Abstract

Resting-state functional connectivity analysis using optical neuroimaging holds the potential to be a powerful bridge between mouse models of disease and clinical neurologic monitoring. However, analysis techniques specific to optical methods are rudimentary, and algorithms from magnetic resonance imaging are not always applicable to optics. We have developed visual processing tools to increase data quality, improve brain segmentation, and average across sessions with better field-of-view. We demonstrate improved performance using resting-state optical intrinsic signal from normal mice. The proposed methods increase the amount of usable data from neuroimaging studies, improve image fidelity, and should be translatable to human optical neuroimaging systems.

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

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References

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    [Crossref]
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    [Crossref]
  7. M. P. Vanni and T. H. Murphy, “Mesoscale transcranial spontaneous activity mapping in GCaMP3 transgenic mice reveals extensive reciprocal connections between areas of somatomotor cortex,” J. Neurosci. 34(48), 15931–15946 (2014).
    [Crossref]
  8. P. W. Wright, L. M. Brier, A. Q. Bauer, G. A. Baxter, A. W. Kraft, M. D. Reisman, A. R. Bice, A. Z. Snyder, J.-M. Lee, and J. P. Culver, “Functional connectivity structure of cortical calcium dynamics in anesthetized and awake mice,” PLoS One 12(10), e0185759 (2017).
    [Crossref]
  9. M. P. Vanni, A. W. Chen, 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]
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    [Crossref]
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    [Crossref]
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    [Crossref]
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  16. D. W. Shattuck, G. Prasad, M. Mirza, K. L. Narr, and A. W. Toga, “Online resource for validation of brain segmentation methods,” NeuroImage 45(2), 431–439 (2009).
    [Crossref]
  17. J. Bai, T. L. H. Trinh, K.-H. Chuang, and A. Qiu, “Atlas-based automatic mouse brain image segmentation revisited: model complexity vs imaging registration,” Magn. Reson. Imaging 30(6), 789–798 (2012).
    [Crossref]
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    [Crossref]
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    [Crossref]
  20. G. Silasi, D. Xiao, M. P. Vanni, A. C. N. Chen, and T. H. Murphy, “Intact skull chronic windows for mesoscopic wide-field imaging in awake mice,” J. Neurosci. Methods 267, 141–149 (2016).
    [Crossref]
  21. Y. Zang, T. Jiang, Y. Lu, Y. He, and L. Tian, “Regional homogeneity approach to fMRI data analysis,” NeuroImage 22(1), 394–400 (2004).
    [Crossref]
  22. R. A. Alexander, “A note on averaging correlations,” Bull. Psychon. Soc. 28(4), 335–336 (1990).
    [Crossref]
  23. N. C. Silver and W. P. Dunlap, “Averaging correlation coefficients: should Fisher’s $z$z transformation be used?” J. App. Psychol. 72(1), 146–148 (1987).
    [Crossref]
  24. J. D. Power, A. Mitra, T. O. Laumann, A. Z. Snyder, B. L. Schlaggar, and S. E. Petersen, “Methods to detect, characterize, and remove motion artifact in resting state fMRI,” NeuroImage 84, 320–341 (2014).
    [Crossref]
  25. J. S. Siegel, J. D. Power, J. W. Dubis, A. C. Vogel, J. A. Church, B. L. Schlaggar, and S. E. Petersen, “Statistical improvements in functional magnetic resonance imaging analyses produced by censoring high-motion data points,” Hum. Brain Mapp. 35(5), 1981–1996 (2014).
    [Crossref]
  26. Y.-F. Zang, Y. He, C.-Z. Zhu, Q.-J. Cao, M.-Q. Sui, M. Liang, L.-X. Tian, T.-Z. Jiang, and Y.-F. Wang, “Altered baseline brain activity in children with ADHD revealed by resting-state functional MRI,” Brain Dev. 29(2), 83–91 (2007).
    [Crossref]
  27. Q.-H. Zou, C.-Z. Zhu, Y. Yang, X.-N. Zuo, X.-Y. Long, Q.-J. Cao, Y.-F. Wang, and Y.-F. Zang, “An improved approach to detection of amplitude of low-frequency fluctuation (ALFF) for resting-state fMRI: Fractional ALFF,” J. Neurosci. Methods 172(1), 137–141 (2008).
    [Crossref]
  28. Z. Li, Y. Zhu, A. Childress, J. Detre, and Z. Wang, “Relations between BOLD fMRI-derived resting brain activity and cerebral blood flow,” PLoS One 7(9), e44556 (2012).
    [Crossref]
  29. R. Yuan, X. Di, E. Kim, S. Barik, B. Rypma, and B. Biswal, “Regional homogeneity of resting-state fMRI contributes to both neurovascular and task activation variations,” Magn. Reson. Imaging 31(9), 1492–1500 (2013).
    [Crossref]
  30. L. Brier, E. Landsness, A. Snyder, P. Wright, G. Baxter, A. Bauer, J.-M. Lee, and J. Culver, “Separability of calcium slow waves and functional connectivity during wake, sleep, and anesthesia,” Neurophotonics 6(03), 1 (2019).
    [Crossref]
  31. K.-H. Chuang, H. Lee, Z. Li, W.-T. Chang, F. Nasrallah, L. Yeow, and K. Singh, “Evaluation of nuisance removal for functional MRI of rodent brain,” NeuroImage 188, 694–709 (2019).
    [Crossref]
  32. T. Satterthwaite, D. Wolf, J. Loughead, K. Ruparel, M. Elliott, H. Hakonarson, R. Gur, and R. Gur, “Impact of in-scanner head motion on multiple measures of functional connectivity: Relevance for studies of neurodevelopment in youth,” NeuroImage 60(1), 623–632 (2012).
    [Crossref]
  33. J. Power, B. Schlaggar, and S. Petersen, “Recent progress and outstanding issues in motion correction in resting state fMRI,” NeuroImage 105, 536–551 (2015).
    [Crossref]
  34. X. Wu, A. T. Eggebrecht, S. L. Ferradal, J. P. Culver, and H. Dehghani, “Quantitative evaluation of atlas-based high-density diffuse optical tomography for imaging of the human visual cortex,” Biomed. Opt. Express 5(11), 3882–3900 (2014).
    [Crossref]
  35. S. L. Ferradal, A. T. Eggebrecht, M. Hassanpour, A. Z. Snyder, and J. P. Culver, “Atlas-based head modeling and spatial normalization for high-density diffuse optical tomography: in vivo validation against fMRI,” NeuroImage 85, 117–126 (2014).
    [Crossref]
  36. A. T. Eggebrecht, S. L. Ferradal, A. Robichaux-Viehoever, M. S. Hassanpour, H. Dehghani, A. Z. Snyder, T. Hershey, and J. P. Culver, “Mapping distributed brain function and networks with diffuse optical tomography,” Nat. Photonics 8(6), 448–454 (2014).
    [Crossref]

2019 (2)

L. Brier, E. Landsness, A. Snyder, P. Wright, G. Baxter, A. Bauer, J.-M. Lee, and J. Culver, “Separability of calcium slow waves and functional connectivity during wake, sleep, and anesthesia,” Neurophotonics 6(03), 1 (2019).
[Crossref]

K.-H. Chuang, H. Lee, Z. Li, W.-T. Chang, F. Nasrallah, L. Yeow, and K. Singh, “Evaluation of nuisance removal for functional MRI of rodent brain,” NeuroImage 188, 694–709 (2019).
[Crossref]

2018 (3)

A. Q. Bauer, A. W. Kraft, G. A. Baxter, P. W. Wright, M. D. Reisman, A. R. Bice, J. J. Park, M. R. Bruchas, A. Z. Snyder, J.-M. Lee, and J. P. Culver, “Effective connectivity measured using optogenetically evoked hemodynamics signals exhibits topography distinct from resting state functional connectivity in the mouse,” Cereb. Cortex 28(1), 370–386 (2018).
[Crossref]

S. Kura, H. Xie, B. Fu, C. Ayata, D. A. Boas, and S. Sakadzic, “Intrinsic optical signal imaging of the blood volume changes is sufficient for mapping the resting state functional connectivity in the rodent cortex,” J. Neural. Eng. 15(3), 035003 (2018).
[Crossref]

M. J. Quattromani, J. Hakon, U. Rauch, A. Q. Bauer, and T. Wieloch, “Changes in resting-state functional connectivity after stroke in a mouse brain lacking extracellular matrix components,” Neurobiol. Dis. 112, 91–105 (2018).
[Crossref]

