Abstract

Functional near-infrared spectroscopy (fNIRS) is an increasingly popular technology for studying brain function because it is non-invasive, non-irradiating and relatively inexpensive. Further, fNIRS potentially allows measurement of hemodynamic activity with high temporal resolution (milliseconds) and in naturalistic settings. However, in comparison with other imaging modalities, namely fMRI, fNIRS has a significant drawback: limited sensitivity to hemodynamic changes in deep-brain regions. To overcome this limitation, we developed a computational method to infer deep-brain activity using fNIRS measurements of cortical activity. Using simultaneous fNIRS and fMRI, we measured brain activity in 17 participants as they completed three cognitive tasks. A support vector regression (SVR) learning algorithm was used to predict activity in twelve deep-brain regions using information from surface fNIRS measurements. We compared these predictions against actual fMRI-measured activity using Pearson’s correlation to quantify prediction performance. To provide a benchmark for comparison, we also used fMRI measurements of cortical activity to infer deep-brain activity. When using fMRI-measured activity from the entire cortex, we were able to predict deep-brain activity in the fusiform cortex with an average correlation coefficient of 0.80 and in all deep-brain regions with an average correlation coefficient of 0.67. The top 15% of predictions using fNIRS signal achieved an accuracy of 0.7. To our knowledge, this study is the first to investigate the feasibility of using cortical activity to infer deep-brain activity. This new method has the potential to extend fNIRS applications in cognitive and clinical neuroscience research.

© 2015 Optical Society of America

Full Article  |  PDF Article
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References

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2012 (1)

X. Cui, D. M. Bryant, and A. L. Reiss, “NIRS-based hyperscanning reveals increased interpersonal coherence in superior frontal cortex during cooperation,” Neuroimage 59(3), 2430–2437 (2012).
[Crossref] [PubMed]

2011 (5)

X. Cui, S. Bray, D. M. Bryant, G. H. Glover, and A. L. Reiss, “A quantitative comparison of NIRS and fMRI across multiple cognitive tasks,” Neuroimage 54(4), 2808–2821 (2011).
[Crossref] [PubMed]

A. S. Garrett, A. L. Reiss, D. J. Miklowitz, T. K. Acquaye, V. E. Cosgrove, M. K. Singh, M. E. Howe, R. G. Kelley, D. Taylor, E. George, and et al.., “Changes in Brain Activation Following Family-Focused Treatment in Youth At-Risk for Bipolar Disorder,” Biol. Psychiatry 69(9), 163S (2011).

C. C. Chang and C. J. Lin, “LIBSVM: A Library for Support Vector Machines,” ACM Trans. Intelligent Systems Technology. 2(3), 1–27 (2011).
[Crossref]

L. Minati, E. Visani, N. G. Dowell, N. Medford, and H. D. Critchley, “Variability comparison of simultaneous brain near-infrared spectroscopy and functional magnetic resonance imaging during visual stimulation,” J. Med. Eng. Technol. 35(6-7), 370–376 (2011).
[Crossref] [PubMed]

F. Tian, H. Niu, B. Khan, G. Alexandrakis, K. Behbehani, and H. Liu, “Enhanced functional brain imaging by using adaptive filtering and a depth compensation algorithm in diffuse optical tomography,” IEEE Trans. Med. Imaging 30(6), 1239–1251 (2011).
[Crossref] [PubMed]

2010 (4)

B. W. Haas, F. Hoeft, Y. M. Searcy, D. Mills, U. Bellugi, and A. L. Reiss, “Individual differences in social behavior predict amygdala response to fearful facial expressions in Williams syndrome,” Neuropsychologia 48(5), 1283–1288 (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]

B. B. Biswal, M. Mennes, X. N. Zuo, S. Gohel, C. Kelly, S. M. Smith, C. F. Beckmann, J. S. Adelstein, R. L. Buckner, S. Colcombe, A. M. Dogonowski, M. Ernst, D. Fair, M. Hampson, M. J. Hoptman, J. S. Hyde, V. J. Kiviniemi, R. Kötter, S. J. Li, C. P. Lin, M. J. Lowe, C. Mackay, D. J. Madden, K. H. Madsen, D. S. Margulies, H. S. Mayberg, K. McMahon, C. S. Monk, S. H. Mostofsky, B. J. Nagel, J. J. Pekar, S. J. Peltier, S. E. Petersen, V. Riedl, S. A. Rombouts, B. Rypma, B. L. Schlaggar, S. Schmidt, R. D. Seidler, G. J. Siegle, C. Sorg, G. J. Teng, J. Veijola, A. Villringer, M. Walter, L. Wang, X. C. Weng, S. Whitfield-Gabrieli, P. Williamson, C. Windischberger, Y. F. Zang, H. Y. Zhang, F. X. Castellanos, and M. P. Milham, “Toward discovery science of human brain function,” Proc. Natl. Acad. Sci. U.S.A. 107(10), 4734–4739 (2010).
[Crossref] [PubMed]

C. Ecker, A. Marquand, J. Mourão-Miranda, P. Johnston, E. M. Daly, M. J. Brammer, S. Maltezos, C. M. Murphy, D. Robertson, S. C. Williams, and D. G. Murphy, “Describing the Brain in Autism in Five Dimensions--Magnetic Resonance Imaging-Assisted Diagnosis of Autism Spectrum Disorder Using a Multiparameter Classification Approach,” J. Neurosci. 30(32), 10612–10623 (2010).
[Crossref] [PubMed]

2009 (6)

R. A. Poldrack, Y. O. Halchenko, and S. J. Hanson, “Decoding the large-scale structure of brain function by classifying mental States across individuals,” Psychol. Sci. 20(11), 1364–1372 (2009).
[Crossref] [PubMed]

R. Chaves, J. Ramírez, J. M. Górriz, M. López, D. Salas-Gonzalez, I. Alvarez, and F. Segovia, “SVM-based computer-aided diagnosis of the Alzheimer’s disease using t-test NMSE feature selection with feature correlation weighting,” Neurosci. Lett. 461(3), 293–297 (2009).
[Crossref] [PubMed]

F. Pereira, T. Mitchell, and M. Botvinick, “Machine learning classifiers and fMRI: a tutorial overview,” Neuroimage 45(1Suppl), S199–S209 (2009).
[Crossref] [PubMed]

E. Bullmore and O. Sporns, “Complex brain networks: graph theoretical analysis of structural and functional systems,” Nat. Rev. Neurosci. 10(3), 186–198 (2009).
[Crossref] [PubMed]

C. Chang, J. P. Cunningham, and G. H. Glover, “Influence of heart rate on the BOLD signal: The cardiac response function,” Neuroimage 44(3), 857–869 (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 (1)

S. Kloppel, C. M. Stonnington, C. Chu, B. Draganski, R. I. Scahill, J. D. Rohrer, N. C. Fox, C. R. Jack, J. Ashburner, and R. S. J. Frackowiak, “Automatic classification of MR scans in Alzheimers disease,” Brain 131(Pt 3), 681–689 (2008).

