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Electrical fingerprint of the amygdala guides neurofeedback training for stress resilience

Nature Human Behaviour volume 3, pages 63–73 (2019)Cite this article

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An Author Correction to this article was published on 28 January 2019

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Abstract

Real-time functional magnetic resonance imaging (rt-fMRI) has revived the translational perspective of neurofeedback (NF)1. Particularly for stress management, targeting deeply located limbic areas involved in stress processing2 has paved new paths for brain-guided interventions. However, the high cost and immobility of fMRI constitute a challenging drawback for the scalability (accessibility and cost-effectiveness) of the approach, particularly for clinical purposes3. The current study aimed to overcome the limited applicability of rt-fMRI by using an electroencephalography (EEG) model endowed with improved spatial resolution, derived from simultaneous EEG–fMRI, to target amygdala activity (termed amygdala electrical fingerprint (Amyg-EFP))4,5,6. Healthy individuals (n = 180) undergoing a stressful military training programme were randomly assigned to six Amyg-EFP-NF sessions or one of two controls (control-EEG-NF or NoNF), taking place at the military training base. The results demonstrated specificity of NF learning to the targeted Amyg-EFP signal, which led to reduced alexithymia and faster emotional Stroop, indicating better stress coping following Amyg-EFP-NF relative to controls. Neural target engagement was demonstrated in a follow-up fMRI-NF, showing greater amygdala blood-oxygen-level-dependent downregulation and amygdala–ventromedial prefrontal cortex functional connectivity following Amyg-EFP-NF relative to NoNF. Together, these results demonstrate limbic specificity and efficacy of Amyg-EFP-NF during a stressful period, pointing to a scalable non-pharmacological yet neuroscience-based training to prevent stress-induced psychopathology.

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Fig. 1: Training protocol and neurofeedback session procedure.
Fig. 2: NF learning.
Fig. 3: Outcomes of NF training per group.
Fig. 4: Amyg-fMRI-NF one month following Amyg-EFP-NF training.

Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Change history

  • 28 January 2019

    The original and corrected Acknowledgements are shown in the accompanying Author Correction.

References

  1. Johnston, S. J., Boehm, S. G., Healy, D., Goebel, R. & Linden, D. E. J. Neurofeedback: a promising tool for the self-regulation of emotion networks. Neuroimage 49, 1066–1072 (2010).

    Article  CAS  Google Scholar 

  2. Pessoa, L. & Adolphs, R. Emotion processing and the amygdala: from a ‘low road’ to ‘many roads’ of evaluating biological significance. Nat. Rev. Neurosci. 11, 773–783 (2010).

    Article  CAS  Google Scholar 

  3. Birbaumer, N., Ruiz, S. & Sitaram, R. Learned regulation of brain metabolism. Trends Cogn. Sci. 17, 295–302 (2013).

    Article  Google Scholar 

  4. Keynan, J. N. et al. Limbic activity modulation guided by functional magnetic resonance imaging-inspired electroencephalography improves implicit emotion regulation. Biol. Psychiatry 80, 490–496 (2016).

    Article  Google Scholar 

  5. Meir-Hasson, Y., Kinreich, S., Podlipsky, I., Hendler, T. & Intrator, N. An EEG finger-print of fMRI deep regional activation. Neuroimage 102, 128–141 (2014).

    Article  Google Scholar 

  6. Meir-Hasson, Y. et al. One-class fMRI-inspired EEG model for self-regulation training. PLoS ONE 11, e0154968 (2016).

    Article  Google Scholar 

  7. Johnstone, T., Reekum, C. M., van, Urry, H. L., Kalin, N. H. & Davidson, R. J. Failure to regulate: counterproductive recruitment of top-down prefrontal–subcortical circuitry in major depression. J. Neurosci. 27, 8877–8884 (2007).

    Article  CAS  Google Scholar 

  8. Gross, J. J. Emotion regulation: current status and future prospects. Psychol. Inq. 26, 1–26 (2015).

    Article  Google Scholar 

  9. Admon, R. et al. Human vulnerability to stress depends on amygdala’s predisposition and hippocampal plasticity. Proc. Natl Acad. Sci. USA 106, 14120–14125 (2009).

    Article  CAS  Google Scholar 

  10. Paret, C. et al. fMRI neurofeedback of amygdala response to aversive stimuli enhances prefrontal–limbic brain connectivity. Neuroimage 125, 182–188 (2016).

    Article  Google Scholar 

  11. Nicholson, A. A. et al. The neurobiology of emotion regulation in posttraumatic stress disorder: amygdala downregulation via real-time fMRI neurofeedback. Hum. Brain Mapp. 38, 541–560 (2017).

