Abstract

In this study, functional near-infrared spectroscopy (fNIRS) and the graph theory approach were used to access the functional connectivity (FC) of the prefrontal cortex (PFC) in a resting state and during increased mental workload. For this very purpose, a pattern recognition-based test was developed, which elicited a strong response throughout the PFC during the test condition. FC parameters obtained during stimulation were found increased compared to those in a resting state after correlation based signal improvement (CBSI), which can attenuate those components of fNIRS signals which are unrelated to neural activity. These results indicate that the cognitive challenge increased the FC in the PFC and suggests a great potential in investigating FC in various cognitive states.

© 2017 Optical Society of America

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2017 (1)

E. S. Dørum, T. Kaufmann, D. Alnæs, O. A. Andreassen, G. Richard, K. K. Kolskår, J. E. Nordvik, and L. T. Westlye, “Increased sensitivity to age-related differences in brain functional connectivity during continuous multiple object tracking compared to resting-state,” Neuroimage 148, 364–372 (2017).
[Crossref] [PubMed]

2016 (3)

C. S. Silva, M. K. Hazrati, A. Keil, and J. C. Principe, “Quantification of neural functional connectivity during an active avoidance task,” Conf. Proc. IEEE Eng. Med. Biol. Soc. 2016, 708–711 (2016).
[PubMed]

S. L. Novi, R. B. Rodrigues, and R. C. Mesquita, “Resting state connectivity patterns with near-infrared spectroscopy data of the whole head,” Biomed. Opt. Express 7(7), 2524–2537 (2016).
[Crossref] [PubMed]

I. Tachtsidis and F. Scholkmann, “False positives and false negatives in functional near-infrared spectroscopy: issues, challenges, and the way forward,” Neurophotonics 3(3), 031405 (2016).
[Crossref] [PubMed]

2015 (2)

J. Xu, X. Liu, J. Zhang, Z. Li, X. Wang, F. Fang, and H. Niu, “FC-NIRS: A Functional Connectivity Analysis Tool for Near-Infrared Spectroscopy Data,” BioMed Res. Int. 2015, 248724 (2015).
[Crossref] [PubMed]

M. De Marco, F. Meneghello, D. Duzzi, J. Rigon, C. Pilosio, and A. Venneri, “Cognitive stimulation of the default-mode network modulates functional connectivity in healthy aging,” Brain Res. Bull. 121, 12126–12141 (2015).
[PubMed]

2014 (7)

C. J. Stam, “Modern network science of neurological disorders,” Nat. Rev. Neurosci. 15(10), 683–695 (2014).
[Crossref] [PubMed]

A. C. Ruocco, A. H. Rodrigo, J. Lam, S. I. Di Domenico, B. Graves, and H. Ayaz, “A problem-solving task specialized for functional neuroimaging: validation of the Scarborough adaptation of the Tower of London (S-TOL) using near-infrared spectroscopy,” Front. Hum. Neurosci. 8, 185 (2014).
[Crossref] [PubMed]

H. Niu and Y. He, “Resting-state functional brain connectivity: lessons from functional near-infrared spectroscopy,” Neuroscientist 20(2), 173–188 (2014).
[Crossref] [PubMed]

C. C. Chuang and C. W. Sun, “Gender-related effects of prefrontal cortex connectivity: a resting-state functional optical tomography study,” Biomed. Opt. Express 5(8), 2503–2516 (2014).
[Crossref] [PubMed]

J. Li and L. Qiu, “Temporal correlation of spontaneous hemodynamic activity in language areas measured with functional near-infrared spectroscopy,” Biomed. Opt. Express 5(2), 587–595 (2014).
[Crossref] [PubMed]

F. Scholkmann, S. Kleiser, A. J. Metz, R. Zimmermann, J. Mata Pavia, U. Wolf, and M. Wolf, “A review on continuous wave functional near-infrared spectroscopy and imaging instrumentation and methodology,” Neuroimage 85(Pt 1), 6–27 (2014).
[Crossref] [PubMed]

S. Basso Moro, S. Bisconti, M. Muthalib, M. Spezialetti, S. Cutini, M. Ferrari, G. Placidi, and V. Quaresima, “A semi-immersive virtual reality incremental swing balance task activates prefrontal cortex: a functional near-infrared spectroscopy study,” Neuroimage 85(Pt 1), 451–460 (2014).
[Crossref] [PubMed]

2013 (7)

C. Herff, D. Heger, O. Fortmann, J. Hennrich, F. Putze, and T. Schultz, “Mental workload during n-back task-quantified in the prefrontal cortex using fNIRS,” Front. Hum. Neurosci. 7, 7935 (2013).
[PubMed]

