Abstract

Resting state connectivity aims to identify spontaneous cerebral hemodynamic fluctuations that reflect neuronal activity at rest. In this study, we investigated the spatial-temporal correlation of hemoglobin concentration signals over the whole head during the resting state. By choosing a source-detector pair as a seed, we calculated the correlation value between its time course and the time course of all other source-detector combinations, and projected them onto a topographic map. In all subjects, we found robust spatial interactions in agreement with previous fMRI and NIRS findings. Strong correlations between the two opposite hemispheres were seen for both sensorimotor and visual cortices. Correlations in the prefrontal cortex were more heterogeneous and dependent on the hemodynamic contrast. HbT provided robust, well defined maps, suggesting that this contrast may be used to better localize functional connectivity. The effects of global systemic physiology were also investigated, particularly low frequency blood pressure oscillations which give rise to broad regions of high correlation and mislead interpretation of the results. These results confirm the feasibility of using functional connectivity with optical methods during the resting state, and validate its use to investigate cortical interactions across the whole head.

© 2010 OSA

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2010 (6)

A. R. Carter, S. V. Astafiev, C. E. Lang, L. T. Connor, J. Rengachary, M. J. Strube, D. L. W. Pope, G. L. Shulman, and M. Corbetta, “Resting interhemispheric functional magnetic resonance imaging connectivity predicts performance after stroke,” Ann. Neurol. 67(3), 365–375 (2010).
[PubMed]

S. Aydöre, M. K. Mihçak, K. Ciftçi, and A. Akin, “On temporal connectivity of PFC via Gauss-Markov modeling of fNIRS signals,” IEEE Trans. Biomed. Eng. 57(3), 761–768 (2010).
[PubMed]

C. M. Lu, Y. J. Zhang, B. B. Biswal, Y. F. Zang, D. L. Peng, and C. Z. Zhu, “Use of fNIRS to assess resting state functional connectivity,” J. Neurosci. Methods 186(2), 242–249 (2010).
[CrossRef] [PubMed]

H. Zhang, Y. J. Zhang, C. M. Lu, S. Y. Ma, Y. F. Zang, and C. Z. Zhu, “Functional connectivity as revealed by independent component analysis of resting-state fNIRS measurements,” Neuroimage 51(3), 1150–1161 (2010).
[CrossRef] [PubMed]

B. L. Edlow, M. N. Kim, T. Durduran, C. Zhou, M. E. Putt, A. G. Yodh, J. H. Greenberg, and J. A. Detre, “The effects of healthy aging on cerebral hemodynamic responses to posture change,” Physiol. Meas. 31(4), 477–495 (2010).
[CrossRef] [PubMed]

A. Custo, D. A. Boas, D. Tsuzuki, I. Dan, R. C. Mesquita, B. Fischl, W. E. L. Grimson, and W. Wells, “Anatomical atlas-guided diffuse optical tomography of brain activation,” Neuroimage 49(1), 561–567 (2010).
[CrossRef] [PubMed]

2009 (3)

J. Markham, B. R. White, B. W. Zeff, and J. P. Culver, “Blind identification of evoked human brain activity with independent component analysis of optical data,” Hum. Brain Mapp. 30(8), 2382–2392 (2009).
[CrossRef] [PubMed]

B. R. White, A. Z. Snyder, A. L. Cohen, S. E. Petersen, M. E. Raichle, B. L. Schlaggar, and J. P. Culver, “Resting-state functional connectivity in the human brain revealed with diffuse optical tomography,” Neuroimage 47(1), 148–156 (2009).
[CrossRef] [PubMed]

K. Murphy, R. M. Birn, D. A. Handwerker, T. B. Jones, and P. A. Bandettini, “The impact of global signal regression on resting state correlations: are anti-correlated networks introduced?” Neuroimage 44(3), 893–905 (2009).
[CrossRef] [PubMed]

2008 (3)

R. L. Buckner, J. R. Andrews-Hanna, and D. L. Schacter, “The brain’s default network: anatomy, function, and relevance to disease,” Ann. N. Y. Acad. Sci. 1124(1), 1–38 (2008).
[CrossRef] [PubMed]

S. Lui, L. Ouyang, Q. Chen, X. Huang, H. Tang, H. Chen, D. Zhou, G. J. Kemp, and Q. Gong, “Differential interictal activity of the precuneus/posterior cingulate cortex revealed by resting state functional MRI at 3T in generalized vs. partial seizure,” J. Magn. Reson. Imaging 27(6), 1214–1220 (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(5), 054031 (2008).
[CrossRef] [PubMed]