2007 (2)

F. Hoeft, A. Hernandez, S. Parthasarathy, C. L. Watson, S. S. Hall, and A. L. Reiss, “Fronto-striatal dysfunction and potential compensatory mechanisms in male adolescents with fragile X syndrome,” Hum. Brain Mapp. 28(6), 543–554 (2007).
[Crossref] [PubMed]

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

2006 (2)

S. Caspers, S. Geyer, A. Schleicher, H. Mohlberg, K. Amunts, and K. Zilles, “The human inferior parietal cortex: cytoarchitectonic parcellation and interindividual variability,” Neuroimage 33(2), 430–448 (2006).
[Crossref] [PubMed]

J. Steinbrink, A. Villringer, F. Kempf, D. Haux, S. Boden, and H. Obrig, “Illuminating the BOLD signal: combined fMRI-fNIRS studies,” Magn. Reson. Imaging 24(4), 495–505 (2006).
[Crossref] [PubMed]

2005 (2)

Y. Kamitani and F. Tong, “Decoding the visual and subjective contents of the human brain,” Nat. Neurosci. 8(5), 679–685 (2005).
[Crossref] [PubMed]

J. Mourão-Miranda, A. L. Bokde, C. Born, H. Hampel, and M. Stetter, “Classifying brain states and determining the discriminating activation patterns: Support Vector Machine on functional MRI data,” Neuroimage 28(4), 980–995 (2005).
[Crossref] [PubMed]

2004 (4)

A. J. Smola and B. Schölkopf, “A tutorial on support vector regression,” Stat. Comput. 14(3), 199–222 (2004).
[Crossref]

M. Okamoto, H. Dan, K. Shimizu, K. Takeo, T. Amita, I. Oda, I. Konishi, K. Sakamoto, S. Isobe, T. Suzuki, K. Kohyama, and I. Dan, “Multimodal assessment of cortical activation during apple peeling by NIRS and fMRI,” Neuroimage 21(4), 1275–1288 (2004).
[Crossref] [PubMed]

A. J. Smola and B. Scholkopf, “A tutorial on support vector regression,” Stat. Comput. 14(3), 199–222 (2004).
[Crossref]

A. Grinsted, J. C. Moore, and S. Jevrejeva, “Application of the cross wavelet transform and wavelet coherence to geophysical time series,” Nonlinear Process. Geophys. 11(5/6), 561–566 (2004).
[Crossref]

2002 (6)

C. C. Chang and C. J. Lin, “Training nu-support vector regression: Theory and algorithms,” Neural Comput. 14(8), 1959–1977 (2002).
[Crossref] [PubMed]

M. Wolf, U. Wolf, V. Toronov, A. Michalos, L. A. Paunescu, J. H. Choi, and E. Gratton, “Different time evolution of oxyhemoglobin and deoxyhemoglobin concentration changes in the visual and motor cortices during functional stimulation: a near-infrared spectroscopy study,” Neuroimage 16(3), 704–712 (2002).
[Crossref] [PubMed]

N. Tzourio-Mazoyer, B. Landeau, D. Papathanassiou, F. Crivello, O. Etard, N. Delcroix, B. Mazoyer, and M. Joliot, “Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain,” Neuroimage 15(1), 273–289 (2002).
[Crossref] [PubMed]

S. Del Bianco, F. Martelli, and G. Zaccanti, “Penetration depth of light re-emitted by a diffusive medium: theoretical and experimental investigation,” Phys. Med. Biol. 47(23), 4131–4144 (2002).
[Crossref] [PubMed]

P. R. Montague, G. S. Berns, J. D. Cohen, S. M. McClure, G. Pagnoni, M. Dhamala, M. C. Wiest, I. Karpov, R. D. King, N. Apple, and R. E. Fisher, “Hyperscanning: simultaneous fMRI during linked social interactions,” Neuroimage 16(4), 1159–1164 (2002).
[Crossref] [PubMed]

J. Downar, A. P. Crawley, D. J. Mikulis, and K. D. Davis, “A cortical network sensitive to stimulus salience in a neutral behavioral context across multiple sensory modalities,” J. Neurophysiol. 87(1), 615–620 (2002).
[PubMed]

2001 (1)

G. H. Glover and C. S. Law, “Spiral-in/out BOLD fMRI for increased SNR and reduced susceptibility artifacts,” Magn. Reson. Med. 46(3), 515–522 (2001).
[Crossref] [PubMed]

2000 (1)

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(1), 162–167 (2000).
[Crossref] [PubMed]

1998 (1)

K. Grill-Spector, T. Kushnir, T. Hendler, S. Edelman, Y. Itzchak, and R. Malach, “A sequence of object-processing stages revealed by fMRI in the human occipital lobe,” Hum. Brain Mapp. 6(4), 316–328 (1998).
[Crossref] [PubMed]

1997 (1)

H. Drucker, C. J. C. Burges, L. Kaufman, A. Smola, and V. Vapnik, “Support vector regression machines,” Adv. Neural Inf. Process. Syst. 9(9), 155–161 (1997).

1996 (1)

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]

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).
[Crossref] [PubMed]

1964 (1)

V. Vapnik and A. Chervonenkis, “A note on one class of perceptrons,” Autom. Remote Control 25, 25 (1964).

1963 (1)

V. Vapnik and A. Lerner, “Pattern recognition using generalized portrait method,” Autom. Remote Control 24, 774 (1963).

Acquaye, T. K.

A. S. Garrett, A. L. Reiss, D. J. Miklowitz, T. K. Acquaye, V. E. Cosgrove, M. K. Singh, M. E. Howe, R. G. Kelley, D. Taylor, E. George, and et al.., “Changes in Brain Activation Following Family-Focused Treatment in Youth At-Risk for Bipolar Disorder,” Biol. Psychiatry 69(9), 163S (2011).

Adelstein, J. S.

