Heart Rate Variability as an Index of Differential Brain Dynamics at Rest and After Acute Stress Induction

The brain continuously receives input from the internal and external environment. Using this information, the brain exerts its influence on both itself and the body to facilitate an appropriate response. The dynamic interplay between the brain and the heart and how external conditions modulate this relationship deserves attention. In high-stress situations, synchrony between various brain regions such as the prefrontal cortex and the heart may alter. This flexibility is believed to facilitate transitions between functional states related to cognitive, emotional, and especially autonomic activity. This study examined the dynamic temporal functional association of heart rate variability (HRV) with the interaction between three main canonical brain networks in 38 healthy male subjects at rest and directly after a psychosocial stress task. A sliding window approach was used to estimate the functional connectivity (FC) among the salience network (SN), central executive network (CEN), and default mode network (DMN) in 60-s windows on time series of blood-oxygen-level dependent (BOLD) signal. FC between brain networks was calculated by Pearson correlation. A multilevel linear mixed model was conducted to examine the window-by-window association between the root mean square of successive differences between normal heartbeats (RMSSD) and FC of network-pairs across sessions. Our findings showed that the minute-by-minute correlation between the FC and RMSSD was significantly stronger between DMN and CEN than for SN and CEN in the baseline session [b = 4.36, t(5025) = 3.20, p = 0.006]. Additionally, this differential relationship between network pairs and RMSSD disappeared after the stress task; FC between DMN and CEN showed a weaker correlation with RMSSD in comparison to baseline [b = −3.35, t(5025) = −3.47, p = 0.006]. These results suggest a dynamic functional interplay between HRV and the functional association between brain networks that varies depending on the needs created by changing conditions.

[1]  E. Benarroch The central autonomic network: functional organization, dysfunction, and perspective. , 1993, Mayo Clinic proceedings.

[2]  G. Breithardt,et al.  Heart rate variability: standards of measurement, physiological interpretation and clinical use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. , 1996 .

[3]  Jan de Leeuw,et al.  Introducing Multilevel Modeling , 1998 .

[4]  L. Amaral,et al.  Multifractality in human heartbeat dynamics , 1998, Nature.

[5]  C. Peng,et al.  Cardiac interbeat interval dynamics from childhood to senescence : comparison of conventional and new measures based on fractals and chaos theory. , 1999, Circulation.

[6]  R. Lane,et al.  A model of neurovisceral integration in emotion regulation and dysregulation. , 2000, Journal of affective disorders.

[7]  G L Shulman,et al.  INAUGURAL ARTICLE by a Recently Elected Academy Member:A default mode of brain function , 2001 .

[8]  H. Huikuri,et al.  Ectopic Beats in Heart Rate Variability Analysis: Effects of Editing on Time and Frequency Domain Measures , 2001, Annals of noninvasive electrocardiology : the official journal of the International Society for Holter and Noninvasive Electrocardiology, Inc.

[9]  Christiane,et al.  World Medical Association Declaration of Helsinki: ethical principles for medical research involving human subjects. , 2004, Journal international de bioethique = International journal of bioethics.

[10]  J. Thayer,et al.  Psychosomatics and psychopathology: looking up and down from the brain , 2005, Psychoneuroendocrinology.

[11]  Peter A. Bandettini,et al.  Separating respiratory-variation-related fluctuations from neuronal-activity-related fluctuations in fMRI , 2006, NeuroImage.

[12]  Jeff H. Duyn,et al.  Low-frequency fluctuations in the cardiac rate as a source of variance in the resting-state fMRI BOLD signal , 2007, NeuroImage.

[13]  Thomas T. Liu,et al.  A component based noise correction method (CompCor) for BOLD and perfusion based fMRI , 2007, NeuroImage.

[14]  Myeong Gi Jeong,et al.  Ultra Short Term Analysis of Heart Rate Variability for Monitoring Mental Stress in Mobile Settings , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[15]  Olaf Sporns,et al.  Network structure of cerebral cortex shapes functional connectivity on multiple time scales , 2007, Proceedings of the National Academy of Sciences.

[16]  G. Glover,et al.  Dissociable Intrinsic Connectivity Networks for Salience Processing and Executive Control , 2007, The Journal of Neuroscience.

[17]  D. Schacter,et al.  The Brain's Default Network , 2008, Annals of the New York Academy of Sciences.

