Abstract

Systemic physiology and motion-induced artifacts represent two major sources of confounding noise in functional near infrared spectroscopy (fNIRS) imaging that can reduce the performance of analyses and inflate false positive rates (i.e., type I errors) of detecting evoked hemodynamic responses. In this work, we demonstrated a general algorithm for solving the general linear model (GLM) for both deconvolution (finite impulse response) and canonical regression models based on designing optimal pre-whitening filters using autoregressive models and employing iteratively reweighted least squares. We evaluated the performance of the new method by performing receiver operating characteristic (ROC) analyses using synthetic data, in which serial correlations, motion artifacts, and evoked responses were controlled via simulations, as well as using experimental data from children (3–5 years old) as a source baseline physiological noise and motion artifacts. The new method outperformed ordinary least squares (OLS) with no motion correction, wavelet based motion correction, or spline interpolation based motion correction in the presence of physiological and motion related noise. In the experimental data, false positive rates were as high as 37% when the estimated p-value was 0.05 for the OLS methods. The false positive rate was reduced to 5–9% with the proposed method. Overall, the method improves control of type I errors and increases performance when motion artifacts are present.

© 2013 osa

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  1. F. F. Jöbsis, “Noninvasive, infrared monitoring of cerebral and myocardial oxygen sufficiency and circulatory parameters,” Science 198, 1264–1267 (1977).
    [Crossref] [PubMed]
  2. M. Cope, D. T. Delpy, E. O. Reynolds, S. Wray, J. Wyatt, and P. van der Zee, “Methods of quantitating cerebral near infrared spectroscopy data,” Adv. Exp. Med. Biol. 222, 183–189 (1988).
    [Crossref] [PubMed]
  3. I. Miyai, H. C. Tanabe, I. Sase, H. Eda, I. Oda, I. Konishi, Y. Tsunazawa, T. Suzuki, T. Yanagida, and K. Kubota, “Cortical mapping of gait in humans: a near-infrared spectroscopic topography study,” Neuroimage 14, 1186–1192 (2001).
    [Crossref] [PubMed]
  4. M. Suzuki, I. Miyai, T. Ono, and K. Kubota, “Activities in the frontal cortex and gait performance are modulated by preparation: an fNIRS study,” Neuroimage 39, 600–607 (2008).
    [Crossref]
  5. H. Karim, S. I. Fuhrman, P. Sparto, J. Furman, and T. Huppert, “Functional brain imaging of multi-sensory vestibular processing during computerized dynamic posturography using near-infrared spectroscopy,” Neuroimage 74, 318–325 (2013).
    [Crossref] [PubMed]
  6. H. Karim, B. Schmidt, D. Dart, N. Beluk, and T. Huppert, “Functional near-infrared spectroscopy (fNIRS) of brain function during active balancing using a video game system,” Gait Posture 35, 367–372 (2012).
    [Crossref]
  7. H. T. Karim, S. I. Fuhrman, J. M. Furman, and T. J. Huppert, “Neuroimaging to detect cortical projection of vestibular response to caloric stimulation in young and older adults using functional near-infrared spectroscopy (fNIRS),” Neuroimage 76, 1–10 (2013).
    [Crossref] [PubMed]
  8. X. Cui, D. M. Bryant, and A. L. Reiss, “NIRS-based hyperscanning reveals increased interpersonal coherence in superior frontal cortex during cooperation,” Neuroimage 59, 2430–2437 (2012).
    [Crossref]
  9. 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, 280–298 (2009).
    [Crossref]
  10. F. Scholkmann, S. Spichtig, T. Muehlemann, and M. Wolf, “How to detect and reduce movement artifacts in near-infrared imaging using moving standard deviation and spline interpolation,” Physiol. Meas. 31, 649–662 (2010).
    [Crossref] [PubMed]
  11. B. Molavi and G. A. Dumont, “Wavelet-based motion artifact removal for functional near-infrared spectroscopy,” Physiol. Meas. 33, 259–270 (2012).
    [Crossref] [PubMed]
  12. M. Izzetoglu, P. Chitrapu, S. Bunce, and B. Onaral, “Motion artifact cancellation in NIR spectroscopy using discrete Kalman filtering,” Biomed. Eng. Online 9, 16 (2010).
    [Crossref] [PubMed]
  13. R. J. Cooper, J. Selb, L. Gagnon, D. Phillip, H. W. Schytz, H. K. Iversen, M. Ashina, and D. A. Boas, “A systematic comparison of motion artifact correction techniques for functional near-infrared spectroscopy,” Front. Neurosci. 6, 147 (2012).
    [Crossref] [PubMed]
  14. J. C. Ye, S. Tak, K. E. Jang, J. Jung, and J. Jang, “NIRS-SPM: statistical parametric mapping for near-infrared spectroscopy,” Neuroimage 44, 428–447 (2009).
    [Crossref]
  15. G. H. Orcutt and D. Cochrane, “A sampling study of the merits of autoregressive and reduced form transformation in regression analysis,” J. Am. Stat. Assoc. 44, 356–372 (1949).
    [Crossref] [PubMed]
  16. M. J. Hofmann, M. J. Herrmann, I. Dan, H. Obrig, M. Conrad, L. Kuchinke, A. M. Jacobs, and A. J. Fallgatter, “Differential activation of frontal and parietal regions during visual word recognition: an optical topography study,” Neuroimage 40, 1340–1349 (2008).
    [Crossref] [PubMed]
  17. M. M. Plichta, M. J. Herrmann, C. G. Baehne, A. C. Ehlis, M. M. Richter, P. Pauli, and A. J. Fallgatter, “Event-related functional near-infrared spectroscopy (fNIRS): are the measurements reliable?” Neuroimage 31, 116–124 (2006).
    [Crossref] [PubMed]
  18. M. M. Plichta, S. Heinzel, A. C. Ehlis, P. Pauli, and A. J. Fallgatter, “Model-based analysis of rapid event-related functional near-infrared spectroscopy (NIRS) data: a parametric validation study,” Neuroimage 35, 625–634 (2007).
    [Crossref] [PubMed]
  19. A. C. Harvey, The Econometric Analysis of Time Series(MIT Press, 1990).
  20. T. Speed and B. Yu, “Model selection and prediction: normal regression,” Ann. Inst. Stat. Math. 45, 35–54 (1993).
    [Crossref]
  21. K. J. Friston, J. T. Ashburner, S. J. Kiebel, T. E. Nichols, and W. D. Penny, Statistical Parametric Mapping: The Analysis of Functional Brain Images: The Analysis of Functional Brain Images(Academic Press, 2011).
  22. A. M. Dale, “Optimal experimental design for event-related fmri,” Hum. Brain Mapp. 8, 109–114 (1999).
    [Crossref] [PubMed]
  23. 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, 88–101 (2006).
    [Crossref]
  24. P. W. Holland and R. E. Welsch, “Robust regression using iteratively reweighted least-squares,” Commun. Stat.-Theory Methods 6, 813–827 (1977).
    [Crossref]
  25. A. E. Beaton and J. W. Tukey, “The fitting of power series, meaning polynomials, illustrated on band-spectroscopic data,” Technometrics 16, 147–185 (1974).
    [Crossref]
  26. P. J. Huber, “Robust regression: asymptotics, conjectures and monte carlo,” Ann. Stat. 1, 799–821 (1973).
    [Crossref]
  27. S. B. Perlman, B. Luna, T. Hein, and T. J. Huppert, “fNIRS evidence of prefrontal regulation of frustration in early childhood,” Neuroimage (to be published).
    [PubMed]