B. B. Biswal, M. Mennes, X. N. Zuo, S. Gohel, C. Kelly, S. M. Smith, C. F. Beckmann, J. S. Adelstein, R. L. Buckner, S. Colcombe, A. M. Dogonowski, M. Ernst, D. Fair, M. Hampson, M. J. Hoptman, J. S. Hyde, V. J. Kiviniemi, R. Kötter, S. J. Li, C. P. Lin, M. J. Lowe, C. Mackay, D. J. Madden, K. H. Madsen, D. S. Margulies, H. S. Mayberg, K. McMahon, C. S. Monk, S. H. Mostofsky, B. J. Nagel, J. J. Pekar, S. J. Peltier, S. E. Petersen, V. Riedl, S. A. Rombouts, B. Rypma, B. L. Schlaggar, S. Schmidt, R. D. Seidler, G. J. Siegle, C. Sorg, G. J. Teng, J. Veijola, A. Villringer, M. Walter, L. Wang, X. C. Weng, S. Whitfield-Gabrieli, P. Williamson, C. Windischberger, Y. F. Zang, H. Y. Zhang, F. X. Castellanos, and M. P. Milham, “Toward discovery science of human brain function,” Proc. Natl. Acad. Sci. U.S.A. 107(10), 4734–4739 (2010).
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S. Caspers, S. Geyer, A. Schleicher, H. Mohlberg, K. Amunts, and K. Zilles, “The human inferior parietal cortex: cytoarchitectonic parcellation and interindividual variability,” Neuroimage 33(2), 430–448 (2006).
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P. R. Montague, G. S. Berns, J. D. Cohen, S. M. McClure, G. Pagnoni, M. Dhamala, M. C. Wiest, I. Karpov, R. D. King, N. Apple, and R. E. Fisher, “Hyperscanning: simultaneous fMRI during linked social interactions,” Neuroimage 16(4), 1159–1164 (2002).
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B. B. Biswal, M. Mennes, X. N. Zuo, S. Gohel, C. Kelly, S. M. Smith, C. F. Beckmann, J. S. Adelstein, R. L. Buckner, S. Colcombe, A. M. Dogonowski, M. Ernst, D. Fair, M. Hampson, M. J. Hoptman, J. S. Hyde, V. J. Kiviniemi, R. Kötter, S. J. Li, C. P. Lin, M. J. Lowe, C. Mackay, D. J. Madden, K. H. Madsen, D. S. Margulies, H. S. Mayberg, K. McMahon, C. S. Monk, S. H. Mostofsky, B. J. Nagel, J. J. Pekar, S. J. Peltier, S. E. Petersen, V. Riedl, S. A. Rombouts, B. Rypma, B. L. Schlaggar, S. Schmidt, R. D. Seidler, G. J. Siegle, C. Sorg, G. J. Teng, J. Veijola, A. Villringer, M. Walter, L. Wang, X. C. Weng, S. Whitfield-Gabrieli, P. Williamson, C. Windischberger, Y. F. Zang, H. Y. Zhang, F. X. Castellanos, and M. P. Milham, “Toward discovery science of human brain function,” Proc. Natl. Acad. Sci. U.S.A. 107(10), 4734–4739 (2010).
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B. B. Biswal, M. Mennes, X. N. Zuo, S. Gohel, C. Kelly, S. M. Smith, C. F. Beckmann, J. S. Adelstein, R. L. Buckner, S. Colcombe, A. M. Dogonowski, M. Ernst, D. Fair, M. Hampson, M. J. Hoptman, J. S. Hyde, V. J. Kiviniemi, R. Kötter, S. J. Li, C. P. Lin, M. J. Lowe, C. Mackay, D. J. Madden, K. H. Madsen, D. S. Margulies, H. S. Mayberg, K. McMahon, C. S. Monk, S. H. Mostofsky, B. J. Nagel, J. J. Pekar, S. J. Peltier, S. E. Petersen, V. Riedl, S. A. Rombouts, B. Rypma, B. L. Schlaggar, S. Schmidt, R. D. Seidler, G. J. Siegle, C. Sorg, G. J. Teng, J. Veijola, A. Villringer, M. Walter, L. Wang, X. C. Weng, S. Whitfield-Gabrieli, P. Williamson, C. Windischberger, Y. F. Zang, H. Y. Zhang, F. X. Castellanos, and M. P. Milham, “Toward discovery science of human brain function,” Proc. Natl. Acad. Sci. U.S.A. 107(10), 4734–4739 (2010).
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X. Cui, D. M. Bryant, and A. L. Reiss, “NIRS-based hyperscanning reveals increased interpersonal coherence in superior frontal cortex during cooperation,” Neuroimage 59(3), 2430–2437 (2012).
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B. B. Biswal, M. Mennes, X. N. Zuo, S. Gohel, C. Kelly, S. M. Smith, C. F. Beckmann, J. S. Adelstein, R. L. Buckner, S. Colcombe, A. M. Dogonowski, M. Ernst, D. Fair, M. Hampson, M. J. Hoptman, J. S. Hyde, V. J. Kiviniemi, R. Kötter, S. J. Li, C. P. Lin, M. J. Lowe, C. Mackay, D. J. Madden, K. H. Madsen, D. S. Margulies, H. S. Mayberg, K. McMahon, C. S. Monk, S. H. Mostofsky, B. J. Nagel, J. J. Pekar, S. J. Peltier, S. E. Petersen, V. Riedl, S. A. Rombouts, B. Rypma, B. L. Schlaggar, S. Schmidt, R. D. Seidler, G. J. Siegle, C. Sorg, G. J. Teng, J. Veijola, A. Villringer, M. Walter, L. Wang, X. C. Weng, S. Whitfield-Gabrieli, P. Williamson, C. Windischberger, Y. F. Zang, H. Y. Zhang, F. X. Castellanos, and M. P. Milham, “Toward discovery science of human brain function,” Proc. Natl. Acad. Sci. U.S.A. 107(10), 4734–4739 (2010).
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S. Caspers, S. Geyer, A. Schleicher, H. Mohlberg, K. Amunts, and K. Zilles, “The human inferior parietal cortex: cytoarchitectonic parcellation and interindividual variability,” Neuroimage 33(2), 430–448 (2006).
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B. B. Biswal, M. Mennes, X. N. Zuo, S. Gohel, C. Kelly, S. M. Smith, C. F. Beckmann, J. S. Adelstein, R. L. Buckner, S. Colcombe, A. M. Dogonowski, M. Ernst, D. Fair, M. Hampson, M. J. Hoptman, J. S. Hyde, V. J. Kiviniemi, R. Kötter, S. J. Li, C. P. Lin, M. J. Lowe, C. Mackay, D. J. Madden, K. H. Madsen, D. S. Margulies, H. S. Mayberg, K. McMahon, C. S. Monk, S. H. Mostofsky, B. J. Nagel, J. J. Pekar, S. J. Peltier, S. E. Petersen, V. Riedl, S. A. Rombouts, B. Rypma, B. L. Schlaggar, S. Schmidt, R. D. Seidler, G. J. Siegle, C. Sorg, G. J. Teng, J. Veijola, A. Villringer, M. Walter, L. Wang, X. C. Weng, S. Whitfield-Gabrieli, P. Williamson, C. Windischberger, Y. F. Zang, H. Y. Zhang, F. X. Castellanos, and M. P. Milham, “Toward discovery science of human brain function,” Proc. Natl. Acad. Sci. U.S.A. 107(10), 4734–4739 (2010).
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Cohen, J. D.

P. R. Montague, G. S. Berns, J. D. Cohen, S. M. McClure, G. Pagnoni, M. Dhamala, M. C. Wiest, I. Karpov, R. D. King, N. Apple, and R. E. Fisher, “Hyperscanning: simultaneous fMRI during linked social interactions,” Neuroimage 16(4), 1159–1164 (2002).
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B. B. Biswal, M. Mennes, X. N. Zuo, S. Gohel, C. Kelly, S. M. Smith, C. F. Beckmann, J. S. Adelstein, R. L. Buckner, S. Colcombe, A. M. Dogonowski, M. Ernst, D. Fair, M. Hampson, M. J. Hoptman, J. S. Hyde, V. J. Kiviniemi, R. Kötter, S. J. Li, C. P. Lin, M. J. Lowe, C. Mackay, D. J. Madden, K. H. Madsen, D. S. Margulies, H. S. Mayberg, K. McMahon, C. S. Monk, S. H. Mostofsky, B. J. Nagel, J. J. Pekar, S. J. Peltier, S. E. Petersen, V. Riedl, S. A. Rombouts, B. Rypma, B. L. Schlaggar, S. Schmidt, R. D. Seidler, G. J. Siegle, C. Sorg, G. J. Teng, J. Veijola, A. Villringer, M. Walter, L. Wang, X. C. Weng, S. Whitfield-Gabrieli, P. Williamson, C. Windischberger, Y. F. Zang, H. Y. Zhang, F. X. Castellanos, and M. P. Milham, “Toward discovery science of human brain function,” Proc. Natl. Acad. Sci. U.S.A. 107(10), 4734–4739 (2010).
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Cope, M.

