Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Spontaneous fluctuations in neural responses to heartbeats predict visual detection

Abstract

Spontaneous fluctuations of ongoing neural activity substantially affect sensory and cognitive performance. Because bodily signals are constantly relayed up to the neocortex, neural responses to bodily signals are likely to shape ongoing activity. Here, using magnetoencephalography, we show that in humans, neural events locked to heartbeats before stimulus onset predict the detection of a faint visual grating in the posterior right inferior parietal lobule and the ventral anterior cingulate cortex, two regions that have multiple functional correlates and that belong to the same resting-state network. Neither fluctuations in measured bodily parameters nor overall cortical excitability could account for this finding. Neural events locked to heartbeats therefore shape visual conscious experience, potentially by contributing to the neural maps of the organism that might underlie subjectivity. Beyond conscious vision, our results show that neural events locked to a basic physiological input such as heartbeats underlie behaviorally relevant differential activation in multifunctional cortical areas.

Your institute does not have access to this article

Relevant articles

Open Access articles citing this article.

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Figure 1: Experimental paradigm and rationale.
Figure 2: Post-decisional heart slowing.
Figure 3: Neural events locked to heartbeats before stimulus onset predicted conscious perception of the stimulus.
Figure 4: Neural sources of the differential response to heartbeats.
Figure 5: Pupil diameter, alpha power and warning-evoked response do not predict detection (n = 17).

References

  1. Arieli, A., Sterkin, A., Grinvald, A. & Aertsen, A. Dynamics of ongoing activity: explanation of the large variability in evoked cortical responses. Science 273, 1868–1871 (1996).

    CAS  PubMed  Google Scholar 

  2. Greicius, M.D. & Menon, V. Default-mode activity during a passive sensory task: uncoupled from deactivation but impacting activation. J. Cogn. Neurosci. 16, 1484–1492 (2004).

    PubMed  Google Scholar 

  3. Lakatos, P. et al. An oscillatory hierarchy controlling neuronal excitability and stimulus processing in the auditory cortex. J. Neurophysiol. 94, 1904–1911 (2005).

    PubMed  Google Scholar 

  4. Poulet, J.F. & Petersen, C.C. Internal brain state regulates membrane potential synchrony in barrel cortex of behaving mice. Nature 454, 881–885 (2008).

    CAS  PubMed  Google Scholar 

  5. Marguet, S.L. & Harris, K.D. State-dependent representation of amplitude-modulated noise stimuli in rat auditory cortex. J. Neurosci. 31, 6414–6420 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. He, B.J. Spontaneous and task-evoked brain activity negatively interact. J. Neurosci. 33, 4672–4682 (2013).

    PubMed  PubMed Central  Google Scholar 

  7. Linkenkaer-Hansen, K., Nikulin, V.V., Palva, S., Ilmoniemi, R.J. & Palva, J.M. Prestimulus oscillations enhance psychophysical performance in humans. J. Neurosci. 24, 10186–10190 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Boly, M. et al. Baseline brain activity fluctuations predict somatosensory perception in humans. Proc. Natl. Acad. Sci. USA 104, 12187–12192 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Sadaghiani, S., Hesselmann, G. & Kleinschmidt, A. Distributed and antagonistic contributions of ongoing activity fluctuations to auditory stimulus detection. J. Neurosci. 29, 13410–13417 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Palva, J.M. et al. Neuronal long-range temporal correlations and avalanche dynamics are correlated with behavioral scaling laws. Proc. Natl. Acad. Sci. USA 110, 3585–3590 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Vinnik, E., Itskov, P.M. & Balaban, E. Beta- and gamma-band EEG power predicts illusory auditory continuity perception. J. Neurophysiol. 108, 2717–2724 (2012).

    PubMed  Google Scholar 

  12. Fox, M.D. & Raichle, M.E. Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat. Rev. Neurosci. 8, 700–711 (2007).

    CAS  PubMed  Google Scholar 

  13. Deco, G., Jirsa, V.K. & McIntosh, A.R. Emerging concepts for the dynamical organization of resting-state activity in the brain. Nat. Rev. Neurosci. 12, 43–56 (2011).

    CAS  PubMed  Google Scholar 

  14. Blanke, O. Multisensory brain mechanisms of bodily self-consciousness. Nat. Rev. Neurosci. 13, 556–571 (2012).

    CAS  PubMed  Google Scholar 

  15. Craig, A.D. How do you feel? Interoception: the sense of the physiological condition of the body. Nat. Rev. Neurosci. 3, 655–666 (2002).

