Abstract

The prefrontal cortex (PFC) is thought to play an important role in “higher” brain functions such as personality and emotion that may associated with several gender-related mental disorders. In this study, the gender effects of functional connectivity, cortical lateralization and significantly differences in the PFC were investigated by using resting-state functional optical tomography (fOT) measurement. A total of forty subjects including twenty healthy male and twenty healthy female adults were recruited for this study. In the results, the hemoglobin responses are higher in the male group. Additionally, male group exhibited the stronger connectivity in the PFC regions. In the result of lateralization, leftward dominant was observed in the male group but bilateral dominance in the female group. Finally, the 11 channels of the inferior PFC regions (corresponding to the region of Brodmann area 45) are significant different with spectrum analysis. Our findings suggest that the resting-state fOT method can provide high potential to apply to clinical neuroscience for several gender-related mental disorders diagnosis.

© 2014 Optical Society of America

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    [CrossRef] [PubMed]
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    [CrossRef] [PubMed]
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    [CrossRef] [PubMed]

2014 (3)

A. V. Medvedev, “Does the resting state connectivity have hemispheric asymmetry? A near-infrared spectroscopy study,” Neuroimage85(Pt 1), 400–407 (2014).
[CrossRef] [PubMed]

S. Lloyd-Fox, M. Papademetriou, M. K. Darboe, N. L. Everdell, R. Wegmuller, A. M. Prentice, S. E. Moore, and C. E. Elwell, “Functional near infrared spectroscopy (fNIRS) to assess cognitive function in infants in rural Africa,” Sci. Rep.4, 4740 (2014).
[CrossRef] [PubMed]

H. Niu and Y. He, “Resting-State Functional Brain Connectivity: Lessons from Functional Near-Infrared Spectroscopy,” Neuroscientist20(2), 173–188 (2014).
[CrossRef] [PubMed]

2013 (1)

C.-C. Chuang, C.-M. Chen, Y.-S. Hsieh, T.-C. Liu, and C.-W. Sun, “Brain structure and spatial sensitivity profile assessing by near-infrared spectroscopy modeling based on 3D MRI data,” J. Biophotonics6(3), 267–274 (2013).
[CrossRef] [PubMed]

2012 (6)

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,” Neuroimage63(1), 179–193 (2012).
[CrossRef] [PubMed]

D. Tomasi and N. D. Volkow, “Gender differences in brain functional connectivity density,” Hum. Brain Mapp.33(4), 849–860 (2012).
[CrossRef] [PubMed]

Y. Tong, L. M. Hocke, S. C. Licata, and B. Frederick, “Low-frequency oscillations measured in the periphery with near-infrared spectroscopy are strongly correlated with blood oxygen level-dependent functional magnetic resonance imaging signals,” J. Biomed. Opt.17(10), 106004 (2012).
[CrossRef] [PubMed]

H. Niu, J. Wang, T. Zhao, N. Shu, and Y. He, “Revealing topological organization of human brain functional networks with resting-state functional near infrared spectroscopy,” PLoS ONE7(9), e45771 (2012).
[CrossRef] [PubMed]

E. Glerean, J. Salmi, J. M. Lahnakoski, I. P. Jääskeläinen, and M. Sams, “Functional magnetic resonance imaging phase synchronization as a measure of dynamic functional connectivity,” Brain Connect.2(2), 91–101 (2012).
[CrossRef] [PubMed]

D. Tomasi and N. D. Volkow, “Laterality patterns of brain functional connectivity: gender effects,” Cereb. Cortex22(6), 1455–1462 (2012).
[CrossRef] [PubMed]

2011 (6)

L. Tian, J. Wang, C. Yan, and Y. He, “Hemisphere- and gender-related differences in small-world brain networks: A resting-state functional MRI study,” Neuroimage54(1), 191–202 (2011).
[CrossRef] [PubMed]

X. W. Song, Z. Y. Dong, X. Y. Long, S. F. Li, X. N. Zuo, C. Z. Zhu, Y. He, C. G. Yan, and Y. F. Zang, “REST: A Toolkit for resting-state functional magnetic resonance imaging data processing,” PLoS ONE6(9), e25031 (2011).
[CrossRef] [PubMed]

C. Rosazza and L. Minati, “Resting-state brain networks: literature review and clinical applications,” Neurol. Sci.32(5), 773–785 (2011).
[CrossRef] [PubMed]