D. T. Delpy, M. Cope, P. van der Zee, S. Arridge, S. Wray, and J. Wyatt, “Estimation of Optical Pathlength through Tissue from Direct Time of Flight Measurement,” Phys. Med. Biol. 33(12), 1433–1442 (1988).
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A. S. Garrett, A. L. Reiss, D. J. Miklowitz, T. K. Acquaye, V. E. Cosgrove, M. K. Singh, M. E. Howe, R. G. Kelley, D. Taylor, E. George, and et al.., “Changes in Brain Activation Following Family-Focused Treatment in Youth At-Risk for Bipolar Disorder,” Biol. Psychiatry 69(9), 163S (2011).

Crawley, A. P.

J. Downar, A. P. Crawley, D. J. Mikulis, and K. D. Davis, “A cortical network sensitive to stimulus salience in a neutral behavioral context across multiple sensory modalities,” J. Neurophysiol. 87(1), 615–620 (2002).
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L. Minati, E. Visani, N. G. Dowell, N. Medford, and H. D. Critchley, “Variability comparison of simultaneous brain near-infrared spectroscopy and functional magnetic resonance imaging during visual stimulation,” J. Med. Eng. Technol. 35(6-7), 370–376 (2011).
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X. Cui, D. M. Bryant, and A. L. Reiss, “NIRS-based hyperscanning reveals increased interpersonal coherence in superior frontal cortex during cooperation,” Neuroimage 59(3), 2430–2437 (2012).
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X. Cui, S. Bray, D. M. Bryant, G. H. Glover, and A. L. Reiss, “A quantitative comparison of NIRS and fMRI across multiple cognitive tasks,” Neuroimage 54(4), 2808–2821 (2011).
[Crossref] [PubMed]

Cunningham, J. P.

C. Chang, J. P. Cunningham, and G. H. Glover, “Influence of heart rate on the BOLD signal: The cardiac response function,” Neuroimage 44(3), 857–869 (2009).
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C. Ecker, A. Marquand, J. Mourão-Miranda, P. Johnston, E. M. Daly, M. J. Brammer, S. Maltezos, C. M. Murphy, D. Robertson, S. C. Williams, and D. G. Murphy, “Describing the Brain in Autism in Five Dimensions--Magnetic Resonance Imaging-Assisted Diagnosis of Autism Spectrum Disorder Using a Multiparameter Classification Approach,” J. Neurosci. 30(32), 10612–10623 (2010).
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M. Okamoto, H. Dan, K. Shimizu, K. Takeo, T. Amita, I. Oda, I. Konishi, K. Sakamoto, S. Isobe, T. Suzuki, K. Kohyama, and I. Dan, “Multimodal assessment of cortical activation during apple peeling by NIRS and fMRI,” Neuroimage 21(4), 1275–1288 (2004).
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M. Okamoto, H. Dan, K. Shimizu, K. Takeo, T. Amita, I. Oda, I. Konishi, K. Sakamoto, S. Isobe, T. Suzuki, K. Kohyama, and I. Dan, “Multimodal assessment of cortical activation during apple peeling by NIRS and fMRI,” Neuroimage 21(4), 1275–1288 (2004).
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Davis, K. D.

J. Downar, A. P. Crawley, D. J. Mikulis, and K. D. Davis, “A cortical network sensitive to stimulus salience in a neutral behavioral context across multiple sensory modalities,” J. Neurophysiol. 87(1), 615–620 (2002).
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N. Tzourio-Mazoyer, B. Landeau, D. Papathanassiou, F. Crivello, O. Etard, N. Delcroix, B. Mazoyer, and M. Joliot, “Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain,” Neuroimage 15(1), 273–289 (2002).
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Delpy, D. T.

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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Dhamala, M.

P. R. Montague, G. S. Berns, J. D. Cohen, S. M. McClure, G. Pagnoni, M. Dhamala, M. C. Wiest, I. Karpov, R. D. King, N. Apple, and R. E. Fisher, “Hyperscanning: simultaneous fMRI during linked social interactions,” Neuroimage 16(4), 1159–1164 (2002).
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[Crossref] [PubMed]

Dowell, N. G.

L. Minati, E. Visani, N. G. Dowell, N. Medford, and H. D. Critchley, “Variability comparison of simultaneous brain near-infrared spectroscopy and functional magnetic resonance imaging during visual stimulation,” J. Med. Eng. Technol. 35(6-7), 370–376 (2011).
[Crossref] [PubMed]

Downar, J.

J. Downar, A. P. Crawley, D. J. Mikulis, and K. D. Davis, “A cortical network sensitive to stimulus salience in a neutral behavioral context across multiple sensory modalities,” J. Neurophysiol. 87(1), 615–620 (2002).
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S. Kloppel, C. M. Stonnington, C. Chu, B. Draganski, R. I. Scahill, J. D. Rohrer, N. C. Fox, C. R. Jack, J. Ashburner, and R. S. J. Frackowiak, “Automatic classification of MR scans in Alzheimers disease,” Brain 131(Pt 3), 681–689 (2008).

Drucker, H.

H. Drucker, C. J. C. Burges, L. Kaufman, A. Smola, and V. Vapnik, “Support vector regression machines,” Adv. Neural Inf. Process. Syst. 9(9), 155–161 (1997).

Ecker, C.

C. Ecker, A. Marquand, J. Mourão-Miranda, P. Johnston, E. M. Daly, M. J. Brammer, S. Maltezos, C. M. Murphy, D. Robertson, S. C. Williams, and D. G. Murphy, “Describing the Brain in Autism in Five Dimensions--Magnetic Resonance Imaging-Assisted Diagnosis of Autism Spectrum Disorder Using a Multiparameter Classification Approach,” J. Neurosci. 30(32), 10612–10623 (2010).
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B. B. Biswal, M. Mennes, X. N. Zuo, S. Gohel, C. Kelly, S. M. Smith, C. F. Beckmann, J. S. Adelstein, R. L. Buckner, S. Colcombe, A. M. Dogonowski, M. Ernst, D. Fair, M. Hampson, M. J. Hoptman, J. S. Hyde, V. J. Kiviniemi, R. Kötter, S. J. Li, C. P. Lin, M. J. Lowe, C. Mackay, D. J. Madden, K. H. Madsen, D. S. Margulies, H. S. Mayberg, K. McMahon, C. S. Monk, S. H. Mostofsky, B. J. Nagel, J. J. Pekar, S. J. Peltier, S. E. Petersen, V. Riedl, S. A. Rombouts, B. Rypma, B. L. Schlaggar, S. Schmidt, R. D. Seidler, G. J. Siegle, C. Sorg, G. J. Teng, J. Veijola, A. Villringer, M. Walter, L. Wang, X. C. Weng, S. Whitfield-Gabrieli, P. Williamson, C. Windischberger, Y. F. Zang, H. Y. Zhang, F. X. Castellanos, and M. P. Milham, “Toward discovery science of human brain function,” Proc. Natl. Acad. Sci. U.S.A. 107(10), 4734–4739 (2010).
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N. Tzourio-Mazoyer, B. Landeau, D. Papathanassiou, F. Crivello, O. Etard, N. Delcroix, B. Mazoyer, and M. Joliot, “Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain,” Neuroimage 15(1), 273–289 (2002).
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Fair, D.

