Autonomic and Neural Activity Dynamically Couple During Infant Attention

Autonomic and Neural Activity Dynamically Couple During Infant Attention | 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 Autonomic and Neural Activity Dynamically Couple During Infant Attention Annie Aitken, John Richards, Amy Hume, Isabella Stallworthy, Stephen Braren, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7143406/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 The developing brain does not regulate attention in isolation, but coordinates with the body’s peripheral signals. However, empirical research quantifying the dynamic interplay between neural oscillations and autonomic signals during infancy is lacking. Here, we provide the first evidence of real-time coupling between cortical activity and parasympathetic tone in 3-month-old infants during a sustained attention task. Using simultaneous EEG and ECG, we extracted continuous time series of respiratory sinus arrhythmia (RSA) and EEG power across theta, alpha, and beta bands. Cross-correlation and generalized additive mixed models revealed frequency- and region-specific coupling: theta and alpha power were positively linked to RSA, peaking when RSA preceded neural activity while beta power showed inverse coupling. Coupling was strongest for theta power in the frontal cortex and predicted the magnitude of sustained attention. Notably, RSA–neural coupling peaked immediately prior to the onset of sustained attention while the infant was orienting to the stimulus. These findings evidence a dynamic, bidirectional autonomic-neural signal attunement that scaffolds attention from the earliest months of life. Biological sciences/Neuroscience/Cognitive neuroscience/Attention Biological sciences/Physiology/Respiration Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction The autonomic nervous system (ANS) and the brain are intricately interconnected, working together as a dynamic system that enables adaptive responses to environmental stimuli 1 , 2 . This bidirectional communication serves as a critical interface between stress physiology and behavior, connecting the body’s physiological responses to processes of learning and self-regulation 3 – 6 . In early development, when the brain and ANS are rapidly developing, understanding this relationship is critical, as it lays the foundation for more complex forms of self-regulation later in life 7 – 10 . Despite the dynamic nature of both systems, research has often focused on neural and physiological mechanisms within single-time-point contexts. This approach limits our ability to capture the temporal coordination that may underlie emerging cognitive and regulatory capacities. Despite advancements in the field, a comprehensive understanding of how distinct neural oscillatory dynamics, across cortical regions and frequency bands, interact with real-time fluctuations in the ANS activity remains lacking. Attention is a cornerstone of cognitive development and is influenced by both the sympathetic (SNS) and parasympathetic (PNS) branches of the autonomic nervous system (ANS), which regulate physiological states to support engagement with the environment. The SNS facilitates attentional readiness by increasing arousal through activation of the locus coeruleus (LC) in the brainstem and the release of norepinephrine, which primes cognitive systems for sustained focus 11 . Meanwhile, the PNS moderates arousal levels to maintain an optimal range that promotes sensitivity of the attention system to external stimuli. 12 – 15 . The primary biological mechanism thought to underlie PNS modulation of attention is through the vagus nerve. The vagus nerve contains both efferent and afferent fibers, enabling bidirectional communication between the brain and the heart. Efferent fibers originate in the brainstem and project to the sinoatrial node, the heart’s pacemaker, where they exert an inhibitory influence that slows heart rate. In turn, afferent fibers transmit signals from the heart back to the brainstem, forming a continuous feedback loop between the brain and the parasympathetic nervous system 16 , 17 . Respiratory sinus arrhythmia (RSA)—the rhythmic fluctuation of heart rate across the respiratory cycle, also known as high-frequency heart rate variability (HRV)—is a widely recognized index of vagal activity 18 , 19 . Some researchers have hypothesized that RSA is associated with activity in the prefrontal cortex (PFC) 20 . Thayer’s neurovisceral integration theory proposes that the medial PFC plays a central role in modulating autonomic activity by exerting a top-down inhibitory control over the PNS 21 . Indeed, empirical evidence has demonstrated that increases in RSA predict PFC-dependent cognitive functions such as executive function 22 . However, theories of bidirectional connectivity between the RSA and PFC remain largely speculative, with much of the evidence drawn from animal models rather than human research, and there are almost no studies conducted within developmental contexts. Preliminary findings in adults have shown associations between autonomic signals and medial PFC activity, amygdala-PFC functional connectivity 23 – 25 , and global brain activity 26 . Adult research has also demonstrated that fluctuations in aperiodic neural activity are phase-locked to the respiratory cycle 27 , suggesting a dynamic link between neural and autonomic rhythms. In contrast, research by Nguyen and colleagues (2022) found no concurrent associations between RSA activity and mPFC activity in infants using fNIRS. Consequently, significant questions remain about the existence, directionality, and regional specificity of coupling between RSA and neural activity, particularly in infant populations. To address these gaps, we can leverage the precise temporal resolution of electroencephalography (EEG), which, with its millisecond-level accuracy, offers valuable insights into the underlying mechanisms of neural and autonomic interactions. Indeed, emerging research linking RSA to ERP EEG components has found that negative coupling between RSA and event-related negativity is evident among young children with anxiety disorder symptoms 28 . Emerging conceptual frameworks and empirical findings have begun to establish links between neural oscillations, as measured through EEG, and parasympathetic activity. In children with ADHD, research has demonstrated that theta power is negatively associated with SNS activity; however, these studies did not specifically investigate the role of PNS mechanisms in modulating theta activity 29 . Similarly, alpha activity has been inversely associated with physiological arousal. For instance, in cold pressor studies that temporarily activate the SNS, decreases in alpha power have been observed 30 . This relationship is evident in infancy, where increased arousal, as measured by skin conductance, is associated with reduced alpha activity, such that higher baseline arousal predicts lower alpha activity. Similarly, in college students, resting-state SNS activity has been shown to negatively correlate with alpha activity 31 . However, the specific role of vagal activity in shaping alpha oscillations during infancy remains poorly understood. Some studies suggest a connection between PNS activity and alpha oscillations, with evidence indicating positive associations between high-frequency heart rate variability (HF-HRV) and alpha activity during relaxed mental states 32 . Moreover, adult studies utilizing vagus nerve stimulation have found concordance between increases in HRV and simultaneous increases in alpha activity 33 . In contrast to theta and alpha, beta oscillations are more commonly associated with heightened arousal and has been primarily studied in adult populations. Findings from cold pressor experiments have revealed a positive relationship between SNS activity and beta power 29 , 34 . Although prior research has provided some insight into non-task-related associations between ANS activity and oscillatory frequency bands in children and adults, substantial gaps remain in our understanding of how PNS activity relates to oscillatory dynamics during cognitive tasks in infancy. While prior research has provided preliminary evidence linking autonomic activity to neural oscillations, far less is known about how these systems interact during active cognitive engagement. Understanding autonomic–neural oscillatory dynamics in task contexts is essential, as these interactions may reveal how the peripheral and central nervous systems coordinate to regulate attention and contribute to individual differences in self-regulation across development. Distinct neural frequency bands are known to support cognitive functions in developmentally specific ways and may show differential associations with ANS activity. For example, task-dependent theta oscillations have been linked to top-down processes and are theorized to originate in subcortical regions such as the hippocampus and nucleus accumbens, with cortico-subcortical feedback loops to facilitate learning and memory 35 , 36 . In infancy, theta synchronization during attention tasks has been associated with improved attentional performance and active engagement 37 – 39 . Alpha oscillations also show developmental changes in their functional significance. In 9-month-old infants, alpha desynchronization has been observed during sustained attention, potentially reflecting perceptual tuning and suppression of motor areas 39 . However, in younger infants (i.e., 3-month-olds), recent findings suggest that alpha may instead show weak synchronization during attentional states, possibly indicating a developmental shift in how alpha rhythms support cognitive engagement 3 . Research on beta oscillations in infancy remains limited, particularly within the context of sustained attention. One study found no significant changes in beta activity during periods of sustained attention compared to inattention in infants aged 5–12 months 39 . In adults, however, beta activity is often linked to inhibitory control and is thought to reflect the balance of excitatory and inhibitory neural signaling, potentially modulated by GABAergic neurotransmission 40 . Although beta rhythms appear to play a less prominent role in early attention processes, their developmental trajectory and potential relevance for cognitive control warrant further investigation 39 , 41 . Despite growing recognition of its importance, relatively little is known about the dynamic interplay between vagal regulation and neural oscillatory activity, particularly the functional implications of this coupling for attention in infancy. Prior work from John Richard’s group has established that infant sustained attention is characterized by a deceleration of HR that is a result of an increase in function in the parasympathetic nervous system 42 . However, evidence linking time-resolved, lag-sensitive associations between vagal activity, neural oscillations, and HR-defined sustained attention remains limited. Notably, the moment-to-moment feedback loops between vagal input and neural activity during attentional tasks are underexplored. Additionally, the directionality of influence, whether vagal activity drives neural oscillations or vice versa, has not been thoroughly examined. Investigating these dynamics in developmental contexts, particularly in infancy, is critical for understanding how coupling between the ANS and the brain supports attentional development. Further, a vagal-neural coupling index could serve as a novel metric, reflecting either arousal-mediated or vagal-mediated attention depending on its measurement. Current Study The primary goal of this study was to investigate how fluctuations in vagal activity dynamically co-vary with neural activity during an attention task in 3-month-old infants. To do this, we concurrently recorded infant ECG and EEG during a free-viewing attention-eliciting task. We derived continuous time series estimates of respiratory sinus arrhythmia (RSA) as an index of vagal activity. EEG data were analyzed in the theta, alpha, and beta frequency bands from frontal and parietal regions to capture ongoing neural dynamics. Our key analytic objectives were the following: 1) Assess RSA-EEG Coupling : Characterize the coupling between vagal activity (RSA) and EEG power across low- and high-frequency bands during an attention task. 2) Examine Regional and Frequency-Specific Patterns : Determine whether the strength of RSA-EEG coupling varies across frontal vs. parietal regions and among different oscillatory frequency bands. 3) Test Temporal Directionality : Use lagged analyses to evaluate if peak coupling occurs when RSA precedes or follows EEG activity, offering insight into the direction of influence. 4) Link to Behavioral and Physiological Engagement : Assess whether the strength or direction of RSA-EEG coupling predicts behavioral indices of attention and physiological measures of engagement during the attention task. Methods Participants The initial sample included 100 infants (63 males; age M = 3.46 months, SD = 0.38) recruited from community events, family services, health care providers, and flyers posted at local businesses around New York City. The final sample only included infants with usable EEG and ECG data (N = 81). Participants were excluded from the present study based on birth before 36 weeks of gestation, multiple births, or the presence of developmental disorders. Families were invited to participate in the study when infants were 3 months of age. See Table 1 for participant demographics on the analytic sample. Testing for the current study began in March 2018 and was halted in March 2020. The present study was conducted according to guidelines in the Declaration of Helsinki, with written informed consent obtained from a parent or guardian for each child before any assessment or data collection. All research procedures were approved by the [MASKED FOR BLINDING] IRB. Table 1 Mean (SD) or N (%) Infant Age at Visit 1 (months) 3.46 (0.38) Gestational age (weeks) 39.16 (1.24) Income-to-Needs at Visit 1 5.60 (5.36) Maternal Education (years) 15.62 (3.81) Infant sex (male) 47 (61%) Ethnicity Hispanic/Latino 40 (53%) Not Hispanic/Latino 33 (44%) Unreported 2 (3%) Race Two or More/Other 29 (39%) White 26 (35%) Black/African-American 12 (16%) Asian 5 (7%) Unreported 3 (4%) Protocol Infants and their caregivers visited the lab when infants were 3-months of age (Age M = 3.48, SD = 0.39). EEG and ECG were recorded from the infant during an attention task and at rest. Out of the initial sample (N = 100), 7 infants did not provide EEG data due to infant fussiness, 5 EEG files were unusable due to excessive artifacts, and 7 infants did not provide sufficient sustained attention EEG data (minimum required 20s), see EEG Data Acquisition & Processing . Thus, the analytic sample included 81 infants. Family and Household Characteristic Measures Families were given questionnaires to obtain demographic information, including maternal and infant age, race, and ethnicity. Caregivers also reported on their highest level of education attained and annual household income. Family income-to-needs ratio (ITN) is the total household income divided by the federal poverty line for the corresponding number of adults and children in the home and is used as the measure of socioeconomic status within the analyses. EEG Data Acquisition & Processing. EEG data at rest and during the visual attention task were acquired while the infants were seated on their caregivers’ laps. The recording room was dimly lit and an experimenter was nearby to soothe the infant with bubbles or a toy if the infant became too fussy. EEG was recorded using a 64-channel HydroCel Geodesic Sensory Net and amplifier (Electrical Geodesic, Inc., Eugene, OR). Electrode impedances were kept below 100 KΩ and the sampling rate was recorded at 1000Hz. All EEG files were processed in the Batch EEG Automated Processing Platform (BEAPP) software to ensure standardization in data processing and cleaning across all files (Levin et al. 2018). Continuous resting EEG files were converted from NetStation format to Matlab (2018b) format. Data preprocessing was carried out using the Harvard Automated Processing Pipeline for EEG (HAPPE), an automated preprocessing pipeline designed for infant EEG data (Gabard-Durnam et al. 2018). First, a 1 Hz high-pass and 100 Hz low-pass filter was applied to each EEG dataset. The third step involved artifact removal and included CleanLine’s multitaper approach to removing 60 Hz electrical noise, bad channel rejection, and wavelet-enhanced ICA for artifact rejection with automated component rejection through the Multiple Artifact Rejection Algorithm (Winkler et al. 2011) in EEGLAB. A subset of spatially distributed electrodes was selected for analysis with MARA: electrodes #: 2, 3, 5, 6, 8, 9, 10, 11, 12, 13, 14, 18, 20, 24, 25, 28, 30, 31, 34, 35, 39, 40, 42, 44, 48, 50, 52, 57, 58, 59, 60. NetStation Geodesic 64- Channel Net). Bad channels that were initially rejected were repopulated using spherical interpolation to reduce bias in re-referencing and the signal was mean detrended. Finally, each EEG file was segmented into 200 ms windows for power decomposition. The resultant EEG data had a time resolution of 5 Hz, corresponding with the RSA data. Continuous EEG Power Timeseries Processing. EEG data was transformed to continuous time series using wavelet transformation via MATLAB’s continuous wavelet transform (cwt) function with the Wavelet Type = Analytic Morlet wavelet ("amor") and Resolution: 48 voices per octave. The output of this step is a wavelet-transformed EEG activity series, which contains the EEG time series filtered by the wavelet filterbank. The wavelet-transformed EEG series is selected within the theta (4–6 Hz), alpha (6–9 Hz), and beta (13–20 Hz) frequency ranges for each 10–20 electrode. Finally, instantaneous neural power is calculated as the logarithm of the summed absolute values within each 5Hz time bin: log(sum(∣time series∣). Summed power was then separately averaged across frontal (electrode #:2, 3, 9, 11, 12, 13, 15, 57, 58, 59, 60) and parietal (electrode#: 28, 30, 31, 34, 40, 42, 44) regions (Fig. 2 a), for each frequency band. ECG Data Acquisition and Respiratory Sinus Arrhythmia Processing ECG Data Acquisition. During EEG acquisition, infant ECG data was collected using a Physio16 (EGI) device while infants were seated on their mothers' lap, while EEG was also simultaneously recorded. Infants provided an average of 277.90 + /- 20.84 s of ECG data (min = 90 s, max = 281 s). Data were edited and processed using the software QRSTool to remove artifacts and identify heartbeats. R-R intervals were extracted from the processed ECG data to receive inter-beat intervals (IBIs). Figure 1 Description: A schematic overview of RSA estimation. (1) Inter-beat intervals (IBI) were extracted from ECG signals and resampled to 5 Hz to align with EEG data. (2) Wavelet decomposition of the IBI time series was conducted using a continuous wavelet transform (CWT) with an analytic Morlet wavelet. (3) An inverse CWT (iCWT) constrained to the 0.42–1.2 Hz infant respiratory frequency range was applied to isolate RSA components. (4) Instantaneous RSA power was computed as the log-transformed variance of the filtered time series. This high-resolution RSA time series was synchronized with EEG data for coupling analyses. Continuous Respiratory Sinus Arrythmia Timeseries Processing. Step 1: Transforming IBI Series into a Sample-Rate Time Series To prepare IBI data for wavelet transformation, the IBI series is converted into a sample-rate time series at 5 Hz. This rate was chosen to ensure synchronization with the EEG data time frequency. Step 2: CWT Transformation to IBI data The wavelet transformation uses MATLAB’s cwt function with the Wavelet Type = Analytic Morlet wavelet ("amor") and Resolution: 48 voices per octave. The output of this step is a wavelet-transformed IBI series, which contains the IBI time series filtered by the wavelet filterbank. Step 2: Inverse CWT (icwt) for Age-Appropriate Respiratory Bands The wavelet-transformed IBI series is processed using MATLAB’s inverse continuous wavelet transform (icwt) function, constrained to the frequency bands corresponding to age-appropriate respiratory norms. The icwt function used the same analytic Morlet wavelet (‘amor’) and reconstructed the signal to using only the wavelets that corresponded to infant respiration range (0.42–1.2 Hz), to capture the variability in infant IBI that was produced by