2007 (8)

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]

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).
[CrossRef] [PubMed]

M. A. Pinsk and S. Kastner, “Neuroscience: unconscious networking,” Nature 447(7140), 46–47 (2007).
[CrossRef] [PubMed]

M. D. Fox and M. E. Raichle, “Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging,” Nat. Rev. Neurosci. 8(9), 700–711 (2007).
[CrossRef] [PubMed]

R. L. Buckner and J. L. Vincent, “Unrest at rest: default activity and spontaneous network correlations,” Neuroimage 37(4), 1091–1096, discussion 1097–1099 (2007).
[CrossRef] [PubMed]

A. G. Garrity, G. D. Pearlson, K. McKiernan, D. Lloyd, K. A. Kiehl, and V. D. Calhoun, “Aberrant “default mode” functional connectivity in schizophrenia,” Am. J. Psychiatry 164(3), 450–457 (2007).
[CrossRef] [PubMed]

H. Lu, Y. Zuo, H. Gu, J. A. Waltz, W. Zhan, C. A. Scholl, W. Rea, Y. Yang, and E. A. Stein, “Synchronized delta oscillations correlate with the resting-state functional MRI signal,” Proc. Natl. Acad. Sci. U.S.A. 104(46), 18265–18269 (2007).
[CrossRef] [PubMed]

J. L. Vincent, G. H. Patel, M. D. Fox, A. Z. Snyder, J. T. Baker, D. C. Van Essen, J. M. Zempel, L. H. Snyder, M. Corbetta, and M. E. Raichle, “Intrinsic functional architecture in the anaesthetized monkey brain,” Nature 447(7140), 83–86 (2007).
[CrossRef] [PubMed]

2006 (10)

M. De Luca, C. F. Beckmann, N. De Stefano, P. M. Matthews, and S. M. Smith, “fMRI resting state networks define distinct modes of long-distance interactions in the human brain,” Neuroimage 29(4), 1359–1367 (2006).
[CrossRef] [PubMed]

J. S. Damoiseaux, S. A. Rombouts, F. Barkhof, P. Scheltens, C. J. Stam, S. M. Smith, and C. F. Beckmann, “Consistent resting-state networks across healthy subjects,” Proc. Natl. Acad. Sci. U.S.A. 103(37), 13848–13853 (2006).
[CrossRef] [PubMed]

D. P. Kennedy, E. Redcay, and E. Courchesne, “Failing to deactivate: resting functional abnormalities in autism,” Proc. Natl. Acad. Sci. U.S.A. 103(21), 8275–8280 (2006).
[CrossRef] [PubMed]

M. A. Just, V. L. Cherkassky, T. A. Keller, R. K. Kana, and N. J. Minshew, “Functional and anatomical cortical underconnectivity in autism: evidence from an FMRI study of an executive function task and corpus callosum morphometry,” Cereb. Cortex 17(4), 951–961 (2006).
[CrossRef] [PubMed]

T. E. Lund, K. H. Madsen, K. Sidaros, W. L. Luo, and T. E. Nichols, “Non-white noise in fMRI: does modelling have an impact?” Neuroimage 29(1), 54–66 (2006).
[CrossRef] [PubMed]

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(4), 1536–1548 (2006).
[CrossRef] [PubMed]

J. L. Vincent, A. Z. Snyder, M. D. Fox, B. J. Shannon, J. R. Andrews, M. E. Raichle, and R. L. Buckner, “Coherent spontaneous activity identifies a hippocampal-parietal memory network,” J. Neurophysiol. 96(6), 3517–3531 (2006).
[CrossRef] [PubMed]

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

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

D. K. Joseph, T. J. Huppert, M. A. Franceschini, and D. A. Boas, “Diffuse optical tomography system to image brain activation with improved spatial resolution and validation with functional magnetic resonance imaging,” Appl. Opt. 45(31), 8142–8151 (2006).
[CrossRef] [PubMed]

2005 (3)

J. P. Culver, A. M. Siegel, M. A. Franceschini, J. B. Mandeville, and D. A. Boas, “Evidence that cerebral blood volume can provide brain activation maps with better spatial resolution than deoxygenated hemoglobin,” Neuroimage 27(4), 947–959 (2005).
[CrossRef] [PubMed]

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

C. F. Beckmann, M. DeLuca, J. T. Devlin, and S. M. Smith, “Investigations into resting-state connectivity using independent component analysis,” Philos. Trans. R. Soc. Lond. B Biol. Sci. 360(1457), 1001–1013 (2005).
[CrossRef] [PubMed]

2004 (5)