B. B. Biswal, M. Mennes, X. N. Zuo, S. Gohel, C. Kelly, S. M. Smith, C. F. Beckmann, J. S. Adelstein, R. L. Buckner, S. Colcombe, A. M. Dogonowski, M. Ernst, D. Fair, M. Hampson, M. J. Hoptman, J. S. Hyde, V. J. Kiviniemi, R. Kötter, S. J. Li, C. P. Lin, M. J. Lowe, C. Mackay, D. J. Madden, K. H. Madsen, D. S. Margulies, H. S. Mayberg, K. McMahon, C. S. Monk, S. H. Mostofsky, B. J. Nagel, J. J. Pekar, S. J. Peltier, S. E. Petersen, V. Riedl, S. A. Rombouts, B. Rypma, B. L. Schlaggar, S. Schmidt, R. D. Seidler, G. J. Siegle, C. Sorg, G. J. Teng, J. Veijola, A. Villringer, M. Walter, L. Wang, X. C. Weng, S. Whitfield-Gabrieli, P. Williamson, C. Windischberger, Y. F. Zang, H. Y. Zhang, F. X. Castellanos, and M. P. Milham, “Toward discovery science of human brain function,” Proc. Natl. Acad. Sci. U.S.A. 107(10), 4734–4739 (2010).
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P. R. Montague, G. S. Berns, J. D. Cohen, S. M. McClure, G. Pagnoni, M. Dhamala, M. C. Wiest, I. Karpov, R. D. King, N. Apple, and R. E. Fisher, “Hyperscanning: simultaneous fMRI during linked social interactions,” Neuroimage 16(4), 1159–1164 (2002).
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S. Kloppel, C. M. Stonnington, C. Chu, B. Draganski, R. I. Scahill, J. D. Rohrer, N. C. Fox, C. R. Jack, J. Ashburner, and R. S. J. Frackowiak, “Automatic classification of MR scans in Alzheimers disease,” Brain 131(Pt 3), 681–689 (2008).

Frackowiak, R. S. J.

S. Kloppel, C. M. Stonnington, C. Chu, B. Draganski, R. I. Scahill, J. D. Rohrer, N. C. Fox, C. R. Jack, J. Ashburner, and R. S. J. Frackowiak, “Automatic classification of MR scans in Alzheimers disease,” Brain 131(Pt 3), 681–689 (2008).

Frahm, J.

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).
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George, E.

A. S. Garrett, A. L. Reiss, D. J. Miklowitz, T. K. Acquaye, V. E. Cosgrove, M. K. Singh, M. E. Howe, R. G. Kelley, D. Taylor, E. George, and et al.., “Changes in Brain Activation Following Family-Focused Treatment in Youth At-Risk for Bipolar Disorder,” Biol. Psychiatry 69(9), 163S (2011).

Geyer, S.

S. Caspers, S. Geyer, A. Schleicher, H. Mohlberg, K. Amunts, and K. Zilles, “The human inferior parietal cortex: cytoarchitectonic parcellation and interindividual variability,” Neuroimage 33(2), 430–448 (2006).
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X. Cui, S. Bray, D. M. Bryant, G. H. Glover, and A. L. Reiss, “A quantitative comparison of NIRS and fMRI across multiple cognitive tasks,” Neuroimage 54(4), 2808–2821 (2011).
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R. Chaves, J. Ramírez, J. M. Górriz, M. López, D. Salas-Gonzalez, I. Alvarez, and F. Segovia, “SVM-based computer-aided diagnosis of the Alzheimer’s disease using t-test NMSE feature selection with feature correlation weighting,” Neurosci. Lett. 461(3), 293–297 (2009).
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K. Grill-Spector, T. Kushnir, T. Hendler, S. Edelman, Y. Itzchak, and R. Malach, “A sequence of object-processing stages revealed by fMRI in the human occipital lobe,” Hum. Brain Mapp. 6(4), 316–328 (1998).
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Grinsted, A.

A. Grinsted, J. C. Moore, and S. Jevrejeva, “Application of the cross wavelet transform and wavelet coherence to geophysical time series,” Nonlinear Process. Geophys. 11(5/6), 561–566 (2004).
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Haas, B. W.

B. W. Haas, F. Hoeft, Y. M. Searcy, D. Mills, U. Bellugi, and A. L. Reiss, “Individual differences in social behavior predict amygdala response to fearful facial expressions in Williams syndrome,” Neuropsychologia 48(5), 1283–1288 (2010).
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Halchenko, Y. O.

R. A. Poldrack, Y. O. Halchenko, and S. J. Hanson, “Decoding the large-scale structure of brain function by classifying mental States across individuals,” Psychol. Sci. 20(11), 1364–1372 (2009).
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Hall, S. S.

F. Hoeft, A. Hernandez, S. Parthasarathy, C. L. Watson, S. S. Hall, and A. L. Reiss, “Fronto-striatal dysfunction and potential compensatory mechanisms in male adolescents with fragile X syndrome,” Hum. Brain Mapp. 28(6), 543–554 (2007).
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Hampel, H.

J. Mourão-Miranda, A. L. Bokde, C. Born, H. Hampel, and M. Stetter, “Classifying brain states and determining the discriminating activation patterns: Support Vector Machine on functional MRI data,” Neuroimage 28(4), 980–995 (2005).
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Hampson, M.

B. B. Biswal, M. Mennes, X. N. Zuo, S. Gohel, C. Kelly, S. M. Smith, C. F. Beckmann, J. S. Adelstein, R. L. Buckner, S. Colcombe, A. M. Dogonowski, M. Ernst, D. Fair, M. Hampson, M. J. Hoptman, J. S. Hyde, V. J. Kiviniemi, R. Kötter, S. J. Li, C. P. Lin, M. J. Lowe, C. Mackay, D. J. Madden, K. H. Madsen, D. S. Margulies, H. S. Mayberg, K. McMahon, C. S. Monk, S. H. Mostofsky, B. J. Nagel, J. J. Pekar, S. J. Peltier, S. E. Petersen, V. Riedl, S. A. Rombouts, B. Rypma, B. L. Schlaggar, S. Schmidt, R. D. Seidler, G. J. Siegle, C. Sorg, G. J. Teng, J. Veijola, A. Villringer, M. Walter, L. Wang, X. C. Weng, S. Whitfield-Gabrieli, P. Williamson, C. Windischberger, Y. F. Zang, H. Y. Zhang, F. X. Castellanos, and M. P. Milham, “Toward discovery science of human brain function,” Proc. Natl. Acad. Sci. U.S.A. 107(10), 4734–4739 (2010).
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Hanson, S. J.

R. A. Poldrack, Y. O. Halchenko, and S. J. Hanson, “Decoding the large-scale structure of brain function by classifying mental States across individuals,” Psychol. Sci. 20(11), 1364–1372 (2009).
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J. Steinbrink, A. Villringer, F. Kempf, D. Haux, S. Boden, and H. Obrig, “Illuminating the BOLD signal: combined fMRI-fNIRS studies,” Magn. Reson. Imaging 24(4), 495–505 (2006).
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Hendler, T.

K. Grill-Spector, T. Kushnir, T. Hendler, S. Edelman, Y. Itzchak, and R. Malach, “A sequence of object-processing stages revealed by fMRI in the human occipital lobe,” Hum. Brain Mapp. 6(4), 316–328 (1998).
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Hernandez, A.