F. Scholkmann, U. Gerber, M. Wolf, and U. Wolf, “End-tidal CO2: an important parameter for a correct interpretation in functional brain studies using speech tasks,” Neuroimage 66, 71–79 (2013).
[Crossref] [PubMed]

A. K. Barbey, M. Koenigs, and J. Grafman, “Dorsolateral prefrontal contributions to human working memory,” Cortex 49(5), 1195–1205 (2013).
[Crossref] [PubMed]

F. Agosta, S. Sala, P. Valsasina, A. Meani, E. Canu, G. Magnani, S. F. Cappa, E. Scola, P. Quatto, M. A. Horsfield, A. Falini, G. Comi, and M. Filippi, “Brain network connectivity assessed using graph theory in frontotemporal dementia,” Neurology 81(2), 134–143 (2013).
[Crossref] [PubMed]

M. P. van den Heuvel and O. Sporns, “Network hubs in the human brain,” Trends Cogn. Sci. (Regul. Ed.) 17(12), 683–696 (2013).
[Crossref] [PubMed]

L. Wang, H. Li, Y. Liang, J. Zhang, X. Li, N. Shu, Y. Y. Wang, and Z. Zhang, “Amnestic mild cognitive impairment: topological reorganization of the default-mode network,” Radiology 268(2), 501–514 (2013).
[Crossref] [PubMed]

B. M. Tijms, A. M. Wink, W. de Haan, W. M. van der Flier, C. J. Stam, P. Scheltens, and F. Barkhof, “Alzheimer’s disease: connecting findings from graph theoretical studies of brain networks,” Neurobiol. Aging 34(8), 2023–2036 (2013).
[Crossref] [PubMed]

2012 (6)

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

T. Yamada, S. Umeyama, and K. Matsuda, “Separation of fNIRS signals into functional and systemic components based on differences in hemodynamic modalities,” PLoS One 7(11), e50271 (2012).
[Crossref] [PubMed]

M. Ferrari and V. Quaresima, “A brief review on the history of human functional near-infrared spectroscopy (fNIRS) development and fields of application,” Neuroimage 63(2), 921–935 (2012).
[Crossref] [PubMed]

A. C. Dieler, S. V. Tupak, and A. J. Fallgatter, “Functional near-infrared spectroscopy for the assessment of speech related tasks,” Brain Lang. 121(2), 90–109 (2012).
[Crossref] [PubMed]

S. Sasai, F. Homae, H. Watanabe, A. T. Sasaki, H. C. Tanabe, N. Sadato, and G. Taga, “A NIRS-fMRI study of resting state network,” Neuroimage 63(1), 179–193 (2012).
[Crossref] [PubMed]

L. Duan, Y. J. Zhang, and C. Z. Zhu, “Quantitative comparison of resting-state functional connectivity derived from fNIRS and fMRI: a simultaneous recording study,” Neuroimage 60(4), 2008–2018 (2012).
[Crossref] [PubMed]

2011 (4)

O. Sporns, “The human connectome: a complex network,” Ann. N. Y. Acad. Sci. 1224(1), 109–125 (2011).
[Crossref] [PubMed]

M. Pievani, W. de Haan, T. Wu, W. W. Seeley, and G. B. Frisoni, “Functional network disruption in the degenerative dementias,” Lancet Neurol. 10(9), 829–843 (2011).
[Crossref] [PubMed]

N. Shu, Y. Liu, K. Li, Y. Duan, J. Wang, C. Yu, H. Dong, J. Ye, and Y. He, “Diffusion tensor tractography reveals disrupted topological efficiency in white matter structural networks in multiple sclerosis,” Cereb. Cortex 21(11), 2565–2577 (2011).
[Crossref] [PubMed]

F. Skidmore, D. Korenkevych, Y. Liu, G. He, E. Bullmore, and P. M. Pardalos, “Connectivity brain networks based on wavelet correlation analysis in Parkinson fMRI data,” Neurosci. Lett. 499(1), 47–51 (2011).
[Crossref] [PubMed]

2010 (3)

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]

M. Rubinov and O. Sporns, “Complex network measures of brain connectivity: uses and interpretations,” Neuroimage 52(3), 1059–1069 (2010).
[Crossref] [PubMed]

X. Cui, S. Bray, and A. L. Reiss, “Functional near infrared spectroscopy (NIRS) signal improvement based on negative correlation between oxygenated and deoxygenated hemoglobin dynamics,” Neuroimage 49(4), 3039–3046 (2010).
[Crossref] [PubMed]

2009 (5)

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]

