⁻¹) or Bhastrika Pranayama (90-120 breaths min⁻¹) for 5 minutes, following baseline spontaneous breathing. Relative to baseline, Bhastrika (n=9) significantly decreased median interbeat interval (Δ ≈ –20 ms), during breathing, t(8)=7.62, p<0.001, indicating sympathetic activation, whereas RNYB (n=8) selectively increased IBI variance by ~30%, t(8)=–3.55, p=0.008, reflecting enhanced vagal tone. Concurrent fNIRS revealed that RNYB elevated oxyhemoglobin concentration in the right‑lateral prefrontal cortex (median Δ ≈ +0.15 µM; p =0.021), with peak activation exceeding that of Bhastrika (p = 0.034). Behaviorally, RNYB reduced Stroop interference by ~15% (p = 0.029) and improved 2‑back accuracy by ~8% (p < 0.05), whereas Bhastrika yielded faster but less accurate responses. Crucially, across participants, increases in HRV correlated with right‑lateral PFC HbO (ρ ≈ 0.6; p < 0.05) and predicted lower Stroop interference (ρ ≈ –0.5; p < 0.05). These findings demonstrate that volitional respiration can be harnessed to tune an integrated autonomic–cortical–executive control feedback network, offering a blueprint for noninvasive interventions in stress, cognitive aging, cardiovascular and neuropsychiatric disorders. Biological sciences/Neuroscience/Cognitive neuroscience/Cognitive control Biological sciences/Neuroscience/Peripheral nervous system/Autonomic nervous system Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction At its simplest, breathing sustains life; yet it also exerts a profound, bidirectional influence on mind and body, it serves as a dynamic bridge between bodily physiology and conscious experience​. Respiration rhythmically gates sensory inflow and autonomic tone, synchronizes neural oscillations, and calibrates cortical excitability 1 , 2 . By wielding breath as a voluntary lever—even for minutes at a time—one can tune heart–brain circuits and shape higher‑order cognition 2 , 3 . Throughout history and across cultures, breath has been revered not merely as a metabolic necessity but as a powerful regulator of mental, emotional, and physical state. Ancient contemplative traditions (e.g. yoga prāṇāyāma, Buddhist ānāpānasati ) treat the breath as a gateway to self-awareness and emotional balance. In classical Yoga (Patanjali’s Yoga Sutra), prāṇāyāma (breath control and manipulation) is the key “limb” linking physiological and psychological states, encapsulated in the aphorism “mind and breath are so interlinked that controlling the breath can regulate the mind” 4 , 5 . Early Western scientists similarly recognized this link: Wundt noted influences of respiration on emotion and volitional control, and Hering and Bainbridge described its central role in cardiovascular reflexes 6 , 7 . Modern psychophysiology confirms that respiration exerts profound effects on neural and autonomic systems. Voluntary breathing patterns alter vagal tone and heart-rate variability, shaping prefrontal–limbic balance and affecting arousal and mood 3 , 4 ​. Recent studies confirm slow, deep breathing tends to lower heart rate and elevate vagal tone, whereas rapid forceful breathing activates sympathetic arousal 4 , 8 . In short, breathing provides an unusual window into otherwise “hidden” brain–body networks, allowing conscious modulation of autonomic state. Contemporary neuroscience provides mechanistic models for this breath–brain link. The polyvagal and neurovisceral integration frameworks posit a central autonomic network (including prefrontal cortex, insula, brainstem nuclei) whose activity co-varies with cardiac vagal modulation 9 , 10 . Heart-rate variability (HRV), an index of cardiac vagal tone, has emerged as a marker of self-regulation: higher resting HRV predicts better performance on executive tasks (attention, inhibition, working memory) 9 , 11 . Indeed, individual differences in HRV correlate with prefrontal activity and cognitive flexibility 9 , 11 . Interventions that raise HRV (biofeedback, meditation, controlled breathing) often yield improvements in cognitive control and stress resilience 2 . Beyond its essential role in gas exchange, respiration plays a far more dynamic and integrative role in shaping neural and physiological systems. A growing body of research reveals that respiration modulates not only arousal and pain perception 12 , 13 , but also cognitive performance through phase-synchronous modulation of neural activity. Studies have shown that the timing of the respiratory cycle entrains oscillatory activity in the hippocampus and cortex 1 , 14 , influences olfactory and sensory integration 1 , and alters task performance based on inhalation-exhalation phase. These respiratory rhythms extend to impact motor function, language processing, and emotional regulation, suggesting breath as a powerful modulator of brain-wide information flow 4 , 15 . Among the many tools to probe brain function during respiration, functional near-infrared spectroscopy (fNIRS) offers a noninvasive, temporally sensitive, and motion-tolerant imaging modality. Prior studies using fNIRS have shown that yogic breathing elicits region-specific changes in prefrontal cortex (PFC) oxygenation. For instance, unilateral right-nostril breathing (RNYB) increases contralateral (left) PFC oxyhemoglobin 16 . In Singh et al. (2016), RNYB raised left PFC oxy-Hb significantly above left-nostril or control breathing 16 . Similarly, high-frequency yogic breathing (Bhastrika-type) has been associated with enhanced cortical activation and arousal 16 , 17 . Together these suggest that breathing pattern can modulate PFC hemodynamics. Importantly, enhanced PFC oxygenation often accompanies better cognitive performance: for instance, faster Stroop task responses are linked to higher dorsolateral PFC oxy-Hb 18 . Executive functions are a set of top-down cognitive processes—including inhibition, cognitive flexibility, and working memory—that are essential for goal-directed behavior and adaptivity in changing environments. These functions are supported by a distributed network anchored in the PFC and modulated by autonomic input via pathways like the vagus nerve 19 . Within the broader executive domain, cognitive control refers specifically to the ability to flexibly adjust behavior in response to goals or conflicting demands 19 . Working memory refers to a system of temporary storage and manipulation of information no longer perceptually present, its widely measured by n-back test 19 . The Stroop task is a canonical probe of this ability, requiring the inhibition of an automatic response (reading a word) in favor of a task-relevant one (naming the ink color). Because cognitive control and working memory are sensitive to changes in arousal, vagal tone, and PFC activation, it provides a useful behavioral assay of breath-linked neurophysiological modulation. Despite a substantial body of research examining the individual effects of breath-based interventions on autonomic physiology, cortical activation, or cognitive performance, these components are often studied in isolation. Most investigations target only one segment of the system—such as the impact of breathing on HRV, or the correlation between HRV and prefrontal activation—without capturing their concurrent modulation or downstream cognitive consequences. In effect, past work treats each link in the pathway—breath → autonomic nervous system (ANS), ANS → PFC, PFC → executive functions (EF)—as a silo. Yet these components form a tightly coupled system, and the integrated breath–ANS–PFC–EF circuit may exhibit emergent properties that cannot be inferred from its parts alone. Furthermore, few studies contrast distinct respiratory strategies that differentially engage autonomic branches (e.g., vagal-enhancing RNYB vs. sympatho-excitatory Bhastrika), or employ synchronized multimodal acquisition to quantify respiration-linked modulation across physiology, cortex, and behavior. This study addresses that critical gap by implementing a rigorously controlled, cross-condition design integrating ECG, fNIRS, and task-based measures to empirically map the full functional loop from breath to brain and to behavior. Here we test how acute breathing pattern and style alter a coupled autonomic–cortical–cognitive loop. Specifically, we compare two well-defined yogic interventions: RNYB—a slow, focused technique associated with increased parasympathetic activity—and Bhastrika Pranayama—a rapid, forceful breathing associated with sympathetic arousal. In healthy participants, we recorded ECG-derived interbeat intervals (IBIs), fNIRS-based oxyhemoglobin responses in the prefrontal cortex, and performance on executive function tasks (Stroop and 2-back). We hypothesized that RNYB would increase HRV (via IBI variance) and PFC oxygenation, with corresponding gains in executive functions, whereas Bhastrika would elevate heart rate (lower median IBI) with less improvement in neural or behavioral outcomes. This study thus aims to uncover the mechanistic path by which respiration can modulate core human functions via breath-modulated biofeedback and regulatory loops. Methods Participants: Twenty participants (M = 25.48, SD = 15.32), aged between 19 to 73, were recruited via flyers, distributed literature, campus-wide emails at Luddy School, Indiana University Bloomington, and SONA advertisements through the Psychology and Brain Sciences IU Bloomington, student subject pool. Of these participants, 19 were right-handed. Exclusion criteria included the presence of psychiatric or neurological