OSA's Digital Library

Biomedical Optics Express

Biomedical Optics Express

  • Editor: Joseph A. Izatt
  • Vol. 5, Iss. 6 — Jun. 1, 2014
  • pp: 1778–1798

State-space models of impulse hemodynamic responses over motor, somatosensory, and visual cortices

Keum-Shik Hong and Hoang-Dung Nguyen  »View Author Affiliations


Biomedical Optics Express, Vol. 5, Issue 6, pp. 1778-1798 (2014)
http://dx.doi.org/10.1364/BOE.5.001778


View Full Text Article

Enhanced HTML    Acrobat PDF (4014 KB)





Browse Journals / Lookup Meetings

Browse by Journal and Year


   


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools

Share
Citations

Abstract

The paper presents state space models of the hemodynamic response (HR) of fNIRS to an impulse stimulus in three brain regions: motor cortex (MC), somatosensory cortex (SC), and visual cortex (VC). Nineteen healthy subjects were examined. For each cortex, three impulse HRs experimentally obtained were averaged. The averaged signal was converted to a state space equation by using the subspace method. The activation peak and the undershoot peak of the oxy-hemoglobin (HbO) in MC are noticeably higher than those in SC and VC. The time-to-peaks of the HbO in three brain regions are almost the same (about 6.76 76 ± 0.2 s). The time to undershoot peak in VC is the largest among three. The HbO decreases in the early stage (~0.46 s) in MC and VC, but it is not so in SC. These findings were well described with the developed state space equations. Another advantage of the proposed method is its easy applicability in generating the expected HR to arbitrary stimuli in an online (or real-time) imaging. Experimental results are demonstrated.

© 2014 Optical Society of America

OCIS Codes
(100.2960) Image processing : Image analysis
(170.5380) Medical optics and biotechnology : Physiology
(300.0300) Spectroscopy : Spectroscopy
(170.2655) Medical optics and biotechnology : Functional monitoring and imaging

ToC Category:
Neuroscience and Brain Imaging

History
Original Manuscript: March 18, 2014
Revised Manuscript: May 3, 2014
Manuscript Accepted: May 3, 2014
Published: May 9, 2014

Citation
Keum-Shik Hong and Hoang-Dung Nguyen, "State-space models of impulse hemodynamic responses over motor, somatosensory, and visual cortices," Biomed. Opt. Express 5, 1778-1798 (2014)
http://www.opticsinfobase.org/boe/abstract.cfm?URI=boe-5-6-1778


