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

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2014 (5)

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]

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,” Neuroimage 85(Pt 1), 104–116 (2014).
[CrossRef] [PubMed]

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,” Neuroimage 85(Pt 1), 181–191 (2014).
[CrossRef] [PubMed]

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]

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]

2013 (8)

J. W. Barker, A. Aarabi, and T. J. Huppert, “Autoregressive model based algorithm for correcting motion and serially correlated errors in fNIRS,” Biomed. Opt. Express 4(8), 1366–1379 (2013).
[CrossRef] [PubMed]

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. Express 4(10), 2231–2246 (2013).
[CrossRef] [PubMed]

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. Express 4(11), 2411–2432 (2013).
[CrossRef] [PubMed]

Z. Yuan, “Combining independent component analysis and Granger causality to investigate brain network dynamics with fNIRS measurements,” Biomed. Opt. Express 4(11), 2629–2643 (2013).
[CrossRef] [PubMed]

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]

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]

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]

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]

2012 (3)

I. Schelkanova and V. Toronov, “Independent component analysis of broadband near-infrared spectroscopy data acquired on adult human head,” Biomed. Opt. Express 3(1), 64–74 (2012).
[CrossRef] [PubMed]

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,” Neuroimage 63(1), 553–568 (2012).
[CrossRef] [PubMed]

X. S. Hu, K. S. Hong, and S. S. Ge, “fNIRS-based online deception decoding,” J. Neural Eng. 9(2), 026012 (2012).
[CrossRef] [PubMed]

2011 (2)

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,” Neuroimage 55(4), 1679–1685 (2011).
[CrossRef] [PubMed]

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]

2010 (1)

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. Online 9(1), 82 (2010).
[CrossRef] [PubMed]

2009 (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,” Neuroimage 45(1Suppl), S187–S198 (2009).
[CrossRef] [PubMed]

J. C. Ye, S. Tak, K. E. Jang, J. Jung, and J. Jang, “NIRS-SPM: statistical parametric mapping for near-infrared spectroscopy,” Neuroimage 44(2), 428–447 (2009).
[CrossRef] [PubMed]

2008 (1)

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]

2007 (3)

K. E. Stephan, N. Weiskopf, P. M. Drysdale, P. A. Robinson, and K. J. Friston, “Comparing hemodynamic models with DCM,” Neuroimage 38(3), 387–401 (2007).
[CrossRef] [PubMed]

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]

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,” Neuroimage 37(1), 189–201 (2007).
[CrossRef] [PubMed]

2005 (1)

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. Imaging 23(1), 83–88 (2005).
[CrossRef] [PubMed]

2003 (3)

K. J. Friston, L. Harrison, and W. Penny, “Dynamic causal modelling,” Neuroimage 19(4), 1273–1302 (2003).
[CrossRef] [PubMed]

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,” Neuroimage 20(1), 479–488 (2003).
[CrossRef] [PubMed]

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,” Psychophysiology 40(4), 548–560 (2003).
[CrossRef] [PubMed]

2001 (2)

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]

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]

2000 (2)

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,” Neuroimage 11(6), 735–759 (2000).
[CrossRef] [PubMed]

K. J. Friston, A. Mechelli, R. Turner, and C. J. Price, “Nonlinear responses in fMRI: the balloon model, volterra kernels, and other hemodynamics,” Neuroimage 12(4), 466–477 (2000).
[CrossRef] [PubMed]

1998 (2)

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]

K. J. Friston, P. Fletcher, O. Josephs, A. Holmes, M. D. Rugg, and R. Turner, “Event-related fMRI: characterizing differential responses,” Neuroimage 7(1), 30–40 (1998).
[CrossRef] [PubMed]

1997 (1)

A. Villringer and B. Chance, “Non-invasive optical spectroscopy and imaging of human brain function,” Trends Neurosci. 20(10), 435–442 (1997).
[CrossRef] [PubMed]

1988 (1)

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]

Aarabi, A.

Adriany, G.

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]

Akin, A.

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]

Amita, T.

Aqil, M.

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,” Neuroimage 63(1), 553–568 (2012).
[CrossRef] [PubMed]

Atlas, L. Y.

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,” Neuroimage 45(1Suppl), S187–S198 (2009).
[CrossRef] [PubMed]

Baraldi, P.