2013 (2)

H. Karim, S. I. Fuhrman, P. Sparto, J. Furman, and T. Huppert, “Functional brain imaging of multi-sensory vestibular processing during computerized dynamic posturography using near-infrared spectroscopy,” Neuroimage 74, 318–325 (2013).
[Crossref] [PubMed]

H. T. Karim, S. I. Fuhrman, J. M. Furman, and T. J. Huppert, “Neuroimaging to detect cortical projection of vestibular response to caloric stimulation in young and older adults using functional near-infrared spectroscopy (fNIRS),” Neuroimage 76, 1–10 (2013).
[Crossref] [PubMed]

2012 (4)

X. Cui, D. M. Bryant, and A. L. Reiss, “NIRS-based hyperscanning reveals increased interpersonal coherence in superior frontal cortex during cooperation,” Neuroimage 59, 2430–2437 (2012).
[Crossref]

H. Karim, B. Schmidt, D. Dart, N. Beluk, and T. Huppert, “Functional near-infrared spectroscopy (fNIRS) of brain function during active balancing using a video game system,” Gait Posture 35, 367–372 (2012).
[Crossref]

B. Molavi and G. A. Dumont, “Wavelet-based motion artifact removal for functional near-infrared spectroscopy,” Physiol. Meas. 33, 259–270 (2012).
[Crossref] [PubMed]

R. J. Cooper, J. Selb, L. Gagnon, D. Phillip, H. W. Schytz, H. K. Iversen, M. Ashina, and D. A. Boas, “A systematic comparison of motion artifact correction techniques for functional near-infrared spectroscopy,” Front. Neurosci. 6, 147 (2012).
[Crossref] [PubMed]

2010 (2)

M. Izzetoglu, P. Chitrapu, S. Bunce, and B. Onaral, “Motion artifact cancellation in NIR spectroscopy using discrete Kalman filtering,” Biomed. Eng. Online 9, 16 (2010).
[Crossref] [PubMed]

F. Scholkmann, S. Spichtig, T. Muehlemann, and M. Wolf, “How to detect and reduce movement artifacts in near-infrared imaging using moving standard deviation and spline interpolation,” Physiol. Meas. 31, 649–662 (2010).
[Crossref] [PubMed]

2009 (2)

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

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, 280–298 (2009).
[Crossref]

2008 (2)

M. Suzuki, I. Miyai, T. Ono, and K. Kubota, “Activities in the frontal cortex and gait performance are modulated by preparation: an fNIRS study,” Neuroimage 39, 600–607 (2008).
[Crossref]

M. J. Hofmann, M. J. Herrmann, I. Dan, H. Obrig, M. Conrad, L. Kuchinke, A. M. Jacobs, and A. J. Fallgatter, “Differential activation of frontal and parietal regions during visual word recognition: an optical topography study,” Neuroimage 40, 1340–1349 (2008).
[Crossref] [PubMed]

2007 (1)

M. M. Plichta, S. Heinzel, A. C. Ehlis, P. Pauli, and A. J. Fallgatter, “Model-based analysis of rapid event-related functional near-infrared spectroscopy (NIRS) data: a parametric validation study,” Neuroimage 35, 625–634 (2007).
[Crossref] [PubMed]

2006 (2)

M. M. Plichta, M. J. Herrmann, C. G. Baehne, A. C. Ehlis, M. M. Richter, P. Pauli, and A. J. Fallgatter, “Event-related functional near-infrared spectroscopy (fNIRS): are the measurements reliable?” Neuroimage 31, 116–124 (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, 88–101 (2006).
[Crossref]

2001 (1)

I. Miyai, H. C. Tanabe, I. Sase, H. Eda, I. Oda, I. Konishi, Y. Tsunazawa, T. Suzuki, T. Yanagida, and K. Kubota, “Cortical mapping of gait in humans: a near-infrared spectroscopic topography study,” Neuroimage 14, 1186–1192 (2001).
[Crossref] [PubMed]

1999 (1)

A. M. Dale, “Optimal experimental design for event-related fmri,” Hum. Brain Mapp. 8, 109–114 (1999).
[Crossref] [PubMed]

1993 (1)

T. Speed and B. Yu, “Model selection and prediction: normal regression,” Ann. Inst. Stat. Math. 45, 35–54 (1993).
[Crossref]

1988 (1)

M. Cope, D. T. Delpy, E. O. Reynolds, S. Wray, J. Wyatt, and P. van der Zee, “Methods of quantitating cerebral near infrared spectroscopy data,” Adv. Exp. Med. Biol. 222, 183–189 (1988).
[Crossref] [PubMed]

1977 (2)

F. F. Jöbsis, “Noninvasive, infrared monitoring of cerebral and myocardial oxygen sufficiency and circulatory parameters,” Science 198, 1264–1267 (1977).
[Crossref] [PubMed]

P. W. Holland and R. E. Welsch, “Robust regression using iteratively reweighted least-squares,” Commun. Stat.-Theory Methods 6, 813–827 (1977).
[Crossref]

1974 (1)

A. E. Beaton and J. W. Tukey, “The fitting of power series, meaning polynomials, illustrated on band-spectroscopic data,” Technometrics 16, 147–185 (1974).
[Crossref]

1973 (1)

P. J. Huber, “Robust regression: asymptotics, conjectures and monte carlo,” Ann. Stat. 1, 799–821 (1973).
[Crossref]

1949 (1)

G. H. Orcutt and D. Cochrane, “A sampling study of the merits of autoregressive and reduced form transformation in regression analysis,” J. Am. Stat. Assoc. 44, 356–372 (1949).
[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, 88–101 (2006).
[Crossref]

Ashburner, J. T.