S. Sasai, F. Homae, H. Watanabe, and G. Taga, “Frequency-specific functional connectivity in the brain during resting state revealed by NIRS,” Neuroimage56(1), 252–257 (2011).
[CrossRef] [PubMed]

G. Gong, Y. He, and A. C. Evans, “Brain connectivity: Gender makes a difference,” Neuroscientist17(5), 575–591 (2011).
[CrossRef] [PubMed]

H. Zhang, L. Duan, Y. J. Zhang, C. M. Lu, H. Liu, and C. Z. Zhu, “Test-retest assessment of independent component analysis-derived resting-state functional connectivity based on functional near-infrared spectroscopy,” Neuroimage55(2), 607–615 (2011).
[CrossRef] [PubMed]

2010 (9)

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,” Neuroimage51(3), 1150–1161 (2010).
[CrossRef] [PubMed]

Y. J. Zhang, C. M. Lu, B. B. Biswal, Y. F. Zang, D. L. Peng, and C. Z. Zhu, “Detecting resting-state functional connectivity in the language system using functional near-infrared spectroscopy,” J. Biomed. Opt.15(4), 047003 (2010).
[CrossRef] [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. Methods186(2), 242–249 (2010).
[CrossRef] [PubMed]

N. Jaušovec and K. Jaušovec, “Resting brain activity: Differences between genders,” Neuropsychologia48(13), 3918–3925 (2010).
[CrossRef] [PubMed]

Z. F. Zaidi, “Gender differences in human brain: A review,” Open Anat. J.2, 37–55 (2010).
[CrossRef]

M. P. van den Heuvel and H. E. Hulshoff Pol, “Exploring the brain network: A review on resting-state fMRI functional connectivity,” Eur. Neuropsychopharmacol.20(8), 519–534 (2010).
[CrossRef] [PubMed]

M. D. Fox and M. Greicius, “Clinical applications of resting state functional connectivity,” Front Syst. Neurosci.4, 19 (2010).
[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. Express1(1), 324–336 (2010).
[CrossRef] [PubMed]

W. Koch, S. Teipel, S. Mueller, K. Buerger, A. L. W. Bokde, H. Hampel, U. Coates, M. Reiser, and T. Meindl, “Effects of aging on default mode network activity in resting state fMRI: Does the method of analysis matter?” Neuroimage51(1), 280–287 (2010).
[CrossRef] [PubMed]

2009 (4)

M. Wallentin, “Putative sex differences in verbal abilities and language cortex: A critical review,” Brain Lang.108(3), 175–183 (2009).
[CrossRef] [PubMed]

J. M. Andreano and L. Cahill, “Sex influences on the neurobiology of learning and memory,” Learn. Mem.16(4), 248–266 (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]

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,” Neuroimage47(1), 148–156 (2009).
[CrossRef] [PubMed]

2008 (1)

N. Shimoda, K. Takeda, I. Imai, J. Kaneko, and H. Kato, “Cerebral laterality differences in handedness: A mental rotation study with NIRS,” Neurosci. Lett.430(1), 43–47 (2008).
[CrossRef] [PubMed]

2007 (6)

K. Takeda, Y. Gomi, I. Imai, N. Shimoda, M. Hiwatari, and H. Kato, “Shift of motor activation areas during recovery from hemiparesis after cerebral infarction: A longitudinal study with near-infrared spectroscopy,” Neurosci. Res.59(2), 136–144 (2007).
[CrossRef] [PubMed]

S. M. Coyle, T. E. Ward, and C. M. Markham, “Brain-computer interface using a simplified functional near-infrared spectroscopy system,” J. Neural Eng.4(3), 219–226 (2007).
[CrossRef] [PubMed]

Y. Hoshi, “Functional near-infrared spectroscopy: current status and future prospects,” J. Biomed. Opt.12(6), 062106 (2007).
[CrossRef] [PubMed]

P. H. Koh, D. E. Glaser, G. Flandin, S. Kiebel, B. Butterworth, A. Maki, D. T. Delpy, and C. E. Elwell, “Functional optical signal analysis: a software tool for near-infrared spectroscopy data processing incorporating statistical parametric mapping,” J. Biomed. Opt.12(6), 064010 (2007).
[CrossRef] [PubMed]

P. Fransson, B. Skiöld, S. Horsch, A. Nordell, M. Blennow, H. Lagercrantz, and U. Åden, “Resting-state networks in the infant brain,” Proc. Natl. Acad. Sci. U.S.A.104(39), 15531–15536 (2007).
[CrossRef] [PubMed]