F. Tian, B. Chance, and H. Liu, “Investigation of the prefrontal cortex in response to duration-variable anagram tasks using functional near-infrared spectroscopy,” J. Biomed. Opt. 14(5), 054016 (2009).
[Crossref] [PubMed]

M. Rubinov, S. A. Knock, C. J. Stam, S. Micheloyannis, A. W. Harris, L. M. Williams, and M. Breakspear, “Small-world properties of nonlinear brain activity in schizophrenia,” Hum. Brain Mapp. 30(2), 403–416 (2009).
[Crossref] [PubMed]

Y. He, A. Dagher, Z. Chen, A. Charil, A. Zijdenbos, K. Worsley, and A. Evans, “Impaired small-world efficiency in structural cortical networks in multiple sclerosis associated with white matter lesion load,” Brain 132(Pt 12), 3366–3379 (2009).
[Crossref] [PubMed]

R. Debreczeni, I. Amrein, A. Kamondi, and I. Szirmai, “Hypocapnia induced by involuntary hyperventilation during mental arithmetic reduces cerebral blood flow velocity,” Tohoku J. Exp. Med. 217(2), 147–154 (2009).
[Crossref] [PubMed]

2008 (3)

I. Tachtsidis, T. S. Leung, L. Devoto, D. T. Delpy, and C. E. Elwell, “Measurement of frontal lobe functional activation and related systemic effects: a near-infrared spectroscopy investigation,” Adv. Exp. Med. Biol. 614, 397–403 (2008).
[Crossref] [PubMed]

Y. Liu, M. Liang, Y. Zhou, Y. He, Y. Hao, M. Song, C. Yu, H. Liu, Z. Liu, and T. Jiang, “Disrupted small-world networks in schizophrenia,” Brain 131(Pt 4), 945–961 (2008).
[Crossref] [PubMed]

I. Tachtsidis, T. S. Leung, M. . Tisdall, P. Devendra, M. Smith, D. T. Delpy, and C. E. Elwell, “Investigation of frontal cortex, motor cortex and systemic haemodynamic changes during anagram solving,” Adv. Exp. Med. Biol. 614, 21–28 (2008).
[Crossref] [PubMed]

2007 (2)

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]

B. Chance, S. Nioka, and Z. Zhao, “A wearable brain imager,” IEEE Eng. Med. Biol. Mag. 26(4), 30–37 (2007).
[Crossref] [PubMed]

2006 (1)

L. Kocsis, P. Herman, and A. Eke, “The modified Beer-Lambert law revisited,” Phys. Med. Biol. 51(5), N91–N98 (2006).
[Crossref] [PubMed]

2005 (1)

I. Szirmai, I. Amrein, L. Pálvölgyi, R. Debreczeni, and A. Kamondi, “Correlation between blood flow velocity in the middle cerebral artery and EEG during cognitive effort,” Brain Res. Cogn. Brain Res. 24(1), 33–40 (2005).
[Crossref] [PubMed]

2004 (2)

A. Gazzaley, J. Rissman, and M. D’Esposito, “Functional connectivity during working memory maintenance,” Cogn. Affect. Behav. Neurosci. 4(4), 580–599 (2004).
[Crossref] [PubMed]

J. Rissman, A. Gazzaley, and M. D’Esposito, “Measuring functional connectivity during distinct stages of a cognitive task,” Neuroimage 23(2), 752–763 (2004).
[Crossref] [PubMed]

2003 (6)

X. Delbeuck, M. Van der Linden, and F. Collette, “Alzheimer’s disease as a disconnection syndrome?” Neuropsychol. Rev. 13(2), 79–92 (2003).
[Crossref] [PubMed]

T. S. Leung, C. E. Elwell, J. R. Henty, and D. T. Delpy, “Simultaneous measurement of cerebral tissue oxygenation over the adult frontal and motor cortex during rest and functional activation,” Adv. Exp. Med. Biol. 510, 385–389 (2003).
[Crossref] [PubMed]

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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G. Taga, K. Asakawa, A. Maki, Y. Konishi, and H. Koizumi, “Brain imaging in awake infants by near-infrared optical topography,” Proc. Natl. Acad. Sci. U.S.A. 100(19), 10722–10727 (2003).
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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]

Y. Hoshi, “Functional near-infrared optical imaging: utility and limitations in human brain mapping,” Psychophysiology 40(4), 511–520 (2003).
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2002 (1)

D. Attwell and C. Iadecola, “The neural basis of functional brain imaging signals,” Trends Neurosci. 25(12), 621–625 (2002).
[Crossref] [PubMed]

2001 (3)