    Article  Google Scholar 

  12. Zotev, V., Phillips, R., Young, K. D., Drevets, W. C. & Bodurka, J. Prefrontal control of the amygdala during real-time fMRI neurofeedback training of emotion regulation. PLoS ONE 8, e79184 (2013).

    Article  CAS  Google Scholar 

  13. Paret, C. et al. Alterations of amygdala–prefrontal connectivity with real-time fMRI neurofeedback in BPD patients. Soc. Cogn. Affect. Neurosci. 11, 952–960 (2016).

    Article  Google Scholar 

  14. Marxen, M. et al. Amygdala regulation following fMRI-neurofeedback without instructed strategies. Front. Hum. Neurosci. 10, 183 (2016).

    Article  Google Scholar 

  15. Young, K. D. et al. Randomized clinical trial of real-time fMRI amygdala neurofeedback for major depressive disorder: effects on symptoms and autobiographical memory recall. Am. J. Psychiatry 174, 748–755 (2017).

    Article  Google Scholar 

  16. Thibault, R. T., Lifshitz, M., Birbaumer, N. & Raz, A. Neurofeedback, self-regulation, and brain imaging: clinical science and fad in the service of mental disorders. Psychother. Psychosom. 84, 193–207 (2015).

    Article  Google Scholar 

  17. Hendler, T., Intrator, N., Klovatch, I., Kinreich, S. & Meir-Hasson, Y. Method and system for use in analyzing neural activity in a subject’s brain. US Patent US20140148657 A1, WO2012104853 A3, EP2670299 A2 (2011).

  18. Gold, M. A. Cadet basic training: an ethnographic study of stress and coping. Mil. Med. 165, 147–152 (2000).

    Article  CAS  Google Scholar 

  19. Larson, G. E. Physical symptoms as indicators of depression and anxiety. Mil. Med. 166, 796–799 (2001).

    Article  CAS  Google Scholar 

  20. Spielberger, C. D., Gorsuch, R. L. & Lushene, R. Test Manual for the State-Trait Anxiety Inventory (Consulting Psychologists Press, Palo Alto, 1970).

  21. Taylor, G. J., Bagby, R. M. & Parker, J. D. A. Disorders of Affect Regulation (Cambridge Univ. Press, Cambridge, 1997).

  22. Etkin, A., Prater, K. E., Hoeft, F., Menon, V. & Schatzberg, A. F. Failure of anterior cingulate activation and connectivity with the amygdala during implicit regulation of emotional processing in generalized anxiety disorder. Am. J. Psychiatry 167, 545–554 (2010).

    Article  Google Scholar 

  23. Sitaram, R. et al. Closed-loop brain training: the science of neurofeedback. Nat. Rev. Neurosci. 18, 86–100 (2017).

    Article  CAS  Google Scholar 

  24. Alegria, A. A. et al. Real-time fMRI neurofeedback in adolescents with attention deficit hyperactivity disorder. Hum. Brain Mapp. 38, 3190–3209 (2017).

    Article  Google Scholar 

  25. Gruzelier, J. H. EEG-neurofeedback for optimising performance. III: A review of methodological and theoretical considerations. Neurosci. Biobehav. Rev. 44, 159–182 (2014).

    Article  Google Scholar 

  26. Cohen, A. et al. Multi-modal virtual scenario enhances neurofeedback learning. Front. Robot. AI 3, 52 (2016).

    Article  Google Scholar 

  27. Etkin, A., Egner, T., Peraza, D. M., Kandel, E. R. & Hirsch, J. Resolving emotional conflict: a role for the rostral anterior cingulate cortex in modulating activity in the amygdala. Neuron 51, 871–882 (2006).

    Article  CAS  Google Scholar 

  28. Durham, C. N. Posttraumatic stress disorder and resilience in Iraq and Afghanistan veterans: the mediator roles of masculine gender role stress and alexithymia (New Mexico State Univ., Las Cruces, 2016).

  29. Frewen, P. A., Pain, C., Dozois, D. J. A. & Lanius, R. A. Alexithymia in PTSD. Ann. N. Y. Acad. Sci. 1071, 397–400 (2006).

    Article  Google Scholar 

  30. Thibault, R. T., Lifshitz, M. & Raz, A. The self-regulating brain and neurofeedback: experimental science and clinical promise. Cortex 74, 247–261 (2016).

    Article  Google Scholar 

  31. Franz, M. et al. Alexithymia in the German general population. Soc. Psychiatry Psychiatr. Epidemiol. 43, 54–62 (2008).