2017 (4)

P. W. Wright, A. S. Archambault, S. Peek, A. Q. Bauer, S. M. Culican, B. M. Ances, J. P. Culver, and G. F. Wu, “Functional connectivity alterations in a murine model of optic neuritis,” Exp. Neurol. 295, 18–22 (2017).
[Crossref]

P. W. Wright, L. M. Brier, A. Q. Bauer, G. A. Baxter, A. W. Kraft, M. D. Reisman, A. R. Bice, A. Z. Snyder, J.-M. Lee, and J. P. Culver, “Functional connectivity structure of cortical calcium dynamics in anesthetized and awake mice,” PLoS One 12(10), e0185759 (2017).
[Crossref]

M. P. Vanni, A. W. Chen, 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]

M. Gorges, F. Roselli, H.-P. Muller, A. C. Ludolph, V. Rasche, and J. Kassubek, “Functional connectivity mapping in the animal model: principles and applications of resting-state fMRI,” Front. Neurol. 8, 200 (2017).
[Crossref]

2016 (2)

T. H. Murphy, J. D. Boyd, F. Bolanos, M. P. Vanni, G. Silasi, D. Haupt, and J. M. LeDue, “High-throughput automated home-cage mesoscopic functional imaging of mouse cortex,” Nat. Commun. 7(1), 11611 (2016).
[Crossref]

G. Silasi, D. Xiao, M. P. Vanni, A. C. N. Chen, and T. H. Murphy, “Intact skull chronic windows for mesoscopic wide-field imaging in awake mice,” J. Neurosci. Methods 267, 141–149 (2016).
[Crossref]

2015 (1)

J. Power, B. Schlaggar, and S. Petersen, “Recent progress and outstanding issues in motion correction in resting state fMRI,” NeuroImage 105, 536–551 (2015).
[Crossref]

2014 (7)

X. Wu, A. T. Eggebrecht, S. L. Ferradal, J. P. Culver, and H. Dehghani, “Quantitative evaluation of atlas-based high-density diffuse optical tomography for imaging of the human visual cortex,” Biomed. Opt. Express 5(11), 3882–3900 (2014).
[Crossref]

S. L. Ferradal, A. T. Eggebrecht, M. Hassanpour, A. Z. Snyder, and J. P. Culver, “Atlas-based head modeling and spatial normalization for high-density diffuse optical tomography: in vivo validation against fMRI,” NeuroImage 85, 117–126 (2014).
[Crossref]

A. T. Eggebrecht, S. L. Ferradal, A. Robichaux-Viehoever, M. S. Hassanpour, H. Dehghani, A. Z. Snyder, T. Hershey, and J. P. Culver, “Mapping distributed brain function and networks with diffuse optical tomography,” Nat. Photonics 8(6), 448–454 (2014).
[Crossref]

J. D. Power, A. Mitra, T. O. Laumann, A. Z. Snyder, B. L. Schlaggar, and S. E. Petersen, “Methods to detect, characterize, and remove motion artifact in resting state fMRI,” NeuroImage 84, 320–341 (2014).
[Crossref]

J. S. Siegel, J. D. Power, J. W. Dubis, A. C. Vogel, J. A. Church, B. L. Schlaggar, and S. E. Petersen, “Statistical improvements in functional magnetic resonance imaging analyses produced by censoring high-motion data points,” Hum. Brain Mapp. 35(5), 1981–1996 (2014).
[Crossref]

M. P. Vanni and T. H. Murphy, “Mesoscale transcranial spontaneous activity mapping in GCaMP3 transgenic mice reveals extensive reciprocal connections between areas of somatomotor cortex,” J. Neurosci. 34(48), 15931–15946 (2014).
[Crossref]

A. Q. Bauer, A. W. Kraft, P. W. Wright, A. Z. Snyder, J.-M. Lee, and J. P. Culver, “Optical imaging of disrupted functional connectivity following ischemic stroke in mice,” NeuroImage 99, 388–401 (2014).
[Crossref]

2013 (2)

M. H. Mohajerani, A. W. Chan, M. Mohsenvand, J. LeDue, R. Liu, D. A. McVea, J. D. Boyd, Y. T. Wang, M. Reimers, and T. H. Murphy, “Spontaneous cortical activity alternates between motifs defined by regional axonal projections,” Nat. Neurosci. 16(10), 1426–1435 (2013).
[Crossref]

R. Yuan, X. Di, E. Kim, S. Barik, B. Rypma, and B. Biswal, “Regional homogeneity of resting-state fMRI contributes to both neurovascular and task activation variations,” Magn. Reson. Imaging 31(9), 1492–1500 (2013).
[Crossref]

2012 (4)

T. Satterthwaite, D. Wolf, J. Loughead, K. Ruparel, M. Elliott, H. Hakonarson, R. Gur, and R. Gur, “Impact of in-scanner head motion on multiple measures of functional connectivity: Relevance for studies of neurodevelopment in youth,” NeuroImage 60(1), 623–632 (2012).
[Crossref]

Z. Li, Y. Zhu, A. Childress, J. Detre, and Z. Wang, “Relations between BOLD fMRI-derived resting brain activity and cerebral blood flow,” PLoS One 7(9), e44556 (2012).
[Crossref]

A. W. Bero, A. Q. Bauer, F. R. Stewart, B. R. White, J. R. Cirrito, M. E. Raichle, J. P. Culver, and D. M. Holtzman, “Bidirectional relationship between functional connectivity and amyloid-$\beta$β deposition in mouse brain,” J. Neurosci. 32(13), 4334–4340 (2012).
[Crossref]

J. Bai, T. L. H. Trinh, K.-H. Chuang, and A. Qiu, “Atlas-based automatic mouse brain image segmentation revisited: model complexity vs imaging registration,” Magn. Reson. Imaging 30(6), 789–798 (2012).
[Crossref]

2011 (1)

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]

2010 (1)

M. D. Fox and M. Greicius, “Clinical applications of resting state functional connectivity,” Front. Syst. Neurosci. 4, 19 (2010).
[Crossref]

2009 (1)

D. W. Shattuck, G. Prasad, M. Mirza, K. L. Narr, and A. W. Toga, “Online resource for validation of brain segmentation methods,” NeuroImage 45(2), 431–439 (2009).
[Crossref]

2008 (1)

Q.-H. Zou, C.-Z. Zhu, Y. Yang, X.-N. Zuo, X.-Y. Long, Q.-J. Cao, Y.-F. Wang, and Y.-F. Zang, “An improved approach to detection of amplitude of low-frequency fluctuation (ALFF) for resting-state fMRI: Fractional ALFF,” J. Neurosci. Methods 172(1), 137–141 (2008).
[Crossref]

2007 (2)

Y.-F. Zang, Y. He, C.-Z. Zhu, Q.-J. Cao, M.-Q. Sui, M. Liang, L.-X. Tian, T.-Z. Jiang, and Y.-F. Wang, “Altered baseline brain activity in children with ADHD revealed by resting-state functional MRI,” Brain Dev. 29(2), 83–91 (2007).
[Crossref]

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]

2004 (2)

D. E. Rex, D. W. Shattuck, R. P. Woods, K. L. Narr, E. Luders, K. Rehm, S. E. Stolzner, D. A. Rottenberg, and A. W. Toga, “A meta-algorithm for brain extraction in MRI,” NeuroImage 23(2), 625–637 (2004).
[Crossref]

Y. Zang, T. Jiang, Y. Lu, Y. He, and L. Tian, “Regional homogeneity approach to fMRI data analysis,” NeuroImage 22(1), 394–400 (2004).
[Crossref]

2002 (1)

B. Fischl, D. H. Salat, E. Busa, M. Albert, M. Dieterich, C. Haselgrove, A. van der Kouwe, R. Killiany, D. Kennedy, S. Klaveness, A. Montillo, N. Makris, B. Rosen, and A. M. Dale, “Whole brain segmentation: automated labelling of neuroanatomical structures in the human brain,” Neuron 33(3), 341–355 (2002).
[Crossref]

1990 (1)

R. A. Alexander, “A note on averaging correlations,” Bull. Psychon. Soc. 28(4), 335–336 (1990).
[Crossref]

1987 (1)

N. C. Silver and W. P. Dunlap, “Averaging correlation coefficients: should Fisher’s $z$z transformation be used?” J. App. Psychol. 72(1), 146–148 (1987).
[Crossref]

Albert, M.