K. J. Friston, J. T. Ashburner, S. J. Kiebel, T. E. Nichols, and W. D. Penny, Statistical Parametric Mapping: The Analysis of Functional Brain Images: The Analysis of Functional Brain Images(Academic Press, 2011).

Ashina, M.

R. J. Cooper, J. Selb, L. Gagnon, D. Phillip, H. W. Schytz, H. K. Iversen, M. Ashina, and D. A. Boas, “A systematic comparison of motion artifact correction techniques for functional near-infrared spectroscopy,” Front. Neurosci. 6, 147 (2012).
[Crossref] [PubMed]

Baehne, C. G.

M. M. Plichta, M. J. Herrmann, C. G. Baehne, A. C. Ehlis, M. M. Richter, P. Pauli, and A. J. Fallgatter, “Event-related functional near-infrared spectroscopy (fNIRS): are the measurements reliable?” Neuroimage 31, 116–124 (2006).
[Crossref] [PubMed]

Beaton, A. E.

A. E. Beaton and J. W. Tukey, “The fitting of power series, meaning polynomials, illustrated on band-spectroscopic data,” Technometrics 16, 147–185 (1974).
[Crossref]

Beluk, N.

H. Karim, B. Schmidt, D. Dart, N. Beluk, and T. Huppert, “Functional near-infrared spectroscopy (fNIRS) of brain function during active balancing using a video game system,” Gait Posture 35, 367–372 (2012).
[Crossref]

Boas, D. A.

R. J. Cooper, J. Selb, L. Gagnon, D. Phillip, H. W. Schytz, H. K. Iversen, M. Ashina, and D. A. Boas, “A systematic comparison of motion artifact correction techniques for functional near-infrared spectroscopy,” Front. Neurosci. 6, 147 (2012).
[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, 280–298 (2009).
[Crossref]

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, 88–101 (2006).
[Crossref]

Bryant, D. M.

X. Cui, D. M. Bryant, and A. L. Reiss, “NIRS-based hyperscanning reveals increased interpersonal coherence in superior frontal cortex during cooperation,” Neuroimage 59, 2430–2437 (2012).
[Crossref]

Bunce, S.

M. Izzetoglu, P. Chitrapu, S. Bunce, and B. Onaral, “Motion artifact cancellation in NIR spectroscopy using discrete Kalman filtering,” Biomed. Eng. Online 9, 16 (2010).
[Crossref] [PubMed]

Chitrapu, P.

M. Izzetoglu, P. Chitrapu, S. Bunce, and B. Onaral, “Motion artifact cancellation in NIR spectroscopy using discrete Kalman filtering,” Biomed. Eng. Online 9, 16 (2010).
[Crossref] [PubMed]

Cochrane, D.

G. H. Orcutt and D. Cochrane, “A sampling study of the merits of autoregressive and reduced form transformation in regression analysis,” J. Am. Stat. Assoc. 44, 356–372 (1949).
[Crossref] [PubMed]

Conrad, M.

M. J. Hofmann, M. J. Herrmann, I. Dan, H. Obrig, M. Conrad, L. Kuchinke, A. M. Jacobs, and A. J. Fallgatter, “Differential activation of frontal and parietal regions during visual word recognition: an optical topography study,” Neuroimage 40, 1340–1349 (2008).
[Crossref] [PubMed]

Cooper, R. J.

R. J. Cooper, J. Selb, L. Gagnon, D. Phillip, H. W. Schytz, H. K. Iversen, M. Ashina, and D. A. Boas, “A systematic comparison of motion artifact correction techniques for functional near-infrared spectroscopy,” Front. Neurosci. 6, 147 (2012).
[Crossref] [PubMed]

Cope, M.

M. Cope, D. T. Delpy, E. O. Reynolds, S. Wray, J. Wyatt, and P. van der Zee, “Methods of quantitating cerebral near infrared spectroscopy data,” Adv. Exp. Med. Biol. 222, 183–189 (1988).
[Crossref] [PubMed]

Cui, X.

X. Cui, D. M. Bryant, and A. L. Reiss, “NIRS-based hyperscanning reveals increased interpersonal coherence in superior frontal cortex during cooperation,” Neuroimage 59, 2430–2437 (2012).
[Crossref]

Dale, A. M.

A. M. Dale, “Optimal experimental design for event-related fmri,” Hum. Brain Mapp. 8, 109–114 (1999).
[Crossref] [PubMed]

Dan, I.

M. J. Hofmann, M. J. Herrmann, I. Dan, H. Obrig, M. Conrad, L. Kuchinke, A. M. Jacobs, and A. J. Fallgatter, “Differential activation of frontal and parietal regions during visual word recognition: an optical topography study,” Neuroimage 40, 1340–1349 (2008).
[Crossref] [PubMed]

Dart, D.

H. Karim, B. Schmidt, D. Dart, N. Beluk, and T. Huppert, “Functional near-infrared spectroscopy (fNIRS) of brain function during active balancing using a video game system,” Gait Posture 35, 367–372 (2012).
[Crossref]

Delpy, D. T.

M. Cope, D. T. Delpy, E. O. Reynolds, S. Wray, J. Wyatt, and P. van der Zee, “Methods of quantitating cerebral near infrared spectroscopy data,” Adv. Exp. Med. Biol. 222, 183–189 (1988).
[Crossref] [PubMed]

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, 280–298 (2009).
[Crossref]

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, 88–101 (2006).
[Crossref]

Dumont, G. A.

B. Molavi and G. A. Dumont, “Wavelet-based motion artifact removal for functional near-infrared spectroscopy,” Physiol. Meas. 33, 259–270 (2012).
[Crossref] [PubMed]

Eda, H.

I. Miyai, H. C. Tanabe, I. Sase, H. Eda, I. Oda, I. Konishi, Y. Tsunazawa, T. Suzuki, T. Yanagida, and K. Kubota, “Cortical mapping of gait in humans: a near-infrared spectroscopic topography study,” Neuroimage 14, 1186–1192 (2001).
[Crossref] [PubMed]

Ehlis, A. C.

M. M. Plichta, S. Heinzel, A. C. Ehlis, P. Pauli, and A. J. Fallgatter, “Model-based analysis of rapid event-related functional near-infrared spectroscopy (NIRS) data: a parametric validation study,” Neuroimage 35, 625–634 (2007).
[Crossref] [PubMed]

M. M. Plichta, M. J. Herrmann, C. G. Baehne, A. C. Ehlis, M. M. Richter, P. Pauli, and A. J. Fallgatter, “Event-related functional near-infrared spectroscopy (fNIRS): are the measurements reliable?” Neuroimage 31, 116–124 (2006).
[Crossref] [PubMed]

Fallgatter, A. J.