[18]  Emery N. Brown,et al.  Brain correlates of autonomic modulation: Combining heart rate variability with fMRI , 2008, NeuroImage.

[19]  Catie Chang,et al.  Effects of model-based physiological noise correction on default mode network anti-correlations and correlations , 2009, NeuroImage.

[20]  A. Hamm,et al.  Individual differences in fear-potentiated startle as a function of resting heart rate variability: implications for panic disorder. , 2009, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[21]  M. Corbetta,et al.  Learning sculpts the spontaneous activity of the resting human brain , 2009, Proceedings of the National Academy of Sciences.

[22]  R. Lane,et al.  Claude Bernard and the heart–brain connection: Further elaboration of a model of neurovisceral integration , 2009, Neuroscience & Biobehavioral Reviews.

[23]  R. Mccraty,et al.  The Coherent Heart Heart-Brain Interactions, Psychophysiological Coherence, and the Emergence of System-Wide Order , 2009 .

[24]  Edwin M. Robertson,et al.  The Resting Human Brain and Motor Learning , 2009, Current Biology.

[25]  J. Thayer,et al.  Relationship between heart rate variability and cognitive function during threat of shock , 2009, Anxiety, stress, and coping.

[26]  R. Mccraty,et al.  Coherence: bridging personal, social, and global health. , 2010, Alternative therapies in health and medicine.

[27]  Catie Chang,et al.  Time–frequency dynamics of resting-state brain connectivity measured with fMRI , 2010, NeuroImage.

[28]  N. Makris,et al.  Sex Differences in Stress Response Circuitry Activation Dependent on Female Hormonal Cycle , 2010, The Journal of Neuroscience.

[29]  Erno J. Hermans,et al.  Enhanced resting-state connectivity of amygdala in the immediate aftermath of acute psychological stress , 2010, NeuroImage.

[30]  Pieter R. Roelfsema,et al.  The Brain's Router: A Cortical Network Model of Serial Processing in the Primate Brain , 2010, PLoS Comput. Biol..

[31]  R. Oostenveld,et al.  Stress-Related Noradrenergic Activity Prompts Large-Scale Neural Network Reconfiguration , 2011, Science.

[32]  Kaustubh Supekar,et al.  Dynamic Reconfiguration of Structural and Functional Connectivity Across Core Neurocognitive Brain Networks with Development , 2011, The Journal of Neuroscience.

[33]  Timothy D. Verstynen,et al.  Using pulse oximetry to account for high and low frequency physiological artifacts in the BOLD signal , 2011, NeuroImage.

[34]  M. Sigman,et al.  The human Turing machine: a neural framework for mental programs , 2011, Trends in Cognitive Sciences.

[35]  V. Menon Large-scale brain networks and psychopathology: a unifying triple network model , 2011, Trends in Cognitive Sciences.

[36]  Mirja A. Peltola Role of Editing of R–R Intervals in the Analysis of Heart Rate Variability , 2011, Front. Physio..

[37]  Susan L. Whitfield-Gabrieli,et al.  Conn: A Functional Connectivity Toolbox for Correlated and Anticorrelated Brain Networks , 2012, Brain Connect..

[38]  Mariano Sigman,et al.  From a single decision to a multi-step algorithm , 2012, Current Opinion in Neurobiology.

[39]  M. Greicius,et al.  Decoding subject-driven cognitive states with whole-brain connectivity patterns. , 2012, Cerebral cortex.

[40]  Mark D'Esposito,et al.  The continuing challenge of understanding and modeling hemodynamic variation in fMRI , 2012, NeuroImage.

[41]  Y. J. Kang,et al.  Correction: Corrigendum: MicroRNA122 is a key regulator of α-fetoprotein expression and influences the aggressiveness of hepatocellular carcinoma , 2012 .

[42]  Amir Bashan,et al.  Network physiology reveals relations between network topology and physiological function , 2012, Nature Communications.

[43]  J. Thayer,et al.  eview meta-analysis of heart rate variability and neuroimaging studies : Implications or heart rate variability as a marker of stress and health , 2012 .

[44]  Dost Öngür,et al.  Anticorrelations in resting state networks without global signal regression , 2012, NeuroImage.

[45]  Ajay B. Satpute,et al.  Large-scale brain networks in affective and social neuroscience: towards an integrative functional architecture of the brain , 2013, Current Opinion in Neurobiology.

[46]  T. Hendler,et al.  Neural traces of stress: cortisol related sustained enhancement of amygdala-hippocampal functional connectivity , 2013, Front. Hum. Neurosci..