    CAS  PubMed  Google Scholar 

  16. Mayer, E.A. Gut feelings: the emerging biology of gut-brain communication. Nat. Rev. Neurosci. 12, 453–466 (2011).

    CAS  PubMed  Google Scholar 

  17. Critchley, H.D. & Harrison, N.A. Visceral influences on brain and behavior. Neuron 77, 624–638 (2013).

    CAS  PubMed  Google Scholar 

  18. Damasio, A. & Carvalho, G.B. The nature of feelings: evolutionary and neurobiological origins. Nat. Rev. Neurosci. 14, 143–152 (2013).

    CAS  PubMed  Google Scholar 

  19. Christoff, K., Cosmelli, D., Legrand, D. & Thompson, E. Specifying the self for cognitive neuroscience. Trends Cogn. Sci. 15, 104–112 (2011).

    PubMed  Google Scholar 

  20. Patterson, J.C. II, Ungerleider, L.G. & Bandettini, P.A. Task-independent functional brain activity correlation with skin conductance changes: an fMRI study. Neuroimage 17, 1797–1806 (2002).

    PubMed  Google Scholar 

  21. Nagai, Y., Critchley, H.D., Featherstone, E., Trimble, M.R. & Dolan, R.J. Activity in ventromedial prefrontal cortex covaries with sympathetic skin conductance level: a physiological account of a “default mode” of brain function. Neuroimage 22, 243–251 (2004).

    CAS  PubMed  Google Scholar 

  22. Fan, J. et al. Spontaneous brain activity relates to autonomic arousal. J. Neurosci. 32, 11176–11186 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Wong, S.W., Masse, N., Kimmerly, D.S., Menon, R.S. & Shoemaker, J.K. Ventral medial prefrontal cortex and cardiovagal control in conscious humans. Neuroimage 35, 698–708 (2007).

    PubMed  Google Scholar 

  24. Ziegler, G., Dahnke, R., Yeragani, V.K. & Bar, K.J. The relation of ventromedial prefrontal cortex activity and heart rate fluctuations at rest. Eur. J. Neurosci. 30, 2205–2210 (2009).

    PubMed  Google Scholar 

  25. Schandry, R. & Montoya, P. Event-related brain potentials and the processing of cardiac activity. Biol. Psychol. 42, 75–85 (1996).

    CAS  PubMed  Google Scholar 

  26. Gray, M.A. et al. A cortical potential reflecting cardiac function. Proc. Natl. Acad. Sci. USA 104, 6818–6823 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Kern, M., Aertsen, A., Schulze-Bonhage, A. & Ball, T. Heart cycle-related effects on event-related potentials, spectral power changes, and connectivity patterns in the human ECoG. Neuroimage 81, 178–190 (2013).

    PubMed  Google Scholar 

  28. Montoya, P., Schandry, R. & Muller, A. Heartbeat evoked potentials (HEP): topography and influence of cardiac awareness and focus of attention. Electroencephalogr. Clin. Neurophysiol. 88, 163–172 (1993).

    CAS  PubMed  Google Scholar 

  29. Fukushima, H., Terasawa, Y. & Umeda, S. Association between interoception and empathy: evidence from heart-beat evoked brain potential. Int. J. Psychophysiol. 79, 259–265 (2011).

    PubMed  Google Scholar 

  30. Amour, J.A. & Ardell, J.L. Basic and Clinical Neurocardiology (Oxford University Press, Oxford, 2004).

  31. Vogt, B.A. & Derbyshire, S.W.G. Visceral circuits and cingulate-mediated autonomic functions. in Cingulate Neurobiology and Disease (ed. Vogt, B.A.) 220–235 (Oxford University Press, Oxford, 2009).

  32. Lacey, B.C. & Lacey, J.I. Studies of heart rate and other bodily processes in sensorimotor behavior. in Cardiovascular Psychophysiology (ed. Obrist, P.A., Black, A.H., Brener, J. & DiCara, L.) 538–564 (Aldine Press, Chicago, 1974).

  33. Dirlich, G., Dietl, T., Vogl, L. & Strian, F. Topography and morphology of heart action-related EEG potentials. Electroencephalogr. Clin. Neurophysiol. 108, 299–305 (1998).

    CAS  PubMed  Google Scholar 

  34. Devinsky, O., Morrell, M.J. & Vogt, B.A. Contributions of anterior cingulate cortex to behaviour. Brain 118, 279–306 (1995).

    PubMed  Google Scholar 

  35. Fox, M.D. et al. The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc. Natl. Acad. Sci. USA 102, 9673–9678 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Critchley, H.D., Wiens, S., Rotshtein, P., Ohman, A. & Dolan, R.J. Neural systems supporting interoceptive awareness. Nat. Neurosci. 7, 189–195 (2004).

