Abstract

Analysis of cerebral hemodynamics reveals a wide spectrum of oscillations ranging from 0.0095 to 2 Hz. While most of these oscillations can be filtered out during analysis of functional near-infrared spectroscopy (fNIRS) signals when estimating stimulus evoked hemodynamic responses, oscillations around 0.1 Hz are an exception. This is due to the fact that they share a common spectral range with typical stimulus evoked hemodynamic responses from the brain. Here we investigate the effect of hemodynamic oscillations around 0.1 Hz on the estimation of hemodynamic response functions from fNIRS data. Our results show that for an expected response of ~1 µM in oxygenated hemoglobin concentration (HbO), Mayer wave oscillations with an amplitude > ~1 µM at 0.1 Hz reduce the accuracy of the estimated response as quantified by a 3 fold increase in the mean squared error and decrease in correlation (R2 below 0.78) when compared to the true HRF. These results indicate that the amplitude of oscillations at 0.1 Hz can serve as an objective metric of the expected HRF estimation accuracy. In addition, we investigated the effect of short separation regression on the recovered HRF, and found that this improves the recovered HRF when large amplitude 0.1 Hz oscillations are present in fNIRS data. We suspect that the development of other filtering strategies may provide even further improvement.

© 2016 Optical Society of America

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    [Crossref] [PubMed]
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    [Crossref] [PubMed]
  25. M. A. Yücel, J. Selb, R. J. Cooper, and D. A. Boas, “Targeted principle component analysis: A new motion artifact correction approach for near-infrared spectroscopy,” J. Innov. Opt. Health Sci. 7(2), 1–8 (2014).
    [Crossref] [PubMed]
  26. 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]
  27. D. T. Delpy, M. Cope, P. van der Zee, S. Arridge, S. Wray, and J. Wyatt, “Estimation of optical pathlength through tissue from direct time of flight measurement,” Phys. Med. Biol. 33(12), 1433–1442 (1988).
    [Crossref] [PubMed]
  28. D. A. Boas, A. M. Dale, and M. A. Franceschini, “Diffuse optical imaging of brain activation: Approaches to optimizing image sensitivity, resolution, and accuracy,” Neuroimage 23, S275–S288 (2004).
  29. 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]
  30. T. Yamada, S. Umeyama, and K. Matsuda, “Multidistance probe arrangement to eliminate artifacts in functional near-infrared spectroscopy,” J. Biomed. Opt. 14(6), 064034 (2009).
    [Crossref] [PubMed]
  31. Q. Zhang, G. E. Strangman, and G. Ganis, “Adaptive filtering to reduce global interference in non-invasive NIRS measures of brain activation: how well and when does it work?” Neuroimage 45(3), 788–794 (2009).
    [Crossref] [PubMed]
  32. Y. Zhang, F. Tan, X. Xu, L. Duan, H. Liu, F. Tian, and C. Z. Zhu, “Multiregional functional near-infrared spectroscopy reveals globally symmetrical and frequency-specific patterns of superficial interference,” Biomed. Opt. Express 6(8), 2786–2802 (2015).
    [Crossref] [PubMed]
  33. L. Gagnon, R. J. Cooper, M. A. Yücel, K. L. Perdue, D. N. Greve, and D. A. Boas, “Short separation channel location impacts the performance of short channel regression in NIRS,” Neuroimage 59(3), 2518–2528 (2012).
    [Crossref] [PubMed]

2016 (1)

I. Tachtsidis and F. Scholkmann, “Erratum: Publisher’s note: False positives and false negatives in functional near-infrared spectroscopy: issues, challenges, and the way forward,” Neurophotonics 3(3), 039801 (2016).
[Crossref] [PubMed]

2015 (1)

2014 (1)

M. A. Yücel, J. Selb, R. J. Cooper, and D. A. Boas, “Targeted principle component analysis: A new motion artifact correction approach for near-infrared spectroscopy,” J. Innov. Opt. Health Sci. 7(2), 1–8 (2014).
[Crossref] [PubMed]

2013 (1)

E. Kirilina, N. Yu, A. Jelzow, H. Wabnitz, A. M. Jacobs, and I. Tachtsidis, “Identifying and quantifying main components of physiological noise in functional near infrared spectroscopy on the prefrontal cortex,” Front. Hum. Neurosci. 7, 864 (2013).
[PubMed]

2012 (4)

L. Gagnon, R. J. Cooper, M. A. Yücel, K. L. Perdue, D. N. Greve, and D. A. Boas, “Short separation channel location impacts the performance of short channel regression in NIRS,” Neuroimage 59(3), 2518–2528 (2012).
[Crossref] [PubMed]

Y. Zhan, A. T. Eggebrecht, J. P. Culver, and H. Dehghani, “Image quality analysis of high-density diffuse optical tomography incorporating a subject-specific head model,” Front. Neuroenergetics 4, 6 (2012).
[PubMed]

Y. Tong, L. M. Hocke, S. C. Licata, and B. Frederick, “Low-frequency oscillations measured in the periphery with near-infrared spectroscopy are strongly correlated with blood oxygen level-dependent functional magnetic resonance imaging signals,” J. Biomed. Opt. 17(10), 106004 (2012).
[Crossref] [PubMed]

A. Sassaroli, M. Pierro, P. R. Bergethon, and S. Fantini, “Low-frequency spontaneous oscillations of cerebral hemodynamics investigated with near-infrared spectroscopy: A review,” IEEE J. Sel. Top. Quantum Electron. 18(4), 1478–1492 (2012).
[Crossref]

2011 (3)

C. Aalkjær, D. Boedtkjer, and V. Matchkov, “Vasomotion - what is currently thought?” Acta Physiol. (Oxf.) 202(3), 253–269 (2011).
[Crossref] [PubMed]

L. Gagnon, K. Perdue, D. N. Greve, D. Goldenholz, G. Kaskhedikar, and D. A. Boas, “Improved recovery of the hemodynamic response in diffuse optical imaging using short optode separations and state-space modeling,” Neuroimage 56(3), 1362–1371 (2011).
[Crossref] [PubMed]

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]

2009 (3)

T. Yamada, S. Umeyama, and K. Matsuda, “Multidistance probe arrangement to eliminate artifacts in functional near-infrared spectroscopy,” J. Biomed. Opt. 14(6), 064034 (2009).
[Crossref] [PubMed]