disorders, recent use of tobacco products (within 24 hours), consumption of more than three alcoholic beverages in the past 24 hours, hospitalization in the past year due to head injury, major surgery, or COVID-19, and any respiratory issues such as shortness of breath or difficulty breathing in the past year. Additional exclusions applied to individuals with tuberculosis within the past 10 years, chronic obstructive pulmonary disease, asthma, pacemakers, or ongoing cardiac or blood pressure medication use (excluding low-dose aspirin). Participants with allergies to adhesives, use of non-prescription drugs, or ingestion of caffeine or alcohol within two hours prior to the experimental session were also excluded. Participants were pre-screened via phone or email to assess health habits and general medical history. No participant had a medical condition that posed a risk or could interfere with autonomic measurements. Furthermore, none were taking prescription medications, such as central nervous system depressants, stimulants, hypertension medications, or anticholinergic agents, that could impact autonomic regulation. This comprehensive screening ensured a consistent and reliable sample for the study. Of the 20 participants recruited, data from 3 were excluded due to [protocol non-compliance, excessive motion artifacts, or incomplete data], resulting in a final sample of 17 participants (n = 17) included in the analyses. Data Collection (Equipment and Stimulus Materials): In compliance with the Indiana University Bloomington Institutional Review Board, participants read and signed informed consent forms and were screened for health status to ensure adherence to exclusion criteria (e.g., brain injury, chronic bronchitis, smoking, as detailed earlier). Data collection took place at the Psychology and Brain Sciences Department of Indiana University, following all ethical standards and protocols for human research. After the consent and screening process, three gel electrodes were placed on the chest to record electrocardiography (ECG) for beat-to-beat heart rate measurements. A respiratory band was fitted around the chest to monitor continuous changes in tidal volume, while two electrodes placed on the palm recorded electrodermal activity (EDA). Photoplethysmography (PPG) was measured using a pulse oximeter on the left index finger. These psychophysiological signals were recorded with the Biopac MP160 system (Biopac Systems, USA), interfaced with a Windows 10-operated Dell computer. Hemodynamic responses were captured using Biopac’s functional near-infrared spectroscopy (fNIRS) system (FNIR-2000S, Imager 2000S, Biopac Systems, USA) with 18-channel sensor pads and accompanying fNIRSoft software. This setup monitored cortical activity during breathing and executive function tasks. The Stroop and Two-Back tasks were designed and administered using PsychoPy software, ensuring precise stimulus timing and synchronization with physiological data. Subjects were seated in a reclining chair within a dedicated dark room, which was temperature-controlled at approximately 68°F. The room was shielded to minimize interference and divided into two sections separated by curtains: one for participants and another for research assistants. A split-screen setup allowed research assistants to monitor participant responses and adjust experimental conditions as needed. Throughout the session, physiological signals were recorded continuously. Participants completed preliminary demographic surveys and questionnaires in the dark room before starting the tasks. During the breathing manipulation phases, they were encouraged to follow the instructions carefully. Typically, two research assistants facilitated the study, ensuring smooth data collection, monitoring participant compliance, and administering breathing styles and EF tasks. This controlled environment minimized distractions and optimized fNIRS data accuracy. Protocol: The study, formally called DIVNE (Dynamic respiratory control’s Impact on Vagus nerve, Neurological activation of the prefrontal Cortex and Executive functions) study employs a sequential and structured protocol to examine the relationship between dynamic respiratory control, autonomic responses, prefrontal cortex activity, and executive function under various task conditions. Each participant was randomly assigned to one of the two breathing conditions (RNYB or Bhastrika) on the day of the study, using a stratified online randomization tool, which maintained approximately equal group sizes (n = 10 per condition), during which psychophysiological, and PFC responses are assessed across multiple experimental phases. The sequence is designed to isolate the effects of controlled breathing patterns on executive function performance and autonomic regulation. The protocol begins with a 5-minute session of natural spontaneous breathing (NSB1), allowing participants’ baseline physiological states to stabilize without external interference. Following this, participants perform a computerized Stroop task (Stroop1), consisting of 150 randomized trials across congruent, neutral, and incongruent conditions. This task captures baseline cognitive load metrics, such as reaction time and accuracy, under varying levels of executive demand. Subsequently, participants went through one of two breathing patterns, assigned at start of study, (Dynamic Respiratory Control 1) —Bhastrika or Right Nostril Yogic Breathing—for a 5-minute session, during which autonomic and neural metrics are continuously monitored. Bhastrika, a pranayama technique involving rapid and forceful diaphragmatic breaths (~ 90–120 breaths per minute), was performed with active abdominal engagement and guided by a standardized instructional video. Participants received real-time encouragement and compliance monitoring from a trained research assistant. RNYB consisted of slow, deliberate inhalations through the right nostril only (~ 4–6 breaths per minute), with the left nostril occluded using the participant’s thumb. Breathing pace was paced using the same visual cue video, and participants were continuously reminded to maintain consistency and focus throughout breathing task. After the breathing intervention, participants complete a second Stroop task (Stroop2), consisting of 150 randomized trials across congruent, neutral, and incongruent conditions, to assess EF changes attributable to the respiratory modulation. A second baseline breathing 5-minute session (NSB2) follows to examine recovery and return-to-baseline effects. The protocol then transitions to the two-back task (2Back1), a working memory test consisting of 30 trials, which serves as a pre-intervention EF performance benchmark. A final round of breathing pattern intervention-5- minute session (Dynamic Respiratory Control 2), which would be same as randomly assigned at the beginning (Bhastrika or RNYB), and a post-intervention two-back task, consisting of 30 trials (2Back2) conclude the study, enabling comparisons of behavioral and neural metrics between pre- and post-intervention conditions. Data Reduction: ECG and fNIRS data for each session were transferred offline to a Windows-based system for preprocessing and analysis. fNIRS (16-channel system, 2 wavelengths) measured oxy- and deoxy-hemoglobin in the prefrontal cortex. Optodes were arranged to cover bilateral lateral and medial PFC. Channels 1–4 were assigned to left-lateral PFC, 5–8 left-medial, 9–12 right-medial, 13–16 right-lateral. fNIRS data were preprocessed using COBI Studio software (Biopac Systems, Inc., USA), following procedures adapted from Ayaz et al. (2010) 20 . Preprocessing involved these main steps. First, ambient light correction was performed using dedicated ambient channels to remove environmental interference and light saturation artifacts. Second, motion artifacts were addressed using a sliding-window approach (SMAR), which applies a coefficient of variation–based filter across ~ 10-second windows to identify abrupt fluctuations in signal intensity; artifact detection thresholds were empirically. Third, a Finite Impulse Response (FIR) low-pass filter with a 0.1 Hz cutoff and a Hamming window (order ~ 20) was applied to suppress high-frequency noise, including cardiac and respiratory components. The cleaned optical density signals were then converted into concentration changes in oxyhemoglobin (HbO) and deoxyhemoglobin (HbR) using the Modified Beer–Lambert Law. For each breathing condition (e.g., NSB vs. RNYB or Bhastrika), the median and maximum HbO values were extracted from each prefrontal subregion to quantify both tonic and peak changes in cortical oxygenation. Continuous ECG was recorded (lead II) to capture cardiac cycles. ECG data were processed using CardioEdit (Brain-Body Center, Chicago, IL), which enabled visual inspection and correction of missed R-wave detections and artifacts caused by electrode movement or electrical interference. Inter-beat interval (IBI) values, essential for Heart Rate Variability calculations, were extracted following this quality-control step. From each phase (NSB, breathing manipulation), we extracted the median IBI (an index of heart rate) and variance of IBI (a time-domain HRV measure). Because both breathing techniques fell outside the normal respiratory sinus arrhythmia band (fast for Bhastrika, unilateral slow for RNYB), we focused on HRV (variance) rather than spectral RSA. Further processing of fNIRS and ECG data was carried out in Python 3.10 (Python