Abstract

Near-infrared spectroscopy (NIRS) is a noninvasive neuroimaging tool for studying evoked hemodynamic changes within the brain. By this technique, changes in the optical absorption of light are recorded over time and are used to estimate the functionally evoked changes in cerebral oxyhemoglobin and deoxyhemoglobin concentrations that result from local cerebral vascular and oxygen metabolic effects during brain activity. Over the past three decades this technology has continued to grow, and today NIRS studies have found many niche applications in the fields of psychology, physiology, and cerebral pathology. The growing popularity of this technique is in part associated with a lower cost and increased portability of NIRS equipment when compared with other imaging modalities, such as functional magnetic resonance imaging and positron emission tomography. With this increasing number of applications, new techniques for the processing, analysis, and interpretation of NIRS data are continually being developed. We review some of the time-series and functional analysis techniques that are currently used in NIRS studies, we describe the practical implementation of various signal processing techniques for removing physiological, instrumental, and motion-artifact noise from optical data, and we discuss the unique aspects of NIRS analysis in comparison with other brain imaging modalities. These methods are described within the context of the MATLAB-based graphical user interface program, HomER, which we have developed and distributed to facilitate the processing of optical functional brain data.

© 2009 Optical Society of America

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  126. G. Boverman, Q. Fang, S. A. Carp, E. L. Miller, D. H. Brooks, J. Selb, R. H. Moore, D. B. Kopans, and D. A. Boas, “Spatio-temporal imaging of the hemoglobin in the compressed breast with diffuse optical tomography,” Phys. Med. Biol. 52, 3619-3641 (2007).
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  127. T. Deneux and O. Faugeras, “Using nonlinear models in fMRI data analysis: model selection and activation detection,” NeuroImage 32, 1669-1689 (2006).
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  134. R. D. Hoge, M. A. Franceschini, R. J. Covolan, T. Huppert, J. B. Mandeville, and D. A. Boas, “Simultaneous recording of task-induced changes in blood oxygenation, volume, and flow using diffuse optical imaging and arterial spin-labeling MRI,” NeuroImage 25, 701-707 (2005).
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  135. J. Steinbrink, H. Wabnitz, H. Obrig, A. Villringer, and H. Rinneberg, “Determining changes in NIR absorption using a layered model of the human head,” Phys. Med. Biol. 46, 879-896 (2001).
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  138. A. Li, Q. Zhang, J. P. Culver, E. L. Miller, and D. A. Boas, “Reconstructing chromosphere concentration images directly by continuous-wave diffuse optical tomography,” Opt. Lett. 29, 256-258 (2004).
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2009 (1)

T. Huppert, M. Franceschini, and D. Boas, “Non-invasive imaging of cerebral activation with diffuse optical tomography,” in In Vivo Optical Imaging of Brain Function, R. Frostig, ed., 2nd ed. (CRC Press, 2009).
[CrossRef]

2008 (3)

S. Perrey, “Non-invasive NIR spectroscopy of human brain function during exercise,” Methods 45, 289-299 (2008).
[CrossRef] [PubMed]

T. J. Huppert, S. G. Diamond, and D. A. Boas, “Direct estimation of evoked hemoglobin changes by multimodality fusion imaging,” J. Biomed. Opt. 13, 054031 (2008).
[CrossRef] [PubMed]

T. J. Huppert, M. S. Allen, S. G. Diamond, and D. A. Boas, “Estimating cerebral oxygen metabolism from fMRI with a dynamic multicompartment Windkessel model,” Hum. Brain Mapp. (2008).
[PubMed]

2007 (6)

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]

G. Boverman, Q. Fang, S. A. Carp, E. L. Miller, D. H. Brooks, J. Selb, R. H. Moore, D. B. Kopans, and D. A. Boas, “Spatio-temporal imaging of the hemoglobin in the compressed breast with diffuse optical tomography,” Phys. Med. Biol. 52, 3619-3641 (2007).
[CrossRef] [PubMed]

H. Luo and S. Puthusserypady, “fMRI data analysis with nonstationary noise models: a Bayesian approach,” IEEE Trans. Biomed. Eng. 54, 1621-1630 (2007).
[CrossRef] [PubMed]

P. K. Yalavarthy, B. W. Pogue, H. Dehghani, and K. D. Paulsen, “Weight-matrix structured regularization provides optimal generalized least-squares estimate in diffuse optical tomography,” Med. Phys. 34, 2085-2098 (2007).
[CrossRef] [PubMed]

J. Cohen-Adad, S. Chapuisat, J. Doyon, S. Rossignol, J. M. Lina, H. Benali, and F. Lesage, “Activation detection in diffuse optical imaging by means of the general linear model,” Med. Image Anal. 11, 616-629 (2007).
[CrossRef] [PubMed]

K. J. Friston., Statistical Parametric Mapping : the Analysis of Functional Brain Images (Elsevier/Academic, 2007).

2006 (15)

D. K. Joseph, T. J. Huppert, M. A. Franceschini, and D. A. Boas, “Design and validation of a time division multiplexed continuous wave diffuse optical tomography (DOT) system optimized for brain function imaging,” Appl. Opt. 45, 8142-8151 (2006).
[CrossRef] [PubMed]

L. Kocsis, P. Herman, and A. Eke, “The modified Beer-Lambert law revisited,” Phys. Med. Biol. 51, N91-N98 (2006).
[CrossRef] [PubMed]

H. Sato, M. Kiguchi, A. Maki, Y. Fuchino, A. Obata, T. Yoro, and H. Koizumi, “Within-subject reproducibility of near-infrared spectroscopy signals in sensorimotor activation after 6 months,” J. Biomed. Opt. 11, 014021 (2006).
[CrossRef] [PubMed]

D. C. Montgomery, E. A. Peck, and G. G. Vining, Introduction to Linear Regression Analysis (Wiley-Interscience, 2006).

H. Sato, N. Tanaka, M. Uchida, Y. Hirabayashi, M. Kanai, T. Ashida, I. Konishi, and A. Maki, “Wavelet analysis for detecting body-movement artifacts in optical topography signals,” NeuroImage 33, 580-587 (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]

R. M. Birn, J. B. Diamond, M. A. Smith, and P. A. Bandettini, “Separating respiratory-variation-related fluctuations from neuronal-activity-related fluctuations in fMRI,” NeuroImage 31, 1536-1548 (2006).
[CrossRef] [PubMed]

T. Deneux and O. Faugeras, “Using nonlinear models in fMRI data analysis: model selection and activation detection,” NeuroImage 32, 1669-1689 (2006).
[CrossRef] [PubMed]

A. Custo, W. M. Wells III, A. H. Barnett, E. M. Hillman, and D. A. Boas, “Effective scattering coefficient of the cerebral spinal fluid in adult head models for diffuse optical imaging,” Appl. Opt. 45, 4747-4755 (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, 054007 (2006).
[CrossRef] [PubMed]

T. Huppert, R. D. Hoge, A. M. Dale, M. A. Franceschini, and D. A. Boas, “A quantitative spatial comparison of diffuse optical imaging with BOLD- and ASL-based fMRI,” J. Biomed. Opt. 11, 064018 (2006).
[CrossRef]

T. J. Huppert, R. D. Hoge, S. G. Diamond, M. A. Franceschini, and D. A. Boas, “A temporal comparison of BOLD, ASL, and NIRS hemodynamic responses to motor stimuli in adult humans,” NeuroImage 29, 368-382 (2006).
[CrossRef]

M. Kameyama, M. Fukuda, Y. Yamagishi, T. Sato, T. Uehara, M. Ito, T. Suto, and M. Mikuni, “Frontal lobe function in bipolar disorder: a multichannel near-infrared spectroscopy study,” NeuroImage 29, 172-184 (2006).
[CrossRef]

S. Nagamitsu, M. Nagano, Y. Yamashita, S. Takashima, and T. Matsuishi, “Prefrontal cerebral blood volume patterns while playing video games-a near-infrared spectroscopy study,” Brain Dev. 28, 315-321 (2006).
[CrossRef] [PubMed]

M. Okamoto, M. Matsunami, H. Dan, T. Kohata, K. Kohyama, and I. Dan, “Prefrontal activity during taste encoding: an fNIRS study,” NeuroImage 31, 796-806 (2006).
[CrossRef] [PubMed]

2005 (20)

S. Sumitani, T. Tanaka, S. Tayoshi, K. Ota, N. Kameoka, M. Morimune, S. Shibuya-Tayoshi, S. Kinouchi, S. Ueno, and T. Ohmori, “Hemodynamic changes in the prefrontal cortex during mental works as measured by multi channel near-infrared spectroscopy (NIRS),” J. Med. Invest. 52(Suppl.), 302-303 (2005).
[CrossRef] [PubMed]

A. P. Gibson, J. C. Hebden, and S. R. Arridge, “Recent advances in diffuse optical imaging,” Phys. Med. Biol. 50, R1-R43 (2005).
[CrossRef] [PubMed]

E. Gratton, V. Toronov, U. Wolf, M. Wolf, and A. Webb, “Measurement of brain activity by near-infrared light,” J. Biomed. Opt. 10, 011008 (2005).
[CrossRef]

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, 011014 (2005).
[CrossRef]

J. Selb, J. J. Stott, M. A. Franceschini, A. G. Sorensen, and D. A. Boas, “Improved sensitivity to cerebral hemodynamics during brain activation with a time-gated optical system: analytical model and experimental validation,” J. Biomed. Opt. 10, 011013 (2005).
[CrossRef]

X. Zhang, V. Y. Toronov, and A. G. Webb, “Spatial and temporal hemodynamic study of human primary visual cortex using simultaneous functional MRI and diffuse optical tomography,” in 27th Annual International Conference of the Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005 (IEEE, 2005), pp. 727-730.
[PubMed]

A. Liebert, H. Wabnitz, J. Steinbrink, M. Moller, R. Macdonald, H. Rinneberg, A. Villringer, and H. Obrig, “Bed-side assessment of cerebral perfusion in stroke patients based on optical monitoring of a dye bolus by time-resolved diffuse reflectance,” NeuroImage 24, 426-435 (2005).
[CrossRef] [PubMed]

J. R. Thiagarajah, M. C. Papadopoulos, and A. S. Verkman, “Noninvasive early detection of brain edema in mice by near-infrared light scattering,” J. Neorosci. Res. 80, 293-299 (2005).
[CrossRef]

Y. Kubota, M. Toichi, M. Shimizu, R. A. Mason, C. M. Coconcea, R. L. Findling, K. Yamamoto, and J. R. Calabrese, “Prefrontal activation during verbal fluency tests in schizophrenia--a near-infrared spectroscopy (NIRS) study,” Schizophr. Res. 77, 65-73 (2005).
[CrossRef] [PubMed]

C. E. Elwell, J. R. Henty, T. S. Leung, T. Austin, J. H. Meek, D. T. Delpy, and J. S. Wyatt, “Measurement of CMRO2 in neonates undergoing intensive care using near infrared spectroscopy,” Adv. Exp. Med. Biol. 566, 263-268 (2005).
[CrossRef]

T. Wilcox, H. Bortfeld, R. Woods, E. Wruck, and D. A. Boas, “Using near-infrared spectroscopy to assess neural activation during object processing in infants,” J. Biomed. Opt. 10, 11010 (2005).
[CrossRef] [PubMed]

H. Chen, D. Yao, and Z. Liu, “A comparison of gamma and Gaussian dynamic convolution models of the fMRI BOLD response,” Magn. Reson. Imaging 23, 83-88 (2005).
[CrossRef] [PubMed]

Y. Zhang, D. H. Brooks, and D. A. Boas, “A haemodynamic response function model in spatio-temporal diffuse optical tomography,” Phys. Med. Biol. 50, 4625-4644 (2005).
[CrossRef] [PubMed]

S. G. Diamond, T. J. Huppert, V. Kolehmainen, M. A. Franceschini, J. P. Kaipio, S. R. Arridge, and D. A. Boas, “Physiological system identification with the Kalman filter in diffuse optical tomography,” in Medical Image Computing and Computer-Assisted Intervention - MICCAI 2005, Vol. 3750 of Lecture Notes in Computer Science (Springer, 2005), pp. 649-656.
[CrossRef]

R. D. Hoge, M. A. Franceschini, R. J. Covolan, T. Huppert, J. B. Mandeville, and D. A. Boas, “Simultaneous recording of task-induced changes in blood oxygenation, volume, and flow using diffuse optical imaging and arterial spin-labeling MRI,” NeuroImage 25, 701-707 (2005).
[CrossRef] [PubMed]

B. Brooksby, S. Srinivasan, S. Jiang, H. Dehghani, B. W. Pogue, K. D. Paulsen, J. Weaver, C. Kogel, and S. P. Poplack, “Spectral priors improve near-infrared diffuse tomography more than spatial priors,” Opt. Lett. 30, 1968-1970 (2005).
[CrossRef] [PubMed]

M. Izzetoglu, A. Devaraj, S. Bunce, and B. Onaral, “Motion artifact cancellation in NIR spectroscopy using Wiener filtering,” IEEE Trans. Biomed. Eng. 52, 934-938(2005).
[CrossRef] [PubMed]

H. Sato, Y. Fuchino, M. Kiguchi, T. Katura, A. Maki, T. Yoro, and H. Koizumi, “Intersubject variability of near-infrared spectroscopy signals during sensorimotor cortex activation,” J. Biomed. Opt. 10, 44001 (2005).
[CrossRef] [PubMed]

D. A. Boas and A. M. Dale, “Simulation study of magnetic resonance imaging-guided cortically constrained diffuse optical tomography of human brain function,” Appl. Opt. 44, 1957-1968 (2005).
[CrossRef] [PubMed]

N. Okui and E. Okada, “Wavelength dependence of crosstalk in dual-wavelength measurement of oxy- and deoxy-hemoglobin,” J. Biomed. Opt. 10, 11015 (2005).
[CrossRef] [PubMed]

2004 (17)

H. Sato, M. Kiguchi, F. Kawaguchi, and A. Maki, “Practicality of wavelength selection to improve signal-to-noise ratio in near-infrared spectroscopy,” NeuroImage 21, 1554-1562 (2004).
[CrossRef] [PubMed]

D. A. Boas, K. Chen, D. Grebert, and M. A. Franceschini, “Improving the diffuse optical imaging spatial resolution of the cerebral hemodynamic response to brain activation in humans,” Opt. Lett. 29, 1506-1508 (2004).
[CrossRef] [PubMed]

R. B. Buxton, K. Uludag, D. J. Dubowitz, and T. T. Liu, “Modeling the hemodynamic response to brain activation,” NeuroImage 23(Suppl. 1), S220-S233 (2004).
[CrossRef] [PubMed]

A. Sassaroli and S. Fantini, “Comment on the modified Beer-Lambert law for scattering media,” Phys. Med. Biol. 49, N255-N257 (2004).
[CrossRef] [PubMed]

C. Long, E. N. Brown, D. Manoach, and V. Solo, “Spatiotemporal wavelet analysis for functional MRI,” NeuroImage 23, 500-516 (2004).
[CrossRef] [PubMed]

Y. Shimizu, M. Barth, C. Windischberger, E. Moser, and S. Thurner, “Wavelet-based multifractal analysis of fMRI time series,” NeuroImage 22, 1195-1202 (2004).
[CrossRef] [PubMed]

A. Li, Q. Zhang, J. P. Culver, E. L. Miller, and D. A. Boas, “Reconstructing chromosphere concentration images directly by continuous-wave diffuse optical tomography,” Opt. Lett. 29, 256-258 (2004).
[CrossRef] [PubMed]

J. J. Riera, J. Watanabe, I. Kazuki, M. Naoki, E. Aubert, T. Ozaki, and R. Kawashima, “A state-space model of the hemodynamic approach: nonlinear filtering of BOLD signals,” NeuroImage 21, 547-567 (2004).
[CrossRef] [PubMed]

M. L. Schroeter, M. M. Bucheler, K. Muller, K. Uludag, H. Obrig, G. Lohmann, M. Tittgemeyer, A. Villringer, and D. Y. von Cramon, “Towards a standard analysis for functional near-infrared imaging,” NeuroImage 21, 283-290 (2004).
[CrossRef] [PubMed]

E. R. Sowell, P. M. Thompson, C. M. Leonard, S. E. Welcome, E. Kan, and A. W. Toga, “Longitudinal mapping of cortical thickness and brain growth in normal children,” J. Neurosci. 24, 8223-8231 (2004).
[CrossRef] [PubMed]

T. Shinba, M. Nagano, N. Kariya, K. Ogawa, T. Shinozaki, S. Shimosato, and Y. Hoshi, “Near-infrared spectroscopy analysis of frontal lobe dysfunction in schizophrenia,” Biol. Psychiatry 55, 154-164 (2004).
[CrossRef] [PubMed]

A. Watanabe and T. Kato, “Cerebrovascular response to cognitive tasks in patients with schizophrenia measured by near-infrared spectroscopy,” Schizophr. Bull. 30, 435-444 (2004).
[PubMed]

T. Suto, M. Fukuda, M. Ito, T. Uehara, and M. Mikuni, “Multichannel near-infrared spectroscopy in depression and schizophrenia: cognitive brain activation study,” Biol. Psychiatry 55, 501-511 (2004).
[CrossRef] [PubMed]

K. Orihashi, T. Sueda, K. Okada, and K. Imai, “Near-infrared spectroscopy for monitoring cerebral ischemia during selective cerebral perfusion,” Eur. J. Cardiothorac Surg. 26, 907-911 (2004).
[CrossRef] [PubMed]

C. Terborg, S. Bramer, S. Harscher, M. Simon, and O. W. Witte, “Bedside assessment of cerebral perfusion reductions in patients with acute ischaemic stroke by near-infrared spectroscopy and indocyanine green,” J. Neurol. Neurosurg. Psychiatry 75, 38-42 (2004).
[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 (Suppl. 1), S275-S288 (2004).
[CrossRef] [PubMed]

K. Buchheim, H. Obrig, W. v. Pannwitz, A. Muller, H. Heekeren, A. Villringer, and H. Meierkord, “Decrease in haemoglobin oxygenation during absence seizures in adult humans,” Neurosci. Lett. Suppl. 354, 119-122 (2004).
[CrossRef]

2003 (10)

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, 548-560 (2003).
[CrossRef] [PubMed]

A. Yodh and D. Boas, eds., Functional Imaging with Diffusing Light (CRC Press, 2003).

Y. Fukui, Y. Ajichi, and E. Okada, “Monte Carlo prediction of near-infrared light propagation in realistic adult and neonatal head models,” Appl. Opt. 42, 2881-2887 (2003).
[CrossRef] [PubMed]

E. Okada and D. T. Delpy, “Near-infrared light propagation in an adult head model. II. Effect of superficial tissue thickness on the sensitivity of the near-infrared spectroscopy signal,” Appl. Opt. 42, 2915-2922 (2003).
[CrossRef] [PubMed]

E. Okada and D. T. Delpy, “Near-infrared light propagation in an adult head model. I. Modeling of low-level scattering in the cerebrospinal fluid layer,” Appl. Opt. 42, 2906-2914 (2003).
[CrossRef] [PubMed]

S. Prince, V. Kolehmainen, J. P. Kaipio, M. A. Franceschini, D. Boas, and S. R. Arridge, “Time-series estimation of biological factors in optical diffusion tomography,” Phys. Med. Biol. 48, 1491-1504 (2003).
[CrossRef] [PubMed]

M. Wolf, M. A. Franceschini, L. A. Paunescu, V. Toronov, A. Michalos, U. Wolf, E. Gratton, and S. Fantini, “Absolute frequency-domain pulse oximetry of the brain: methodology and measurements,” Adv. Exp. Med. Biol. 530, 61-73 (2003).
[CrossRef] [PubMed]

T. D. Wager and T. E. Nichols, “Optimization of experimental design in fMRI: a general framework using a genetic algorithm,” NeuroImage 18, 293-309 (2003).
[CrossRef] [PubMed]

G. Strangman, M. A. Franceschini, and D. A. Boas, “Factors affecting the accuracy of near-infrared spectroscopy concentration calculations for focal changes in oxygenation parameters,” NeuroImage 18, 865-879 (2003).
[CrossRef] [PubMed]

Y. Hoshi, “Functional near-infrared optical imaging: utility and limitations in human brain mapping,” Psychophysiology 40, 511-520 (2003).
[CrossRef] [PubMed]

2002 (6)

G. Strangman, D. A. Boas, and J. P. Sutton, “Non-invasive neuroimaging using near-infrared light,” Biol. Psychiatry 52, 679-693 (2002).
[CrossRef] [PubMed]

E. R. Cohen, K. Ugurbil, and S. G. Kim, “Effect of basal conditions on the magnitude and dynamics of the blood oxygenation level-dependent fMRI response,” J. Cereb. Blood Flow Metab. 22, 1042-1053 (2002).
[CrossRef] [PubMed]

D. Boas, J. Culver, J. Stott, and A. Dunn, “Three dimensional Monte Carlo code for photon migration through complex heterogeneous media including the adult human head,” Opt. Express 10, 159-170 (2002).
[PubMed]

S. Chen, K. Sakatani, W. Lichty, P. Ning, S. Zhao, and H. Zuo, “Auditory-evoked cerebral oxygenation changes in hypoxic-ischemic encephalopathy of newborn infants monitored by near infrared spectroscopy,” Early Hum. Dev. 67, 113-121(2002).
[CrossRef] [PubMed]

E. Watanabe, Y. Nagahori, and Y. Mayanagi, “Focus diagnosis of epilepsy using near-infrared spectroscopy,” Epilepsia (1909-1915, 1937-1950, 1952-1955, 1959-) 43(Suppl. 9), 50-55 (2002).

K. Matsuo, N. Kato, and T. Kato, “Decreased cerebral haemodynamic response to cognitive and physiological tasks in mood disorders as shown by near-infrared spectroscopy,” Psychol. Med. 32, 1029-1037 (2002).
[CrossRef] [PubMed]

2001 (5)

D. J. Mehagnoul-Schipper, W. N. Colier, and R. W. Jansen, “Reproducibility of orthostatic changes in cerebral oxygenation in healthy subjects aged 70 years or older,” Clin. Physiol. 21, 77-84 (2001).
[CrossRef] [PubMed]

P. Zaramella, F. Freato, A. Amigoni, S. Salvadori, P. Marangoni, A. Suppjei, B. Schiavo, and L. Chiandetti, “Brain auditory activation measured by near-infrared spectroscopy (NIRS) in neonates,” Pediatr. Res. 49, 213-219 (2001).
[CrossRef] [PubMed]

J. Steinbrink, H. Wabnitz, H. Obrig, A. Villringer, and H. Rinneberg, “Determining changes in NIR absorption using a layered model of the human head,” Phys. Med. Biol. 46, 879-896 (2001).
[CrossRef] [PubMed]

D. A. Boas, T. Gaudette, G. Strangman, X. Cheng, J. J. Marota, and J. B. Mandeville, “The accuracy of near infrared spectroscopy and imaging during focal changes in cerebral hemodynamics,” NeuroImage 13, 76-90 (2001).
[CrossRef] [PubMed]

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]

2000 (14)

K. J. Friston, “Experimental design and statistical issues,” in Brain Mapping: The Disorders, J. C.Mazziotta, A. W.Toga, and R. S. J.Frackowiak, eds. (Academic, 2000), pp. 33-58.
[CrossRef]

H. Obrig, M. Neufang, R. Wenzel, M. Kohl, J. Steinbrink, K. Einhaupl, and A. Villringer, “Spontaneous low frequency oscillations of cerebral hemodynamics and metabolism in human adults,” NeuroImage 12, 623-639 (2000).
[CrossRef] [PubMed]

V. Toronov, M. A. Franceschini, M. Filiaci, S. Fantini, M. Wolf, A. Michalos, and E. Gratton, “Near-infrared study of fluctuations in cerebral hemodynamics during rest and motor stimulation: temporal analysis and spatial mapping,” Med. Phys. 27, 801-815 (2000).
[CrossRef] [PubMed]

G. H. Glover, T. Q. Li, and D. Ress, “Image-based method for retrospective correction of physiological motion effects in fMRI: RETROICOR,” Magn. Reson. Med. 44, 162-167(2000).
[CrossRef] [PubMed]

K. J. Friston, A. Mechelli, R. Turner, and C. J. Price, “Nonlinear responses in fMRI: the Balloon model, Volterra kernels, and other hemodynamics,” NeuroImage 12, 466-477(2000).
[PubMed]

E. Watanabe, A. Maki, F. Kawaguchi, Y. Yamashita, H. Koizumi, and Y. Mayanagi, “Noninvasive cerebral blood volume measurement during seizures using multichannel near infrared spectroscopic topography,” J. Biomed. Opt. 5, 287-290 (2000).
[CrossRef] [PubMed]

D. K. Sokol, O. N. Markand, E. C. Daly, T. G. Luerssen, and M. D. Malkoff, “Near infrared spectroscopy (NIRS) distinguishes seizure types,” Seizure : the Journal of the British Epilepsy Association 9, 323-327 (2000).
[CrossRef]

K. Ide and N. H. Secher, “Cerebral blood flow and metabolism during exercise,” Prog. Neurobiol. (Oxford) 61, 397-414(2000).
[CrossRef]

W. G. Chen, P. C. Li, Q. M. Luo, S. Q. Zeng, and B. Hu, “Hemodynamic assessment of ischemic stroke with near-infrared spectroscopy,” Space Med. Med. Eng. (Beijing) 13, 84-89(2000).

E. M. Nemoto, H. Yonas, and A. Kassam, “Clinical experience with cerebral oximetry in stroke and cardiac arrest,” Crit. Care Med. 28, 1052-1054 (2000).
[CrossRef] [PubMed]

H. Saitou, H. Yanagi, S. Hara, S. Tsuchiya, and S. Tomura, “Cerebral blood volume and oxygenation among poststroke hemiplegic patients: effects of 13 rehabilitation tasks measured by near-infrared spectroscopy,” Arch. Phys. Med. Rehabil. 81, 1348-1356 (2000).
[CrossRef] [PubMed]

A. J. Fallgatter, and W. K. Strik, “Reduced frontal functional asymmetry in schizophrenia during a cued continuous performance test assessed with near-infrared spectroscopy,” Schizophr. Bull. 26, 913-919 (2000).
[PubMed]

G. W. Eschweiler, C. Wegerer, W. Schlotter, C. Spandl, A. Stevens, M. Bartels, and G. Buchkremer, “Left prefrontal activation predicts therapeutic effects of repetitive transcranial magnetic stimulation (rTMS) in major depression,” Psychiatry Res. 99, 161-172 (2000).
[CrossRef] [PubMed]

K. Matsuo, T. Kato, M. Fukuda, and N. Kato, “Alteration of hemoglobin oxygenation in the frontal region in elderly depressed patients as measured by near-infrared spectroscopy,” J. Neuropsychiatry Clin. Neurosci. 12, 465-471 (2000).
[CrossRef] [PubMed]

1999 (11)

F. Vernieri, N. Rosato, F. Pauri, F. Tibuzzi, F. Passarelli, and P. M. Rossini, “Near infrared spectroscopy and transcranial Doppler in monohemispheric stroke,” Eur. Neurol. 41, 159-162 (1999).
[CrossRef] [PubMed]

E. B. Hanlon, I. Itzkan, R. R. Dasari, M. S. Feld, R. J. Ferrante, A. C. McKee, D. Lathi, and N. W. Kowall, “Near-infrared fluorescence spectroscopy detects Alzheimer's disease in vitro,” Photochem. Photobiol. 70, 236-242 (1999).
[PubMed]

W. N. Colier, V. Quaresima, B. Oeseburg, and M. Ferrari, “Human motor-cortex oxygenation changes induced by cyclic coupled movements of hand and foot,” Exp. Brain. Res. 129, 457-461 (1999).
[CrossRef] [PubMed]

K. Sakatani, S. Chen, W. Lichty, H. Zuo, and Y. P. Wang, “Cerebral blood oxygenation changes induced by auditory stimulation in newborn infants measured by near infrared spectroscopy,” Early Hum. Dev. 55, 229-236 (1999).
[CrossRef] [PubMed]

H. Sato, T. Takeuchi, and K. L. Sakai, “Temporal cortex activation during speech recognition: an optical topography study,” Cognition 73, B55-66 (1999).
[CrossRef] [PubMed]

P. D. Adelson, E. Nemoto, M. Scheuer, M. Painter, J. Morgan, and H. Yonas, “Noninvasive continuous monitoring of cerebral oxygenation periictally using near-infrared spectroscopy: a preliminary report,” Epilepsia (1909-1915, 1937-1950, 1952-1955, 1959-) 40, 1484-1489 (1999).

G. H. Glover, “Deconvolution of impulse response in event-related BOLD fMRI,” NeuroImage 9, 416-429 (1999). doi: 10.1002/hbm.20628
[CrossRef] [PubMed]

S. Baillet, L. Garnero, G. Marin, and J. P. Hugonin, “Combined MEG and EEG source imaging by minimization of mutual information,” IEEE Trans. Biomed. Eng. 46, 522-534 (1999).
[CrossRef] [PubMed]

K. von Siebenthal, J. Beran, M. Wolf, M. Keel, V. Dietz, S. Kundu, and H. U. Bucher, “Cyclical fluctuations in blood pressure, heart rate and cerebral blood volume in preterm infants,” Brain Dev. 21, 529-534 (1999).
[CrossRef] [PubMed]

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

O. Josephs and R. N. Henson, “Event-related functional magnetic resonance imaging: modelling, inference and optimization,” Philos. Trans. R. Soc. London Ser. B 354, 1215-1228 (1999).
[CrossRef]

1998 (2)

M. Kohl, C. Nolte, H. R. Heekeren, S. Horst, U. Scholz, H. Obrig, and A. Villringer, “Determination of the wavelength dependence of the differential pathlength factor from near-infrared pulse signals,” Phys. Med. Biol. 43, 1771-1782 (1998).
[CrossRef] [PubMed]

A. F. Cannestra, N. Pouratian, M. H. Shomer, and A. W. Toga, “Refractory periods observed by intrinsic signal and fluorescent dye imaging,” J. Neurophysiol. 80, 1522-1532(1998).
[PubMed]

1997 (6)

A. J. Fallgatter, M. Roesler, L. Sitzmann, A. Heidrich, T. J. Mueller, and W. K. Strik, “Loss of functional hemispheric asymmetry in Alzheimer's dementia assessed with near-infrared spectroscopy,” Brain Res. Cognit. Brain Res. 6, 67-72 (1997).
[CrossRef]

J. Ruben, R. Wenzel, H. Obrig, K. Villringer, J. Bernarding, C. Hirth, H. Heekeren, U. Dirnagl, and A. Villringer, “Haemoglobin oxygenation changes during visual stimulation in the occipital cortex,” Adv. Exp. Med. Biol. 428, 181-187 (1997).
[CrossRef] [PubMed]

H. R. Heekeren, H. Obrig, R. Wenzel, K. Eberle, J. Ruben, K. Villringer, R. Kurth, and A. Villringer, “Cerebral haemoglobin oxygenation during sustained visual stimulation--a near-infrared spectroscopy study,” Philos. Trans. R. Soc. London Ser. B 352, 743-750 (1997).
[CrossRef]

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

J. Chang, H. L. Graber, P. C. Koo, R. Aronson, S. L. Barbour, and R. L. Barbour, “Optical imaging of anatomical maps derived from magnetic resonance images using time-independent optical sources,” IEEE Trans. Med. Imaging 16, 68-77 (1997).
[CrossRef] [PubMed]

E. Gratton, S. Fantini, M. A. Franceschini, G. Gratton, and M. Fabiani, “Measurements of scattering and absorption changes in muscle and brain,” Philos. Trans. R. Soc. London Ser. B 352, 727-735 (1997).
[CrossRef]

1996 (7)

C. Hock, K. Villringer, F. Muller-Spahn, M. Hofmann, S. Schuh-Hofer, H. Heekeren, R. Wenzel, U. Dirnagl, and A. Villringer, “Near infrared spectroscopy in the diagnosis of Alzheimer's disease,” Ann. N.Y. Acad. Sci. 777, 22-29 (1996).
[CrossRef] [PubMed]

F. Okada, N. Takahashi, and Y. Tokumitsu, “Dominance of the 'nondominant' hemisphere in depression,” J. Affect Disord. 37, 13-21 (1996).
[CrossRef] [PubMed]

C. Hirth, H. Obrig, K. Villringer, A. Thiel, J. Bernarding, W. Muhlnickel, H. Flor, U. Dirnagl, and A. Villringer, “Non-invasive functional mapping of the human motor cortex using near-infrared spectroscopy,” NeuroReport 7, 1977-1981(1996).
[CrossRef] [PubMed]

A. Kleinschmidt, H. Obrig, M. Requardt, K. D. Merboldt, U. Dirnagl, A. Villringer, and J. Frahm, “Simultaneous recording of cerebral blood oxygenation changes during human brain activation by magnetic resonance imaging and near-infrared spectroscopy,” J. Cereb. Blood Flow Metab. 16, 817-826 (1996).
[CrossRef] [PubMed]

B. J. Steinhoff, G. Herrendorf, and C. Kurth, “Ictal near infrared spectroscopy in temporal lobe epilepsy: a pilot study,” Seizure: Eur. J. Epilepsy 5, 97-101 (1996).

H. Obrig, T. Wolf, C. Doge, J. J. Hulsing, U. Dirnagl, and A. Villringer, “Cerebral oxygenation changes during motor and somatosensory stimulation in humans, as measured by near-infrared spectroscopy,” Adv. Exp. Med. Biol. 388, 219-224 (1996).
[CrossRef] [PubMed]

H. Obrig, C. Hirth, J. G. Junge-Hulsing, C. Doge, T. Wolf, U. Dirnagl, and A. Villringer, “Cerebral oxygenation changes in response to motor stimulation,” J. Appl. Physiol. 81, 1174-1183 (1996).
[PubMed]

1995 (4)

J. H. Meek, C. E. Elwell, M. J. Khan, J. Romaya, J. S. Wyatt, D. T. Delpy, and S. Zeki, “Regional changes in cerebral haemodynamics as a result of a visual stimulus measured by near infrared spectroscopy,” Proc. R. Soc. London Ser. B 261, 351-356 (1995).
[CrossRef]

L. Wang, S. L. Jacques, and L. Zheng, “MCML--Monte Carlo modeling of light transport in multi-layered tissues,” Comput. Methods Programs Biomed. 47, 131-146 (1995).
[CrossRef] [PubMed]

R. L. Barbour, H. L. Graber, J. Chang, S.-L. S. Barbour, P. C. Koo, and R. Aronson, “MRI-guided optical tomography: prospectives and computation for a new imaging method,” IEEE Comput. Sci. Eng. 2, 63-77 (1995).
[CrossRef]

J. L. Devore, “Simple linear regression and correlation,” in Probability and Statistics for Engineering and the Sciences (Wadsworth, 1995), pp. 474-522.

1994 (1)

F. Okada, Y. Tokumitsu, Y. Hoshi, and M. Tamura, “Impaired interhemispheric integration in brain oxygenation and hemodynamics in schizophrenia,” Eur. Arch. Psychiatry Clin. Neurosci. 244, 17-25 (1994).
[CrossRef] [PubMed]

1993 (1)

M. Hiraoka, M. Firbank, M. Essenpreis, M. Cope, S. R. Arridge, P. van der Zee, and D. T. Delpy, “A Monte Carlo investigation of optical pathlength in inhomogeneous tissue and its application to near-infrared spectroscopy,” Phys. Med. Biol. 38, 1859-1876 (1993).
[CrossRef] [PubMed]

1992 (1)

P. Rolfe, Y. A. Wickramasinghe, M. S. Thorniley, F. Faris, R. Houston, Z. Kai, K. Yamakoshi, S. O'Brien, M. Doyle, K. Palmer, and S. Spencer, “Fetal and neonatal cerebral oxygen monitoring with NIRS: theory and practice,” Early Hum. Dev. 29, 269-273 (1992).
[CrossRef] [PubMed]

1989 (2)

S. T. Flock, M. S. Patterson, B. C. Wilson, and D. R. Wyman, “Monte Carlo modeling of light propagation in highly scattering tissue--I: Model predictions and comparison with diffusion theory,” IEEE Trans. Biomed. Eng. 36, 1162-1168(1989).
[CrossRef] [PubMed]

S. T. Flock, B. C. Wilson, and M. S. Patterson, “Monte Carlo modeling of light propagation in highly scattering tissues--II: Comparison with measurements in phantoms,” IEEE Trans. Biomed. Eng. 36, 1169-1173 (1989).
[CrossRef] [PubMed]

1988 (3)

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).
[PubMed]

B. Chance, J. S. Leigh, H. Miyake, D. S. Smith, S. Nioka, R. Greenfeld, M. Finander, K. Kaufmann, W. Levy, and M. Young, “Comparison of time-resolved and -unresolved measurements of deoxyhemoglobin in brain,” Proc. Natl. Acad. Sci. USA 85, 4971-4975 (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, 1433-1442 (1988).
[CrossRef] [PubMed]

1986 (1)

J. S. Wyatt, M. Cope, D. T. Delpy, S. Wray, and E. O. Reynolds, “Quantification of cerebral oxygenation and haemodynamics in sick newborn infants by near infrared spectrophotometry,” Lancet 2, 1063-1066 (1986) [online ed.Lancet 328, 1063-1066 (2003).
[CrossRef] [PubMed]

1977 (1)

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

Adelson, P. D.

P. D. Adelson, E. Nemoto, M. Scheuer, M. Painter, J. Morgan, and H. Yonas, “Noninvasive continuous monitoring of cerebral oxygenation periictally using near-infrared spectroscopy: a preliminary report,” Epilepsia (1909-1915, 1937-1950, 1952-1955, 1959-) 40, 1484-1489 (1999).

Ajichi, Y.

Allen, M. S.

T. J. Huppert, M. S. Allen, S. G. Diamond, and D. A. Boas, “Estimating cerebral oxygen metabolism from fMRI with a dynamic multicompartment Windkessel model,” Hum. Brain Mapp. (2008).
[PubMed]

Amigoni, A.

P. Zaramella, F. Freato, A. Amigoni, S. Salvadori, P. Marangoni, A. Suppjei, B. Schiavo, and L. Chiandetti, “Brain auditory activation measured by near-infrared spectroscopy (NIRS) in neonates,” Pediatr. Res. 49, 213-219 (2001).
[CrossRef] [PubMed]

Aronson, R.

J. Chang, H. L. Graber, P. C. Koo, R. Aronson, S. L. Barbour, and R. L. Barbour, “Optical imaging of anatomical maps derived from magnetic resonance images using time-independent optical sources,” IEEE Trans. Med. Imaging 16, 68-77 (1997).
[CrossRef] [PubMed]

R. L. Barbour, H. L. Graber, J. Chang, S.-L. S. Barbour, P. C. Koo, and R. Aronson, “MRI-guided optical tomography: prospectives and computation for a new imaging method,” IEEE Comput. Sci. Eng. 2, 63-77 (1995).
[CrossRef]

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, 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, 88-101 (2006).
[CrossRef]

S. G. Diamond, T. J. Huppert, V. Kolehmainen, M. A. Franceschini, J. P. Kaipio, S. R. Arridge, and D. A. Boas, “Physiological system identification with the Kalman filter in diffuse optical tomography,” in Medical Image Computing and Computer-Assisted Intervention - MICCAI 2005, Vol. 3750 of Lecture Notes in Computer Science (Springer, 2005), pp. 649-656.
[CrossRef]

A. P. Gibson, J. C. Hebden, and S. R. Arridge, “Recent advances in diffuse optical imaging,” Phys. Med. Biol. 50, R1-R43 (2005).
[CrossRef] [PubMed]

S. Prince, V. Kolehmainen, J. P. Kaipio, M. A. Franceschini, D. Boas, and S. R. Arridge, “Time-series estimation of biological factors in optical diffusion tomography,” Phys. Med. Biol. 48, 1491-1504 (2003).
[CrossRef] [PubMed]

M. Hiraoka, M. Firbank, M. Essenpreis, M. Cope, S. R. Arridge, P. van der Zee, and D. T. Delpy, “A Monte Carlo investigation of optical pathlength in inhomogeneous tissue and its application to near-infrared spectroscopy,” Phys. Med. Biol. 38, 1859-1876 (1993).
[CrossRef] [PubMed]

Ashida, T.

H. Sato, N. Tanaka, M. Uchida, Y. Hirabayashi, M. Kanai, T. Ashida, I. Konishi, and A. Maki, “Wavelet analysis for detecting body-movement artifacts in optical topography signals,” NeuroImage 33, 580-587 (2006).
[CrossRef] [PubMed]

Aubert, E.

J. J. Riera, J. Watanabe, I. Kazuki, M. Naoki, E. Aubert, T. Ozaki, and R. Kawashima, “A state-space model of the hemodynamic approach: nonlinear filtering of BOLD signals,” NeuroImage 21, 547-567 (2004).
[CrossRef] [PubMed]

Austin, T.

C. E. Elwell, J. R. Henty, T. S. Leung, T. Austin, J. H. Meek, D. T. Delpy, and J. S. Wyatt, “Measurement of CMRO2 in neonates undergoing intensive care using near infrared spectroscopy,” Adv. Exp. Med. Biol. 566, 263-268 (2005).
[CrossRef]

Baillet, S.

S. Baillet, L. Garnero, G. Marin, and J. P. Hugonin, “Combined MEG and EEG source imaging by minimization of mutual information,” IEEE Trans. Biomed. Eng. 46, 522-534 (1999).
[CrossRef] [PubMed]

Bandettini, P. A.

R. M. Birn, J. B. Diamond, M. A. Smith, and P. A. Bandettini, “Separating respiratory-variation-related fluctuations from neuronal-activity-related fluctuations in fMRI,” NeuroImage 31, 1536-1548 (2006).
[CrossRef] [PubMed]

Barbour, R. L.

J. Chang, H. L. Graber, P. C. Koo, R. Aronson, S. L. Barbour, and R. L. Barbour, “Optical imaging of anatomical maps derived from magnetic resonance images using time-independent optical sources,” IEEE Trans. Med. Imaging 16, 68-77 (1997).
[CrossRef] [PubMed]

R. L. Barbour, H. L. Graber, J. Chang, S.-L. S. Barbour, P. C. Koo, and R. Aronson, “MRI-guided optical tomography: prospectives and computation for a new imaging method,” IEEE Comput. Sci. Eng. 2, 63-77 (1995).
[CrossRef]

Barbour, S. L.

J. Chang, H. L. Graber, P. C. Koo, R. Aronson, S. L. Barbour, and R. L. Barbour, “Optical imaging of anatomical maps derived from magnetic resonance images using time-independent optical sources,” IEEE Trans. Med. Imaging 16, 68-77 (1997).
[CrossRef] [PubMed]

Barbour, S.-L. S.

R. L. Barbour, H. L. Graber, J. Chang, S.-L. S. Barbour, P. C. Koo, and R. Aronson, “MRI-guided optical tomography: prospectives and computation for a new imaging method,” IEEE Comput. Sci. Eng. 2, 63-77 (1995).
[CrossRef]

Barnett, A. H.

Bartels, M.

G. W. Eschweiler, C. Wegerer, W. Schlotter, C. Spandl, A. Stevens, M. Bartels, and G. Buchkremer, “Left prefrontal activation predicts therapeutic effects of repetitive transcranial magnetic stimulation (rTMS) in major depression,” Psychiatry Res. 99, 161-172 (2000).
[CrossRef] [PubMed]

Barth, M.

Y. Shimizu, M. Barth, C. Windischberger, E. Moser, and S. Thurner, “Wavelet-based multifractal analysis of fMRI time series,” NeuroImage 22, 1195-1202 (2004).
[CrossRef] [PubMed]

Benali, H.

J. Cohen-Adad, S. Chapuisat, J. Doyon, S. Rossignol, J. M. Lina, H. Benali, and F. Lesage, “Activation detection in diffuse optical imaging by means of the general linear model,” Med. Image Anal. 11, 616-629 (2007).
[CrossRef] [PubMed]

Beran, J.

K. von Siebenthal, J. Beran, M. Wolf, M. Keel, V. Dietz, S. Kundu, and H. U. Bucher, “Cyclical fluctuations in blood pressure, heart rate and cerebral blood volume in preterm infants,” Brain Dev. 21, 529-534 (1999).
[CrossRef] [PubMed]

Bernarding, J.

J. Ruben, R. Wenzel, H. Obrig, K. Villringer, J. Bernarding, C. Hirth, H. Heekeren, U. Dirnagl, and A. Villringer, “Haemoglobin oxygenation changes during visual stimulation in the occipital cortex,” Adv. Exp. Med. Biol. 428, 181-187 (1997).
[CrossRef] [PubMed]

C. Hirth, H. Obrig, K. Villringer, A. Thiel, J. Bernarding, W. Muhlnickel, H. Flor, U. Dirnagl, and A. Villringer, “Non-invasive functional mapping of the human motor cortex using near-infrared spectroscopy,” NeuroReport 7, 1977-1981(1996).
[CrossRef] [PubMed]

Birn, R. M.

R. M. Birn, J. B. Diamond, M. A. Smith, and P. A. Bandettini, “Separating respiratory-variation-related fluctuations from neuronal-activity-related fluctuations in fMRI,” NeuroImage 31, 1536-1548 (2006).
[CrossRef] [PubMed]

Boas, D.

T. Huppert, M. Franceschini, and D. Boas, “Non-invasive imaging of cerebral activation with diffuse optical tomography,” in In Vivo Optical Imaging of Brain Function, R. Frostig, ed., 2nd ed. (CRC Press, 2009).
[CrossRef]

A. Yodh and D. Boas, eds., Functional Imaging with Diffusing Light (CRC Press, 2003).

S. Prince, V. Kolehmainen, J. P. Kaipio, M. A. Franceschini, D. Boas, and S. R. Arridge, “Time-series estimation of biological factors in optical diffusion tomography,” Phys. Med. Biol. 48, 1491-1504 (2003).
[CrossRef] [PubMed]

D. Boas, J. Culver, J. Stott, and A. Dunn, “Three dimensional Monte Carlo code for photon migration through complex heterogeneous media including the adult human head,” Opt. Express 10, 159-170 (2002).
[PubMed]

Boas, D. A.

T. J. Huppert, S. G. Diamond, and D. A. Boas, “Direct estimation of evoked hemoglobin changes by multimodality fusion imaging,” J. Biomed. Opt. 13, 054031 (2008).
[CrossRef] [PubMed]

T. J. Huppert, M. S. Allen, S. G. Diamond, and D. A. Boas, “Estimating cerebral oxygen metabolism from fMRI with a dynamic multicompartment Windkessel model,” Hum. Brain Mapp. (2008).
[PubMed]

G. Boverman, Q. Fang, S. A. Carp, E. L. Miller, D. H. Brooks, J. Selb, R. H. Moore, D. B. Kopans, and D. A. Boas, “Spatio-temporal imaging of the hemoglobin in the compressed breast with diffuse optical tomography,” Phys. Med. Biol. 52, 3619-3641 (2007).
[CrossRef] [PubMed]

A. Custo, W. M. Wells III, A. H. Barnett, E. M. Hillman, and D. A. Boas, “Effective scattering coefficient of the cerebral spinal fluid in adult head models for diffuse optical imaging,” Appl. Opt. 45, 4747-4755 (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, 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, 88-101 (2006).
[CrossRef]

D. K. Joseph, T. J. Huppert, M. A. Franceschini, and D. A. Boas, “Design and validation of a time division multiplexed continuous wave diffuse optical tomography (DOT) system optimized for brain function imaging,” Appl. Opt. 45, 8142-8151 (2006).
[CrossRef] [PubMed]

T. J. Huppert, R. D. Hoge, S. G. Diamond, M. A. Franceschini, and D. A. Boas, “A temporal comparison of BOLD, ASL, and NIRS hemodynamic responses to motor stimuli in adult humans,” NeuroImage 29, 368-382 (2006).
[CrossRef]

T. Huppert, R. D. Hoge, A. M. Dale, M. A. Franceschini, and D. A. Boas, “A quantitative spatial comparison of diffuse optical imaging with BOLD- and ASL-based fMRI,” J. Biomed. Opt. 11, 064018 (2006).
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D. A. Boas and A. M. Dale, “Simulation study of magnetic resonance imaging-guided cortically constrained diffuse optical tomography of human brain function,” Appl. Opt. 44, 1957-1968 (2005).
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S. G. Diamond, T. J. Huppert, V. Kolehmainen, M. A. Franceschini, J. P. Kaipio, S. R. Arridge, and D. A. Boas, “Physiological system identification with the Kalman filter in diffuse optical tomography,” in Medical Image Computing and Computer-Assisted Intervention - MICCAI 2005, Vol. 3750 of Lecture Notes in Computer Science (Springer, 2005), pp. 649-656.
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T. Wilcox, H. Bortfeld, R. Woods, E. Wruck, and D. A. Boas, “Using near-infrared spectroscopy to assess neural activation during object processing in infants,” J. Biomed. Opt. 10, 11010 (2005).
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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, 011014 (2005).
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J. Selb, J. J. Stott, M. A. Franceschini, A. G. Sorensen, and D. A. Boas, “Improved sensitivity to cerebral hemodynamics during brain activation with a time-gated optical system: analytical model and experimental validation,” J. Biomed. Opt. 10, 011013 (2005).
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Y. Zhang, D. H. Brooks, and D. A. Boas, “A haemodynamic response function model in spatio-temporal diffuse optical tomography,” Phys. Med. Biol. 50, 4625-4644 (2005).
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R. D. Hoge, M. A. Franceschini, R. J. Covolan, T. Huppert, J. B. Mandeville, and D. A. Boas, “Simultaneous recording of task-induced changes in blood oxygenation, volume, and flow using diffuse optical imaging and arterial spin-labeling MRI,” NeuroImage 25, 701-707 (2005).
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A. Li, Q. Zhang, J. P. Culver, E. L. Miller, and D. A. Boas, “Reconstructing chromosphere concentration images directly by continuous-wave diffuse optical tomography,” Opt. Lett. 29, 256-258 (2004).
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Y. Zhang, D. H. Brooks, and D. A. Boas, “A haemodynamic response function model in spatio-temporal diffuse optical tomography,” Phys. Med. Biol. 50, 4625-4644 (2005).
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C. Terborg, S. Bramer, S. Harscher, M. Simon, and O. W. Witte, “Bedside assessment of cerebral perfusion reductions in patients with acute ischaemic stroke by near-infrared spectroscopy and indocyanine green,” J. Neurol. Neurosurg. Psychiatry 75, 38-42 (2004).
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Figures (8)

Fig. 1
Fig. 1

Typical setup for a NIRS experiment. (a) Sensitivity of a NIRS measurement determined by the propagation of light emanated from a source position and recorded by a detector placed several centimeters away. (b) Sample NIRS probe used to measure the primary motor cortex [10]. (c) Absorption spectrum (extinction coefficients) for oxyhemoglobin and deoxyhemoglobin over the range of wavelengths typically used for optical imaging [11].

Fig. 2
Fig. 2

Tomographic optical probe: sample arrangement for a tomographic-style probe [97] used for a study of visual activation. To acquire optical signals from multiple source–detector distances, a time-multiplexing scheme is used in which our system switches between three sets of laser on–off states and detector gains (lower right). In our current system, a complete cycle can be imaged at up to 3 Hz . Overlapping (tomographic) measurements provide more uniform spatial sensitivity and coverage of the underlying brain. The theoretical sensitivity profile for this probe is shown in the upper right-hand panel with contour lines at 5 dB intervals based on a semi-infinite homogeneous ( μ a = 0.1 cm 1 and μ s = 10 cm 1 ) slab geometry.

Fig. 3
Fig. 3

Physiological fluctuations in optical signals: physiology fluctuations are generally the dominant source of noise in NIRS measurements because of the superficial sensitivity of the technique. This figure illustrates cardiac, respiratory, and blood pressure (Mayer wave) oscillations recorded during a resting period for the subject. The data also demonstrate a motion artifact, where the probes shifted during recording and generated a large perturbation of the signal intensity.

Fig. 4
Fig. 4

Example of motion artifact removal by principal component (PCA) filtering. The raw data shown in (a) is experimental data measured in the inferior temporal cortex in an infant presented with an object recognition task [64]. The data were dominated by several motion artifacts that resulted from the movement of the probe on the head’s surface. These motion artifacts were highly covariant across the entire probe. (b) and (c) show the filtered data after the removal of the first and second principal components. After reduction of the covariance in the data by the removal of these components, the motion artifacts were greatly reduced. The gray shading indicates the presentation of stimuli.

Fig. 5
Fig. 5

Removing systemic physiological noise by PCA: NIRS measurements are often sensitive to systemic fluctuations arising from blood pressure changes, respiration, or the cardiac cycle. As a complication, this physiology may change during the performance of intense stimulus tasks, such as motor activity. As is shown in (a), these changes can result in a systemic response giving the appearance of global functional activation. (b) When the PCA filter described in the text is used to remove this systemic effect by reducing this covariance, the activation region is localized to the motor area. These data were previously published in [69].

Fig. 6
Fig. 6

Linear filtering of systemic physiology based on auxiliary measurements. Here we demonstrate the use of auxiliary measurements to improve the estimate of the functional hemodynamic response. Without the removal of these signals by bandpass filtering, the calculated hemodynamic response is heavily corrupted by these fluctuations. When the cardiac cycle and blood pressure fluctuations are used as additional regression variables, the functional hemodynamic response is more clearly separated from the effects of these systemic variables. The regression of this data with the external physiological measurements allows the separation of the data into the functional and systemic contributions. The panels on the right (b)–(d) show the separated system components for the evoked response (b), the cardiac related response (c) and the blood pressure component (d) composing the raw data (a).

Fig. 7
Fig. 7

Screen shot of the HomER program. The layout of the HomER program is based around an interactive graphical display of the NIRS probe, shown in the upper right (b). The user specifies this probe geometry within the data file imported into HomER as described in the text. By selecting source (displayed as “x”) or detector (‘o”) positions on this probe layout, the user navigates through the display of their data. The original data are shown in (a) and the average evoked response is shown in (c). The data presented are described in [134] and were recorded during a 20 s finger-tapping task. Data shown are from a single run of one of the subjects.

Fig. 8
Fig. 8

Levels of analysis in HomER. The HomER program architecture is based on three levels of analysis and processing: a single experimental scan, a session (single subject), and group analysis. At each level, various options for processing, visualization, and data management are offered.

Equations (15)

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Φ ^ i j λ ( t ) = r ( γ ) Φ ^ i j λ ( 0 ) e Δ μ λ abs ( r ) · d r ,
Δ OD λ i j ( t ) = Ln [ Φ ^ i j λ ( 0 ) Φ ^ i j λ ( t ) ] ,
Δ OD i j λ = L i j λ ( ε HbR λ Δ [ HbR ] + ε HbO 2 λ Δ [ HbO 2 ] ) ,
Δ OD i j λ = L i j λ DPF λ ( ε HbR λ Δ [ HbR ] + ε HbO 2 λ Δ [ HbO 2 ] ) ,
[ Δ [ HbO 2 ] Δ [ HbR ] ] = ( E T R 1 E ) 1 E T R 1 [ Δ OD λ 1 / L λ 1 DPF λ 1 Δ OD λ N / L λ N DPF λ N ] ,
E = [ ε HbO 2 λ 1 ε HbR λ 1 ε HbO 2 λ N ε HbR λ N ] .
C = U · Σ · V H ,
C = [ Δ OD { src - det } λ 1 Δ OD { src - det } λ 2 ] [ Δ OD { src - det } λ 1 ] .
Δ OD Filtered = Δ OD f t SVD ,
f t SVD = i w i · σ i · v i t ,
W = Δ OD · V · Σ 1 ,
Y = U stim β response + U Cardiac β Cardiac + U BP β BP + .
Y = U stim β + e ,
Y = G · β + e .
β ^ = ( G t · G ) 1 · G t · Y .

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