G. Buzsáki and A. Draguhn, “Neuronal oscillations in cortical networks,” Science 304(5679), 1926–1929 (2004).
[CrossRef] [PubMed]

M. D. Greicius, G. Srivastava, A. L. Reiss, and V. Menon, “Default-mode network activity distinguishes Alzheimer’s disease from healthy aging: evidence from functional MRI,” Proc. Natl. Acad. Sci. U.S.A. 101(13), 4637–4642 (2004).
[CrossRef] [PubMed]

S. A. Sheth, M. Nemoto, M. Guiou, M. Walker, N. Pouratian, N. Hageman, and A. W. Toga, “Columnar specificity of microvascular oxygenation and volume responses: implications for functional brain mapping,” J. Neurosci. 24(3), 634–641 (2004).
[CrossRef] [PubMed]

G. Morren, M. Wolf, P. Lemmerling, U. Wolf, J. H. Choi, E. Gratton, L. Lathauwer, and S. Huffel, “Detection of fast neuronal signals in the motor cortex from functional near infrared spectroscopy measurements using independent component analysis,” Med. Biol. Eng. Comput. 42(1), 92–99 (2004).
[CrossRef] [PubMed]

I. Tachtsidis, C. E. Elwell, T. S. Leung, C. W. Lee, M. Smith, and D. T. Delpy, “Investigation of cerebral haemodynamics by near-infrared spectroscopy in young healthy volunteers reveals posture-dependent spontaneous oscillations,” Physiol. Meas. 25(2), 437–445 (2004).
[CrossRef] [PubMed]

2003 (3)

H. Laufs, K. Krakow, P. Sterzer, E. Eger, A. Beyerle, A. Salek-Haddadi, and A. Kleinschmidt, “Electroencephalographic signatures of attentional and cognitive default modes in spontaneous brain activity fluctuations at rest,” Proc. Natl. Acad. Sci. U.S.A. 100(19), 11053–11058 (2003).
[CrossRef] [PubMed]

V. Kiviniemi, J. H. Kantola, J. Jauhiainen, A. Hyvärinen, and O. Tervonen, “Independent component analysis of nondeterministic fMRI signal sources,” Neuroimage 19(2), 253–260 (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 (1)

M. J. Lowe, M. D. Phillips, J. T. Lurito, D. Mattson, M. Dzemidzic, and V. P. Mathews, “Multiple sclerosis: low-frequency temporal blood oxygen level-dependent fluctuations indicate reduced functional connectivity initial results,” Radiology 224(1), 184–192 (2002).
[CrossRef] [PubMed]

2001 (1)

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

2000 (3)

H. Obrig, M. Neufang, R. Wenzel, M. Kohl, J. Steinbrink, K. Einhäupl, and A. Villringer, “Spontaneous low frequency oscillations of cerebral hemodynamics and metabolism in human adults,” Neuroimage 12(6), 623–639 (2000).
[CrossRef] [PubMed]

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(4), 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. Rees. Med. 44(1), 162–167 (2000).
[CrossRef]

1999 (1)

J. Xiong, L. M. Parsons, J. H. Gao, and P. T. Fox, “Interregional connectivity to primary motor cortex revealed using MRI resting state images,” Hum. Brain Mapp. 8(2-3), 151–156 (1999).
[CrossRef] [PubMed]

1998 (1)

M. J. Lowe, B. J. Mock, and J. A. Sorenson, “Functional connectivity in single and multislice echoplanar imaging using resting-state fluctuations,” Neuroimage 7(2), 119–132 (1998).
[CrossRef] [PubMed]

1996 (1)

A. Arieli, A. Sterkin, A. Grinvald, and A. Aertsen, “Dynamics of ongoing activity: explanation of the large variability in evoked cortical responses,” Science 273(5283), 1868–1871 (1996).
[CrossRef] [PubMed]

1995 (1)

B. Biswal, F. Zerrin 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]

Aertsen, A.

A. Arieli, A. Sterkin, A. Grinvald, and A. Aertsen, “Dynamics of ongoing activity: explanation of the large variability in evoked cortical responses,” Science 273(5283), 1868–1871 (1996).
[CrossRef] [PubMed]

Akin, A.

S. Aydöre, M. K. Mihçak, K. Ciftçi, and A. Akin, “On temporal connectivity of PFC via Gauss-Markov modeling of fNIRS signals,” IEEE Trans. Biomed. Eng. 57(3), 761–768 (2010).
[PubMed]

Amita, T.

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

Andrews, J. R.