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,” Neuroimage 37(1), 189–201 (2007).
[CrossRef] [PubMed]

Barker, J. W.

Benali, H.

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]

Berger, A. J.

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,” Neuroimage 55(4), 1679–1685 (2011).
[CrossRef] [PubMed]

Bhutta, M. R.

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]

Boas, D. A.

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,” Neuroimage 85(Pt 1), 181–191 (2014).
[CrossRef] [PubMed]

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,” Neuroimage 20(1), 479–488 (2003).
[CrossRef] [PubMed]

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,” Psychophysiology 40(4), 548–560 (2003).
[CrossRef] [PubMed]

Brigadoi, S.

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,” Neuroimage 85(Pt 1), 181–191 (2014).
[CrossRef] [PubMed]

Buckner, R. L.

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,” Neuroimage 11(6), 735–759 (2000).
[CrossRef] [PubMed]

Buxton, R. B.

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]

Caffini, M.

Ceccherini, L.

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,” Neuroimage 85(Pt 1), 181–191 (2014).
[CrossRef] [PubMed]

Chance, B.

A. Villringer and B. Chance, “Non-invasive optical spectroscopy and imaging of human brain function,” Trends Neurosci. 20(10), 435–442 (1997).
[CrossRef] [PubMed]

Chapuisat, S.

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]

Chen, H. F.

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. Imaging 23(1), 83–88 (2005).
[CrossRef] [PubMed]

Ciftçi, K.

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]

Cohen-Adad, J.

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]

Contini, D.

Cooper, R. J.

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,” Neuroimage 85(Pt 1), 181–191 (2014).
[CrossRef] [PubMed]

Cope, M.

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]

Cubeddu, R.

Culver, J. P.

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,” Neuroimage 85(Pt 1), 104–116 (2014).
[CrossRef] [PubMed]

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,” Psychophysiology 40(4), 548–560 (2003).
[CrossRef] [PubMed]

Cutini, S.

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,” Neuroimage 85(Pt 1), 181–191 (2014).
[CrossRef] [PubMed]

Delpy, D. T.

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]

Doyon, J.

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]

Drysdale, P. M.

K. E. Stephan, N. Weiskopf, P. M. Drysdale, P. A. Robinson, and K. J. Friston, “Comparing hemodynamic models with DCM,” Neuroimage 38(3), 387–401 (2007).
[CrossRef] [PubMed]

Eggebrecht, A. T.

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,” Neuroimage 85(Pt 1), 104–116 (2014).
[CrossRef] [PubMed]

Fantini, S.

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,” Psychophysiology 40(4), 548–560 (2003).
[CrossRef] [PubMed]

Ferradal, S. L.

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,” Neuroimage 85(Pt 1), 104–116 (2014).
[CrossRef] [PubMed]

Fletcher, P.

K. J. Friston, P. Fletcher, O. Josephs, A. Holmes, M. D. Rugg, and R. Turner, “Event-related fMRI: characterizing differential responses,” Neuroimage 7(1), 30–40 (1998).
[CrossRef] [PubMed]

Franceschini, M. A.

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,” Psychophysiology 40(4), 548–560 (2003).
[CrossRef] [PubMed]

Frank, L. R.

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]

Friston, K. J.

K. E. Stephan, N. Weiskopf, P. M. Drysdale, P. A. Robinson, and K. J. Friston, “Comparing hemodynamic models with DCM,” Neuroimage 38(3), 387–401 (2007).
[CrossRef] [PubMed]

K. J. Friston, L. Harrison, and W. Penny, “Dynamic causal modelling,” Neuroimage 19(4), 1273–1302 (2003).
[CrossRef] [PubMed]

K. J. Friston, A. Mechelli, R. Turner, and C. J. Price, “Nonlinear responses in fMRI: the balloon model, volterra kernels, and other hemodynamics,” Neuroimage 12(4), 466–477 (2000).
[CrossRef] [PubMed]

K. J. Friston, P. Fletcher, O. Josephs, A. Holmes, M. D. Rugg, and R. Turner, “Event-related fMRI: characterizing differential responses,” Neuroimage 7(1), 30–40 (1998).
[CrossRef] [PubMed]

Gagnon, L.

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,” Neuroimage 85(Pt 1), 181–191 (2014).
[CrossRef] [PubMed]

Ge, S. S.