F. Hoeft, A. Hernandez, S. Parthasarathy, C. L. Watson, S. S. Hall, and A. L. Reiss, “Fronto-striatal dysfunction and potential compensatory mechanisms in male adolescents with fragile X syndrome,” Hum. Brain Mapp. 28(6), 543–554 (2007).
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Hoeft, F.

B. W. Haas, F. Hoeft, Y. M. Searcy, D. Mills, U. Bellugi, and A. L. Reiss, “Individual differences in social behavior predict amygdala response to fearful facial expressions in Williams syndrome,” Neuropsychologia 48(5), 1283–1288 (2010).
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F. Hoeft, A. Hernandez, S. Parthasarathy, C. L. Watson, S. S. Hall, and A. L. Reiss, “Fronto-striatal dysfunction and potential compensatory mechanisms in male adolescents with fragile X syndrome,” Hum. Brain Mapp. 28(6), 543–554 (2007).
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Hoptman, M. J.

B. B. Biswal, M. Mennes, X. N. Zuo, S. Gohel, C. Kelly, S. M. Smith, C. F. Beckmann, J. S. Adelstein, R. L. Buckner, S. Colcombe, A. M. Dogonowski, M. Ernst, D. Fair, M. Hampson, M. J. Hoptman, J. S. Hyde, V. J. Kiviniemi, R. Kötter, S. J. Li, C. P. Lin, M. J. Lowe, C. Mackay, D. J. Madden, K. H. Madsen, D. S. Margulies, H. S. Mayberg, K. McMahon, C. S. Monk, S. H. Mostofsky, B. J. Nagel, J. J. Pekar, S. J. Peltier, S. E. Petersen, V. Riedl, S. A. Rombouts, B. Rypma, B. L. Schlaggar, S. Schmidt, R. D. Seidler, G. J. Siegle, C. Sorg, G. J. Teng, J. Veijola, A. Villringer, M. Walter, L. Wang, X. C. Weng, S. Whitfield-Gabrieli, P. Williamson, C. Windischberger, Y. F. Zang, H. Y. Zhang, F. X. Castellanos, and M. P. Milham, “Toward discovery science of human brain function,” Proc. Natl. Acad. Sci. U.S.A. 107(10), 4734–4739 (2010).
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Howe, M. E.

A. S. Garrett, A. L. Reiss, D. J. Miklowitz, T. K. Acquaye, V. E. Cosgrove, M. K. Singh, M. E. Howe, R. G. Kelley, D. Taylor, E. George, and et al.., “Changes in Brain Activation Following Family-Focused Treatment in Youth At-Risk for Bipolar Disorder,” Biol. Psychiatry 69(9), 163S (2011).

Hyde, J. S.

B. B. Biswal, M. Mennes, X. N. Zuo, S. Gohel, C. Kelly, S. M. Smith, C. F. Beckmann, J. S. Adelstein, R. L. Buckner, S. Colcombe, A. M. Dogonowski, M. Ernst, D. Fair, M. Hampson, M. J. Hoptman, J. S. Hyde, V. J. Kiviniemi, R. Kötter, S. J. Li, C. P. Lin, M. J. Lowe, C. Mackay, D. J. Madden, K. H. Madsen, D. S. Margulies, H. S. Mayberg, K. McMahon, C. S. Monk, S. H. Mostofsky, B. J. Nagel, J. J. Pekar, S. J. Peltier, S. E. Petersen, V. Riedl, S. A. Rombouts, B. Rypma, B. L. Schlaggar, S. Schmidt, R. D. Seidler, G. J. Siegle, C. Sorg, G. J. Teng, J. Veijola, A. Villringer, M. Walter, L. Wang, X. C. Weng, S. Whitfield-Gabrieli, P. Williamson, C. Windischberger, Y. F. Zang, H. Y. Zhang, F. X. Castellanos, and M. P. Milham, “Toward discovery science of human brain function,” Proc. Natl. Acad. Sci. U.S.A. 107(10), 4734–4739 (2010).
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Ishikawa, A.

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

M. Okamoto, H. Dan, K. Shimizu, K. Takeo, T. Amita, I. Oda, I. Konishi, K. Sakamoto, S. Isobe, T. Suzuki, K. Kohyama, and I. Dan, “Multimodal assessment of cortical activation during apple peeling by NIRS and fMRI,” Neuroimage 21(4), 1275–1288 (2004).
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Itzchak, Y.

K. Grill-Spector, T. Kushnir, T. Hendler, S. Edelman, Y. Itzchak, and R. Malach, “A sequence of object-processing stages revealed by fMRI in the human occipital lobe,” Hum. Brain Mapp. 6(4), 316–328 (1998).
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Jack, C. R.

S. Kloppel, C. M. Stonnington, C. Chu, B. Draganski, R. I. Scahill, J. D. Rohrer, N. C. Fox, C. R. Jack, J. Ashburner, and R. S. J. Frackowiak, “Automatic classification of MR scans in Alzheimers disease,” Brain 131(Pt 3), 681–689 (2008).

Jevrejeva, S.

A. Grinsted, J. C. Moore, and S. Jevrejeva, “Application of the cross wavelet transform and wavelet coherence to geophysical time series,” Nonlinear Process. Geophys. 11(5/6), 561–566 (2004).
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Johnston, P.

C. Ecker, A. Marquand, J. Mourão-Miranda, P. Johnston, E. M. Daly, M. J. Brammer, S. Maltezos, C. M. Murphy, D. Robertson, S. C. Williams, and D. G. Murphy, “Describing the Brain in Autism in Five Dimensions--Magnetic Resonance Imaging-Assisted Diagnosis of Autism Spectrum Disorder Using a Multiparameter Classification Approach,” J. Neurosci. 30(32), 10612–10623 (2010).
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Joliot, M.

N. Tzourio-Mazoyer, B. Landeau, D. Papathanassiou, F. Crivello, O. Etard, N. Delcroix, B. Mazoyer, and M. Joliot, “Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain,” Neuroimage 15(1), 273–289 (2002).
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Y. Kamitani and F. Tong, “Decoding the visual and subjective contents of the human brain,” Nat. Neurosci. 8(5), 679–685 (2005).
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P. R. Montague, G. S. Berns, J. D. Cohen, S. M. McClure, G. Pagnoni, M. Dhamala, M. C. Wiest, I. Karpov, R. D. King, N. Apple, and R. E. Fisher, “Hyperscanning: simultaneous fMRI during linked social interactions,” Neuroimage 16(4), 1159–1164 (2002).
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Kelley, R. G.

A. S. Garrett, A. L. Reiss, D. J. Miklowitz, T. K. Acquaye, V. E. Cosgrove, M. K. Singh, M. E. Howe, R. G. Kelley, D. Taylor, E. George, and et al.., “Changes in Brain Activation Following Family-Focused Treatment in Youth At-Risk for Bipolar Disorder,” Biol. Psychiatry 69(9), 163S (2011).

Kelly, C.