B. Fischl, D. H. Salat, E. Busa, M. Albert, M. Dieterich, C. Haselgrove, A. van der Kouwe, R. Killiany, D. Kennedy, S. Klaveness, A. Montillo, N. Makris, B. Rosen, and A. M. Dale, “Whole brain segmentation: automated labelling of neuroanatomical structures in the human brain,” Neuron 33(3), 341–355 (2002).
[Crossref]

Alexander, R. A.

R. A. Alexander, “A note on averaging correlations,” Bull. Psychon. Soc. 28(4), 335–336 (1990).
[Crossref]

Ances, B. M.

P. W. Wright, A. S. Archambault, S. Peek, A. Q. Bauer, S. M. Culican, B. M. Ances, J. P. Culver, and G. F. Wu, “Functional connectivity alterations in a murine model of optic neuritis,” Exp. Neurol. 295, 18–22 (2017).
[Crossref]

Archambault, A. S.

P. W. Wright, A. S. Archambault, S. Peek, A. Q. Bauer, S. M. Culican, B. M. Ances, J. P. Culver, and G. F. Wu, “Functional connectivity alterations in a murine model of optic neuritis,” Exp. Neurol. 295, 18–22 (2017).
[Crossref]

Ayata, C.

S. Kura, H. Xie, B. Fu, C. Ayata, D. A. Boas, and S. Sakadzic, “Intrinsic optical signal imaging of the blood volume changes is sufficient for mapping the resting state functional connectivity in the rodent cortex,” J. Neural. Eng. 15(3), 035003 (2018).
[Crossref]

Bai, J.

J. Bai, T. L. H. Trinh, K.-H. Chuang, and A. Qiu, “Atlas-based automatic mouse brain image segmentation revisited: model complexity vs imaging registration,” Magn. Reson. Imaging 30(6), 789–798 (2012).
[Crossref]

Balbi, M.

M. P. Vanni, A. W. Chen, 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]

Barik, S.

R. Yuan, X. Di, E. Kim, S. Barik, B. Rypma, and B. Biswal, “Regional homogeneity of resting-state fMRI contributes to both neurovascular and task activation variations,” Magn. Reson. Imaging 31(9), 1492–1500 (2013).
[Crossref]

Bauer, A.

L. Brier, E. Landsness, A. Snyder, P. Wright, G. Baxter, A. Bauer, J.-M. Lee, and J. Culver, “Separability of calcium slow waves and functional connectivity during wake, sleep, and anesthesia,” Neurophotonics 6(03), 1 (2019).
[Crossref]

Bauer, A. Q.

M. J. Quattromani, J. Hakon, U. Rauch, A. Q. Bauer, and T. Wieloch, “Changes in resting-state functional connectivity after stroke in a mouse brain lacking extracellular matrix components,” Neurobiol. Dis. 112, 91–105 (2018).
[Crossref]

A. Q. Bauer, A. W. Kraft, G. A. Baxter, P. W. Wright, M. D. Reisman, A. R. Bice, J. J. Park, M. R. Bruchas, A. Z. Snyder, J.-M. Lee, and J. P. Culver, “Effective connectivity measured using optogenetically evoked hemodynamics signals exhibits topography distinct from resting state functional connectivity in the mouse,” Cereb. Cortex 28(1), 370–386 (2018).
[Crossref]

P. W. Wright, L. M. Brier, A. Q. Bauer, G. A. Baxter, A. W. Kraft, M. D. Reisman, A. R. Bice, A. Z. Snyder, J.-M. Lee, and J. P. Culver, “Functional connectivity structure of cortical calcium dynamics in anesthetized and awake mice,” PLoS One 12(10), e0185759 (2017).
[Crossref]

P. W. Wright, A. S. Archambault, S. Peek, A. Q. Bauer, S. M. Culican, B. M. Ances, J. P. Culver, and G. F. Wu, “Functional connectivity alterations in a murine model of optic neuritis,” Exp. Neurol. 295, 18–22 (2017).
[Crossref]

A. Q. Bauer, A. W. Kraft, P. W. Wright, A. Z. Snyder, J.-M. Lee, and J. P. Culver, “Optical imaging of disrupted functional connectivity following ischemic stroke in mice,” NeuroImage 99, 388–401 (2014).
[Crossref]

A. W. Bero, A. Q. Bauer, F. R. Stewart, B. R. White, J. R. Cirrito, M. E. Raichle, J. P. Culver, and D. M. Holtzman, “Bidirectional relationship between functional connectivity and amyloid-$\beta$β deposition in mouse brain,” J. Neurosci. 32(13), 4334–4340 (2012).
[Crossref]

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]

Baxter, G.

L. Brier, E. Landsness, A. Snyder, P. Wright, G. Baxter, A. Bauer, J.-M. Lee, and J. Culver, “Separability of calcium slow waves and functional connectivity during wake, sleep, and anesthesia,” Neurophotonics 6(03), 1 (2019).
[Crossref]

Baxter, G. A.

A. Q. Bauer, A. W. Kraft, G. A. Baxter, P. W. Wright, M. D. Reisman, A. R. Bice, J. J. Park, M. R. Bruchas, A. Z. Snyder, J.-M. Lee, and J. P. Culver, “Effective connectivity measured using optogenetically evoked hemodynamics signals exhibits topography distinct from resting state functional connectivity in the mouse,” Cereb. Cortex 28(1), 370–386 (2018).
[Crossref]

P. W. Wright, L. M. Brier, A. Q. Bauer, G. A. Baxter, A. W. Kraft, M. D. Reisman, A. R. Bice, A. Z. Snyder, J.-M. Lee, and J. P. Culver, “Functional connectivity structure of cortical calcium dynamics in anesthetized and awake mice,” PLoS One 12(10), e0185759 (2017).
[Crossref]

Bero, A. W.