D. A. Boas, T. Gaudette, G. Strangman, X. Cheng, J. J. Marota, and J. B. Mandeville, “The accuracy of near infrared spectroscopy and imaging during focal changes in cerebral hemodynamics,” Neuroimage 13(1), 76–90 (2001).
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T. Mildner, D. G. Norris, C. Schwarzbauer, and C. J. Wiggins, “A qualitative test of the balloon model for BOLD-based MR signal changes at 3T,” Magn. Reson. Med. 46(5), 891–899 (2001).
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V. Latora and M. Marchiori, “Efficient behavior of small-world networks,” Phys. Rev. Lett. 87(19), 198701 (2001).
[Crossref] [PubMed]

1998 (3)

M. Firbank, E. Okada, and D. T. Delpy, “A theoretical study of the signal contribution of regions of the adult head to near-infrared spectroscopy studies of visual evoked responses,” Neuroimage 8(1), 69–78 (1998).
[Crossref] [PubMed]

R. B. Buxton, E. C. Wong, and L. R. Frank, “Dynamics of blood flow and oxygenation changes during brain activation: the balloon model,” Magn. Reson. Med. 39(6), 855–864 (1998).
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D. J. Watts and S. H. Strogatz, “Collective dynamics of ‘small-world’ networks,” Nature 393(6684), 440–442 (1998).
[Crossref] [PubMed]

1997 (1)

A. Villringer and B. Chance, “Non-invasive optical spectroscopy and imaging of human brain function,” Trends Neurosci. 20(10), 435–442 (1997).
[Crossref] [PubMed]

1995 (2)

A. Maki, Y. Yamashita, Y. Ito, E. Watanabe, Y. Mayanagi, and H. Koizumi, “Spatial and temporal analysis of human motor activity using noninvasive NIR topography,” Med. Phys. 22(12), 1997–2005 (1995).
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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]

1993 (1)

K. J. Friston, C. D. Frith, P. F. Liddle, and R. S. Frackowiak, “Functional connectivity: the principal-component analysis of large (PET) data sets,” J. Cereb. Blood Flow Metab. 13(1), 5–14 (1993).
[Crossref] [PubMed]

1992 (2)

A. Baddeley, “Working memory,” Science 255(5044), 556–559 (1992).
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M. A. Goodale and A. D. Milner, “Separate visual pathways for perception and action,” Trends Neurosci. 15(1), 20–25 (1992).
[Crossref] [PubMed]

1988 (1)

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

Agosta, F.

F. Agosta, S. Sala, P. Valsasina, A. Meani, E. Canu, G. Magnani, S. F. Cappa, E. Scola, P. Quatto, M. A. Horsfield, A. Falini, G. Comi, and M. Filippi, “Brain network connectivity assessed using graph theory in frontotemporal dementia,” Neurology 81(2), 134–143 (2013).
[Crossref] [PubMed]

Alnæs, D.

E. S. Dørum, T. Kaufmann, D. Alnæs, O. A. Andreassen, G. Richard, K. K. Kolskår, J. E. Nordvik, and L. T. Westlye, “Increased sensitivity to age-related differences in brain functional connectivity during continuous multiple object tracking compared to resting-state,” Neuroimage 148, 364–372 (2017).
[Crossref] [PubMed]

Amrein, I.

R. Debreczeni, I. Amrein, A. Kamondi, and I. Szirmai, “Hypocapnia induced by involuntary hyperventilation during mental arithmetic reduces cerebral blood flow velocity,” Tohoku J. Exp. Med. 217(2), 147–154 (2009).
[Crossref] [PubMed]

I. Szirmai, I. Amrein, L. Pálvölgyi, R. Debreczeni, and A. Kamondi, “Correlation between blood flow velocity in the middle cerebral artery and EEG during cognitive effort,” Brain Res. Cogn. Brain Res. 24(1), 33–40 (2005).
[Crossref] [PubMed]

Andreassen, O. A.

E. S. Dørum, T. Kaufmann, D. Alnæs, O. A. Andreassen, G. Richard, K. K. Kolskår, J. E. Nordvik, and L. T. Westlye, “Increased sensitivity to age-related differences in brain functional connectivity during continuous multiple object tracking compared to resting-state,” Neuroimage 148, 364–372 (2017).
[Crossref] [PubMed]

Asakawa, K.

G. Taga, K. Asakawa, A. Maki, Y. Konishi, and H. Koizumi, “Brain imaging in awake infants by near-infrared optical topography,” Proc. Natl. Acad. Sci. U.S.A. 100(19), 10722–10727 (2003).
[Crossref] [PubMed]

Ashina, M.

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

Attwell, D.

D. Attwell and C. Iadecola, “The neural basis of functional brain imaging signals,” Trends Neurosci. 25(12), 621–625 (2002).
[Crossref] [PubMed]

Ayaz, H.