B. B. Biswal, M. Mennes, X. N. Zuo, S. Gohel, C. Kelly, S. M. Smith, C. F. Beckmann, J. S. Adelstein, R. L. Buckner, S. Colcombe, A. M. Dogonowski, M. Ernst, D. Fair, M. Hampson, M. J. Hoptman, J. S. Hyde, V. J. Kiviniemi, R. Kötter, S. J. Li, C. P. Lin, M. J. Lowe, C. Mackay, D. J. Madden, K. H. Madsen, D. S. Margulies, H. S. Mayberg, K. McMahon, C. S. Monk, S. H. Mostofsky, B. J. Nagel, J. J. Pekar, S. J. Peltier, S. E. Petersen, V. Riedl, S. A. Rombouts, B. Rypma, B. L. Schlaggar, S. Schmidt, R. D. Seidler, G. J. Siegle, C. Sorg, G. J. Teng, J. Veijola, A. Villringer, M. Walter, L. Wang, X. C. Weng, S. Whitfield-Gabrieli, P. Williamson, C. Windischberger, Y. F. Zang, H. Y. Zhang, F. X. Castellanos, and M. P. Milham, “Toward discovery science of human brain function,” Proc. Natl. Acad. Sci. U.S.A. 107(10), 4734–4739 (2010).
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Kempf, F.

J. Steinbrink, A. Villringer, F. Kempf, D. Haux, S. Boden, and H. Obrig, “Illuminating the BOLD signal: combined fMRI-fNIRS studies,” Magn. Reson. Imaging 24(4), 495–505 (2006).
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Khan, B.

F. Tian, H. Niu, B. Khan, G. Alexandrakis, K. Behbehani, and H. Liu, “Enhanced functional brain imaging by using adaptive filtering and a depth compensation algorithm in diffuse optical tomography,” IEEE Trans. Med. Imaging 30(6), 1239–1251 (2011).
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King, R. D.

P. R. Montague, G. S. Berns, J. D. Cohen, S. M. McClure, G. Pagnoni, M. Dhamala, M. C. Wiest, I. Karpov, R. D. King, N. Apple, and R. E. Fisher, “Hyperscanning: simultaneous fMRI during linked social interactions,” Neuroimage 16(4), 1159–1164 (2002).
[Crossref] [PubMed]

Kiviniemi, V. J.

B. B. Biswal, M. Mennes, X. N. Zuo, S. Gohel, C. Kelly, S. M. Smith, C. F. Beckmann, J. S. Adelstein, R. L. Buckner, S. Colcombe, A. M. Dogonowski, M. Ernst, D. Fair, M. Hampson, M. J. Hoptman, J. S. Hyde, V. J. Kiviniemi, R. Kötter, S. J. Li, C. P. Lin, M. J. Lowe, C. Mackay, D. J. Madden, K. H. Madsen, D. S. Margulies, H. S. Mayberg, K. McMahon, C. S. Monk, S. H. Mostofsky, B. J. Nagel, J. J. Pekar, S. J. Peltier, S. E. Petersen, V. Riedl, S. A. Rombouts, B. Rypma, B. L. Schlaggar, S. Schmidt, R. D. Seidler, G. J. Siegle, C. Sorg, G. J. Teng, J. Veijola, A. Villringer, M. Walter, L. Wang, X. C. Weng, S. Whitfield-Gabrieli, P. Williamson, C. Windischberger, Y. F. Zang, H. Y. Zhang, F. X. Castellanos, and M. P. Milham, “Toward discovery science of human brain function,” Proc. Natl. Acad. Sci. U.S.A. 107(10), 4734–4739 (2010).
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Kleinschmidt, A.

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]

Kloppel, S.

S. Kloppel, C. M. Stonnington, C. Chu, B. Draganski, R. I. Scahill, J. D. Rohrer, N. C. Fox, C. R. Jack, J. Ashburner, and R. S. J. Frackowiak, “Automatic classification of MR scans in Alzheimers disease,” Brain 131(Pt 3), 681–689 (2008).

Kohno, S.

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

M. Okamoto, H. Dan, K. Shimizu, K. Takeo, T. Amita, I. Oda, I. Konishi, K. Sakamoto, S. Isobe, T. Suzuki, K. Kohyama, and I. Dan, “Multimodal assessment of cortical activation during apple peeling by NIRS and fMRI,” Neuroimage 21(4), 1275–1288 (2004).
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Konishi, I.

M. Okamoto, H. Dan, K. Shimizu, K. Takeo, T. Amita, I. Oda, I. Konishi, K. Sakamoto, S. Isobe, T. Suzuki, K. Kohyama, and I. Dan, “Multimodal assessment of cortical activation during apple peeling by NIRS and fMRI,” Neuroimage 21(4), 1275–1288 (2004).
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B. W. Haas, F. Hoeft, Y. M. Searcy, D. Mills, U. Bellugi, and A. L. Reiss, “Individual differences in social behavior predict amygdala response to fearful facial expressions in Williams syndrome,” Neuropsychologia 48(5), 1283–1288 (2010).
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Requardt, M.

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Rombouts, S. A.

B. B. Biswal, M. Mennes, X. N. Zuo, S. Gohel, C. Kelly, S. M. Smith, C. F. Beckmann, J. S. Adelstein, R. L. Buckner, S. Colcombe, A. M. Dogonowski, M. Ernst, D. Fair, M. Hampson, M. J. Hoptman, J. S. Hyde, V. J. Kiviniemi, R. Kötter, S. J. Li, C. P. Lin, M. J. Lowe, C. Mackay, D. J. Madden, K. H. Madsen, D. S. Margulies, H. S. Mayberg, K. McMahon, C. S. Monk, S. H. Mostofsky, B. J. Nagel, J. J. Pekar, S. J. Peltier, S. E. Petersen, V. Riedl, S. A. Rombouts, B. Rypma, B. L. Schlaggar, S. Schmidt, R. D. Seidler, G. J. Siegle, C. Sorg, G. J. Teng, J. Veijola, A. Villringer, M. Walter, L. Wang, X. C. Weng, S. Whitfield-Gabrieli, P. Williamson, C. Windischberger, Y. F. Zang, H. Y. Zhang, F. X. Castellanos, and M. P. Milham, “Toward discovery science of human brain function,” Proc. Natl. Acad. Sci. U.S.A. 107(10), 4734–4739 (2010).
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Rypma, B.

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Salas-Gonzalez, D.

R. Chaves, J. Ramírez, J. M. Górriz, M. López, D. Salas-Gonzalez, I. Alvarez, and F. Segovia, “SVM-based computer-aided diagnosis of the Alzheimer’s disease using t-test NMSE feature selection with feature correlation weighting,” Neurosci. Lett. 461(3), 293–297 (2009).
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Scahill, R. I.

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Schlaggar, B. L.

