Examining the Neural Correlates of Incidental Facial Emotion Encoding Within the Prefrontal Cortex Using Functional Near-Infrared Spectroscopy

  • Achala H. RodrigoEmail author
  • Hasan Ayaz
  • Anthony C. Ruocco
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9743)


Previous neuroimaging research has implicated the prefrontal cortex (PFC) as a region of the brain that is vital for various aspects of emotion processing. The present study sought to examine the neural correlates of incidental facial emotion encoding, with regard to neutral and fearful faces, within the PFC. Thirty-nine healthy adults were presented briefly with neutral and fearful faces and the evoked hemodynamic oxygenation within the PFC was measured using 16-channel continuous-wave functional near-infrared spectroscopy. When viewing fearful as compared to neutral faces, participants demonstrated higher levels of activation within the right medial PFC. On the other hand, participants demonstrated lower levels of activation within the left medial PFC and left lateral PFC when viewing fearful faces, as compared to neutral faces.These findings are consistent with previous fMRI research, and suggest that fearful faces are linked to a neural response within the right medial PFC, whereas neutral faces appear to elicit a neural response within left medial and lateral areas of the PFC.


fNIRS Prefrontal cortex Facial emotions Incidental encoding 



The authors would like to thank Stefano I. Di Domenico for his assistance with statistical analysis. The authors would also like to thank the research team at the Clinical Neurosciences Laboratory at the University of Toronto Scarborough for data collection and processing.