A. C. Ruocco, A. H. Rodrigo, J. Lam, S. I. Di Domenico, B. Graves, and H. Ayaz, “A problem-solving task specialized for functional neuroimaging: validation of the Scarborough adaptation of the Tower of London (S-TOL) using near-infrared spectroscopy,” Front. Hum. Neurosci. 8, 185 (2014).
[Crossref] [PubMed]

Baddeley, A.

A. Baddeley, “Working memory,” Science 255(5044), 556–559 (1992).
[Crossref] [PubMed]

Barbey, A. K.

A. K. Barbey, M. Koenigs, and J. Grafman, “Dorsolateral prefrontal contributions to human working memory,” Cortex 49(5), 1195–1205 (2013).
[Crossref] [PubMed]

Barkhof, F.

B. M. Tijms, A. M. Wink, W. de Haan, W. M. van der Flier, C. J. Stam, P. Scheltens, and F. Barkhof, “Alzheimer’s disease: connecting findings from graph theoretical studies of brain networks,” Neurobiol. Aging 34(8), 2023–2036 (2013).
[Crossref] [PubMed]

Basso Moro, S.

S. Basso Moro, S. Bisconti, M. Muthalib, M. Spezialetti, S. Cutini, M. Ferrari, G. Placidi, and V. Quaresima, “A semi-immersive virtual reality incremental swing balance task activates prefrontal cortex: a functional near-infrared spectroscopy study,” Neuroimage 85(Pt 1), 451–460 (2014).
[Crossref] [PubMed]

Bisconti, S.

S. Basso Moro, S. Bisconti, M. Muthalib, M. Spezialetti, S. Cutini, M. Ferrari, G. Placidi, and V. Quaresima, “A semi-immersive virtual reality incremental swing balance task activates prefrontal cortex: a functional near-infrared spectroscopy study,” Neuroimage 85(Pt 1), 451–460 (2014).
[Crossref] [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]

Boas, D. A.

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

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]

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

Bray, S.

X. Cui, S. Bray, and A. L. Reiss, “Functional near infrared spectroscopy (NIRS) signal improvement based on negative correlation between oxygenated and deoxygenated hemoglobin dynamics,” Neuroimage 49(4), 3039–3046 (2010).
[Crossref] [PubMed]

Breakspear, M.

M. Rubinov, S. A. Knock, C. J. Stam, S. Micheloyannis, A. W. Harris, L. M. Williams, and M. Breakspear, “Small-world properties of nonlinear brain activity in schizophrenia,” Hum. Brain Mapp. 30(2), 403–416 (2009).
[Crossref] [PubMed]

Bullmore, E.

F. Skidmore, D. Korenkevych, Y. Liu, G. He, E. Bullmore, and P. M. Pardalos, “Connectivity brain networks based on wavelet correlation analysis in Parkinson fMRI data,” Neurosci. Lett. 499(1), 47–51 (2011).
[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]

Buxton, R. B.

R. B. Buxton, E. C. Wong, and L. R. Frank, “Dynamics of blood flow and oxygenation changes during brain activation: the balloon model,” Magn. Reson. Med. 39(6), 855–864 (1998).
[Crossref] [PubMed]

Canu, E.

F. Agosta, S. Sala, P. Valsasina, A. Meani, E. Canu, G. Magnani, S. F. Cappa, E. Scola, P. Quatto, M. A. Horsfield, A. Falini, G. Comi, and M. Filippi, “Brain network connectivity assessed using graph theory in frontotemporal dementia,” Neurology 81(2), 134–143 (2013).
[Crossref] [PubMed]

Cappa, S. F.

F. Agosta, S. Sala, P. Valsasina, A. Meani, E. Canu, G. Magnani, S. F. Cappa, E. Scola, P. Quatto, M. A. Horsfield, A. Falini, G. Comi, and M. Filippi, “Brain network connectivity assessed using graph theory in frontotemporal dementia,” Neurology 81(2), 134–143 (2013).
[Crossref] [PubMed]

Chance, B.

F. Tian, B. Chance, and H. Liu, “Investigation of the prefrontal cortex in response to duration-variable anagram tasks using functional near-infrared spectroscopy,” J. Biomed. Opt. 14(5), 054016 (2009).
[Crossref] [PubMed]

B. Chance, S. Nioka, and Z. Zhao, “A wearable brain imager,” IEEE Eng. Med. Biol. Mag. 26(4), 30–37 (2007).
[Crossref] [PubMed]

A. Villringer and B. Chance, “Non-invasive optical spectroscopy and imaging of human brain function,” Trends Neurosci. 20(10), 435–442 (1997).
[Crossref] [PubMed]

M. Izzetoglu, S. Nioka, B. Chance, and B. Onaral, “Single trial hemodynamic response estimation in a block anagram solution study using fNIR spectroscopy,” 2005 Ieee International Conference on Acoustics, Speech, and Signal Processing, 1–5633–636 (2005).
[Crossref]

Charil, A.