Software Foundation, www.python.org ) and MATLAB 2023a (MathWorks Inc., Massachusetts, USA). Data visualization, reduction, and statistical analysis were conducted using IBM SPSS Statistics 30.0.0 (IBM Corp., Released 2023). Data Segmentation and Analysis Metrics : The prefrontal cortex was anatomically segmented into four regions: left lateral (Optodes 1–4), left medial (Optodes 5–8), right medial (Optodes 9–12), and right lateral (Optodes 13–16). This division facilitated regional neural activity analyses under experimental conditions. Executive Tasks After each breathing session, participants performed a computerized color-word Stroop test and a 2-back working memory task. The Stroop comprised neutral, congruent, and incongruent trials. Executive task performance was evaluated using Stroop task metrics, including interference Score (which is difference of median Response Time of incongruent conditions and median Response Time of congruent conditions) and facilitation scores (which is difference of median Response Time of neutral conditions and median Response Time of congruent conditions), however for our analysis we have used interference Score only. For the 2-back task, accuracy rate (correct responses as a percentage of total stimuli (total number of trials, which is 30 here)) and reaction time (median response time for correct trials) were assessed. Analysis All physiological and behavioral measures were first tested for normality; nonparametric methods were used for non-normal distributions. We used paired t-tests (Wilcoxon signed-rank) to compare NSB vs breathing phases within each group, and independent t-tests for between-group comparisons (RNYB vs Bhastrika). Correlations (Spearman’s ρ) were computed between changes in HRV/HR and changes in PFC HbO or cognitive scores. Significance was set at α = 0.05 (two-tailed) with correction for multiple comparisons (FDR) where appropriate. All analyses were performed in Python and SPSS 2 . Results Autonomic Effects Breathing manipulations produced the expected autonomic shifts. Relative to baseline, Bhastrika pranayama significantly decreased the median IBI (median NSB→Bhastrika: − 20 ms, p < 0.05), indicating a higher heart rate. In contrast, RNYB did not change median IBI significantly. Conversely, RNYB increased HRV: variance of IBI rose by ~ 30% from NSB to RNYB (p < 0.05), whereas Bhastrika showed no HRV increase. These effects align with previous reports that fast forceful breathing triggers sympathetic activation, whereas slow unilateral breathing enhances vagal tone 4,17 . Figures 1 and 2 represents change in median IBI and change in IBI variance respectively. Notably, RNYB’s HRV increase correlated with changes in PFC oxygenation (see below). Prefrontal Hemodynamics We next examined PFC oxygenation. RNYB led to significantly higher median HbO in the right-lateral PFC region compared to NSB (p = 0.02), and marginally higher in right-medial PFC. Bhastrika’s effect was more bilateral but weaker: a slight increase in left-medial HbO was observed (p = 0.08 uncorrected). In terms of peak activation, RNYB produced a higher max HbO in the right-lateral PFC than Bhastrika (p < 0.05), whereas Bhastrika tended to peak in left-medial PFC. Thus, RNYB preferentially augmented right PFC oxygenation in our sample, perhaps reflecting contralateral control (right- nostril→left-PFC in Singh’s (2016) finding, but here right-PFC may reflect respiration-linked networks). Across participants, individuals who had larger HRV gains under RNYB also showed larger increases in right- lateral PFC oxy-Hb (Spearman ρ ≈ 0.65, p < 0.01). Importantly, changes in IBI variance (ΔHRV) correlated with Δ(maximum HbO) in right-lateral PFC. This coupling was robust even after excluding an outlier. In line with the neurovisceral integration model, higher vagal activity coincided with stronger prefrontal recruitment 9,18 . Cognitive Performance The two breathing conditions also differed in cognitive effects. After RNYB, Stroop interference scores were significantly reduced (mean interference RT down by ~ 15%, p < 0.03), indicating improved inhibitory control. No significant Stroop change was seen with Bhastrika. For the 2-back task, RNYB participants showed a significant increase in accuracy (+ 8%, p < 0.04) with a slight (non-significant) slowing of reaction time, consistent with a more cautious yet effective strategy. Bhastrika had the opposite trend: participants responded faster (− 20 ms, p < 0.05) but without accuracy gain, suggesting heightened arousal at the cost of precision. Across subjects, greater right-lateral PFC oxygenation (median HbO) was associated with higher 2-back accuracy (ρ ≈ 0.6, p < 0.05), echoing reports that better working memory relates to increased PFC oxygen availability. Similarly, higher HRV predicted lower Stroop interference (ρ≈−0.5), consistent with vagal tone underpinning executive performance 11,18 . Stroop fNIRS Contrast Maps To probe task-specific PFC activation, we computed F-statistic maps of oxy-Hb change between Stroop conditions (Fig. 3). Comparing congruent vs neutral trials in RNYB sessions showed a focal increase in left- medial PFC (optodes 5–8; F ≈ 4.5, p < 0.05 uncorrected) during congruent blocks. Incongruent vs neutral showed more diffuse right-lateral activation, but these differences did not survive multiple-comparison correction (likely due to small N). In brief, breath-induced baseline shifts modulated typical Stroop-related activation patterns: RNYB’s vagal state sharpened PFC hemodynamics even in easy (congruent) trials, hinting at heightened top-down readiness. Discussion The intertwining of respiratory rhythms with cardiac and cognitive processes has fascinated thinkers for over a century. In the late 19th century, Wilhelm Wundt’s pioneering psychophysiological work revealed that shifts in heart rate paralleled fluctuations in attention and volitional control, presaging modern autonomic–cognitive theories 2,7 . Shortly thereafter, Francis Bainbridge characterized the reflexive adjustments of heart rate to venous return—the Bainbridge reflex —demonstrating that mechanical changes in the heart feed back into autonomic circuits 7,21 . Claude Bernard’s concept of the milieu intérieur and Walter Cannon’s subsequent framing of homeostasis further underscored the vagus nerve as a central conduit linking visceral states to behavioral regulation. Together, these early insights laid the groundwork for today’s understanding of how breathmediated vagal afferents can both sense and shape cortical activity. Contemporary models build directly on this lineage. The neurovisceral integration model (Thayer & Lane) posits that heartbrain connectivity, indexed by HRV and prefrontal engagement, underlies adaptive emotion and executive control 22 . Stephen Porges’s polyvagal theory further elaborates how specialized vagal branches support social engagement and selfregulation 10 . Antonio Damasio’s notion of somatic markers likewise highlights bodily feedback—especially cardiovascular signals—as integral to decisionmaking and higher cognition 23 . Our findings converge with these frameworks, showing that volitional breath modulation can transiently tune vagal tone and prefrontal hemodynamics, thereby sharpening executive functions, suggesting that controlled respiration may be harnessed as a tool for enhancing facets of human intelligence, cognition, emotion and overall mental health. This study demonstrates that dynamic Respiratory Control can orchestrate a closed-loop modulation of autonomic state and prefrontal function, with concomitant behavioral consequences and executive function performances. RNYB produced a parasympathetic-dominant profile: heart rate slowed (median IBI stable) while HRV increased, alongside enhanced right-prefrontal oxygenation and improved executive task performance. In contrast, Bhastrika yielded a sympathetic-dominant profile: heart rate rose (lower IBI) with no HRV boost, modest PFC activation, and mainly faster but not more accurate cognitive responses. Crucially, these multi-domain changes were interlinked: participants with the largest RNYB-induced HRV gains also showed the largest PFC oxy-Hb increases and cognitive benefits. This coherence supports a mechanistic biofeedback loop: paced breathing alters baroreflex and vagal output, which influences prefrontal neural activity (through brainstem and insular pathways), thereby modulating cognitive control networks (Fig. 4). Our findings align with prior research. The increase in HRV under slow unilateral breathing matches reports that voluntary breath-slowing engages vagal circuits 4 . Bhastrika’s heart rate elevation and LF/HF shift mirror Malhotra et al.’s report of heightened sympathetic activity during fast yogic breathing 17 . The lateralized PFC effects fit fNIRS studies: Singh et al. observed that RNYB selectively increases contra-lesional PFC oxygenation 16 . Here, RNYB boosted right lateral PFC oxygenation, possibly reflecting individual differences or asymmetric baseline breathing rhythm. Importantly, our data extend these findings by linking them to HRV and to concrete cognitive outcomes. The observed Stroop, working-memory enhancements are consistent with literature showing that higher resting HRV is associated with better inhibitory and memory performance 9,11,24 . This suggests RNYB may prime PFC circuits for efficient cognitive control and working memory, enhancing overall goal directed behavior. This work is methodologically