  1. 1.
    Phan, K.L., Wager, T., Taylor, S.F., Liberzon, I.: Functional neuroanatomy of emotion: a meta-analysis of emotion activation studies in PET and fMRI. Neuroimage 16(2), 331–348 (2002)CrossRefGoogle Scholar
  2. 2.
    Narumoto, J., Yamada, H., Iidaka, T., Sadato, N., Fukui, K., Itoh, H., Yonekura, Y.: Brain regions involved in verbal or non-verbal aspects of facial emotion recognition. Neuroreport 11(11), 2571–2574 (2000)CrossRefGoogle Scholar
  3. 3.
    Doi, H., Nishitani, S., Shinohara, K.: NIRS as a tool for assaying emotional function in the prefrontal cortex. Front. Hum. Neurosci. 7, 770 (2013). doi: 10.3389/fnhum.2013.00770 CrossRefGoogle Scholar
  4. 4.
    Liberati, G., Federici, S., Pasqualotto, E.: Extracting neurophysiological signals reflecting users’ emotional and affective responses to BCI use: a systematic literature review. NeuroRehabilitation 37, 341–358 (2015). doi: 10.3233/NRE-151266 CrossRefGoogle Scholar
  5. 5.
    Ochsner, K.N., Silvers, J.A., Buhle, J.T.: Functional imaging studies of emotion regulation: a synthetic review and evolving model of the cognitive control of emotion. Ann. N. Y. Acad. Sci. 1251, E1–E24 (2012). doi: 10.1111/j.1749-6632.2012.06751.x CrossRefGoogle Scholar
  6. 6.
    Etkin, A., Egner, T., Kalisch, R.: Emotional processing in anterior cingulate and medial prefrontal cortex. Trends Cogn. Sci. 15(2), 85–93 (2011). doi: 10.1016/j.tics.2010.11.004 CrossRefGoogle Scholar
  7. 7.
    Gorno-Tempini, M.L., Pradelli, S., Serafini, M., Pagnoni, G., Baraldi, P., Porro, C., Nichelli, P.: Explicit and incidental facial expression processing: an fMRI study. Neuroimage 14(2), 465–473 (2001)CrossRefGoogle Scholar
  8. 8.
    Fusar-Poli, P., Placentino, A., Carletti, F., Landi, P., Abbamonte, M.: Functional atlas of emotional faces processing: a voxel-based meta-analysis of 105 functional magnetic resonance imaging studies. J. Psychiatry Neurosci. JPN 34(6), 418 (2009)Google Scholar
  9. 9.
    Wager, T.D., Barrett, L.F., Bliss-Moreau, E., Lindquist, K., Duncan, S., Kober, H., Mize, J.: The neuroimaging of emotion. Handb. Emot. 3, 249–271 (2008)Google Scholar
  10. 10.
    Wager, T.D., Davidson, M.L., Hughes, B.L., Lindquist, M.A., Ochsner, K.N.: Prefrontal-subcortical pathways mediating successful emotion regulation. Neuron 59(6), 1037–1050 (2008)CrossRefGoogle Scholar
  11. 11.
    Kober, H., Barrett, L.F., Joseph, J., Bliss-Moreau, E., Lindquist, K., Wager, T.D.: Functional grouping and cortical–subcortical interactions in emotion: a meta-analysis of neuroimaging studies. Neuroimage 42(2), 998–1031 (2008)CrossRefGoogle Scholar
  12. 12.
    Irani, F., Platek, S.M., Bunce, S., Ruocco, A.C., Chute, D.: Functional near infrared spectroscopy (fNIRS): an emerging neuroimaging technology with important applications for the study of brain disorders. Clin. Neuropsychologist 21(1), 9–37 (2007)CrossRefGoogle Scholar
  13. 13.
    Ayaz, H., Shewokis, P.A., Bunce, S., Izzetoglu, K., Willems, B., Onaral, B.: Optical brain monitoring for operator training and mental workload assessment. Neuroimage 59(1), 36–47 (2012). doi: 10.1016/j.neuroimage.2011.06.023 CrossRefGoogle Scholar
  14. 14.
    Ayaz, H., Shewokis, P.A., Curtin, A., Izzetoglu, M., Izzetoglu, K., Onaral, B.: Using MazeSuite and functional near infrared spectroscopy to study learning in spa-tial navigation. J. Vis. Exp. 56, e3443 (2014). doi: 10.3791/3443 Google Scholar
  15. 15.
    Rodrigo, A.H., Di Domenico, S.I., Ayaz, H., Gulrajani, S., Lam, J., Ruocco, A.C.: Differentiating functions of the lateral and medial prefrontal cortex in motor response inhibition. Neuroimage 85, 423–431 (2014)CrossRefGoogle Scholar
  16. 16.
    Ruocco, A.C., Rodrigo, A.H., Lam, J., Di Domenico, S.I., Graves, B., Ayaz, H.: A problem-solving task specialized for functional neuroimaging: validation of the Scarborough adaptation of the Tower of London (S-TOL) using near-infrared spectroscopy. Front. Hum. Neurosci. 8, 185 (2013)Google Scholar
  17. 17.
    Di Domenico, S.I., Rodrigo, A.H., Ayaz, H., Fournier, M.A., Ruocco, A.C.: Decision-making conflict and the neural efficiency hypothesis of intelligence: a functional near-infrared spectroscopy investigation. NeuroImage 109, 307–317 (2015)CrossRefGoogle Scholar
  18. 18.
    Ayaz, H., Onaral, B., Izzetoglu, K., Shewokis, P.A., McKendrick, R., Parasuraman, R.: Continuous monitoring of brain dynamics with functional near infrared spectroscopy as a tool for neuroergonomic research: Empirical examples and a technological development. Front. Hum. Neurosci. 7, 1–13 (2013). doi: 10.3389/fnhum.2013.00871 CrossRefGoogle Scholar
  19. 19.