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K.-H. Chuang, H. Lee, Z. Li, W.-T. Chang, F. Nasrallah, L. Yeow, and K. Singh, “Evaluation of nuisance removal for functional MRI of rodent brain,” NeuroImage 188, 694–709 (2019).
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A. Q. Bauer, A. W. Kraft, G. A. Baxter, P. W. Wright, M. D. Reisman, A. R. Bice, J. J. Park, M. R. Bruchas, A. Z. Snyder, J.-M. Lee, and J. P. Culver, “Effective connectivity measured using optogenetically evoked hemodynamics signals exhibits topography distinct from resting state functional connectivity in the mouse,” Cereb. Cortex 28(1), 370–386 (2018).
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X. Wu, A. T. Eggebrecht, S. L. Ferradal, J. P. Culver, and H. Dehghani, “Quantitative evaluation of atlas-based high-density diffuse optical tomography for imaging of the human visual cortex,” Biomed. Opt. Express 5(11), 3882–3900 (2014).
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A. T. Eggebrecht, S. L. Ferradal, A. Robichaux-Viehoever, M. S. Hassanpour, H. Dehghani, A. Z. Snyder, T. Hershey, and J. P. Culver, “Mapping distributed brain function and networks with diffuse optical tomography,” Nat. Photonics 8(6), 448–454 (2014).
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M. D. Fox and M. Greicius, “Clinical applications of resting state functional connectivity,” Front. Syst. Neurosci. 4, 19 (2010).
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B. Fischl, D. H. Salat, E. Busa, M. Albert, M. Dieterich, C. Haselgrove, A. van der Kouwe, R. Killiany, D. Kennedy, S. Klaveness, A. Montillo, N. Makris, B. Rosen, and A. M. Dale, “Whole brain segmentation: automated labelling of neuroanatomical structures in the human brain,” Neuron 33(3), 341–355 (2002).
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S. L. Ferradal, A. T. Eggebrecht, M. Hassanpour, A. Z. Snyder, and J. P. Culver, “Atlas-based head modeling and spatial normalization for high-density diffuse optical tomography: in vivo validation against fMRI,” NeuroImage 85, 117–126 (2014).
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A. T. Eggebrecht, S. L. Ferradal, A. Robichaux-Viehoever, M. S. Hassanpour, H. Dehghani, A. Z. Snyder, T. Hershey, and J. P. Culver, “Mapping distributed brain function and networks with diffuse optical tomography,” Nat. Photonics 8(6), 448–454 (2014).
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T. H. Murphy, J. D. Boyd, F. Bolanos, M. P. Vanni, G. Silasi, D. Haupt, and J. M. LeDue, “High-throughput automated home-cage mesoscopic functional imaging of mouse cortex,” Nat. Commun. 7(1), 11611 (2016).
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A. T. Eggebrecht, S. L. Ferradal, A. Robichaux-Viehoever, M. S. Hassanpour, H. Dehghani, A. Z. Snyder, T. Hershey, and J. P. Culver, “Mapping distributed brain function and networks with diffuse optical tomography,” Nat. Photonics 8(6), 448–454 (2014).
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Y.-F. Zang, Y. He, C.-Z. Zhu, Q.-J. Cao, M.-Q. Sui, M. Liang, L.-X. Tian, T.-Z. Jiang, and Y.-F. Wang, “Altered baseline brain activity in children with ADHD revealed by resting-state functional MRI,” Brain Dev. 29(2), 83–91 (2007).
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M. Gorges, F. Roselli, H.-P. Muller, A. C. Ludolph, V. Rasche, and J. Kassubek, “Functional connectivity mapping in the animal model: principles and applications of resting-state fMRI,” Front. Neurol. 8, 200 (2017).
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B. Fischl, D. H. Salat, E. Busa, M. Albert, M. Dieterich, C. Haselgrove, A. van der Kouwe, R. Killiany, D. Kennedy, S. Klaveness, A. Montillo, N. Makris, B. Rosen, and A. M. Dale, “Whole brain segmentation: automated labelling of neuroanatomical structures in the human brain,” Neuron 33(3), 341–355 (2002).
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B. Fischl, D. H. Salat, E. Busa, M. Albert, M. Dieterich, C. Haselgrove, A. van der Kouwe, R. Killiany, D. Kennedy, S. Klaveness, A. Montillo, N. Makris, B. Rosen, and A. M. Dale, “Whole brain segmentation: automated labelling of neuroanatomical structures in the human brain,” Neuron 33(3), 341–355 (2002).
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R. Yuan, X. Di, E. Kim, S. Barik, B. Rypma, and B. Biswal, “Regional homogeneity of resting-state fMRI contributes to both neurovascular and task activation variations,” Magn. Reson. Imaging 31(9), 1492–1500 (2013).
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B. Fischl, D. H. Salat, E. Busa, M. Albert, M. Dieterich, C. Haselgrove, A. van der Kouwe, R. Killiany, D. Kennedy, S. Klaveness, A. Montillo, N. Makris, B. Rosen, and A. M. Dale, “Whole brain segmentation: automated labelling of neuroanatomical structures in the human brain,” Neuron 33(3), 341–355 (2002).
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A. Q. Bauer, A. W. Kraft, G. A. Baxter, P. W. Wright, M. D. Reisman, A. R. Bice, J. J. Park, M. R. Bruchas, A. Z. Snyder, J.-M. Lee, and J. P. Culver, “Effective connectivity measured using optogenetically evoked hemodynamics signals exhibits topography distinct from resting state functional connectivity in the mouse,” Cereb. Cortex 28(1), 370–386 (2018).
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P. W. Wright, L. M. Brier, A. Q. Bauer, G. A. Baxter, A. W. Kraft, M. D. Reisman, A. R. Bice, A. Z. Snyder, J.-M. Lee, and J. P. Culver, “Functional connectivity structure of cortical calcium dynamics in anesthetized and awake mice,” PLoS One 12(10), e0185759 (2017).
[Crossref]

A. Q. Bauer, A. W. Kraft, P. W. Wright, A. Z. Snyder, J.-M. Lee, and J. P. Culver, “Optical imaging of disrupted functional connectivity following ischemic stroke in mice,” NeuroImage 99, 388–401 (2014).
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S. Kura, H. Xie, B. Fu, C. Ayata, D. A. Boas, and S. Sakadzic, “Intrinsic optical signal imaging of the blood volume changes is sufficient for mapping the resting state functional connectivity in the rodent cortex,” J. Neural. Eng. 15(3), 035003 (2018).
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L. Brier, E. Landsness, A. Snyder, P. Wright, G. Baxter, A. Bauer, J.-M. Lee, and J. Culver, “Separability of calcium slow waves and functional connectivity during wake, sleep, and anesthesia,” Neurophotonics 6(03), 1 (2019).
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M. H. Mohajerani, A. W. Chan, M. Mohsenvand, J. LeDue, R. Liu, D. A. McVea, J. D. Boyd, Y. T. Wang, M. Reimers, and T. H. Murphy, “Spontaneous cortical activity alternates between motifs defined by regional axonal projections,” Nat. Neurosci. 16(10), 1426–1435 (2013).
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T. H. Murphy, J. D. Boyd, F. Bolanos, M. P. Vanni, G. Silasi, D. Haupt, and J. M. LeDue, “High-throughput automated home-cage mesoscopic functional imaging of mouse cortex,” Nat. Commun. 7(1), 11611 (2016).
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K.-H. Chuang, H. Lee, Z. Li, W.-T. Chang, F. Nasrallah, L. Yeow, and K. Singh, “Evaluation of nuisance removal for functional MRI of rodent brain,” NeuroImage 188, 694–709 (2019).
[Crossref]

Lee, J.-M.

L. Brier, E. Landsness, A. Snyder, P. Wright, G. Baxter, A. Bauer, J.-M. Lee, and J. Culver, “Separability of calcium slow waves and functional connectivity during wake, sleep, and anesthesia,” Neurophotonics 6(03), 1 (2019).
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A. Q. Bauer, A. W. Kraft, G. A. Baxter, P. W. Wright, M. D. Reisman, A. R. Bice, J. J. Park, M. R. Bruchas, A. Z. Snyder, J.-M. Lee, and J. P. Culver, “Effective connectivity measured using optogenetically evoked hemodynamics signals exhibits topography distinct from resting state functional connectivity in the mouse,” Cereb. Cortex 28(1), 370–386 (2018).
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P. W. Wright, L. M. Brier, A. Q. Bauer, G. A. Baxter, A. W. Kraft, M. D. Reisman, A. R. Bice, A. Z. Snyder, J.-M. Lee, and J. P. Culver, “Functional connectivity structure of cortical calcium dynamics in anesthetized and awake mice,” PLoS One 12(10), e0185759 (2017).
[Crossref]

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M. P. Vanni, A. W. Chen, 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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D. W. Shattuck, G. Prasad, M. Mirza, K. L. Narr, and A. W. Toga, “Online resource for validation of brain segmentation methods,” NeuroImage 45(2), 431–439 (2009).
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J. D. Power, A. Mitra, T. O. Laumann, A. Z. Snyder, B. L. Schlaggar, and S. E. Petersen, “Methods to detect, characterize, and remove motion artifact in resting state fMRI,” NeuroImage 84, 320–341 (2014).
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J. D. Power, A. Mitra, T. O. Laumann, A. Z. Snyder, B. L. Schlaggar, and S. E. Petersen, “Methods to detect, characterize, and remove motion artifact in resting state fMRI,” NeuroImage 84, 320–341 (2014).
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M. H. Mohajerani, A. W. Chan, M. Mohsenvand, J. LeDue, R. Liu, D. A. McVea, J. D. Boyd, Y. T. Wang, M. Reimers, and T. H. Murphy, “Spontaneous cortical activity alternates between motifs defined by regional axonal projections,” Nat. Neurosci. 16(10), 1426–1435 (2013).
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A. Q. Bauer, A. W. Kraft, G. A. Baxter, P. W. Wright, M. D. Reisman, A. R. Bice, J. J. Park, M. R. Bruchas, A. Z. Snyder, J.-M. Lee, and J. P. Culver, “Effective connectivity measured using optogenetically evoked hemodynamics signals exhibits topography distinct from resting state functional connectivity in the mouse,” Cereb. Cortex 28(1), 370–386 (2018).
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D. E. Rex, D. W. Shattuck, R. P. Woods, K. L. Narr, E. Luders, K. Rehm, S. E. Stolzner, D. A. Rottenberg, and A. W. Toga, “A meta-algorithm for brain extraction in MRI,” NeuroImage 23(2), 625–637 (2004).
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M. Gorges, F. Roselli, H.-P. Muller, A. C. Ludolph, V. Rasche, and J. Kassubek, “Functional connectivity mapping in the animal model: principles and applications of resting-state fMRI,” Front. Neurol. 8, 200 (2017).
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B. Fischl, D. H. Salat, E. Busa, M. Albert, M. Dieterich, C. Haselgrove, A. van der Kouwe, R. Killiany, D. Kennedy, S. Klaveness, A. Montillo, N. Makris, B. Rosen, and A. M. Dale, “Whole brain segmentation: automated labelling of neuroanatomical structures in the human brain,” Neuron 33(3), 341–355 (2002).
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J. D. Power, A. Mitra, T. O. Laumann, A. Z. Snyder, B. L. Schlaggar, and S. E. Petersen, “Methods to detect, characterize, and remove motion artifact in resting state fMRI,” NeuroImage 84, 320–341 (2014).
[Crossref]