breathing. The output represents the RSA time series filtered for the infant respiratory frequency range. Step 3: Calculation of RSA RSA is quantified as the logarithm of the variance of the inverse CWT time series: RSA = log(var(icwt time series)). This approach aligns with the Porges-Bohrer Moving Polynomial Filter (PB-MPF) method, ensuring consistency with prior RSA research methodologies. Step 4: Instantaneous RSA Power The instantaneous amplitude of RSA is derived from the CWT-transformed data to calculate RSA at each time point of the filtered series. Instantaneous RSA power is calculated as the logarithm of the summed absolute values log(sum(∣time series∣). The power calculation provides measures of instantaneous RSA at each time point, offering insights into temporal dynamics in RSA activity. The CWT-based approach enhances resolution for frequency-domain analysis, offering better drop-off characteristics and flexibility for adapting to respiratory norms across developmental stages. Additionally, the ability to compute instantaneous RSA provides a refined understanding of RSA fluctuations over time. Estimation of RSA-EEG Coupling RSA-EEG coupling was calculated from synchronized RSA and EEG time series that were each sampled at 5Hz during the sustained attention task. Both of these signals were detrended using the detrend function from the gsignal R package (van Boxtel, 2021). The detrended signals were submitted to a cross-correlation analysis with lags ranging from − 10 to 10 in 200ms time windows, where negative lags indicate RSA leading EEG and positive lags indicate EEG leading RSA. The lag window and resolution parameters were selected to capture a range of physiologically plausible directional interactions between RSA and EEG, taking into account the high temporal resolution of both ECG and EEG signals and aligning with prior literature 26 , 43 , 44 . Figure 2 Description EEG and ECG signals were recorded simultaneously during a sustained attention task. EEG preprocessing included filtering, artifact removal using HAPPE, ICA decomposition, and wavelet transformation into theta (4–6 Hz), alpha (6–9 Hz), and beta (13–20 Hz) frequency bands. Power was computed in 200 ms windows and averaged over frontal and parietal regions. RSA signals were extracted from ECG using CWT and iCWT within the infant respiratory frequency band. EEG and RSA time series were downsampled to 5 Hz and entered into cross-correlation coupling analyses over ± 2 s (lags − 10 to + 10). Sustained Attention Task This study uses the same stimuli and procedure as Xie & Richards (2017) and Brandes-Aitken et al. (2022) to measure sustained attention (See Fig. 3 ). Infants sat on their caregivers’ lap while they were presented with a dynamic Sesame Street video on a large computer monitor. A camera in front of the infant recorded the infants’ faces, while a camera behind the participants recorded the stimulus. The 4-minute video consisted of several characters from Sesame Street , such as ‘Elmo’ and ‘Big Bird,’ that moved from side to side, disappeared, sang, and danced. These videos have been repeatedly demonstrated to elicit periods of sustained attention in young infants (Xie & Richards, 2017). Visual attention to stimuli was manually coded retroactively with the Net Station 5.1 software. Periods of infant sustained attention were categorized based on infant looking and heart rate deceleration. The criteria for categorizing periods of infant sustained attention required infant visual fixation to the stimulus paired with decreased heart rate (Richards, 2010). Specifically, HR-defined sustained attention phases began when the infant was looking at the screen and the median of five consecutive IBI values was higher than the median of the five IBIs preceding a look onset. HR-defined sustained attention phases ended (and inattention phases began) when the median of five consecutive IBI values was lower than the median of the five IBIs preceding a look onset. All phases of attention occur during looks to the experimental stimuli. Here, we evaluate the entire task, including both phases of HR-defined attention and attention termination as one continuous stream. To evaluate sustained attention engagement as an outcome measure, we calculated the proportion of time in HR-defined sustained attention phases (seconds in sustained attention/total seconds of visual looking and magnitude of heart rate deceleration from inattention to sustained attention (Tonnsen et al., 2018; Xie & Richards, 2016). Figure 3 Description. A dynamic audiovisual stimulus (Sesame Street video) was presented to infants while seated on a caregiver’s lap. Visual attention was manually coded, and sustained attention was defined by concurrent visual fixation and heart rate (HR) deceleration. The plot displays time series of inter-beat intervals (IBI), showing typical transitions between attention phases, with HR-defined sustained attention indicated by increased IBIs. Analysis Plan Richards (2025) 45 has presented a method for computing RSA with continuous wavelet transformation to produce continuous RSA timeseries based on age-appropriate respiration bands. This approach is particularly well-suited for high-resolution analyses of millisecond-level synchrony. The procedure for calculating continuous RSA using this method includes the following steps: To evaluate statistically significant coupling, we will compare between observed EEG-RSA coupling and randomly paired surrogate combinations of EEG and RSA time series to assess the degree of coupling across regions, frequencies, and lags. By establishing a baseline of coupling through surrogate analysis (e.g., Abney et al., 2015; Nguyen, Abney, et al., 2021; Nguyen, Hoehl, et al., 2021), we can compare the properties of the observed EEG-RSA coupling against what might be expected by chance or spurious correlation. For the surrogate analysis, we created surrogate datasets by randomly pairing the EEG time series of each participant with the RSA time series of other participants. We created distributions of 1000 non-repeated surrogate pairings for each participant and compute coupling for each pairing across all regions, lags, and frequencies of interest. This surrogate analysis allows us to estimate a distribution of coupling values that can be attributed to chance. A single model will be estimated for all participants, with coupling as the dependent variable. Fixed effects will include pairing type (observed vs. surrogate), region (e.g., frontal vs. parietal), frequency band (e.g., theta vs. alpha vs. beta), lag, and their interactions. This approach will allow us to determine whether the observed levels of EEG-RSA coupling across regions, frequencies, and lags are significantly greater than those expected by chance. Generalized additive mixed models (GAMMs) were employed to evaluate non-linear lag-specific influences on RSA-EEG coupling to understand the directionality and influence of lag. Moreover, parametric effects can be estimated with GAMMs to understand differences in average coupling by region. We fit hierarchical generalized additive mixed models (GAMMs) with random effects of individual intercepts to model nonlinear trajectories of coupling (R package, mgcv76). Models were examined for correctness using the gam.check function to ensure correct specification on the basis of dimension ( k ) and distribution of residuals. Models were also cross validated against LMMs incorporating both linear and polynomial terms for the time lag. GAMMs consistently outperformed LMMs in both model fit (AIC difference > 3) and explained variance (Rsq difference > 5). Non-linearity from GAMMs was defined as effective degrees of freedom (edf) greater than 2, and linear relations were defined by edf values closer to 1. Two separate GAMMs were fit to predict each frequency band: $$\:1)\:EEG-RSA\:Coupling\:\:\sim\:Region\:+\:s(lag,\:k=8,\:bs="cr")\:+\:s(ID,\:bs={\prime\:}re{\prime\:})$$ $$\:2)\:EEG-RSA\:Coupling\:\sim\:Region\:+\:s(lag,\:k=8,\:bs="cr")\:+s(lag,by=Region,\:k=8,\:bs="cr")\:+\:s(ID,\:bs={\prime\:}re{\prime\:})\:\:$$ Within this equation, s(lag, k = 8, bs="cr") is a smoothed time term, and ID is a random effect. In the first, only region was included as both a main effect (regional differences in average coupling) and in the second, both the main effect of region and a region X lag interaction effect (regional differences in peak coupling) were included. These two models were compared by ANOVA to account for any additional variance explained in the more complex model. In all three models, the ANOVA was not significant (p < 0.05); thus, model one, with the main effect of region, was selected. This approach was supported by visual inspection of cross-correlation plots, which revealed similar patterns of correlations across lags for both the frontal and parietal regions. To control for multiple comparisons, a false discovery rate (FDR) was applied using the Benjamini-Hochberg method. To further understand how EEG-RSA coupling differences were associated with sustained attention behavior during the task, we fit GAMM models for each frequency x region combination with a behavioral attention term included in a first set of models and with cardiac-orienting-response in a second set of models. Behavioral attention is defined as the proportion of time an infant spends in sustained attention, and cardiac-orienting response is defined as the magnitude of heart rate deceleration from inattention to attention. The attention variables were first evaluated as continuous variables and then dichotomized based on the median split in order to probe the significant interactions. $$\:EEG-RSA\:Coupling\:\sim\:Attention\:+\:s(lag,\:k=8,\:bs="cr")\:+s(lag,by=Attention,\:k=8,\:bs="cr")\:+\:s(ID,\:bs={\prime\:}re{\prime\:})\:\:$$ To examine time-varying associations between physiological coupling and distinct phases of attention, we segmented the continuous time series data into four attention phases: pre-attention, orienting, sustained attention, and attention termination. The pre-attention phase was defined as the 5-second interval preceding orienting. The orienting phase spanned from the onset of heart rate (HR) deceleration to the point at which sustained attention was established. Sustained attention, as previously defined, referred to the period of maintained HR deceleration. Finally, the attention termination phase encompassed the 5 seconds following the end of sustained attention. We applied a linear mixed-effects model (LMER package in R) to the time-series data, specifying EEG power as the outcome variable. Respiratory sinus arrhythmia (RSA) was decomposed into between-person and within-person components, both of which were included as predictors. Time-varying within-person fluctuations in RSA were modeled as an interaction with the attention phase to capture dynamic coupling effects. Post hoc estimated marginal means were used to derive phase-specific coupling coefficients. Results Evidence of Neural-RSA Coupling We used cross-correlation functions to evaluate the coupling between RSA and EEG activity across a time window of -2 to 2 seconds in 5Hz (200ms) time bins, where negative lags indicate RSA leading EEG and positive lags indicate EEG leading RSA. To assess the statistical significance of the observed coupling, a null distribution of beta weights was generated by re-computing the cross-correlation analysis with surrogate respiration time series (k = 1000). The resultant cross-correlation coefficients were entered into a linear mixed effects model (LMEM) with pairwise contrasts between surrogate and observed data between every combination of lag-by-region-by-frequency. Pairwise contrasts employed FDR correction for multiple comparisons. Coupling was observed to be significantly greater than that of the surrogate pairs for Theta and Alpha frequencies in every lag and every region. Statistical significance was not met in the Beta frequency band for the frontal region at lags 3–10 (~ 1500- 2000ms EEG leading RSA) and in the parietal region at any lag. Interestingly, while theta and alpha demonstrated positive coupling with RSA, Frontal Beta activity demonstrated negative coupling with RSA (increase in RSA associated with a decrease in Beta) at lags − 10 − 2 (2000ms to 0 RSA leading EEG and 0-400ms EEG lead RSA). To summarize, there is evidence of a bidirectional relationship between RSA-EEG coupling in Theta and Alpha frequency bins across frontal and parietal regions and a unidirectional inverse correlation of Beta-leading-RSA coupling in the frontal region (See Fig. 4 ). Figure 4 Description. Topographic maps depict cross-correlation coefficients between EEG power and RSA for theta, alpha, and beta bands across lags (− 2000 to + 2000 ms). Lineplots in grey show null distributions of coupling coefficients derived from 1,000 surrogate RSA-EEG pairings. Observed coupling coefficients (colored lines) significantly exceed surrogate distributions for theta and alpha across all lags and regions. Beta coupling was significantly negative in frontal regions and negative lags but not significantly different from surrogate in parietal regions or in positive lags. Distinct Patterns of Coupling based on Frequency and Topography We fit three GAMM models for each frequency band, including region as a main effect and lag as a smoothed term to evaluate linear or non-linear effects of lag on peak coupling. The frontal region was set as the reference factor, in line with our a priori hypothesis that greater coupling would occur in the frontal region. Results demonstrated that across alpha and theta frequency bands, RSA coupling was strongest in the frontal region relative to the parietal region. The inverse correlation between RSA and beta was also strongest (higher negative value) in the frontal region relative to parietal (See Fig. 5 and Table 2 ). Table 2 Theta Alpha Beta Fixed Effect Terms Est (Std. Error) T Statistic Est (Std. Error) T Statistic Est (Std. Error) T Statistic (Intercept) 0.047 (0.007) 5.44*** 0.04 (0.01) 5.29*** -0.013 (0.01) -2.27* region [parietal] −0.004 (0.001) −2.60** -0.01(0.001) -7.70*** 0.01(0.001) 8.84*** Smoothed Terms Edf (df) F Statistic Edf (df) F Statistic Edf (df) F Statistic lag 2.05 (19) 0.67*** 1.065 (19) 0.180* 1.013 (19) 0.20* ID 79.52 (80) 165.89*** 79.51 (80) 160.89*** 79.39 (80) 130.60*** Model Fit Rsq (adj) Rsq (adj) Rsq (adj) 0.80 0.79 0.76 Within the Theta band, smoothed lag terms revealed significant non-linear relationships between lag and RSA-EEG, with effective degrees of freedom indicating non-linear lag effects for Theta and Alpha, and a linear lag effect for Beta. A qualitative inspection of the cross-correlation plot (Fig. 4 ) demonstrates that theta-RSA coupling is stronger when RSA leads theta, with a peak coupling occurring around − 5 lags (approximately 1000 ms RSA leading EEG). Coupling then decreases as EEG leads RSA. For the Alpha band, a similar non-linear pattern to Theta emerges. Coupling is highest when RSA leads Alpha, and it decreases as Alpha activity begins to lead RSA. Finally, Beta activity exhibits a more linear lag effect, with less pronounced non-linear coupling dynamics compared to Theta and Alpha. Figure 5 Description. Bar plots show mean RSA–EEG coupling strength, aggregated by frequency band (theta, alpha, beta), scalp region (frontal, blue; parietal, purple), and lag direction. Lag bins represent RSA leading EEG (− 2000 to − 500 ms), instantaneous coupling (0 ms), and EEG leading RSA (+ 500 to + 2000 ms). Frontal theta coupling was strongest when RSA preceded EEG activity, followed by peak coupling at 0-lag in the same band and region. In contrast, beta activity exhibited the strongest inverse coupling in the frontal cortex when RSA preceeds EEG, consistent with a unidirectional, suppressive pattern. Increases in Physiological Engagement in Attention with Greater by EEG-RSA Coupling We next examined whether RSA–EEG coupling was associated with the physiological mechanisms underlying sustained attention, specifically, heart rate deceleration (HRD), or the cardiac orienting response. To do this, we re-estimated the GAMM models, this time including each infant’s average magnitude of HR deceleration (i.e., the difference in heart rate between inattention and sustained attention phases) as a continuous predictor. There were no significant main effects of HRD on overall RSA–EEG coupling across frequency, region, or lags (p > .15). Significant lag × HRD interactions emerged specifically for theta band coupling in the frontal and parietal regions: In the frontal theta model, the interaction was significant (F = 1.21, edf = 2.22, p = .006). In the high-HRD group, Theta–RSA coupling showed a non-linear pattern, peaking when RSA led EEG by approximately 500 ms and decreasing when EEG preceded RSA (F = 3.20, edf = 2.64, p < .001). The low-HRD group exhibited no discernible lag effect; visual inspection revealed no clear peaks in coupling direction. In the parietal theta model, a similar interaction was found (F = 2.14, edf = 1.80, p < .001). Among high-HRD infants, peak coupling occurred near or just before 0-lag, while the low-HRD group did not show any identifiable peaks. No significant lag effects were found for alpha frequency in either the frontal or parietal regions. For beta frequencies, which were only evaluated in the frontal region (due to non-significant coupling in the parietal region relative to surrogate pairs), a significant lag × HRD interaction was observed (F = 1.61, edf = 1.83, df = 7, p = .001). However, this effect did not replicate when HRD was modeled as a binary high/low variable; no significant interaction was found p \(\:\ge\:.08\) , and when visually inspected only a small trend toward greater inverse coupling in the high-HRD group was observed. Interpretation of this finding is limited by the overall small magnitude of cross-correlation values in the beta band. Increases in Sustained Attention Behavior with Greater EEG-RSA Coupling Behavioral attention during the experimental task was initially included as a continuous predictor, reflecting the proportion of time spent in sustained attention. Throughout the sustained attention task, infants weave in and out of sustained attention over the course of the task. There is within-person variability with the proportion of time spent in sustained attention vs. inattention throughout the task (Brandes-Aitken et al., 2022). To facilitate interpretation of interaction effects in the GAMM models, a median split of sustained attention was used in post-hoc analyses to define high- and low-attention groups. No significant main effects of behavioral attention were found on overall RSA–EEG coupling across frequency or regional domains (all p > .15). However, significant interactions between time lag and behavioral attention emerged for theta band coupling in both the frontal and parietal regions. In the frontal theta model, a significant lag × attention interaction was observed (F = 0.72, edf = 1.72, p = .027). For high-attention infants, Theta–RSA coupling exhibited a non-linear pattern, peaking when RSA led EEG by approximately 500 ms and declining when EEG preceded RSA (F = 1.81, edf = 1.66, p = .001). In contrast, the low-attention group showed no discernible lag effect; visual inspection revealed no identifiable peaks in coupling directionality (F = 0,0, edf = 0.0, p = .61). Similarly, in the parietal theta model, a significant lag × attention interaction was found (F = 0.88, edf = 1.80, p = .025). Among high-attention infants, peak Theta–RSA coupling occurred around 0-lag. No clear coupling peaks were evident in the low-attention group. There were no significant lag effects observed in alpha frequency bands for either the frontal or parietal regions. For beta frequencies, analyses were limited to the frontal region due to non-significant coupling in the parietal region relative to surrogate pairs. A significant lag × attention interaction was found in the frontal region (F = 2.35, edf = 1.61, df = 7, p < .0001). This effect was more linear, with stronger RSA-leading-EEG coupling observed in the high-attention group from − 2000 ms to 0 ms. In contrast, no significant effect was observed in the low-attention group (F = 0.47, edf = 0.88, df = 7, p = .037). Figure 6 Description. a) Schematic illustration of a single participant’s interbeat interval (IBI) time series, highlighting heart-rate-defined sustained attention (SA) episodes. Blue-shaded regions indicate periods of HR-defined sustained attention, while red ruler figures represent the calculated