M. J. Hofmann, M. J. Herrmann, I. Dan, H. Obrig, M. Conrad, L. Kuchinke, A. M. Jacobs, and A. J. Fallgatter, “Differential activation of frontal and parietal regions during visual word recognition: an optical topography study,” Neuroimage 40, 1340–1349 (2008).
[Crossref] [PubMed]

M. M. Plichta, S. Heinzel, A. C. Ehlis, P. Pauli, and A. J. Fallgatter, “Model-based analysis of rapid event-related functional near-infrared spectroscopy (NIRS) data: a parametric validation study,” Neuroimage 35, 625–634 (2007).
[Crossref] [PubMed]

M. M. Plichta, M. J. Herrmann, C. G. Baehne, A. C. Ehlis, M. M. Richter, P. Pauli, and A. J. Fallgatter, “Event-related functional near-infrared spectroscopy (fNIRS): are the measurements reliable?” Neuroimage 31, 116–124 (2006).
[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, 280–298 (2009).
[Crossref]

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, 88–101 (2006).
[Crossref]

Friston, K. J.

K. J. Friston, J. T. Ashburner, S. J. Kiebel, T. E. Nichols, and W. D. Penny, Statistical Parametric Mapping: The Analysis of Functional Brain Images: The Analysis of Functional Brain Images(Academic Press, 2011).

Fuhrman, S. I.

H. Karim, S. I. Fuhrman, P. Sparto, J. Furman, and T. Huppert, “Functional brain imaging of multi-sensory vestibular processing during computerized dynamic posturography using near-infrared spectroscopy,” Neuroimage 74, 318–325 (2013).
[Crossref] [PubMed]

H. T. Karim, S. I. Fuhrman, J. M. Furman, and T. J. Huppert, “Neuroimaging to detect cortical projection of vestibular response to caloric stimulation in young and older adults using functional near-infrared spectroscopy (fNIRS),” Neuroimage 76, 1–10 (2013).
[Crossref] [PubMed]

Furman, J.

H. Karim, S. I. Fuhrman, P. Sparto, J. Furman, and T. Huppert, “Functional brain imaging of multi-sensory vestibular processing during computerized dynamic posturography using near-infrared spectroscopy,” Neuroimage 74, 318–325 (2013).
[Crossref] [PubMed]

Furman, J. M.

H. T. Karim, S. I. Fuhrman, J. M. Furman, and T. J. Huppert, “Neuroimaging to detect cortical projection of vestibular response to caloric stimulation in young and older adults using functional near-infrared spectroscopy (fNIRS),” Neuroimage 76, 1–10 (2013).
[Crossref] [PubMed]

Gagnon, L.

R. J. Cooper, J. Selb, L. Gagnon, D. Phillip, H. W. Schytz, H. K. Iversen, M. Ashina, and D. A. Boas, “A systematic comparison of motion artifact correction techniques for functional near-infrared spectroscopy,” Front. Neurosci. 6, 147 (2012).
[Crossref] [PubMed]

Harvey, A. C.

A. C. Harvey, The Econometric Analysis of Time Series(MIT Press, 1990).

Hein, T.

S. B. Perlman, B. Luna, T. Hein, and T. J. Huppert, “fNIRS evidence of prefrontal regulation of frustration in early childhood,” Neuroimage (to be published).
[PubMed]

Heinzel, S.

M. M. Plichta, S. Heinzel, A. C. Ehlis, P. Pauli, and A. J. Fallgatter, “Model-based analysis of rapid event-related functional near-infrared spectroscopy (NIRS) data: a parametric validation study,” Neuroimage 35, 625–634 (2007).
[Crossref] [PubMed]

Herrmann, M. J.

M. J. Hofmann, M. J. Herrmann, I. Dan, H. Obrig, M. Conrad, L. Kuchinke, A. M. Jacobs, and A. J. Fallgatter, “Differential activation of frontal and parietal regions during visual word recognition: an optical topography study,” Neuroimage 40, 1340–1349 (2008).
[Crossref] [PubMed]

M. M. Plichta, M. J. Herrmann, C. G. Baehne, A. C. Ehlis, M. M. Richter, P. Pauli, and A. J. Fallgatter, “Event-related functional near-infrared spectroscopy (fNIRS): are the measurements reliable?” Neuroimage 31, 116–124 (2006).
[Crossref] [PubMed]

Hofmann, M. J.

M. J. Hofmann, M. J. Herrmann, I. Dan, H. Obrig, M. Conrad, L. Kuchinke, A. M. Jacobs, and A. J. Fallgatter, “Differential activation of frontal and parietal regions during visual word recognition: an optical topography study,” Neuroimage 40, 1340–1349 (2008).
[Crossref] [PubMed]

Holland, P. W.

P. W. Holland and R. E. Welsch, “Robust regression using iteratively reweighted least-squares,” Commun. Stat.-Theory Methods 6, 813–827 (1977).
[Crossref]

Huber, P. J.

P. J. Huber, “Robust regression: asymptotics, conjectures and monte carlo,” Ann. Stat. 1, 799–821 (1973).
[Crossref]

Huppert, T.

H. Karim, S. I. Fuhrman, P. Sparto, J. Furman, and T. Huppert, “Functional brain imaging of multi-sensory vestibular processing during computerized dynamic posturography using near-infrared spectroscopy,” Neuroimage 74, 318–325 (2013).
[Crossref] [PubMed]

H. Karim, B. Schmidt, D. Dart, N. Beluk, and T. Huppert, “Functional near-infrared spectroscopy (fNIRS) of brain function during active balancing using a video game system,” Gait Posture 35, 367–372 (2012).
[Crossref]

Huppert, T. J.

H. T. Karim, S. I. Fuhrman, J. M. Furman, and T. J. Huppert, “Neuroimaging to detect cortical projection of vestibular response to caloric stimulation in young and older adults using functional near-infrared spectroscopy (fNIRS),” Neuroimage 76, 1–10 (2013).
[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, 280–298 (2009).
[Crossref]

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, 88–101 (2006).
[Crossref]

S. B. Perlman, B. Luna, T. Hein, and T. J. Huppert, “fNIRS evidence of prefrontal regulation of frustration in early childhood,” Neuroimage (to be published).
[PubMed]

Iversen, H. K.