Q. Zhang, G. E. Strangman, and G. Ganis, “Adaptive filtering to reduce global interference in non-invasive NIRS measures of brain activation: how well and when does it work?” Neuroimage 45(3), 788–794 (2009).
[Crossref] [PubMed]

T. J. Huppert, S. G. Diamond, M. A. Franceschini, and D. A. Boas, “HomER: a review of time-series analysis methods for near-infrared spectroscopy of the brain,” Appl. Opt. 48(10), D280–D298 (2009).
[Crossref] [PubMed]

2008 (1)

2007 (2)

S. Kohno, I. Miyai, A. Seiyama, I. Oda, A. Ishikawa, S. Tsuneishi, T. Amita, and K. Shimizu, “Removal of the skin blood flow artifact in functional near-infrared spectroscopic imaging data through independent component analysis,” J. Biomed. Opt. 12(6), 062111 (2007).
[Crossref] [PubMed]

A. Stefanovska, “Coupled Oscillators,” Eng. Med. Biol. Mag. 7, 25–29 (2007).

2006 (3)

C. Julien, “The enigma of Mayer waves: Facts and models,” Cardiovasc. Res. 70(1), 12–21 (2006).
[Crossref] [PubMed]

M. A. Franceschini, D. K. Joseph, T. J. Huppert, S. G. Diamond, and D. A. Boas, “Diffuse optical imaging of the whole head,” J. Biomed. Opt. 11(5), 054007 (2006).
[Crossref] [PubMed]

S. G. Diamond, T. J. Huppert, V. Kolehmainen, M. A. Franceschini, J. P. Kaipio, S. R. Arridge, and D. A. Boas, “Dynamic physiological modeling for functional diffuse optical tomography,” Neuroimage 30(1), 88–101 (2006).
[Crossref] [PubMed]

2005 (2)

Y. Zhang, D. H. Brooks, M. A. Franceschini, and D. A. Boas, “Eigenvector-based spatial filtering for reduction of physiological interference in diffuse optical imaging,” J. Biomed. Opt. 10(1), 011014 (2005).
[Crossref] [PubMed]

R. B. Saager and A. J. Berger, “Direct characterization and removal of interfering absorption trends in two-layer turbid media,” J. Opt. Soc. Am. A 22(9), 1874–1882 (2005).
[Crossref] [PubMed]

2004 (1)

D. A. Boas, A. M. Dale, and M. A. Franceschini, “Diffuse optical imaging of brain activation: Approaches to optimizing image sensitivity, resolution, and accuracy,” Neuroimage 23, S275–S288 (2004).

2003 (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]

2001 (1)

D. Cordes, V. M. Haughton, K. Arfanakis, J. D. Carew, P. A. Turski, C. H. Moritz, M. A. Quigley, and M. E. Meyerand, “Frequencies Contributing to Functional Connectivity in the Cerebral Cortex in “Resting-state” Data,” AJNR Am. J. Neuroradiol. 22(7), 1326–1333 (2001).
[PubMed]

2000 (1)

H. Obrig, M. Neufang, R. Wenzel, M. Kohl, J. Steinbrink, K. Einhäupl, and A. Villringer, “Spontaneous Low Frequency Oscillations of Cerebral Hemodynamics and Metabolism in Human Adults,” Neuroimage 12(6), 623–639 (2000).
[Crossref] [PubMed]

1999 (1)

C. E. Elwell, R. Springett, E. Hillman, and D. T. Delpy, “Oscillations in cerebral haemodynamics. Implications for functional activation studies,” Adv. Exp. Med. Biol. 471, 57–65 (1999).
[Crossref] [PubMed]

1998 (2)

H. D. Kvernmo, A. Stefanovska, M. Bracic, K. A. Kirkebøen, and K. Kvernebo, “Spectral analysis of the laser Doppler perfusion signal in human skin before and after exercise,” Microvasc. Res. 56(3), 173–182 (1998).
[Crossref] [PubMed]

Y. Hoshi, S. Kosaka, Y. Xie, S. Kohri, and M. Tamura, “Relationship between fluctuations in the cerebral hemoglobin oxygenation state and neuronal activity under resting conditions in man,” Neurosci. Lett. 245(3), 147–150 (1998).
[Crossref] [PubMed]

1995 (1)

G. Gratton, M. Fabiani, D. Friedman, M. A. Franceschini, S. Fantini, P. Corballis, and E. Gratton, “Rapid changes of optical parameters in the human brain during a tapping task,” J. Cogn. Neurosci. 7(4), 446–456 (1995).
[Crossref] [PubMed]

1992 (1)

L. N. Livera, Y. Wickramasinghe, S. Spencer, P. Rolfe, and M. S. Thorniley, “Cyclical fluctuations in cerebral blood volume,” Arch. Dis. Child. 67(1), 62–63 (1992).

1988 (2)

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]

D. T. Delpy, M. Cope, P. van der Zee, S. Arridge, S. Wray, and J. Wyatt, “Estimation of optical pathlength through tissue from direct time of flight measurement,” Phys. Med. Biol. 33(12), 1433–1442 (1988).
[Crossref] [PubMed]

Aalkjær, C.

C. Aalkjær, D. Boedtkjer, and V. Matchkov, “Vasomotion - what is currently thought?” Acta Physiol. (Oxf.) 202(3), 253–269 (2011).
[Crossref] [PubMed]

Amita, T.

S. Kohno, I. Miyai, A. Seiyama, I. Oda, A. Ishikawa, S. Tsuneishi, T. Amita, and K. Shimizu, “Removal of the skin blood flow artifact in functional near-infrared spectroscopic imaging data through independent component analysis,” J. Biomed. Opt. 12(6), 062111 (2007).
[Crossref] [PubMed]

Arfanakis, K.

D. Cordes, V. M. Haughton, K. Arfanakis, J. D. Carew, P. A. Turski, C. H. Moritz, M. A. Quigley, and M. E. Meyerand, “Frequencies Contributing to Functional Connectivity in the Cerebral Cortex in “Resting-state” Data,” AJNR Am. J. Neuroradiol. 22(7), 1326–1333 (2001).
[PubMed]

Arridge, S.

D. T. Delpy, M. Cope, P. van der Zee, S. Arridge, S. Wray, and J. Wyatt, “Estimation of optical pathlength through tissue from direct time of flight measurement,” Phys. Med. Biol. 33(12), 1433–1442 (1988).
[Crossref] [PubMed]

Arridge, S. R.