Sort:  Author  |  Year  |  Journal  |  Reset  

References

  1. K. J. Friston, P. Fletcher, O. Josephs, A. Holmes, M. D. Rugg, and R. Turner, “Event-related fMRI: characterizing differential responses,” Neuroimage7(1), 30–40 (1998). [CrossRef] [PubMed]
  2. M. A. Lindquist, J. Meng Loh, L. Y. Atlas, and T. D. Wager, “Modeling the hemodynamic response function in fMRI: efficiency, bias and mis-modeling,” Neuroimage45(1Suppl), S187–S198 (2009). [CrossRef] [PubMed]
  3. H. F. Chen, D. Z. Yao, and Z. X. Liu, “A comparison of Gamma and Gaussian dynamic convolution models of the fMRI BOLD response,” Magn. Reson. Imaging23(1), 83–88 (2005). [CrossRef] [PubMed]
  4. K. Ciftçi, B. Sankur, Y. P. Kahya, and A. Akin, “Constraining the general linear model for sensible hemodynamic response function waveforms,” Med. Biol. Eng. Comput.46(8), 779–787 (2008). [CrossRef] [PubMed]
  5. X. S. Hu, K. S. Hong, S. S. Ge, and M. Y. Jeong, “Kalman estimator- and general linear model-based on-line brain activation mapping by near-infrared spectroscopy,” Biomed. Eng. Online9(1), 82 (2010). [CrossRef] [PubMed]
  6. R. B. Buxton, E. C. Wong, and L. R. Frank, “Dynamics of blood flow and oxygenation changes during brain activation: the balloon model,” Magn. Reson. Med.39(6), 855–864 (1998). [CrossRef] [PubMed]
  7. K. J. Friston, A. Mechelli, R. Turner, and C. J. Price, “Nonlinear responses in fMRI: the balloon model, volterra kernels, and other hemodynamics,” Neuroimage12(4), 466–477 (2000). [CrossRef] [PubMed]
  8. K. J. Friston, L. Harrison, and W. Penny, “Dynamic causal modelling,” Neuroimage19(4), 1273–1302 (2003). [CrossRef] [PubMed]
  9. K. E. Stephan, N. Weiskopf, P. M. Drysdale, P. A. Robinson, and K. J. Friston, “Comparing hemodynamic models with DCM,” Neuroimage38(3), 387–401 (2007). [CrossRef] [PubMed]
  10. J. Cohen-Adad, S. Chapuisat, J. Doyon, S. Rossignol, J. M. Lina, H. Benali, and F. Lesage, “Activation detection in diffuse optical imaging by means of the general linear model,” Med. Image Anal.11(6), 616–629 (2007). [CrossRef] [PubMed]
  11. M. A. Kamran and K. S. Hong, “Linear parameter-varying model and adaptive filtering technique for detecting neuronal activities: an fNIRS study,” J. Neural Eng.10(5), 056002 (2013). [CrossRef] [PubMed]
  12. N. Naseer, M. J. Hong, and K. S. Hong, “Online binary decision decoding using functional near-infrared spectroscopy for the development of brain-computer interface,” Exp. Brain Res.232(2), 555–564 (2014). [CrossRef] [PubMed]
  13. X. S. Hu, K. S. Hong, and S. S. Ge, “Recognition of stimulus-evoked neuronal optical response by identifying chaos levels of near-infrared spectroscopy time series,” Neurosci. Lett.504(2), 115–120 (2011). [CrossRef] [PubMed]
  14. T. Shimokawa, T. Kosaka, O. Yamashita, N. Hiroe, T. Amita, Y. Inoue, and M. A. Sato, “Extended hierarchical Bayesian diffuse optical tomography for removing scalp artifact,” Biomed. Opt. Express4(11), 2411–2432 (2013). [CrossRef] [PubMed]
  15. R. Re, D. Contini, M. Turola, L. Spinelli, L. Zucchelli, M. Caffini, R. Cubeddu, and A. Torricelli, “Multi-channel medical device for time domain functional near infrared spectroscopy based on wavelength space multiplexing,” Biomed. Opt. Express4(10), 2231–2246 (2013). [CrossRef] [PubMed]
  16. M. Cope and D. T. Delpy, “System for long-term measurement of cerebral blood and tissue oxygenation on newborn infants by near infra-red transillumination,” Med. Biol. Eng. Comput.26(3), 289–294 (1988). [CrossRef] [PubMed]
  17. R. B. Saager, N. L. Telleri, and A. J. Berger, “Two-detector corrected near infrared spectroscopy (C-NIRS) detects hemodynamic activation responses more robustly than single-detector NIRS,” Neuroimage55(4), 1679–1685 (2011). [CrossRef] [PubMed]
  18. J. C. Ye, S. Tak, K. E. Jang, J. Jung, and J. Jang, “NIRS-SPM: statistical parametric mapping for near-infrared spectroscopy,” Neuroimage44(2), 428–447 (2009). [CrossRef] [PubMed]
  19. M. S. Hassanpour, B. R. White, A. T. Eggebrecht, S. L. Ferradal, A. Z. Snyder, and J. P. Culver, “Statistical analysis of high density diffuse optical tomography,” Neuroimage85(Pt 1), 104–116 (2014). [CrossRef] [PubMed]
  20. M. Aqil, K. S. Hong, M. Y. Jeong, and S. S. Ge, “Detection of event-related hemodynamic response to neuroactivation by dynamic modeling of brain activity,” Neuroimage63(1), 553–568 (2012). [CrossRef] [PubMed]