[47]  Hans-Jochen Heinze,et al.  Association between heart rate variability and fluctuations in resting-state functional connectivity , 2013, NeuroImage.

[48]  Ilya M. Veer,et al.  The impact of “physiological correction” on functional connectivity analysis of pharmacological resting state fMRI , 2013, NeuroImage.

[49]  L. Nilsson Respiration Signals from Photoplethysmography , 2013, Anesthesia and analgesia.

[50]  J. Vagedes,et al.  How accurate is pulse rate variability as an estimate of heart rate variability? A review on studies comparing photoplethysmographic technology with an electrocardiogram. , 2013, International journal of cardiology.

[51]  V. Napadow,et al.  The Autonomic Brain: An Activation Likelihood Estimation Meta-Analysis for Central Processing of Autonomic Function , 2013, The Journal of Neuroscience.

[52]  David A. Leopold,et al.  Dynamic functional connectivity: Promise, issues, and interpretations , 2013, NeuroImage.

[53]  Mary Beth Nebel,et al.  Reduction of motion-related artifacts in resting state fMRI using aCompCor , 2014, NeuroImage.

[54]  Jocelyn E. Bolin,et al.  Multilevel Modeling Using R , 2019 .

[55]  A. Flatt,et al.  Ultra-short-term heart rate variability indexes at rest and post-exercise in athletes: evaluating the agreement with accepted recommendations. , 2014, Journal of sports science & medicine.

[56]  John J. B. Allen,et al.  Increased association over time between regional frontal lobe BOLD change magnitude and cardiac vagal control with sertraline treatment for major depression , 2014, Psychiatry Research: Neuroimaging.

[57]  S. Dehaene Consciousness and the brain : deciphering how the brain codes our thoughts , 2014 .

[58]  M. Rietschel,et al.  A functional variant in the neuropeptide S receptor 1 gene moderates the influence of urban upbringing on stress processing in the amygdala , 2014, Stress.

[59]  G. Fernández,et al.  Dynamic adaptation of large-scale brain networks in response to acute stressors , 2014, Trends in Neurosciences.

[60]  R. Dampney Central mechanisms regulating coordinated cardiovascular and respiratory function during stress and arousal. , 2015, American journal of physiology. Regulatory, integrative and comparative physiology.

[61]  B. Oken,et al.  A systems approach to stress, stressors and resilience in humans , 2015, Behavioural Brain Research.

[62]  Dimitri Van De Ville,et al.  On spurious and real fluctuations of dynamic functional connectivity during rest , 2015, NeuroImage.

[63]  M. Saladin,et al.  Menstrual cycle phase effects in the gender dimorphic stress cue reactivity of smokers. , 2015, Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco.

[64]  J. Shoemaker,et al.  Forebrain neurocircuitry associated with human reflex cardiovascular control , 2015, Front. Physiol..

[65]  Laura C. Buchanan,et al.  Tracking ongoing cognition in individuals using brief, whole-brain functional connectivity patterns , 2015, Proceedings of the National Academy of Sciences.

[66]  Michael J. Tobia,et al.  Differential lateralization of hippocampal connectivity reflects features of recent context and ongoing demands: An examination of immediate post‐task activity , 2015, Human brain mapping.

[67]  Kang K. L. Liu,et al.  Network Physiology: How Organ Systems Dynamically Interact , 2015, PLoS ONE.

[68]  J. Woo,et al.  Reliability of ultra-short-term analysis as a surrogate of standard 5-min analysis of heart rate variability. , 2015, Telemedicine journal and e-health : the official journal of the American Telemedicine Association.

[69]  V. van de Ven,et al.  Temporal Dynamics of Stress-Induced Alternations of Intrinsic Amygdala Connectivity and Neuroendocrine Levels , 2015, PloS one.

[70]  S. Whitfield-Gabrieli,et al.  Dynamic Resting-State Functional Connectivity in Major Depression , 2016, Neuropsychopharmacology.

[71]  Kenneth Knoblauch,et al.  Effects of aging on low luminance contrast processing in humans , 2016, NeuroImage.

[72]  Kang K. L. Liu,et al.  Focus on the emerging new fields of network physiology and network medicine , 2016, New journal of physics.

[73]  L. F. Barrett Navigating the science of emotion , 2021, Emotion Measurement.