S. G. Diamond, T. J. Huppert, V. Kolehmainen, M. A. Franceschini, J. P. Kaipio, S. R. Arridge, and D. A. Boas, “Dynamic physiological modeling for functional diffuse optical tomography,” Neuroimage 30(1), 88–101 (2006).
[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]

R. B. Saager and A. J. Berger, “Direct characterization and removal of interfering absorption trends in two-layer turbid media,” J. Opt. Soc. Am. A 22(9), 1874–1882 (2005).
[Crossref] [PubMed]

Bergethon, P. R.

A. Sassaroli, M. Pierro, P. R. Bergethon, and S. Fantini, “Low-frequency spontaneous oscillations of cerebral hemodynamics investigated with near-infrared spectroscopy: A review,” IEEE J. Sel. Top. Quantum Electron. 18(4), 1478–1492 (2012).
[Crossref]

Boas, D. A.

M. A. Yücel, J. Selb, R. J. Cooper, and D. A. Boas, “Targeted principle component analysis: A new motion artifact correction approach for near-infrared spectroscopy,” J. Innov. Opt. Health Sci. 7(2), 1–8 (2014).
[Crossref] [PubMed]

L. Gagnon, R. J. Cooper, M. A. Yücel, K. L. Perdue, D. N. Greve, and D. A. Boas, “Short separation channel location impacts the performance of short channel regression in NIRS,” Neuroimage 59(3), 2518–2528 (2012).
[Crossref] [PubMed]

L. Gagnon, K. Perdue, D. N. Greve, D. Goldenholz, G. Kaskhedikar, and D. A. Boas, “Improved recovery of the hemodynamic response in diffuse optical imaging using short optode separations and state-space modeling,” Neuroimage 56(3), 1362–1371 (2011).
[Crossref] [PubMed]

T. J. Huppert, S. G. Diamond, M. A. Franceschini, and D. A. Boas, “HomER: a review of time-series analysis methods for near-infrared spectroscopy of the brain,” Appl. Opt. 48(10), D280–D298 (2009).
[Crossref] [PubMed]

S. G. Diamond, T. J. Huppert, V. Kolehmainen, M. A. Franceschini, J. P. Kaipio, S. R. Arridge, and D. A. Boas, “Dynamic physiological modeling for functional diffuse optical tomography,” Neuroimage 30(1), 88–101 (2006).
[Crossref] [PubMed]

M. A. Franceschini, D. K. Joseph, T. J. Huppert, S. G. Diamond, and D. A. Boas, “Diffuse optical imaging of the whole head,” J. Biomed. Opt. 11(5), 054007 (2006).
[Crossref] [PubMed]

Y. Zhang, D. H. Brooks, M. A. Franceschini, and D. A. Boas, “Eigenvector-based spatial filtering for reduction of physiological interference in diffuse optical imaging,” J. Biomed. Opt. 10(1), 011014 (2005).
[Crossref] [PubMed]

D. A. Boas, A. M. Dale, and M. A. Franceschini, “Diffuse optical imaging of brain activation: Approaches to optimizing image sensitivity, resolution, and accuracy,” Neuroimage 23, S275–S288 (2004).

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]

Boedtkjer, D.

C. Aalkjær, D. Boedtkjer, and V. Matchkov, “Vasomotion - what is currently thought?” Acta Physiol. (Oxf.) 202(3), 253–269 (2011).
[Crossref] [PubMed]

Bracic, M.

H. D. Kvernmo, A. Stefanovska, M. Bracic, K. A. Kirkebøen, and K. Kvernebo, “Spectral analysis of the laser Doppler perfusion signal in human skin before and after exercise,” Microvasc. Res. 56(3), 173–182 (1998).
[Crossref] [PubMed]

Brooks, D. H.

Y. Zhang, D. H. Brooks, M. A. Franceschini, and D. A. Boas, “Eigenvector-based spatial filtering for reduction of physiological interference in diffuse optical imaging,” J. Biomed. Opt. 10(1), 011014 (2005).
[Crossref] [PubMed]

Carew, J. D.

D. Cordes, V. M. Haughton, K. Arfanakis, J. D. Carew, P. A. Turski, C. H. Moritz, M. A. Quigley, and M. E. Meyerand, “Frequencies Contributing to Functional Connectivity in the Cerebral Cortex in “Resting-state” Data,” AJNR Am. J. Neuroradiol. 22(7), 1326–1333 (2001).
[PubMed]

Cooper, R. J.

M. A. Yücel, J. Selb, R. J. Cooper, and D. A. Boas, “Targeted principle component analysis: A new motion artifact correction approach for near-infrared spectroscopy,” J. Innov. Opt. Health Sci. 7(2), 1–8 (2014).
[Crossref] [PubMed]

L. Gagnon, R. J. Cooper, M. A. Yücel, K. L. Perdue, D. N. Greve, and D. A. Boas, “Short separation channel location impacts the performance of short channel regression in NIRS,” Neuroimage 59(3), 2518–2528 (2012).
[Crossref] [PubMed]

Cope, M.

D. T. Delpy, M. Cope, P. van der Zee, S. Arridge, S. Wray, and J. Wyatt, “Estimation of optical pathlength through tissue from direct time of flight measurement,” Phys. Med. Biol. 33(12), 1433–1442 (1988).
[Crossref] [PubMed]

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]

Corballis, P.

G. Gratton, M. Fabiani, D. Friedman, M. A. Franceschini, S. Fantini, P. Corballis, and E. Gratton, “Rapid changes of optical parameters in the human brain during a tapping task,” J. Cogn. Neurosci. 7(4), 446–456 (1995).
[Crossref] [PubMed]

Cordes, D.

D. Cordes, V. M. Haughton, K. Arfanakis, J. D. Carew, P. A. Turski, C. H. Moritz, M. A. Quigley, and M. E. Meyerand, “Frequencies Contributing to Functional Connectivity in the Cerebral Cortex in “Resting-state” Data,” AJNR Am. J. Neuroradiol. 22(7), 1326–1333 (2001).
[PubMed]

Culver, J. P.

Y. Zhan, A. T. Eggebrecht, J. P. Culver, and H. Dehghani, “Image quality analysis of high-density diffuse optical tomography incorporating a subject-specific head model,” Front. Neuroenergetics 4, 6 (2012).
[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]

Dale, A. M.