    Article  Google Scholar 

  32. Salminen, J. K., Saarijärvi, S., Äärelä, E., Toikka, T. & Kauhanen, J. Prevalence of alexithymia and its association with sociodemographic variables in the general population of Finland. J. Psychosom. Res. 46, 75–82 (1999).

    Article  CAS  Google Scholar 

  33. Lindholm, T., Lehtinen, V., Hyyppä, M. T. & Puukka, P. Alexithymic features in relation to the dexamethasone suppression test in a Finnish population sample. Am. J. Psychiatry 147, 1216–1219 (1990).

    Article  CAS  Google Scholar 

  34. Linden, D. E. J. et al. Real-time self-regulation of emotion networks in patients with depression. PLoS ONE 7, e38115 (2012).

    Article  CAS  Google Scholar 

  35. Censor, N., Sagi, D. & Cohen, L. G. Common mechanisms of human perceptual and motor learning. Nat. Rev. Neurosci. 13, 658–664 (2012).

    Article  CAS  Google Scholar 

  36. Thibault, R. T., Lifshitz, M. & Raz, A. Neurofeedback or neuroplacebo? Brain 140, 862–864 (2017).

    Article  Google Scholar 

  37. De Vente, W., Kamphuis, J. H. & Emmelkamp, P. M. G. Alexithymia, risk factor or consequence of work-related stress? Psychother. Psychosom. 75, 304–311 (2006).

    Article  Google Scholar 

  38. Frewen, P. A., Dozois, D. J. A., Neufeld, R. W. J. & Lanius, R. A. Meta-analysis of alexithymia in posttraumatic stress disorder. J. Trauma Stress 21, 243–246 (2008).

    Article  Google Scholar 

  39. Etkin, A., Egner, T. & Kalisch, R. Emotional processing in anterior cingulate and medial prefrontal cortex. Trends Cogn. Sci. 15, 85–93 (2011).

    Article  Google Scholar 

  40. Zotev, V. et al. Real-time fMRI neurofeedback training of the amygdala activity with simultaneous EEG in veterans with combat-related PTSD. Neuroimage Clin. 19, 106–121 (2018).

    Article  Google Scholar 

  41. Kinreich, S., Podlipsky, I., Intrator, N. & Hendler, T. Categorized EEG neurofeedback performance unveils simultaneous fMRI deep brain activation. Mach. Learn. Interpret. Neuroimaging 7263, 108–115 (2012).

    Article  Google Scholar 

  42. Öhman, A. & Mineka, S. Fears, phobias, and preparedness: toward an evolved module of fear and fear learning. Psychol. Rev. 108, 483–522 (2001).

    Article  Google Scholar 

  43. Dolan, R. J. & Vuilleumier, P. Amygdala automaticity in emotional processing. Ann. N. Y. Acad. Sci. 985, 348–355 (2006).

    Article  Google Scholar 

  44. Morris, J. S., Öhman, A. & Dolan, R. J. A subcortical pathway to the right amygdala mediating “unseen” fear. Proc. Natl Acad. Sci. USA 96, 1680–1685 (1999).

    Article  CAS  Google Scholar 

  45. LeDoux, J. The emotional brain, fear, and the amygdala. Cell. Mol. Neurobiol. 23, 727–738 (2003).

    Article  Google Scholar 

  46. Haber, S. N. & Knutson, B. The reward circuit: linking primate anatomy and human imaging. Neuropsychopharmacology 35, 4–26 (2010).

    Article  Google Scholar 

  47. Barrett, L. F. & Simmons, W. K. Interoceptive predictions in the brain. Nat. Rev. Neurosci. 16, 419–429 (2015).

    Article  CAS  Google Scholar 

  48. Cavazza, M. et al. Towards emotional regulation through neurofeedback. In Proc. 5th Augmented Human International Conference (ed. Terada, T.) 1–8 (ACM, 2014); https://doi.org/10.1145/2582051.2582093

  49. Shibata, K., Watanabe, T., Sasaki, Y. & Kawato, M. Perceptual learning incepted by decoded fMRI neurofeedback without stimulus presentation. Science 334, 1413–1415 (2011).

    Article  CAS  Google Scholar 

  50. Taylor, G. J., Bagby, R. M. & Parker, J. D. A. The 20-Item Toronto Alexithymia Scale: IV. Reliability and factorial validity in different languages and cultures. J. Psychosom. Res. 55, 277–283 (2003).

    Article  Google Scholar 

  51. Teichman, Y. & Melnick, H. The Hebrew Manual for the State-Trait Anxiety Inventory (Ramot Press, Tel-Aviv University, Tel-Aviv, 1980).