[74]  Yikai Wang,et al.  An Efficient and Reliable Statistical Method for Estimating Functional Connectivity in Large Scale Brain Networks Using Partial Correlation , 2016, Front. Neurosci..

[75]  C. Nemeroff,et al.  The Links Between Stress and Depression: Psychoneuroendocrinological, Genetic, and Environmental Interactions. , 2016, The Journal of neuropsychiatry and clinical neurosciences.

[76]  L. Faes,et al.  Predictability decomposition detects the impairment of brain–heart dynamical networks during sleep disorders and their recovery with treatment , 2016, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[77]  T. Hendler,et al.  A large-scale perspective on stress-induced alterations in resting-state networks , 2016, Scientific Reports.

[78]  T. Hendler,et al.  Dynamic Shifts in Large-Scale Brain Network Balance As a Function of Arousal , 2017, The Journal of Neuroscience.

[79]  M. Rietschel,et al.  Sex-specific association between functional neuropeptide S receptor gene (NPSR1) variants and cortisol and central stress responses , 2017, Psychoneuroendocrinology.

[80]  F. Shaffer,et al.  An Overview of Heart Rate Variability Metrics and Norms , 2017, Front. Public Health.

[81]  C. Beckmann,et al.  How the brain connects in response to acute stress: A review at the human brain systems level , 2017, Neuroscience & Biobehavioral Reviews.

[82]  Yuanyuan Chen,et al.  Age-Related Decline in the Variation of Dynamic Functional Connectivity: A Resting State Analysis , 2017, Front. Aging Neurosci..

[83]  B. T. Thomas Yeo,et al.  Interpreting temporal fluctuations in resting-state functional connectivity MRI , 2017, NeuroImage.

[84]  T. Hendler,et al.  Dynamic Shifts in Large-Scale Brain Network Balance As a Function of Arousal. , 2017, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[85]  J. Thayer,et al.  Heart Rate Variability and Cardiac Vagal Tone in Psychophysiological Research – Recommendations for Experiment Planning, Data Analysis, and Data Reporting , 2017, Front. Psychol..

[86]  César Caballero-Gaudes,et al.  Methods for cleaning the BOLD fMRI signal , 2016, NeuroImage.

[87]  S. Khalsa,et al.  The hierarchical basis of neurovisceral integration , 2017, Neuroscience & Biobehavioral Reviews.

[88]  Tobias U. Hauser,et al.  The PhysIO Toolbox for Modeling Physiological Noise in fMRI Data , 2017, Journal of Neuroscience Methods.

[89]  D. Bai,et al.  Stress and Heart Rate Variability: A Meta-Analysis and Review of the Literature , 2018, Psychiatry investigation.

[90]  G. Mashour,et al.  Timescales of Intrinsic BOLD Signal Dynamics and Functional Connectivity in Pharmacologic and Neuropathologic States of Unconsciousness , 2018, The Journal of Neuroscience.

[91]  H. Walter,et al.  Differences in Neural Recovery From Acute Stress Between Cortisol Responders and Non-responders , 2018, Front. Psychiatry.

[92]  Hangsik Shin,et al.  Quantitative Analysis of the Effect of an Ectopic Beat on the Heart Rate Variability in the Resting Condition , 2018, Front. Physiol..

[93]  C. Tallon-Baudry,et al.  Visceral Signals Shape Brain Dynamics and Cognition , 2019, Trends in Cognitive Sciences.

[94]  Wei Zhang,et al.  Acute stress alters the ‘default’ brain processing , 2019, NeuroImage.

[95]  Rossana Castaldo,et al.  Ultra-short term HRV features as surrogates of short term HRV: a case study on mental stress detection in real life , 2019, BMC Medical Informatics and Decision Making.

[96]  Jong-Hwan Lee,et al.  Mediation analysis of triple networks revealed functional feature of mindfulness from real-time fMRI neurofeedback , 2019, NeuroImage.

[97]  R. Buckner,et al.  The brain’s default network: updated anatomy, physiology and evolving insights , 2019, Nature Reviews Neuroscience.

[98]  P. Hastings,et al.  Resting heart rate variability is negatively associated with mirror neuron and limbic response to emotional faces , 2019, Biological Psychology.

[99]  M. Barbieri,et al.  Autonomic Nervous System and Cognitive Impairment in Older Patients: Evidence From Long-Term Heart Rate Variability in Real-Life Setting , 2020, Frontiers in Aging Neuroscience.