D. A. Boas, A. M. Dale, and M. A. Franceschini, “Diffuse optical imaging of brain activation: Approaches to optimizing image sensitivity, resolution, and accuracy,” Neuroimage 23, S275–S288 (2004).

Dehaes, M.

Dehghani, H.

Y. Zhan, A. T. Eggebrecht, J. P. Culver, and H. Dehghani, “Image quality analysis of high-density diffuse optical tomography incorporating a subject-specific head model,” Front. Neuroenergetics 4, 6 (2012).
[PubMed]

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C. E. Elwell, R. Springett, E. Hillman, and D. T. Delpy, “Oscillations in cerebral haemodynamics. Implications for functional activation studies,” Adv. Exp. Med. Biol. 471, 57–65 (1999).
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D. T. Delpy, M. Cope, P. van der Zee, S. Arridge, S. Wray, and J. Wyatt, “Estimation of optical pathlength through tissue from direct time of flight measurement,” Phys. Med. Biol. 33(12), 1433–1442 (1988).
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Diamond, S. G.

T. J. Huppert, S. G. Diamond, M. A. Franceschini, and D. A. Boas, “HomER: a review of time-series analysis methods for near-infrared spectroscopy of the brain,” Appl. Opt. 48(10), D280–D298 (2009).
[Crossref] [PubMed]

M. A. Franceschini, D. K. Joseph, T. J. Huppert, S. G. Diamond, and D. A. Boas, “Diffuse optical imaging of the whole head,” J. Biomed. Opt. 11(5), 054007 (2006).
[Crossref] [PubMed]

S. G. Diamond, T. J. Huppert, V. Kolehmainen, M. A. Franceschini, J. P. Kaipio, S. R. Arridge, and D. A. Boas, “Dynamic physiological modeling for functional diffuse optical tomography,” Neuroimage 30(1), 88–101 (2006).
[Crossref] [PubMed]

Duan, L.

Eggebrecht, A. T.

Y. Zhan, A. T. Eggebrecht, J. P. Culver, and H. Dehghani, “Image quality analysis of high-density diffuse optical tomography incorporating a subject-specific head model,” Front. Neuroenergetics 4, 6 (2012).
[PubMed]

Einhäupl, K.

H. Obrig, M. Neufang, R. Wenzel, M. Kohl, J. Steinbrink, K. Einhäupl, and A. Villringer, “Spontaneous Low Frequency Oscillations of Cerebral Hemodynamics and Metabolism in Human Adults,” Neuroimage 12(6), 623–639 (2000).
[Crossref] [PubMed]

Elwell, C. E.

C. E. Elwell, R. Springett, E. Hillman, and D. T. Delpy, “Oscillations in cerebral haemodynamics. Implications for functional activation studies,” Adv. Exp. Med. Biol. 471, 57–65 (1999).
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G. Gratton, M. Fabiani, D. Friedman, M. A. Franceschini, S. Fantini, P. Corballis, and E. Gratton, “Rapid changes of optical parameters in the human brain during a tapping task,” J. Cogn. Neurosci. 7(4), 446–456 (1995).
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A. Sassaroli, M. Pierro, P. R. Bergethon, and S. Fantini, “Low-frequency spontaneous oscillations of cerebral hemodynamics investigated with near-infrared spectroscopy: A review,” IEEE J. Sel. Top. Quantum Electron. 18(4), 1478–1492 (2012).
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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]

G. Gratton, M. Fabiani, D. Friedman, M. A. Franceschini, S. Fantini, P. Corballis, and E. Gratton, “Rapid changes of optical parameters in the human brain during a tapping task,” J. Cogn. Neurosci. 7(4), 446–456 (1995).
[Crossref] [PubMed]

Franceschini, M. A.

T. J. Huppert, S. G. Diamond, M. A. Franceschini, and D. A. Boas, “HomER: a review of time-series analysis methods for near-infrared spectroscopy of the brain,” Appl. Opt. 48(10), D280–D298 (2009).
[Crossref] [PubMed]

M. A. Franceschini, D. K. Joseph, T. J. Huppert, S. G. Diamond, and D. A. Boas, “Diffuse optical imaging of the whole head,” J. Biomed. Opt. 11(5), 054007 (2006).
[Crossref] [PubMed]

S. G. Diamond, T. J. Huppert, V. Kolehmainen, M. A. Franceschini, J. P. Kaipio, S. R. Arridge, and D. A. Boas, “Dynamic physiological modeling for functional diffuse optical tomography,” Neuroimage 30(1), 88–101 (2006).
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Y. Zhang, D. H. Brooks, M. A. Franceschini, and D. A. Boas, “Eigenvector-based spatial filtering for reduction of physiological interference in diffuse optical imaging,” J. Biomed. Opt. 10(1), 011014 (2005).
[Crossref] [PubMed]

D. A. Boas, A. M. Dale, and M. A. Franceschini, “Diffuse optical imaging of brain activation: Approaches to optimizing image sensitivity, resolution, and accuracy,” Neuroimage 23, S275–S288 (2004).

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]

G. Gratton, M. Fabiani, D. Friedman, M. A. Franceschini, S. Fantini, P. Corballis, and E. Gratton, “Rapid changes of optical parameters in the human brain during a tapping task,” J. Cogn. Neurosci. 7(4), 446–456 (1995).
[Crossref] [PubMed]

Frederick, B.

Y. Tong, L. M. Hocke, S. C. Licata, and B. Frederick, “Low-frequency oscillations measured in the periphery with near-infrared spectroscopy are strongly correlated with blood oxygen level-dependent functional magnetic resonance imaging signals,” J. Biomed. Opt. 17(10), 106004 (2012).
[Crossref] [PubMed]

Friedman, D.

G. Gratton, M. Fabiani, D. Friedman, M. A. Franceschini, S. Fantini, P. Corballis, and E. Gratton, “Rapid changes of optical parameters in the human brain during a tapping task,” J. Cogn. Neurosci. 7(4), 446–456 (1995).
[Crossref] [PubMed]

Gagnon, L.