R. J. Cooper, J. Selb, L. Gagnon, D. Phillip, H. W. Schytz, H. K. Iversen, M. Ashina, and D. A. Boas, “A systematic comparison of motion artifact correction techniques for functional near-infrared spectroscopy,” Front. Neurosci. 6, 147 (2012).
[Crossref] [PubMed]

Izzetoglu, M.

M. Izzetoglu, P. Chitrapu, S. Bunce, and B. Onaral, “Motion artifact cancellation in NIR spectroscopy using discrete Kalman filtering,” Biomed. Eng. Online 9, 16 (2010).
[Crossref] [PubMed]

Jacobs, A. M.

M. J. Hofmann, M. J. Herrmann, I. Dan, H. Obrig, M. Conrad, L. Kuchinke, A. M. Jacobs, and A. J. Fallgatter, “Differential activation of frontal and parietal regions during visual word recognition: an optical topography study,” Neuroimage 40, 1340–1349 (2008).
[Crossref] [PubMed]

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, 428–447 (2009).
[Crossref]

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, 428–447 (2009).
[Crossref]

Jöbsis, F. F.

F. F. Jöbsis, “Noninvasive, infrared monitoring of cerebral and myocardial oxygen sufficiency and circulatory parameters,” Science 198, 1264–1267 (1977).
[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, 428–447 (2009).
[Crossref]

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, 88–101 (2006).
[Crossref]

Karim, H.

H. Karim, S. I. Fuhrman, P. Sparto, J. Furman, and T. Huppert, “Functional brain imaging of multi-sensory vestibular processing during computerized dynamic posturography using near-infrared spectroscopy,” Neuroimage 74, 318–325 (2013).
[Crossref] [PubMed]

H. Karim, B. Schmidt, D. Dart, N. Beluk, and T. Huppert, “Functional near-infrared spectroscopy (fNIRS) of brain function during active balancing using a video game system,” Gait Posture 35, 367–372 (2012).
[Crossref]

Karim, H. T.

H. T. Karim, S. I. Fuhrman, J. M. Furman, and T. J. Huppert, “Neuroimaging to detect cortical projection of vestibular response to caloric stimulation in young and older adults using functional near-infrared spectroscopy (fNIRS),” Neuroimage 76, 1–10 (2013).
[Crossref] [PubMed]

Kiebel, S. J.

K. J. Friston, J. T. Ashburner, S. J. Kiebel, T. E. Nichols, and W. D. Penny, Statistical Parametric Mapping: The Analysis of Functional Brain Images: The Analysis of Functional Brain Images(Academic Press, 2011).

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, 88–101 (2006).
[Crossref]

Konishi, I.

I. Miyai, H. C. Tanabe, I. Sase, H. Eda, I. Oda, I. Konishi, Y. Tsunazawa, T. Suzuki, T. Yanagida, and K. Kubota, “Cortical mapping of gait in humans: a near-infrared spectroscopic topography study,” Neuroimage 14, 1186–1192 (2001).
[Crossref] [PubMed]

Kubota, K.

M. Suzuki, I. Miyai, T. Ono, and K. Kubota, “Activities in the frontal cortex and gait performance are modulated by preparation: an fNIRS study,” Neuroimage 39, 600–607 (2008).
[Crossref]

I. Miyai, H. C. Tanabe, I. Sase, H. Eda, I. Oda, I. Konishi, Y. Tsunazawa, T. Suzuki, T. Yanagida, and K. Kubota, “Cortical mapping of gait in humans: a near-infrared spectroscopic topography study,” Neuroimage 14, 1186–1192 (2001).
[Crossref] [PubMed]

Kuchinke, L.

M. J. Hofmann, M. J. Herrmann, I. Dan, H. Obrig, M. Conrad, L. Kuchinke, A. M. Jacobs, and A. J. Fallgatter, “Differential activation of frontal and parietal regions during visual word recognition: an optical topography study,” Neuroimage 40, 1340–1349 (2008).
[Crossref] [PubMed]

Luna, B.

S. B. Perlman, B. Luna, T. Hein, and T. J. Huppert, “fNIRS evidence of prefrontal regulation of frustration in early childhood,” Neuroimage (to be published).
[PubMed]

Miyai, I.

M. Suzuki, I. Miyai, T. Ono, and K. Kubota, “Activities in the frontal cortex and gait performance are modulated by preparation: an fNIRS study,” Neuroimage 39, 600–607 (2008).
[Crossref]

I. Miyai, H. C. Tanabe, I. Sase, H. Eda, I. Oda, I. Konishi, Y. Tsunazawa, T. Suzuki, T. Yanagida, and K. Kubota, “Cortical mapping of gait in humans: a near-infrared spectroscopic topography study,” Neuroimage 14, 1186–1192 (2001).
[Crossref] [PubMed]

Molavi, B.

B. Molavi and G. A. Dumont, “Wavelet-based motion artifact removal for functional near-infrared spectroscopy,” Physiol. Meas. 33, 259–270 (2012).
[Crossref] [PubMed]

Muehlemann, T.

F. Scholkmann, S. Spichtig, T. Muehlemann, and M. Wolf, “How to detect and reduce movement artifacts in near-infrared imaging using moving standard deviation and spline interpolation,” Physiol. Meas. 31, 649–662 (2010).
[Crossref] [PubMed]

Nichols, T. E.

K. J. Friston, J. T. Ashburner, S. J. Kiebel, T. E. Nichols, and W. D. Penny, Statistical Parametric Mapping: The Analysis of Functional Brain Images: The Analysis of Functional Brain Images(Academic Press, 2011).

Obrig, H.

M. J. Hofmann, M. J. Herrmann, I. Dan, H. Obrig, M. Conrad, L. Kuchinke, A. M. Jacobs, and A. J. Fallgatter, “Differential activation of frontal and parietal regions during visual word recognition: an optical topography study,” Neuroimage 40, 1340–1349 (2008).
[Crossref] [PubMed]

Oda, I.

I. Miyai, H. C. Tanabe, I. Sase, H. Eda, I. Oda, I. Konishi, Y. Tsunazawa, T. Suzuki, T. Yanagida, and K. Kubota, “Cortical mapping of gait in humans: a near-infrared spectroscopic topography study,” Neuroimage 14, 1186–1192 (2001).
[Crossref] [PubMed]

Onaral, B.

M. Izzetoglu, P. Chitrapu, S. Bunce, and B. Onaral, “Motion artifact cancellation in NIR spectroscopy using discrete Kalman filtering,” Biomed. Eng. Online 9, 16 (2010).
[Crossref] [PubMed]

Ono, T.