B. B. Biswal, M. Mennes, X. N. Zuo, S. Gohel, C. Kelly, S. M. Smith, C. F. Beckmann, J. S. Adelstein, R. L. Buckner, S. Colcombe, A. M. Dogonowski, M. Ernst, D. Fair, M. Hampson, M. J. Hoptman, J. S. Hyde, V. J. Kiviniemi, R. Kötter, S. J. Li, C. P. Lin, M. J. Lowe, C. Mackay, D. J. Madden, K. H. Madsen, D. S. Margulies, H. S. Mayberg, K. McMahon, C. S. Monk, S. H. Mostofsky, B. J. Nagel, J. J. Pekar, S. J. Peltier, S. E. Petersen, V. Riedl, S. A. Rombouts, B. Rypma, B. L. Schlaggar, S. Schmidt, R. D. Seidler, G. J. Siegle, C. Sorg, G. J. Teng, J. Veijola, A. Villringer, M. Walter, L. Wang, X. C. Weng, S. Whitfield-Gabrieli, P. Williamson, C. Windischberger, Y. F. Zang, H. Y. Zhang, F. X. Castellanos, and M. P. Milham, “Toward discovery science of human brain function,” Proc. Natl. Acad. Sci. U.S.A. 107(10), 4734–4739 (2010).
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B. W. Haas, F. Hoeft, Y. M. Searcy, D. Mills, U. Bellugi, and A. L. Reiss, “Individual differences in social behavior predict amygdala response to fearful facial expressions in Williams syndrome,” Neuropsychologia 48(5), 1283–1288 (2010).
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Segovia, F.

R. Chaves, J. Ramírez, J. M. Górriz, M. López, D. Salas-Gonzalez, I. Alvarez, and F. Segovia, “SVM-based computer-aided diagnosis of the Alzheimer’s disease using t-test NMSE feature selection with feature correlation weighting,” Neurosci. Lett. 461(3), 293–297 (2009).
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Seidler, R. D.

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Seiyama, A.

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Shimizu, K.

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Figures (11)

Fig. 1
Fig. 1 Noise determination. (a) The wavelet transform map of a poor-quality fNIRS signal; note that there is no heartbeat frequency band at 1 Hz. (b) The wavelet transform map of a good-quality fNIRS signal; note that there is a clear heartbeat frequency band showed at 1 Hz. (c) and (d) are the time series corresponding to (a) and (b). The blue curves represent the original signals, and the red curves represent the signals after bandpass filtering. (e) Number of noisy fNIRS channels for all subjects.
Fig. 2
Fig. 2 Diagram of SVR algorithm.
Fig. 3
Fig. 3 Cross correlation of fNIRS and fMRI signals for a representative subject at four representative channels. In each panel, the arrow indicates the optimum time lag (i.e. the one with the highest correlation) within a task cycle (36 seconds per cycle). The fMRI signal was interpolated to match the fNIRS sample rate (10Hz), and both fMRI and fNIRS signals were normalized before conducting cross-correlation. Notice that all optimum time lags were smaller than 4 seconds.
Fig. 4
Fig. 4 Process of extracting the fMRI signals from the cortex. (a) Location of uniformly distributed points on a spherical cap. (b) Location of retrieved voxels on the brain surface with uniform distribution. (c) Projection of twenty-four fNIRS channels on the brain. The fMRI signals were extracted from those voxels. (d) Two 3x3 patches were placed over the left and right prefrontal brain region. Source probes (not shown) were plugged in the red holes on the patches, and detector probes (not shown) were plugged in the blue holes.
Fig. 5
Fig. 5 Prediction performance at twelve deep-brain regions when using fMRI signals as input to SVR. (a) Prediction performance for a representative subject, and (b) prediction performance averaged across all subjects. The input signals to the SVR prediction model for (a) and (b) were 4,225 fMRI signals from the entire cortex (fMRI-EC signals). (c) The overall mean correlation between predicted and measured signals plotted as a function of the number of input fMRI-EC signals. The correlation coefficients in (c) were averaged across all subjects, regions, and tasks, and the error bars represent the standard deviation. (d) Prediction performance averaged across all subjects, where the input signals to the SVR prediction model were 24 fMRI signals from the prefrontal cortex (fMRI-PFC). The error bars represent the standard deviation.
Fig. 6
Fig. 6 Prediction performance when using fNIRS-PFC signals as input to SVR. The correlation coefficients were averaged across eleven subjects (those that remained after exclusion for noise). The error bars represent the standard deviation across the subjects. The top figure is the prediction performance when using the fNIRS-PFC-1 signals, which included 80 time-lag fNIRS signals from −4 to + 4 seconds in 0.1-second intervals (3,840 total signals per subject). The bottom figure is the prediction performance when using the fNIRS-PFC-2 signals, which included only the fNIRS signals at the optimal latency time, i.e., those with the highest correlation with corresponding BOLD signals (48 total signals per subject).
Fig. 7
Fig. 7 Distribution and mean of correlations between predicted and observed signals for three groups of input signals: fMRI-EC, fMRI-PFC, and fNIRS-PFC-1. (a) Distribution of correlations for the three groups of input signal. The white, vertical, dashed lines indicate 0.5-correlation. (b) Mean correlations for the three groups of input signals. Light blue bars indicate the averaged correlation coefficients across all subjects, regions and tasks, i.e., averaged across 396 values. Magenta bars indicate the mean correlations averaged over the top 15% of the 396 correlation values.
Fig. 8
Fig. 8 Weight maps associated with predicting: (a-c) left fusiform activity in a representative subject during nogo, faces, and complex-visual tasks; (d) left and (e) right fusiform activity, averaged over all subjects and all tasks; and the activity at (f) insula, (g) hippocampus, (h) parahippocampal gyrus, (i) amygdala and (j) caudate, averaged over all subjects and all tasks. The weights combine voxels for left and right predictions in (f-j). Red points mark the voxels with normalized weight > 0.6 in the top panel, and > 0.7 in the bottom panel.
Fig. 9
Fig. 9 The fNIRS signal (HbO) change before and after respiratory denoising for a representative subject. The HbO signals were first down-sampled to match the fMRI BOLD signal sampling rate, then the standard RETROICOR method (Glover et al., 2000) was used to remove respiratory noise. The corresponding BOLD signal was used as a benchmark to identify the efficiency of the denoising. Specifically, the mean correlation between the HbO signal and the corresponding BOLD signal across all channels was calculated for both uncorrected and corrected HbO signals. A paired t-test between those two cases (utilizing Fisher z-transformed correlation coefficients) indicated that there was no difference between them (p = 0.13, df = 23, T-value = 1.56). The result implies that the global physiological noise was very small and thus was likely not a confound that influenced the findings or the resulting conclusions. (a) The HbO signals before (blue) and after (red) RETROICOR correction for one representative channel. (b) The mean correlation between HbO signal and the corresponding BOLD signal across all channels for both uncorrected and corrected HbO signals. The error bar represents the standard deviation.
Fig. 10
Fig. 10 Two representative cases with different prediction performance. The blue lines represent the measured BOLD signals (Target signals), and the green lines represent the predicted signals. (a) The correlation coefficient between the target signal and the predicted signal was very high: r = 0.99. (b) The correlation coefficient between the target signal and the predicted signal was moderate: r = 0.79.
Fig. 11
Fig. 11 Location of uniformly distributed points on a spherical cap. (a) 241 points; (b) 565 points.

Tables (3)

Tables Icon

Table 1 Two-way ANOVA: prediction performance versus deep-brain regions, for fMRI-EC input signal (4,225 fMRI signals extracted uniformly from the entire cortex).

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Table 2 Two-way ANOVA: prediction performance versus deep-brain regions, for fMRI-PFC input signal.

Tables Icon

Table 3 Summary of the cortical regions with large weight (> 0.7) in predictions of activity in the six deep-brain regions.

Equations (2)

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( w T ϕ( x i )+b ) y i ε+ ξ i , y i ( w T ϕ( x i )+b )ε+ ξ i * , ξ i , ξ i * 0,i=1,...,m,ε0.

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