H. Yang, X. Y. Long, Y. Yang, H. Yan, C. Z. Zhu, X. P. Zhou, Y. F. Zang, and Q. Y. Gong, “Amplitude of low frequency fluctuation within visual areas revealed by resting-state functional MRI,” Neuroimage36(1), 144–152 (2007).
[CrossRef] [PubMed]

2006 (5)

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,” Neuroimage31(4), 1536–1548 (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?” Neuroimage29(1), 54–66 (2006).
[CrossRef] [PubMed]

S. C. Bunce, M. Izzetoglu, K. Izzetoglu, B. Onaral, and K. Pourrezaei, “Functional near-infrared spectroscopy,” IEEE Eng. Med. Biol. Mag.25(4), 54–62 (2006).
[CrossRef] [PubMed]

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

E. M. Weiss, J. D. Ragland, C. M. Brensinger, W. B. Bilker, E. A. Deisenhammer, and M. Delazer, “Sex differences in clustering and switching in verbal fluency tasks,” J. Int. Neuropsychol. Soc.12(4), 502–509 (2006).
[CrossRef] [PubMed]

2005 (1)

A. A. Fingelkurts, A. A. Fingelkurts, and S. Kähkönen, “Functional connectivity in the brain-is it an elusive concept?” Neurosci. Biobehav. Rev.28(8), 827–836 (2005).
[CrossRef] [PubMed]

2004 (3)

G. Boas, “Noninvasive imaging of the brain,” Opt. Photonics News15, 52–55 (2004).

M. Okamoto, H. Dan, K. Sakamoto, K. Takeo, K. Shimizu, S. Kohno, I. Oda, S. Isobe, T. Suzuki, K. Kohyama, and I. Dan, “Three-dimensional probabilistic anatomical cranio-cerebral correlation via the international 10-20 system oriented for transcranial functional brain mapping,” Neuroimage21(1), 99–111 (2004).
[CrossRef] [PubMed]

N. F. Watson, C. Dodrill, D. Farrell, M. D. Holmes, and J. W. Miller, “Determination of language dominance with near-infrared spectroscopy: comparison with the intracarotid amobarbital procedure,” Seizure13(6), 399–402 (2004).
[CrossRef] [PubMed]

2003 (1)

B. Horwitz, “The elusive concept of brain connectivity,” Neuroimage19(2), 466–470 (2003).
[CrossRef] [PubMed]

2002 (1)

R. P. Kennan, D. Kim, A. Maki, H. Koizumi, and R. T. Constable, “Non-invasive assessment of language lateralization by transcranial near infrared optical topography and functional MRI,” Hum. Brain Mapp.16(3), 183–189 (2002).
[CrossRef] [PubMed]

2001 (1)

D. Cordes, V. M. Haughton, K. Arfanakis, J. D. Carew, P. A. Turski, C. H. Moritz, M. A. Quigley, and M. E. Meyerand, “Frequencies contributing to functional connectivity in the cerebral cortex in “resting-state” data,” AJNR Am. J. Neuroradiol.22(7), 1326–1333 (2001).
[PubMed]

1994 (1)

K. J. Friston, “Functional and effective connectivity in neuroimaging: A synthesis,” Hum. Brain Mapp.2(1-2), 56–78 (1994).
[CrossRef]

1993 (1)

Y. Hoshi and M. Tamura, “Dynamic multichannel near-infrared optical imaging of human brain activity,” J. Appl. Physiol.75(4), 1842–1846 (1993).
[PubMed]

Åden, U.

P. Fransson, B. Skiöld, S. Horsch, A. Nordell, M. Blennow, H. Lagercrantz, and U. Åden, “Resting-state networks in the infant brain,” Proc. Natl. Acad. Sci. U.S.A.104(39), 15531–15536 (2007).
[CrossRef] [PubMed]

Andreano, J. M.

J. M. Andreano and L. Cahill, “Sex influences on the neurobiology of learning and memory,” Learn. Mem.16(4), 248–266 (2009).
[CrossRef] [PubMed]

Arfanakis, K.

D. Cordes, V. M. Haughton, K. Arfanakis, J. D. Carew, P. A. Turski, C. H. Moritz, M. A. Quigley, and M. E. Meyerand, “Frequencies contributing to functional connectivity in the cerebral cortex in “resting-state” data,” AJNR Am. J. Neuroradiol.22(7), 1326–1333 (2001).
[PubMed]

Bandettini, P. A.