J. L. Vincent, A. Z. Snyder, M. D. Fox, B. J. Shannon, J. R. Andrews, M. E. Raichle, and R. L. Buckner, “Coherent spontaneous activity identifies a hippocampal-parietal memory network,” J. Neurophysiol. 96(6), 3517–3531 (2006).
[CrossRef] [PubMed]

Andrews-Hanna, J. R.

R. L. Buckner, J. R. Andrews-Hanna, and D. L. Schacter, “The brain’s default network: anatomy, function, and relevance to disease,” Ann. N. Y. Acad. Sci. 1124(1), 1–38 (2008).
[CrossRef] [PubMed]

Arieli, A.

A. Arieli, A. Sterkin, A. Grinvald, and A. Aertsen, “Dynamics of ongoing activity: explanation of the large variability in evoked cortical responses,” Science 273(5283), 1868–1871 (1996).
[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]

Astafiev, S. V.

A. R. Carter, S. V. Astafiev, C. E. Lang, L. T. Connor, J. Rengachary, M. J. Strube, D. L. W. Pope, G. L. Shulman, and M. Corbetta, “Resting interhemispheric functional magnetic resonance imaging connectivity predicts performance after stroke,” Ann. Neurol. 67(3), 365–375 (2010).
[PubMed]

Aydöre, S.

S. Aydöre, M. K. Mihçak, K. Ciftçi, and A. Akin, “On temporal connectivity of PFC via Gauss-Markov modeling of fNIRS signals,” IEEE Trans. Biomed. Eng. 57(3), 761–768 (2010).
[PubMed]

Baker, J. T.

J. L. Vincent, G. H. Patel, M. D. Fox, A. Z. Snyder, J. T. Baker, D. C. Van Essen, J. M. Zempel, L. H. Snyder, M. Corbetta, and M. E. Raichle, “Intrinsic functional architecture in the anaesthetized monkey brain,” Nature 447(7140), 83–86 (2007).
[CrossRef] [PubMed]

Bandettini, P. A.

K. Murphy, R. M. Birn, D. A. Handwerker, T. B. Jones, and P. A. Bandettini, “The impact of global signal regression on resting state correlations: are anti-correlated networks introduced?” Neuroimage 44(3), 893–905 (2009).
[CrossRef] [PubMed]

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(4), 1536–1548 (2006).
[CrossRef] [PubMed]

Barkhof, F.

J. S. Damoiseaux, S. A. Rombouts, F. Barkhof, P. Scheltens, C. J. Stam, S. M. Smith, and C. F. Beckmann, “Consistent resting-state networks across healthy subjects,” Proc. Natl. Acad. Sci. U.S.A. 103(37), 13848–13853 (2006).
[CrossRef] [PubMed]

Beckmann, C. F.

J. S. Damoiseaux, S. A. Rombouts, F. Barkhof, P. Scheltens, C. J. Stam, S. M. Smith, and C. F. Beckmann, “Consistent resting-state networks across healthy subjects,” Proc. Natl. Acad. Sci. U.S.A. 103(37), 13848–13853 (2006).
[CrossRef] [PubMed]

M. De Luca, C. F. Beckmann, N. De Stefano, P. M. Matthews, and S. M. Smith, “fMRI resting state networks define distinct modes of long-distance interactions in the human brain,” Neuroimage 29(4), 1359–1367 (2006).
[CrossRef] [PubMed]

C. F. Beckmann, M. DeLuca, J. T. Devlin, and S. M. Smith, “Investigations into resting-state connectivity using independent component analysis,” Philos. Trans. R. Soc. Lond. B Biol. Sci. 360(1457), 1001–1013 (2005).
[CrossRef] [PubMed]

Beyerle, A.

H. Laufs, K. Krakow, P. Sterzer, E. Eger, A. Beyerle, A. Salek-Haddadi, and A. Kleinschmidt, “Electroencephalographic signatures of attentional and cognitive default modes in spontaneous brain activity fluctuations at rest,” Proc. Natl. Acad. Sci. U.S.A. 100(19), 11053–11058 (2003).
[CrossRef] [PubMed]

Birn, R. M.

K. Murphy, R. M. Birn, D. A. Handwerker, T. B. Jones, and P. A. Bandettini, “The impact of global signal regression on resting state correlations: are anti-correlated networks introduced?” Neuroimage 44(3), 893–905 (2009).
[CrossRef] [PubMed]

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(4), 1536–1548 (2006).
[CrossRef] [PubMed]

Biswal, B.

B. Biswal, F. Zerrin 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.

C. M. Lu, Y. J. Zhang, B. B. Biswal, Y. F. Zang, D. L. Peng, and C. Z. Zhu, “Use of fNIRS to assess resting state functional connectivity,” J. Neurosci. Methods 186(2), 242–249 (2010).
[CrossRef] [PubMed]

Boas, D. A.

A. Custo, D. A. Boas, D. Tsuzuki, I. Dan, R. C. Mesquita, B. Fischl, W. E. L. Grimson, and W. Wells, “Anatomical atlas-guided diffuse optical tomography of brain activation,” Neuroimage 49(1), 561–567 (2010).
[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(5), 054031 (2008).
[CrossRef] [PubMed]

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

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

D. K. Joseph, T. J. Huppert, M. A. Franceschini, and D. A. Boas, “Diffuse optical tomography system to image brain activation with improved spatial resolution and validation with functional magnetic resonance imaging,” Appl. Opt. 45(31), 8142–8151 (2006).
[CrossRef] [PubMed]

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

J. P. Culver, A. M. Siegel, M. A. Franceschini, J. B. Mandeville, and D. A. Boas, “Evidence that cerebral blood volume can provide brain activation maps with better spatial resolution than deoxygenated hemoglobin,” Neuroimage 27(4), 947–959 (2005).
[CrossRef] [PubMed]

Brooks, D. H.

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

Brown, E. N.

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).
[CrossRef] [PubMed]

Buckner, R. L.

R. L. Buckner, J. R. Andrews-Hanna, and D. L. Schacter, “The brain’s default network: anatomy, function, and relevance to disease,” Ann. N. Y. Acad. Sci. 1124(1), 1–38 (2008).
[CrossRef] [PubMed]

R. L. Buckner and J. L. Vincent, “Unrest at rest: default activity and spontaneous network correlations,” Neuroimage 37(4), 1091–1096, discussion 1097–1099 (2007).
[CrossRef] [PubMed]

J. L. Vincent, A. Z. Snyder, M. D. Fox, B. J. Shannon, J. R. Andrews, M. E. Raichle, and R. L. Buckner, “Coherent spontaneous activity identifies a hippocampal-parietal memory network,” J. Neurophysiol. 96(6), 3517–3531 (2006).
[CrossRef] [PubMed]

Buzsáki, G.

G. Buzsáki and A. Draguhn, “Neuronal oscillations in cortical networks,” Science 304(5679), 1926–1929 (2004).
[CrossRef] [PubMed]

Calhoun, V. D.

A. G. Garrity, G. D. Pearlson, K. McKiernan, D. Lloyd, K. A. Kiehl, and V. D. Calhoun, “Aberrant “default mode” functional connectivity in schizophrenia,” Am. J. Psychiatry 164(3), 450–457 (2007).
[CrossRef] [PubMed]

Carter, A. R.

A. R. Carter, S. V. Astafiev, C. E. Lang, L. T. Connor, J. Rengachary, M. J. Strube, D. L. W. Pope, G. L. Shulman, and M. Corbetta, “Resting interhemispheric functional magnetic resonance imaging connectivity predicts performance after stroke,” Ann. Neurol. 67(3), 365–375 (2010).
[PubMed]

Chen, H.

S. Lui, L. Ouyang, Q. Chen, X. Huang, H. Tang, H. Chen, D. Zhou, G. J. Kemp, and Q. Gong, “Differential interictal activity of the precuneus/posterior cingulate cortex revealed by resting state functional MRI at 3T in generalized vs. partial seizure,” J. Magn. Reson. Imaging 27(6), 1214–1220 (2008).
[CrossRef] [PubMed]

Chen, Q.

S. Lui, L. Ouyang, Q. Chen, X. Huang, H. Tang, H. Chen, D. Zhou, G. J. Kemp, and Q. Gong, “Differential interictal activity of the precuneus/posterior cingulate cortex revealed by resting state functional MRI at 3T in generalized vs. partial seizure,” J. Magn. Reson. Imaging 27(6), 1214–1220 (2008).
[CrossRef] [PubMed]

Cherkassky, V. L.

M. A. Just, V. L. Cherkassky, T. A. Keller, R. K. Kana, and N. J. Minshew, “Functional and anatomical cortical underconnectivity in autism: evidence from an FMRI study of an executive function task and corpus callosum morphometry,” Cereb. Cortex 17(4), 951–961 (2006).
[CrossRef] [PubMed]

Choi, J. H.

G. Morren, M. Wolf, P. Lemmerling, U. Wolf, J. H. Choi, E. Gratton, L. Lathauwer, and S. Huffel, “Detection of fast neuronal signals in the motor cortex from functional near infrared spectroscopy measurements using independent component analysis,” Med. Biol. Eng. Comput. 42(1), 92–99 (2004).
[CrossRef] [PubMed]

Ciftçi, K.

S. Aydöre, M. K. Mihçak, K. Ciftçi, and A. Akin, “On temporal connectivity of PFC via Gauss-Markov modeling of fNIRS signals,” IEEE Trans. Biomed. Eng. 57(3), 761–768 (2010).
[PubMed]

Cohen, A. L.

B. R. White, A. Z. Snyder, A. L. Cohen, S. E. Petersen, M. E. Raichle, B. L. Schlaggar, and J. P. Culver, “Resting-state functional connectivity in the human brain revealed with diffuse optical tomography,” Neuroimage 47(1), 148–156 (2009).
[CrossRef] [PubMed]

Connor, L. T.

A. R. Carter, S. V. Astafiev, C. E. Lang, L. T. Connor, J. Rengachary, M. J. Strube, D. L. W. Pope, G. L. Shulman, and M. Corbetta, “Resting interhemispheric functional magnetic resonance imaging connectivity predicts performance after stroke,” Ann. Neurol. 67(3), 365–375 (2010).
[PubMed]

Corbetta, M.

A. R. Carter, S. V. Astafiev, C. E. Lang, L. T. Connor, J. Rengachary, M. J. Strube, D. L. W. Pope, G. L. Shulman, and M. Corbetta, “Resting interhemispheric functional magnetic resonance imaging connectivity predicts performance after stroke,” Ann. Neurol. 67(3), 365–375 (2010).
[PubMed]

J. L. Vincent, G. H. Patel, M. D. Fox, A. Z. Snyder, J. T. Baker, D. C. Van Essen, J. M. Zempel, L. H. Snyder, M. Corbetta, and M. E. Raichle, “Intrinsic functional architecture in the anaesthetized monkey brain,” Nature 447(7140), 83–86 (2007).
[CrossRef] [PubMed]

Courchesne, E.

D. P. Kennedy, E. Redcay, and E. Courchesne, “Failing to deactivate: resting functional abnormalities in autism,” Proc. Natl. Acad. Sci. U.S.A. 103(21), 8275–8280 (2006).
[CrossRef] [PubMed]

Culver, J. P.

B. R. White, A. Z. Snyder, A. L. Cohen, S. E. Petersen, M. E. Raichle, B. L. Schlaggar, and J. P. Culver, “Resting-state functional connectivity in the human brain revealed with diffuse optical tomography,” Neuroimage 47(1), 148–156 (2009).
[CrossRef] [PubMed]

J. Markham, B. R. White, B. W. Zeff, and J. P. Culver, “Blind identification of evoked human brain activity with independent component analysis of optical data,” Hum. Brain Mapp. 30(8), 2382–2392 (2009).
[CrossRef] [PubMed]

J. P. Culver, A. M. Siegel, M. A. Franceschini, J. B. Mandeville, and D. A. Boas, “Evidence that cerebral blood volume can provide brain activation maps with better spatial resolution than deoxygenated hemoglobin,” Neuroimage 27(4), 947–959 (2005).
[CrossRef] [PubMed]

Custo, A.

A. Custo, D. A. Boas, D. Tsuzuki, I. Dan, R. C. Mesquita, B. Fischl, W. E. L. Grimson, and W. Wells, “Anatomical atlas-guided diffuse optical tomography of brain activation,” Neuroimage 49(1), 561–567 (2010).
[CrossRef] [PubMed]

Damoiseaux, J. S.

J. S. Damoiseaux, S. A. Rombouts, F. Barkhof, P. Scheltens, C. J. Stam, S. M. Smith, and C. F. Beckmann, “Consistent resting-state networks across healthy subjects,” Proc. Natl. Acad. Sci. U.S.A. 103(37), 13848–13853 (2006).
[CrossRef] [PubMed]

Dan, I.

A. Custo, D. A. Boas, D. Tsuzuki, I. Dan, R. C. Mesquita, B. Fischl, W. E. L. Grimson, and W. Wells, “Anatomical atlas-guided diffuse optical tomography of brain activation,” Neuroimage 49(1), 561–567 (2010).
[CrossRef] [PubMed]

De Luca, M.

M. De Luca, C. F. Beckmann, N. De Stefano, P. M. Matthews, and S. M. Smith, “fMRI resting state networks define distinct modes of long-distance interactions in the human brain,” Neuroimage 29(4), 1359–1367 (2006).
[CrossRef] [PubMed]

De Stefano, N.

M. De Luca, C. F. Beckmann, N. De Stefano, P. M. Matthews, and S. M. Smith, “fMRI resting state networks define distinct modes of long-distance interactions in the human brain,” Neuroimage 29(4), 1359–1367 (2006).
[CrossRef] [PubMed]

Delpy, D. T.

I. Tachtsidis, C. E. Elwell, T. S. Leung, C. W. Lee, M. Smith, and D. T. Delpy, “Investigation of cerebral haemodynamics by near-infrared spectroscopy in young healthy volunteers reveals posture-dependent spontaneous oscillations,” Physiol. Meas. 25(2), 437–445 (2004).
[CrossRef] [PubMed]

DeLuca, M.

C. F. Beckmann, M. DeLuca, J. T. Devlin, and S. M. Smith, “Investigations into resting-state connectivity using independent component analysis,” Philos. Trans. R. Soc. Lond. B Biol. Sci. 360(1457), 1001–1013 (2005).
[CrossRef] [PubMed]

Detre, J. A.

B. L. Edlow, M. N. Kim, T. Durduran, C. Zhou, M. E. Putt, A. G. Yodh, J. H. Greenberg, and J. A. Detre, “The effects of healthy aging on cerebral hemodynamic responses to posture change,” Physiol. Meas. 31(4), 477–495 (2010).
[CrossRef] [PubMed]

Devlin, J. T.

C. F. Beckmann, M. DeLuca, J. T. Devlin, and S. M. Smith, “Investigations into resting-state connectivity using independent component analysis,” Philos. Trans. R. Soc. Lond. B Biol. Sci. 360(1457), 1001–1013 (2005).
[CrossRef] [PubMed]

Diamond, J. B.

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(4), 1536–1548 (2006).
[CrossRef] [PubMed]

Diamond, S. G.

T. J. Huppert, S. G. Diamond, and D. A. Boas, “Direct estimation of evoked hemoglobin changes by multimodality fusion imaging,” J. Biomed. Opt. 13(5), 054031 (2008).
[CrossRef] [PubMed]

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

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

Draguhn, A.

G. Buzsáki and A. Draguhn, “Neuronal oscillations in cortical networks,” Science 304(5679), 1926–1929 (2004).
[CrossRef] [PubMed]

Durduran, T.

B. L. Edlow, M. N. Kim, T. Durduran, C. Zhou, M. E. Putt, A. G. Yodh, J. H. Greenberg, and J. A. Detre, “The effects of healthy aging on cerebral hemodynamic responses to posture change,” Physiol. Meas. 31(4), 477–495 (2010).
[CrossRef] [PubMed]

Dzemidzic, M.

M. J. Lowe, M. D. Phillips, J. T. Lurito, D. Mattson, M. Dzemidzic, and V. P. Mathews, “Multiple sclerosis: low-frequency temporal blood oxygen level-dependent fluctuations indicate reduced functional connectivity initial results,” Radiology 224(1), 184–192 (2002).
[CrossRef] [PubMed]

Edlow, B. L.

B. L. Edlow, M. N. Kim, T. Durduran, C. Zhou, M. E. Putt, A. G. Yodh, J. H. Greenberg, and J. A. Detre, “The effects of healthy aging on cerebral hemodynamic responses to posture change,” Physiol. Meas. 31(4), 477–495 (2010).
[CrossRef] [PubMed]

Eger, E.

H. Laufs, K. Krakow, P. Sterzer, E. Eger, A. Beyerle, A. Salek-Haddadi, and A. Kleinschmidt, “Electroencephalographic signatures of attentional and cognitive default modes in spontaneous brain activity fluctuations at rest,” Proc. Natl. Acad. Sci. U.S.A. 100(19), 11053–11058 (2003).
[CrossRef] [PubMed]

Einhäupl, K.

H. Obrig, M. Neufang, R. Wenzel, M. Kohl, J. Steinbrink, K. Einhäupl, and A. Villringer, “Spontaneous low frequency oscillations of cerebral hemodynamics and metabolism in human adults,” Neuroimage 12(6), 623–639 (2000).
[CrossRef] [PubMed]

Elwell, C. E.

I. Tachtsidis, C. E. Elwell, T. S. Leung, C. W. Lee, M. Smith, and D. T. Delpy, “Investigation of cerebral haemodynamics by near-infrared spectroscopy in young healthy volunteers reveals posture-dependent spontaneous oscillations,” Physiol. Meas. 25(2), 437–445 (2004).
[CrossRef] [PubMed]

Fantini, S.

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(4), 801–815 (2000).
[CrossRef] [PubMed]

Filiaci, M.