HR deceleration values from inattention to sustained attention transitions. b) RSA–EEG cross-correlation plots stratified by high vs. low HR deceleration (median split). Infants with high HR deceleration exhibited stronger coupling overall, particularly in the frontal theta band, where coupling peaked when RSA preceded EEG by approximately 500 ms. In the beta band, high-HRD infants showed inverse coupling; however, this effect was not statistically significant in the model including the binary HRD variable. c) RSA–EEG coupling plots stratified by high vs. low sustained attention (SA) levels. High-SA infants showed stronger coupling, especially in the theta band, with peak coupling observed when RSA led EEG by 0–500 ms. A similar but attenuated pattern was seen in the alpha band. High-SA infants also demonstrated greater RSA–beta coupling during periods when RSA preceded EEG, though this effect was modest. Upregulation of EEG-RSA Coupling Prior To Sustained Attention Phase To evaluate how RSA, neural activity, and RSA–EEG coupling predicted dynamic fluctuations in sustained attention behavior, we modeled time-varying associations among RSA, EEG power, and attention phase using linear mixed-effects models (LMER). RSA was lagged by 600 ms, based on prior analyses showing peak RSA–EEG cross-correlations at that offset. Analyses focused on frontal theta and alpha frequency bands, where coupling was strongest. Attention was segmented into four phases derived from heart rate dynamics: pre-attention, orienting (HR deceleration onset), sustained attention (continued HR deceleration), and termination (HR re-acceleration), with pre-attention as the reference level. In the uncoupled models, RSA was significantly elevated during the orienting (β = 0.22, 95% CI [0.21, 0.24], p < .001) and sustained attention (β = 0.22, 95% CI [0.20, 0.24], p < .001) phases compared to pre-attention (see Fig. 7 ). Similarly, theta power was significantly higher during orienting (β = 0.04, 95% CI [0.03, 0.05], p < .001) and sustained attention (β = 0.05, 95% CI [0.04, 0.06], p < .001) with no difference between pre-attention and attention termination. Alpha power showed the same pattern of increasing during orienting (β = 0.04, 95% CI [0.03, 0.05], p < .001) and sustained attention (β = 0.05, 95% CI [0.04, 0.06], p < .001), and no significant difference at termination. In the RSA–EEG coupling models, we observed significant RSA × Phase interactions. For theta, coupling peaked during orienting (β = 0.02, 95% CI [0.00, 0.04], p = .017) and decreased significantly during sustained attention (β = − 0.02, 95% CI [–0.04, − 0.01], p = .006). The termination phase did not show a significant interaction. For alpha, coupling was also strongest during orienting (β = 0.04, 95% CI [0.02, 0.06], p < .001), with trend level increased coupling during sustained attention (β = 0.01, 95% CI [–0.00, 0.03], p = .08) and termination (β = 0.02, 95% CI [–0.00, 0.03], p = .07; See Full Table of Results in SI). ​​ Figure 7 . Time-to-attention dynamics of RSA, EEG power, and RSA–EEG coupling. Discussion Our findings provide robust evidence of dynamic coupling between electroencephalography (EEG) and respiratory sinus arrhythmia (RSA) activity in 3-month-old infants during an attention task. Among the frequency bands examined, theta activity exhibited the strongest positive coupling with RSA, followed by alpha, suggesting that increased activity in one system is associated with upregulation in the other. In contrast, beta activity demonstrated the weakest coupling and was negatively correlated with RSA, indicating a potentially suppressive or inhibitory interaction. Coupling was observed across the scalp, with stronger effects localized in the frontal region relative to parietal areas. Cross-correlation analyses revealed bidirectional coupling for both theta and alpha bands, with peak coupling occurring when RSA preceded EEG activity, suggesting that vagal-mediated signals may play a leading role in shaping oscillatory dynamics in lower frequency bands. Conversely, beta activity showed minimal evidence of reciprocal interaction, instead displaying RSA-driven suppression rather than mutual modulation. Notably, infants who exhibited greater RSA-lead coupling with theta, alpha, and beta activity, demonstrated enhanced sustained attention during the task, as indexed by prolonged looking and greater heart rate deceleration. Finally, the temporal dynamics of RSA–EEG coupling followed a dynamic profile whereby coupling increased during phases of orienting, plateaued during sustained attention, and declined during disengagement or inattention. These findings highlight the coordinated roles of vagus nerve activity and cortical oscillatory activity in supporting early attentional engagement and regulation. There was evidence of varying degrees of coupling across all frequency bands; however, the strongest RSA-EEG co-variation emerged within the theta frequency band. This finding aligns with prior research demonstrating synchronized activity between vagal signals and theta oscillations 46 , 47 . Theta power is widely recognized as a neural mechanism supporting top-down, effortful control of attention 35 , 48 , while vagal activity is a key physiological marker of behavioral regulation and self-regulatory capacity. Notably, during an infant stress-eliciting paradigm, both RSA activity and theta power increase across the scalp, corresponding with greater infant attention to social stimuli 48 . Converging lines of evidence suggest that vagal-mediated signals may influence active learning and attentional processes through modulation of theta rhythms. Experimental studies have shown that vagus nerve stimulation induces theta oscillations originating in the hippocampus 49 – 51 . Task-evoked theta power is thought to originate from subcortical regions such as the hippocampus and nucleus accumbens, which engage in feedback loops with cortical regions to facilitate active learning 36 . In rodent models, task-dependent theta activity has been shown to induce long-term potentiation, a core mechanism of learning and memory 52 . Importantly, theta and high-frequency HRV oscillations operate within similar frequency ranges, making their coupling physiologically plausible. Respiration-entrained theta oscillations have been causally demonstrated in rodent models 53 , and recent theoretical work has proposed that theta oscillations may, in part, originate within the vagus nerve itself 54 . This causal evidence aligns with our directionality analysis, demonstrating that the strongest RSA-Theta coupling occurred when RSA preceded theta activity. Altogether, these findings support the idea that vagal signals may be communicated to and represented in the brain via theta rhythms, which are known to support long-range neural communication and facilitate attentional engagement. In contrast, there is relatively less research linking vagal-mediated signals to alpha and beta oscillatory activity. Adult studies have shown that vagal stimulation can increase alpha power and reduce high frequency (30-40hz) power 55 . Task-related beta activity has been associated with GABAA-mediated inhibitory processes, although the precise mechanisms remain under investigation. In the current study, increases in RSA were associated with decreases in beta power. One possible explanation is that vagal input modulates theta rhythms, which in turn influence higher-frequency oscillations such as beta through cross-frequency phase-amplitude coupling 56 . This indirect pathway could account for the lack of bidirectional effects in our beta analyses, specifically, that beta activity did not exert reciprocal influence on RSA. This pattern aligns with existing literature showing that increases in task-related low-frequency power are often accompanied by decreases in high-frequency power 39 , 57 . Together, these findings collectively point toward a multi-level mechanism by which parasympathetic activity, mediated by vagal signals, modulate neural oscillatory dynamics in support of attention and cognitive engagement in early development. Although both frontal and parietal regions demonstrated significant RSA–neural coupling, the frontal region consistently exhibited stronger coupling across all frequency bands and time lags. This is a noteworthy finding that aligns with and extends existing developmental literature, which posits robust bidirectional connections between vagal input and frontal cortical activity—an area critical for top-down attentional control 18 , 58 . These results provide empirical support for neurobiological models of vagal–frontal integration. Specifically, vagal afferent fibers project to the medullary nucleus of the solitary tract (NTS), which plays a central role in coordinating autonomic regulation. From the NTS, signals follow multiple ascending and descending pathways: (1) efferent feedback to modulate peripheral physiological states, (2) projections to the reticular formation, and (3) ascending pathways to the forebrain via the parabrachial nucleus and locus coeruleus 21 , 59 . These ascending circuits ultimately reach limbic and cortical regions involved in the regulation of frontal cortical activity. The observed dominance of frontal coupling underscores the importance of this network in supporting the integration of physiological regulation and attentional processes during early development. RSA-neural coupling predicted the magnitude of physiological engagement during transitions into sustained attention. Prior research has shown that infant HR deceleration in response to engaging stimuli is associated with baseline RSA, providing converging evidence for the interrelation between vagal activity and subsequent physiologically induced attention episodes 5 , 60 . Additionally, we found that individual differences in the magnitude of RSA–EEG coupling distinguished infants in the ‘high attention’ versus ‘low attention’ subgroups. Within the theta frequency band, high-attention infants exhibited a pronounced increase in RSA-leading-EEG coupling in the frontal region, along with concurrent coupling in the parietal region. In contrast, low-attention infants showed markedly reduced coupling in both regions. Notably, among high-attention infants, RSA preceding EEG activity in the frontal cortex was associated with strong negative correlations, a pattern not observed in the low-attention group, where coupling was relatively flat. These findings suggest a functional role for RSA–neural coupling in supporting sustained attention: infants who showed stronger coupling, particularly when RSA activity led neural oscillatory dynamics, spent more time engaged in sustained attention during the task. These findings support the idea that the coordination and upregulation of vagal activity, in temporal synchrony with neural oscillations, facilitates attentional control in early infancy. This is the first study, to our knowledge, to examine real-time neural-physiological coupling in infants in relation to behavioral attention, offering a novel framework for understanding early regulatory mechanisms. To better understand how RSA-EEG modulates sustained attention in real-time we evaluated time-to-attention changes in coupling between phases of pre-attention, attention orienting, sustained attention, and attention termination. When modeled independently, RSA, Theta and Alpha power demonstrated marked increases during orienting and sustained attention, as has previously been demonstrated 39 . However, when examining the time-varying interaction between neural activity and attention phase as a function of RSA change, we found that the highest degree of coupling occurred for theta and alpha activity during orienting. This is a novel finding suggesting that vagal-cortical coupling may reflect a mechanism for recruiting physiological resources that mobilize heart-rate defined sustained attention. Limitations and Future Directions The present study represents a significant advancement in the field of developmental neurophysiological research, but replication and expansion are warranted. We first suggest that future research explicitly measure infant respiration and cardiac action potentials to better evaluate what other physiological mechanisms may be directly entraining neural oscillatory bursts outside of vagal input. Further disaggregating periodic versus 1/f aperiodic associations with RSA could provide more insight into the neural mechanism most sensitive to changes in physiological regulation 27 . Finally, a longitudinal study would be useful for better understanding how neural-vagal coupling shows maturational changes from early to late infancy. In summary, we demonstrated some of the first evidence that significant RSA-neural coupling exists in infants at 3-months of age during a sustained attention task. We observed a positive coupling between RSA and both theta and alpha activity, as well as an inverse-coupling between RSA and beta activity. We found stronger effects in the frontal cortex, particularly when RSA preceded EEG activity. Infants who spent more time in sustained attention exhibited greater RSA-leading coupling in theta and alpha, and stronger RSA-leading beta decoupling. These results suggest that vagal modulation of neural dynamics plays a central role in attentional engagement in early infancy. Collectively, our findings propose a novel conceptual and methodological framework for studying infant neurophysiology, offering new insight into how autonomic-neural coordination shapes the development of attentional control. References Arnsten, A.F.: Catecholamine modulation of prefrontal cortical cognitive function. Trends Cogn. Sci. 2 , 436–447 (1998) Arnsten, A.F.T., Li, B.-M.: Neurobiology of executive functions: catecholamine influences on prefrontal cortical functions. Biol. Psychiatry. 57 , 1377–1384 (2005) Brandes-Aitken, A., Metser, M., Braren, S.H., Vogel, S.C., Brito, N.H.: Neurophysiology of sustained attention in early infancy: Investigating longitudinal relations with recognition memory outcomes. Infant Behav. Dev. 70 , 101807 (2023) Brandes-Aitken, A., et al.: Within-person changes in basal cortisol and caregiving modulate executive attention across infancy. Dev. Psychopathol. 34 , 1386–1399 (2022) Richards, J.E., Casey, B.J.: Heart rate variability during attention phases in young infants. Psychophysiology. 28 , 43–53 (1991) de Barbaro, K., Clackson, K., Wass, S.V.: Infant Attention Is Dynamically Modulated With Changing Arousal Levels. Child. Dev. 88 , 629–639 (2017) Brandes-Aitken, A., Braren, S., Swingler, M., Voegtline, K., Blair, C.: Sustained attention in infancy: A foundation for the development of multiple aspects of self-regulation for children in poverty. J. Exp. Child. Psychol. 184 , 192–209 (2019) Wass, S.V., Clackson, K., de Barbaro, K.: Temporal dynamics of arousal and attention in 12-month-old infants: Temporal Dynamics of Arousal in Infants. Dev. Psychobiol. 58 , 623–639 (2016) Amadó, M.P., et al.: The neural and physiological substrates of real-world attention change across development. eLife (2023). 10.7554/elife.92171.1 Wass, S.V., de Barbaro, K., Clackson, K.: Tonic and phasic co-variation of peripheral arousal indices in infants. Biol. Psychol. 111 , 26–39 (2015) Aston-Jones, G., Rajkowski, J., Cohen, J.: Role of locus coeruleus in attention and behavioral flexibility. Biol. Psychiatry. 46 , 1309–1320 (1999) Braren, S.H., et al.: Baseline hypothalamic–pituitary–adrenal axis and parasympathetic nervous system activity interact to predict executive functions in low-income children. Mind Brain Educ. 15 , 61–66 (2021) Suess, P.E., Porges, S.W., Plude, D.J.: Cardiac vagal tone and sustained attention in school-age children. Psychophysiology. 31 , 17–22 (1994) Richards, J.E., Cameron, D.: Infant heart-rate variability and behavioral developmental status. Infant Behav. Dev. 12 , 45–58 (1989) Porges, S.W.: Autonomic regulation and attention. In: Attention and information processing in infants and adults 201–223. Psychology (2014) Porges, S.W., Doussard-Roosevelt, J.A., Portales, A.L., Greenspan, S.: I. Infant regulation of the vagal brake predicts child behavior problems: a psychobiological model of social behavior. Dev. Psychobiol. 29 , 697–712 (1996) Levy, M.N., Warner, M.R.: Parasympathetic effects on cardiac function. Neurocardiology (1994) Beauchaine, T.P.: Respiratory sinus arrhythmia: A transdiagnostic biomarker of emotion dysregulation and psychopathology. Curr. Opin. Psychol. 3 , 43–47 (2015) Porges, S.W.: Orienting in a defensive world: mammalian modifications of our evolutionary heritage. Polyvagal Theory Psychophysiology. 32 , 301–318 (1995) Beauchaine, T.P., Thayer, J.F.: Heart rate variability as a transdiagnostic biomarker of psychopathology. Int. J. Psychophysiol. 98 , 338–350 (2015) Thayer, J.F., Lane, R.D.: A model of neurovisceral integration in emotion regulation and dysregulation. J. Affect. Disord. 61 , 201–216 (2000) Thayer, J.F., Hansen, A.L., Saus-Rose, E., Johnsen, B.H.: Heart rate variability, prefrontal neural function, and cognitive performance: the neurovisceral integration perspective on self-regulation, adaptation, and health. Ann. Behav. Med. 37 , 141–153 (2009) Sakaki, M., et al.: Heart rate variability is associated with amygdala functional connectivity with MPFC across younger and older adults. Neuroimage. 139 , 44–52 (2016) Lane, R.D., et al.: Neural correlates of heart rate variability during emotion. Neuroimage. 44 , 213–222 (2009) Weissman, D.G., Guyer, A.E., Ferrer, E., Robins, R.W., Hastings, P.D.: Adolescents’ brain-autonomic coupling during emotion processing. Neuroimage. 183 , 818–827 (2018) Bolt, T., et al.: Autonomic physiological coupling of the global fMRI signal. Nat. Neurosci. 1–9 (2025) Kluger, D.S., et al.: Modulatory dynamics of periodic and aperiodic activity in respiration-brain coupling. Nat. Commun. 14 , 4699 (2023) Peoples, S.G., Davis, E.L., Brooker, R.J.: Variation in coupling across neural and cardiac systems of regulation is linked to markers of anxiety risk in preschool. Dev. Psychopathol. 1–13 (2024) Barry, R.J., Clarke, A.R., Johnstone, S.J., McCarthy, R., Selikowitz, M.: Electroencephalogram theta/beta ratio and arousal in attention-deficit/hyperactivity disorder: evidence of independent processes. Biol. Psychiatry. 66 , 398–401 (2009) Rho, G., Callara, A.L., Bernardi, G., Scilingo, E.P., Greco, A.: EEG cortical activity and connectivity correlates of early sympathetic response during cold pressor test. Sci. Rep. 13 , 1338 (2023) Barry, R.J., De Blasio, F.M., Fogarty, J.S., Clarke, A.R.: Natural alpha frequency components in resting EEG and their relation to arousal. Clin. Neurophysiol. 131 , 205–212 (2020) Kawashima, T., Shiratori, H., Amano, K.: The relationship between alpha power and heart rate variability commonly seen in various mental states. PLoS One. 19 , e0298961 (2024) Kang, D., Choi, Y., Lee, J., Park, E., Kim, I.Y.: Analysis of taVNS effects on autonomic and central nervous systems in healthy young adults based on HRV, EEG parameters. J. Neural Eng. 21 , 046012 (2024) Chang, P.F., Arendt-Nielsen, L., Chen, A.C.: N. Dynamic changes and spatial correlation of EEG activities during cold pressor test in man. Brain Res. Bull. 57 , 667–675 (2002) Begus, K., Bonawitz, E.: The rhythm of learning: Theta oscillations as an index of active learning in infancy. Dev. Cogn. Neurosci. 45 , 100810 (2020) Cohen, M.X., et al.: Top-down-directed synchrony from medial frontal cortex to nucleus accumbens during reward anticipation. Hum. Brain Mapp. 33 , 246–252 (2012) Braithwaite, E.K., Jones, E.J.H., Johnson, M.H., Holmboe, K.: Dynamic modulation of frontal theta power predicts cognitive ability in infancy. Dev. Cogn. Neurosci. 45 , 100818 (2020) Anderson, A.J., Perone, S., Gartstein, M.A.: Context matters: Cortical rhythms in infants across baseline and play. Infant Behav. Dev. 66 , 101665 (2022) Xie, W., Mallin, B.M., Richards, J.E.: Development of infant sustained attention and its relation to EEG oscillations: an EEG and cortical source analysis study. Dev. Sci. e12562 (2017) Fingelkurts, A.A., et al.: Enhancement of GABA-related signalling is associated with increase of functional connectivity in human cortex: GABA Inhibition and Cortical Connectivity. Hum. Brain Mapp. 22 , 27–39 (2004) Porjesz, B., et al.: Linkage disequilibrium between the beta frequency of the human EEG and a GABAA receptor gene locus. Proc. Natl. Acad. Sci. U. S. A. 99, 3729–3733 (2002) Reynolds, G.D., Richards, J.E.: Infant heart rate: A developmental psychophysiological perspective. In: Schmidt, L.A. (ed.) Developmental psychophysiology: Theory, systems, and methods, pp. 173–212. Cambridge University Press, xxii, pp., New York, NY, US, (2008) Yuan, H., Zotev, V., Phillips, R., Bodurka, J.: Correlated slow fluctuations in respiration, EEG, and BOLD fMRI. Neuroimage. 