  52. Allen, P. J., Polizzi, G., Krakow, K., Fish, D. R. & Lemieux, L. Identification of EEG events in the MR scanner: the problem of pulse artifact and a method for its subtraction. Neuroimage 8, 229–239 (1998).

    Article  CAS  Google Scholar 

  53. Sterne, J. A. C. et al. Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls. BMJ 338, b2393 (2009).

    Article  Google Scholar 

  54. van Ginkel, J. R. & Kroonenberg, P. M. Analysis of variance of multiply imputed data. Multivariate Behav. Res. 49, 78–91 (2014).

    Article  Google Scholar 

  55. Gilam, G. et al. Neural substrates underlying the tendency to accept anger-infused ultimatum offers during dynamic social interactions. Neuroimage 120, 400–411 (2015).

    Article  Google Scholar 

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Acknowledgements

We thank I. Rashap, D. Torjeman, Y. Roll, S. Dushy, I. Teshner, N. Shani, L. Frumer, T. Yeheskely, R. Bashan, L. Wiezman, O. Shani, A. Greental, T. Jacoby and M. Halevy for assisting in this study. This project was supported by the following grants: US Department of Defense—grant agreement no. W81XWH-11–2–0008. Mafat, IDF, I-Core cognitive studies grant agreement no. 693210. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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Authors and Affiliations

  1. Sagol Brain Institute, Wohl Institute for Advanced Imaging, Tel-Aviv Sourasky Medical Center, Tel-Aviv, Israel

    Jackob N. Keynan, Avihay Cohen, Gilan Jackont, Nili Green, Noam Goldway, Gal Raz & Talma Hendler

  2. The School of Psychological Sciences, Tel-Aviv University, Tel-Aviv, Israel

    Jackob N. Keynan, Avihay Cohen, Gilan Jackont, Nili Green & Talma Hendler

  3. Sagol School of Neuroscience, Tel-Aviv University, Tel-Aviv, Israel

    Noam Goldway, Gal Raz, Nathan Intrator & Talma Hendler

  4. The Mental Health Department, Medical Corps, IDF, Ramat-Gan, Israel

    Alexander Davidov, Eyal Fruchter & Keren Ginat

  5. Blavatnik School of Computer Science, Tel-Aviv University, Tel-Aviv, Israel

    Yehudit Meir-Hasson & Nathan Intrator

  6. The Steve Tisch School of Film and Television, Tel-Aviv University, Tel-Aviv, Israel

    Gal Raz

  7. Department of Psychiatry, New York University Langone School of Medicine, New York, NY, USA

    Eugene Laska

  8. School of Engineering and Digital Arts, University of Kent, Canterbury, UK

    Marc Cavazza

  9. Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel

    Talma Hendler

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Contributions

T.H. conceived the study. T.H. and J.N.K. designed the study. J.N.K., A.C., N.Green and A.D. collected the data. G.J. developed the online analysis techniques and the custom-made randomization, blinding and data management software. T.H. conceptualized the Amyg-EFP model. Y.M.-H. and N.I. carried out the computational programming of the Amyg-EFP model. G.R. and M.C. developed the animated scenario interface. A.D., E.F. and K.G. managed the contact with the IDF. E.L. provided statistical advice. J.N.K. analysed the data. N.Goldway assisted in data analysis, figure preparation and proofing. J.N.K. and T.H. wrote the paper.

Corresponding author

Correspondence to Talma Hendler.

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Competing interests

T.H., N.I. and Y.M.-H. are inventors of related patent applications entitled ‘Method and system for use in monitoring neural activity in a subject’s brain’ (US20140148657 A1, WO2012104853 A3 and EP2670299 A2). This does not alter the authors’ adherence to all Nature Human Behaviour policies.

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Supplementary information

Supplementary Information

Supplementary Figures 1–7, Supplementary Tables 1–6, Supplementary Methods, Supplementary References

Reporting Summary

Supplementary Video 1

An illustration of NF training guided by the animated scenario. Real-time modulations in the Amyg–EFP signal are reflected by audiovisual changes in the unrest level of a virtual 3D scenario (a typical hospital waiting room), manifested as the ratio between characters sitting down and those loudly protesting at the counter. The video shows an example both for down- and up-regulation training; however, in the current study, only down-regulation training was conducted. The participant consented to appear in the video. Video reproduced from ref. 26

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Keynan, J.N., Cohen, A., Jackont, G. et al. Electrical fingerprint of the amygdala guides neurofeedback training for stress resilience. Nat Hum Behav 3, 63–73 (2019). https://doi.org/10.1038/s41562-018-0484-3

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