    Balconi, M., Molteni, E.: Past and future of near-infrared spectroscopy in studies of emotion and social neuroscience. J. Cogn. Psychol. 28, 1–18 (2015). doi: 10.1080/20445911.2015.1102919 Google Scholar
  20. 20.
    Balters, S., Steinert, M.: Capturing emotion reactivity through physiology measurement as a foundation for affective engineering in engineering design science and engineering practices. J. Intell. Manuf. 28, 1–18 (2015). doi: 10.1007/s10845-015-1145-2 Google Scholar
  21. 21.
    Heller, A.S., Johnstone, T., Peterson, M.J., Kolden, G.G., Kalin, N.H., Davidson, R.J.: Increased prefrontal cortex activity during negative emotion regulation as a predictor of depression symptom severity trajectory over 6 months. JAMA Psychiatry 70(11), 1181–1189 (2013). doi: 10.1001/jamapsychiatry.2013.2430 CrossRefGoogle Scholar
  22. 22.
    Ozawa, S., Matsuda, G., Hiraki, K.: Negative emotion modulates prefrontal cortex activity during a working memory task: A NIRS study. Front. Hum. Neurosci. 8, 46 (2014). doi: 10.3389/fnhum.2014.00046 CrossRefGoogle Scholar
  23. 23.
    Sun, Y., Ayaz, H., Akansu, A.N.: Neural correlates of affective context in facial expression analysis: a simultaneous EEG-fNIRS study. In: Paper Presented at the 3rd IEEE GlobalSIP Conference, Symposium on Signal Processing Challenges in Human Brain Connectomics, Orlando, FL (2015)Google Scholar
  24. 24.
    Cowie, R., Douglas-Cowie, E., Tsapatsoulis, N., Votsis, G., Kollias, S., Fellenz, W., Taylor, J.G.: Emotion recognition in human-computer interaction. IEEE Signal Process. Mag. 18(1), 32–80 (2001)CrossRefGoogle Scholar
  25. 25.
    Parasuraman, R., Christensen, J., Grafton, S.: Neuroergonomics: the brain in action and at work. Neuroimage 59(1), 1–3 (2012)CrossRefGoogle Scholar
  26. 26.
    Parasuraman, R., Rizzo, M.: Neuroergonomics: The Brain at Work. Oxford University Press, New York (2007)Google Scholar
  27. 27.
    Zander, T.O., Kothe, C., Jatzev, S., Gaertner, M.: Enhancing human-computer interaction with input from active and passive brain-computer interfaces. In: Tan, D.S., Nijholt, A. (eds.) Brain-Computer Interfaces, pp. 181–199. Springer, London (2010)CrossRefGoogle Scholar
  28. 28.
    Gur, R.C., Ragland, J.D., Moberg, P.J., Turner, T.H., Bilker, W.B., Kohler, C., Siegel, S.J., Gur, R.E.: Computerized neurocognitive scanning: I. Methodology and validation in healthy people. Neuropsychopharmacology 25(5), 766–776 (2001)CrossRefGoogle Scholar
  29. 29.
    Ayaz, H., Izzetoglu, M., Platek, S.M., Bunce, S., Izzetoglu, K., Pourrezaei, K., Onaral, B.: Registering fNIR data to brain surface image using MRI templates. In: Conference Proceedings of the IEEE Engineering in Medicine and Biology Society, pp. 2671–2674 (2006). doi: 10.1109/IEMBS.2006.260835
  30. 30.
    Jasper, H.H.: Report of the committee on methods of clinical examination in electroencephalography. Electroencephalogr. Clin. Neurophysiol. 10, 370–375 (1958)CrossRefGoogle Scholar
  31. 31.
    Ayaz, H.: Functional near infrared spectroscopy based brain computer interface. (Ph.D. thesis), Drexel University, Philadelphia, PA (2010)Google Scholar
  32. 32.
    Bryk, A.S., Raudenbush, S.W.: Application of hierarchical linear models to assessing change. Psychol. Bull. 101(1), 147 (1987)CrossRefGoogle Scholar
  33. 33.
    Schluchter, M.D., Elashoff, J.T.: Small-sample adjustments to tests with unbalanced repeated measures assuming several covariance structures. J. Stat. Comput. Simul. 37(1–2), 69–87 (1990)CrossRefzbMATHGoogle Scholar
  34. 34.
    Benjamini, Y., Drai, D., Elmer, G., Kafkafi, N., Golani, I.: Controlling the false discovery rate in behavior genetics research. Behav. Brain Res. 125(1), 279–284 (2001)CrossRefGoogle Scholar
  35. 35.
    Benjamini, Y., Hochberg, Y.: Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. Roy. Stat. Soc.: Ser. B (Methodol.) 57, 289–300 (1995)MathSciNetzbMATHGoogle Scholar
  36. 36.
    Kilts, C.D., Egan, G., Gideon, D.A., Ely, T.D., Hoffman, J.M.: Dissociable neural pathways are involved in the recognition of emotion in static and dynamic facial expressions. Neuroimage 18(1), 156–168 (2003)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Achala H. Rodrigo
    • 1
    Email author
  • Hasan Ayaz
    • 2
    • 3
    • 4
  • Anthony C. Ruocco
    • 5
  1. 1.Department of PsychologyUniversity of Toronto ScarboroughTorontoCanada
  2. 2.School of Biomedical Engineering, Science and Health SystemsDrexel UniversityPhiladelphiaUSA
  3. 3.Department of Family and Community HealthUniversity of PennsylvaniaPhiladelphiaUSA
  4. 4.Division of General PediatricsChildren’s Hospital of PhiladelphiaPhiladelphiaUSA
  5. 5.Mood and Anxiety DivisionCentre for Addiction and Mental HealthTorontoCanada

Personalised recommendations