rigorous: we combined device-based physiological measures (ECG, fNIRS) with standardized cognitive tests in a controlled within-subjects design. We accounted for artifacts (motion, light) in fNIRS and for respiratory confounds in HRV. By examining both median and variance of IBI, and median and peak HbO per PFC region, we captured nuanced effects. We also computed Stroop interference (a robust index of frontal inhibition). Such multimodal integration – linking ECG, neuroimaging, and behavior – is rare in human breath studies, making our “breath→ANS→PFC→EF” nexus novel. Clinically, these results underscore the therapeutic potential of targeted breathing. RNYB, by enhancing vagal tone and PFC efficiency, could be a non-pharmacological tool for stress reduction and cognitive enhancement in anxiety or ADHD populations. Indeed, HRV is considered a biomarker for stress resilience, and practices that elevate HRV tend to improve mood and attention 25 . Conversely, although Bhastrika raises alertness, its limited HRV effect suggests it may be more useful for acute arousal (e.g. combating fatigue) than for cognitive control. Future work should test these interventions in clinical samples (e.g. anxiety disorders, stroke) where ANS and PFC dysregulation co-occur. Several limitations warrant note. Our sample was modest and group assignment was between- subject, so replication is needed. fNIRS spatial resolution is coarse; future fMRI could map these effects more precisely. We did not control for placebo or expectancy effects – participants knew the breathing type – though physiological data help anchor results in objective measures. We also focused on immediate breathing effects; longitudinal training may yield stronger or different adaptations. In sum, this integrated study provides mechanistic evidence that breath modulation yields bidirectional shifts in autonomic and cognitive systems. It bridges ancient wisdom and modern science: pranayama exercises modulate a central feedback loop linking the vagus nerve, prefrontal cortex, and executive functions 9,18 . Our approach is generalizable to other respiratory techniques and could inform brain– body therapies. Ultimately, understanding breath’s role in neurocognition may inspire novel interventions for mental health and cognitive aging. In conclusion, breathing control, style, pattern: (pauses between breaths (inhalation or exhalation), inhalation to exhalation ratio, diaphragmatic to abdominal) produce measurable, interrelated changes in heart-rate dynamics, prefrontal oxygenation, and executive task performance. Right-nostril breathing enhances vagal tone and PFC-mediated cognitive control, whereas forceful breathing induces sympathetic arousal. These results highlight a concrete biofeedback loop: conscious respiration can be used to tune autonomic state and prefrontal cognitive resources and cognitive executive functions. This breath–brain coupling is ripe for clinical translation and underscores the need to consider breathing as a central variable in neuroscience and psychology. 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Behav Brain Res 193:248–256 Shah S, Kuber J, Lewis GF (2025) Executive Functions in relation to Autonomic Control: An Overview of Neuropsychological Evaluation Methods. bioRxiv 2025.02.25.640212 10.1101/2025.02.25.640212 Software A (2005) and Stimulus-Presentation Platform to Utilize, Visualize and Analyze Near-infrared Spectroscopy Measures - ProQuest. https://www.proquest.com/docview/2871971626 Bainbridge FA (1920) The relation between respiration and the pulse-rate. J Physiol 54:192–202 Thayer J (2021) Brain and vagus nerve stimulation: a neurovisceral integration perspective. Brain Stimul 14:1750 Damasio AR (1996) The somatic marker hypothesis and the possible functions of the prefrontal cortex. Philosophical Trans Royal Soc B: Biol Sci 351:1413–1420 Berntson GG, Cacioppo JT (1999) Heart Rate Variability: A Neuroscientific Perspective for Further Studies. Card Electrophysiol Rev 3:279–282 Porges SW (2022) Heart Rate Variability: A Personal Journey. Appl Psychophysiol Biofeedback 47:259–271 Additional Declarations There is NO Competing Interest. Cite Share Download PDF Status: Posted 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-7255999","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":493965949,"identity":"6121745c-02f2-45cb-8881-3114c7ba2beb","order_by":0,"name":"Shyama Shah","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/ElEQVRIiWNgGAWjYBACeyBmZmA4wGAA4n0AYjZ2AloMG5C0MM4AaWEmoMXgAJIWZh4GiKX4tdw+wPy5oOaOvblE8tPNNr+2yfMxMzB++JiDR8u5BDbpGceeJe6ckWZ2O7fvtmEbMwOz5MxteLScATqeh+1wgsGNBKCWntuMQC1szLz4tTB/5vl32N7gRvq325Y9t+2J0cIgzdt2mHHDjRyz2ww/bicS1GLYw9gmzdt3OHHDmTdlN3sbbie3MTM24/WLPQ/z4c8834AOO56+7caPP7dt57c3H/zwEY8WYPw1QGiBBCC7DVmEIOA/ACT+EKl4FIyCUTAKRhQAAOAMVnNOkyzFAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0009-0007-8894-424X","institution":"Indiana University","correspondingAuthor":true,"prefix":"","firstName":"Shyama","middleName":"","lastName":"Shah","suffix":""},{"id":493965950,"identity":"72e32dc1-23a0-4ea2-810d-3d13e46e4f05","order_by":1,"name":"Gregory Lewis","email":"","orcid":"","institution":"indiana university","correspondingAuthor":false,"prefix":"","firstName":"Gregory","middleName":"","lastName":"Lewis","suffix":""}],"badges":[],"createdAt":"2025-07-30 19:40:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7255999/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7255999/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89386824,"identity":"43cd49d9-7420-410b-b3be-65f06b561975","added_by":"auto","created_at":"2025-08-19 12:37:01","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":31171,"visible":true,"origin":"","legend":"\u003cp\u003ea: Change in median IBI (RNYB vs Bhastrika)\u003c/p\u003e\n\u003cp\u003eb: Change in IBI variance (HRV proxy)\u003c/p\u003e\n\u003cp\u003ea, and b, Bar graphs of median IBI and IBI variance by group (RNYB vs Bhastrika) to illustrate autonomic effects.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7255999/v1/758e6ba78c1091087ffbb7bc.jpg"},{"id":89386826,"identity":"e2a624e3-7c1c-4f6b-bd6a-3c83fc94d278","added_by":"auto","created_at":"2025-08-19 12:37:01","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":51570,"visible":true,"origin":"","legend":"\u003cp\u003ea: Stroop Interference (pre vs post for RNYB, Bhastrika), error bar: +/- 2SE\u003c/p\u003e\n\u003cp\u003eb : 2-back Accuracy (pre vs post for both conditions), error bar: +/- 2SE\u003c/p\u003e\n\u003cp\u003e(2) a, and b, Bar graphs of Stroop interference and 2-back accuracy for each condition, showing cognitive performance differences\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7255999/v1/695c2c81a24f84cc47bd7b2d.jpg"},{"id":89386828,"identity":"70c9b4e1-0419-4292-b29b-0127441d9fd2","added_by":"auto","created_at":"2025-08-19 12:37:02","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":40960,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e: Neutral condition (F-stat heatmap)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB\u003c/strong\u003e: Congruent condition (F-stat heatmap)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC\u003c/strong\u003e: InCongruent condition (F-stat heatmap)\u003c/p\u003e\n\u003cp\u003ea, b, and c, Topographic heatmaps of PFC optode activation (F-statistics) for Stroop (neutral vs congruent vs incongruent) to visualize fNIRS results.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7255999/v1/f6921c0bce5cb659da9cff41.jpg"},{"id":89389968,"identity":"e736c7de-e573-4342-974d-d8f4549d9236","added_by":"auto","created_at":"2025-08-19 12:53:02","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":20584,"visible":true,"origin":"","legend":"\u003cp\u003ePathway diagram summarizing the proposed mechanism.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7255999/v1/2fc429e481da068ae1786a89.jpg"},{"id":89390992,"identity":"2f1aa3ff-3c11-4b29-8b1b-179296526c88","added_by":"auto","created_at":"2025-08-19 13:01:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":596392,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7255999/v1/80dbe587-6c54-42fc-b752-7139117cd608.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Breath-Modulated Biofeedback Loops for Enhancing Autonomic Regulation and Executive Functions through fNIRS and ECG","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAt its simplest, breathing sustains life; yet it also exerts a profound, bidirectional influence on mind and body, it serves as a dynamic bridge between bodily physiology and conscious experience​. Respiration rhythmically gates sensory inflow and autonomic tone, synchronizes neural oscillations, and calibrates cortical excitability \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. By wielding breath as a voluntary lever\u0026mdash;even for minutes at a time\u0026mdash;one can tune heart\u0026ndash;brain circuits and shape higher‑order cognition \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Throughout history and across cultures, breath has been revered not merely as a metabolic necessity but as a powerful regulator of mental, emotional, and physical state. Ancient contemplative traditions (e.g. yoga prāṇāyāma, Buddhist \u003cem\u003eānāpānasati\u003c/em\u003e) treat the breath as a gateway to self-awareness and emotional balance. In classical Yoga (Patanjali\u0026rsquo;s Yoga Sutra), prāṇāyāma (breath control and manipulation) is the key \u0026ldquo;limb\u0026rdquo; linking physiological and psychological states, encapsulated in the aphorism \u0026ldquo;mind and breath are so interlinked that controlling the breath can regulate the mind\u0026rdquo; \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Early Western scientists similarly recognized this link: Wundt noted influences of respiration on emotion and volitional control, and Hering and Bainbridge described its central role in cardiovascular reflexes \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Modern psychophysiology confirms that respiration exerts profound effects on neural and autonomic systems. Voluntary breathing patterns alter vagal tone and heart-rate variability, shaping prefrontal\u0026ndash;limbic balance and affecting arousal and mood \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e ​. Recent studies confirm slow, deep breathing tends to lower heart rate and elevate vagal tone, whereas rapid forceful breathing activates sympathetic arousal \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. In short, breathing provides an unusual window into otherwise \u0026ldquo;hidden\u0026rdquo; brain\u0026ndash;body networks, allowing conscious modulation of autonomic state.