Y. He, A. Dagher, Z. Chen, A. Charil, A. Zijdenbos, K. Worsley, and A. Evans, “Impaired small-world efficiency in structural cortical networks in multiple sclerosis associated with white matter lesion load,” Brain 132(Pt 12), 3366–3379 (2009).
[Crossref] [PubMed]

Chen, Z.

Y. He, A. Dagher, Z. Chen, A. Charil, A. Zijdenbos, K. Worsley, and A. Evans, “Impaired small-world efficiency in structural cortical networks in multiple sclerosis associated with white matter lesion load,” Brain 132(Pt 12), 3366–3379 (2009).
[Crossref] [PubMed]

Cheng, X.

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

Chuang, C. C.

Collette, F.

X. Delbeuck, M. Van der Linden, and F. Collette, “Alzheimer’s disease as a disconnection syndrome?” Neuropsychol. Rev. 13(2), 79–92 (2003).
[Crossref] [PubMed]

Comi, G.

F. Agosta, S. Sala, P. Valsasina, A. Meani, E. Canu, G. Magnani, S. F. Cappa, E. Scola, P. Quatto, M. A. Horsfield, A. Falini, G. Comi, and M. Filippi, “Brain network connectivity assessed using graph theory in frontotemporal dementia,” Neurology 81(2), 134–143 (2013).
[Crossref] [PubMed]

Cooper, R. J.

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

Cope, M.

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

Cui, X.

X. Cui, S. Bray, and A. L. Reiss, “Functional near infrared spectroscopy (NIRS) signal improvement based on negative correlation between oxygenated and deoxygenated hemoglobin dynamics,” Neuroimage 49(4), 3039–3046 (2010).
[Crossref] [PubMed]

Cutini, S.

S. Basso Moro, S. Bisconti, M. Muthalib, M. Spezialetti, S. Cutini, M. Ferrari, G. Placidi, and V. Quaresima, “A semi-immersive virtual reality incremental swing balance task activates prefrontal cortex: a functional near-infrared spectroscopy study,” Neuroimage 85(Pt 1), 451–460 (2014).
[Crossref] [PubMed]

D’Esposito, M.

A. Gazzaley, J. Rissman, and M. D’Esposito, “Functional connectivity during working memory maintenance,” Cogn. Affect. Behav. Neurosci. 4(4), 580–599 (2004).
[Crossref] [PubMed]

J. Rissman, A. Gazzaley, and M. D’Esposito, “Measuring functional connectivity during distinct stages of a cognitive task,” Neuroimage 23(2), 752–763 (2004).
[Crossref] [PubMed]

Dagher, A.

Y. He, A. Dagher, Z. Chen, A. Charil, A. Zijdenbos, K. Worsley, and A. Evans, “Impaired small-world efficiency in structural cortical networks in multiple sclerosis associated with white matter lesion load,” Brain 132(Pt 12), 3366–3379 (2009).
[Crossref] [PubMed]

de Haan, W.

B. M. Tijms, A. M. Wink, W. de Haan, W. M. van der Flier, C. J. Stam, P. Scheltens, and F. Barkhof, “Alzheimer’s disease: connecting findings from graph theoretical studies of brain networks,” Neurobiol. Aging 34(8), 2023–2036 (2013).
[Crossref] [PubMed]

M. Pievani, W. de Haan, T. Wu, W. W. Seeley, and G. B. Frisoni, “Functional network disruption in the degenerative dementias,” Lancet Neurol. 10(9), 829–843 (2011).
[Crossref] [PubMed]

De Marco, M.

M. De Marco, F. Meneghello, D. Duzzi, J. Rigon, C. Pilosio, and A. Venneri, “Cognitive stimulation of the default-mode network modulates functional connectivity in healthy aging,” Brain Res. Bull. 121, 12126–12141 (2015).
[PubMed]

Debreczeni, R.

R. Debreczeni, I. Amrein, A. Kamondi, and I. Szirmai, “Hypocapnia induced by involuntary hyperventilation during mental arithmetic reduces cerebral blood flow velocity,” Tohoku J. Exp. Med. 217(2), 147–154 (2009).
[Crossref] [PubMed]

I. Szirmai, I. Amrein, L. Pálvölgyi, R. Debreczeni, and A. Kamondi, “Correlation between blood flow velocity in the middle cerebral artery and EEG during cognitive effort,” Brain Res. Cogn. Brain Res. 24(1), 33–40 (2005).
[Crossref] [PubMed]

Delbeuck, X.