79 , 81–93 (2013) Nguyen, T., Hoehl, S., Bertenthal, B.I., Abney, D.H.: Coupling between prefrontal brain activity and respiratory sinus arrhythmia in infants and adults. Dev. Cogn. Neurosci. 58 , 101184 (2022) Richards, J.E.: Prospectus for using continuous wavelet transform for respiratory sinus arrhythmia. Res. Gate. (2025). 10.13140/RG.2.2.24125.58081 Brandes-Aitken, A., et al.: Maternal heart rate variability at 3-months postpartum is associated with maternal mental health and infant neurophysiology. Sci. Rep. 14 , 18766 (2024) Bazhenova, O.V., Stroganova, T.A., Doussard-Roosevelt, J.A., Posikera, I.A., Porges, S.: W. Physiological responses of 5-month-old infants to smiling and blank faces. Int. J. Psychophysiol. 63 , 64–76 (2007) Orekhova, E.V., Stroganova, T.A., Posikera, I.N.: Theta synchronization during sustained anticipatory attention in infants over the second half of the first year of life. Int. J. Psychophysiol. 32 , 151–172 (1999) Broncel, A., Bocian, R., Konopacki, J.: Vagal nerve stimulation: The effect on the brain oscillatory field potential. Neuroscience. 483 , 127–138 (2022) Broncel, A., Bocian, R., Kłos-Wojtczak, P., Konopacki, J.: Hippocampal theta rhythm induced by vagal nerve stimulation: The effect of modulation of electrical coupling. Brain Res. Bull. 152 , 236–245 (2019) Karalis, N., Sirota, A.: Breathing coordinates cortico-hippocampal dynamics in mice during offline states. Nat. Commun. 13 , 467 (2022) Greenstein, Y.J., Pavlides, C., Winson, J.: Long-term potentiation in the dentate gyrus is preferentially induced at theta rhythm periodicity. Brain Res. 438 , 331–334 (1988) Yanovsky, Y., Ciatipis, M., Draguhn, A., Tort, A.B.L., Brankačk, J.: Slow oscillations in the mouse hippocampus entrained by nasal respiration. J. Neurosci. 34 , 5949–5964 (2014) Shaffer, C., Barrett, L.F., Quigley, K.S.: Signal processing in the vagus nerve: Hypotheses based on new genetic and anatomical evidence. Biol. Psychol. 182 , 108626 (2023) Vanneste, S., Martin, J., Rennaker, R.L. 2nd, Kilgard, M.P.: Pairing sound with vagus nerve stimulation modulates cortical synchrony and phase coherence in tinnitus: An exploratory retrospective study. Sci. Rep. 7 , 17345 (2017) Brændholt, M., et al.: Breathing in waves: Understanding respiratory-brain coupling as a gradient of predictive oscillations. Neurosci. Biobehav Rev. 152 , 105262 (2023) Zavala, B., et al.: Cognitive control involves theta power within trials and beta power across trials in the prefrontal-subthalamic network. Brain. 141 , 3361–3376 (2018) Porges, S.W.: The Polyvagal Theory: phylogenetic contributions to social behavior. Physiol. Behav. 79 , 503–513 (2003) Benarroch, E.: Central autonomic network: functional organization and clinical correlations. Chapters 1&2. Preprint at (1998) Bradshaw, J., Abney, D.H.: Infant physiological activity and the early emergence of social communication. Dev. Psychobiol. 63 , e22145 (2021) Additional Declarations There is NO Competing Interest. Supplementary Files RSAEEGNatCommSupp1.docx Supplemental Information nrreportingsummary2.pdf Reporting Summary 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. 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-7143406","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":516017729,"identity":"22a597ef-8e65-4ad5-b987-7be182994f19","order_by":0,"name":"Annie Aitken","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIiWNgGAWjYDACZiCEMhgfACkePsJamOFamA1AWtiIsIYZxmKTAJOENPC38x82+LjDLs+cnflY5dccOxk2BuaHj27g0SJxmJk5ceaZ5GLLZra027LbkoEOYzM2zsFnDVDLYd425sQNh3nMbktuYwZq4WGTxqdFHqTlb1s9UAv/t2LJbfWEtRgAtSQzth0G2cLG+HHbYcJaDA8zGxv2th0HamEzlmbcdpyHjZmAX+TOH3ws8bOtOnHD+cMPP/7cVm3Pz9788DFe7yMDZh4wSaxyEGD8QYrqUTAKRsEoGDEAAA1nQS3eYas1AAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-7227-6888","institution":"New York University","correspondingAuthor":true,"prefix":"","firstName":"Annie","middleName":"","lastName":"Aitken","suffix":""},{"id":516017730,"identity":"b15d87b6-94e8-4391-a103-e21146a987f9","order_by":1,"name":"John Richards","email":"","orcid":"","institution":"University of South Carolina","correspondingAuthor":false,"prefix":"","firstName":"John","middleName":"","lastName":"Richards","suffix":""},{"id":516017731,"identity":"db020e91-caaf-4c34-bdac-66f103e78d83","order_by":2,"name":"Amy Hume","email":"","orcid":"","institution":"New York University","correspondingAuthor":false,"prefix":"","firstName":"Amy","middleName":"","lastName":"Hume","suffix":""},{"id":516017732,"identity":"dc96d319-068f-440d-be0f-e66b1acc5395","order_by":3,"name":"Isabella Stallworthy","email":"","orcid":"","institution":"University of Pennsylvania","correspondingAuthor":false,"prefix":"","firstName":"Isabella","middleName":"","lastName":"Stallworthy","suffix":""},{"id":516017733,"identity":"818ea64d-0f0b-4e50-a691-5f97259c5d05","order_by":4,"name":"Stephen Braren","email":"","orcid":"","institution":"Social Creatures Applied Research Non-Profit","correspondingAuthor":false,"prefix":"","firstName":"Stephen","middleName":"","lastName":"Braren","suffix":""},{"id":516017734,"identity":"42b58883-f553-48aa-b634-9ae9eda26914","order_by":5,"name":"Tram Vo","email":"","orcid":"","institution":"New York University","correspondingAuthor":false,"prefix":"","firstName":"Tram","middleName":"","lastName":"Vo","suffix":""},{"id":516017735,"identity":"2f9d155b-3057-4c0d-a944-5a0774b8844e","order_by":6,"name":"Natalie Brito","email":"","orcid":"","institution":"Columbia University","correspondingAuthor":false,"prefix":"","firstName":"Natalie","middleName":"","lastName":"Brito","suffix":""}],"badges":[],"createdAt":"2025-07-16 21:45:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7143406/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7143406/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":93936153,"identity":"80d0e52c-d5b4-4f9b-b88d-5ba4e334a879","added_by":"auto","created_at":"2025-10-20 12:53:32","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":9383342,"visible":true,"origin":"","legend":"","description":"","filename":"RSAEEGMSFinal.docx","url":"https://assets-eu.researchsquare.com/files/rs-7143406/v1/30f011c7e71bb2b5b93d0580.docx"},{"id":93936091,"identity":"8af391df-6710-4e4a-a4b2-08d71c5278f5","added_by":"auto","created_at":"2025-10-20 12:53:27","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":7896,"visible":true,"origin":"","legend":"","description":"","filename":"COMMSBIO257248T.json","url":"https://assets-eu.researchsquare.com/files/rs-7143406/v1/dcf5fee191f58ca6f118db7e.json"},{"id":93936101,"identity":"0b9fcbf8-b207-4788-9660-af05c421c835","added_by":"auto","created_at":"2025-10-20 12:53:28","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":15729,"visible":true,"origin":"","legend":"","description":"","filename":"RSAEEGNatCommSupp1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7143406/v1/21f1381c431379d4e3765128.docx"},{"id":93936111,"identity":"b764ddcd-0e80-499e-91f3-c6772b4617d7","added_by":"auto","created_at":"2025-10-20 12:53:29","extension":"xml","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":154533,"visible":true,"origin":"","legend":"","description":"","filename":"COMMSBIO257248T0enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7143406/v1/c297dda1afe4ce9bf4fb910f.xml"},{"id":93936138,"identity":"78ccd668-6303-4cc6-a355-1238a525ed50","added_by":"auto","created_at":"2025-10-20 12:53:31","extension":"png","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1799571,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7143406/v1/9bc2db791c8735de863f0ea9.png"},{"id":93936147,"identity":"5977deab-4d9c-4057-8e71-d33b3d8cc80f","added_by":"auto","created_at":"2025-10-20 12:53:31","extension":"png","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":46474,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7143406/v1/77fd6f3a89186d2cd3601545.png"},{"id":93936143,"identity":"2932406b-bbca-4c56-aa25-21e527cca281","added_by":"auto","created_at":"2025-10-20 12:53:31","extension":"png","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":189443,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7143406/v1/851a7a2c9b7282050c6d6ef3.png"},{"id":93936128,"identity":"e9cc9266-9420-488f-b680-c6029c9aea24","added_by":"auto","created_at":"2025-10-20 12:53:30","extension":"png","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":223146,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7143406/v1/9683245e44c2ae479a991053.png"},{"id":93936173,"identity":"d7735f6c-248f-4682-a9eb-52770a38488e","added_by":"auto","created_at":"2025-10-20 12:53:32","extension":"png","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":87873,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7143406/v1/748e8c326fd4c8048d4ebee3.png"},{"id":93936100,"identity":"2b22408c-7808-4b34-b88b-2bbc1740848e","added_by":"auto","created_at":"2025-10-20 12:53:28","extension":"png","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":20772,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7143406/v1/77019c394f09331c93b96cf6.png"},{"id":93936152,"identity":"7e6377c9-a205-4768-8dd9-e97da1dab09a","added_by":"auto","created_at":"2025-10-20 12:53:31","extension":"png","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":88275,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7143406/v1/c21372807cfebdece2157edf.png"},{"id":93936148,"identity":"f5491265-ac4f-4145-b416-239b8eb3397b","added_by":"auto","created_at":"2025-10-20 12:53:31","extension":"png","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":51916,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-7143406/v1/10999e23ef669d9bd392a430.png"},{"id":93936112,"identity":"78e86321-ffd8-43a1-9a90-c71ea84b7828","added_by":"auto","created_at":"2025-10-20 12:53:29","extension":"xml","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":152063,"visible":true,"origin":"","legend":"","description":"","filename":"COMMSBIO257248T0structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7143406/v1/5494815cc804d75d652db1f1.xml"},{"id":93936157,"identity":"a6a55f61-cfa7-4507-a0ee-996bc0a8bdc9","added_by":"auto","created_at":"2025-10-20 12:53:32","extension":"html","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":165469,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7143406/v1/7717eeb0c04ab1f80929c9fd.html"},{"id":93936122,"identity":"c3da8fa8-5389-43d8-b251-2bd9235069ec","added_by":"auto","created_at":"2025-10-20 12:53:30","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":122094,"visible":true,"origin":"","legend":"\u003cp\u003eA schematic overview of RSA estimation. (1) Inter-beat intervals (IBI) were extracted from ECG signals and resampled to 5 Hz to align with EEG data. (2) Wavelet decomposition of the IBI time series was conducted using a continuous wavelet transform (CWT) with an analytic Morlet wavelet. (3) An inverse CWT (iCWT) constrained to the 0.42–1.2 Hz infant respiratory frequency range was applied to isolate RSA components. (4) Instantaneous RSA power was computed as the log-transformed variance of the filtered time series. This high-resolution RSA time series was synchronized with EEG data for coupling analyses.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7143406/v1/cdd55712bf7546807a736b96.png"},{"id":93936204,"identity":"d1dd8d02-0386-4cb4-afdf-694cf7a242cc","added_by":"auto","created_at":"2025-10-20 12:53:34","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":720985,"visible":true,"origin":"","legend":"\u003cp\u003eEEG and ECG signals were recorded simultaneously during a sustained attention task. EEG preprocessing included filtering, artifact removal using HAPPE, ICA decomposition, and wavelet transformation into theta (4–6 Hz), alpha (6–9 Hz), and beta (13–20 Hz) frequency bands. Power was computed in 200 ms windows and averaged over frontal and parietal regions. RSA signals were extracted from ECG using CWT and iCWT within the infant respiratory frequency band. EEG and RSA time series were downsampled to 5 Hz and entered into cross-correlation coupling analyses over ±2 s (lags −10 to +10).\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7143406/v1/567f67934242f7afea5cff5f.jpeg"},{"id":93936145,"identity":"36873d85-9fb1-4f9e-8d67-715575e6dabe","added_by":"auto","created_at":"2025-10-20 12:53:31","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1830139,"visible":true,"origin":"","legend":"\u003cp\u003eA dynamic audiovisual stimulus (Sesame Street video) was presented to infants while seated on a caregiver’s lap. Visual attention was manually coded, and sustained attention was defined by concurrent visual fixation and heart rate (HR) deceleration. The plot displays time series of inter-beat intervals (IBI), showing typical transitions between attention phases, with HR-defined sustained attention indicated by increased IBIs.\u003c/p\u003e","description":"","filename":"Screenshot20251019at9.04.00PM.png","url":"https://assets-eu.researchsquare.com/files/rs-7143406/v1/f00e8695660001c3cf07dec6.png"},{"id":93936179,"identity":"28501941-e09e-423d-9339-195805a7f64b","added_by":"auto","created_at":"2025-10-20 12:53:33","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":471499,"visible":true,"origin":"","legend":"\u003cp\u003eTopographic maps depict cross-correlation coefficients between EEG power and RSA for theta, alpha, and beta bands across lags (−2000 to +2000 ms). Lineplots in grey show null distributions of coupling coefficients derived from 1,000 surrogate RSA-EEG pairings. Observed coupling coefficients (colored lines) significantly exceed surrogate distributions for theta and alpha across all lags and regions. Beta coupling was significantly negative in frontal regions and negative lags but not significantly different from surrogate in parietal regions or in positive lags.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7143406/v1/8631e64e8e2599726a5ca1b2.png"},{"id":93936150,"identity":"92645ffb-090c-4a5d-93a6-002893a839d5","added_by":"auto","created_at":"2025-10-20 12:53:31","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":144875,"visible":true,"origin":"","legend":"\u003cp\u003eBar plots show mean RSA–EEG coupling strength, aggregated by frequency band (theta, alpha, beta), scalp region (frontal, blue; parietal, purple), and lag direction. Lag bins represent RSA leading EEG (−2000 to −500 ms), instantaneous coupling (0 ms), and EEG leading RSA (+500 to +2000 ms). Frontal theta coupling was strongest when RSA preceded EEG activity, followed by peak coupling at 0-lag in the same band and region. In contrast, beta activity exhibited the strongest inverse coupling in the frontal cortex when RSA preceeds EEG, consistent with a unidirectional, suppressive pattern.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7143406/v1/d0e65d75721c1e10ef38c27e.png"},{"id":93936074,"identity":"f32b7398-2107-42f4-985e-f7e07656ac6a","added_by":"auto","created_at":"2025-10-20 12:53:26","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":665559,"visible":true,"origin":"","legend":"\u003cp\u003ea) Schematic illustration of a single participant’s interbeat interval (IBI) time series, highlighting heart-rate-defined sustained attention (SA) episodes. Blue-shaded regions indicate periods of HR-defined sustained attention, while red ruler figures represent the calculated HR deceleration values from inattention to sustained attention transitions. b) RSA–EEG cross-correlation plots stratified by high vs. low HR deceleration (median split). Infants with high HR deceleration exhibited stronger coupling overall, particularly in the frontal theta band, where coupling peaked when RSA preceded EEG by approximately 500 ms. In the beta band, high-HRD infants showed inverse coupling; however, this effect was not statistically significant in the model including the binary HRD variable. c) RSA–EEG coupling plots stratified by high vs. low sustained attention (SA) levels. High-SA infants showed stronger coupling, especially in the theta band, with peak coupling observed when RSA led EEG by 0–500 ms. A similar but attenuated pattern was seen in the alpha band. High-SA infants also demonstrated greater RSA–beta coupling during periods when RSA preceded EEG, though this effect was modest.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7143406/v1/b4495da6dc838d6dad2bfb97.png"},{"id":93936198,"identity":"e38a8edc-f1e4-44e4-8601-ef7beea4cbd8","added_by":"auto","created_at":"2025-10-20 12:53:34","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":432457,"visible":true,"origin":"","legend":"\u003cp\u003eLine plots depicting mean RSA, EEG power (theta, alpha, beta), and RSA–EEG coupling (RSA lagged by 600 ms) across four attention phases: pre-attention, orienting, sustained attention, and termination. RSA, theta, and alpha power peaked during the orienting phase. RSA–EEG coupling also peaked during orienting and decreased during sustained attention and termination, highlighting phase-specific coordination between physiological and neural processes.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-7143406/v1/879bfca59276ac94809dbb4b.png"},{"id":93936522,"identity":"7bbfbf21-2a7b-4f76-9e18-0d96b52d5497","added_by":"auto","created_at":"2025-10-20 12:53:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5098987,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7143406/v1/c7c86e0c-7353-4c32-ab89-60e3cb31a9de.pdf"},{"id":93936096,"identity":"d06c80e3-d8c5-459d-be27-18dcba6d04e7","added_by":"auto","created_at":"2025-10-20 12:53:28","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":15729,"visible":true,"origin":"","legend":"Supplemental Information","description":"","filename":"RSAEEGNatCommSupp1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7143406/v1/b8ef3ea89a5f5b7836e8a9f8.docx"},{"id":93936097,"identity":"95b1b55d-0161-4207-8f7b-3e2feade3638","added_by":"auto","created_at":"2025-10-20 12:53:28","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1664933,"visible":true,"origin":"","legend":"Reporting Summary","description":"","filename":"nrreportingsummary2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7143406/v1/5424f6016ae408b72e0d9b3b.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Autonomic and Neural Activity Dynamically Couple During Infant Attention","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe autonomic nervous system (ANS) and the brain are intricately interconnected, working together as a dynamic system that enables adaptive responses to environmental stimuli \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. This bidirectional communication serves as a critical interface between stress physiology and behavior, connecting the body’s physiological responses to processes of learning and self-regulation \u003csup\u003e\u003cspan additionalcitationids=\"CR4 CR5\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e–\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. In early development, when the brain and ANS are rapidly developing, understanding this relationship is critical, as it lays the foundation for more complex forms of self-regulation later in life \u003csup\u003e\u003cspan additionalcitationids=\"CR8 CR9\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e–\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Despite the dynamic nature of both systems, research has often focused on neural and physiological mechanisms within single-time-point contexts. This approach limits our ability to capture the temporal coordination that may underlie emerging cognitive and regulatory capacities. Despite advancements in the field, a comprehensive understanding of how distinct neural oscillatory dynamics, across cortical regions and frequency bands, interact with real-time fluctuations in the ANS activity remains lacking.\u003c/p\u003e\u003cp\u003eAttention is a cornerstone of cognitive development and is influenced by both the sympathetic (SNS) and parasympathetic (PNS) branches of the autonomic nervous system (ANS), which regulate physiological states to support engagement with the environment. The SNS facilitates attentional readiness by increasing arousal through activation of the locus coeruleus (LC) in the brainstem and the release of norepinephrine, which primes cognitive systems for sustained focus \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Meanwhile, the PNS moderates arousal levels to maintain an optimal range that promotes sensitivity of the attention system to external stimuli. \u003csup\u003e\u003cspan additionalcitationids=\"CR13 CR14\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e–\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. The primary biological mechanism thought to underlie PNS modulation of attention is through the vagus nerve.