\u003c/p\u003e\u003cp\u003eContemporary neuroscience provides mechanistic models for this breath\u0026ndash;brain link. The polyvagal and neurovisceral integration frameworks posit a central autonomic network (including prefrontal cortex, insula, brainstem nuclei) whose activity co-varies with cardiac vagal modulation \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Heart-rate variability (HRV), an index of cardiac vagal tone, has emerged as a marker of self-regulation: higher resting HRV predicts better performance on executive tasks (attention, inhibition, working memory) \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Indeed, individual differences in HRV correlate with prefrontal activity and cognitive flexibility \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Interventions that raise HRV (biofeedback, meditation, controlled breathing) often yield improvements in cognitive control and stress resilience \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eBeyond its essential role in gas exchange, respiration plays a far more dynamic and integrative role in shaping neural and physiological systems. A growing body of research reveals that respiration modulates not only arousal and pain perception \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, but also cognitive performance through phase-synchronous modulation of neural activity. Studies have shown that the timing of the respiratory cycle entrains oscillatory activity in the hippocampus and cortex \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e, influences olfactory and sensory integration \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e, and alters task performance based on inhalation-exhalation phase. These respiratory rhythms extend to impact motor function, language processing, and emotional regulation, suggesting breath as a powerful modulator of brain-wide information flow \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAmong the many tools to probe brain function during respiration, functional near-infrared spectroscopy (fNIRS) offers a noninvasive, temporally sensitive, and motion-tolerant imaging modality. Prior studies using fNIRS have shown that yogic breathing elicits region-specific changes in prefrontal cortex (PFC) oxygenation. For instance, unilateral right-nostril breathing (RNYB) increases contralateral (left) PFC oxyhemoglobin \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. In Singh et al. (2016), RNYB raised left PFC oxy-Hb significantly above left-nostril or control breathing \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Similarly, high-frequency yogic breathing (Bhastrika-type) has been associated with enhanced cortical activation and arousal \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Together these suggest that breathing pattern can modulate PFC hemodynamics. Importantly, enhanced PFC oxygenation often accompanies better cognitive performance: for instance, faster Stroop task responses are linked to higher dorsolateral PFC oxy-Hb \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e .\u003c/p\u003e\u003cp\u003eExecutive functions are a set of top-down cognitive processes\u0026mdash;including inhibition, cognitive flexibility, and working memory\u0026mdash;that are essential for goal-directed behavior and adaptivity in changing environments. These functions are supported by a distributed network anchored in the PFC and modulated by autonomic input via pathways like the vagus nerve \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Within the broader executive domain, cognitive control refers specifically to the ability to flexibly adjust behavior in response to goals or conflicting demands \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Working memory refers to a system of temporary storage and manipulation of information no longer perceptually present, its widely measured by n-back test \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. The Stroop task is a canonical probe of this ability, requiring the inhibition of an automatic response (reading a word) in favor of a task-relevant one (naming the ink color). Because cognitive control and working memory are sensitive to changes in arousal, vagal tone, and PFC activation, it provides a useful behavioral assay of breath-linked neurophysiological modulation.\u003c/p\u003e\u003cp\u003eDespite a substantial body of research examining the individual effects of breath-based interventions on autonomic physiology, cortical activation, or cognitive performance, these components are often studied in isolation. Most investigations target only one segment of the system\u0026mdash;such as the impact of breathing on HRV, or the correlation between HRV and prefrontal activation\u0026mdash;without capturing their concurrent modulation or downstream cognitive consequences. In effect, past work treats each link in the pathway\u0026mdash;breath \u0026rarr; autonomic nervous system (ANS), ANS \u0026rarr; PFC, PFC \u0026rarr; executive functions (EF)\u0026mdash;as a silo. Yet these components form a tightly coupled system, and the integrated breath\u0026ndash;ANS\u0026ndash;PFC\u0026ndash;EF circuit may exhibit emergent properties that cannot be inferred from its parts alone. Furthermore, few studies contrast distinct respiratory strategies that differentially engage autonomic branches (e.g., vagal-enhancing RNYB vs. sympatho-excitatory Bhastrika), or employ synchronized multimodal acquisition to quantify respiration-linked modulation across physiology, cortex, and behavior. This study addresses that critical gap by implementing a rigorously controlled, cross-condition design integrating ECG, fNIRS, and task-based measures to empirically map the full functional loop from breath to brain and to behavior.\u003c/p\u003e\u003cp\u003eHere we test how acute breathing pattern and style alter a coupled autonomic\u0026ndash;cortical\u0026ndash;cognitive loop. Specifically, we compare two well-defined yogic interventions: RNYB\u0026mdash;a slow, focused technique associated with increased parasympathetic activity\u0026mdash;and Bhastrika Pranayama\u0026mdash;a rapid, forceful breathing associated with sympathetic arousal. In healthy participants, we recorded ECG-derived interbeat intervals (IBIs), fNIRS-based oxyhemoglobin responses in the prefrontal cortex, and performance on executive function tasks (Stroop and 2-back). We hypothesized that RNYB would increase HRV (via IBI variance) and PFC oxygenation, with corresponding gains in executive functions, whereas Bhastrika would elevate heart rate (lower median IBI) with less improvement in neural or behavioral outcomes. This study thus aims to uncover the mechanistic path by which respiration can modulate core human functions via breath-modulated biofeedback and regulatory loops.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eParticipants:\u003c/h2\u003e\u003cp\u003eTwenty participants (M\u0026thinsp;=\u0026thinsp;25.48, SD\u0026thinsp;=\u0026thinsp;15.32), aged between 19 to 73, were recruited via flyers, distributed literature, campus-wide emails at Luddy School, Indiana University Bloomington, and SONA advertisements through the Psychology and Brain Sciences IU Bloomington, student subject pool. Of these participants, 19 were right-handed. Exclusion criteria included the presence of psychiatric or neurological disorders, recent use of tobacco products (within 24 hours), consumption of more than three alcoholic beverages in the past 24 hours, hospitalization in the past year due to head injury, major surgery, or COVID-19, and any respiratory issues such as shortness of breath or difficulty breathing in the past year. Additional exclusions applied to individuals with tuberculosis within the past 10 years, chronic obstructive pulmonary disease, asthma, pacemakers, or ongoing cardiac or blood pressure medication use (excluding low-dose aspirin). Participants with allergies to adhesives, use of non-prescription drugs, or ingestion of caffeine or alcohol within two hours prior to the experimental session were also excluded.