X. Delbeuck, M. Van der Linden, and F. Collette, “Alzheimer’s disease as a disconnection syndrome?” Neuropsychol. Rev. 13(2), 79–92 (2003).
[Crossref] [PubMed]

Delpy, D. T.

I. Tachtsidis, T. S. Leung, M. . Tisdall, P. Devendra, M. Smith, D. T. Delpy, and C. E. Elwell, “Investigation of frontal cortex, motor cortex and systemic haemodynamic changes during anagram solving,” Adv. Exp. Med. Biol. 614, 21–28 (2008).
[Crossref] [PubMed]

I. Tachtsidis, T. S. Leung, L. Devoto, D. T. Delpy, and C. E. Elwell, “Measurement of frontal lobe functional activation and related systemic effects: a near-infrared spectroscopy investigation,” Adv. Exp. Med. Biol. 614, 397–403 (2008).
[Crossref] [PubMed]

T. S. Leung, C. E. Elwell, J. R. Henty, and D. T. Delpy, “Simultaneous measurement of cerebral tissue oxygenation over the adult frontal and motor cortex during rest and functional activation,” Adv. Exp. Med. Biol. 510, 385–389 (2003).
[Crossref] [PubMed]

M. Firbank, E. Okada, and D. T. Delpy, “A theoretical study of the signal contribution of regions of the adult head to near-infrared spectroscopy studies of visual evoked responses,” Neuroimage 8(1), 69–78 (1998).
[Crossref] [PubMed]

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

Devendra, P.

I. Tachtsidis, T. S. Leung, M. . Tisdall, P. Devendra, M. Smith, D. T. Delpy, and C. E. Elwell, “Investigation of frontal cortex, motor cortex and systemic haemodynamic changes during anagram solving,” Adv. Exp. Med. Biol. 614, 21–28 (2008).
[Crossref] [PubMed]

Devoto, L.

I. Tachtsidis, T. S. Leung, L. Devoto, D. T. Delpy, and C. E. Elwell, “Measurement of frontal lobe functional activation and related systemic effects: a near-infrared spectroscopy investigation,” Adv. Exp. Med. Biol. 614, 397–403 (2008).
[Crossref] [PubMed]

Di Domenico, S. I.

A. C. Ruocco, A. H. Rodrigo, J. Lam, S. I. Di Domenico, B. Graves, and H. Ayaz, “A problem-solving task specialized for functional neuroimaging: validation of the Scarborough adaptation of the Tower of London (S-TOL) using near-infrared spectroscopy,” Front. Hum. Neurosci. 8, 185 (2014).
[Crossref] [PubMed]

Dieler, A. C.

A. C. Dieler, S. V. Tupak, and A. J. Fallgatter, “Functional near-infrared spectroscopy for the assessment of speech related tasks,” Brain Lang. 121(2), 90–109 (2012).
[Crossref] [PubMed]

Dong, H.

N. Shu, Y. Liu, K. Li, Y. Duan, J. Wang, C. Yu, H. Dong, J. Ye, and Y. He, “Diffusion tensor tractography reveals disrupted topological efficiency in white matter structural networks in multiple sclerosis,” Cereb. Cortex 21(11), 2565–2577 (2011).
[Crossref] [PubMed]

Dørum, E. S.

E. S. Dørum, T. Kaufmann, D. Alnæs, O. A. Andreassen, G. Richard, K. K. Kolskår, J. E. Nordvik, and L. T. Westlye, “Increased sensitivity to age-related differences in brain functional connectivity during continuous multiple object tracking compared to resting-state,” Neuroimage 148, 364–372 (2017).
[Crossref] [PubMed]

Duan, L.

L. Duan, Y. J. Zhang, and C. Z. Zhu, “Quantitative comparison of resting-state functional connectivity derived from fNIRS and fMRI: a simultaneous recording study,” Neuroimage 60(4), 2008–2018 (2012).
[Crossref] [PubMed]

Duan, Y.

N. Shu, Y. Liu, K. Li, Y. Duan, J. Wang, C. Yu, H. Dong, J. Ye, and Y. He, “Diffusion tensor tractography reveals disrupted topological efficiency in white matter structural networks in multiple sclerosis,” Cereb. Cortex 21(11), 2565–2577 (2011).
[Crossref] [PubMed]

Duzzi, D.