\u003c/p\u003e\u003cp\u003eThe vagus nerve contains both efferent and afferent fibers, enabling bidirectional communication between the brain and the heart. Efferent fibers originate in the brainstem and project to the sinoatrial node, the heart’s pacemaker, where they exert an inhibitory influence that slows heart rate. In turn, afferent fibers transmit signals from the heart back to the brainstem, forming a continuous feedback loop between the brain and the parasympathetic nervous system \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Respiratory sinus arrhythmia (RSA)—the rhythmic fluctuation of heart rate across the respiratory cycle, also known as high-frequency heart rate variability (HRV)—is a widely recognized index of vagal activity \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Some researchers have hypothesized that RSA is associated with activity in the prefrontal cortex (PFC) \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Thayer’s neurovisceral integration theory proposes that the medial PFC plays a central role in modulating autonomic activity by exerting a top-down inhibitory control over the PNS \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Indeed, empirical evidence has demonstrated that increases in RSA predict PFC-dependent cognitive functions such as executive function \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eHowever, theories of bidirectional connectivity between the RSA and PFC remain largely speculative, with much of the evidence drawn from animal models rather than human research, and there are almost no studies conducted within developmental contexts. Preliminary findings in adults have shown associations between autonomic signals and medial PFC activity, amygdala-PFC functional connectivity \u003csup\u003e\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e–\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e, and global brain activity \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Adult research has also demonstrated that fluctuations in aperiodic neural activity are phase-locked to the respiratory cycle \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e, suggesting a dynamic link between neural and autonomic rhythms. In contrast, research by Nguyen and colleagues (2022) found no concurrent associations between RSA activity and mPFC activity in infants using fNIRS. Consequently, significant questions remain about the existence, directionality, and regional specificity of coupling between RSA and neural activity, particularly in infant populations. To address these gaps, we can leverage the precise temporal resolution of electroencephalography (EEG), which, with its millisecond-level accuracy, offers valuable insights into the underlying mechanisms of neural and autonomic interactions. Indeed, emerging research linking RSA to ERP EEG components has found that negative coupling between RSA and event-related negativity is evident among young children with anxiety disorder symptoms \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eEmerging conceptual frameworks and empirical findings have begun to establish links between neural oscillations, as measured through EEG, and parasympathetic activity. In children with ADHD, research has demonstrated that theta power is negatively associated with SNS activity; however, these studies did not specifically investigate the role of PNS mechanisms in modulating theta activity \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Similarly, alpha activity has been inversely associated with physiological arousal. For instance, in cold pressor studies that temporarily activate the SNS, decreases in alpha power have been observed \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. This relationship is evident in infancy, where increased arousal, as measured by skin conductance, is associated with reduced alpha activity, such that higher baseline arousal predicts lower alpha activity. Similarly, in college students, resting-state SNS activity has been shown to negatively correlate with alpha activity \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. However, the specific role of vagal activity in shaping alpha oscillations during infancy remains poorly understood. Some studies suggest a connection between PNS activity and alpha oscillations, with evidence indicating positive associations between high-frequency heart rate variability (HF-HRV) and alpha activity during relaxed mental states \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Moreover, adult studies utilizing vagus nerve stimulation have found concordance between increases in HRV and simultaneous increases in alpha activity \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. In contrast to theta and alpha, beta oscillations are more commonly associated with heightened arousal and has been primarily studied in adult populations. Findings from cold pressor experiments have revealed a positive relationship between SNS activity and beta power \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Although prior research has provided some insight into non-task-related associations between ANS activity and oscillatory frequency bands in children and adults, substantial gaps remain in our understanding of how PNS activity relates to oscillatory dynamics during cognitive tasks in infancy.\u003c/p\u003e\u003cp\u003eWhile prior research has provided preliminary evidence linking autonomic activity to neural oscillations, far less is known about how these systems interact during active cognitive engagement. Understanding autonomic–neural oscillatory dynamics in task contexts is essential, as these interactions may reveal how the peripheral and central nervous systems coordinate to regulate attention and contribute to individual differences in self-regulation across development. Distinct neural frequency bands are known to support cognitive functions in developmentally specific ways and may show differential associations with ANS activity. For example, task-dependent theta oscillations have been linked to top-down processes and are theorized to originate in subcortical regions such as the hippocampus and nucleus accumbens, with cortico-subcortical feedback loops to facilitate learning and memory \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. In infancy, theta synchronization during attention tasks has been associated with improved attentional performance and active engagement \u003csup\u003e\u003cspan additionalcitationids=\"CR38\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e–\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Alpha oscillations also show developmental changes in their functional significance. In 9-month-old infants, alpha desynchronization has been observed during sustained attention, potentially reflecting perceptual tuning and suppression of motor areas \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. However, in younger infants (i.e., 3-month-olds), recent findings suggest that alpha may instead show weak synchronization during attentional states, possibly indicating a developmental shift in how alpha rhythms support cognitive engagement \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Research on beta oscillations in infancy remains limited, particularly within the context of sustained attention. One study found no significant changes in beta activity during periods of sustained attention compared to inattention in infants aged 5–12 months \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. In adults, however, beta activity is often linked to inhibitory control and is thought to reflect the balance of excitatory and inhibitory neural signaling, potentially modulated by GABAergic neurotransmission \u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. Although beta rhythms appear to play a less prominent role in early attention processes, their developmental trajectory and potential relevance for cognitive control warrant further investigation \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eDespite growing recognition of its importance, relatively little is known about the dynamic interplay between vagal regulation and neural oscillatory activity, particularly the functional implications of this coupling for attention in infancy. Prior work from John Richard’s group has established that infant sustained attention is characterized by a deceleration of HR that is a result of an increase in function in the parasympathetic nervous system \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. However, evidence linking time-resolved, lag-sensitive associations between vagal activity, neural oscillations, and HR-defined sustained attention remains limited. Notably, the moment-to-moment feedback loops between vagal input and neural activity during attentional tasks are underexplored. Additionally, the directionality of influence, whether vagal activity drives neural oscillations or vice versa, has not been thoroughly examined. Investigating these dynamics in developmental contexts, particularly in infancy, is critical for understanding how coupling between the ANS and the brain supports attentional development. Further, a vagal-neural coupling index could serve as a novel metric, reflecting either arousal-mediated or vagal-mediated attention depending on its measurement.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCurrent Study\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe primary goal of this study was to investigate how fluctuations in vagal activity dynamically co-vary with neural activity during an attention task in 3-month-old infants. To do this, we concurrently recorded infant ECG and EEG during a free-viewing attention-eliciting task. We derived continuous time series estimates of respiratory sinus arrhythmia (RSA) as an index of vagal activity. EEG data were analyzed in the theta, alpha, and beta frequency bands from frontal and parietal regions to capture ongoing neural dynamics. Our key analytic objectives were the following:\u003c/p\u003e\u003cp\u003e1) \u003cb\u003eAssess RSA-EEG Coupling\u003c/b\u003e: Characterize the coupling between vagal activity (RSA) and EEG power across low- and high-frequency bands during an attention task.\u003c/p\u003e\u003cp\u003e2) \u003cb\u003eExamine Regional and Frequency-Specific Patterns\u003c/b\u003e: Determine whether the strength of RSA-EEG coupling varies across frontal vs. parietal regions and among different oscillatory frequency bands.\u003c/p\u003e\u003cp\u003e3) \u003cb\u003eTest Temporal Directionality\u003c/b\u003e: Use lagged analyses to evaluate if peak coupling occurs when RSA precedes or follows EEG activity, offering insight into the direction of influence.\u003c/p\u003e\u003cp\u003e4) \u003cb\u003eLink to Behavioral and Physiological Engagement\u003c/b\u003e: Assess whether the strength or direction of RSA-EEG coupling predicts behavioral indices of attention and physiological measures of engagement during the attention task.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cb\u003eParticipants\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe initial sample included 100 infants (63 males; age \u003cem\u003eM\u003c/em\u003e = 3.46 months, \u003cem\u003eSD\u003c/em\u003e = 0.38) recruited from community events, family services, health care providers, and flyers posted at local businesses around New York City. The final sample only included infants with usable EEG and ECG data (N = 81). Participants were excluded from the present study based on birth before 36 weeks of gestation, multiple births, or the presence of developmental disorders. Families were invited to participate in the study when infants were 3 months of age. See Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e for participant demographics on the analytic sample. Testing for the current study began in March 2018 and was halted in March 2020. The present study was conducted according to guidelines in the Declaration of Helsinki, with written informed consent obtained from a parent or guardian for each child before any assessment or data collection. All research procedures were approved by the [MASKED FOR BLINDING] IRB.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean (SD) or N (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInfant Age at Visit 1 (months)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.46 (0.38)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGestational age (weeks)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e39.16 (1.24)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIncome-to-Needs at Visit 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.60 (5.36)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaternal Education (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15.62 (3.81)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInfant sex (male)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e47 (61%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEthnicity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHispanic/Latino\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40 (53%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNot Hispanic/Latino\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33 (44%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnreported\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 (3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRace\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTwo or More/Other\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e29 (39%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWhite\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26 (35%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlack/African-American\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12 (16%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAsian\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5 (7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnreported\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3 (4%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003cb\u003eProtocol\u003c/b\u003e\u003c/p\u003e\u003cp\u003eInfants and their caregivers visited the lab when infants were 3-months of age (Age \u003cem\u003eM\u003c/em\u003e = 3.48, \u003cem\u003eSD\u003c/em\u003e = 0.39). EEG and ECG were recorded from the infant during an attention task and at rest. Out of the initial sample (N = 100), 7 infants did not provide EEG data due to infant fussiness, 5 EEG files were unusable due to excessive artifacts, and 7 infants did not provide sufficient sustained attention EEG data (minimum required 20s), see \u003cem\u003eEEG Data Acquisition \u0026amp; Processing\u003c/em\u003e. Thus, the analytic sample included 81 infants.\u003c/p\u003e\u003cp\u003e\u003cb\u003eFamily and Household Characteristic Measures\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFamilies were given questionnaires to obtain demographic information, including maternal and infant age, race, and ethnicity. Caregivers also reported on their highest level of education attained and annual household income. Family income-to-needs ratio (ITN) is the total household income divided by the federal poverty line for the corresponding number of adults and children in the home and is used as the measure of socioeconomic status within the analyses.\u003c/p\u003e\u003cp\u003e\u003cb\u003eEEG Data Acquisition \u0026amp; Processing.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eEEG data at rest and during the visual attention task were acquired while the infants were seated on their caregivers’ laps. The recording room was dimly lit and an experimenter was nearby to soothe the infant with bubbles or a toy if the infant became too fussy. EEG was recorded using a 64-channel HydroCel Geodesic Sensory Net and amplifier (Electrical Geodesic, Inc., Eugene, OR). Electrode impedances were kept below 100 KΩ and the sampling rate was recorded at 1000Hz. All EEG files were processed in the Batch EEG Automated Processing Platform (BEAPP) software to ensure standardization in data processing and cleaning across all files (Levin et al. 2018). Continuous resting EEG files were converted from NetStation format to Matlab (2018b) format. Data preprocessing was carried out using the Harvard Automated Processing Pipeline for EEG (HAPPE), an automated preprocessing pipeline designed for infant EEG data (Gabard-Durnam et al. 2018). First, a 1 Hz high-pass and 100 Hz low-pass filter was applied to each EEG dataset. The third step involved artifact removal and included CleanLine’s multitaper approach to removing 60 Hz electrical noise, bad channel rejection, and wavelet-enhanced ICA for artifact rejection with automated component rejection through the Multiple Artifact Rejection Algorithm (Winkler et al. 2011) in EEGLAB. A subset of spatially distributed electrodes was selected for analysis with MARA: electrodes #: 2, 3, 5, 6, 8, 9, 10, 11, 12, 13, 14, 18, 20, 24, 25, 28, 30, 31, 34, 35, 39, 40, 42, 44, 48, 50, 52, 57, 58, 59, 60. NetStation Geodesic 64- Channel Net). Bad channels that were initially rejected were repopulated using spherical interpolation to reduce bias in re-referencing and the signal was mean detrended. Finally, each EEG file was segmented into 200 ms windows for power decomposition. The resultant EEG data had a time resolution of 5 Hz, corresponding with the RSA data.\u003c/p\u003e\u003cp\u003e\u003cb\u003eContinuous EEG Power Timeseries Processing.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eEEG data was transformed to continuous time series using wavelet transformation via MATLAB’s continuous wavelet transform (cwt) function with the Wavelet Type = Analytic Morlet wavelet (\"amor\") and Resolution: 48 voices per octave. The output of this step is a wavelet-transformed EEG activity series, which contains the EEG time series filtered by the wavelet filterbank. The wavelet-transformed EEG series is selected within the theta (4–6 Hz), alpha (6–9 Hz), and beta (13–20 Hz) frequency ranges for each 10–20 electrode. Finally, instantaneous neural power is calculated as the logarithm of the summed absolute values within each 5Hz time bin: log(sum(∣time series∣). Summed power was then separately averaged across frontal (electrode #:2, 3, 9, 11, 12, 13, 15, 57, 58, 59, 60) and parietal (electrode#: 28, 30, 31, 34, 40, 42, 44) regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea), for each frequency band.\u003c/p\u003e\u003cp\u003e\u003cb\u003eECG Data Acquisition and Respiratory Sinus Arrhythmia Processing\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eECG Data Acquisition.\u003c/b\u003e During EEG acquisition, infant ECG data was collected using a Physio16 (EGI) device while infants were seated on their mothers' lap, while EEG was also simultaneously recorded. Infants provided an average of 277.90 + /- 20.84 s of ECG data (min = 90 s, max = 281 s). Data were edited and processed using the software QRSTool to remove artifacts and identify heartbeats. R-R intervals were extracted from the processed ECG data to receive inter-beat intervals (IBIs).\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u003cem\u003eDescription: A schematic overview of RSA estimation. (1) Inter-beat intervals (IBI) were extracted from ECG signals and resampled to 5 Hz to align with EEG data. (2) Wavelet decomposition of the IBI time series was conducted using a continuous wavelet transform (CWT) with an analytic Morlet wavelet. (3) An inverse CWT (iCWT) constrained to the 0.42–1.2 Hz infant respiratory frequency range was applied to isolate RSA components. (4) Instantaneous RSA power was computed as the log-transformed variance of the filtered time series. This high-resolution RSA time series was synchronized with EEG data for coupling analyses.\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eContinuous Respiratory Sinus Arrythmia Timeseries Processing.\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eStep 1: Transforming IBI Series into a Sample-Rate Time Series\u003c/em\u003e\u003c/p\u003e\u003cp\u003eTo prepare IBI data for wavelet transformation, the IBI series is converted into a sample-rate time series at 5 Hz. This rate was chosen to ensure synchronization with the EEG data time frequency.