\u003c/p\u003e\u003cp\u003eParticipants were pre-screened via phone or email to assess health habits and general medical history. No participant had a medical condition that posed a risk or could interfere with autonomic measurements. Furthermore, none were taking prescription medications, such as central nervous system depressants, stimulants, hypertension medications, or anticholinergic agents, that could impact autonomic regulation. This comprehensive screening ensured a consistent and reliable sample for the study.\u003c/p\u003e\u003cp\u003eOf the 20 participants recruited, data from 3 were excluded due to [protocol non-compliance, excessive motion artifacts, or incomplete data], resulting in a final sample of 17 participants (n\u0026thinsp;=\u0026thinsp;17) included in the analyses.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eData Collection (Equipment and Stimulus Materials):\u003c/h3\u003e\n\u003cp\u003e In compliance with the Indiana University Bloomington Institutional Review Board, participants read and signed informed consent forms and were screened for health status to ensure adherence to exclusion criteria (e.g., brain injury, chronic bronchitis, smoking, as detailed earlier). Data collection took place at the Psychology and Brain Sciences Department of Indiana University, following all ethical standards and protocols for human research. After the consent and screening process, three gel electrodes were placed on the chest to record electrocardiography (ECG) for beat-to-beat heart rate measurements. A respiratory band was fitted around the chest to monitor continuous changes in tidal volume, while two electrodes placed on the palm recorded electrodermal activity (EDA). Photoplethysmography (PPG) was measured using a pulse oximeter on the left index finger. These psychophysiological signals were recorded with the Biopac MP160 system (Biopac Systems, USA), interfaced with a Windows 10-operated Dell computer.\u003c/p\u003e\u003cp\u003eHemodynamic responses were captured using Biopac\u0026rsquo;s functional near-infrared spectroscopy (fNIRS) system (FNIR-2000S, Imager 2000S, Biopac Systems, USA) with 18-channel sensor pads and accompanying fNIRSoft software. This setup monitored cortical activity during breathing and executive function tasks. The Stroop and Two-Back tasks were designed and administered using PsychoPy software, ensuring precise stimulus timing and synchronization with physiological data. Subjects were seated in a reclining chair within a dedicated dark room, which was temperature-controlled at approximately 68\u0026deg;F. The room was shielded to minimize interference and divided into two sections separated by curtains: one for participants and another for research assistants. A split-screen setup allowed research assistants to monitor participant responses and adjust experimental conditions as needed. Throughout the session, physiological signals were recorded continuously. Participants completed preliminary demographic surveys and questionnaires in the dark room before starting the tasks. During the breathing manipulation phases, they were encouraged to follow the instructions carefully. Typically, two research assistants facilitated the study, ensuring smooth data collection, monitoring participant compliance, and administering breathing styles and EF tasks. This controlled environment minimized distractions and optimized fNIRS data accuracy.\u003c/p\u003e\n\u003ch3\u003eProtocol:\u003c/h3\u003e\n\u003cp\u003eThe study, formally called DIVNE (Dynamic respiratory control\u0026rsquo;s Impact on Vagus nerve, Neurological activation of the prefrontal Cortex and Executive functions) study employs a sequential and structured protocol to examine the relationship between dynamic respiratory control, autonomic responses, prefrontal cortex activity, and executive function under various task conditions. Each participant was randomly assigned to one of the two breathing conditions (RNYB or Bhastrika) on the day of the study, using a stratified online randomization tool, which maintained approximately equal group sizes (n\u0026thinsp;=\u0026thinsp;10 per condition), during which psychophysiological, and PFC responses are assessed across multiple experimental phases. The sequence is designed to isolate the effects of controlled breathing patterns on executive function performance and autonomic regulation.\u003c/p\u003e\u003cp\u003e The protocol begins with a 5-minute session of natural spontaneous breathing (NSB1), allowing participants\u0026rsquo; baseline physiological states to stabilize without external interference. Following this, participants perform a computerized Stroop task (Stroop1), consisting of 150 randomized trials across congruent, neutral, and incongruent conditions. This task captures baseline cognitive load metrics, such as reaction time and accuracy, under varying levels of executive demand. Subsequently, participants went through one of two breathing patterns, assigned at start of study, (Dynamic Respiratory Control 1) \u0026mdash;Bhastrika or Right Nostril Yogic Breathing\u0026mdash;for a 5-minute session, during which autonomic and neural metrics are continuously monitored. Bhastrika, a pranayama technique involving rapid and forceful diaphragmatic breaths (~\u0026thinsp;90\u0026ndash;120 breaths per minute), was performed with active abdominal engagement and guided by a standardized instructional video. Participants received real-time encouragement and compliance monitoring from a trained research assistant. RNYB consisted of slow, deliberate inhalations through the right nostril only (~\u0026thinsp;4\u0026ndash;6 breaths per minute), with the left nostril occluded using the participant\u0026rsquo;s thumb. Breathing pace was paced using the same visual cue video, and participants were continuously reminded to maintain consistency and focus throughout breathing task.\u003c/p\u003e\u003cp\u003e After the breathing intervention, participants complete a second Stroop task (Stroop2), consisting of 150 randomized trials across congruent, neutral, and incongruent conditions, to assess EF changes attributable to the respiratory modulation. A second baseline breathing 5-minute session (NSB2) follows to examine recovery and return-to-baseline effects. The protocol then transitions to the two-back task (2Back1), a working memory test consisting of 30 trials, which serves as a pre-intervention EF performance benchmark. A final round of breathing pattern intervention-5- minute session (Dynamic Respiratory Control 2), which would be same as randomly assigned at the beginning (Bhastrika or RNYB), and a post-intervention two-back task, consisting of 30 trials (2Back2) conclude the study, enabling comparisons of behavioral and neural metrics between pre- and post-intervention conditions.\u003c/p\u003e\n\u003ch3\u003eData Reduction:\u003c/h3\u003e\n\u003cp\u003eECG and fNIRS data for each session were transferred offline to a Windows-based system for preprocessing and analysis. fNIRS (16-channel system, 2 wavelengths) measured oxy- and deoxy-hemoglobin in the prefrontal cortex. Optodes were arranged to cover bilateral lateral and medial PFC. Channels 1\u0026ndash;4 were assigned to left-lateral PFC, 5\u0026ndash;8 left-medial, 9\u0026ndash;12 right-medial, 13\u0026ndash;16 right-lateral.\u003c/p\u003e\u003cp\u003efNIRS data were preprocessed using COBI Studio software (Biopac Systems, Inc., USA), following procedures adapted from Ayaz et al. (2010) \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Preprocessing involved these main steps. First, ambient light correction was performed using dedicated ambient channels to remove environmental interference and light saturation artifacts. Second, motion artifacts were addressed using a sliding-window approach (SMAR), which applies a coefficient of variation\u0026ndash;based filter across ~\u0026thinsp;10-second windows to identify abrupt fluctuations in signal intensity; artifact detection thresholds were empirically. Third, a Finite Impulse Response (FIR) low-pass filter with a 0.1 Hz cutoff and a Hamming window (order\u0026thinsp;~\u0026thinsp;20) was applied to suppress high-frequency noise, including cardiac and respiratory components. The cleaned optical density signals were then converted into concentration changes in oxyhemoglobin (HbO) and deoxyhemoglobin (HbR) using the Modified Beer\u0026ndash;Lambert Law. For each breathing condition (e.g., NSB vs. RNYB or Bhastrika), the median and maximum HbO values were extracted from each prefrontal subregion to quantify both tonic and peak changes in cortical oxygenation.\u003c/p\u003e\u003cp\u003eContinuous ECG was recorded (lead II) to capture cardiac cycles. ECG data were processed using CardioEdit (Brain-Body Center, Chicago, IL), which enabled visual inspection and correction of missed R-wave detections and artifacts caused by electrode movement or electrical interference. Inter-beat interval (IBI) values, essential for Heart Rate Variability calculations, were extracted following this quality-control step. From each phase (NSB, breathing manipulation), we extracted the median IBI (an index of heart rate) and variance of IBI (a time-domain HRV measure). Because both breathing techniques fell outside the normal respiratory sinus arrhythmia band (fast for Bhastrika, unilateral slow for RNYB), we focused on HRV (variance) rather than spectral RSA.\u003c/p\u003e\u003cp\u003eFurther processing of fNIRS and ECG data was carried out in Python 3.10 (Python Software Foundation, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"http://www.python.org\" target=\"_blank\"\u003ewww.python.org\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.python.