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[Crossref]

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[Crossref]

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D. W. Shattuck, G. Prasad, M. Mirza, K. L. Narr, and A. W. Toga, “Online resource for validation of brain segmentation methods,” NeuroImage 45(2), 431–439 (2009).
[Crossref]

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J. S. Siegel, J. D. Power, J. W. Dubis, A. C. Vogel, J. A. Church, B. L. Schlaggar, and S. E. Petersen, “Statistical improvements in functional magnetic resonance imaging analyses produced by censoring high-motion data points,” Hum. Brain Mapp. 35(5), 1981–1996 (2014).
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M. P. Vanni, A. W. Chen, 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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G. Silasi, D. Xiao, M. P. Vanni, A. C. N. Chen, and T. H. Murphy, “Intact skull chronic windows for mesoscopic wide-field imaging in awake mice,” J. Neurosci. Methods 267, 141–149 (2016).
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T. H. Murphy, J. D. Boyd, F. Bolanos, M. P. Vanni, G. Silasi, D. Haupt, and J. M. LeDue, “High-throughput automated home-cage mesoscopic functional imaging of mouse cortex,” Nat. Commun. 7(1), 11611 (2016).
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[Crossref]

A. Q. Bauer, A. W. Kraft, P. W. Wright, A. Z. Snyder, J.-M. Lee, and J. P. Culver, “Optical imaging of disrupted functional connectivity following ischemic stroke in mice,” NeuroImage 99, 388–401 (2014).
[Crossref]

J. D. Power, A. Mitra, T. O. Laumann, A. Z. Snyder, B. L. Schlaggar, and S. E. Petersen, “Methods to detect, characterize, and remove motion artifact in resting state fMRI,” NeuroImage 84, 320–341 (2014).
[Crossref]

S. L. Ferradal, A. T. Eggebrecht, M. Hassanpour, A. Z. Snyder, and J. P. Culver, “Atlas-based head modeling and spatial normalization for high-density diffuse optical tomography: in vivo validation against fMRI,” NeuroImage 85, 117–126 (2014).
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A. T. Eggebrecht, S. L. Ferradal, A. Robichaux-Viehoever, M. S. Hassanpour, H. Dehghani, A. Z. Snyder, T. Hershey, and J. P. Culver, “Mapping distributed brain function and networks with diffuse optical tomography,” Nat. Photonics 8(6), 448–454 (2014).
[Crossref]

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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Y.-F. Zang, Y. He, C.-Z. Zhu, Q.-J. Cao, M.-Q. Sui, M. Liang, L.-X. Tian, T.-Z. Jiang, and Y.-F. Wang, “Altered baseline brain activity in children with ADHD revealed by resting-state functional MRI,” Brain Dev. 29(2), 83–91 (2007).
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Y. Zang, T. Jiang, Y. Lu, Y. He, and L. Tian, “Regional homogeneity approach to fMRI data analysis,” NeuroImage 22(1), 394–400 (2004).
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Y.-F. Zang, Y. He, C.-Z. Zhu, Q.-J. Cao, M.-Q. Sui, M. Liang, L.-X. Tian, T.-Z. Jiang, and Y.-F. Wang, “Altered baseline brain activity in children with ADHD revealed by resting-state functional MRI,” Brain Dev. 29(2), 83–91 (2007).
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D. W. Shattuck, G. Prasad, M. Mirza, K. L. Narr, and A. W. Toga, “Online resource for validation of brain segmentation methods,” NeuroImage 45(2), 431–439 (2009).
[Crossref]

D. E. Rex, D. W. Shattuck, R. P. Woods, K. L. Narr, E. Luders, K. Rehm, S. E. Stolzner, D. A. Rottenberg, and A. W. Toga, “A meta-algorithm for brain extraction in MRI,” NeuroImage 23(2), 625–637 (2004).
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J. Bai, T. L. H. Trinh, K.-H. Chuang, and A. Qiu, “Atlas-based automatic mouse brain image segmentation revisited: model complexity vs imaging registration,” Magn. Reson. Imaging 30(6), 789–798 (2012).
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B. Fischl, D. H. Salat, E. Busa, M. Albert, M. Dieterich, C. Haselgrove, A. van der Kouwe, R. Killiany, D. Kennedy, S. Klaveness, A. Montillo, N. Makris, B. Rosen, and A. M. Dale, “Whole brain segmentation: automated labelling of neuroanatomical structures in the human brain,” Neuron 33(3), 341–355 (2002).
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M. P. Vanni, A. W. Chen, 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]

G. Silasi, D. Xiao, M. P. Vanni, A. C. N. Chen, and T. H. Murphy, “Intact skull chronic windows for mesoscopic wide-field imaging in awake mice,” J. Neurosci. Methods 267, 141–149 (2016).
[Crossref]

T. H. Murphy, J. D. Boyd, F. Bolanos, M. P. Vanni, G. Silasi, D. Haupt, and J. M. LeDue, “High-throughput automated home-cage mesoscopic functional imaging of mouse cortex,” Nat. Commun. 7(1), 11611 (2016).
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M. P. Vanni and T. H. Murphy, “Mesoscale transcranial spontaneous activity mapping in GCaMP3 transgenic mice reveals extensive reciprocal connections between areas of somatomotor cortex,” J. Neurosci. 34(48), 15931–15946 (2014).
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J. S. Siegel, J. D. Power, J. W. Dubis, A. C. Vogel, J. A. Church, B. L. Schlaggar, and S. E. Petersen, “Statistical improvements in functional magnetic resonance imaging analyses produced by censoring high-motion data points,” Hum. Brain Mapp. 35(5), 1981–1996 (2014).
[Crossref]

Wang, Y. T.

M. H. Mohajerani, A. W. Chan, M. Mohsenvand, J. LeDue, R. Liu, D. A. McVea, J. D. Boyd, Y. T. Wang, M. Reimers, and T. H. Murphy, “Spontaneous cortical activity alternates between motifs defined by regional axonal projections,” Nat. Neurosci. 16(10), 1426–1435 (2013).
[Crossref]

Wang, Y.-F.

Q.-H. Zou, C.-Z. Zhu, Y. Yang, X.-N. Zuo, X.-Y. Long, Q.-J. Cao, Y.-F. Wang, and Y.-F. Zang, “An improved approach to detection of amplitude of low-frequency fluctuation (ALFF) for resting-state fMRI: Fractional ALFF,” J. Neurosci. Methods 172(1), 137–141 (2008).
[Crossref]

Y.-F. Zang, Y. He, C.-Z. Zhu, Q.-J. Cao, M.-Q. Sui, M. Liang, L.-X. Tian, T.-Z. Jiang, and Y.-F. Wang, “Altered baseline brain activity in children with ADHD revealed by resting-state functional MRI,” Brain Dev. 29(2), 83–91 (2007).
[Crossref]

Wang, Z.

Z. Li, Y. Zhu, A. Childress, J. Detre, and Z. Wang, “Relations between BOLD fMRI-derived resting brain activity and cerebral blood flow,” PLoS One 7(9), e44556 (2012).
[Crossref]

White, B. R.

A. W. Bero, A. Q. Bauer, F. R. Stewart, B. R. White, J. R. Cirrito, M. E. Raichle, J. P. Culver, and D. M. Holtzman, “Bidirectional relationship between functional connectivity and amyloid-$\beta$β deposition in mouse brain,” J. Neurosci. 32(13), 4334–4340 (2012).
[Crossref]

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]

Wieloch, T.

M. J. Quattromani, J. Hakon, U. Rauch, A. Q. Bauer, and T. Wieloch, “Changes in resting-state functional connectivity after stroke in a mouse brain lacking extracellular matrix components,” Neurobiol. Dis. 112, 91–105 (2018).
[Crossref]

Wolf, D.

T. Satterthwaite, D. Wolf, J. Loughead, K. Ruparel, M. Elliott, H. Hakonarson, R. Gur, and R. Gur, “Impact of in-scanner head motion on multiple measures of functional connectivity: Relevance for studies of neurodevelopment in youth,” NeuroImage 60(1), 623–632 (2012).
[Crossref]

Woods, R. P.