\u003c/p\u003e\u003cp\u003e\u003cem\u003eStep 2: CWT Transformation to IBI data\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe wavelet transformation uses MATLAB’s cwt function with the Wavelet Type = Analytic Morlet wavelet (\"amor\") and Resolution: 48 voices per octave. The output of this step is a wavelet-transformed IBI series, which contains the IBI time series filtered by the wavelet filterbank.\u003c/p\u003e\u003cp\u003e\u003cem\u003eStep 2: Inverse CWT (icwt) for Age-Appropriate Respiratory Bands\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe wavelet-transformed IBI series is processed using MATLAB’s inverse continuous wavelet transform (icwt) function, constrained to the frequency bands corresponding to age-appropriate respiratory norms. The icwt function used the same analytic Morlet wavelet (‘amor’) and reconstructed the signal to using only the wavelets that corresponded to infant respiration range (0.42–1.2 Hz), to capture the variability in infant IBI that was produced by breathing. The output represents the RSA time series filtered for the infant respiratory frequency range.\u003c/p\u003e\u003cp\u003e\u003cem\u003eStep 3: Calculation of RSA\u003c/em\u003e\u003c/p\u003e\u003cp\u003eRSA is quantified as the logarithm of the variance of the inverse CWT time series: RSA = log(var(icwt time series)). This approach aligns with the Porges-Bohrer Moving Polynomial Filter (PB-MPF) method, ensuring consistency with prior RSA research methodologies.\u003c/p\u003e\u003cp\u003e\u003cem\u003eStep 4: Instantaneous RSA Power\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe instantaneous amplitude of RSA is derived from the CWT-transformed data to calculate RSA at each time point of the filtered series. Instantaneous RSA power is calculated as the logarithm of the summed absolute values log(sum(∣time series∣). The power calculation provides measures of instantaneous RSA at each time point, offering insights into temporal dynamics in RSA activity.\u003c/p\u003e\u003cp\u003eThe CWT-based approach enhances resolution for frequency-domain analysis, offering better drop-off characteristics and flexibility for adapting to respiratory norms across developmental stages. Additionally, the ability to compute instantaneous RSA provides a refined understanding of RSA fluctuations over time.\u003c/p\u003e\u003cp\u003e\u003cb\u003eEstimation of RSA-EEG Coupling\u003c/b\u003e\u003c/p\u003e\u003cp\u003eRSA-EEG coupling was calculated from synchronized RSA and EEG time series that were each sampled at 5Hz during the sustained attention task. Both of these signals were detrended using the detrend function from the gsignal R package (van Boxtel, 2021). The detrended signals were submitted to a cross-correlation analysis with lags ranging from − 10 to 10 in 200ms time windows, where negative lags indicate RSA leading EEG and positive lags indicate EEG leading RSA. The lag window and resolution parameters were selected to capture a range of physiologically plausible directional interactions between RSA and EEG, taking into account the high temporal resolution of both ECG and EEG signals and aligning with prior literature\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e,\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e \u003cem\u003eDescription EEG and ECG signals were recorded simultaneously during a sustained attention task. EEG preprocessing included filtering, artifact removal using HAPPE, ICA decomposition, and wavelet transformation into theta (4–6 Hz), alpha (6–9 Hz), and beta (13–20 Hz) frequency bands. Power was computed in 200 ms windows and averaged over frontal and parietal regions. RSA signals were extracted from ECG using CWT and iCWT within the infant respiratory frequency band. EEG and RSA time series were downsampled to 5 Hz and entered into cross-correlation coupling analyses over ± 2 s (lags − 10 to + 10).\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eSustained Attention Task\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study uses the same stimuli and procedure as Xie \u0026amp; Richards (2017) and Brandes-Aitken et al. (2022) to measure sustained attention (See Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Infants sat on their caregivers’ lap while they were presented with a dynamic Sesame Street video on a large computer monitor. A camera in front of the infant recorded the infants’ faces, while a camera behind the participants recorded the stimulus. The 4-minute video consisted of several characters from \u003cem\u003eSesame Street\u003c/em\u003e, such as ‘Elmo’ and ‘Big Bird,’ that moved from side to side, disappeared, sang, and danced. These videos have been repeatedly demonstrated to elicit periods of sustained attention in young infants (Xie \u0026amp; Richards, 2017). Visual attention to stimuli was manually coded retroactively with the Net Station 5.1 software.\u003c/p\u003e\u003cp\u003ePeriods of infant sustained attention were categorized based on infant looking and heart rate deceleration. The criteria for categorizing periods of infant sustained attention required infant visual fixation to the stimulus paired with decreased heart rate (Richards, 2010). Specifically, HR-defined sustained attention phases began when the infant was looking at the screen \u003cem\u003eand\u003c/em\u003e the median of five consecutive IBI values was higher than the median of the five IBIs preceding a look onset. HR-defined sustained attention phases ended (and inattention phases began) when the median of five consecutive IBI values was lower than the median of the five IBIs preceding a look onset. All phases of attention occur during looks to the experimental stimuli. Here, we evaluate the entire task, including both phases of HR-defined attention and attention termination as one continuous stream.\u003c/p\u003e\u003cp\u003eTo evaluate sustained attention engagement as an outcome measure, we calculated the proportion of time in HR-defined sustained attention phases (seconds in sustained attention/total seconds of visual looking and magnitude of heart rate deceleration from inattention to sustained attention (Tonnsen et al., 2018; Xie \u0026amp; Richards, 2016).\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e \u003cem\u003eDescription. A dynamic audiovisual stimulus (Sesame Street video) was presented to infants while seated on a caregiver’s lap. Visual attention was manually coded, and sustained attention was defined by concurrent visual fixation and heart rate (HR) deceleration. The plot displays time series of inter-beat intervals (IBI), showing typical transitions between attention phases, with HR-defined sustained attention indicated by increased IBIs.\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eAnalysis Plan\u003c/b\u003e\u003c/p\u003e\u003cp\u003eRichards (2025)\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e has presented a method for computing RSA with continuous wavelet transformation to produce continuous RSA timeseries based on age-appropriate respiration bands. This approach is particularly well-suited for high-resolution analyses of millisecond-level synchrony. The procedure for calculating continuous RSA using this method includes the following steps:\u003c/p\u003e\u003cp\u003eTo evaluate statistically significant coupling, we will compare between observed EEG-RSA coupling and randomly paired surrogate combinations of EEG and RSA time series to assess the degree of coupling across regions, frequencies, and lags. By establishing a baseline of coupling through surrogate analysis (e.g., Abney et al., 2015; Nguyen, Abney, et al., 2021; Nguyen, Hoehl, et al., 2021), we can compare the properties of the observed EEG-RSA coupling against what might be expected by chance or spurious correlation. For the surrogate analysis, we created surrogate datasets by randomly pairing the EEG time series of each participant with the RSA time series of other participants. We created distributions of 1000 non-repeated surrogate pairings for each participant and compute coupling for each pairing across all regions, lags, and frequencies of interest. This surrogate analysis allows us to estimate a distribution of coupling values that can be attributed to chance.\u003c/p\u003e\u003cp\u003eA single model will be estimated for all participants, with coupling as the dependent variable. Fixed effects will include pairing type (observed vs. surrogate), region (e.g., frontal vs. parietal), frequency band (e.g., theta vs. alpha vs. beta), lag, and their interactions. This approach will allow us to determine whether the observed levels of EEG-RSA coupling across regions, frequencies, and lags are significantly greater than those expected by chance.\u003c/p\u003e\u003cp\u003eGeneralized additive mixed models (GAMMs) were employed to evaluate non-linear lag-specific influences on RSA-EEG coupling to understand the directionality and influence of lag. Moreover, parametric effects can be estimated with GAMMs to understand differences in average coupling by region. We fit hierarchical generalized additive mixed models (GAMMs) with random effects of individual intercepts to model nonlinear trajectories of coupling (R package, mgcv76). Models were examined for correctness using the gam.check function to ensure correct specification on the basis of dimension (\u003cem\u003ek\u003c/em\u003e) and distribution of residuals. Models were also cross validated against LMMs incorporating both linear and polynomial terms for the time lag. GAMMs consistently outperformed LMMs in both model fit (AIC difference \u0026gt; 3) and explained variance (Rsq difference \u0026gt; 5). Non-linearity from GAMMs was defined as effective degrees of freedom (edf) greater than 2, and linear relations were defined by edf values closer to 1.\u003c/p\u003e\u003cp\u003eTwo separate GAMMs were fit to predict each frequency band:\u003c/p\u003e\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:1)\\:EEG-RSA\\:Coupling\\:\\:\\sim\\:Region\\:+\\:s(lag,\\:k=8,\\:bs=\"cr\")\\:+\\:s(ID,\\:bs={\\prime\\:}re{\\prime\\:})$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:2)\\:EEG-RSA\\:Coupling\\:\\sim\\:Region\\:+\\:s(lag,\\:k=8,\\:bs=\"cr\")\\:+s(lag,by=Region,\\:k=8,\\:bs=\"cr\")\\:+\\:s(ID,\\:bs={\\prime\\:}re{\\prime\\:})\\:\\:$$\u003c/div\u003e\u003c/div\u003e\u003cp\u003eWithin this equation, s(lag, k = 8, bs=\"cr\") is a smoothed time term, and ID is a random effect. In the first, only region was included as both a main effect (regional differences in average coupling) and in the second, both the main effect of region and a region X lag interaction effect (regional differences in peak coupling) were included. These two models were compared by ANOVA to account for any additional variance explained in the more complex model. In all three models, the ANOVA was not significant (p \u0026lt; 0.05); thus, model one, with the main effect of region, was selected. This approach was supported by visual inspection of cross-correlation plots, which revealed similar patterns of correlations across lags for both the frontal and parietal regions. To control for multiple comparisons, a false discovery rate (FDR) was applied using the Benjamini-Hochberg method.\u003c/p\u003e\u003cp\u003eTo further understand how EEG-RSA coupling differences were associated with sustained attention behavior during the task, we fit GAMM models for each frequency x region combination with a behavioral attention term included in a first set of models and with cardiac-orienting-response in a second set of models. Behavioral attention is defined as the proportion of time an infant spends in sustained attention, and cardiac-orienting response is defined as the magnitude of heart rate deceleration from inattention to attention. The attention variables were first evaluated as continuous variables and then dichotomized based on the median split in order to probe the significant interactions.\u003c/p\u003e\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:EEG-RSA\\:Coupling\\:\\sim\\:Attention\\:+\\:s(lag,\\:k=8,\\:bs=\"cr\")\\:+s(lag,by=Attention,\\:k=8,\\:bs=\"cr\")\\:+\\:s(ID,\\:bs={\\prime\\:}re{\\prime\\:})\\:\\:$$\u003c/div\u003e\u003c/div\u003e\u003cp\u003eTo examine time-varying associations between physiological coupling and distinct phases of attention, we segmented the continuous time series data into four attention phases: pre-attention, orienting, sustained attention, and attention termination. The pre-attention phase was defined as the 5-second interval preceding orienting. The orienting phase spanned from the onset of heart rate (HR) deceleration to the point at which sustained attention was established. Sustained attention, as previously defined, referred to the period of maintained HR deceleration. Finally, the attention termination phase encompassed the 5 seconds following the end of sustained attention. We applied a linear mixed-effects model (LMER package in R) to the time-series data, specifying EEG power as the outcome variable. Respiratory sinus arrhythmia (RSA) was decomposed into between-person and within-person components, both of which were included as predictors. Time-varying within-person fluctuations in RSA were modeled as an interaction with the attention phase to capture dynamic coupling effects. Post hoc estimated marginal means were used to derive phase-specific coupling coefficients.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eEvidence of Neural-RSA Coupling\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe used cross-correlation functions to evaluate the coupling between RSA and EEG activity across a time window of -2 to 2 seconds in 5Hz (200ms) time bins, where negative lags indicate RSA leading EEG and positive lags indicate EEG leading RSA. To assess the statistical significance of the observed coupling, a null distribution of beta weights was generated by re-computing the cross-correlation analysis with surrogate respiration time series (k\u0026thinsp;=\u0026thinsp;1000). The resultant cross-correlation coefficients were entered into a linear mixed effects model (LMEM) with pairwise contrasts between surrogate and observed data between every combination of lag-by-region-by-frequency. Pairwise contrasts employed FDR correction for multiple comparisons.\u003c/p\u003e\u003cp\u003eCoupling was observed to be significantly greater than that of the surrogate pairs for Theta and Alpha frequencies in every lag and every region. Statistical significance was \u003cem\u003enot\u003c/em\u003e met in the Beta frequency band for the frontal region at lags 3\u0026ndash;10 (~\u0026thinsp;1500- 2000ms EEG leading RSA) and in the parietal region at \u003cem\u003eany\u003c/em\u003e lag. Interestingly, while theta and alpha demonstrated positive coupling with RSA, Frontal Beta activity demonstrated negative coupling with RSA (increase in RSA associated with a decrease in Beta) at lags \u0026minus;\u0026thinsp;10\u0026thinsp;\u0026minus;\u0026thinsp;2 (2000ms to 0 RSA leading EEG and 0-400ms EEG lead RSA). To summarize, there is evidence of a bidirectional relationship between RSA-EEG coupling in Theta and Alpha frequency bins across frontal and parietal regions and a unidirectional inverse correlation of Beta-leading-RSA coupling in the frontal region (See Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e \u003cem\u003eDescription. Topographic maps depict cross-correlation coefficients between EEG power and RSA for theta, alpha, and beta bands across lags (\u0026minus;\u0026thinsp;2000 to +\u0026thinsp;2000 ms). Lineplots in grey show null distributions of coupling coefficients derived from 1,000 surrogate RSA-EEG pairings. Observed coupling coefficients (colored lines) significantly exceed surrogate distributions for theta and alpha across all lags and regions. Beta coupling was significantly negative in frontal regions and negative lags but not significantly different from surrogate in parietal regions or in positive lags.\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eDistinct Patterns of Coupling based on Frequency and Topography\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe fit three GAMM models for each frequency band, including region as a main effect and lag as a smoothed term to evaluate linear or non-linear effects of lag on peak coupling. The frontal region was set as the reference factor, in line with our a priori hypothesis that greater coupling would occur in the frontal region. Results demonstrated that across alpha and theta frequency bands, RSA coupling was strongest in the frontal region relative to the parietal region. The inverse correlation between RSA and beta was also strongest (higher negative value) in the frontal region relative to parietal (See Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eTheta\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eAlpha\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eBeta\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFixed Effect Terms\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eEst (Std. Error)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eT Statistic\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eEst (Std. Error)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003eT Statistic\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003eEst (Std. Error)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003eT Statistic\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e(Intercept)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.047 (0.007)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.44***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.04 (0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.29***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.013 (0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-2.27*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eregion [parietal]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;0.004 (0.001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026minus;2.60**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.01(0.001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-7.70***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.01(0.001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8.84***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSmoothed Terms\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eEdf (df)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eF Statistic\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eEdf (df)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003eF Statistic\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003eEdf (df)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003eF Statistic\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003elag\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.05 (19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.67***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.065 (19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.180*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.013 (19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.20*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eID\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e79.52 (80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e165.89***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e79.51 (80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e160.89***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e79.39 (80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e130.60***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eModel Fit\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eRsq (adj)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eRsq (adj)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003eRsq (adj)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWithin the Theta band, smoothed lag terms revealed significant non-linear relationships between lag and RSA-EEG, with effective degrees of freedom indicating non-linear lag effects for Theta and Alpha, and a linear lag effect for Beta. A qualitative inspection of the cross-correlation plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) demonstrates that theta-RSA coupling is stronger when RSA leads theta, with a peak coupling occurring around \u0026minus;\u0026thinsp;5 lags (approximately 1000 ms RSA leading EEG). Coupling then decreases as EEG leads RSA. For the Alpha band, a similar non-linear pattern to Theta emerges. Coupling is highest when RSA leads Alpha, and it decreases as Alpha activity begins to lead RSA. Finally, Beta activity exhibits a more linear lag effect, with less pronounced non-linear coupling dynamics compared to Theta and Alpha.