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and MATLAB 2023a (MathWorks Inc., Massachusetts, USA). Data visualization, reduction, and statistical analysis were conducted using IBM SPSS Statistics 30.0.0 (IBM Corp., Released 2023).\u003c/p\u003e\u003cp\u003e\u003cb\u003eData Segmentation and Analysis Metrics\u003c/b\u003e: The prefrontal cortex was anatomically segmented into four regions: left lateral (Optodes 1\u0026ndash;4), left medial (Optodes 5\u0026ndash;8), right medial (Optodes 9\u0026ndash;12), and right lateral (Optodes 13\u0026ndash;16). This division facilitated regional neural activity analyses under experimental conditions.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eExecutive Tasks\u003c/strong\u003e\u003cp\u003e After each breathing session, participants performed a computerized color-word Stroop test and a 2-back working memory task. The Stroop comprised neutral, congruent, and incongruent trials. Executive task performance was evaluated using Stroop task metrics, including interference Score (which is difference of median Response Time of incongruent conditions and median Response Time of congruent conditions) and facilitation scores (which is difference of median Response Time of neutral conditions and median Response Time of congruent conditions), however for our analysis we have used interference Score only. For the 2-back task, accuracy rate (correct responses as a percentage of total stimuli (total number of trials, which is 30 here)) and reaction time (median response time for correct trials) were assessed.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eAnalysis\u003c/strong\u003e\u003cp\u003eAll physiological and behavioral measures were first tested for normality; nonparametric methods were used for non-normal distributions. We used paired t-tests (Wilcoxon signed-rank) to compare NSB vs breathing phases within each group, and independent t-tests for between-group comparisons (RNYB vs Bhastrika). Correlations (Spearman\u0026rsquo;s ρ) were computed between changes in HRV/HR and changes in PFC HbO or cognitive scores. Significance was set at α\u0026thinsp;=\u0026thinsp;0.05 (two-tailed) with correction for multiple comparisons (FDR) where appropriate. All analyses were performed in Python and SPSS \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e .\u003c/p\u003e\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\"\u003e\n \u003ch2\u003eAutonomic Effects\u003c/h2\u003e\n \u003cp\u003eBreathing manipulations produced the expected autonomic shifts. Relative to baseline, Bhastrika pranayama significantly decreased the median IBI (median NSB→Bhastrika: − 20 ms, p \u0026lt; 0.05), indicating a higher heart rate. In contrast, RNYB did not change median IBI significantly. Conversely, RNYB increased HRV: variance of IBI rose by ~ 30% from NSB to RNYB (p \u0026lt; 0.05), whereas Bhastrika showed no HRV increase. These effects align with previous reports that fast forceful breathing triggers sympathetic activation, whereas slow unilateral breathing enhances vagal tone \u003csup\u003e4,17\u003c/sup\u003e. Figures 1 and 2 represents change in median IBI and change in IBI variance respectively. Notably, RNYB’s HRV increase correlated with changes in PFC oxygenation (see below).\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003ePrefrontal Hemodynamics\u003c/h3\u003e\n\u003cp\u003eWe next examined PFC oxygenation. RNYB led to significantly higher median HbO in the right-lateral PFC region compared to NSB (p = 0.02), and marginally higher in right-medial PFC. Bhastrika’s effect was more bilateral but weaker: a slight increase in left-medial HbO was observed (p = 0.08 uncorrected). In terms of peak activation, RNYB produced a higher max HbO in the right-lateral PFC than Bhastrika (p \u0026lt; 0.05), whereas Bhastrika tended to peak in left-medial PFC. Thus, RNYB preferentially augmented right PFC oxygenation in our sample, perhaps reflecting contralateral control (right- nostril→left-PFC in Singh’s (2016) finding, but here right-PFC may reflect respiration-linked networks).\u003c/p\u003e\n\u003cp\u003eAcross participants, individuals who had larger HRV gains under RNYB also showed larger increases in right- lateral PFC oxy-Hb (Spearman ρ ≈ 0.65, p \u0026lt; 0.01). Importantly, changes in IBI variance (ΔHRV) correlated with Δ(maximum HbO) in right-lateral PFC. This coupling was robust even after excluding an outlier. In line with the neurovisceral integration model, higher vagal activity coincided with stronger prefrontal recruitment \u003csup\u003e9,18\u003c/sup\u003e .\u003c/p\u003e\n\u003ch3\u003eCognitive Performance\u003c/h3\u003e\n\u003cp\u003eThe two breathing conditions also differed in cognitive effects. After RNYB, Stroop interference scores were significantly reduced (mean interference RT down by ~ 15%, p \u0026lt; 0.03), indicating improved inhibitory control. No significant Stroop change was seen with Bhastrika. For the 2-back task, RNYB participants showed a significant increase in accuracy (+ 8%, p \u0026lt; 0.04) with a slight (non-significant) slowing of reaction time, consistent with a more cautious yet effective strategy. Bhastrika had the opposite trend: participants responded faster (− 20 ms, p \u0026lt; 0.05) but without accuracy gain, suggesting heightened arousal at the cost of precision. Across subjects, greater right-lateral PFC oxygenation (median HbO) was associated with higher 2-back accuracy (ρ ≈ 0.6, p \u0026lt; 0.05), echoing reports that better working memory relates to increased PFC oxygen availability. Similarly, higher HRV predicted lower Stroop interference (ρ≈−0.5), consistent with vagal tone underpinning executive performance \u003csup\u003e11,18\u003c/sup\u003e .\u003c/p\u003e\n\u003cdiv id=\"Sec11\"\u003e\n \u003ch2\u003eStroop fNIRS Contrast Maps\u003c/h2\u003e\n \u003cp\u003eTo probe task-specific PFC activation, we computed F-statistic maps of oxy-Hb change between Stroop conditions (Fig. 3). Comparing congruent vs neutral trials in RNYB sessions showed a focal increase in left- medial PFC (optodes 5–8; F ≈ 4.5, p \u0026lt; 0.05 uncorrected) during congruent blocks. Incongruent vs neutral showed more diffuse right-lateral activation, but these differences did not survive multiple-comparison correction (likely due to small N). In brief, breath-induced baseline shifts modulated typical Stroop-related activation patterns: RNYB’s vagal state sharpened PFC hemodynamics even in easy (congruent) trials, hinting at heightened top-down readiness.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe intertwining of respiratory rhythms with cardiac and cognitive processes has fascinated thinkers for over a century. In the late 19th century, Wilhelm Wundt’s pioneering psychophysiological work revealed that shifts in heart rate paralleled fluctuations in attention and volitional control, presaging modern autonomic–cognitive theories \u003csup\u003e2,7\u003c/sup\u003e. Shortly thereafter, Francis Bainbridge characterized the reflexive adjustments of heart rate to venous return—the \u003cem\u003eBainbridge reflex\u003c/em\u003e—demonstrating that mechanical changes in the heart feed back into autonomic circuits \u003csup\u003e7,21\u003c/sup\u003e. Claude Bernard’s concept of the \u003cem\u003emilieu intérieur\u003c/em\u003e and Walter Cannon’s subsequent framing of homeostasis further underscored the vagus nerve as a central conduit linking visceral states to behavioral regulation. Together, these early insights laid the groundwork for today’s understanding of how breathmediated vagal afferents can both sense and shape cortical activity.\u003c/p\u003e\n\u003cp\u003eContemporary models build directly on this lineage. The neurovisceral integration model (Thayer \u0026amp; Lane) posits that heartbrain connectivity, indexed by HRV and prefrontal engagement, underlies adaptive emotion and executive control \u003csup\u003e22\u003c/sup\u003e. Stephen Porges’s polyvagal theory further elaborates how specialized vagal branches support social engagement and selfregulation \u003csup\u003e10\u003c/sup\u003e. Antonio Damasio’s notion of \u003cem\u003esomatic markers\u003c/em\u003e likewise highlights bodily feedback—especially cardiovascular signals—as integral to decisionmaking and higher cognition \u003csup\u003e23\u003c/sup\u003e. Our findings converge with these frameworks, showing that volitional breath modulation can transiently tune vagal tone and prefrontal hemodynamics, thereby sharpening executive functions, suggesting that controlled respiration may be harnessed as a tool for enhancing facets of human intelligence, cognition, emotion and overall mental health.\u003c/p\u003e\n\u003cp\u003eThis study demonstrates that dynamic Respiratory Control can orchestrate a closed-loop modulation of autonomic state and prefrontal function, with concomitant behavioral consequences and executive function performances. RNYB produced a parasympathetic-dominant profile: heart rate slowed (median IBI stable) while HRV increased, alongside enhanced right-prefrontal oxygenation and improved executive task performance. In contrast, Bhastrika yielded a sympathetic-dominant profile: heart rate rose (lower IBI) with no HRV boost, modest PFC activation, and mainly faster but not more accurate cognitive responses. Crucially, these multi-domain changes were interlinked: participants with the largest RNYB-induced HRV gains also showed the largest PFC oxy-Hb increases and cognitive benefits. This coherence supports a mechanistic biofeedback loop: paced breathing alters baroreflex and vagal output, which influences prefrontal neural activity (through brainstem and insular pathways), thereby modulating cognitive control networks (Fig. 4).