R. M. Birn, J. B. Diamond, M. A. Smith, and P. A. Bandettini, “Separating respiratory-variation-related fluctuations from neuronal-activity-related fluctuations in fMRI,” Neuroimage31(4), 1536–1548 (2006).
[CrossRef] [PubMed]

Bilker, W. B.

E. M. Weiss, J. D. Ragland, C. M. Brensinger, W. B. Bilker, E. A. Deisenhammer, and M. Delazer, “Sex differences in clustering and switching in verbal fluency tasks,” J. Int. Neuropsychol. Soc.12(4), 502–509 (2006).
[CrossRef] [PubMed]

Birn, R. M.

R. M. Birn, J. B. Diamond, M. A. Smith, and P. A. Bandettini, “Separating respiratory-variation-related fluctuations from neuronal-activity-related fluctuations in fMRI,” Neuroimage31(4), 1536–1548 (2006).
[CrossRef] [PubMed]

Biswal, B. B.

Y. J. Zhang, C. M. Lu, B. B. Biswal, Y. F. Zang, D. L. Peng, and C. Z. Zhu, “Detecting resting-state functional connectivity in the language system using functional near-infrared spectroscopy,” J. Biomed. Opt.15(4), 047003 (2010).
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P. H. Koh, D. E. Glaser, G. Flandin, S. Kiebel, B. Butterworth, A. Maki, D. T. Delpy, and C. E. Elwell, “Functional optical signal analysis: a software tool for near-infrared spectroscopy data processing incorporating statistical parametric mapping,” J. Biomed. Opt.12(6), 064010 (2007).
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R. P. Kennan, D. Kim, A. Maki, H. Koizumi, and R. T. Constable, “Non-invasive assessment of language lateralization by transcranial near infrared optical topography and functional MRI,” Hum. Brain Mapp.16(3), 183–189 (2002).
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E. Glerean, J. Salmi, J. M. Lahnakoski, I. P. Jääskeläinen, and M. Sams, “Functional magnetic resonance imaging phase synchronization as a measure of dynamic functional connectivity,” Brain Connect.2(2), 91–101 (2012).
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X. W. Song, Z. Y. Dong, X. Y. Long, S. F. Li, X. N. Zuo, C. Z. Zhu, Y. He, C. G. Yan, and Y. F. Zang, “REST: A Toolkit for resting-state functional magnetic resonance imaging data processing,” PLoS ONE6(9), e25031 (2011).
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Y. Tong, L. M. Hocke, S. C. Licata, and B. Frederick, “Low-frequency oscillations measured in the periphery with near-infrared spectroscopy are strongly correlated with blood oxygen level-dependent functional magnetic resonance imaging signals,” J. Biomed. Opt.17(10), 106004 (2012).
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H. Zhang, L. Duan, Y. J. Zhang, C. M. Lu, H. Liu, and C. Z. Zhu, “Test-retest assessment of independent component analysis-derived resting-state functional connectivity based on functional near-infrared spectroscopy,” Neuroimage55(2), 607–615 (2011).
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C.-C. Chuang, C.-M. Chen, Y.-S. Hsieh, T.-C. Liu, and C.-W. Sun, “Brain structure and spatial sensitivity profile assessing by near-infrared spectroscopy modeling based on 3D MRI data,” J. Biophotonics6(3), 267–274 (2013).
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X. W. Song, Z. Y. Dong, X. Y. Long, S. F. Li, X. N. Zuo, C. Z. Zhu, Y. He, C. G. Yan, and Y. F. Zang, “REST: A Toolkit for resting-state functional magnetic resonance imaging data processing,” PLoS ONE6(9), e25031 (2011).
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H. Yang, X. Y. Long, Y. Yang, H. Yan, C. Z. Zhu, X. P. Zhou, Y. F. Zang, and Q. Y. Gong, “Amplitude of low frequency fluctuation within visual areas revealed by resting-state functional MRI,” Neuroimage36(1), 144–152 (2007).
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H. Zhang, L. Duan, Y. J. Zhang, C. M. Lu, H. Liu, and C. Z. Zhu, “Test-retest assessment of independent component analysis-derived resting-state functional connectivity based on functional near-infrared spectroscopy,” Neuroimage55(2), 607–615 (2011).
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Y. J. Zhang, C. M. Lu, B. B. Biswal, Y. F. Zang, D. L. Peng, and C. Z. Zhu, “Detecting resting-state functional connectivity in the language system using functional near-infrared spectroscopy,” J. Biomed. Opt.15(4), 047003 (2010).
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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?” Neuroimage29(1), 54–66 (2006).
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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?” Neuroimage29(1), 54–66 (2006).
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S. M. Coyle, T. E. Ward, and C. M. Markham, “Brain-computer interface using a simplified functional near-infrared spectroscopy system,” J. Neural Eng.4(3), 219–226 (2007).
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N. F. Watson, C. Dodrill, D. Farrell, M. D. Holmes, and J. W. Miller, “Determination of language dominance with near-infrared spectroscopy: comparison with the intracarotid amobarbital procedure,” Seizure13(6), 399–402 (2004).
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S. Lloyd-Fox, M. Papademetriou, M. K. Darboe, N. L. Everdell, R. Wegmuller, A. M. Prentice, S. E. Moore, and C. E. Elwell, “Functional near infrared spectroscopy (fNIRS) to assess cognitive function in infants in rural Africa,” Sci. Rep.4, 4740 (2014).
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S. Lloyd-Fox, M. Papademetriou, M. K. Darboe, N. L. Everdell, R. Wegmuller, A. M. Prentice, S. E. Moore, and C. E. Elwell, “Functional near infrared spectroscopy (fNIRS) to assess cognitive function in infants in rural Africa,” Sci. Rep.4, 4740 (2014).
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Y. J. Zhang, C. M. Lu, B. B. Biswal, Y. F. Zang, D. L. Peng, and C. Z. Zhu, “Detecting resting-state functional connectivity in the language system using functional near-infrared spectroscopy,” J. Biomed. Opt.15(4), 047003 (2010).
[CrossRef] [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. Methods186(2), 242–249 (2010).
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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,” Neuroimage47(1), 148–156 (2009).
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S. C. Bunce, M. Izzetoglu, K. Izzetoglu, B. Onaral, and K. Pourrezaei, “Functional near-infrared spectroscopy,” IEEE Eng. Med. Biol. Mag.25(4), 54–62 (2006).
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S. Lloyd-Fox, M. Papademetriou, M. K. Darboe, N. L. Everdell, R. Wegmuller, A. M. Prentice, S. E. Moore, and C. E. Elwell, “Functional near infrared spectroscopy (fNIRS) to assess cognitive function in infants in rural Africa,” Sci. Rep.4, 4740 (2014).
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D. Cordes, V. M. Haughton, K. Arfanakis, J. D. Carew, P. A. Turski, C. H. Moritz, M. A. Quigley, and M. E. Meyerand, “Frequencies contributing to functional connectivity in the cerebral cortex in “resting-state” data,” AJNR Am. J. Neuroradiol.22(7), 1326–1333 (2001).
[PubMed]