D. E. Rex, D. W. Shattuck, R. P. Woods, K. L. Narr, E. Luders, K. Rehm, S. E. Stolzner, D. A. Rottenberg, and A. W. Toga, “A meta-algorithm for brain extraction in MRI,” NeuroImage 23(2), 625–637 (2004).
[Crossref]

Wright, P.

L. Brier, E. Landsness, A. Snyder, P. Wright, G. Baxter, A. Bauer, J.-M. Lee, and J. Culver, “Separability of calcium slow waves and functional connectivity during wake, sleep, and anesthesia,” Neurophotonics 6(03), 1 (2019).
[Crossref]

Wright, P. W.

A. Q. Bauer, A. W. Kraft, G. A. Baxter, P. W. Wright, M. D. Reisman, A. R. Bice, J. J. Park, M. R. Bruchas, A. Z. Snyder, J.-M. Lee, and J. P. Culver, “Effective connectivity measured using optogenetically evoked hemodynamics signals exhibits topography distinct from resting state functional connectivity in the mouse,” Cereb. Cortex 28(1), 370–386 (2018).
[Crossref]

P. W. Wright, L. M. Brier, A. Q. Bauer, G. A. Baxter, A. W. Kraft, M. D. Reisman, A. R. Bice, A. Z. Snyder, J.-M. Lee, and J. P. Culver, “Functional connectivity structure of cortical calcium dynamics in anesthetized and awake mice,” PLoS One 12(10), e0185759 (2017).
[Crossref]

P. W. Wright, A. S. Archambault, S. Peek, A. Q. Bauer, S. M. Culican, B. M. Ances, J. P. Culver, and G. F. Wu, “Functional connectivity alterations in a murine model of optic neuritis,” Exp. Neurol. 295, 18–22 (2017).
[Crossref]

A. Q. Bauer, A. W. Kraft, P. W. Wright, A. Z. Snyder, J.-M. Lee, and J. P. Culver, “Optical imaging of disrupted functional connectivity following ischemic stroke in mice,” NeuroImage 99, 388–401 (2014).
[Crossref]

Wu, G. F.

P. W. Wright, A. S. Archambault, S. Peek, A. Q. Bauer, S. M. Culican, B. M. Ances, J. P. Culver, and G. F. Wu, “Functional connectivity alterations in a murine model of optic neuritis,” Exp. Neurol. 295, 18–22 (2017).
[Crossref]

Wu, X.

Xiao, D.

G. Silasi, D. Xiao, M. P. Vanni, A. C. N. Chen, and T. H. Murphy, “Intact skull chronic windows for mesoscopic wide-field imaging in awake mice,” J. Neurosci. Methods 267, 141–149 (2016).
[Crossref]

Xie, H.

S. Kura, H. Xie, B. Fu, C. Ayata, D. A. Boas, and S. Sakadzic, “Intrinsic optical signal imaging of the blood volume changes is sufficient for mapping the resting state functional connectivity in the rodent cortex,” J. Neural. Eng. 15(3), 035003 (2018).
[Crossref]

Yang, Y.

Q.-H. Zou, C.-Z. Zhu, Y. Yang, X.-N. Zuo, X.-Y. Long, Q.-J. Cao, Y.-F. Wang, and Y.-F. Zang, “An improved approach to detection of amplitude of low-frequency fluctuation (ALFF) for resting-state fMRI: Fractional ALFF,” J. Neurosci. Methods 172(1), 137–141 (2008).
[Crossref]

Yeow, L.

K.-H. Chuang, H. Lee, Z. Li, W.-T. Chang, F. Nasrallah, L. Yeow, and K. Singh, “Evaluation of nuisance removal for functional MRI of rodent brain,” NeuroImage 188, 694–709 (2019).
[Crossref]

Yuan, R.

R. Yuan, X. Di, E. Kim, S. Barik, B. Rypma, and B. Biswal, “Regional homogeneity of resting-state fMRI contributes to both neurovascular and task activation variations,” Magn. Reson. Imaging 31(9), 1492–1500 (2013).
[Crossref]

Zang, Y.

Y. Zang, T. Jiang, Y. Lu, Y. He, and L. Tian, “Regional homogeneity approach to fMRI data analysis,” NeuroImage 22(1), 394–400 (2004).
[Crossref]

Zang, Y.-F.

Q.-H. Zou, C.-Z. Zhu, Y. Yang, X.-N. Zuo, X.-Y. Long, Q.-J. Cao, Y.-F. Wang, and Y.-F. Zang, “An improved approach to detection of amplitude of low-frequency fluctuation (ALFF) for resting-state fMRI: Fractional ALFF,” J. Neurosci. Methods 172(1), 137–141 (2008).
[Crossref]

Y.-F. Zang, Y. He, C.-Z. Zhu, Q.-J. Cao, M.-Q. Sui, M. Liang, L.-X. Tian, T.-Z. Jiang, and Y.-F. Wang, “Altered baseline brain activity in children with ADHD revealed by resting-state functional MRI,” Brain Dev. 29(2), 83–91 (2007).
[Crossref]

Zhu, C.-Z.

Q.-H. Zou, C.-Z. Zhu, Y. Yang, X.-N. Zuo, X.-Y. Long, Q.-J. Cao, Y.-F. Wang, and Y.-F. Zang, “An improved approach to detection of amplitude of low-frequency fluctuation (ALFF) for resting-state fMRI: Fractional ALFF,” J. Neurosci. Methods 172(1), 137–141 (2008).
[Crossref]

Y.-F. Zang, Y. He, C.-Z. Zhu, Q.-J. Cao, M.-Q. Sui, M. Liang, L.-X. Tian, T.-Z. Jiang, and Y.-F. Wang, “Altered baseline brain activity in children with ADHD revealed by resting-state functional MRI,” Brain Dev. 29(2), 83–91 (2007).
[Crossref]

Zhu, Y.

Z. Li, Y. Zhu, A. Childress, J. Detre, and Z. Wang, “Relations between BOLD fMRI-derived resting brain activity and cerebral blood flow,” PLoS One 7(9), e44556 (2012).
[Crossref]

Zou, Q.-H.

Q.-H. Zou, C.-Z. Zhu, Y. Yang, X.-N. Zuo, X.-Y. Long, Q.-J. Cao, Y.-F. Wang, and Y.-F. Zang, “An improved approach to detection of amplitude of low-frequency fluctuation (ALFF) for resting-state fMRI: Fractional ALFF,” J. Neurosci. Methods 172(1), 137–141 (2008).
[Crossref]

Zuo, X.-N.

Q.-H. Zou, C.-Z. Zhu, Y. Yang, X.-N. Zuo, X.-Y. Long, Q.-J. Cao, Y.-F. Wang, and Y.-F. Zang, “An improved approach to detection of amplitude of low-frequency fluctuation (ALFF) for resting-state fMRI: Fractional ALFF,” J. Neurosci. Methods 172(1), 137–141 (2008).
[Crossref]

Biomed. Opt. Express (1)

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[Crossref]

Exp. Neurol. (1)

P. W. Wright, A. S. Archambault, S. Peek, A. Q. Bauer, S. M. Culican, B. M. Ances, J. P. Culver, and G. F. Wu, “Functional connectivity alterations in a murine model of optic neuritis,” Exp. Neurol. 295, 18–22 (2017).
[Crossref]

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Front. Syst. Neurosci. (1)

M. D. Fox and M. Greicius, “Clinical applications of resting state functional connectivity,” Front. Syst. Neurosci. 4, 19 (2010).
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Hum. Brain Mapp. (1)

J. S. Siegel, J. D. Power, J. W. Dubis, A. C. Vogel, J. A. Church, B. L. Schlaggar, and S. E. Petersen, “Statistical improvements in functional magnetic resonance imaging analyses produced by censoring high-motion data points,” Hum. Brain Mapp. 35(5), 1981–1996 (2014).
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S. Kura, H. Xie, B. Fu, C. Ayata, D. A. Boas, and S. Sakadzic, “Intrinsic optical signal imaging of the blood volume changes is sufficient for mapping the resting state functional connectivity in the rodent cortex,” J. Neural. Eng. 15(3), 035003 (2018).
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M. P. Vanni and T. H. Murphy, “Mesoscale transcranial spontaneous activity mapping in GCaMP3 transgenic mice reveals extensive reciprocal connections between areas of somatomotor cortex,” J. Neurosci. 34(48), 15931–15946 (2014).
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M. P. Vanni, A. W. Chen, 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]