M. Suzuki, I. Miyai, T. Ono, and K. Kubota, “Activities in the frontal cortex and gait performance are modulated by preparation: an fNIRS study,” Neuroimage 39, 600–607 (2008).
[Crossref]

Orcutt, G. H.

G. H. Orcutt and D. Cochrane, “A sampling study of the merits of autoregressive and reduced form transformation in regression analysis,” J. Am. Stat. Assoc. 44, 356–372 (1949).
[Crossref] [PubMed]

Pauli, P.

M. M. Plichta, S. Heinzel, A. C. Ehlis, P. Pauli, and A. J. Fallgatter, “Model-based analysis of rapid event-related functional near-infrared spectroscopy (NIRS) data: a parametric validation study,” Neuroimage 35, 625–634 (2007).
[Crossref] [PubMed]

M. M. Plichta, M. J. Herrmann, C. G. Baehne, A. C. Ehlis, M. M. Richter, P. Pauli, and A. J. Fallgatter, “Event-related functional near-infrared spectroscopy (fNIRS): are the measurements reliable?” Neuroimage 31, 116–124 (2006).
[Crossref] [PubMed]

Penny, W. D.

K. J. Friston, J. T. Ashburner, S. J. Kiebel, T. E. Nichols, and W. D. Penny, Statistical Parametric Mapping: The Analysis of Functional Brain Images: The Analysis of Functional Brain Images(Academic Press, 2011).

Perlman, S. B.

S. B. Perlman, B. Luna, T. Hein, and T. J. Huppert, “fNIRS evidence of prefrontal regulation of frustration in early childhood,” Neuroimage (to be published).
[PubMed]

Phillip, D.

R. J. Cooper, J. Selb, L. Gagnon, D. Phillip, H. W. Schytz, H. K. Iversen, M. Ashina, and D. A. Boas, “A systematic comparison of motion artifact correction techniques for functional near-infrared spectroscopy,” Front. Neurosci. 6, 147 (2012).
[Crossref] [PubMed]

Plichta, M. M.

M. M. Plichta, S. Heinzel, A. C. Ehlis, P. Pauli, and A. J. Fallgatter, “Model-based analysis of rapid event-related functional near-infrared spectroscopy (NIRS) data: a parametric validation study,” Neuroimage 35, 625–634 (2007).
[Crossref] [PubMed]

M. M. Plichta, M. J. Herrmann, C. G. Baehne, A. C. Ehlis, M. M. Richter, P. Pauli, and A. J. Fallgatter, “Event-related functional near-infrared spectroscopy (fNIRS): are the measurements reliable?” Neuroimage 31, 116–124 (2006).
[Crossref] [PubMed]

Reiss, A. L.

X. Cui, D. M. Bryant, and A. L. Reiss, “NIRS-based hyperscanning reveals increased interpersonal coherence in superior frontal cortex during cooperation,” Neuroimage 59, 2430–2437 (2012).
[Crossref]

Reynolds, E. O.

M. Cope, D. T. Delpy, E. O. Reynolds, S. Wray, J. Wyatt, and P. van der Zee, “Methods of quantitating cerebral near infrared spectroscopy data,” Adv. Exp. Med. Biol. 222, 183–189 (1988).
[Crossref] [PubMed]

Richter, M. M.

M. M. Plichta, M. J. Herrmann, C. G. Baehne, A. C. Ehlis, M. M. Richter, P. Pauli, and A. J. Fallgatter, “Event-related functional near-infrared spectroscopy (fNIRS): are the measurements reliable?” Neuroimage 31, 116–124 (2006).
[Crossref] [PubMed]

Sase, I.

I. Miyai, H. C. Tanabe, I. Sase, H. Eda, I. Oda, I. Konishi, Y. Tsunazawa, T. Suzuki, T. Yanagida, and K. Kubota, “Cortical mapping of gait in humans: a near-infrared spectroscopic topography study,” Neuroimage 14, 1186–1192 (2001).
[Crossref] [PubMed]

Schmidt, B.

H. Karim, B. Schmidt, D. Dart, N. Beluk, and T. Huppert, “Functional near-infrared spectroscopy (fNIRS) of brain function during active balancing using a video game system,” Gait Posture 35, 367–372 (2012).
[Crossref]

Scholkmann, F.

F. Scholkmann, S. Spichtig, T. Muehlemann, and M. Wolf, “How to detect and reduce movement artifacts in near-infrared imaging using moving standard deviation and spline interpolation,” Physiol. Meas. 31, 649–662 (2010).
[Crossref] [PubMed]

Schytz, H. W.

R. J. Cooper, J. Selb, L. Gagnon, D. Phillip, H. W. Schytz, H. K. Iversen, M. Ashina, and D. A. Boas, “A systematic comparison of motion artifact correction techniques for functional near-infrared spectroscopy,” Front. Neurosci. 6, 147 (2012).
[Crossref] [PubMed]

Selb, J.

R. J. Cooper, J. Selb, L. Gagnon, D. Phillip, H. W. Schytz, H. K. Iversen, M. Ashina, and D. A. Boas, “A systematic comparison of motion artifact correction techniques for functional near-infrared spectroscopy,” Front. Neurosci. 6, 147 (2012).
[Crossref] [PubMed]

Sparto, P.

H. Karim, S. I. Fuhrman, P. Sparto, J. Furman, and T. Huppert, “Functional brain imaging of multi-sensory vestibular processing during computerized dynamic posturography using near-infrared spectroscopy,” Neuroimage 74, 318–325 (2013).
[Crossref] [PubMed]

Speed, T.

T. Speed and B. Yu, “Model selection and prediction: normal regression,” Ann. Inst. Stat. Math. 45, 35–54 (1993).
[Crossref]

Spichtig, S.

F. Scholkmann, S. Spichtig, T. Muehlemann, and M. Wolf, “How to detect and reduce movement artifacts in near-infrared imaging using moving standard deviation and spline interpolation,” Physiol. Meas. 31, 649–662 (2010).
[Crossref] [PubMed]

Suzuki, M.

M. Suzuki, I. Miyai, T. Ono, and K. Kubota, “Activities in the frontal cortex and gait performance are modulated by preparation: an fNIRS study,” Neuroimage 39, 600–607 (2008).
[Crossref]

Suzuki, T.

I. Miyai, H. C. Tanabe, I. Sase, H. Eda, I. Oda, I. Konishi, Y. Tsunazawa, T. Suzuki, T. Yanagida, and K. Kubota, “Cortical mapping of gait in humans: a near-infrared spectroscopic topography study,” Neuroimage 14, 1186–1192 (2001).
[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, 428–447 (2009).
[Crossref]

Tanabe, H. C.

I. Miyai, H. C. Tanabe, I. Sase, H. Eda, I. Oda, I. Konishi, Y. Tsunazawa, T. Suzuki, T. Yanagida, and K. Kubota, “Cortical mapping of gait in humans: a near-infrared spectroscopic topography study,” Neuroimage 14, 1186–1192 (2001).
[Crossref] [PubMed]

Tsunazawa, Y.

I. Miyai, H. C. Tanabe, I. Sase, H. Eda, I. Oda, I. Konishi, Y. Tsunazawa, T. Suzuki, T. Yanagida, and K. Kubota, “Cortical mapping of gait in humans: a near-infrared spectroscopic topography study,” Neuroimage 14, 1186–1192 (2001).
[Crossref] [PubMed]

Tukey, J. W.

A. E. Beaton and J. W. Tukey, “The fitting of power series, meaning polynomials, illustrated on band-spectroscopic data,” Technometrics 16, 147–185 (1974).
[Crossref]

van der Zee, P.

M. Cope, D. T. Delpy, E. O. Reynolds, S. Wray, J. Wyatt, and P. van der Zee, “Methods of quantitating cerebral near infrared spectroscopy data,” Adv. Exp. Med. Biol. 222, 183–189 (1988).
[Crossref] [PubMed]

Welsch, R. E.

P. W. Holland and R. E. Welsch, “Robust regression using iteratively reweighted least-squares,” Commun. Stat.-Theory Methods 6, 813–827 (1977).
[Crossref]

Wolf, M.

F. Scholkmann, S. Spichtig, T. Muehlemann, and M. Wolf, “How to detect and reduce movement artifacts in near-infrared imaging using moving standard deviation and spline interpolation,” Physiol. Meas. 31, 649–662 (2010).
[Crossref] [PubMed]

Wray, S.

M. Cope, D. T. Delpy, E. O. Reynolds, S. Wray, J. Wyatt, and P. van der Zee, “Methods of quantitating cerebral near infrared spectroscopy data,” Adv. Exp. Med. Biol. 222, 183–189 (1988).
[Crossref] [PubMed]

Wyatt, J.

M. Cope, D. T. Delpy, E. O. Reynolds, S. Wray, J. Wyatt, and P. van der Zee, “Methods of quantitating cerebral near infrared spectroscopy data,” Adv. Exp. Med. Biol. 222, 183–189 (1988).
[Crossref] [PubMed]

Yanagida, T.

I. Miyai, H. C. Tanabe, I. Sase, H. Eda, I. Oda, I. Konishi, Y. Tsunazawa, T. Suzuki, T. Yanagida, and K. Kubota, “Cortical mapping of gait in humans: a near-infrared spectroscopic topography study,” Neuroimage 14, 1186–1192 (2001).
[Crossref] [PubMed]

Ye, J. C.

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

Yu, B.

T. Speed and B. Yu, “Model selection and prediction: normal regression,” Ann. Inst. Stat. Math. 45, 35–54 (1993).
[Crossref]

Adv. Exp. Med. Biol. (1)

M. Cope, D. T. Delpy, E. O. Reynolds, S. Wray, J. Wyatt, and P. van der Zee, “Methods of quantitating cerebral near infrared spectroscopy data,” Adv. Exp. Med. Biol. 222, 183–189 (1988).
[Crossref] [PubMed]

Ann. Inst. Stat. Math. (1)

T. Speed and B. Yu, “Model selection and prediction: normal regression,” Ann. Inst. Stat. Math. 45, 35–54 (1993).
[Crossref]

Ann. Stat. (1)

P. J. Huber, “Robust regression: asymptotics, conjectures and monte carlo,” Ann. Stat. 1, 799–821 (1973).
[Crossref]

Appl. Opt. (1)

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, 280–298 (2009).
[Crossref]

Biomed. Eng. Online (1)

M. Izzetoglu, P. Chitrapu, S. Bunce, and B. Onaral, “Motion artifact cancellation in NIR spectroscopy using discrete Kalman filtering,” Biomed. Eng. Online 9, 16 (2010).
[Crossref] [PubMed]

Commun. Stat.-Theory Methods (1)

P. W. Holland and R. E. Welsch, “Robust regression using iteratively reweighted least-squares,” Commun. Stat.-Theory Methods 6, 813–827 (1977).
[Crossref]

Front. Neurosci. (1)

R. J. Cooper, J. Selb, L. Gagnon, D. Phillip, H. W. Schytz, H. K. Iversen, M. Ashina, and D. A. Boas, “A systematic comparison of motion artifact correction techniques for functional near-infrared spectroscopy,” Front. Neurosci. 6, 147 (2012).
[Crossref] [PubMed]

Gait Posture (1)

H. Karim, B. Schmidt, D. Dart, N. Beluk, and T. Huppert, “Functional near-infrared spectroscopy (fNIRS) of brain function during active balancing using a video game system,” Gait Posture 35, 367–372 (2012).
[Crossref]

Hum. Brain Mapp. (1)

A. M. Dale, “Optimal experimental design for event-related fmri,” Hum. Brain Mapp. 8, 109–114 (1999).
[Crossref] [PubMed]

J. Am. Stat. Assoc. (1)

G. H. Orcutt and D. Cochrane, “A sampling study of the merits of autoregressive and reduced form transformation in regression analysis,” J. Am. Stat. Assoc. 44, 356–372 (1949).
[Crossref] [PubMed]

Neuroimage (11)

M. J. Hofmann, M. J. Herrmann, I. Dan, H. Obrig, M. Conrad, L. Kuchinke, A. M. Jacobs, and A. J. Fallgatter, “Differential activation of frontal and parietal regions during visual word recognition: an optical topography study,” Neuroimage 40, 1340–1349 (2008).
[Crossref] [PubMed]

M. M. Plichta, M. J. Herrmann, C. G. Baehne, A. C. Ehlis, M. M. Richter, P. Pauli, and A. J. Fallgatter, “Event-related functional near-infrared spectroscopy (fNIRS): are the measurements reliable?” Neuroimage 31, 116–124 (2006).
[Crossref] [PubMed]

M. M. Plichta, S. Heinzel, A. C. Ehlis, P. Pauli, and A. J. Fallgatter, “Model-based analysis of rapid event-related functional near-infrared spectroscopy (NIRS) data: a parametric validation study,” Neuroimage 35, 625–634 (2007).
[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, 428–447 (2009).
[Crossref]