L. Gagnon, R. J. Cooper, M. A. Yücel, K. L. Perdue, D. N. Greve, and D. A. Boas, “Short separation channel location impacts the performance of short channel regression in NIRS,” Neuroimage 59(3), 2518–2528 (2012).
[Crossref] [PubMed]

L. Gagnon, K. Perdue, D. N. Greve, D. Goldenholz, G. Kaskhedikar, and D. A. Boas, “Improved recovery of the hemodynamic response in diffuse optical imaging using short optode separations and state-space modeling,” Neuroimage 56(3), 1362–1371 (2011).
[Crossref] [PubMed]

Ganis, G.

Q. Zhang, G. E. Strangman, and G. Ganis, “Adaptive filtering to reduce global interference in non-invasive NIRS measures of brain activation: how well and when does it work?” Neuroimage 45(3), 788–794 (2009).
[Crossref] [PubMed]

Goldenholz, D.

L. Gagnon, K. Perdue, D. N. Greve, D. Goldenholz, G. Kaskhedikar, and D. A. Boas, “Improved recovery of the hemodynamic response in diffuse optical imaging using short optode separations and state-space modeling,” Neuroimage 56(3), 1362–1371 (2011).
[Crossref] [PubMed]

Gratton, E.

G. Gratton, M. Fabiani, D. Friedman, M. A. Franceschini, S. Fantini, P. Corballis, and E. Gratton, “Rapid changes of optical parameters in the human brain during a tapping task,” J. Cogn. Neurosci. 7(4), 446–456 (1995).
[Crossref] [PubMed]

Gratton, G.

G. Gratton, M. Fabiani, D. Friedman, M. A. Franceschini, S. Fantini, P. Corballis, and E. Gratton, “Rapid changes of optical parameters in the human brain during a tapping task,” J. Cogn. Neurosci. 7(4), 446–456 (1995).
[Crossref] [PubMed]

Greve, D. N.

L. Gagnon, R. J. Cooper, M. A. Yücel, K. L. Perdue, D. N. Greve, and D. A. Boas, “Short separation channel location impacts the performance of short channel regression in NIRS,” Neuroimage 59(3), 2518–2528 (2012).
[Crossref] [PubMed]

L. Gagnon, K. Perdue, D. N. Greve, D. Goldenholz, G. Kaskhedikar, and D. A. Boas, “Improved recovery of the hemodynamic response in diffuse optical imaging using short optode separations and state-space modeling,” Neuroimage 56(3), 1362–1371 (2011).
[Crossref] [PubMed]

Haughton, V. M.

D. Cordes, V. M. Haughton, K. Arfanakis, J. D. Carew, P. A. Turski, C. H. Moritz, M. A. Quigley, and M. E. Meyerand, “Frequencies Contributing to Functional Connectivity in the Cerebral Cortex in “Resting-state” Data,” AJNR Am. J. Neuroradiol. 22(7), 1326–1333 (2001).
[PubMed]

Hillman, E.

C. E. Elwell, R. Springett, E. Hillman, and D. T. Delpy, “Oscillations in cerebral haemodynamics. Implications for functional activation studies,” Adv. Exp. Med. Biol. 471, 57–65 (1999).
[Crossref] [PubMed]

Hocke, L. M.

Y. Tong, L. M. Hocke, S. C. Licata, and B. Frederick, “Low-frequency oscillations measured in the periphery with near-infrared spectroscopy are strongly correlated with blood oxygen level-dependent functional magnetic resonance imaging signals,” J. Biomed. Opt. 17(10), 106004 (2012).
[Crossref] [PubMed]

Hoshi, Y.

Y. Hoshi, S. Kosaka, Y. Xie, S. Kohri, and M. Tamura, “Relationship between fluctuations in the cerebral hemoglobin oxygenation state and neuronal activity under resting conditions in man,” Neurosci. Lett. 245(3), 147–150 (1998).
[Crossref] [PubMed]

Huppert, T. J.

T. J. Huppert, S. G. Diamond, M. A. Franceschini, and D. A. Boas, “HomER: a review of time-series analysis methods for near-infrared spectroscopy of the brain,” Appl. Opt. 48(10), D280–D298 (2009).
[Crossref] [PubMed]

M. A. Franceschini, D. K. Joseph, T. J. Huppert, S. G. Diamond, and D. A. Boas, “Diffuse optical imaging of the whole head,” J. Biomed. Opt. 11(5), 054007 (2006).
[Crossref] [PubMed]

S. G. Diamond, T. J. Huppert, V. Kolehmainen, M. A. Franceschini, J. P. Kaipio, S. R. Arridge, and D. A. Boas, “Dynamic physiological modeling for functional diffuse optical tomography,” Neuroimage 30(1), 88–101 (2006).
[Crossref] [PubMed]

Ishikawa, A.

S. Kohno, I. Miyai, A. Seiyama, I. Oda, A. Ishikawa, S. Tsuneishi, T. Amita, and K. Shimizu, “Removal of the skin blood flow artifact in functional near-infrared spectroscopic imaging data through independent component analysis,” J. Biomed. Opt. 12(6), 062111 (2007).
[Crossref] [PubMed]

Jacobs, A. M.

E. Kirilina, N. Yu, A. Jelzow, H. Wabnitz, A. M. Jacobs, and I. Tachtsidis, “Identifying and quantifying main components of physiological noise in functional near infrared spectroscopy on the prefrontal cortex,” Front. Hum. Neurosci. 7, 864 (2013).
[PubMed]

Jelzow, A.

E. Kirilina, N. Yu, A. Jelzow, H. Wabnitz, A. M. Jacobs, and I. Tachtsidis, “Identifying and quantifying main components of physiological noise in functional near infrared spectroscopy on the prefrontal cortex,” Front. Hum. Neurosci. 7, 864 (2013).
[PubMed]

Joseph, D. K.

M. A. Franceschini, D. K. Joseph, T. J. Huppert, S. G. Diamond, and D. A. Boas, “Diffuse optical imaging of the whole head,” J. Biomed. Opt. 11(5), 054007 (2006).
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Julien, C.

C. Julien, “The enigma of Mayer waves: Facts and models,” Cardiovasc. Res. 70(1), 12–21 (2006).
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Kaipio, J. P.

S. G. Diamond, T. J. Huppert, V. Kolehmainen, M. A. Franceschini, J. P. Kaipio, S. R. Arridge, and D. A. Boas, “Dynamic physiological modeling for functional diffuse optical tomography,” Neuroimage 30(1), 88–101 (2006).
[Crossref] [PubMed]

Kaskhedikar, G.