    CAS  PubMed  Google Scholar 

  37. Makeig, S. & Inlow, M. Lapses in alertness: coherence of fluctuations in performance and EEG spectrum. Electroencephalogr. Clin. Neurophysiol. 86, 23–35 (1993).

    CAS  PubMed  Google Scholar 

  38. Sadaghiani, S. et al. Intrinsic connectivity networks, alpha oscillations, and tonic alertness: a simultaneous electroencephalography/functional magnetic resonance imaging study. J. Neurosci. 30, 10243–10250 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Park, H.D. & Tallon-Baudry, C. The neural subjective frame: from bodily signals to perceptual consciousness. Phil. Trans. R. Soc. B (in the press).

  40. Crick, F. & Koch, C. Towards a neurobiological theory of consciousness. Semin. Neurosci. 2, 263–275 (1990).

    Google Scholar 

  41. Vogeley, K. & Fink, G.R. Neural correlates of the first-person-perspective. Trends Cogn. Sci. 7, 38–42 (2003).

    PubMed  Google Scholar 

  42. Ruby, P. & Decety, J. Effect of subjective perspective taking during simulation of action: a PET investigation of agency. Nat. Neurosci. 4, 546–550 (2001).

    CAS  PubMed  Google Scholar 

  43. Desmurget, M. et al. Movement intention after parietal cortex stimulation in humans. Science 324, 811–813 (2009).

    CAS  PubMed  Google Scholar 

  44. Lou, H.C. et al. Parietal cortex and representation of the mental Self. Proc. Natl. Acad. Sci. USA 101, 6827–6832 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. Fliessbach, K. et al. Social comparison affects reward-related brain activity in the human ventral striatum. Science 318, 1305–1308 (2007).

    CAS  PubMed  Google Scholar 

  46. Lindquist, K.A., Wager, T.D., Kober, H., Bliss-Moreau, E. & Barrett, L.F. The brain basis of emotion: a meta-analytic review. Behav. Brain Sci. 35, 121–143 (2012).

    PubMed  PubMed Central  Google Scholar 

  47. Qin, P. & Northoff, G. How is our self related to midline regions and the default-mode network? Neuroimage 57, 1221–1233 (2011).

    PubMed  Google Scholar 

  48. Kable, J.W. & Glimcher, P.W. The neural correlates of subjective value during intertemporal choice. Nat. Neurosci. 10, 1625–1633 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. Laird, A.R. et al. Investigating the functional heterogeneity of the default mode network using coordinate-based meta-analytic modeling. J. Neurosci. 29, 14496–14505 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Andrews-Hanna, J.R., Reidler, J.S., Sepulcre, J., Poulin, R. & Buckner, R.L. Functional-anatomic fractionation of the brain's default network. Neuron 65, 550–562 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. Macmillan, N.A. & Creelman, C.D. Detection Theory: a User's Guide (Lawrence Erlbaum Associates, 2005).

  52. Tadel, F., Baillet, S., Mosher, J.C., Pantazis, D. & Leahy, R.M. Brainstorm: a user-friendly application for MEG/EEG analysis. Comput. Intell. Neurosci. 2011, 879716 (2011).

    PubMed  PubMed Central  Google Scholar 

  53. Tzourio-Mazoyer, N. et al. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 15, 273–289 (2002).

    CAS  PubMed  Google Scholar 

  54. Oostenveld, R., Fries, P., Maris, E. & Schoffelen, J.M. FieldTrip: open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data. Comput. Intell. Neurosci. 2011, 156869 (2011).

    PubMed  Google Scholar 

  55. Boyle, J., Bidargaddi, N., Sarela, A. & Karunanithi, M. Automatic detection of respiration rate from ambulatory single-lead ECG. IEEE Trans. Inf. Technol. Biomed. 13, 890–896 (2009).

    PubMed  Google Scholar 

  56. Busch, N.A., Dubois, J. & VanRullen, R. The phase of ongoing EEG oscillations predicts visual perception. J. Neurosci. 29, 7869–7876 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. Maris, E. & Oostenveld, R. Nonparametric statistical testing of EEG- and MEG-data. J. Neurosci. Methods 164, 177–190 (2007).

    PubMed  Google Scholar 

Download references

Acknowledgements

This work was supported by Agence Nationale de la Recherche grants ANR-BLAN-12-BSH2-0002-01 to C.T.B., ANR-10-LABX-0087 IEC and ANR-10-IDEX-0001-02 PSL*. We thank E. Koechlin for useful suggestions on the manuscript and C. Gitton for excellent technical support during data acquisition.

Author information

Authors and Affiliations

Authors

Contributions

H.-D.P. and C.T.-B. designed the experiment. H.-D.P. and S.C. acquired the data. H.-D.P., A.D. and C.T.-B. analyzed the data. H.-D.P. and C.T.-B. wrote the paper.