A. W. Bero, A. Q. Bauer, F. R. Stewart, B. R. White, J. R. Cirrito, M. E. Raichle, J. P. Culver, and D. M. Holtzman, “Bidirectional relationship between functional connectivity and amyloid-$\beta$β deposition in mouse brain,” J. Neurosci. 32(13), 4334–4340 (2012).
[Crossref]

J. Neurosci. Methods (2)

G. Silasi, D. Xiao, M. P. Vanni, A. C. N. Chen, and T. H. Murphy, “Intact skull chronic windows for mesoscopic wide-field imaging in awake mice,” J. Neurosci. Methods 267, 141–149 (2016).
[Crossref]

Q.-H. Zou, C.-Z. Zhu, Y. Yang, X.-N. Zuo, X.-Y. Long, Q.-J. Cao, Y.-F. Wang, and Y.-F. Zang, “An improved approach to detection of amplitude of low-frequency fluctuation (ALFF) for resting-state fMRI: Fractional ALFF,” J. Neurosci. Methods 172(1), 137–141 (2008).
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J. Bai, T. L. H. Trinh, K.-H. Chuang, and A. Qiu, “Atlas-based automatic mouse brain image segmentation revisited: model complexity vs imaging registration,” Magn. Reson. Imaging 30(6), 789–798 (2012).
[Crossref]

R. Yuan, X. Di, E. Kim, S. Barik, B. Rypma, and B. Biswal, “Regional homogeneity of resting-state fMRI contributes to both neurovascular and task activation variations,” Magn. Reson. Imaging 31(9), 1492–1500 (2013).
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T. H. Murphy, J. D. Boyd, F. Bolanos, M. P. Vanni, G. Silasi, D. Haupt, and J. M. LeDue, “High-throughput automated home-cage mesoscopic functional imaging of mouse cortex,” Nat. Commun. 7(1), 11611 (2016).
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M. H. Mohajerani, A. W. Chan, M. Mohsenvand, J. LeDue, R. Liu, D. A. McVea, J. D. Boyd, Y. T. Wang, M. Reimers, and T. H. Murphy, “Spontaneous cortical activity alternates between motifs defined by regional axonal projections,” Nat. Neurosci. 16(10), 1426–1435 (2013).
[Crossref]

Nat. Photonics (1)

A. T. Eggebrecht, S. L. Ferradal, A. Robichaux-Viehoever, M. S. Hassanpour, H. Dehghani, A. Z. Snyder, T. Hershey, and J. P. Culver, “Mapping distributed brain function and networks with diffuse optical tomography,” Nat. Photonics 8(6), 448–454 (2014).
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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).
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M. J. Quattromani, J. Hakon, U. Rauch, A. Q. Bauer, and T. Wieloch, “Changes in resting-state functional connectivity after stroke in a mouse brain lacking extracellular matrix components,” Neurobiol. Dis. 112, 91–105 (2018).
[Crossref]

NeuroImage (9)

A. Q. Bauer, A. W. Kraft, P. W. Wright, A. Z. Snyder, J.-M. Lee, and J. P. Culver, “Optical imaging of disrupted functional connectivity following ischemic stroke in mice,” NeuroImage 99, 388–401 (2014).
[Crossref]

D. E. Rex, D. W. Shattuck, R. P. Woods, K. L. Narr, E. Luders, K. Rehm, S. E. Stolzner, D. A. Rottenberg, and A. W. Toga, “A meta-algorithm for brain extraction in MRI,” NeuroImage 23(2), 625–637 (2004).
[Crossref]

D. W. Shattuck, G. Prasad, M. Mirza, K. L. Narr, and A. W. Toga, “Online resource for validation of brain segmentation methods,” NeuroImage 45(2), 431–439 (2009).
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S. L. Ferradal, A. T. Eggebrecht, M. Hassanpour, A. Z. Snyder, and J. P. Culver, “Atlas-based head modeling and spatial normalization for high-density diffuse optical tomography: in vivo validation against fMRI,” NeuroImage 85, 117–126 (2014).
[Crossref]

K.-H. Chuang, H. Lee, Z. Li, W.-T. Chang, F. Nasrallah, L. Yeow, and K. Singh, “Evaluation of nuisance removal for functional MRI of rodent brain,” NeuroImage 188, 694–709 (2019).
[Crossref]

T. Satterthwaite, D. Wolf, J. Loughead, K. Ruparel, M. Elliott, H. Hakonarson, R. Gur, and R. Gur, “Impact of in-scanner head motion on multiple measures of functional connectivity: Relevance for studies of neurodevelopment in youth,” NeuroImage 60(1), 623–632 (2012).
[Crossref]

J. Power, B. Schlaggar, and S. Petersen, “Recent progress and outstanding issues in motion correction in resting state fMRI,” NeuroImage 105, 536–551 (2015).
[Crossref]

Y. Zang, T. Jiang, Y. Lu, Y. He, and L. Tian, “Regional homogeneity approach to fMRI data analysis,” NeuroImage 22(1), 394–400 (2004).
[Crossref]

J. D. Power, A. Mitra, T. O. Laumann, A. Z. Snyder, B. L. Schlaggar, and S. E. Petersen, “Methods to detect, characterize, and remove motion artifact in resting state fMRI,” NeuroImage 84, 320–341 (2014).
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B. Fischl, D. H. Salat, E. Busa, M. Albert, M. Dieterich, C. Haselgrove, A. van der Kouwe, R. Killiany, D. Kennedy, S. Klaveness, A. Montillo, N. Makris, B. Rosen, and A. M. Dale, “Whole brain segmentation: automated labelling of neuroanatomical structures in the human brain,” Neuron 33(3), 341–355 (2002).
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Neurophotonics (1)

L. Brier, E. Landsness, A. Snyder, P. Wright, G. Baxter, A. Bauer, J.-M. Lee, and J. Culver, “Separability of calcium slow waves and functional connectivity during wake, sleep, and anesthesia,” Neurophotonics 6(03), 1 (2019).
[Crossref]

PLoS One (3)

Z. Li, Y. Zhu, A. Childress, J. Detre, and Z. Wang, “Relations between BOLD fMRI-derived resting brain activity and cerebral blood flow,” PLoS One 7(9), e44556 (2012).
[Crossref]

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]

P. W. Wright, L. M. Brier, A. Q. Bauer, G. A. Baxter, A. W. Kraft, M. D. Reisman, A. R. Bice, A. Z. Snyder, J.-M. Lee, and J. P. Culver, “Functional connectivity structure of cortical calcium dynamics in anesthetized and awake mice,” PLoS One 12(10), e0185759 (2017).
[Crossref]

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Figures (11)