L. Gagnon, K. Perdue, D. N. Greve, D. Goldenholz, G. Kaskhedikar, and D. A. Boas, “Improved recovery of the hemodynamic response in diffuse optical imaging using short optode separations and state-space modeling,” Neuroimage 56(3), 1362–1371 (2011).
[Crossref] [PubMed]

Kirilina, E.

E. Kirilina, N. Yu, A. Jelzow, H. Wabnitz, A. M. Jacobs, and I. Tachtsidis, “Identifying and quantifying main components of physiological noise in functional near infrared spectroscopy on the prefrontal cortex,” Front. Hum. Neurosci. 7, 864 (2013).
[PubMed]

Kirkebøen, K. A.

H. D. Kvernmo, A. Stefanovska, M. Bracic, K. A. Kirkebøen, and K. Kvernebo, “Spectral analysis of the laser Doppler perfusion signal in human skin before and after exercise,” Microvasc. Res. 56(3), 173–182 (1998).
[Crossref] [PubMed]

Kohl, M.

H. Obrig, M. Neufang, R. Wenzel, M. Kohl, J. Steinbrink, K. Einhäupl, and A. Villringer, “Spontaneous Low Frequency Oscillations of Cerebral Hemodynamics and Metabolism in Human Adults,” Neuroimage 12(6), 623–639 (2000).
[Crossref] [PubMed]

Kohno, S.

S. Kohno, I. Miyai, A. Seiyama, I. Oda, A. Ishikawa, S. Tsuneishi, T. Amita, and K. Shimizu, “Removal of the skin blood flow artifact in functional near-infrared spectroscopic imaging data through independent component analysis,” J. Biomed. Opt. 12(6), 062111 (2007).
[Crossref] [PubMed]

Kohri, S.

Y. Hoshi, S. Kosaka, Y. Xie, S. Kohri, and M. Tamura, “Relationship between fluctuations in the cerebral hemoglobin oxygenation state and neuronal activity under resting conditions in man,” Neurosci. Lett. 245(3), 147–150 (1998).
[Crossref] [PubMed]

Kolehmainen, V.

S. G. Diamond, T. J. Huppert, V. Kolehmainen, M. A. Franceschini, J. P. Kaipio, S. R. Arridge, and D. A. Boas, “Dynamic physiological modeling for functional diffuse optical tomography,” Neuroimage 30(1), 88–101 (2006).
[Crossref] [PubMed]

Kosaka, S.

Y. Hoshi, S. Kosaka, Y. Xie, S. Kohri, and M. Tamura, “Relationship between fluctuations in the cerebral hemoglobin oxygenation state and neuronal activity under resting conditions in man,” Neurosci. Lett. 245(3), 147–150 (1998).
[Crossref] [PubMed]

Kvernebo, K.

H. D. Kvernmo, A. Stefanovska, M. Bracic, K. A. Kirkebøen, and K. Kvernebo, “Spectral analysis of the laser Doppler perfusion signal in human skin before and after exercise,” Microvasc. Res. 56(3), 173–182 (1998).
[Crossref] [PubMed]

Kvernmo, H. D.

H. D. Kvernmo, A. Stefanovska, M. Bracic, K. A. Kirkebøen, and K. Kvernebo, “Spectral analysis of the laser Doppler perfusion signal in human skin before and after exercise,” Microvasc. Res. 56(3), 173–182 (1998).
[Crossref] [PubMed]

Lesage, F.

Licata, S. C.

Y. Tong, L. M. Hocke, S. C. Licata, and B. Frederick, “Low-frequency oscillations measured in the periphery with near-infrared spectroscopy are strongly correlated with blood oxygen level-dependent functional magnetic resonance imaging signals,” J. Biomed. Opt. 17(10), 106004 (2012).
[Crossref] [PubMed]

Lina, J.-M.

Liu, H.

Livera, L. N.

L. N. Livera, Y. Wickramasinghe, S. Spencer, P. Rolfe, and M. S. Thorniley, “Cyclical fluctuations in cerebral blood volume,” Arch. Dis. Child. 67(1), 62–63 (1992).

Matchkov, V.

C. Aalkjær, D. Boedtkjer, and V. Matchkov, “Vasomotion - what is currently thought?” Acta Physiol. (Oxf.) 202(3), 253–269 (2011).
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Matsuda, K.

T. Yamada, S. Umeyama, and K. Matsuda, “Multidistance probe arrangement to eliminate artifacts in functional near-infrared spectroscopy,” J. Biomed. Opt. 14(6), 064034 (2009).
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Matteau-Pelletier, C.

Meyerand, M. E.

D. Cordes, V. M. Haughton, K. Arfanakis, J. D. Carew, P. A. Turski, C. H. Moritz, M. A. Quigley, and M. E. Meyerand, “Frequencies Contributing to Functional Connectivity in the Cerebral Cortex in “Resting-state” Data,” AJNR Am. J. Neuroradiol. 22(7), 1326–1333 (2001).
[PubMed]

Miyai, I.

S. Kohno, I. Miyai, A. Seiyama, I. Oda, A. Ishikawa, S. Tsuneishi, T. Amita, and K. Shimizu, “Removal of the skin blood flow artifact in functional near-infrared spectroscopic imaging data through independent component analysis,” J. Biomed. Opt. 12(6), 062111 (2007).
[Crossref] [PubMed]

Moritz, C. H.

D. Cordes, V. M. Haughton, K. Arfanakis, J. D. Carew, P. A. Turski, C. H. Moritz, M. A. Quigley, and M. E. Meyerand, “Frequencies Contributing to Functional Connectivity in the Cerebral Cortex in “Resting-state” Data,” AJNR Am. J. Neuroradiol. 22(7), 1326–1333 (2001).
[PubMed]

Neufang, M.

H. Obrig, M. Neufang, R. Wenzel, M. Kohl, J. Steinbrink, K. Einhäupl, and A. Villringer, “Spontaneous Low Frequency Oscillations of Cerebral Hemodynamics and Metabolism in Human Adults,” Neuroimage 12(6), 623–639 (2000).
[Crossref] [PubMed]

Obrig, H.

H. Obrig, M. Neufang, R. Wenzel, M. Kohl, J. Steinbrink, K. Einhäupl, and A. Villringer, “Spontaneous Low Frequency Oscillations of Cerebral Hemodynamics and Metabolism in Human Adults,” Neuroimage 12(6), 623–639 (2000).
[Crossref] [PubMed]

Oda, I.