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e \u003cem\u003eDescription. Bar plots show mean RSA\u0026ndash;EEG coupling strength, aggregated by frequency band (theta, alpha, beta), scalp region (frontal, blue; parietal, purple), and lag direction. Lag bins represent RSA leading EEG (\u0026minus;\u0026thinsp;2000 to \u0026minus;\u0026thinsp;500 ms), instantaneous coupling (0 ms), and EEG leading RSA (+\u0026thinsp;500 to +\u0026thinsp;2000 ms). Frontal theta coupling was strongest when RSA preceded EEG activity, followed by peak coupling at 0-lag in the same band and region. In contrast, beta activity exhibited the strongest inverse coupling in the frontal cortex when RSA preceeds EEG, consistent with a unidirectional, suppressive pattern.\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eIncreases in Physiological Engagement in Attention with Greater by EEG-RSA Coupling\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe next examined whether RSA\u0026ndash;EEG coupling was associated with the physiological mechanisms underlying sustained attention, specifically, heart rate deceleration (HRD), or the cardiac orienting response. To do this, we re-estimated the GAMM models, this time including each infant\u0026rsquo;s average magnitude of HR deceleration (i.e., the difference in heart rate between inattention and sustained attention phases) as a continuous predictor.\u003c/p\u003e\u003cp\u003eThere were no significant main effects of HRD on overall RSA\u0026ndash;EEG coupling across frequency, region, or lags (p\u0026thinsp;\u0026gt;\u0026thinsp;.15). Significant lag \u0026times; HRD interactions emerged specifically for theta band coupling in the frontal and parietal regions: In the frontal theta model, the interaction was significant (F\u0026thinsp;=\u0026thinsp;1.21, edf\u0026thinsp;=\u0026thinsp;2.22, p\u0026thinsp;=\u0026thinsp;.006). In the high-HRD group, Theta\u0026ndash;RSA coupling showed a non-linear pattern, peaking when RSA led EEG by approximately 500 ms and decreasing when EEG preceded RSA (F\u0026thinsp;=\u0026thinsp;3.20, edf\u0026thinsp;=\u0026thinsp;2.64, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). The low-HRD group exhibited no discernible lag effect; visual inspection revealed no clear peaks in coupling direction. In the parietal theta model, a similar interaction was found (F\u0026thinsp;=\u0026thinsp;2.14, edf\u0026thinsp;=\u0026thinsp;1.80, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). Among high-HRD infants, peak coupling occurred near or just before 0-lag, while the low-HRD group did not show any identifiable peaks.\u003c/p\u003e\u003cp\u003eNo significant lag effects were found for alpha frequency in either the frontal or parietal regions.\u003c/p\u003e\u003cp\u003eFor beta frequencies, which were only evaluated in the frontal region (due to non-significant coupling in the parietal region relative to surrogate pairs), a significant lag \u0026times; HRD interaction was observed (F\u0026thinsp;=\u0026thinsp;1.61, edf\u0026thinsp;=\u0026thinsp;1.83, df\u0026thinsp;=\u0026thinsp;7, p\u0026thinsp;=\u0026thinsp;.001). However, this effect did not replicate when HRD was modeled as a binary high/low variable; no significant interaction was found p\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\ge\\:.08\\)\u003c/span\u003e\u003c/span\u003e, and when visually inspected only a small trend toward greater inverse coupling in the high-HRD group was observed. Interpretation of this finding is limited by the overall small magnitude of cross-correlation values in the beta band.\u003c/p\u003e\u003cp\u003e\u003cb\u003eIncreases in Sustained Attention Behavior with Greater EEG-RSA Coupling\u003c/b\u003e\u003c/p\u003e\u003cp\u003eBehavioral attention during the experimental task was initially included as a continuous predictor, reflecting the proportion of time spent in sustained attention. Throughout the sustained attention task, infants weave in and out of sustained attention over the course of the task. There is within-person variability with the proportion of time spent in sustained attention vs. inattention throughout the task (Brandes-Aitken et al., 2022).\u003c/p\u003e\u003cp\u003eTo facilitate interpretation of interaction effects in the GAMM models, a median split of sustained attention was used in post-hoc analyses to define high- and low-attention groups. No significant main effects of behavioral attention were found on overall RSA\u0026ndash;EEG coupling across frequency or regional domains (all p\u0026thinsp;\u0026gt;\u0026thinsp;.15). However, significant interactions between time lag and behavioral attention emerged for theta band coupling in both the frontal and parietal regions.\u003c/p\u003e\u003cp\u003eIn the frontal theta model, a significant lag \u0026times; attention interaction was observed (F\u0026thinsp;=\u0026thinsp;0.72, edf\u0026thinsp;=\u0026thinsp;1.72, p\u0026thinsp;=\u0026thinsp;.027). For high-attention infants, Theta\u0026ndash;RSA coupling exhibited a non-linear pattern, peaking when RSA led EEG by approximately 500 ms and declining when EEG preceded RSA (F\u0026thinsp;=\u0026thinsp;1.81, edf\u0026thinsp;=\u0026thinsp;1.66, p\u0026thinsp;=\u0026thinsp;.001). In contrast, the low-attention group showed no discernible lag effect; visual inspection revealed no identifiable peaks in coupling directionality (F\u0026thinsp;=\u0026thinsp;0,0, edf\u0026thinsp;=\u0026thinsp;0.0, p\u0026thinsp;=\u0026thinsp;.61). Similarly, in the parietal theta model, a significant lag \u0026times; attention interaction was found (F\u0026thinsp;=\u0026thinsp;0.88, edf\u0026thinsp;=\u0026thinsp;1.80, p\u0026thinsp;=\u0026thinsp;.025). Among high-attention infants, peak Theta\u0026ndash;RSA coupling occurred around 0-lag. No clear coupling peaks were evident in the low-attention group.\u003c/p\u003e\u003cp\u003eThere were no significant lag effects observed in alpha frequency bands for either the frontal or parietal regions.\u003c/p\u003e\u003cp\u003eFor beta frequencies, analyses were limited to the frontal region due to non-significant coupling in the parietal region relative to surrogate pairs. A significant lag \u0026times; attention interaction was found in the frontal region (F\u0026thinsp;=\u0026thinsp;2.35, edf\u0026thinsp;=\u0026thinsp;1.61, df\u0026thinsp;=\u0026thinsp;7, p\u0026thinsp;\u0026lt;\u0026thinsp;.0001). This effect was more linear, with stronger RSA-leading-EEG coupling observed in the high-attention group from \u0026minus;\u0026thinsp;2000 ms to 0 ms. In contrast, no significant effect was observed in the low-attention group (F\u0026thinsp;=\u0026thinsp;0.47, edf\u0026thinsp;=\u0026thinsp;0.88, df\u0026thinsp;=\u0026thinsp;7, p\u0026thinsp;=\u0026thinsp;.037).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e \u003cem\u003eDescription. a) Schematic illustration of a single participant\u0026rsquo;s interbeat interval (IBI) time series, highlighting heart-rate-defined sustained attention (SA) episodes. Blue-shaded regions indicate periods of HR-defined sustained attention, while red ruler figures represent the calculated HR deceleration values from inattention to sustained attention transitions. b) RSA\u0026ndash;EEG cross-correlation plots stratified by high vs. low HR deceleration (median split). Infants with high HR deceleration exhibited stronger coupling overall, particularly in the frontal theta band, where coupling peaked when RSA preceded EEG by approximately 500 ms. In the beta band, high-HRD infants showed inverse coupling; however, this effect was not statistically significant in the model including the binary HRD variable. c) RSA\u0026ndash;EEG coupling plots stratified by high vs. low sustained attention (SA) levels. High-SA infants showed stronger coupling, especially in the theta band, with peak coupling observed when RSA led EEG by 0\u0026ndash;500 ms. A similar but attenuated pattern was seen in the alpha band. High-SA infants also demonstrated greater RSA\u0026ndash;beta coupling during periods when RSA preceded EEG, though this effect was modest.\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eUpregulation of EEG-RSA Coupling Prior To Sustained Attention Phase\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo evaluate how RSA, neural activity, and RSA\u0026ndash;EEG coupling predicted dynamic fluctuations in sustained attention behavior, we modeled time-varying associations among RSA, EEG power, and attention phase using linear mixed-effects models (LMER). RSA was lagged by 600 ms, based on prior analyses showing peak RSA\u0026ndash;EEG cross-correlations at that offset. Analyses focused on frontal theta and alpha frequency bands, where coupling was strongest. Attention was segmented into four phases derived from heart rate dynamics: pre-attention, orienting (HR deceleration onset), sustained attention (continued HR deceleration), and termination (HR re-acceleration), with pre-attention as the reference level. In the uncoupled models, RSA was significantly elevated during the orienting (β\u0026thinsp;=\u0026thinsp;0.22, 95% CI [0.21, 0.24], p\u0026thinsp;\u0026lt;\u0026thinsp;.001) and sustained attention (β\u0026thinsp;=\u0026thinsp;0.22, 95% CI [0.20, 0.24], p\u0026thinsp;\u0026lt;\u0026thinsp;.001) phases compared to pre-attention (see Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Similarly, theta power was significantly higher during orienting (β\u0026thinsp;=\u0026thinsp;0.04, 95% CI [0.03, 0.05], p\u0026thinsp;\u0026lt;\u0026thinsp;.001) and sustained attention (β\u0026thinsp;=\u0026thinsp;0.05, 95% CI [0.04, 0.06], p\u0026thinsp;\u0026lt;\u0026thinsp;.001) with no difference between pre-attention and attention termination. Alpha power showed the same pattern of increasing during orienting (β\u0026thinsp;=\u0026thinsp;0.04, 95% CI [0.03, 0.05], p\u0026thinsp;\u0026lt;\u0026thinsp;.001) and sustained attention (β\u0026thinsp;=\u0026thinsp;0.05, 95% CI [0.04, 0.06], p\u0026thinsp;\u0026lt;\u0026thinsp;.001), and no significant difference at termination.\u003c/p\u003e\u003cp\u003eIn the RSA\u0026ndash;EEG coupling models, we observed significant RSA \u0026times; Phase interactions. For theta, coupling peaked during orienting (β\u0026thinsp;=\u0026thinsp;0.02, 95% CI [0.00, 0.04], p\u0026thinsp;=\u0026thinsp;.017) and decreased significantly during sustained attention (β = \u0026minus;\u0026thinsp;0.02, 95% CI [\u0026ndash;0.04, \u0026minus;\u0026thinsp;0.01], p\u0026thinsp;=\u0026thinsp;.006). The termination phase did not show a significant interaction. For alpha, coupling was also strongest during orienting (β\u0026thinsp;=\u0026thinsp;0.04, 95% CI [0.02, 0.06], p\u0026thinsp;\u0026lt;\u0026thinsp;.001), with trend level increased coupling during sustained attention (β\u0026thinsp;=\u0026thinsp;0.01, 95% CI [\u0026ndash;0.00, 0.03], p\u0026thinsp;=\u0026thinsp;.08) and termination (β\u0026thinsp;=\u0026thinsp;0.02, 95% CI [\u0026ndash;0.00, 0.03], p\u0026thinsp;=\u0026thinsp;.07; See Full Table of Results in SI).\u003c/p\u003e\u003cp\u003e\u003cb\u003e​​\u003c/b\u003eFigure \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. \u003cb\u003eTime-to-attention dynamics of RSA, EEG power, and RSA\u0026ndash;EEG coupling.\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur findings provide robust evidence of dynamic coupling between electroencephalography (EEG) and respiratory sinus arrhythmia (RSA) activity in 3-month-old infants during an attention task. Among the frequency bands examined, theta activity exhibited the strongest positive coupling with RSA, followed by alpha, suggesting that increased activity in one system is associated with upregulation in the other. In contrast, beta activity demonstrated the weakest coupling and was negatively correlated with RSA, indicating a potentially suppressive or inhibitory interaction. Coupling was observed across the scalp, with stronger effects localized in the frontal region relative to parietal areas. Cross-correlation analyses revealed bidirectional coupling for both theta and alpha bands, with peak coupling occurring when RSA preceded EEG activity, suggesting that vagal-mediated signals may play a leading role in shaping oscillatory dynamics in lower frequency bands. Conversely, beta activity showed minimal evidence of reciprocal interaction, instead displaying RSA-driven suppression rather than mutual modulation. Notably, infants who exhibited greater RSA-lead coupling with theta, alpha, and beta activity, demonstrated enhanced sustained attention during the task, as indexed by prolonged looking and greater heart rate deceleration. Finally, the temporal dynamics of RSA\u0026ndash;EEG coupling followed a dynamic profile whereby coupling increased during phases of orienting, plateaued during sustained attention, and declined during disengagement or inattention. These findings highlight the coordinated roles of vagus nerve activity and cortical oscillatory activity in supporting early attentional engagement and regulation.\u003c/p\u003e\u003cp\u003eThere was evidence of varying degrees of coupling across all frequency bands; however, the strongest RSA-EEG co-variation emerged within the theta frequency band. This finding aligns with prior research demonstrating synchronized activity between vagal signals and theta oscillations \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e,\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. Theta power is widely recognized as a neural mechanism supporting top-down, effortful control of attention \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e, while vagal activity is a key physiological marker of behavioral regulation and self-regulatory capacity. Notably, during an infant stress-eliciting paradigm, both RSA activity and theta power increase across the scalp, corresponding with greater infant attention to social stimuli \u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. Converging lines of evidence suggest that vagal-mediated signals may influence active learning and attentional processes through modulation of theta rhythms. Experimental studies have shown that vagus nerve stimulation induces theta oscillations originating in the hippocampus \u003csup\u003e\u003cspan additionalcitationids=\"CR50\" citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. Task-evoked theta power is thought to originate from subcortical regions such as the hippocampus and nucleus accumbens, which engage in feedback loops with cortical regions to facilitate active learning \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. In rodent models, task-dependent theta activity has been shown to induce long-term potentiation, a core mechanism of learning and memory \u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. Importantly, theta and high-frequency HRV oscillations operate within similar frequency ranges, making their coupling physiologically plausible. Respiration-entrained theta oscillations have been causally demonstrated in rodent models \u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e, and recent theoretical work has proposed that theta oscillations may, in part, originate within the vagus nerve itself \u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. This causal evidence aligns with our directionality analysis, demonstrating that the strongest RSA-Theta coupling occurred when RSA preceded theta activity. Altogether, these findings support the idea that vagal signals may be communicated to and represented in the brain via theta rhythms, which are known to support long-range neural communication and facilitate attentional engagement.\u003c/p\u003e\u003cp\u003eIn contrast, there is relatively less research linking vagal-mediated signals to alpha and beta oscillatory activity. Adult studies have shown that vagal stimulation can increase alpha power and reduce high frequency (30-40hz) power\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. Task-related beta activity has been associated with GABAA-mediated inhibitory processes, although the precise mechanisms remain under investigation. In the current study, increases in RSA were associated with decreases in beta power. One possible explanation is that vagal input modulates theta rhythms, which in turn influence higher-frequency oscillations such as beta through cross-frequency phase-amplitude coupling \u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. This indirect pathway could account for the lack of bidirectional effects in our beta analyses, specifically, that beta activity did not exert reciprocal influence on RSA. This pattern aligns with existing literature showing that increases in task-related low-frequency power are often accompanied by decreases in high-frequency power \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. Together, these findings collectively point toward a multi-level mechanism by which parasympathetic activity, mediated by vagal signals, modulate neural oscillatory dynamics in support of attention and cognitive engagement in early development.\u003c/p\u003e\u003cp\u003eAlthough both frontal and parietal regions demonstrated significant RSA\u0026ndash;neural coupling, the frontal region consistently exhibited stronger coupling across all frequency bands and time lags. This is a noteworthy finding that aligns with and extends existing developmental literature, which posits robust bidirectional connections between vagal input and frontal cortical activity\u0026mdash;an area critical for top-down attentional control \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. These results provide empirical support for neurobiological models of vagal\u0026ndash;frontal integration. Specifically, vagal afferent fibers project to the medullary nucleus of the solitary tract (NTS), which plays a central role in coordinating autonomic regulation. From the NTS, signals follow multiple ascending and descending pathways: (1) efferent feedback to modulate peripheral physiological states, (2) projections to the reticular formation, and (3) ascending pathways to the forebrain via the parabrachial nucleus and locus coeruleus \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e. These ascending circuits ultimately reach limbic and cortical regions involved in the regulation of frontal cortical activity. The observed dominance of frontal coupling underscores the importance of this network in supporting the integration of physiological regulation and attentional processes during early development.\u003c/p\u003e\u003cp\u003eRSA-neural coupling predicted the magnitude of physiological engagement during transitions into sustained attention. Prior research has shown that infant HR deceleration in response to engaging stimuli is associated with baseline RSA, providing converging evidence for the interrelation between vagal activity and subsequent physiologically induced attention episodes \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e. Additionally, we found that individual differences in the magnitude of RSA\u0026ndash;EEG coupling distinguished infants in the \u0026lsquo;high attention\u0026rsquo; versus \u0026lsquo;low attention\u0026rsquo; subgroups. Within the theta frequency band, high-attention infants exhibited a pronounced increase in RSA-leading-EEG coupling in the frontal region, along with concurrent coupling in the parietal region. In contrast, low-attention infants showed markedly reduced coupling in both regions. Notably, among high-attention infants, RSA preceding EEG activity in the frontal cortex was associated with strong negative correlations, a pattern not observed in the low-attention group, where coupling was relatively flat. These findings suggest a functional role for RSA\u0026ndash;neural coupling in supporting sustained attention: infants who showed stronger coupling, particularly when RSA activity led neural oscillatory dynamics, spent more time engaged in sustained attention during the task. These findings support the idea that the coordination and upregulation of vagal activity, in temporal synchrony with neural oscillations, facilitates attentional control in early infancy. This is the first study, to our knowledge, to examine real-time neural-physiological coupling in infants in relation to behavioral attention, offering a novel framework for understanding early regulatory mechanisms.