Abstract

We developed a single-camera two-channel hemodynamic imaging system that uses near-infrared light to monitor the mouse brain in vivo with an exposed, un-thinned, and intact skull to explore the effect of Parkinson’s disease on the resting state functional connectivity of the brain. To demonstrate our system’s ability to monitor cerebral hemodynamics, we first performed direct electrical stimulation of an anesthetized healthy mouse brain and detected hemodynamic changes localized to the stimulated area. Subsequently, we developed a unilaterally lesioned 6-hydroxydopamine (hemi-parkinsonian) mouse model and detected the differences in functional connectivity between the normal and hemi-parkinsonian mouse brains by comparing the hemispheric hemodynamic correlations during the resting state. Seed-based correlation for the oxy-hemoglobin channel from the left and right hemispheres of healthy mice was much higher and more symmetric than in hemi-parkinsonian mice. Through a k-means clustering of the hemodynamic signals, the healthy mouse brains were segmented according to brain region, but the hemi-parkinsonian mice did not show a similar segmentation. Overall, this study highlights the development of a spatial multiplexing hemodynamic imaging system that reveals the resting state hemodynamic connectivity in healthy and hemi-parkinsonian mice.

© 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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References

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2018 (1)

X. Liu, J. A. de Zwart, M. L. Schölvinck, C. Chang, F. Q. Ye, D. A. Leopold, and J. H. Duyn, “Subcortical evidence for a contribution of arousal to fMRI studies of brain activity,” Nat. Commun. 9(1), 395 (2018).
[Crossref] [PubMed]

2017 (3)

M. P. Vanni, A. W. Chan, M. Balbi, G. Silasi, and T. H. Murphy, “Mesoscale mapping of mouse cortex reveals frequency-dependent cycling between distinct macroscale functional modules,” J. Neurosci. 37(31), 7513–7533 (2017).
[Crossref] [PubMed]

H. K. Jin, T. Y. Hwang, and S. H. Cho, “Effect of electrical stimulation on blood flow velocity and vessel size,” Open Med. (Wars.) 12(1), 5–11 (2017).
[Crossref] [PubMed]

J. R. Bumstead, A. Q. Bauer, P. W. Wright, and J. P. Culver, “Cerebral functional connectivity and Mayer waves in mice: Phenomena and separability,” J. Cereb. Blood Flow Metab. 37(2), 471–484 (2017).
[Crossref] [PubMed]

2015 (3)

2014 (1)

C. Luo, W. Song, Q. Chen, Z. Zheng, K. Chen, B. Cao, J. Yang, J. Li, X. Huang, Q. Gong, and H. F. Shang, “Reduced functional connectivity in early-stage drug-naive Parkinson’s disease: a resting-state fMRI study,” Neurobiol. Aging 35(2), 431–441 (2014).
[Crossref] [PubMed]

2013 (3)

E. Guevara, N. Sadekova, H. Girouard, and F. Lesage, “Optical imaging of resting-state functional connectivity in a novel arterial stiffness model,” Biomed. Opt. Express 4(11), 2332–2346 (2013).
[Crossref] [PubMed]

D. N. Guilfoyle, S. V. Gerum, J. L. Sanchez, A. Balla, H. Sershen, D. C. Javitt, and M. J. Hoptman, “Functional connectivity fMRI in mouse brain at 7T using isoflurane,” J. Neurosci. Methods 214(2), 144–148 (2013).
[Crossref] [PubMed]

T. M. Kodinariya and P. R. Makwana, “Review on determining number of Cluster in K-Means Clustering,” Int. J. 1(6), 90–95 (2013).

2012 (2)

S. Lee, D. Koh, A. Jo, H. Y. Lim, Y. J. Jung, C. K. Kim, Y. Seo, C. H. Im, B. M. Kim, and M. Suh, “Depth-dependent cerebral hemodynamic responses following direct cortical electrical stimulation (DCES) revealed by in vivo dual-optical imaging techniques,” Opt. Express 20(7), 6932–6943 (2012).
[Crossref] [PubMed]

A. Tessitore, M. Amboni, F. Esposito, A. Russo, M. Picillo, L. Marcuccio, M. T. Pellecchia, C. Vitale, M. Cirillo, G. Tedeschi, and P. Barone, “Resting-state brain connectivity in patients with Parkinson’s disease and freezing of gait,” Parkinsonism Relat. Disord. 18(6), 781–787 (2012).
[Crossref] [PubMed]

2011 (5)

B. R. White, A. Q. Bauer, A. Z. Snyder, B. L. Schlaggar, J. M. Lee, and J. P. Culver, “Imaging of functional connectivity in the mouse brain,” PLoS One 6(1), e16322 (2011).
[Crossref] [PubMed]

T. Wu, X. Long, L. Wang, M. Hallett, Y. Zang, K. Li, and P. Chan, “Functional connectivity of cortical motor areas in the resting state in Parkinson’s disease,” Hum. Brain Mapp. 32(9), 1443–1457 (2011).
[Crossref] [PubMed]

K. J. Friston, “Functional and effective connectivity: a review,” Brain Connect. 1(1), 13–36 (2011).
[Crossref] [PubMed]

S. E. Joel, B. S. Caffo, P. C. van Zijl, and J. J. Pekar, “On the relationship between seed-based and ICA-based measures of functional connectivity,” Magn. Reson. Med. 66(3), 644–657 (2011).
[Crossref] [PubMed]

F. Cauda, F. D’Agata, K. Sacco, S. Duca, G. Geminiani, and A. Vercelli, “Functional connectivity of the insula in the resting brain,” Neuroimage 55(1), 8–23 (2011).
[Crossref] [PubMed]

2010 (3)

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]

A. K. Jain, “Data clustering: 50 years beyond K-means,” Pattern Recognit. Lett. 31(8), 651–666 (2010).
[Crossref]

C. H. Im, Y. J. Jung, S. Lee, D. Koh, D. W. Kim, and B. M. Kim, “Estimation of directional coupling between cortical areas using Near-Infrared Spectroscopy (NIRS),” Opt. Express 18(6), 5730–5739 (2010).
[Crossref] [PubMed]

2009 (3)

J. Virtanen, T. Noponen, and P. Meriläinen, “Comparison of principal and independent component analysis in removing extracerebral interference from near-infrared spectroscopy signals,” J. Biomed. Opt. 14(5), 054032 (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]

T. Wu, L. Wang, Y. Chen, C. Zhao, K. Li, and P. Chan, “Changes of functional connectivity of the motor network in the resting state in Parkinson’s disease,” Neurosci. Lett. 460(1), 6–10 (2009).
[Crossref] [PubMed]

2007 (1)

F. Irani, S. M. Platek, S. Bunce, A. C. Ruocco, and D. Chute, “Functional near infrared spectroscopy (fNIRS): an emerging neuroimaging technology with important applications for the study of brain disorders,” Clin. Neuropsychol. 21(1), 9–37 (2007).
[Crossref] [PubMed]

2005 (1)

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

2004 (1)

B. M. Ances, “Coupling of changes in cerebral blood flow with neural activity: what must initially dip must come back up,” J. Cereb. Blood Flow Metab. 24(1), 1–6 (2004).
[Crossref] [PubMed]

2003 (1)

P. Ahlgren, B. Jarneving, and R. Rousseau, “Requirements for a cocitation similarity measure, with special reference to Pearson’s correlation coefficient,” J. Am. Soc. Inf. Sci. Technol. 54(6), 550–560 (2003).
[Crossref]

1999 (1)

C. Messier, S. Émond, and K. Ethier, “New techniques in stereotaxic surgery and anesthesia in the mouse,” Pharmacol. Biochem. Behav. 63(2), 313–318 (1999).
[Crossref] [PubMed]

1997 (2)

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

1972 (1)

B. M. Slotnick, “Stereotaxic surgical techniques for the mouse,” Physiol. Behav. 8(1), 139–142 (1972).
[Crossref] [PubMed]

Ahlgren, P.

P. Ahlgren, B. Jarneving, and R. Rousseau, “Requirements for a cocitation similarity measure, with special reference to Pearson’s correlation coefficient,” J. Am. Soc. Inf. Sci. Technol. 54(6), 550–560 (2003).
[Crossref]

Amboni, M.

A. Tessitore, M. Amboni, F. Esposito, A. Russo, M. Picillo, L. Marcuccio, M. T. Pellecchia, C. Vitale, M. Cirillo, G. Tedeschi, and P. Barone, “Resting-state brain connectivity in patients with Parkinson’s disease and freezing of gait,” Parkinsonism Relat. Disord. 18(6), 781–787 (2012).
[Crossref] [PubMed]

Ances, B. M.

B. M. Ances, “Coupling of changes in cerebral blood flow with neural activity: what must initially dip must come back up,” J. Cereb. Blood Flow Metab. 24(1), 1–6 (2004).
[Crossref] [PubMed]

Balbi, M.

M. P. Vanni, A. W. Chan, M. Balbi, G. Silasi, and T. H. Murphy, “Mesoscale mapping of mouse cortex reveals frequency-dependent cycling between distinct macroscale functional modules,” J. Neurosci. 37(31), 7513–7533 (2017).
[Crossref] [PubMed]

Balla, A.

D. N. Guilfoyle, S. V. Gerum, J. L. Sanchez, A. Balla, H. Sershen, D. C. Javitt, and M. J. Hoptman, “Functional connectivity fMRI in mouse brain at 7T using isoflurane,” J. Neurosci. Methods 214(2), 144–148 (2013).
[Crossref] [PubMed]

Barone, P.

A. Tessitore, M. Amboni, F. Esposito, A. Russo, M. Picillo, L. Marcuccio, M. T. Pellecchia, C. Vitale, M. Cirillo, G. Tedeschi, and P. Barone, “Resting-state brain connectivity in patients with Parkinson’s disease and freezing of gait,” Parkinsonism Relat. Disord. 18(6), 781–787 (2012).
[Crossref] [PubMed]

Bauer, A. Q.

J. R. Bumstead, A. Q. Bauer, P. W. Wright, and J. P. Culver, “Cerebral functional connectivity and Mayer waves in mice: Phenomena and separability,” J. Cereb. Blood Flow Metab. 37(2), 471–484 (2017).
[Crossref] [PubMed]

B. R. White, A. Q. Bauer, A. Z. Snyder, B. L. Schlaggar, J. M. Lee, and J. P. Culver, “Imaging of functional connectivity in the mouse brain,” PLoS One 6(1), e16322 (2011).
[Crossref] [PubMed]

Bonomini, V.

Bumstead, J. R.

J. R. Bumstead, A. Q. Bauer, P. W. Wright, and J. P. Culver, “Cerebral functional connectivity and Mayer waves in mice: Phenomena and separability,” J. Cereb. Blood Flow Metab. 37(2), 471–484 (2017).
[Crossref] [PubMed]

Bunce, S.

F. Irani, S. M. Platek, S. Bunce, A. C. Ruocco, and D. Chute, “Functional near infrared spectroscopy (fNIRS): an emerging neuroimaging technology with important applications for the study of brain disorders,” Clin. Neuropsychol. 21(1), 9–37 (2007).
[Crossref] [PubMed]

Caffo, B. S.

S. E. Joel, B. S. Caffo, P. C. van Zijl, and J. J. Pekar, “On the relationship between seed-based and ICA-based measures of functional connectivity,” Magn. Reson. Med. 66(3), 644–657 (2011).
[Crossref] [PubMed]

Cao, B.

C. Luo, W. Song, Q. Chen, Z. Zheng, K. Chen, B. Cao, J. Yang, J. Li, X. Huang, Q. Gong, and H. F. Shang, “Reduced functional connectivity in early-stage drug-naive Parkinson’s disease: a resting-state fMRI study,” Neurobiol. Aging 35(2), 431–441 (2014).
[Crossref] [PubMed]

Cauda, F.

F. Cauda, F. D’Agata, K. Sacco, S. Duca, G. Geminiani, and A. Vercelli, “Functional connectivity of the insula in the resting brain,” Neuroimage 55(1), 8–23 (2011).
[Crossref] [PubMed]

Chan, A. W.

M. P. Vanni, A. W. Chan, M. Balbi, G. Silasi, and T. H. Murphy, “Mesoscale mapping of mouse cortex reveals frequency-dependent cycling between distinct macroscale functional modules,” J. Neurosci. 37(31), 7513–7533 (2017).
[Crossref] [PubMed]

Chan, P.

T. Wu, X. Long, L. Wang, M. Hallett, Y. Zang, K. Li, and P. Chan, “Functional connectivity of cortical motor areas in the resting state in Parkinson’s disease,” Hum. Brain Mapp. 32(9), 1443–1457 (2011).
[Crossref] [PubMed]

T. Wu, L. Wang, Y. Chen, C. Zhao, K. Li, and P. Chan, “Changes of functional connectivity of the motor network in the resting state in Parkinson’s disease,” Neurosci. Lett. 460(1), 6–10 (2009).
[Crossref] [PubMed]

Chance, B.

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

Chang, C.

X. Liu, J. A. de Zwart, M. L. Schölvinck, C. Chang, F. Q. Ye, D. A. Leopold, and J. H. Duyn, “Subcortical evidence for a contribution of arousal to fMRI studies of brain activity,” Nat. Commun. 9(1), 395 (2018).
[Crossref] [PubMed]

Chen, K.

C. Luo, W. Song, Q. Chen, Z. Zheng, K. Chen, B. Cao, J. Yang, J. Li, X. Huang, Q. Gong, and H. F. Shang, “Reduced functional connectivity in early-stage drug-naive Parkinson’s disease: a resting-state fMRI study,” Neurobiol. Aging 35(2), 431–441 (2014).
[Crossref] [PubMed]

Chen, Q.

C. Luo, W. Song, Q. Chen, Z. Zheng, K. Chen, B. Cao, J. Yang, J. Li, X. Huang, Q. Gong, and H. F. Shang, “Reduced functional connectivity in early-stage drug-naive Parkinson’s disease: a resting-state fMRI study,” Neurobiol. Aging 35(2), 431–441 (2014).
[Crossref] [PubMed]

Chen, Y.

T. Wu, L. Wang, Y. Chen, C. Zhao, K. Li, and P. Chan, “Changes of functional connectivity of the motor network in the resting state in Parkinson’s disease,” Neurosci. Lett. 460(1), 6–10 (2009).
[Crossref] [PubMed]

Cho, S. H.

H. K. Jin, T. Y. Hwang, and S. H. Cho, “Effect of electrical stimulation on blood flow velocity and vessel size,” Open Med. (Wars.) 12(1), 5–11 (2017).
[Crossref] [PubMed]

Chute, D.

F. Irani, S. M. Platek, S. Bunce, A. C. Ruocco, and D. Chute, “Functional near infrared spectroscopy (fNIRS): an emerging neuroimaging technology with important applications for the study of brain disorders,” Clin. Neuropsychol. 21(1), 9–37 (2007).
[Crossref] [PubMed]

Cirillo, M.

A. Tessitore, M. Amboni, F. Esposito, A. Russo, M. Picillo, L. Marcuccio, M. T. Pellecchia, C. Vitale, M. Cirillo, G. Tedeschi, and P. Barone, “Resting-state brain connectivity in patients with Parkinson’s disease and freezing of gait,” Parkinsonism Relat. Disord. 18(6), 781–787 (2012).
[Crossref] [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]

Contini, D.

Corbetta, M.

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

Culver, J. P.

J. R. Bumstead, A. Q. Bauer, P. W. Wright, and J. P. Culver, “Cerebral functional connectivity and Mayer waves in mice: Phenomena and separability,” J. Cereb. Blood Flow Metab. 37(2), 471–484 (2017).
[Crossref] [PubMed]

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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,” Neuroimage 47(1), 148–156 (2009).
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F. Cauda, F. D’Agata, K. Sacco, S. Duca, G. Geminiani, and A. Vercelli, “Functional connectivity of the insula in the resting brain,” Neuroimage 55(1), 8–23 (2011).
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F. Cauda, F. D’Agata, K. Sacco, S. Duca, G. Geminiani, and A. Vercelli, “Functional connectivity of the insula in the resting brain,” Neuroimage 55(1), 8–23 (2011).
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X. Liu, J. A. de Zwart, M. L. Schölvinck, C. Chang, F. Q. Ye, D. A. Leopold, and J. H. Duyn, “Subcortical evidence for a contribution of arousal to fMRI studies of brain activity,” Nat. Commun. 9(1), 395 (2018).
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D. N. Guilfoyle, S. V. Gerum, J. L. Sanchez, A. Balla, H. Sershen, D. C. Javitt, and M. J. Hoptman, “Functional connectivity fMRI in mouse brain at 7T using isoflurane,” J. Neurosci. Methods 214(2), 144–148 (2013).
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D. N. Guilfoyle, S. V. Gerum, J. L. Sanchez, A. Balla, H. Sershen, D. C. Javitt, and M. J. Hoptman, “Functional connectivity fMRI in mouse brain at 7T using isoflurane,” J. Neurosci. Methods 214(2), 144–148 (2013).
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H. K. Jin, T. Y. Hwang, and S. H. Cho, “Effect of electrical stimulation on blood flow velocity and vessel size,” Open Med. (Wars.) 12(1), 5–11 (2017).
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B. R. White, A. Q. Bauer, A. Z. Snyder, B. L. Schlaggar, J. M. Lee, and J. P. Culver, “Imaging of functional connectivity in the mouse brain,” PLoS One 6(1), e16322 (2011).
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X. Liu, J. A. de Zwart, M. L. Schölvinck, C. Chang, F. Q. Ye, D. A. Leopold, and J. H. Duyn, “Subcortical evidence for a contribution of arousal to fMRI studies of brain activity,” Nat. Commun. 9(1), 395 (2018).
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Li, J.

C. Luo, W. Song, Q. Chen, Z. Zheng, K. Chen, B. Cao, J. Yang, J. Li, X. Huang, Q. Gong, and H. F. Shang, “Reduced functional connectivity in early-stage drug-naive Parkinson’s disease: a resting-state fMRI study,” Neurobiol. Aging 35(2), 431–441 (2014).
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T. Wu, X. Long, L. Wang, M. Hallett, Y. Zang, K. Li, and P. Chan, “Functional connectivity of cortical motor areas in the resting state in Parkinson’s disease,” Hum. Brain Mapp. 32(9), 1443–1457 (2011).
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T. Wu, L. Wang, Y. Chen, C. Zhao, K. Li, and P. Chan, “Changes of functional connectivity of the motor network in the resting state in Parkinson’s disease,” Neurosci. Lett. 460(1), 6–10 (2009).
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T. Wu, X. Long, L. Wang, M. Hallett, Y. Zang, K. Li, and P. Chan, “Functional connectivity of cortical motor areas in the resting state in Parkinson’s disease,” Hum. Brain Mapp. 32(9), 1443–1457 (2011).
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C. Luo, W. Song, Q. Chen, Z. Zheng, K. Chen, B. Cao, J. Yang, J. Li, X. Huang, Q. Gong, and H. F. Shang, “Reduced functional connectivity in early-stage drug-naive Parkinson’s disease: a resting-state fMRI study,” Neurobiol. Aging 35(2), 431–441 (2014).
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T. M. Kodinariya and P. R. Makwana, “Review on determining number of Cluster in K-Means Clustering,” Int. J. 1(6), 90–95 (2013).

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A. Tessitore, M. Amboni, F. Esposito, A. Russo, M. Picillo, L. Marcuccio, M. T. Pellecchia, C. Vitale, M. Cirillo, G. Tedeschi, and P. Barone, “Resting-state brain connectivity in patients with Parkinson’s disease and freezing of gait,” Parkinsonism Relat. Disord. 18(6), 781–787 (2012).
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J. Virtanen, T. Noponen, and P. Meriläinen, “Comparison of principal and independent component analysis in removing extracerebral interference from near-infrared spectroscopy signals,” J. Biomed. Opt. 14(5), 054032 (2009).
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C. Messier, S. Émond, and K. Ethier, “New techniques in stereotaxic surgery and anesthesia in the mouse,” Pharmacol. Biochem. Behav. 63(2), 313–318 (1999).
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M. P. Vanni, A. W. Chan, M. Balbi, G. Silasi, and T. H. Murphy, “Mesoscale mapping of mouse cortex reveals frequency-dependent cycling between distinct macroscale functional modules,” J. Neurosci. 37(31), 7513–7533 (2017).
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J. Virtanen, T. Noponen, and P. Meriläinen, “Comparison of principal and independent component analysis in removing extracerebral interference from near-infrared spectroscopy signals,” J. Biomed. Opt. 14(5), 054032 (2009).
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Park, S. E.

S. E. Park, K. I. Song, J. K. F. Suh, D. Hwang, and I. Youn, “A time-course study of behavioral and electrophysiological characteristics in a mouse model of different stages of Parkinson’s disease using 6-hydroxydopamine,” Behav. Brain Res. 284, 153–157 (2015).
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S. E. Joel, B. S. Caffo, P. C. van Zijl, and J. J. Pekar, “On the relationship between seed-based and ICA-based measures of functional connectivity,” Magn. Reson. Med. 66(3), 644–657 (2011).
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A. Tessitore, M. Amboni, F. Esposito, A. Russo, M. Picillo, L. Marcuccio, M. T. Pellecchia, C. Vitale, M. Cirillo, G. Tedeschi, and P. Barone, “Resting-state brain connectivity in patients with Parkinson’s disease and freezing of gait,” Parkinsonism Relat. Disord. 18(6), 781–787 (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,” Neuroimage 47(1), 148–156 (2009).
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A. Tessitore, M. Amboni, F. Esposito, A. Russo, M. Picillo, L. Marcuccio, M. T. Pellecchia, C. Vitale, M. Cirillo, G. Tedeschi, and P. Barone, “Resting-state brain connectivity in patients with Parkinson’s disease and freezing of gait,” Parkinsonism Relat. Disord. 18(6), 781–787 (2012).
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F. Irani, S. M. Platek, S. Bunce, A. C. Ruocco, and D. Chute, “Functional near infrared spectroscopy (fNIRS): an emerging neuroimaging technology with important applications for the study of brain disorders,” Clin. Neuropsychol. 21(1), 9–37 (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,” Neuroimage 47(1), 148–156 (2009).
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M. D. Fox, A. Z. Snyder, J. L. Vincent, M. Corbetta, D. C. Van Essen, and M. E. Raichle, “The human brain is intrinsically organized into dynamic, anticorrelated functional networks,” Proc. Natl. Acad. Sci. U.S.A. 102(27), 9673–9678 (2005).
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Rousseau, R.

P. Ahlgren, B. Jarneving, and R. Rousseau, “Requirements for a cocitation similarity measure, with special reference to Pearson’s correlation coefficient,” J. Am. Soc. Inf. Sci. Technol. 54(6), 550–560 (2003).
[Crossref]

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F. Irani, S. M. Platek, S. Bunce, A. C. Ruocco, and D. Chute, “Functional near infrared spectroscopy (fNIRS): an emerging neuroimaging technology with important applications for the study of brain disorders,” Clin. Neuropsychol. 21(1), 9–37 (2007).
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A. Tessitore, M. Amboni, F. Esposito, A. Russo, M. Picillo, L. Marcuccio, M. T. Pellecchia, C. Vitale, M. Cirillo, G. Tedeschi, and P. Barone, “Resting-state brain connectivity in patients with Parkinson’s disease and freezing of gait,” Parkinsonism Relat. Disord. 18(6), 781–787 (2012).
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F. Cauda, F. D’Agata, K. Sacco, S. Duca, G. Geminiani, and A. Vercelli, “Functional connectivity of the insula in the resting brain,” Neuroimage 55(1), 8–23 (2011).
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Sanchez, J. L.

D. N. Guilfoyle, S. V. Gerum, J. L. Sanchez, A. Balla, H. Sershen, D. C. Javitt, and M. J. Hoptman, “Functional connectivity fMRI in mouse brain at 7T using isoflurane,” J. Neurosci. Methods 214(2), 144–148 (2013).
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B. R. White, A. Q. Bauer, A. Z. Snyder, B. L. Schlaggar, J. M. Lee, and J. P. Culver, “Imaging of functional connectivity in the mouse brain,” PLoS One 6(1), e16322 (2011).
[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]

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X. Liu, J. A. de Zwart, M. L. Schölvinck, C. Chang, F. Q. Ye, D. A. Leopold, and J. H. Duyn, “Subcortical evidence for a contribution of arousal to fMRI studies of brain activity,” Nat. Commun. 9(1), 395 (2018).
[Crossref] [PubMed]

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Sershen, H.

D. N. Guilfoyle, S. V. Gerum, J. L. Sanchez, A. Balla, H. Sershen, D. C. Javitt, and M. J. Hoptman, “Functional connectivity fMRI in mouse brain at 7T using isoflurane,” J. Neurosci. Methods 214(2), 144–148 (2013).
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C. Luo, W. Song, Q. Chen, Z. Zheng, K. Chen, B. Cao, J. Yang, J. Li, X. Huang, Q. Gong, and H. F. Shang, “Reduced functional connectivity in early-stage drug-naive Parkinson’s disease: a resting-state fMRI study,” Neurobiol. Aging 35(2), 431–441 (2014).
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M. P. Vanni, A. W. Chan, M. Balbi, G. Silasi, and T. H. Murphy, “Mesoscale mapping of mouse cortex reveals frequency-dependent cycling between distinct macroscale functional modules,” J. Neurosci. 37(31), 7513–7533 (2017).
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B. R. White, A. Q. Bauer, A. Z. Snyder, B. L. Schlaggar, J. M. Lee, and J. P. Culver, “Imaging of functional connectivity in the mouse brain,” PLoS One 6(1), e16322 (2011).
[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]

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

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S. E. Park, K. I. Song, J. K. F. Suh, D. Hwang, and I. Youn, “A time-course study of behavioral and electrophysiological characteristics in a mouse model of different stages of Parkinson’s disease using 6-hydroxydopamine,” Behav. Brain Res. 284, 153–157 (2015).
[Crossref] [PubMed]

Song, W.

C. Luo, W. Song, Q. Chen, Z. Zheng, K. Chen, B. Cao, J. Yang, J. Li, X. Huang, Q. Gong, and H. F. Shang, “Reduced functional connectivity in early-stage drug-naive Parkinson’s disease: a resting-state fMRI study,” Neurobiol. Aging 35(2), 431–441 (2014).
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Suh, J. K. F.

S. E. Park, K. I. Song, J. K. F. Suh, D. Hwang, and I. Youn, “A time-course study of behavioral and electrophysiological characteristics in a mouse model of different stages of Parkinson’s disease using 6-hydroxydopamine,” Behav. Brain Res. 284, 153–157 (2015).
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Tedeschi, G.

A. Tessitore, M. Amboni, F. Esposito, A. Russo, M. Picillo, L. Marcuccio, M. T. Pellecchia, C. Vitale, M. Cirillo, G. Tedeschi, and P. Barone, “Resting-state brain connectivity in patients with Parkinson’s disease and freezing of gait,” Parkinsonism Relat. Disord. 18(6), 781–787 (2012).
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A. Tessitore, M. Amboni, F. Esposito, A. Russo, M. Picillo, L. Marcuccio, M. T. Pellecchia, C. Vitale, M. Cirillo, G. Tedeschi, and P. Barone, “Resting-state brain connectivity in patients with Parkinson’s disease and freezing of gait,” Parkinsonism Relat. Disord. 18(6), 781–787 (2012).
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van den Heuvel, M. P.

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]

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M. D. Fox, A. Z. Snyder, J. L. Vincent, M. Corbetta, D. C. Van Essen, and M. E. Raichle, “The human brain is intrinsically organized into dynamic, anticorrelated functional networks,” Proc. Natl. Acad. Sci. U.S.A. 102(27), 9673–9678 (2005).
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S. E. Joel, B. S. Caffo, P. C. van Zijl, and J. J. Pekar, “On the relationship between seed-based and ICA-based measures of functional connectivity,” Magn. Reson. Med. 66(3), 644–657 (2011).
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M. P. Vanni, A. W. Chan, M. Balbi, G. Silasi, and T. H. Murphy, “Mesoscale mapping of mouse cortex reveals frequency-dependent cycling between distinct macroscale functional modules,” J. Neurosci. 37(31), 7513–7533 (2017).
[Crossref] [PubMed]

Vercelli, A.

F. Cauda, F. D’Agata, K. Sacco, S. Duca, G. Geminiani, and A. Vercelli, “Functional connectivity of the insula in the resting brain,” Neuroimage 55(1), 8–23 (2011).
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M. D. Fox, A. Z. Snyder, J. L. Vincent, M. Corbetta, D. C. Van Essen, and M. E. Raichle, “The human brain is intrinsically organized into dynamic, anticorrelated functional networks,” Proc. Natl. Acad. Sci. U.S.A. 102(27), 9673–9678 (2005).
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Virtanen, J.

J. Virtanen, T. Noponen, and P. Meriläinen, “Comparison of principal and independent component analysis in removing extracerebral interference from near-infrared spectroscopy signals,” J. Biomed. Opt. 14(5), 054032 (2009).
[Crossref] [PubMed]

Vitale, C.

A. Tessitore, M. Amboni, F. Esposito, A. Russo, M. Picillo, L. Marcuccio, M. T. Pellecchia, C. Vitale, M. Cirillo, G. Tedeschi, and P. Barone, “Resting-state brain connectivity in patients with Parkinson’s disease and freezing of gait,” Parkinsonism Relat. Disord. 18(6), 781–787 (2012).
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T. Wu, X. Long, L. Wang, M. Hallett, Y. Zang, K. Li, and P. Chan, “Functional connectivity of cortical motor areas in the resting state in Parkinson’s disease,” Hum. Brain Mapp. 32(9), 1443–1457 (2011).
[Crossref] [PubMed]

T. Wu, L. Wang, Y. Chen, C. Zhao, K. Li, and P. Chan, “Changes of functional connectivity of the motor network in the resting state in Parkinson’s disease,” Neurosci. Lett. 460(1), 6–10 (2009).
[Crossref] [PubMed]

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B. R. White, A. Q. Bauer, A. Z. Snyder, B. L. Schlaggar, J. M. Lee, and J. P. Culver, “Imaging of functional connectivity in the mouse brain,” PLoS One 6(1), e16322 (2011).
[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]

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J. R. Bumstead, A. Q. Bauer, P. W. Wright, and J. P. Culver, “Cerebral functional connectivity and Mayer waves in mice: Phenomena and separability,” J. Cereb. Blood Flow Metab. 37(2), 471–484 (2017).
[Crossref] [PubMed]

Wu, T.

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Figures (9)

Fig. 1
Fig. 1 (a) Schematic of the imaging system. M: Mirror. Di: Dichroic Mirror. F1: 850/10–25 nm band pass filter. F2: 780/12–25 nm band pass filter. (b) Picture of the imaging system next to stereotaxic frame
Fig. 2
Fig. 2 (a) Schematic of cortical regions of the mouse brain. (b) In vivo image of cranial window. Red circle is the field of view and the black dot is the bregma. (c) Field of view of our imaging system. (B: bregma, L.S: lambdoidal suture)
Fig. 3
Fig. 3 (a) Placement of the stimulating electrode and measurement window. (b) Hemoglobin (oxy, deoxy, and total) concentration changes over time measured near the electrode during electrical brain stimulation. The dotted black lines indicate the stimulation period. The onset delay of the expected rise in oxy-hemoglobin is the signature of the initial dip related to an immediate increase in cerebral metabolism. (c) Hemodynamic changes in the healthy mouse brain before, during, and after electrical stimulation. The shaded portion of the time scale indicates when stimulation was applied.
Fig. 4
Fig. 4 Correlation of hemodynamic changes in the normal mouse brain before (−10–0 s), during (0–15 s) and immediately after electrical stimulation (15–30 s). At 0-15s, there was a highly correlated change in the oxy-hemoglobin signal in the motor region of both hemispheres, where the stimulus point was located.
Fig. 5
Fig. 5 PCA result in normal and hemi-parkinsonian mice during a 7-minute resting period. (a) Time course corresponding to PC1–PC5 of each mouse. (b) Averaged power spectrum density of each group less than 0.05Hz. (c) Averaged power spectrum density of each principle component less than 0.005 Hz (*p<0.05, **p<0.01).
Fig. 6
Fig. 6 Example of left motor cortex seed correlation map during a 7 minute depends on removal number of PC. N2 shows more clearly segment motor cortex with remove PC1 and PC2 than only remove PC1. In case of PD3, PC2 effect entire brain signal and N3 shows not much change between remove PC1 or PC1 and PC2.
Fig. 7
Fig. 7 Correlation map for oxy-hemoglobin changes between the right and left hemisphere motor cortex in normal (left) and hemi-parkinsonian mice (right) during a 7-minute resting period. Transparency is shown that p < 0.05. The four healthy mice samples (N 1–N 4) show a wider area of bilateral correlation during the resting state than the four hemi-parkinsonian mice samples (PD 1–PD 4).
Fig. 8
Fig. 8 Correlations of connectivity based on the left and right seeds (Fig. 7) for healthy and hemi-parkinsonian mice. As indicated by the reduced correlation values for the hemi-parkinsonian animals, a reduction in symmetry between the left and right hemispheres was observed in the hemi-parkinsonian mice. Only changes in oxy-hemoglobin show a statistically significant difference between the healthy and hemi-parkinsonian mice (* p<0.05).
Fig. 9
Fig. 9 K-means clustering image of oxy-hemoglobin concentration change patterns during the resting state. Four healthy (N 1–N 4; top row) mice and four hemi-parkinsonian (PD 1 – PD 4; bottom row) mice were analyzed. The healthy mice showed clustering corresponding to functionally connected bilateral areas of the brain; however, the hemodynamic signal from the PD mice was not clustered in a similar way.