\u003c/p\u003e\n\u003cp\u003eOur findings align with prior research. The increase in HRV under slow unilateral breathing matches reports that voluntary breath-slowing engages vagal circuits \u003csup\u003e4\u003c/sup\u003e. Bhastrika’s heart rate elevation and LF/HF shift mirror Malhotra et al.’s report of heightened sympathetic activity during fast yogic breathing \u003csup\u003e17\u003c/sup\u003e. The lateralized PFC effects fit fNIRS studies: Singh et al. observed that RNYB selectively increases contra-lesional PFC oxygenation \u003csup\u003e16\u003c/sup\u003e. Here, RNYB boosted right lateral PFC oxygenation, possibly reflecting individual differences or asymmetric baseline breathing rhythm. Importantly, our data extend these findings by linking them to HRV and to concrete cognitive outcomes. The observed Stroop, working-memory enhancements are consistent with literature showing that higher resting HRV is associated with better inhibitory and memory performance \u003csup\u003e9,11,24\u003c/sup\u003e. This suggests RNYB may prime PFC circuits for efficient cognitive control and working memory, enhancing overall goal directed behavior.\u003c/p\u003e\n\u003cp\u003eThis work is methodologically rigorous: we combined device-based physiological measures (ECG, fNIRS) with standardized cognitive tests in a controlled within-subjects design. We accounted for artifacts (motion, light) in fNIRS and for respiratory confounds in HRV. By examining both median and variance of IBI, and median and peak HbO per PFC region, we captured nuanced effects. We also computed Stroop interference (a robust index of frontal inhibition). Such multimodal integration – linking ECG, neuroimaging, and behavior – is rare in human breath studies, making our “breath→ANS→PFC→EF” nexus novel.\u003c/p\u003e\n\u003cp\u003eClinically, these results underscore the therapeutic potential of targeted breathing. RNYB, by enhancing vagal tone and PFC efficiency, could be a non-pharmacological tool for stress reduction and cognitive enhancement in anxiety or ADHD populations. Indeed, HRV is considered a biomarker for stress resilience, and practices that elevate HRV tend to improve mood and attention \u003csup\u003e25\u003c/sup\u003e. Conversely, although Bhastrika raises alertness, its limited HRV effect suggests it may be more useful for acute arousal (e.g. combating fatigue) than for cognitive control. Future work should test these interventions in clinical samples (e.g. anxiety disorders, stroke) where ANS and PFC dysregulation co-occur.\u003c/p\u003e\n\u003cp\u003eSeveral limitations warrant note. Our sample was modest and group assignment was between- subject, so replication is needed. fNIRS spatial resolution is coarse; future fMRI could map these effects more precisely. We did not control for placebo or expectancy effects – participants knew the breathing type – though physiological data help anchor results in objective measures. We also focused on immediate breathing effects; longitudinal training may yield stronger or different adaptations.\u003c/p\u003e\n\u003cp\u003eIn sum, this integrated study provides mechanistic evidence that breath modulation yields bidirectional shifts in autonomic and cognitive systems. It bridges ancient wisdom and modern science: pranayama exercises modulate a central feedback loop linking the vagus nerve, prefrontal cortex, and executive functions \u003csup\u003e9,18\u003c/sup\u003e. Our approach is generalizable to other respiratory techniques and could inform brain– body therapies. Ultimately, understanding breath’s role in neurocognition may inspire novel interventions for mental health and cognitive aging.\u003c/p\u003e\n\u003cp\u003eIn conclusion, breathing control, style, pattern: (pauses between breaths (inhalation or exhalation), inhalation to exhalation ratio, diaphragmatic to abdominal) produce measurable, interrelated changes in heart-rate dynamics, prefrontal oxygenation, and executive task performance. Right-nostril breathing enhances vagal tone and PFC-mediated cognitive control, whereas forceful breathing induces sympathetic arousal. These results highlight a concrete biofeedback loop: conscious respiration can be used to tune autonomic state and prefrontal cognitive resources and cognitive executive functions. This breath–brain coupling is ripe for clinical translation and underscores the need to consider breathing as a central variable in neuroscience and psychology.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eNakamura NH, Oku Y, Fukunaga M (2024) Brain\u0026ndash;breath interactions: respiration-timing\u0026ndash;dependent impact on functional brain networks and beyond. 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J Pain 21:1018\u0026ndash;1030\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIwabe T, Ozaki I, Hashizume A (2014) The respiratory cycle modulates brain potentials, sympathetic activity, and subjective pain sensation induced by noxious stimulation. Neurosci Res 84:47\u0026ndash;59\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHeck DH et al (2017) Breathing as a fundamental rhythm of brain function. Front Neural Circuits 10\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGerritsen RJS, Band GPH (2018) Breath of Life: The Respiratory Vagal Stimulation Model of Contemplative Activity. Front Hum Neurosci 12:9\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSingh K, Bhargav H, Srinivasan T (2016) Effect of uninostril yoga breathing on brain hemodynamics: A functional near-infrared spectroscopy study. 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J Physiol 54:192\u0026ndash;202\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eThayer J (2021) Brain and vagus nerve stimulation: a neurovisceral integration perspective. Brain Stimul 14:1750\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDamasio AR (1996) The somatic marker hypothesis and the possible functions of the prefrontal cortex. Philosophical Trans Royal Soc B: Biol Sci 351:1413\u0026ndash;1420\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBerntson GG, Cacioppo JT (1999) Heart Rate Variability: A Neuroscientific Perspective for Further Studies. Card Electrophysiol Rev 3:279\u0026ndash;282\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePorges SW (2022) Heart Rate Variability: A Personal Journey. Appl Psychophysiol Biofeedback 47:259\u0026ndash;271\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7255999/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7255999/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBreathing is increasingly recognized as a dynamic regulator of autonomic and cognitive systems, yet its integrated impact on cardiac vagal tone, prefrontal hemodynamics, and executive functions remains poorly understood. Here, we introduce a multimodal biofeedback paradigm—combining electrocardiography (ECG), functional near‑infrared spectroscopy (fNIRS), and behavioral cognitive assays—to test how two contrasting yogic breathing techniques acutely modulate the “breath–ANS–PFC–EF” loop in healthy adults (n = 20). Participants performed either Right‑Nostril Yogic Breathing (RNYB; 4-6 breaths min⁻¹) or Bhastrika Pranayama (90-120 breaths min⁻¹) for 5 minutes, following baseline spontaneous breathing. Relative to baseline, Bhastrika (n=9) significantly decreased median interbeat interval (Δ ≈ –20 ms), during breathing, t(8)=7.62, p\u0026lt;0.001, indicating sympathetic activation, whereas RNYB (n=8) selectively increased IBI variance by ~30%, t(8)=–3.55, p=0.008, reflecting enhanced vagal tone. Concurrent fNIRS revealed that RNYB elevated oxyhemoglobin concentration in the right‑lateral prefrontal cortex (median Δ ≈ +0.15 µM; p =0.021), with peak activation exceeding that of Bhastrika (p = 0.034). Behaviorally, RNYB reduced Stroop interference by ~15% (p = 0.029) and improved 2‑back accuracy by ~8% (p \u0026lt; 0.05), whereas Bhastrika yielded faster but less accurate responses. Crucially, across participants, increases in HRV correlated with right‑lateral PFC HbO (ρ ≈ 0.6; p \u0026lt; 0.05) and predicted lower Stroop interference (ρ ≈ –0.5; p \u0026lt; 0.05). These findings demonstrate that volitional respiration can be harnessed to tune an integrated autonomic–cortical–executive control feedback network, offering a blueprint for noninvasive interventions in stress, cognitive aging, cardiovascular and neuropsychiatric disorders.\u003c/p\u003e","manuscriptTitle":"Breath-Modulated Biofeedback Loops for Enhancing Autonomic Regulation and Executive Functions through fNIRS and ECG","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-19 12:36:57","doi":"10.21203/rs.3.rs-7255999/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"70680ba4-81b2-49c7-9cb0-0e615a5da183","owner":[],"postedDate":"August 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":52462010,"name":"Biological sciences/Neuroscience/Cognitive neuroscience/Cognitive control"},{"id":52462011,"name":"Biological sciences/Neuroscience/Peripheral nervous system/Autonomic nervous system"}],"tags":[],"updatedAt":"2025-08-19T12:36:57+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-19 12:36:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7255999","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7255999","identity":"rs-7255999","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}