S. Kohno, I. Miyai, A. Seiyama, I. Oda, A. Ishikawa, S. Tsuneishi, T. Amita, and K. Shimizu, “Removal of the skin blood flow artifact in functional near-infrared spectroscopic imaging data through independent component analysis,” J. Biomed. Opt. 12(6), 062111 (2007).
[Crossref] [PubMed]

Perdue, K.

L. Gagnon, K. Perdue, D. N. Greve, D. Goldenholz, G. Kaskhedikar, and D. A. Boas, “Improved recovery of the hemodynamic response in diffuse optical imaging using short optode separations and state-space modeling,” Neuroimage 56(3), 1362–1371 (2011).
[Crossref] [PubMed]

Perdue, K. L.

L. Gagnon, R. J. Cooper, M. A. Yücel, K. L. Perdue, D. N. Greve, and D. A. Boas, “Short separation channel location impacts the performance of short channel regression in NIRS,” Neuroimage 59(3), 2518–2528 (2012).
[Crossref] [PubMed]

Pierro, M.

A. Sassaroli, M. Pierro, P. R. Bergethon, and S. Fantini, “Low-frequency spontaneous oscillations of cerebral hemodynamics investigated with near-infrared spectroscopy: A review,” IEEE J. Sel. Top. Quantum Electron. 18(4), 1478–1492 (2012).
[Crossref]

Quigley, M. A.

D. Cordes, V. M. Haughton, K. Arfanakis, J. D. Carew, P. A. Turski, C. H. Moritz, M. A. Quigley, and M. E. Meyerand, “Frequencies Contributing to Functional Connectivity in the Cerebral Cortex in “Resting-state” Data,” AJNR Am. J. Neuroradiol. 22(7), 1326–1333 (2001).
[PubMed]

Rolfe, P.

L. N. Livera, Y. Wickramasinghe, S. Spencer, P. Rolfe, and M. S. Thorniley, “Cyclical fluctuations in cerebral blood volume,” Arch. Dis. Child. 67(1), 62–63 (1992).

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).
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R. B. Saager and A. J. Berger, “Direct characterization and removal of interfering absorption trends in two-layer turbid media,” J. Opt. Soc. Am. A 22(9), 1874–1882 (2005).
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Sassaroli, A.

A. Sassaroli, M. Pierro, P. R. Bergethon, and S. Fantini, “Low-frequency spontaneous oscillations of cerebral hemodynamics investigated with near-infrared spectroscopy: A review,” IEEE J. Sel. Top. Quantum Electron. 18(4), 1478–1492 (2012).
[Crossref]

Scholkmann, F.

I. Tachtsidis and F. Scholkmann, “Erratum: Publisher’s note: False positives and false negatives in functional near-infrared spectroscopy: issues, challenges, and the way forward,” Neurophotonics 3(3), 039801 (2016).
[Crossref] [PubMed]

Seiyama, A.

S. Kohno, I. Miyai, A. Seiyama, I. Oda, A. Ishikawa, S. Tsuneishi, T. Amita, and K. Shimizu, “Removal of the skin blood flow artifact in functional near-infrared spectroscopic imaging data through independent component analysis,” J. Biomed. Opt. 12(6), 062111 (2007).
[Crossref] [PubMed]

Selb, J.

M. A. Yücel, J. Selb, R. J. Cooper, and D. A. Boas, “Targeted principle component analysis: A new motion artifact correction approach for near-infrared spectroscopy,” J. Innov. Opt. Health Sci. 7(2), 1–8 (2014).
[Crossref] [PubMed]

Shimizu, K.

S. Kohno, I. Miyai, A. Seiyama, I. Oda, A. Ishikawa, S. Tsuneishi, T. Amita, and K. Shimizu, “Removal of the skin blood flow artifact in functional near-infrared spectroscopic imaging data through independent component analysis,” J. Biomed. Opt. 12(6), 062111 (2007).
[Crossref] [PubMed]

Spencer, S.

L. N. Livera, Y. Wickramasinghe, S. Spencer, P. Rolfe, and M. S. Thorniley, “Cyclical fluctuations in cerebral blood volume,” Arch. Dis. Child. 67(1), 62–63 (1992).

Springett, R.

C. E. Elwell, R. Springett, E. Hillman, and D. T. Delpy, “Oscillations in cerebral haemodynamics. Implications for functional activation studies,” Adv. Exp. Med. Biol. 471, 57–65 (1999).
[Crossref] [PubMed]

Stefanovska, A.

A. Stefanovska, “Coupled Oscillators,” Eng. Med. Biol. Mag. 7, 25–29 (2007).

H. D. Kvernmo, A. Stefanovska, M. Bracic, K. A. Kirkebøen, and K. Kvernebo, “Spectral analysis of the laser Doppler perfusion signal in human skin before and after exercise,” Microvasc. Res. 56(3), 173–182 (1998).
[Crossref] [PubMed]

Steinbrink, J.

H. Obrig, M. Neufang, R. Wenzel, M. Kohl, J. Steinbrink, K. Einhäupl, and A. Villringer, “Spontaneous Low Frequency Oscillations of Cerebral Hemodynamics and Metabolism in Human Adults,” Neuroimage 12(6), 623–639 (2000).
[Crossref] [PubMed]

Strangman, G. E.

Q. Zhang, G. E. Strangman, and G. Ganis, “Adaptive filtering to reduce global interference in non-invasive NIRS measures of brain activation: how well and when does it work?” Neuroimage 45(3), 788–794 (2009).
[Crossref] [PubMed]

Tachtsidis, I.

I. Tachtsidis and F. Scholkmann, “Erratum: Publisher’s note: False positives and false negatives in functional near-infrared spectroscopy: issues, challenges, and the way forward,” Neurophotonics 3(3), 039801 (2016).
[Crossref] [PubMed]

E. Kirilina, N. Yu, A. Jelzow, H. Wabnitz, A. M. Jacobs, and I. Tachtsidis, “Identifying and quantifying main components of physiological noise in functional near infrared spectroscopy on the prefrontal cortex,” Front. Hum. Neurosci. 7, 864 (2013).
[PubMed]

Tamura, M.