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]

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,” Neuroimage 63(1), 553–568 (2012).
[CrossRef] [PubMed]

X. S. Hu, K. S. Hong, and S. S. Ge, “fNIRS-based online deception decoding,” J. Neural Eng. 9(2), 026012 (2012).
[CrossRef] [PubMed]

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]

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. Online 9(1), 82 (2010).
[CrossRef] [PubMed]

Harrison, L.

K. J. Friston, L. Harrison, and W. Penny, “Dynamic causal modelling,” Neuroimage 19(4), 1273–1302 (2003).
[CrossRef] [PubMed]

Hassanpour, M. S.

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,” Neuroimage 85(Pt 1), 104–116 (2014).
[CrossRef] [PubMed]

Hiroe, N.

Holmes, A.

K. J. Friston, P. Fletcher, O. Josephs, A. Holmes, M. D. Rugg, and R. Turner, “Event-related fMRI: characterizing differential responses,” Neuroimage 7(1), 30–40 (1998).
[CrossRef] [PubMed]

Hong, K. S.

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]

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]

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]

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]

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]

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]

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]

X. S. Hu, K. S. Hong, and S. S. Ge, “fNIRS-based online deception decoding,” J. Neural Eng. 9(2), 026012 (2012).
[CrossRef] [PubMed]

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,” Neuroimage 63(1), 553–568 (2012).
[CrossRef] [PubMed]

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]

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. Online 9(1), 82 (2010).
[CrossRef] [PubMed]

Hong, M. J.

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]

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]

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]

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]

Hoshi, Y.

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]

Hu, X. P.

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]

Hu, X. S.

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]

X. S. Hu, K. S. Hong, and S. S. Ge, “fNIRS-based online deception decoding,” J. Neural Eng. 9(2), 026012 (2012).
[CrossRef] [PubMed]

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]

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. Online 9(1), 82 (2010).
[CrossRef] [PubMed]

Huppert, T. J.

Inoue, Y.

Jang, J.

J. C. Ye, S. Tak, K. E. Jang, J. Jung, and J. Jang, “NIRS-SPM: statistical parametric mapping for near-infrared spectroscopy,” Neuroimage 44(2), 428–447 (2009).
[CrossRef] [PubMed]

Jang, K. E.

J. C. Ye, S. Tak, K. E. Jang, J. Jung, and J. Jang, “NIRS-SPM: statistical parametric mapping for near-infrared spectroscopy,” Neuroimage 44(2), 428–447 (2009).
[CrossRef] [PubMed]

Jasdzewski, G.

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,” Neuroimage 20(1), 479–488 (2003).
[CrossRef] [PubMed]

Jeong, M. Y.

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,” Neuroimage 63(1), 553–568 (2012).
[CrossRef] [PubMed]

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. Online 9(1), 82 (2010).
[CrossRef] [PubMed]

Josephs, O.

K. J. Friston, P. Fletcher, O. Josephs, A. Holmes, M. D. Rugg, and R. Turner, “Event-related fMRI: characterizing differential responses,” Neuroimage 7(1), 30–40 (1998).
[CrossRef] [PubMed]

Jung, J.

J. C. Ye, S. Tak, K. E. Jang, J. Jung, and J. Jang, “NIRS-SPM: statistical parametric mapping for near-infrared spectroscopy,” Neuroimage 44(2), 428–447 (2009).
[CrossRef] [PubMed]

Kahya, Y. P.

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]

Kamran, M. A.

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]

Khan, M. J.

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]

Kim, B. M.

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]

Kim, S. P.

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]

Kim, Y. H.

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]

Kobayashi, N.

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]

Kosaka, T.

Kwong, K. K.

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,” Neuroimage 20(1), 479–488 (2003).
[CrossRef] [PubMed]

Lee, S. H.

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]

Lesage, F.

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]

Liberati, D.

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,” Neuroimage 37(1), 189–201 (2007).
[CrossRef] [PubMed]

Lina, J. M.

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]

Lindquist, M. A.

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,” Neuroimage 45(1Suppl), S187–S198 (2009).
[CrossRef] [PubMed]

Liu, Z. X.

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. Imaging 23(1), 83–88 (2005).
[CrossRef] [PubMed]

Maccotta, L.