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(4), 801–815 (2000).
[CrossRef] [PubMed]

Fischl, B.

A. Custo, D. A. Boas, D. Tsuzuki, I. Dan, R. C. Mesquita, B. Fischl, W. E. L. Grimson, and W. Wells, “Anatomical atlas-guided diffuse optical tomography of brain activation,” Neuroimage 49(1), 561–567 (2010).
[CrossRef] [PubMed]

Fox, M. D.

M. D. Fox and M. E. Raichle, “Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging,” Nat. Rev. Neurosci. 8(9), 700–711 (2007).
[CrossRef] [PubMed]

J. L. Vincent, G. H. Patel, M. D. Fox, A. Z. Snyder, J. T. Baker, D. C. Van Essen, J. M. Zempel, L. H. Snyder, M. Corbetta, and M. E. Raichle, “Intrinsic functional architecture in the anaesthetized monkey brain,” Nature 447(7140), 83–86 (2007).
[CrossRef] [PubMed]

J. L. Vincent, A. Z. Snyder, M. D. Fox, B. J. Shannon, J. R. Andrews, M. E. Raichle, and R. L. Buckner, “Coherent spontaneous activity identifies a hippocampal-parietal memory network,” J. Neurophysiol. 96(6), 3517–3531 (2006).
[CrossRef] [PubMed]

Fox, P. T.

J. Xiong, L. M. Parsons, J. H. Gao, and P. T. Fox, “Interregional connectivity to primary motor cortex revealed using MRI resting state images,” Hum. Brain Mapp. 8(2-3), 151–156 (1999).
[CrossRef] [PubMed]

Franceschini, M. A.

D. K. Joseph, T. J. Huppert, M. A. Franceschini, and D. A. Boas, “Diffuse optical tomography system to image brain activation with improved spatial resolution and validation with functional magnetic resonance imaging,” Appl. Opt. 45(31), 8142–8151 (2006).
[CrossRef] [PubMed]

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

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

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

J. P. Culver, A. M. Siegel, M. A. Franceschini, J. B. Mandeville, and D. A. Boas, “Evidence that cerebral blood volume can provide brain activation maps with better spatial resolution than deoxygenated hemoglobin,” Neuroimage 27(4), 947–959 (2005).
[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(4), 801–815 (2000).
[CrossRef] [PubMed]

Gao, J. H.

J. Xiong, L. M. Parsons, J. H. Gao, and P. T. Fox, “Interregional connectivity to primary motor cortex revealed using MRI resting state images,” Hum. Brain Mapp. 8(2-3), 151–156 (1999).
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Figures (4)

Fig. 1
Fig. 1

(a) Probe geometry showing sources (x), detectors (o), and source-detector pairs we used for the analysis (dotted lines). Each channel was classified as part of one of the four lobes showed in the figure, in both hemispheres. (b) Picture of the probe on a subject, as an example. (c) Representative relative hemoglobin time-courses for a single channel of one subject that was used for correlation analysis.

Fig. 2
Fig. 2

Average correlation maps during resting state in a subject for all the three hemoglobin contrasts when the seed was placed on (a) prefrontal, (b) sensorimotor, and (c) visual cortex. The position of the seed is indicated by the black circle, and the colorbar represents the correlation coefficients. Orientation of the head is indicated by the nose pointing up.

Fig. 3
Fig. 3

Grand average of the symmetric correlation values obtained for the three main cortical regions measured, for each hemoglobin contrast. Correlation was calculated between a given seed time course and its corresponding contralateral channel, and then averaged over all possible seeds in each region. Error bars represent the standard deviation across all subjects.

Fig. 4
Fig. 4

(a) Power spectrum of one channel during a single run after each pre-processing step performed, from the acquisition (raw data) to the time course used for analysis. (b) Average correlation maps for one subject after performing all pre-processing steps (bottow row), and with all steps but BP regression (top row). (c) Percentage of channels greater than or equal to a certain threshold correlation value in HbO maps, as function of the correlation values. Error bars represent standard deviation across all runs of all subjects.

Tables (1)

Tables Icon

Table 1 Maximum correlation of HbO, HbR, and HbT maps, separated by 8 ROIs (4 different locations on the 2 hemispheres of the brain), when a seed is positioned at 3 different locations. Regions of high correlation are represented in bold, and uncertainty of the values denotes the standard error across all subjects

Equations (3)

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h n = ( U B P T U B P + λ 2 I ) 1 U B P T y n ,
X n = y n U B P h n ,
X n ( t ) = ( 1 N n = 1 N X n ( t ) ) β n + v n ' ( t ) ,

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