M. De Marco, F. Meneghello, D. Duzzi, J. Rigon, C. Pilosio, and A. Venneri, “Cognitive stimulation of the default-mode network modulates functional connectivity in healthy aging,” Brain Res. Bull. 121, 12126–12141 (2015).
[PubMed]

Eke, A.

L. Kocsis, P. Herman, and A. Eke, “The modified Beer-Lambert law revisited,” Phys. Med. Biol. 51(5), N91–N98 (2006).
[Crossref] [PubMed]

Elwell, C. E.

I. Tachtsidis, T. S. Leung, L. Devoto, D. T. Delpy, and C. E. Elwell, “Measurement of frontal lobe functional activation and related systemic effects: a near-infrared spectroscopy investigation,” Adv. Exp. Med. Biol. 614, 397–403 (2008).
[Crossref] [PubMed]

I. Tachtsidis, T. S. Leung, M. . Tisdall, P. Devendra, M. Smith, D. T. Delpy, and C. E. Elwell, “Investigation of frontal cortex, motor cortex and systemic haemodynamic changes during anagram solving,” Adv. Exp. Med. Biol. 614, 21–28 (2008).
[Crossref] [PubMed]

T. S. Leung, C. E. Elwell, J. R. Henty, and D. T. Delpy, “Simultaneous measurement of cerebral tissue oxygenation over the adult frontal and motor cortex during rest and functional activation,” Adv. Exp. Med. Biol. 510, 385–389 (2003).
[Crossref] [PubMed]

Evans, A.

Y. He, A. Dagher, Z. Chen, A. Charil, A. Zijdenbos, K. Worsley, and A. Evans, “Impaired small-world efficiency in structural cortical networks in multiple sclerosis associated with white matter lesion load,” Brain 132(Pt 12), 3366–3379 (2009).
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Figures (6)

Fig. 1
Fig. 1 Concept and experimental design. The cognitive task consists of locating a subset within a larger visual pattern (A). View of the 16-channel fNIRS imaging setup with optode in the middle, control box on the left, and laptop computer on the right (B). Position of the 16- channel fNIRS optode over the frontal lobe with red and blue circles indicating light sources and detectors respectively (C).
Fig. 2
Fig. 2 fNIRS data preprocessing. Raw HbO and HbR time series (A) were bandpass filtered with cutoff frequencies 0.02 and 0.4 (B), then averaged over the stimulation blocks to increase signal-to-noise ratio (C). For the connectivity analysis, CBSI was performed on the band-pass filtered data first (D), and then the acquired data was averaged over the stimulation blocks (E).
Fig. 3
Fig. 3 Constructing a binary functional connection network from fNIRS-data. 16 channel total hemoglobin (HbT) time series - before and after CBSI (not shown) as well - were token as the sum of HbO and HbR data (A). Pearson correlation coefficients were calculated for all pairwise combination of channels, resulting in a symmetrical crosscorrelation matrix (B). Binary adjacency matrices were obtained by weight-thresholding along 17 different threshold values (C). Network metrics density, clustering coefficient and efficiency were calculated on the functional connection networks described by the adjacency matrices (D).
Fig. 4
Fig. 4 The grand-averaged temporal profiles of ΔHbR (blue curves), ΔHbO (red curves) and rHb (black curves) time series with the topographic projection of the maximal HbO amplitudes. Resting state data shows only random fluctuations with no significant deviation from baseline (A). On the other hand, an increase in HbR, and decrease in HbO and rHb can be seen during and after task performing (indicated by the grey area) with * symbols marking significant difference from the baseline. Topographical projection of the maximal HbO amplitudes shows only small-amplitude random fluctuation in resting-state (C), while it shows increased activity throughout the whole PFC with maximal amplitude in the lateral and central regions (D).
Fig. 5
Fig. 5 Results of density (D), clustering coefficient (C) and efficiency (E) as a function of threshold. Before CBSI was applied (left, circle markers) the only significant differences between the control (blue) and test (red) groups occurred in D between threshold 0.05 and 0.2 (marked by * symbols). Connectivity analysis performed on data after CBSI (right, square markers) revealed robust increase in D, C and E due to cognitive stimulation, along most of the threshold values (significant differences marked by * symbols).
Fig. 6
Fig. 6 The effect of CBSI. The effect of CBSI was marginal on resting-state data (left, blue lines), significantly increasing C and E for only a few threshold values (marked by * symbols). The method had an extensive effect on data registered during task performing (right, red curves), as it increased D, C and E significantly in every case (marked by * symbols)

Equations (3)

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D= 1 2n( n1 ) in jn a ij
C= 1 n iN 1 k i ( k i 1 ) j,hn a ij a ih a jh
E= 1 n in jn,ij d ij 1 n1

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