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,” Neuroimage 11(6), 735–759 (2000).
[CrossRef] [PubMed]

Maieron, M.

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,” Neuroimage 37(1), 189–201 (2007).
[CrossRef] [PubMed]

Manginelli, A. A.

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,” Neuroimage 37(1), 189–201 (2007).
[CrossRef] [PubMed]

Mechelli, A.

K. J. Friston, A. Mechelli, R. Turner, and C. J. Price, “Nonlinear responses in fMRI: the balloon model, volterra kernels, and other hemodynamics,” Neuroimage 12(4), 466–477 (2000).
[CrossRef] [PubMed]

Meng Loh, J.

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,” Neuroimage 45(1Suppl), S187–S198 (2009).
[CrossRef] [PubMed]

Miezin, F. M.

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,” Neuroimage 11(6), 735–759 (2000).
[CrossRef] [PubMed]

Naseer, N.

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]

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]

Ollinger, J. M.

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,” Neuroimage 11(6), 735–759 (2000).
[CrossRef] [PubMed]

Penny, W.

K. J. Friston, L. Harrison, and W. Penny, “Dynamic causal modelling,” Neuroimage 19(4), 1273–1302 (2003).
[CrossRef] [PubMed]

Petersen, S. E.

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,” Neuroimage 11(6), 735–759 (2000).
[CrossRef] [PubMed]

Pfeuffer, J.

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]

Poldrack, R. A.

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,” Neuroimage 20(1), 479–488 (2003).
[CrossRef] [PubMed]

Porro, C. A.

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,” Neuroimage 37(1), 189–201 (2007).
[CrossRef] [PubMed]

Price, C. J.

K. J. Friston, A. Mechelli, R. Turner, and C. J. Price, “Nonlinear responses in fMRI: the balloon model, volterra kernels, and other hemodynamics,” Neuroimage 12(4), 466–477 (2000).
[CrossRef] [PubMed]

Re, R.

Robinson, P. A.

K. E. Stephan, N. Weiskopf, P. M. Drysdale, P. A. Robinson, and K. J. Friston, “Comparing hemodynamic models with DCM,” Neuroimage 38(3), 387–401 (2007).
[CrossRef] [PubMed]

Rossignol, S.

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]

Rugg, M. D.

K. J. Friston, P. Fletcher, O. Josephs, A. Holmes, M. D. Rugg, and R. Turner, “Event-related fMRI: characterizing differential responses,” Neuroimage 7(1), 30–40 (1998).
[CrossRef] [PubMed]

Saager, R. B.

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,” Neuroimage 55(4), 1679–1685 (2011).
[CrossRef] [PubMed]

Sankur, B.

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]

Santosa, H.

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]

Sato, M. A.

Scarpa, F.

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,” Neuroimage 85(Pt 1), 181–191 (2014).
[CrossRef] [PubMed]

Scatturin, P.

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,” Neuroimage 85(Pt 1), 181–191 (2014).
[CrossRef] [PubMed]

Schelkanova, I.

Selb, J.

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,” Neuroimage 85(Pt 1), 181–191 (2014).
[CrossRef] [PubMed]

Shimokawa, T.

Shmuel, A.

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]

Snyder, A. Z.

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,” Neuroimage 85(Pt 1), 104–116 (2014).
[CrossRef] [PubMed]

Spinelli, L.

Stephan, K. E.

K. E. Stephan, N. Weiskopf, P. M. Drysdale, P. A. Robinson, and K. J. Friston, “Comparing hemodynamic models with DCM,” Neuroimage 38(3), 387–401 (2007).
[CrossRef] [PubMed]

Strangman, G.

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,” Neuroimage 20(1), 479–488 (2003).
[CrossRef] [PubMed]

Tak, S.

J. C. Ye, S. Tak, K. E. Jang, J. Jung, and J. Jang, “NIRS-SPM: statistical parametric mapping for near-infrared spectroscopy,” Neuroimage 44(2), 428–447 (2009).
[CrossRef] [PubMed]

Tamura, M.

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]

Telleri, N. L.

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,” Neuroimage 55(4), 1679–1685 (2011).
[CrossRef] [PubMed]

Thompson, J. H.

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,” Psychophysiology 40(4), 548–560 (2003).
[CrossRef] [PubMed]

Toronov, V.

Torricelli, A.

Turner, R.