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E. M. Weiss, J. D. Ragland, C. M. Brensinger, W. B. Bilker, E. A. Deisenhammer, and M. Delazer, “Sex differences in clustering and switching in verbal fluency tasks,” J. Int. Neuropsychol. Soc.12(4), 502–509 (2006).
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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,” Neuroimage47(1), 148–156 (2009).
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W. Koch, S. Teipel, S. Mueller, K. Buerger, A. L. W. Bokde, H. Hampel, U. Coates, M. Reiser, and T. Meindl, “Effects of aging on default mode network activity in resting state fMRI: Does the method of analysis matter?” Neuroimage51(1), 280–287 (2010).
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C. Rosazza and L. Minati, “Resting-state brain networks: literature review and clinical applications,” Neurol. Sci.32(5), 773–785 (2011).
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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,” Neuroimage63(1), 179–193 (2012).
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M. Okamoto, H. Dan, K. Sakamoto, K. Takeo, K. Shimizu, S. Kohno, I. Oda, S. Isobe, T. Suzuki, K. Kohyama, and I. Dan, “Three-dimensional probabilistic anatomical cranio-cerebral correlation via the international 10-20 system oriented for transcranial functional brain mapping,” Neuroimage21(1), 99–111 (2004).
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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,” Neuroimage63(1), 179–193 (2012).
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S. Sasai, F. Homae, H. Watanabe, and G. Taga, “Frequency-specific functional connectivity in the brain during resting state revealed by NIRS,” Neuroimage56(1), 252–257 (2011).
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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,” Neuroimage63(1), 179–193 (2012).
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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,” Neuroimage47(1), 148–156 (2009).
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M. Okamoto, H. Dan, K. Sakamoto, K. Takeo, K. Shimizu, S. Kohno, I. Oda, S. Isobe, T. Suzuki, K. Kohyama, and I. Dan, “Three-dimensional probabilistic anatomical cranio-cerebral correlation via the international 10-20 system oriented for transcranial functional brain mapping,” Neuroimage21(1), 99–111 (2004).
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N. Shimoda, K. Takeda, I. Imai, J. Kaneko, and H. Kato, “Cerebral laterality differences in handedness: A mental rotation study with NIRS,” Neurosci. Lett.430(1), 43–47 (2008).
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K. Takeda, Y. Gomi, I. Imai, N. Shimoda, M. Hiwatari, and H. Kato, “Shift of motor activation areas during recovery from hemiparesis after cerebral infarction: A longitudinal study with near-infrared spectroscopy,” Neurosci. Res.59(2), 136–144 (2007).
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H. Niu, J. Wang, T. Zhao, N. Shu, and Y. He, “Revealing topological organization of human brain functional networks with resting-state functional near infrared spectroscopy,” PLoS ONE7(9), e45771 (2012).
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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?” Neuroimage29(1), 54–66 (2006).
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P. Fransson, B. Skiöld, S. Horsch, A. Nordell, M. Blennow, H. Lagercrantz, and U. Åden, “Resting-state networks in the infant brain,” Proc. Natl. Acad. Sci. U.S.A.104(39), 15531–15536 (2007).
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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,” Neuroimage47(1), 148–156 (2009).
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S. Sasai, F. Homae, H. Watanabe, and G. Taga, “Frequency-specific functional connectivity in the brain during resting state revealed by NIRS,” Neuroimage56(1), 252–257 (2011).
[CrossRef] [PubMed]