Y. Hoshi, S. Kosaka, Y. Xie, S. Kohri, and M. Tamura, “Relationship between fluctuations in the cerebral hemoglobin oxygenation state and neuronal activity under resting conditions in man,” Neurosci. Lett. 245(3), 147–150 (1998).
[Crossref] [PubMed]

Tan, F.

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]

Thorniley, M. S.

L. N. Livera, Y. Wickramasinghe, S. Spencer, P. Rolfe, and M. S. Thorniley, “Cyclical fluctuations in cerebral blood volume,” Arch. Dis. Child. 67(1), 62–63 (1992).

Tian, F.

Tong, Y.

Y. Tong, L. M. Hocke, S. C. Licata, and B. Frederick, “Low-frequency oscillations measured in the periphery with near-infrared spectroscopy are strongly correlated with blood oxygen level-dependent functional magnetic resonance imaging signals,” J. Biomed. Opt. 17(10), 106004 (2012).
[Crossref] [PubMed]

Tsuneishi, S.

S. Kohno, I. Miyai, A. Seiyama, I. Oda, A. Ishikawa, S. Tsuneishi, T. Amita, and K. Shimizu, “Removal of the skin blood flow artifact in functional near-infrared spectroscopic imaging data through independent component analysis,” J. Biomed. Opt. 12(6), 062111 (2007).
[Crossref] [PubMed]

Turski, P. A.

D. Cordes, V. M. Haughton, K. Arfanakis, J. D. Carew, P. A. Turski, C. H. Moritz, M. A. Quigley, and M. E. Meyerand, “Frequencies Contributing to Functional Connectivity in the Cerebral Cortex in “Resting-state” Data,” AJNR Am. J. Neuroradiol. 22(7), 1326–1333 (2001).
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H. Obrig, M. Neufang, R. Wenzel, M. Kohl, J. Steinbrink, K. Einhäupl, and A. Villringer, “Spontaneous Low Frequency Oscillations of Cerebral Hemodynamics and Metabolism in Human Adults,” Neuroimage 12(6), 623–639 (2000).
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[Crossref] [PubMed]

L. Gagnon, R. J. Cooper, M. A. Yücel, K. L. Perdue, D. N. Greve, and D. A. Boas, “Short separation channel location impacts the performance of short channel regression in NIRS,” Neuroimage 59(3), 2518–2528 (2012).
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I. Tachtsidis and F. Scholkmann, “Erratum: Publisher’s note: False positives and false negatives in functional near-infrared spectroscopy: issues, challenges, and the way forward,” Neurophotonics 3(3), 039801 (2016).
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Neurosci. Lett. (1)

Y. Hoshi, S. Kosaka, Y. Xie, S. Kohri, and M. Tamura, “Relationship between fluctuations in the cerebral hemoglobin oxygenation state and neuronal activity under resting conditions in man,” Neurosci. Lett. 245(3), 147–150 (1998).
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Opt. Express (1)

Phys. Med. Biol. (1)

D. T. Delpy, M. Cope, P. van der Zee, S. Arridge, S. Wray, and J. Wyatt, “Estimation of optical pathlength through tissue from direct time of flight measurement,” Phys. Med. Biol. 33(12), 1433–1442 (1988).
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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).
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Figures (6)

Fig. 1
Fig. 1 The locations of the detectors (blue dots), the sources (red dots), the channels (green lines) and the sensitivity profile are shown for one subject with 10-20 EEG locations.
Fig. 2
Fig. 2 Examples of small and large amplitude Mayer waves in the HbO time course are shown by the blue lines in (A) and (C) respectively. The synthetic HRF is indicated by the red lines. The hemodynamic response function recovered for 25 different stimulus paradigms are shown in (B) and (D) for the small and large amplitude Mayer waves respectively. Note that results are shown for HRF estimation after introducing a signal change of 1% from baseline for the 690 nm and 2% for 830 nm in the raw NIRS data (case 1).
Fig. 3
Fig. 3 R2 (A) and normalized MSE (B) vs. Mayer wave amplitude (in Molars) in HbO signal for all runs. Comparison of R2 (C) and absolute MSE (D) obtained using GLM with and without short separation regression. Percentages in the lower panels indicate the percent of data points above/below the midline indicating that short separation regression improved the estimation. The vertical green line shows the median value for the observed Mayer wave amplitude. Note that results are shown for HRF estimation after introducing a signal change of 1% from baseline for the 690 nm and 2% for 830 nm in the raw NIRS data (case 1).
Fig. 4
Fig. 4 R2 (A) and normalized MSE (B) vs. Mayer wave amplitude (in Molars) in HbO signal. Comparison of R2 (C) and absolute MSE (D) obtained using GLM with and without short separation regression. Percentages on the lower panels show the percent of data points above or below the midline. Note that results are shown for HRF estimation after introducing a signal change of 0.2% from baseline for the 690 nm and 0.4% for 830 nm producing an evoked HbO change of of 0.14 µM in the NIRS data (case 2).
Fig. 5
Fig. 5 Receiver Operating Characteristic (ROC) Curves obtained for the estimation of the HRF at low and high Mayer wave power with and without short separation regression for case 1 (Panels A and B) and case 2 (Panels C and D). Solid lines are for HbO estimation and dashed lines are for HbR estimation. The AUC for the ROC curves are shown with and without underline for the HbO and HbR estimations respectively.
Fig. 6
Fig. 6 Mayer wave power of the HbO signal for each channel shown on the probe layout for four different subjects. Sources (red x’s) and detectors (blue circles) and channels (blue lines) are also shown. The color bar indicates the Mayer wave amplitude in Molar units of log10.

Tables (3)

Tables Icon

Table 1 Mean and standard deviation of R2 and MSE for the estimation of the HRF for signals with low and high Mayer wave power. P-values obtained from a paired t-test which compares R2 and MSE at low and high Mayer wave powers in the HbO signal are also presented. The results are obtained from the estimation without short separation regression.

Tables Icon

Table 2 Mean and standard deviation of R2 and MSE obtained using GLM with and without short separation (SS) regression and p-values obtained when a paired t-test is applied for the comparison of methods for case 1 (Fig. 3) and case 2 (Fig. 4).

Tables Icon

Table 3 Minimum, median and maximum values for the Mayer wave amplitude (shown in µM) of the HbO signal across channels within a subject along with the standard deviation. First four rows correspond to the subject results displayed in Fig. 6. The last row shows the minimum, median, maximum and standard deviation averaged across all subjects.

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