K. J. Friston, A. Mechelli, R. Turner, and C. J. Price, “Nonlinear responses in fMRI: the balloon model, volterra kernels, and other hemodynamics,” Neuroimage 12(4), 466–477 (2000).
[CrossRef] [PubMed]

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Turola, M.

Ugurbil, K.

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]

Van De Moortele, P. F.

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]

Villringer, A.

A. Villringer and B. Chance, “Non-invasive optical spectroscopy and imaging of human brain function,” Trends Neurosci. 20(10), 435–442 (1997).
[CrossRef] [PubMed]

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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,” Neuroimage 45(1Suppl), S187–S198 (2009).
[CrossRef] [PubMed]

Wagner, J.

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,” Neuroimage 20(1), 479–488 (2003).
[CrossRef] [PubMed]

Weiskopf, N.

K. E. Stephan, N. Weiskopf, P. M. Drysdale, P. A. Robinson, and K. J. Friston, “Comparing hemodynamic models with DCM,” Neuroimage 38(3), 387–401 (2007).
[CrossRef] [PubMed]

White, B. R.

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,” Neuroimage 85(Pt 1), 104–116 (2014).
[CrossRef] [PubMed]

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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]

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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).
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Biomed. Eng. Online (1)

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. Online 9(1), 82 (2010).
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Exp. Brain Res. (1)

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]

Front. Hum. Neurosci. (1)

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]

J. Appl. Physiol. (1)

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]

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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]

J. Neural Eng. (2)

X. S. Hu, K. S. Hong, and S. S. Ge, “fNIRS-based online deception decoding,” J. Neural Eng. 9(2), 026012 (2012).
[CrossRef] [PubMed]

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]

Magn. Reson. Imaging (1)

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. Imaging 23(1), 83–88 (2005).
[CrossRef] [PubMed]

Magn. Reson. Med. (1)

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]

Med. Biol. Eng. Comput. (2)

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).
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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]

Med. Image Anal. (1)

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).
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Neuroimage (13)

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,” Neuroimage 55(4), 1679–1685 (2011).
[CrossRef] [PubMed]

J. C. Ye, S. Tak, K. E. Jang, J. Jung, and J. Jang, “NIRS-SPM: statistical parametric mapping for near-infrared spectroscopy,” Neuroimage 44(2), 428–447 (2009).
[CrossRef] [PubMed]

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,” Neuroimage 85(Pt 1), 104–116 (2014).
[CrossRef] [PubMed]

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,” Neuroimage 63(1), 553–568 (2012).
[CrossRef] [PubMed]

K. J. Friston, P. Fletcher, O. Josephs, A. Holmes, M. D. Rugg, and R. Turner, “Event-related fMRI: characterizing differential responses,” Neuroimage 7(1), 30–40 (1998).
[CrossRef] [PubMed]

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,” Neuroimage 45(1Suppl), S187–S198 (2009).
[CrossRef] [PubMed]

K. J. Friston, A. Mechelli, R. Turner, and C. J. Price, “Nonlinear responses in fMRI: the balloon model, volterra kernels, and other hemodynamics,” Neuroimage 12(4), 466–477 (2000).
[CrossRef] [PubMed]

K. J. Friston, L. Harrison, and W. Penny, “Dynamic causal modelling,” Neuroimage 19(4), 1273–1302 (2003).
[CrossRef] [PubMed]

K. E. Stephan, N. Weiskopf, P. M. Drysdale, P. A. Robinson, and K. J. Friston, “Comparing hemodynamic models with DCM,” Neuroimage 38(3), 387–401 (2007).
[CrossRef] [PubMed]

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,” Neuroimage 20(1), 479–488 (2003).
[CrossRef] [PubMed]

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,” Neuroimage 85(Pt 1), 181–191 (2014).
[CrossRef] [PubMed]

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,” Neuroimage 11(6), 735–759 (2000).
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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,” Neuroimage 37(1), 189–201 (2007).
[CrossRef] [PubMed]

Neurosci. Lett. (2)

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]

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]

NMR Biomed. (1)

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]

Psychophysiology (1)

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,” Psychophysiology 40(4), 548–560 (2003).
[CrossRef] [PubMed]

Rev. Sci. Instrum. (2)

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).
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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).
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Trends Neurosci. (1)

A. Villringer and B. Chance, “Non-invasive optical spectroscopy and imaging of human brain function,” Trends Neurosci. 20(10), 435–442 (1997).
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T. Katayama, Subspace methods for system identification, E. D. Sontag, M. Thoma, A. Isidori, J. H. vanSchuppen ed. (Springer-Verlag London Limited, 2005).

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Figures (17)

Fig. 1
Fig. 1

Experimental paradigm for obtaining impulse HR functions: For the motor cortex (MC), a right middle-finger tapping (RMFT) was repeated three times; for the somatosensory cortex (SC), the right index-finger poking using a nail was used, and for the visual cortex (VC), a 3-sec checkerboard display was employed.

Fig. 2
Fig. 2

Optodes configuration for MC (black box) and SC (dotted red box): 1 emitter and 10 detectors → 10 channels.

Fig. 3
Fig. 3

Optodes configuration for VC: 1 emitter and 7 detectors → 7 channels.

Fig. 4
Fig. 4

Characteristics of the canonical HR: a1 stands for the peak amplitude of the main response, a2 is the magnitude of undershoot, t1 is the time-to-peak, t2 is the time-to-undershoot, T1 denotes the activation period, T2 denotes the deactivation period, m1 is the mean in T1, and m2 is the mean in T2.

Fig. 5
Fig. 5

Experiment for demonstrating the MC and SC models: (a) Optodes configuration (MC - black box; SC - dotted red box) and (b) the experimental paradigm including 5 taps/pokes at 30 and 90 s (1 tap/poke for 2 s); 10 taps/pokes at 60 and 120 s (1 tap/poke per s); and 1 tap (or poke) at 150, 170, and 190 s.

Fig. 6
Fig. 6

Experiment for demonstrating the VC model: (a) Optodes configuration and (b) experimental paradigm with 10 s checkerboard displays after the initial 30 s rest period.

Fig. 7
Fig. 7

Examples of HRs (blue solid curves) upon middle-finger taps (thick red impulses) in MC (Subject 10): (a) active channel (Ch. 7); (b) de-active channel (Ch. 8); (c) the averaged HR from (a); (d) the averaged HR from (b).

Fig. 8
Fig. 8

HRs in MC (Ch. 7): (a) The averaged HR over three stimuli in Fig. 1, (b) the averaged HR over 15 subjects in (a) and its standard deviation.

Fig. 9
Fig. 9

HRs in SC (Ch. 4): (a) The averaged HR over three stimuli in Fig. 1, (b) the averaged HR over 14 subjects in (a) and its standard deviation.

Fig. 10
Fig. 10

HRs in VC (Ch. 7): (a) The averaged HR over three stimuli in Fig. 1, (b) the averaged HR over 16 subjects in (a) and its standard deviation.

Fig. 11
Fig. 11

Comparison of the impulse HbO (solid line), HbR (dotted line), and HbT (dashed line): (a) MC (Ch. 7), (b) SC (Ch. 4), and (c) VC (Ch. 7).

Fig. 12
Fig. 12

Comparison of the impulse HbOs in three brain regions.

Fig. 13
Fig. 13

Comparison between the experimental HbOs (blue dashed curves) and the reconstructed ones (red solid curves): (a) MC, (b) SC, and (c) VC.

Fig. 14
Fig. 14

Comparison of the predicted HRs (the red dashed curves are through Eq. (3) and the black solid curves are through the state space method): (a) MC, (b) SC, and (c) VC.

Fig. 15
Fig. 15

Identified HRs in MC (Subject 2): (a) Active channel (Ch. 12), (b) de-active channel (Ch. 11).

Fig. 16
Fig. 16

Identified HRs in SC (Subject 1): (a) Active channel (Ch. 13), (b) de-active channel (Ch. 23).

Fig. 17
Fig. 17

Identified HRs in VC (Subject 1): (a) Active channel (Ch. 1), (b) de-active channel (Ch. 3).

Tables (5)

Tables Icon

Table 1 Examples of t-values in MC (Ch. 7, Ch. 8)

Tables Icon

Table 2 Mean value during the activation period (m1): MC

Tables Icon

Table 3 Mean value during the deactivation period (m2): MC

Tables Icon

Table 4 p-value between m1 and m2 for MC

Tables Icon

Table 5 Comparison of HR parameters between experiment and reconstruction: MC, SC, and VC

Equations (13)

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Δ A i ( k ; λ j ) = [ a HbO ( λ j ) Δ c HbO i ( k ) + a HbR ( λ j ) Δ c HbR i ( k ) ] × l i × d i , j = 1 , 2
[ Δ c HbO i ( k ) Δ c HbR i ( k ) ] = [ a HbO ( λ 1 ) a HbR ( λ 1 ) a HbO ( λ 2 ) a HbR ( λ 2 ) ] 1 [ Δ A i ( k ; λ 1 ) Δ A i ( k ; λ 2 ) ] × 1 l i d i .
y M ( k ) = s ( k ) * h ( k ) ,
y H i ( k ) = x 1 ( k ) β 1 i ( k ) + x 2 ( k ) β 2 i ( k ) + x 3 ( k ) β 3 i ( k ) + x 4 ( k ) β 4 i ( k ) + v i ( k ) , = X T ( k ) β i ( k ) + v i ( k )
e i ( k ) = y H i ( k ) y ^ H i ( k ) , y ^ H i ( k ) = X T ( k ) β ^ i ( k 1 ) , β ^ i ( k ) = β ^ i ( k 1 ) + L ( k ) e i ( k ) , L ( k ) = P ( k 1 ) X ( k ) ( 1 + X T ( k ) P ( k 1 ) X ( k ) ) 1 , P ( k ) = P ( k 1 ) L ( k ) X T ( k ) P ( k 1 ) ,
t i ( k ) = c T β ^ i ( k ) σ ^ i 2 ( k ) c T [ j = 1 k X T ( j ) X ( j ) ] 1 c ,
σ ^ i 2 ( k ) = 1 k r j = 1 k [ y H i ( j ) X T ( j ) β ^ i ( j ) ] 2 ,
z ( k + 1 ) = A z ( k ) + B u ( k ) + w ( k ) , y ( k ) = C z ( k ) + D u ( k ) + v ( k ) ,
U = [ u ( 0 ) u ( 1 ) u ( N 1 ) u ( 1 ) u ( 2 ) u ( N ) u ( q 1 ) u ( q ) u ( q + N 2 ) ] q × N ,
Y = [ y ( 0 ) y ( 1 ) y ( N 1 ) y ( 1 ) y ( 2 ) y ( N ) y ( q 1 ) y ( q ) y ( q + N 2 ) ] q × N ,
A = [ a 11 a 12 a 16 a 21 a 22 a 26 a 61 a 62 a 66 ] = [ 100 0.84 0.02 0.17 0 0.04 0.18 99.98 1.41 0.03 0.34 0.01 0.01 0.28 99.63 2.45 0.05 0.52 0 0.02 0.31 100.18 3.72 0.09 0 0 0 0.79 99.98 3.77 0 0 0.02 0.01 0.52 98.53 ] , B = [ b 11 b 12 b 16 ] T = [ 0.08 0.18 0.17 0.01 0.01 0.01 ] T , C = [ c 11 c 12 c 16 ] = [ 0.80 0.66 0.32 0.14 0.07 0.03 ] , D = 4.0565 × 10 8 .
A = [ 100 1.05 0.01 0.18 0.01 0.02 0.16 99.89 1.84 0 0.35 0.01 0.01 0.25 99.97 3.88 0.07 0.36 0 0.03 0.44 99.83 4.61 0.04 0 0.01 0.07 0.47 99.77 6.20 0 0 0 0.05 3.60 81.96 ] , B = [ 0.09 0.16 0.07 0.01 0.01 0 ] T , C = [ 0.81 0.52 0.19 0.09 0.03 0.01 ] , D = 3.422 × 10 8 .
A = [ 99.94 0.39 0.03 0.04 0.12 0.02 0.39 99.76 0.15 0.15 0.39 0.07 0.08 0.21 100.04 0.93 0.86 0.05 0.04 0.19 1.29 99.95 0.46 0.17 0.09 0.31 0.33 0.84 99.62 0.59 0.02 0.02 0.06 0.14 0.18 100.05 ] , B = [ 0.48 0.65 0.24 0.21 0.35 0.07 ] T , C = [ 0.48 0.66 0.17 0.14 0.42 0.11 ] , D = 2.3951 × 10 6 .

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