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N. Shimoda, K. Takeda, I. Imai, J. Kaneko, and H. Kato, “Cerebral laterality differences in handedness: A mental rotation study with NIRS,” Neurosci. Lett.430(1), 43–47 (2008).
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M. Okamoto, H. Dan, K. Sakamoto, K. Takeo, K. Shimizu, S. Kohno, I. Oda, S. Isobe, T. Suzuki, K. Kohyama, and I. Dan, “Three-dimensional probabilistic anatomical cranio-cerebral correlation via the international 10-20 system oriented for transcranial functional brain mapping,” Neuroimage21(1), 99–111 (2004).
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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,” Neuroimage63(1), 179–193 (2012).
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W. Koch, S. Teipel, S. Mueller, K. Buerger, A. L. W. Bokde, H. Hampel, U. Coates, M. Reiser, and T. Meindl, “Effects of aging on default mode network activity in resting state fMRI: Does the method of analysis matter?” Neuroimage51(1), 280–287 (2010).
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L. Tian, J. Wang, C. Yan, and Y. He, “Hemisphere- and gender-related differences in small-world brain networks: A resting-state functional MRI study,” Neuroimage54(1), 191–202 (2011).
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D. Cordes, V. M. Haughton, K. Arfanakis, J. D. Carew, P. A. Turski, C. H. Moritz, M. A. Quigley, and M. E. Meyerand, “Frequencies contributing to functional connectivity in the cerebral cortex in “resting-state” data,” AJNR Am. J. Neuroradiol.22(7), 1326–1333 (2001).
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D. Tomasi and N. D. Volkow, “Gender differences in brain functional connectivity density,” Hum. Brain Mapp.33(4), 849–860 (2012).
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D. Tomasi and N. D. Volkow, “Laterality patterns of brain functional connectivity: gender effects,” Cereb. Cortex22(6), 1455–1462 (2012).
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H. Niu, J. Wang, T. Zhao, N. Shu, and Y. He, “Revealing topological organization of human brain functional networks with resting-state functional near infrared spectroscopy,” PLoS ONE7(9), e45771 (2012).
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L. Tian, J. Wang, C. Yan, and Y. He, “Hemisphere- and gender-related differences in small-world brain networks: A resting-state functional MRI study,” Neuroimage54(1), 191–202 (2011).
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S. M. Coyle, T. E. Ward, and C. M. Markham, “Brain-computer interface using a simplified functional near-infrared spectroscopy system,” J. Neural Eng.4(3), 219–226 (2007).
[CrossRef] [PubMed]

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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,” Neuroimage63(1), 179–193 (2012).
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[CrossRef] [PubMed]

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N. F. Watson, C. Dodrill, D. Farrell, M. D. Holmes, and J. W. Miller, “Determination of language dominance with near-infrared spectroscopy: comparison with the intracarotid amobarbital procedure,” Seizure13(6), 399–402 (2004).
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S. Lloyd-Fox, M. Papademetriou, M. K. Darboe, N. L. Everdell, R. Wegmuller, A. M. Prentice, S. E. Moore, and C. E. Elwell, “Functional near infrared spectroscopy (fNIRS) to assess cognitive function in infants in rural Africa,” Sci. Rep.4, 4740 (2014).
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E. M. Weiss, J. D. Ragland, C. M. Brensinger, W. B. Bilker, E. A. Deisenhammer, and M. Delazer, “Sex differences in clustering and switching in verbal fluency tasks,” J. Int. Neuropsychol. Soc.12(4), 502–509 (2006).
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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,” Neuroimage47(1), 148–156 (2009).
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L. Tian, J. Wang, C. Yan, and Y. He, “Hemisphere- and gender-related differences in small-world brain networks: A resting-state functional MRI study,” Neuroimage54(1), 191–202 (2011).
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X. W. Song, Z. Y. Dong, X. Y. Long, S. F. Li, X. N. Zuo, C. Z. Zhu, Y. He, C. G. Yan, and Y. F. Zang, “REST: A Toolkit for resting-state functional magnetic resonance imaging data processing,” PLoS ONE6(9), e25031 (2011).
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[CrossRef] [PubMed]

Yang, Y.

H. Yang, X. Y. Long, Y. Yang, H. Yan, C. Z. Zhu, X. P. Zhou, Y. F. Zang, and Q. Y. Gong, “Amplitude of low frequency fluctuation within visual areas revealed by resting-state functional MRI,” Neuroimage36(1), 144–152 (2007).
[CrossRef] [PubMed]

Zaidi, Z. F.

Z. F. Zaidi, “Gender differences in human brain: A review,” Open Anat. J.2, 37–55 (2010).
[CrossRef]

Zang, Y. F.

X. W. Song, Z. Y. Dong, X. Y. Long, S. F. Li, X. N. Zuo, C. Z. Zhu, Y. He, C. G. Yan, and Y. F. Zang, “REST: A Toolkit for resting-state functional magnetic resonance imaging data processing,” PLoS ONE6(9), e25031 (2011).
[CrossRef] [PubMed]

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[CrossRef] [PubMed]

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[CrossRef] [PubMed]

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[CrossRef] [PubMed]

H. Yang, X. Y. Long, Y. Yang, H. Yan, C. Z. Zhu, X. P. Zhou, Y. F. Zang, and Q. Y. Gong, “Amplitude of low frequency fluctuation within visual areas revealed by resting-state functional MRI,” Neuroimage36(1), 144–152 (2007).
[CrossRef] [PubMed]

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H. Zhang, L. Duan, Y. J. Zhang, C. M. Lu, H. Liu, and C. Z. Zhu, “Test-retest assessment of independent component analysis-derived resting-state functional connectivity based on functional near-infrared spectroscopy,” Neuroimage55(2), 607–615 (2011).
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[CrossRef] [PubMed]

Zhang, Y. J.

H. Zhang, L. Duan, Y. J. Zhang, C. M. Lu, H. Liu, and C. Z. Zhu, “Test-retest assessment of independent component analysis-derived resting-state functional connectivity based on functional near-infrared spectroscopy,” Neuroimage55(2), 607–615 (2011).
[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,” Neuroimage51(3), 1150–1161 (2010).
[CrossRef] [PubMed]

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[CrossRef] [PubMed]

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[CrossRef] [PubMed]

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H. Yang, X. Y. Long, Y. Yang, H. Yan, C. Z. Zhu, X. P. Zhou, Y. F. Zang, and Q. Y. Gong, “Amplitude of low frequency fluctuation within visual areas revealed by resting-state functional MRI,” Neuroimage36(1), 144–152 (2007).
[CrossRef] [PubMed]

Zhu, C. Z.