  21. X. S. Hu, K. S. Hong, and S. S. Ge, “fNIRS-based online deception decoding,” J. Neural Eng.9(2), 026012 (2012). [CrossRef] [PubMed]
  22. I. Schelkanova and V. Toronov, “Independent component analysis of broadband near-infrared spectroscopy data acquired on adult human head,” Biomed. Opt. Express3(1), 64–74 (2012). [CrossRef] [PubMed]
  23. Z. Yuan, “Combining independent component analysis and Granger causality to investigate brain network dynamics with fNIRS measurements,” Biomed. Opt. Express4(11), 2629–2643 (2013). [CrossRef] [PubMed]
  24. N. Naseer and K. S. Hong, “Classification of functional near-infrared spectroscopy signals corresponding to the right- and left-wrist motor imagery for development of a brain-computer interface,” Neurosci. Lett.553, 84–89 (2013). [CrossRef] [PubMed]
  25. H. Santosa, M. J. Hong, S. P. Kim, and K. S. Hong, “Noise reduction in functional near-infrared spectroscopy signals by independent component analysis,” Rev. Sci. Instrum.84(7), 073106 (2013). [CrossRef] [PubMed]
  26. A. Villringer and B. Chance, “Non-invasive optical spectroscopy and imaging of human brain function,” Trends Neurosci.20(10), 435–442 (1997). [CrossRef] [PubMed]
  27. G. Jasdzewski, G. Strangman, J. Wagner, K. K. Kwong, R. A. Poldrack, and D. A. Boas, “Differences in the hemodynamic response to event-related motor and visual paradigms as measured by near-infrared spectroscopy,” Neuroimage20(1), 479–488 (2003). [CrossRef] [PubMed]
  28. Y. Hoshi, N. Kobayashi, and M. Tamura, “Interpretation of near-infrared spectroscopy signals: a study with a newly developed perfused rat brain model,” J. Appl. Physiol.90(5), 1657–1662 (2001). [PubMed]
  29. S. Brigadoi, L. Ceccherini, S. Cutini, F. Scarpa, P. Scatturin, J. Selb, L. Gagnon, D. A. Boas, and R. J. Cooper, “Motion artifacts in functional near-infrared spectroscopy: a comparison of motion correction techniques applied to real cognitive data,” Neuroimage85(Pt 1), 181–191 (2014). [CrossRef] [PubMed]
  30. J. W. Barker, A. Aarabi, and T. J. Huppert, “Autoregressive model based algorithm for correcting motion and serially correlated errors in fNIRS,” Biomed. Opt. Express4(8), 1366–1379 (2013). [CrossRef] [PubMed]
  31. X. S. Hu, K. S. Hong, and S. S. Ge, “Reduction of trial-to-trial variability in functional near-infrared spectroscopy signals by accounting for resting-state functional connectivity,” J. Biomed. Opt.18(1), 017003 (2013). [CrossRef] [PubMed]
  32. M. A. Franceschini, S. Fantini, J. H. Thompson, J. P. Culver, and D. A. Boas, “Hemodynamic evoked response of the sensorimotor cortex measured noninvasively with near-infrared optical imaging,” Psychophysiology40(4), 548–560 (2003). [CrossRef] [PubMed]
  33. T. Katayama, Subspace methods for system identification, E. D. Sontag, M. Thoma, A. Isidori, J. H. vanSchuppen ed. (Springer-Verlag London Limited, 2005).
  34. F. M. Miezin, L. Maccotta, J. M. Ollinger, S. E. Petersen, and R. L. Buckner, “Characterizing the hemodynamic response: effects of presentation rate, sampling procedure, and the possibility of ordering brain activity based on relative timing,” Neuroimage11(6), 735–759 (2000). [CrossRef] [PubMed]
  35. M. R. Bhutta, K. S. Hong, B. M. Kim, M. J. Hong, Y. H. Kim, and S. H. Lee, “Note: three wavelengths near-infrared spectroscopy system for compensating the light absorbance by water,” Rev. Sci. Instrum.85(2), 026111 (2014). [CrossRef] [PubMed]
  36. M. J. Khan, M. J. Hong, and K. S. Hong, “Decoding of four movement directions using hybrid NIRS-EEG brain-computer interface,” Front. Hum. Neurosci.8, 244 (2014). [CrossRef]
  37. P. Baraldi, A. A. Manginelli, M. Maieron, D. Liberati, and C. A. Porro, “An ARX model-based approach to trial by trial identification of fMRI-BOLD responses,” Neuroimage37(1), 189–201 (2007). [CrossRef] [PubMed]
  38. E. Yacoub, A. Shmuel, J. Pfeuffer, P. F. Van De Moortele, G. Adriany, K. Ugurbil, and X. P. Hu, “Investigation of the initial dip in fMRI at 7 Tesla,” NMR Biomed.14(7-8), 408–412 (2001). [CrossRef] [PubMed]

Cited By

Alert me when this paper is cited

OSA is able to provide readers links to articles that cite this paper by participating in CrossRef's Cited-By Linking service. CrossRef includes content from more than 3000 publishers and societies. In addition to listing OSA journal articles that cite this paper, citing articles from other participating publishers will also be listed.


« Previous Article  |  Next Article »

OSA is a member of CrossRef.

CrossCheck Deposited