Corresponding author

Correspondence to Catherine Tallon-Baudry.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Mean interbeat interval variability.

Data are presented from the interbeat interval during which the warning signal appeared on the screen until interbeat intervals after response delivery. Interbeat interval variability decreased after stimulus onset (* p<0.05), but did not differ between hits and misses (Greenhouse-Geisser corrected 2-way ANOVA with factors Consciousness and Time, main effect of Time F2.0,32.0=11.7, p=2.10—4; main effect of Consciousness F1,16=0.9, p=0.36; interaction F2.39,38.24=2.0, p=0.13). Error bars represent s.e.m.

Supplementary Figure 2 Neural responses to hearbeats in subjects with fast or slow heart rate.

Mean time course across the cluster in hits and misses, in subjects with (a) short (n=9, mean IBI 730 ms) or (b) long (n=8, mean IBI 886 ms) interbeat intervals (IBI). Neural responses to heartbeats within the cluster significantly differed between hits and misses in both groups (short IBI group, t8=3.76, p=0.006; long IBI group, t7=3.19, p=0.015). Longer IBIs did not delay the effect, indicating that the effect corresponds to a differential response to the preceding heartbeat rather than a preparation of the next heartbeat. Error bars represent s.e.m.

Supplementary Figure 3 T-locked electrocardiogram, grand-average across subjects.

Top, vertical derivation. Bottom, horizontal derivation. We did not find any significant difference between hits and misses.

Supplementary Figure 4 Influence of cardiac artefact correction using independent component analysis (ICA) on prestimulus heartbeat-evoked responses.

(a) Topographical map of the difference between prestimulus heartbeat-evoked responses in hits and misses, grand-averaged across 17 subjects, in the 135—171 ms time window, after ICA correction, on magnetometer signals. The topography of the difference was not affected by ICA correction. (b) T-locked magnetic fields at the sensor indicated in white in A, before (top) and after (bottom) ICA correction. The R and T waves could barely be observed on ICA corrected data, but the significant difference between hits and misses in the 135—171 ms time window (shaded area) was preserved. The overall shift toward more negative values in ICA-corrected data is due to data mean-centering by ICA. (c) Amplitude of the prestimulus heartbeat-evoked response, averaged across the cluster separately in hits and misses, and during a separate resting session with eyes open, after ICA correction. The heart-evoked response in the 135—171 ms cluster significantly differed between hits and misses (paired t-test, t16= 4.81, p=2.10—4). It was larger in hits than during rest (paired t-test, t16=3.46, p=0.003) and smaller in misses than during rest (paired t-test, t16=2.39, p=0.03). Because ICA extracts from MEG data the shape of each participant's heartbeat, this analysis further shows that the effect is not dependent on subject-specific heartbeat morphology. Error bars represent s.e.m.

Supplementary Figure 5 Distribution of respiration phase in hits and misses at stimulus onset.

Each point represents a subject and a condition. No preferential phase emerged (Raighley test for non-uniformity of circular data), neither in hits (p=0.81) nor in misses (p=0.67).

Supplementary Figure 6 Prestimulus peripheral blood pressure in hits and misses.

Blood pressure (arbitrary units) was computed over those heartbeats used to compute the heartbeat-evoked neural activity. (a) Time course of pressure, separately in hits and misses. (b) Difference between systolic and diastolic pressure, separately in hits and misses. No significant difference between hits and misses could be found in systolic, diastolic, nor difference between systolic and diastolic pressure. Error bars represent s.e.m.

Supplementary Figure 7 Peripheral blood pressure throughout a trial.

The difference between systolic and diastolic pressure in arbitrary units was measured on the heartbeat preceding warning onset, preceding stimulus onset, preceding response, as well as the first (R+1) and second (R+2) heartbeats following response. Blood pressure evolved in the course of a trial, but did not differ between hits and misses (Greenhouse-Geisser corrected 2-way ANOVA with factors Consciousness and Time, main effect of Time F2.27,22.71=4,15, p=0.025; main effect of Consciousness F1,10=2.14, p=0.17; interaction F1.85,18.54=1.52, p=0.25; paired t-test between hits and misses before stimulus onset: t10=1.50, p=0.16). Error bars represent standard errors of the mean.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–7 and Supplementary Tables 1–3 (PDF 749 kb)

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Park, HD., Correia, S., Ducorps, A. et al. Spontaneous fluctuations in neural responses to heartbeats predict visual detection. Nat Neurosci 17, 612–618 (2014). https://doi.org/10.1038/nn.3671

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nn.3671

Further reading

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing