Abstract

Brain activity can predict a person’s level of engagement in an attentional task. However, estimates of brain activity are often confounded by measurement artifacts and systemic physiological noise. The optimal method for filtering this noise – thereby increasing such state prediction accuracy – remains unclear. To investigate this, we asked study participants to perform an attentional task while we monitored their brain activity with functional near infrared spectroscopy (fNIRS). We observed higher state prediction accuracy when noise in the fNIRS hemoglobin [Hb] signals was filtered with a non-stationary (adaptive) model as compared to static regression (84% ± 6% versus 72% ± 15%).

© 2016 Optical Society of America

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2015 (1)

S. Brigadoi and R. J. Cooper, “How short is short? Optimum source-detector distance for short-separation channels in functional near-infrared spectroscopy,” Neurophotonics 2(2), 025005 (2015).
[Crossref] [PubMed]

2014 (1)

L. Gagnon, M. A. Yücel, D. A. Boas, and R. J. Cooper, “Further improvement in reducing superficial contamination in NIRS using double short separation measurements,” Neuroimage 85(Pt 1), 127–135 (2014).
[Crossref] [PubMed]

2013 (4)

L. Jiménez and A. Méndez, “It is not what you expect: Dissociating conflict adaptation from expectancies in a Stroop task,” J. Exp. Psychol. Hum. Percept. Perform. 39(1), 271–284 (2013).
[Crossref] [PubMed]

G. J. Thompson, M. E. Magnuson, M. D. Merritt, H. Schwarb, W. J. Pan, A. McKinley, L. D. Tripp, E. H. Schumacher, and S. D. Keilholz, “Short-Time Windows of Correlation Between Large-Scale Functional Brain Networks Predict Vigilance Intraindividually and Interindividually,” Hum. Brain Mapp. 34(12), 3280–3298 (2013).
[Crossref] [PubMed]

R. M. Hutchison, T. Womelsdorf, E. A. Allen, P. A. Bandettini, V. D. Calhoun, M. Corbetta, S. Della Penna, J. H. Duyn, G. H. Glover, J. Gonzalez-Castillo, D. A. Handwerker, S. Keilholz, V. Kiviniemi, D. A. Leopold, F. de Pasquale, O. Sporns, M. Walter, and C. Chang, “Dynamic functional connectivity: promise, issues, and interpretations,” Neuroimage 80, 360–378 (2013).
[Crossref] [PubMed]

S. Fairclough, K. Gilleade, K. C. Ewing, and J. Roberts, “Capturing user engagement via psychophysiology: measures and mechanisms for biocybernetic adaptation,” Int. J. of Autonomous and Adaptive Communications Systems. 6(1), 63–79 (2013).
[Crossref]

2012 (7)

A. Sassaroli, M. Pierro, P. R. Bergethon, and S. Fantini, “Low-Frequency Spontaneous Oscillations of Cerebral Hemodynamics Investigated With Near-Infrared Spectroscopy: A Review,” IEEE J. Sel. Top. Quantum Electron. 18(4), 1478–1492 (2012).
[Crossref]

E. Kirilina, A. Jelzow, A. Heine, M. Niessing, H. Wabnitz, R. Brühl, B. Ittermann, A. M. Jacobs, and I. Tachtsidis, “The physiological origin of task-evoked systemic artefacts in functional near infrared spectroscopy,” Neuroimage 61(1), 70–81 (2012).
[Crossref] [PubMed]

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

L. Gagnon, M. A. Yücel, M. Dehaes, R. J. Cooper, K. L. Perdue, J. Selb, T. J. Huppert, R. D. Hoge, and D. A. Boas, “Quantification of the cortical contribution to the NIRS signal over the motor cortex using concurrent NIRS-fMRI measurements,” Neuroimage 59(4), 3933–3940 (2012).
[Crossref] [PubMed]

J. Smallwood, K. Brown, B. Baird, and J. W. Schooler, “Cooperation between the default mode network and the frontal-parietal network in the production of an internal train of thought,” Brain Res. 1428, 60–70 (2012).
[Crossref] [PubMed]

M. Aqil, K. S. Hong, M. Y. Jeong, and S. S. Ge, “Cortical brain imaging by adaptive filtering of NIRS signals,” Neurosci. Lett. 514(1), 35–41 (2012).
[Crossref] [PubMed]

J. A. De Havas, S. Parimal, C. S. Soon, and M. W. L. Chee, “Sleep deprivation reduces default mode network connectivity and anti-correlation during rest and task performance,” Neuroimage 59(2), 1745–1751 (2012).
[Crossref] [PubMed]

2011 (7)

J. Prado and D. H. Weissman, “Heightened interactions between a key default-mode region and a key task-positive region are linked to suboptimal current performance but to enhanced future performance,” Neuroimage 56(4), 2276–2282 (2011).
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J. Kang, L. Wang, C. Yan, J. Wang, X. Liang, and Y. He, “Characterizing dynamic functional connectivity in the resting brain using variable parameter regression and Kalman filtering approaches,” Neuroimage 56(3), 1222–1234 (2011).
[Crossref] [PubMed]

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

M. Gruberger, E. Ben-Simon, Y. Levkovitz, A. Zangen, and T. Hendler, “Towards a neuroscience of mind-wandering,” Front. Hum. Neurosci. 5(56), 56 (2011).
[PubMed]

G. Deco, V. K. Jirsa, and A. R. McIntosh, “Emerging concepts for the dynamical organization of resting-state activity in the brain,” Nat. Rev. Neurosci. 12(1), 43–56 (2011).
[Crossref] [PubMed]

W. Majeed, M. Magnuson, W. Hasenkamp, H. Schwarb, E. H. Schumacher, L. Barsalou, and S. D. Keilholz, “Spatiotemporal dynamics of low frequency BOLD fluctuations in rats and humans,” Neuroimage 54(2), 1140–1150 (2011).
[Crossref] [PubMed]

C. C. Chang and C. J. Lin, “LIBSVM: a library for support vector machines,” ACM Trans. Intell. Syst. Technol. 2(3), 1–27 (2011).
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2010 (3)

F. Orihuela-Espina, D. R. Leff, D. R. C. James, A. W. Darzi, and G. Z. Yang, “Quality control and assurance in functional near infrared spectroscopy (fNIRS) experimentation,” Phys. Med. Biol. 55(13), 3701–3724 (2010).
[Crossref] [PubMed]

C. Chang and G. H. Glover, “Time-frequency dynamics of resting-state brain connectivity measured with fMRI,” Neuroimage 50(1), 81–98 (2010).
[Crossref] [PubMed]

R. C. Mesquita, M. A. Franceschini, and D. A. Boas, “Resting state functional connectivity of the whole head with near-infrared spectroscopy,” Biomed. Opt. Express 1(1), 324–336 (2010).
[Crossref] [PubMed]

2009 (5)

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

I. Tachtsidis, T. S. Leung, A. Chopra, P. H. Koh, C. B. Reid, and C. E. Elwell, “False positives in functional near-infrared topography,” Adv. Exp. Med. Biol. 645, 307–314 (2009).
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K. Christoff, A. M. Gordon, J. Smallwood, R. Smith, and J. W. Schooler, “Experience sampling during fMRI reveals default network and executive system contributions to mind wandering,” Proc. Natl. Acad. Sci. U.S.A. 106(21), 8719–8724 (2009).
[Crossref] [PubMed]

A. F. Abdelnour and T. Huppert, “Real-time imaging of human brain function by near-infrared spectroscopy using an adaptive general linear model,” Neuroimage 46(1), 133–143 (2009).
[Crossref] [PubMed]

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

2008 (4)

F. Matthews, B. A. Pearlmutter, T. E. Ward, C. Soraghan, and C. Markham, “Hemodynamics for braincomputer interfaces,” IEEE Signal Process. Mag. 25(1), 87–94 (2008).
[Crossref]

U. E. Emir, C. Ozturk, and A. Akin, “Multimodal investigation of fMRI and fNIRS derived breath hold BOLD signals with an expanded balloon model,” Physiol. Meas. 29(1), 49–63 (2008).
[Crossref] [PubMed]

M. W. L. Chee, J. C. Tan, H. Zheng, S. Parimal, D. H. Weissman, V. Zagorodnov, and D. F. Dinges, “Lapsing during sleep deprivation is associated with distributed changes in brain activation,” J. Neurosci. 28(21), 5519–5528 (2008).
[Crossref] [PubMed]

A. M. C. Kelly, L. Q. Uddin, B. B. Biswal, F. X. Castellanos, and M. P. Milham, “Competition between functional brain networks mediates behavioral variability,” Neuroimage 39(1), 527–537 (2008).
[Crossref] [PubMed]

2007 (7)

M. E. Raichle and A. Z. Snyder, “A default mode of brain function: a brief history of an evolving idea,” Neuroimage 37(4), 1083–1099 (2007).
[Crossref] [PubMed]

R. Vohn, B. Fimm, J. Weber, R. Schnitker, A. Thron, W. Spijkers, K. Willmes, and W. Sturm, “Management of attentional resources in within-modal and cross-modal divided attention tasks: an fMRI study,” Hum. Brain Mapp. 28(12), 1267–1275 (2007).
[Crossref] [PubMed]

Q. Zhang, E. N. Brown, and G. E. Strangman, “Adaptive filtering for global interference cancellation and real-time recovery of evoked brain activity: a Monte Carlo simulation study,” J. Biomed. Opt. 12(4), 044014 (2007).
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Q. Zhang, E. N. Brown, and G. E. Strangman, “Adaptive filtering to reduce global interference in evoked brain activity detection: a human subject case study,” J. Biomed. Opt. 12(6), 064009 (2007).
[Crossref] [PubMed]

P. M. Arenth, J. H. Ricker, and M. T. Schultheis, “Applications of Functional Near-Infrared Spectroscopy (fNIRS) to Neurorehabilitation of cognitive disabilities,” Clin. Neuropsychol. 21(1), 38–57 (2007).
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S. M. Coyle, T. E. Ward, and C. M. Markham, “Brain-computer interface using a simplified functional near-infrared spectroscopy system,” J. Neural Eng. 4(3), 219–226 (2007).
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S. M. LaConte, S. J. Peltier, and X. P. P. Hu, “Real-time fMRI using brain-state classification,” Hum. Brain Mapp. 28(10), 1033–1044 (2007).
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2006 (9)

C. Julien, “The enigma of Mayer waves: Facts and models,” Cardiovasc. Res. 70(1), 12–21 (2006).
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P. Fransson, “How default is the default mode of brain function? Further evidence from intrinsic BOLD signal fluctuations,” Neuropsychologia 44(14), 2836–2845 (2006).
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G. Bush and L. M. Shin, “The Multi-Source Interference Task: an fMRI task that reliably activates the cingulo-frontal-parietal cognitive/attention network,” Nat. Protoc. 1(1), 308–313 (2006).
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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, “Dynamic physiological modeling for functional diffuse optical tomography,” Neuroimage 30(1), 88–101 (2006).
[Crossref] [PubMed]

T. J. Huppert, R. D. Hoge, A. M. Dale, M. A. Franceschini, and D. A. Boas, “Quantitative spatial comparison of diffuse optical imaging with blood oxygen level-dependent and arterial spin labeling-based functional magnetic resonance imaging,” J. Biomed. Opt. 11(6), 064018 (2006).
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M. L. Schroeter, T. Kupka, T. Mildner, K. Uludağ, and D. Y. von Cramon, “Investigating the post-stimulus undershoot of the BOLD signal--a simultaneous fMRI and fNIRS study,” Neuroimage 30(2), 349–358 (2006).
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S. C. Bunce, M. Izzetoglu, K. Izzetoglu, B. Onaral, and K. Pourrezaei, “Functional near-infrared spectroscopy,” IEEE Eng. Med. Biol. Mag. 25(4), 54–62 (2006).
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D. H. Weissman, K. C. Roberts, K. M. Visscher, and M. G. Woldorff, “The neural bases of momentary lapses in attention,” Nat. Neurosci. 9(7), 971–978 (2006).
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N. U. F. Dosenbach, K. M. Visscher, E. D. Palmer, F. M. Miezin, K. K. Wenger, H. C. Kang, E. D. Burgund, A. L. Grimes, B. L. Schlaggar, and S. E. Petersen, “A core system for the implementation of task sets,” Neuron 50(5), 799–812 (2006).
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2005 (9)

M. D. Fox, A. Z. Snyder, J. L. Vincent, M. Corbetta, D. C. Van Essen, and M. E. Raichle, “The human brain is intrinsically organized into dynamic, anticorrelated functional networks,” Proc. Natl. Acad. Sci. U.S.A. 102(27), 9673–9678 (2005).
[Crossref] [PubMed]

S. P. A. Drummond, A. Bischoff-Grethe, D. F. Dinges, L. Ayalon, S. C. Mednick, and M. J. Meloy, “The neural basis of the psychomotor vigilance task,” Sleep 28(9), 1059–1068 (2005).
[PubMed]

S. P. A. Drummond, M. J. Meloy, M. A. Yanagi, H. J. Orff, and G. G. Brown, “Compensatory recruitment after sleep deprivation and the relationship with performance,” Psychiatry Res. 140(3), 211–223 (2005).
[Crossref] [PubMed]

H. R. Lieberman, G. P. Bathalon, C. M. Falco, C. A. Morgan, P. J. Niro, and W. J. Tharion, “The fog of war: decrements in cognitive performance and mood associated with combat-like stress,” Aviat. Space Environ. Med. 76(7Suppl), C7–C14 (2005).
[PubMed]

K. Nebel, H. Wiese, P. Stude, A. de Greiff, H. C. Diener, and M. Keidel, “On the neural basis of focused and divided attention,” Brain Res. Cogn. Brain Res. 25(3), 760–776 (2005).
[Crossref] [PubMed]

A. P. Gibson, J. C. Hebden, and S. R. Arridge, “Recent advances in diffuse optical imaging,” Phys. Med. Biol. 50(4), R1–R43 (2005).
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E. Gratton, V. Toronov, U. Wolf, M. Wolf, and A. Webb, “Measurement of brain activity by near-infrared light,” J. Biomed. Opt. 10(1), 011008 (2005).
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T. Kojima, H. Tsunashima, T. Shiozawa, H. Takada, and T. Sakai, “Measurement of train driver’s brain activity by functional near-infrared spectroscopy (fNIRS),” Opt. Quantum Electron. 37(13–15), 1319–1338 (2005).
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J. F. Stins, W. M. A. van Leeuwen, and E. J. C. de Geus, “The Multi-Source Interference Task: The Effect of Randomization,” J. Clin. Exp. Neuropsychol. 27(6), 711–717 (2005).
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2004 (3)

K. Izzetoglu, S. Bunce, B. Onaral, K. Pourrezaei, and B. Chance, “Functional optical brain imaging using near-infrared during cognitive tasks,” Int. J. Hum. Comput. Interact. 17(2), 211–227 (2004).
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T. Schnell, Y. Kwon, S. Merchant, and T. Etherington, “Improved flight technical performance in flight decks equipped with synthetic vision information system displays,” Int. J. Aviat. Psychol. 14(1), 79–102 (2004).
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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).
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2003 (6)

H. Obrig and A. Villringer, “Beyond the visible--Imaging the human brain with light,” J. Cereb. Blood Flow Metab. 23(1), 1–18 (2003).
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M. Moosmann, P. Ritter, I. Krastel, A. Brink, S. Thees, F. Blankenburg, B. Taskin, H. Obrig, and A. Villringer, “Correlates of alpha rhythm in functional magnetic resonance imaging and near infrared spectroscopy,” Neuroimage 20(1), 145–158 (2003).
[Crossref] [PubMed]

U. Mayr, E. Awh, and P. Laurey, “Conflict adaptation effects in the absence of executive control,” Nat. Neurosci. 6(5), 450–452 (2003).
[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(4), 865–879 (2003).
[Crossref] [PubMed]

V. Kolehmainen, S. Prince, S. R. Arridge, and J. P. Kaipio, “State-estimation approach to the nonstationary optical tomography problem,” J. Opt. Soc. Am. A 20(5), 876–889 (2003).
[Crossref] [PubMed]

M. D. Greicius, B. Krasnow, A. L. Reiss, and V. Menon, “Functional connectivity in the resting brain: A network analysis of the default mode hypothesis,” Proc. Natl. Acad. Sci. U.S.A. 100(1), 253–258 (2003).
[Crossref] [PubMed]

2002 (2)

G. Strangman, J. P. Culver, J. H. Thompson, and D. A. Boas, “A quantitative comparison of simultaneous BOLD fMRI and NIRS recordings during functional brain activation,” Neuroimage 17(2), 719–731 (2002).
[Crossref] [PubMed]

G. Strangman, D. A. Boas, and J. P. Sutton, “Non-invasive neuroimaging using near-infrared light,” Biol. Psychiatry 52(7), 679–693 (2002).
[Crossref] [PubMed]

2001 (2)

D. A. Boas, T. Gaudette, G. Strangman, X. Cheng, J. J. A. Marota, and J. B. Mandeville, “The accuracy of near infrared spectroscopy and imaging during focal changes in cerebral hemodynamics,” Neuroimage 13(1), 76–90 (2001).
[Crossref] [PubMed]

M. E. Raichle, A. M. MacLeod, A. Z. Snyder, W. J. Powers, D. A. Gusnard, and G. L. Shulman, “A default mode of brain function,” Proc. Natl. Acad. Sci. U.S.A. 98(2), 676–682 (2001).
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2000 (1)

A. W. MacDonald, J. D. Cohen, V. A. Stenger, and C. S. Carter, “Dissociating the Role of the Dorsolateral Prefrontal and Anterior Cingulate Cortex in Cognitive Control,” Science 288(5472), 1835–1838 (2000).
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1998 (1)

C. J. Burgess, “A Tutorial on Support Vector Machines for Pattern Recognition,” Data Min. Knowl. Discov. 2(2), 121–167 (1998).
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1996 (2)

A. Duncan, J. H. Meek, M. Clemence, C. E. Elwell, P. Fallon, L. Tyszczuk, M. Cope, and D. T. Delpy, “Measurement of cranial optical path length as a function of age using phase resolved near infrared spectroscopy,” Pediatr. Res. 39(5), 889–894 (1996).
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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(5), 817–826 (1996).
[Crossref] [PubMed]

1995 (2)

A. T. Pope, E. H. Bogart, and D. S. Bartolome, “Biocybernetic system evaluates indices of operator engagement in automated task,” Biol. Psychol. 40(1-2), 187–195 (1995).
[Crossref] [PubMed]

B. Biswal, F. Z. Yetkin, V. M. Haughton, and J. S. Hyde, “Functional connectivity in the motor cortex of resting human brain using echo-planar MRI,” Magn. Reson. Med. 34(4), 537–541 (1995).
[Crossref] [PubMed]

1988 (1)

D. T. Delpy, M. Cope, P. van der Zee, S. Arridge, S. Wray, and J. Wyatt, “Estimation of optical pathlength through tissue from direct time of flight measurement,” Phys. Med. Biol. 33(12), 1433–1442 (1988).
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1960 (1)

R. E. Kalman, “A new approach to linear filtering and prediction problems,” J. Basic Eng. 82(1), 35–45 (1960).
[Crossref]

Abdelnour, A. F.

A. F. Abdelnour and T. Huppert, “Real-time imaging of human brain function by near-infrared spectroscopy using an adaptive general linear model,” Neuroimage 46(1), 133–143 (2009).
[Crossref] [PubMed]

Akin, A.

U. E. Emir, C. Ozturk, and A. Akin, “Multimodal investigation of fMRI and fNIRS derived breath hold BOLD signals with an expanded balloon model,” Physiol. Meas. 29(1), 49–63 (2008).
[Crossref] [PubMed]

Allen, E. A.

R. M. Hutchison, T. Womelsdorf, E. A. Allen, P. A. Bandettini, V. D. Calhoun, M. Corbetta, S. Della Penna, J. H. Duyn, G. H. Glover, J. Gonzalez-Castillo, D. A. Handwerker, S. Keilholz, V. Kiviniemi, D. A. Leopold, F. de Pasquale, O. Sporns, M. Walter, and C. Chang, “Dynamic functional connectivity: promise, issues, and interpretations,” Neuroimage 80, 360–378 (2013).
[Crossref] [PubMed]

Aqil, M.

M. Aqil, K. S. Hong, M. Y. Jeong, and S. S. Ge, “Cortical brain imaging by adaptive filtering of NIRS signals,” Neurosci. Lett. 514(1), 35–41 (2012).
[Crossref] [PubMed]

Arenth, P. M.

P. M. Arenth, J. H. Ricker, and M. T. Schultheis, “Applications of Functional Near-Infrared Spectroscopy (fNIRS) to Neurorehabilitation of cognitive disabilities,” Clin. Neuropsychol. 21(1), 38–57 (2007).
[Crossref] [PubMed]

Arridge, S.

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

Arridge, S. R.

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

A. P. Gibson, J. C. Hebden, and S. R. Arridge, “Recent advances in diffuse optical imaging,” Phys. Med. Biol. 50(4), R1–R43 (2005).
[Crossref] [PubMed]

V. Kolehmainen, S. Prince, S. R. Arridge, and J. P. Kaipio, “State-estimation approach to the nonstationary optical tomography problem,” J. Opt. Soc. Am. A 20(5), 876–889 (2003).
[Crossref] [PubMed]

Awh, E.

U. Mayr, E. Awh, and P. Laurey, “Conflict adaptation effects in the absence of executive control,” Nat. Neurosci. 6(5), 450–452 (2003).
[PubMed]

Ayalon, L.

S. P. A. Drummond, A. Bischoff-Grethe, D. F. Dinges, L. Ayalon, S. C. Mednick, and M. J. Meloy, “The neural basis of the psychomotor vigilance task,” Sleep 28(9), 1059–1068 (2005).
[PubMed]

Baird, B.

J. Smallwood, K. Brown, B. Baird, and J. W. Schooler, “Cooperation between the default mode network and the frontal-parietal network in the production of an internal train of thought,” Brain Res. 1428, 60–70 (2012).
[Crossref] [PubMed]

Bandettini, P. A.

R. M. Hutchison, T. Womelsdorf, E. A. Allen, P. A. Bandettini, V. D. Calhoun, M. Corbetta, S. Della Penna, J. H. Duyn, G. H. Glover, J. Gonzalez-Castillo, D. A. Handwerker, S. Keilholz, V. Kiviniemi, D. A. Leopold, F. de Pasquale, O. Sporns, M. Walter, and C. Chang, “Dynamic functional connectivity: promise, issues, and interpretations,” Neuroimage 80, 360–378 (2013).
[Crossref] [PubMed]

Barsalou, L.

W. Majeed, M. Magnuson, W. Hasenkamp, H. Schwarb, E. H. Schumacher, L. Barsalou, and S. D. Keilholz, “Spatiotemporal dynamics of low frequency BOLD fluctuations in rats and humans,” Neuroimage 54(2), 1140–1150 (2011).
[Crossref] [PubMed]

Bartolome, D. S.

A. T. Pope, E. H. Bogart, and D. S. Bartolome, “Biocybernetic system evaluates indices of operator engagement in automated task,” Biol. Psychol. 40(1-2), 187–195 (1995).
[Crossref] [PubMed]

Bathalon, G. P.

H. R. Lieberman, G. P. Bathalon, C. M. Falco, C. A. Morgan, P. J. Niro, and W. J. Tharion, “The fog of war: decrements in cognitive performance and mood associated with combat-like stress,” Aviat. Space Environ. Med. 76(7Suppl), C7–C14 (2005).
[PubMed]

Ben-Simon, E.

M. Gruberger, E. Ben-Simon, Y. Levkovitz, A. Zangen, and T. Hendler, “Towards a neuroscience of mind-wandering,” Front. Hum. Neurosci. 5(56), 56 (2011).
[PubMed]

Bergethon, P. R.

A. Sassaroli, M. Pierro, P. R. Bergethon, and S. Fantini, “Low-Frequency Spontaneous Oscillations of Cerebral Hemodynamics Investigated With Near-Infrared Spectroscopy: A Review,” IEEE J. Sel. Top. Quantum Electron. 18(4), 1478–1492 (2012).
[Crossref]

Bischoff-Grethe, A.

S. P. A. Drummond, A. Bischoff-Grethe, D. F. Dinges, L. Ayalon, S. C. Mednick, and M. J. Meloy, “The neural basis of the psychomotor vigilance task,” Sleep 28(9), 1059–1068 (2005).
[PubMed]

Biswal, B.

B. Biswal, F. Z. Yetkin, V. M. Haughton, and J. S. Hyde, “Functional connectivity in the motor cortex of resting human brain using echo-planar MRI,” Magn. Reson. Med. 34(4), 537–541 (1995).
[Crossref] [PubMed]

Biswal, B. B.

A. M. C. Kelly, L. Q. Uddin, B. B. Biswal, F. X. Castellanos, and M. P. Milham, “Competition between functional brain networks mediates behavioral variability,” Neuroimage 39(1), 527–537 (2008).
[Crossref] [PubMed]

Blankenburg, F.

M. Moosmann, P. Ritter, I. Krastel, A. Brink, S. Thees, F. Blankenburg, B. Taskin, H. Obrig, and A. Villringer, “Correlates of alpha rhythm in functional magnetic resonance imaging and near infrared spectroscopy,” Neuroimage 20(1), 145–158 (2003).
[Crossref] [PubMed]

Boas, D. A.

L. Gagnon, M. A. Yücel, D. A. Boas, and R. J. Cooper, “Further improvement in reducing superficial contamination in NIRS using double short separation measurements,” Neuroimage 85(Pt 1), 127–135 (2014).
[Crossref] [PubMed]

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Appl. Opt. (1)

Aviat. Space Environ. Med. (1)

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Figures (8)

Fig. 1
Fig. 1

Optodes used to interrogate the DLPFC and MFG. Source optodes (red) are arranged around detector optodes (green), shown schematically with respect to the International 10-20 locations.

Fig. 2
Fig. 2

Head probe arrays for the DLPFC (left) and the MFG (lower right). Prisms and light pipe assemblies (top right) were used with optical fibers.

Fig. 3
Fig. 3

Schematic of the optical components used in the light pipe assemblies.

Fig. 4
Fig. 4

Average beta fit parameters quantifying the predicted activation and nuisance found on each channel for the static regression processing case are shown for [HbO] measures by location and run. Column numbers in each image scale plot correspond to probe array channel numbers.

Fig. 5
Fig. 5

Average beta fit parameters quantifying the predicted activation and nuisance found on each channel for the static regression processing case are shown for [HbR] measures by location and run. Column numbers in each image scale plot correspond to probe array channel numbers.

Fig. 6
Fig. 6

State prediction accuracy for seven participants with each bar representing one participant, for various fNIRS processing methods: as-measured, static, adaptive and physiological. Error bars show the standard deviation across the 4 runs.

Fig. 7
Fig. 7

In the top panel, black markers at + 1 indicate a classifier output of a more engaged or ‘working’ state, while those at −1 indicate a less engaged or ‘resting’ state. A prediction is made at every instance. The dotted black line indicates the probability estimates of the classifier predictions. In the bottom panel, the six classifier input features generated using static processing are plotted. Truth labels are indicated by the green task indicator function in both the top and bottom panels.

Fig. 8
Fig. 8

In the top panel, black markers at + 1 indicate a classifier output of a more engaged or ‘working’ state, while those at −1 indicate a less engaged or ‘resting’ state. A prediction is made at every instance. The dotted black line indicates the probability estimates of the classifier predictions. In the bottom panel, the six classifier input features generated using adaptive processing are plotted. Truth labels are indicated by the green task indicator function in both the top and bottom panels.

Tables (1)

Tables Icon

Table 1 State prediction accuracy and statistics for various fNIRS processing methods and features

Metrics