Estimation of Skin Blood Flow Artefacts in NIRS Signals During a Verbal Fluency Task

  • Akitoshi SeiyamaEmail author
  • Kotona Higaki
  • Nao Takeuchi
  • Masahiro Uehara
  • Naoko Takayama
Conference paper
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 876)


The aim of this study was to clarify effects of skin (scalp) blood flow on functional near infrared spectroscopy (fNIRS) during a verbal fluency task. In the present study, to estimate the influence of skin blood flow on fNIRS signals, we conducted examinations on 19 healthy volunteers (39.9 ± 13.1 years, 11 male and 8 female subjects). We simultaneously measured the fNIRS signals, skin blood flow (i.e., flow, velocity, and number of red blood cells [RBC]), and pulse wave rates using a multimodal fNIRS system. We found that the effects of skin blood flow, measured by the degree of interference of the flow, velocity, and number of RBCs, and pulse wave rates, on NIRS signals varied considerably across subjects. Further, by using the above physiological parameters, we evaluated application of the independent component analysis algorithm proposed by Molgedey and Schuster (MS-ICA) to remove skin blood flow artefacts from fNIRS signals.


Functional near infrared spectroscopy Verbal fluency task Independent component analysis Pearson’s correlation coefficient Coefficient of spatial uniformity 



This study was supported in part by grants-in-aid from the Ministry of Education, Science and Culture of Japan.


  1. 1.
    Suto T, Fukuda M, Ito M et al (2004) Multichannel near-infrared spectroscopy in depression and schizophrenia: cognitive brain activation study. Biol Psychiatry 55:501–511CrossRefPubMedGoogle Scholar
  2. 2.
    Kameyama M, Fukuda M, Yamagishi Y et al (2006) Frontal lobe function in bipolar disorder: a multichannel near-infrared spectroscopy study. Neuroimage 29:172–184CrossRefPubMedGoogle Scholar
  3. 3.
    Takizawa R, Kasai K, Kawakubo Y et al (2008) Reduced frontopolar activation during verbal fluency task in schizophrenia: a multi-channel near-infrared spectroscopy study. Schizophr Res 99:250–262CrossRefPubMedGoogle Scholar
  4. 4.
    Kinou M, Takizawa R, Marumo K et al (2013) Differential spatiotemporal characteristics of the prefrontal hemodynamic response and their association with functional impairment in schizophrenia and major depression. Schizophr Res 150(2–3):459–467CrossRefPubMedGoogle Scholar
  5. 5.
    Takizawa R, Fukuda M, Kawasaki S et al (2014) Neuroimaging-aided differential diagnosis of the depressive state. Neuroimage 85(Pt 1):498–507CrossRefPubMedGoogle Scholar
  6. 6.
    Takahashi T, Takikawa Y, Kawagoe R et al (2011) Influence of skin blood flow on near-infrared spectroscopy signals measured on the forehead during a verbal fluency task. Neuroimage 57:991–1002CrossRefPubMedGoogle Scholar
  7. 7.
    Kirilina E, Jelzow A, Heine A et al (2012) The physiological origin of task-evoked systemic artifacts in functional near infrared spectroscopy. Neuroimage 61:70–81CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Kohno S, Miyai I, Seiyama A et al (2007) Removal of the skin blood flow artifact in functional near-infrared spectroscopic imaging data through independent component analysis. J Biomed Opt 12(6):062111CrossRefPubMedGoogle Scholar
  9. 9.
    Katura T, Sato H, Fuchino Y et al (2008) Extracting task-related activation components from optical topography measurement using independent components analysis. J Biomed Opt 13:054008CrossRefPubMedGoogle Scholar
  10. 10.
    Markham J, White BR, Zeff BW et al (2009) Blind identification of evoked human brain activity with independent component analysis of optical data. Hum Brain Mapp 30:2382–2392CrossRefPubMedGoogle Scholar
  11. 11.
    Patel S, Katura T, Maki A et al (2011) Quantification of systemic interference in optical topography data during frontal lobe and motor cortex activation: an independent component analysis. Adv Exp Med Biol 915:45–51CrossRefGoogle Scholar
  12. 12.
    Funane T, Atsumori H, Katura T et al (2014) Quantitative evaluation of deep and shallow tissue layers’ contribution to fNIRS signal using multi-distance optodes and independent component analysis. Neuroimage 85:150–165CrossRefPubMedGoogle Scholar
  13. 13.
    Seiyama A, Sasaki Y, Takatsuki A et al (2012) Effects of the autonomic nervous system on functional neuroimaging: analyses based on the vector autoregressive model. Adv Exp Med Biol 737:77–82CrossRefPubMedGoogle Scholar
  14. 14.
    Molgedey L, Schuster HG (1994) Separation of a mixture of independent signals using time delayed correlations. Phys Rev Lett 72:3634–3637CrossRefPubMedGoogle Scholar
  15. 15.
    Shimodera S, Imai Y, Kamimura N et al (2012) Mapping hypofrontality during letter fluency task in schizophrenia: a multi-channel near-infrared spectroscopy study. Schizophr Res 136:63–69CrossRefPubMedGoogle Scholar
  16. 16.
    Kashima S, Ono Y, Sohda A et al (1994) Separate measurement of two components of blood flow velocity in tissue by dynamic light scattering method. Jpn J Appl Phys 33:2123–2127CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, New York 2016

Authors and Affiliations

  • Akitoshi Seiyama
    • 1
    Email author
  • Kotona Higaki
    • 1
    • 2
  • Nao Takeuchi
    • 1
    • 2
  • Masahiro Uehara
    • 1
  • Naoko Takayama
    • 2
  1. 1.Division of Medical Devices for Diagnoses, Human Health Sciences, Graduate School of MedicineKyoto UniversityKyotoJapan
  2. 2.Laboratory of Clinical Examination, Takayama Medical Clinic, Medical Corporation TaihoukaiTakayamaJapan

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