\u003c/p\u003e\u003cp\u003eTo better understand how RSA-EEG modulates sustained attention in real-time we evaluated time-to-attention changes in coupling between phases of pre-attention, attention orienting, sustained attention, and attention termination. When modeled independently, RSA, Theta and Alpha power demonstrated marked increases during orienting and sustained attention, as has previously been demonstrated \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. However, when examining the time-varying interaction between neural activity and attention phase as a function of RSA change, we found that the highest degree of coupling occurred for theta and alpha activity during orienting. This is a novel finding suggesting that vagal-cortical coupling may reflect a mechanism for recruiting physiological resources that mobilize heart-rate defined sustained attention.\u003c/p\u003e\u003cp\u003e\u003cb\u003eLimitations and Future Directions\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe present study represents a significant advancement in the field of developmental neurophysiological research, but replication and expansion are warranted. We first suggest that future research explicitly measure infant respiration and cardiac action potentials to better evaluate what other physiological mechanisms may be directly entraining neural oscillatory bursts outside of vagal input. Further disaggregating periodic versus 1/f aperiodic associations with RSA could provide more insight into the neural mechanism most sensitive to changes in physiological regulation \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Finally, a longitudinal study would be useful for better understanding how neural-vagal coupling shows maturational changes from early to late infancy.\u003c/p\u003e\u003cp\u003eIn summary, we demonstrated some of the first evidence that significant RSA-neural coupling exists in infants at 3-months of age during a sustained attention task. We observed a positive coupling between RSA and both theta and alpha activity, as well as an inverse-coupling between RSA and beta activity. We found stronger effects in the frontal cortex, particularly when RSA preceded EEG activity. Infants who spent more time in sustained attention exhibited greater RSA-leading coupling in theta and alpha, and stronger RSA-leading beta decoupling. These results suggest that vagal modulation of neural dynamics plays a central role in attentional engagement in early infancy. Collectively, our findings propose a novel conceptual and methodological framework for studying infant neurophysiology, offering new insight into how autonomic-neural coordination shapes the development of attentional control.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eArnsten, A.F.: Catecholamine modulation of prefrontal cortical cognitive function. Trends Cogn. Sci. \u003cb\u003e2\u003c/b\u003e, 436\u0026ndash;447 (1998)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eArnsten, A.F.T., Li, B.-M.: Neurobiology of executive functions: catecholamine influences on prefrontal cortical functions. Biol. Psychiatry. \u003cb\u003e57\u003c/b\u003e, 1377\u0026ndash;1384 (2005)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBrandes-Aitken, A., Metser, M., Braren, S.H., Vogel, S.C., Brito, N.H.: Neurophysiology of sustained attention in early infancy: Investigating longitudinal relations with recognition memory outcomes. Infant Behav. Dev. \u003cb\u003e70\u003c/b\u003e, 101807 (2023)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBrandes-Aitken, A., et al.: Within-person changes in basal cortisol and caregiving modulate executive attention across infancy. Dev. Psychopathol. \u003cb\u003e34\u003c/b\u003e, 1386\u0026ndash;1399 (2022)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRichards, J.E., Casey, B.J.: Heart rate variability during attention phases in young infants. Psychophysiology. \u003cb\u003e28\u003c/b\u003e, 43\u0026ndash;53 (1991)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ede Barbaro, K., Clackson, K., Wass, S.V.: Infant Attention Is Dynamically Modulated With Changing Arousal Levels. Child. Dev. \u003cb\u003e88\u003c/b\u003e, 629\u0026ndash;639 (2017)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBrandes-Aitken, A., Braren, S., Swingler, M., Voegtline, K., Blair, C.: Sustained attention in infancy: A foundation for the development of multiple aspects of self-regulation for children in poverty. J. Exp. Child. Psychol. \u003cb\u003e184\u003c/b\u003e, 192\u0026ndash;209 (2019)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWass, S.V., Clackson, K., de Barbaro, K.: Temporal dynamics of arousal and attention in 12-month-old infants: Temporal Dynamics of Arousal in Infants. Dev. Psychobiol. \u003cb\u003e58\u003c/b\u003e, 623\u0026ndash;639 (2016)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAmad\u0026oacute;, M.P., et al.: The neural and physiological substrates of real-world attention change across development. eLife (2023). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.7554/elife.92171.1\u003c/span\u003e\u003cspan address=\"10.7554/elife.92171.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWass, S.V., de Barbaro, K., Clackson, K.: Tonic and phasic co-variation of peripheral arousal indices in infants. Biol. Psychol. \u003cb\u003e111\u003c/b\u003e, 26\u0026ndash;39 (2015)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAston-Jones, G., Rajkowski, J., Cohen, J.: Role of locus coeruleus in attention and behavioral flexibility. Biol. Psychiatry. \u003cb\u003e46\u003c/b\u003e, 1309\u0026ndash;1320 (1999)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBraren, S.H., et al.: Baseline hypothalamic\u0026ndash;pituitary\u0026ndash;adrenal axis and parasympathetic nervous system activity interact to predict executive functions in low-income children. Mind Brain Educ. \u003cb\u003e15\u003c/b\u003e, 61\u0026ndash;66 (2021)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSuess, P.E., Porges, S.W., Plude, D.J.: Cardiac vagal tone and sustained attention in school-age children. Psychophysiology. \u003cb\u003e31\u003c/b\u003e, 17\u0026ndash;22 (1994)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRichards, J.E., Cameron, D.: Infant heart-rate variability and behavioral developmental status. Infant Behav. Dev. \u003cb\u003e12\u003c/b\u003e, 45\u0026ndash;58 (1989)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePorges, S.W.: Autonomic regulation and attention. In: Attention and information processing in infants and adults 201\u0026ndash;223. Psychology (2014)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePorges, S.W., Doussard-Roosevelt, J.A., Portales, A.L., Greenspan, S.: I. Infant regulation of the vagal brake predicts child behavior problems: a psychobiological model of social behavior. Dev. Psychobiol. \u003cb\u003e29\u003c/b\u003e, 697\u0026ndash;712 (1996)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLevy, M.N., Warner, M.R.: Parasympathetic effects on cardiac function. Neurocardiology (1994)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBeauchaine, T.P.: Respiratory sinus arrhythmia: A transdiagnostic biomarker of emotion dysregulation and psychopathology. Curr. Opin. Psychol. \u003cb\u003e3\u003c/b\u003e, 43\u0026ndash;47 (2015)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePorges, S.W.: Orienting in a defensive world: mammalian modifications of our evolutionary heritage. Polyvagal Theory Psychophysiology. \u003cb\u003e32\u003c/b\u003e, 301\u0026ndash;318 (1995)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBeauchaine, T.P., Thayer, J.F.: Heart rate variability as a transdiagnostic biomarker of psychopathology. Int. J. Psychophysiol. \u003cb\u003e98\u003c/b\u003e, 338\u0026ndash;350 (2015)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eThayer, J.F., Lane, R.D.: A model of neurovisceral integration in emotion regulation and dysregulation. J. Affect. Disord. \u003cb\u003e61\u003c/b\u003e, 201\u0026ndash;216 (2000)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eThayer, J.F., Hansen, A.L., Saus-Rose, E., Johnsen, B.H.: Heart rate variability, prefrontal neural function, and cognitive performance: the neurovisceral integration perspective on self-regulation, adaptation, and health. Ann. Behav. Med. \u003cb\u003e37\u003c/b\u003e, 141\u0026ndash;153 (2009)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSakaki, M., et al.: Heart rate variability is associated with amygdala functional connectivity with MPFC across younger and older adults. Neuroimage. \u003cb\u003e139\u003c/b\u003e, 44\u0026ndash;52 (2016)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLane, R.D., et al.: Neural correlates of heart rate variability during emotion. Neuroimage. \u003cb\u003e44\u003c/b\u003e, 213\u0026ndash;222 (2009)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWeissman, D.G., Guyer, A.E., Ferrer, E., Robins, R.W., Hastings, P.D.: Adolescents\u0026rsquo; brain-autonomic coupling during emotion processing. Neuroimage. \u003cb\u003e183\u003c/b\u003e, 818\u0026ndash;827 (2018)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBolt, T., et al.: Autonomic physiological coupling of the global fMRI signal. Nat. Neurosci. 1\u0026ndash;9 (2025)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKluger, D.S., et al.: Modulatory dynamics of periodic and aperiodic activity in respiration-brain coupling. Nat. Commun. \u003cb\u003e14\u003c/b\u003e, 4699 (2023)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePeoples, S.G., Davis, E.L., Brooker, R.J.: Variation in coupling across neural and cardiac systems of regulation is linked to markers of anxiety risk in preschool. Dev. Psychopathol. 1\u0026ndash;13 (2024)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBarry, R.J., Clarke, A.R., Johnstone, S.J., McCarthy, R., Selikowitz, M.: Electroencephalogram theta/beta ratio and arousal in attention-deficit/hyperactivity disorder: evidence of independent processes. Biol. Psychiatry. \u003cb\u003e66\u003c/b\u003e, 398\u0026ndash;401 (2009)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRho, G., Callara, A.L., Bernardi, G., Scilingo, E.P., Greco, A.: EEG cortical activity and connectivity correlates of early sympathetic response during cold pressor test. Sci. Rep. \u003cb\u003e13\u003c/b\u003e, 1338 (2023)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBarry, R.J., De Blasio, F.M., Fogarty, J.S., Clarke, A.R.: Natural alpha frequency components in resting EEG and their relation to arousal. Clin. Neurophysiol. \u003cb\u003e131\u003c/b\u003e, 205\u0026ndash;212 (2020)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKawashima, T., Shiratori, H., Amano, K.: The relationship between alpha power and heart rate variability commonly seen in various mental states. PLoS One. \u003cb\u003e19\u003c/b\u003e, e0298961 (2024)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKang, D., Choi, Y., Lee, J., Park, E., Kim, I.Y.: Analysis of taVNS effects on autonomic and central nervous systems in healthy young adults based on HRV, EEG parameters. J. Neural Eng. \u003cb\u003e21\u003c/b\u003e, 046012 (2024)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChang, P.F., Arendt-Nielsen, L., Chen, A.C.: N. Dynamic changes and spatial correlation of EEG activities during cold pressor test in man. Brain Res. Bull. \u003cb\u003e57\u003c/b\u003e, 667\u0026ndash;675 (2002)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBegus, K., Bonawitz, E.: The rhythm of learning: Theta oscillations as an index of active learning in infancy. Dev. Cogn. Neurosci. \u003cb\u003e45\u003c/b\u003e, 100810 (2020)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCohen, M.X., et al.: Top-down-directed synchrony from medial frontal cortex to nucleus accumbens during reward anticipation. Hum. Brain Mapp. \u003cb\u003e33\u003c/b\u003e, 246\u0026ndash;252 (2012)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBraithwaite, E.K., Jones, E.J.H., Johnson, M.H., Holmboe, K.: Dynamic modulation of frontal theta power predicts cognitive ability in infancy. Dev. Cogn. Neurosci. \u003cb\u003e45\u003c/b\u003e, 100818 (2020)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAnderson, A.J., Perone, S., Gartstein, M.A.: Context matters: Cortical rhythms in infants across baseline and play. Infant Behav. Dev. \u003cb\u003e66\u003c/b\u003e, 101665 (2022)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXie, W., Mallin, B.M., Richards, J.E.: Development of infant sustained attention and its relation to EEG oscillations: an EEG and cortical source analysis study. Dev. Sci. e12562 (2017)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFingelkurts, A.A., et al.: Enhancement of GABA-related signalling is associated with increase of functional connectivity in human cortex: GABA Inhibition and Cortical Connectivity. Hum. Brain Mapp. \u003cb\u003e22\u003c/b\u003e, 27\u0026ndash;39 (2004)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePorjesz, B., et al.: Linkage disequilibrium between the beta frequency of the human EEG and a GABAA receptor gene locus. \u003cem\u003eProc. Natl. Acad. Sci. U. S. A.\u003c/em\u003e 99, 3729\u0026ndash;3733 (2002)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eReynolds, G.D., Richards, J.E.: Infant heart rate: A developmental psychophysiological perspective. In: Schmidt, L.A. (ed.) Developmental psychophysiology: Theory, systems, and methods, pp. 173\u0026ndash;212. Cambridge University Press, xxii, pp., New York, NY, US, (2008)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYuan, H., Zotev, V., Phillips, R., Bodurka, J.: Correlated slow fluctuations in respiration, EEG, and BOLD fMRI. Neuroimage. \u003cb\u003e79\u003c/b\u003e, 81\u0026ndash;93 (2013)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNguyen, T., Hoehl, S., Bertenthal, B.I., Abney, D.H.: Coupling between prefrontal brain activity and respiratory sinus arrhythmia in infants and adults. Dev. Cogn. Neurosci. \u003cb\u003e58\u003c/b\u003e, 101184 (2022)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRichards, J.E.: Prospectus for using continuous wavelet transform for respiratory sinus arrhythmia. Res. Gate. (2025). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.13140/RG.2.2.24125.58081\u003c/span\u003e\u003cspan address=\"10.13140/RG.2.2.24125.58081\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBrandes-Aitken, A., et al.: Maternal heart rate variability at 3-months postpartum is associated with maternal mental health and infant neurophysiology. Sci. Rep. \u003cb\u003e14\u003c/b\u003e, 18766 (2024)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBazhenova, O.V., Stroganova, T.A., Doussard-Roosevelt, J.A., Posikera, I.A., Porges, S.: W. Physiological responses of 5-month-old infants to smiling and blank faces. Int. J. Psychophysiol. \u003cb\u003e63\u003c/b\u003e, 64\u0026ndash;76 (2007)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOrekhova, E.V., Stroganova, T.A., Posikera, I.N.: Theta synchronization during sustained anticipatory attention in infants over the second half of the first year of life. Int. J. Psychophysiol. \u003cb\u003e32\u003c/b\u003e, 151\u0026ndash;172 (1999)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBroncel, A., Bocian, R., Konopacki, J.: Vagal nerve stimulation: The effect on the brain oscillatory field potential. Neuroscience. \u003cb\u003e483\u003c/b\u003e, 127\u0026ndash;138 (2022)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBroncel, A., Bocian, R., Kłos-Wojtczak, P., Konopacki, J.: Hippocampal theta rhythm induced by vagal nerve stimulation: The effect of modulation of electrical coupling. Brain Res. Bull. \u003cb\u003e152\u003c/b\u003e, 236\u0026ndash;245 (2019)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKaralis, N., Sirota, A.: Breathing coordinates cortico-hippocampal dynamics in mice during offline states. Nat. Commun. \u003cb\u003e13\u003c/b\u003e, 467 (2022)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGreenstein, Y.J., Pavlides, C., Winson, J.: Long-term potentiation in the dentate gyrus is preferentially induced at theta rhythm periodicity. Brain Res. \u003cb\u003e438\u003c/b\u003e, 331\u0026ndash;334 (1988)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYanovsky, Y., Ciatipis, M., Draguhn, A., Tort, A.B.L., Brankačk, J.: Slow oscillations in the mouse hippocampus entrained by nasal respiration. J. Neurosci. \u003cb\u003e34\u003c/b\u003e, 5949\u0026ndash;5964 (2014)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShaffer, C., Barrett, L.F., Quigley, K.S.: Signal processing in the vagus nerve: Hypotheses based on new genetic and anatomical evidence. Biol. Psychol. \u003cb\u003e182\u003c/b\u003e, 108626 (2023)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVanneste, S., Martin, J., Rennaker, R.L. 2nd, Kilgard, M.P.: Pairing sound with vagus nerve stimulation modulates cortical synchrony and phase coherence in tinnitus: An exploratory retrospective study. Sci. Rep. \u003cb\u003e7\u003c/b\u003e, 17345 (2017)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBr\u0026aelig;ndholt, M., et al.: Breathing in waves: Understanding respiratory-brain coupling as a gradient of predictive oscillations. Neurosci. Biobehav Rev. \u003cb\u003e152\u003c/b\u003e, 105262 (2023)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZavala, B., et al.: Cognitive control involves theta power within trials and beta power across trials in the prefrontal-subthalamic network. Brain. \u003cb\u003e141\u003c/b\u003e, 3361\u0026ndash;3376 (2018)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePorges, S.W.: The Polyvagal Theory: phylogenetic contributions to social behavior. Physiol. Behav. \u003cb\u003e79\u003c/b\u003e, 503\u0026ndash;513 (2003)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBenarroch, E.: Central autonomic network: functional organization and clinical correlations. Chapters 1\u0026amp;2. Preprint at (1998)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBradshaw, J., Abney, D.H.: Infant physiological activity and the early emergence of social communication. Dev. Psychobiol. \u003cb\u003e63\u003c/b\u003e, e22145 (2021)\u003c/span\u003e\u003c/li\u003e\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":"","lastPublishedDoi":"10.21203/rs.3.rs-7143406/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7143406/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe developing brain does not regulate attention in isolation, but coordinates with the body\u0026rsquo;s peripheral signals. However, empirical research quantifying the dynamic interplay between neural oscillations and autonomic signals during infancy is lacking. Here, we provide the first evidence of real-time coupling between cortical activity and parasympathetic tone in 3-month-old infants during a sustained attention task. Using simultaneous EEG and ECG, we extracted continuous time series of respiratory sinus arrhythmia (RSA) and EEG power across theta, alpha, and beta bands. Cross-correlation and generalized additive mixed models revealed frequency- and region-specific coupling: theta and alpha power were positively linked to RSA, peaking when RSA preceded neural activity while beta power showed inverse coupling. Coupling was strongest for theta power in the frontal cortex and predicted the magnitude of sustained attention. Notably, RSA\u0026ndash;neural coupling peaked immediately prior to the onset of sustained attention while the infant was orienting to the stimulus. These findings evidence a dynamic, bidirectional autonomic-neural signal attunement that scaffolds attention from the earliest months of life.\u003c/p\u003e","manuscriptTitle":"Autonomic and Neural Activity Dynamically Couple During Infant Attention","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-20 12:53:16","doi":"10.21203/rs.3.rs-7143406/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"communications-biology","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"commsbio","sideBox":"Learn more about [Communications Biology](http://www.nature.com/commsbio/)","snPcode":"","submissionUrl":"","title":"Communications Biology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Communications Series","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"a546d6f5-9bfb-437b-b68d-8a25beff48ef","owner":[],"postedDate":"October 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":54820627,"name":"Biological sciences/Neuroscience/Cognitive neuroscience/Attention"},{"id":54820628,"name":"Biological sciences/Physiology/Respiration"}],"tags":[],"updatedAt":"2026-05-11T20:11:31+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-20 12:53:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7143406","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7143406","identity":"rs-7143406","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}