H. T. Karim, S. I. Fuhrman, J. M. Furman, and T. J. Huppert, “Neuroimaging to detect cortical projection of vestibular response to caloric stimulation in young and older adults using functional near-infrared spectroscopy (fNIRS),” Neuroimage 76, 1–10 (2013).
[Crossref] [PubMed]

X. Cui, D. M. Bryant, and A. L. Reiss, “NIRS-based hyperscanning reveals increased interpersonal coherence in superior frontal cortex during cooperation,” Neuroimage 59, 2430–2437 (2012).
[Crossref]

I. Miyai, H. C. Tanabe, I. Sase, H. Eda, I. Oda, I. Konishi, Y. Tsunazawa, T. Suzuki, T. Yanagida, and K. Kubota, “Cortical mapping of gait in humans: a near-infrared spectroscopic topography study,” Neuroimage 14, 1186–1192 (2001).
[Crossref] [PubMed]

M. Suzuki, I. Miyai, T. Ono, and K. Kubota, “Activities in the frontal cortex and gait performance are modulated by preparation: an fNIRS study,” Neuroimage 39, 600–607 (2008).
[Crossref]

H. Karim, S. I. Fuhrman, P. Sparto, J. Furman, and T. Huppert, “Functional brain imaging of multi-sensory vestibular processing during computerized dynamic posturography using near-infrared spectroscopy,” Neuroimage 74, 318–325 (2013).
[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, 88–101 (2006).
[Crossref]

S. B. Perlman, B. Luna, T. Hein, and T. J. Huppert, “fNIRS evidence of prefrontal regulation of frustration in early childhood,” Neuroimage (to be published).
[PubMed]

Physiol. Meas. (2)

F. Scholkmann, S. Spichtig, T. Muehlemann, and M. Wolf, “How to detect and reduce movement artifacts in near-infrared imaging using moving standard deviation and spline interpolation,” Physiol. Meas. 31, 649–662 (2010).
[Crossref] [PubMed]

B. Molavi and G. A. Dumont, “Wavelet-based motion artifact removal for functional near-infrared spectroscopy,” Physiol. Meas. 33, 259–270 (2012).
[Crossref] [PubMed]

Science (1)

F. F. Jöbsis, “Noninvasive, infrared monitoring of cerebral and myocardial oxygen sufficiency and circulatory parameters,” Science 198, 1264–1267 (1977).
[Crossref] [PubMed]

Technometrics (1)

A. E. Beaton and J. W. Tukey, “The fitting of power series, meaning polynomials, illustrated on band-spectroscopic data,” Technometrics 16, 147–185 (1974).
[Crossref]

Other (2)

K. J. Friston, J. T. Ashburner, S. J. Kiebel, T. E. Nichols, and W. D. Penny, Statistical Parametric Mapping: The Analysis of Functional Brain Images: The Analysis of Functional Brain Images(Academic Press, 2011).

A. C. Harvey, The Econometric Analysis of Time Series(MIT Press, 1990).

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Figures (6)

Fig. 1
Fig. 1

A simulated fNIRS signal generated from an AR(5) process with simulated motion artifacts is shown in (a). After generating an optimal pre-whitening filter via fitting an AR(2) model, the whitened signal (b) has significantly reduced autocorrelations (c). An experimental fNIRS signal is shown in (d). After generating an optimal pre-whitening filter via fitting an AR(2) model, the whitened signal (e) has significantly reduced autocorrelations (d).

Fig. 2
Fig. 2

Examples of recovered hemodynamic response functions for simulated block design (a–c) and event-related (d–f) design using the experimental data as baseline physiology/noise.

Fig. 3
Fig. 3

Partial AUC (AUC0.05) for detection of evoked responses with a deconvolution/FIR model for simulated block (a) and event (b) tasks using an AR model as baseline signal with no artifacts (AR/None), spike artifacts (AR/Spike), or shift artifacts (AR/Shift) or with experimental data as a baseline signal containing motion artifacts. Error bars indicate 99% confidence interval.

Fig. 4
Fig. 4

False positive rate of detection as a function of estimated p-value (i.e., estimated false positive rate) with the deconvolution/FIR model for simulated block (top row) and event (bottom row) tasks using a simulated AR model as baseline signal with no artifacts (AR/None), spike artifacts (AR/Spike), or shift artifacts (AR/Shift) or with experimental data as a baseline signal containing motion artifacts.

Fig. 5
Fig. 5

Partial AUC for detection of evoked responses with the canonical regression model for simulated block (a) and event (b) tasks using a simulated AR model as baseline signal with no artifacts (AR/None), spike artifacts (AR/spike), or shift artifacts (AR/shift) or with experimental data as a baseline signal containing motion artifacts. Error bars indicate 99% confidence interval.

Fig. 6
Fig. 6

False positive rate of detection as a function of estimated p-value (i.e., estimated false positive rate) with the canonical regression model for simulated block (top row) and event (bottom row) tasks using a simulated AR model as baseline signal with no artifacts (AR/None), spike artifacts (AR/spike), or shift artifacts (AR/shift) or with experimental data as a baseline signal containing motion artifacts.

Equations (21)

Equations on this page are rendered with MathJax. Learn more.

y t = h t + ε t
ε t ~ 𝒩 ( 0 , σ 2 ) ,
ε t = ρ 1 ε t 1 + ν t
ν t ~ 𝒩 ( 0 , σ 2 ) ,
f * y t = ( y t ρ 1 y t 1 ) = ( h t ρ 1 h t 1 ) + ν t ,
ε t = ρ 1 ε t 1 + ρ 2 ε t 2 + + ρ P ε t P + ν t
f = [ 1 ρ 1 ρ 2 ρ P ] T
( f * y t ) = ( f * h t ) + ν t .
B I C ( P ) = 2 L L + P ln ( n ) ,
y = X β + ε ,
β = ( X T X ) 1 X T y
cov ( β ) = σ 2 ( X T X ) 1 ,
F y = F X β + F ε ,
β = ( X T F T F X ) 1 X T F T F y
cov ( β ) = σ 2 ( X T F T F X ) 1 .
w ( r ) = { ( 1 ( r σ κ ) 2 ) 2 | r | < κ 0 | r | κ ,
β = ( X T F T W F X ) 1 X T F T W F y
[ W i i ] = w ( [ F y F X β ] i ) .
cov ( β ) = σ 2 E [ ψ 2 ( ε / σ ) ] E [ ψ ( ε / σ ) ] 2 ) ( X T X ) 1 ,
t = c T β c T cov ( β ) c .
f ( t ) = A exp ( | t t 0 | b ) ,

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