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H. Zhang, L. Duan, Y. J. Zhang, C. M. Lu, H. Liu, and C. Z. Zhu, “Test-retest assessment of independent component analysis-derived resting-state functional connectivity based on functional near-infrared spectroscopy,” Neuroimage55(2), 607–615 (2011).
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Figures (9)

Fig. 1
Fig. 1

The fOT channels localization. (Top left) the 3-D image of the optode position; (Bottom) The localizations of all 52 channels were positioned according to the international 10-20 system. Red and blue circles indicate near-infrared light emitter and detector positions, respectively. By using the international 10-20 system the detector 26 was positioned at the FZ marker point while the bottom row of sources 11, 13, 14 and 16 were placed on the T3–FP1–FP2–T4 line. Yellow diamonds indicate the measuring region of PFC.

Fig. 2
Fig. 2

The flowchart of resting-state fOT data analysis. (a) fOT channels localization above the PFC; (b) distribution of power spectrum within low-frequency band 0.01 to 0.08 Hz of ΔHbO2, ΔHb and ΔtHb for a single channel of one subject; (c) spectrogram from each channel of group-level statistics on averaging that was used for (d) significant difference analysis; (e) distribution of time series within low-frequency band 0.01 to 0.08 Hz of ΔHbO2, ΔHb and ΔtHb for a single channel of one subject that was used for correlation and lateralization analysis; (f) schematic diagram of group average correlation matrices for all channels; (g) schematic diagram of cortical lateralization analysis; (h) schematic diagram of functional connectivity in PFC; (i) schematic diagram of correlation coefficient between left vs. right PFC.

Fig. 3
Fig. 3

The correlation matrices derived from male and female groups. (a) and (d) are the correlation matrices of each pair channel of ΔHbO2; (b) and (e) are the correlation matrices of each pair channel of ΔHb; (c) and (f) are the correlation matrices of each pair channel of ΔtHb. Each figure shows a 52 × 52 square matrix, where the x and y axes correspond to the channels listed in optode geometry, and where each unit of matrix indicates the mean strength of the functional connectivity between each pair channel.

Fig. 4
Fig. 4

The distributions of correlation coefficient of male and female group of the same channel. (a), (b), and (c) are the distributions of correlation coefficient of ΔHbO2, ΔHb, and ΔtHb of male and female group of the same channel, respectively. The data were derived from correlation matrices. The red lines mean the result of linear regression. The dots mean the correlation coefficient of male and female group of the same channel.

Fig. 5
Fig. 5

Correlation analysis of hemodynamic response between left vs. right (Ch 3 vs. Ch 8, Ch4 vs. Ch7 et al.) bilateral PFC regions from male and female groups. (a), (c), and (e) are the correlation analysis of male bilateral PFC regions of ΔHbO2, ΔHb, and ΔtHb, respectively. (b), (d), and (f) are the correlation analysis of female bilateral PFC regions of ΔHbO2, ΔHb, and ΔtHb, respectively. The color bars indicate the mean concentration changes of hemoglobin include ΔHbO2, ΔHb and ΔtHb.

Fig. 6
Fig. 6

The functional connectivity pattern of left-side and right-side PFC in resting-state of (a) male group and (b) female group. The red dots represent the channels of each PFC region. The orange lines represent the functional connectivity (group-level correlation network) of left-side and right-side PFC region with correlation coefficient rXY larger than 0.6.

Fig. 7
Fig. 7

Cortical dominance channel of PFC region by computing LI of all left vs. right channel pairs in resting-state. (a) The result of cortical dominance of male group. (b) The result of cortical dominance of female group. The channels of blue diamond indicate the dominance region of leftward or rightward, and others indicate bilateral dominance.

Fig. 8
Fig. 8

The spectrogram of ΔtHb of each channel from (a) male and (b) female groups. Color bar indicates the intensity of frequency. The range of frequency 0.01 to 0.08 Hz was used to assess the significantly different channel of PFC between male and female groups. The channels of PFC regions are Ch. 3 to 8; Ch. 13 to 19; Ch. 23 to 30; Ch. 34 to 40 and Ch. 45 to 50 of frequency 0.01 to 0.08 Hz were highlighted on the bottom of figure.

Fig. 9
Fig. 9

The result of two-sample t-test from power spectrum of each channel of PFC between male and female groups. The Ch. 7, 8, 19, 29, 30, 38, and 39 marked with orange diamonds of left-side PFC and Ch. 13, 23, 34, and 45 marked with orange diamonds of right-side PFC are significantly different with p value < 0.001 between male and female groups.

Equations (2)

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r XY = i=1 n ( X i X ¯ )( Y i Y ¯ ) i=1 n ( X i X ¯ ) 2 ( Y i Y ¯ ) 2
LI= T L T R T L + T R

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