Fig. 1.
Fig. 1. Schematic of the optical intrinsic signal (OIS) imaging system. (A) Diagram of system components and light path. (B) Cross-section of the mouse head during imaging. (C) Schematic of the field-of-view demonstrating the goal of segmentation. The exposed brain is shown in light gray, with surrounding hair and skin in dark gray. Major suture landmarks are shown in red. Points used for the atlasing affine transformation are in yellow.
Fig. 2.
Fig. 2. Examples of the saturation masking procedure. Data is shown prior to affine transformation. (A) Image of the maximum light intensity measured over time in mouse 2, session 5. (B) $B_{SAT}$ for this session. In this case, no pixels were excluded, and all pixels have a value of 1 (shown in white). (C) Similar image to A for mouse 3, session 3. In this case, the LED was accidentally set to be too bright, resulting in saturation over the left retrosplenial and parietal cortex. (D) The saturation mask, similar to B, for this session demonstrating exclusion of the saturated pixels (marked in black).
Fig. 3.
Fig. 3. Example data demonstrating the signal-to-noise pixel quality metric. Data is shown prior to affine transformation. (A) Maximum intensity image (same as Fig. 2(A)) shown as an anatomic reference. (B) The standard deviation of each pixel over time is plotted against the square root of the intensity. Data is from mouse 2, session 5. The linear regression line of best fit is shown in green. Pixels meeting the SNR threshold are colored blue while pixels that failed and will be excluded from analysis are shown in red. (C) Image of the standard deviation for each pixel divided by the square root of the mean intensity for the same data. (D) SNR mask created from the data in C. In this session, only a few pixels were excluded based on this metric (shown in black). (E-H) The same analysis as A-D, now for mouse 3, session 3. Here, while the data is noisier, the same linear relationship holds. Pixels at the border of the dental cement (green arrows) show lower SNR and are excluded (black pixels in H). Additionally, there is a small region in the left sensorimotor cortex (blue arrows) that has low signal-to-noise, which upon examination of the data seems to be due to instability in the camera read-out. Individual low quality pixels can be excluded, and the entire run need not be discarded.
Fig. 4.
Fig. 4. Example data demonstrating the local correlation quality metric. Data is shown prior to affine transformation. (A) Maximum intensity image (same as Fig. 2(A)) shown as an anatomic reference. (B) Image of the minimum value of the correlation coefficients from each pixel’s surrounding four pixels. Areas covered with hair (in the four corners of the image) have values around zero; boundary regions (e.g., brain-skin) have low correlations as well. Data is from mouse 2, session 5. (C) Mask created from the data in B (excluded pixels in black). Note that the mask highlights boundary areas where the brain region meets the reflected skin flaps (pink arrows). (D-F) The same as A-C for mouse 3, session 3. As with other metrics, the data from this mouse is shown to be noisier, but the mask highlights the edge of the dental cemented region (green arrows). In all sessions, pixels are often removed from the region of the confluence of venous sinuses where the cerebrum meets the olfactory bulb with this mask (cyan arrows).
Fig. 5.
Fig. 5. Variation in the masks created by the pixel-wise quality metric across multiple runs in the same mouse (mouse 1, sessions 1-5, prior to affine transformation). In this mouse, camera saturation was never present; so, that mask is not shown. Note also, that the mouse was repositioned after Run 1; so that run is slightly shifted relative to the others. For the SNR mask (upper row), an area at the posterior edge of the left visual cortex (red arrow) is masked in most runs, possibly due to pooling mineral oil (used in this mouse) at that location. Additionally, scattered pixels in the center of the field-of-view are excluded in each run. For the local correlation mask (lower row), the masks area are very similar across runs. Common areas excluded were the areas of fur in the upper right and upper left corners, the venous sagittal sinus (blue arrows), and along the brain-skin interface (green arrows).
Fig. 6.
Fig. 6. Demonstration of unintended intra-rater variation in brain segmentation. Data is shown prior to affine transformation. (A) Example false color image from a single imaging frame from the OIS system as used for brain segmentation (mouse 2, session 5). (B) Variation in manual segmentation between two sessions by the same reader. Pixels within the first segmentation are colored red while those in the second segmentation are colored green. The overlap between the two segmentations is shown in yellow. (C) Reduced variation (greater overlap) is seen between two segmentations when using guided segmentation.
Fig. 7.
Fig. 7. Box plots for measures of segmentation reliability. For all metrics, guided segmentation decreased variation for both intra- and inter-rater segmentation. (A) Dice coefficient for intra-rater segmentation. (B) Jaccard coefficient for intra-rater segmentation. (C) Boundary F1 score for intra-rater segmentation. (D-F) Same as A-C for inter-rater segmentation.
Fig. 8.
Fig. 8. The effects of the pixel-wise quality metrics on the results of seed-based functional connectivity analysis in single sessions. Data is shown from mouse 1, session 1 after affine transformation. First a seed was chosen from the left motor cortex (black circle) that had passed all quality metrics. Functional connectivity maps are shown using both manual (A) and guided segmentation (B). The two maps are similar; thus the presence of low quality pixels at distant locations has little effect on the overall map. Then, a nearby pixel also in the left motor cortex was chosen as a seed (black circle). This seed failed the local correlation mask, but was filled in by interpolation during spatial smoothing and thus is present in both segmentations. Maps from manual segmentation (C) are similar to that from guided segmentation (D). Thus, in this basic analysis, the Gaussian spatial smoothing is able to ameliorate the effects of isolated low quality pixels even without masking.
Fig. 9.
Fig. 9. Functional connectivity maps performed using seeds that failed pixel-wise quality metrics. All data is after affine transformation. All seeds (black circles) shown in this figure were surrounded by other pixels that failed the quality metrics and were unable to be interpolated using spatial smoothing; thus all maps are shown using manually segmented data only. (A) A map of correlation coefficients using a seed in the frontal cortex along the sagittal sinus (mouse 1, session 5). This pixel failed the local correlation metric. (B) A seed pixel chosen from the region where the camera was saturated in the left retrosplenial cortex (mouse 3, session 3). (C) A seed pixel chosen from a region of low SNR in the left somatosensory cortex (mouse 3, session 3). (D) A seed pixel chosen from a region of low local correlation in the cingulate cortex and sagittal sinus (mouse 3, session 3). The resulting images all consist of patterns without a sensible neurologic network.
Fig. 10.
Fig. 10. Demonstration of methods for combining data across sessions within a mouse. Data is shown after affine transformation. Data in the first row (A-D) is from mouse 2 with a seed in the left motor cortex (black or red circle). (A) Correlation coefficients from data segmented manually and then merged using the intersect method. (B) Data segmented using the guided segmentation and then merged with the intersect method. (C) Data segmented using the guided segmentation and then merged using the censored method. (D) Image showing the number of sessions contributing data for each pixel’s correlation coefficient when censored averaging is used (here, most pixels use all three sessions). (E-H) Similar data to A-D using data from mouse 3 and a seed in the right motor cortex. Note two large regions that were excluded by the pixel-wise quality metrics from one session cause drop-out in the guided segmentation / intersect method data. In the left parietal cortex, there is a region with signal loss due to camera saturation (green arrows, compare to Fig. 2(D)). In the left somatosensory cortex, there is signal loss due to instability in camera illumination (blue arrows, compare to Fig. 3(H)). The censored averaging method preserves the full field-of-view. (I-L) Similar data to A-D using data from mouse 3 and a seed in the right retrosplenial cortex.
Fig. 11.
Fig. 11. Demonstration of methods for combining data across mice. Data is shown after affine transformation. Each row is a different canonical resting-state network as shown by seed-based functional connectivity with the seed denoted by the circle (black or red). The first column uses manually segmented data merged using the intersect method. The second column is data segmented using the guided segmentation and then merged with the intersect method. The third column is data segmented using the guided segmentation and then merged using the censored method. The fourth column shows the number of sessions contributing data for each pixel’s correlation coefficient when calculated using the censored method. When seeds are selected that are outside of the field-of-view of the intersect method data, then the segmented brain is shown entirely in blue.

Tables (2)

Tables Icon

Table 1. Definitions of variables.

Tables Icon

Table 2. Metrics for intra- and inter-rater agreement using manual and guided segmentation. All values are presented as the median and interquartile range.

Equations (27)

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B S A T ( x , y ) = { 1  if  Φ ( x , y , t ) < 2 14  for all  t ; 0  if  Φ ( x , y , t ) 2 14  for any  t .
M ( x , y ) = 1 T t = 1 T Φ ( x , y , t ) .
Φ ( x , y , t ) = a 1 ( x , y ) × t + a 0 ( x , y ) .
Φ ( x , y , t ) = Φ ( x , y , t ) [ a 1 ( x , y ) × t + a 0 ( x , y ) ] + M ( x , y ) .
S ( x , y ) = 1 T 1 t = 1 T [ Φ ( x , y , t ) M ( x , y ) ] 2 .
S ( x , y ) = b 1 M ( x , y ) + b 0 .
B S N R ( x , y ) = { 1  if  S ( x , y ) λ 1 b 1 M ( x , y ) + b 0 ; 0  if  S ( x , y ) > λ 1 b 1 M ( x , y ) + b 0 .
B L C ( x , y ) = { 1  if all  r > λ 2 ; 0  if any  r < λ 2 .
B M ( x , y ) = { 1  if within manual segmentation ; 0  if outside manual segmentation .
B 0 ( x , y ) = B S A T ( x , y ) × B S N R ( x , y ) × B L C ( x , y ) .
B G ( x , y ) = { 1  if  B 0 ( x , y ) = 1  and within manual segmentation ; 0  if  B 0 ( x , y ) = 0  or outside manual segmentation .
Δ μ a ( x , y , t ) = 1 L ln ( Φ ( x , y , t ) M ( x , y ) ) ,
Δ μ a , M ( x , y , t ) = Δ x = 2 2 Δ y = 2 2 Δ μ a ( x + Δ x , y + Δ x , t ) × g ( Δ x , Δ y ) .
Δ μ a , G ( x , y , t ) = Δ x = 2 2 Δ y = 2 2 Δ μ a ( x + Δ x , y + Δ x , t ) × g ( Δ x , Δ y ) × B G ( x + Δ x , y + Δ y ) Δ x = 2 2 Δ y = 2 2 g ( Δ x , Δ y ) × B G ( x + Δ x , y + Δ y ) .
P ( x , y ) = Δ x = 2 2 Δ y = 2 2 B G ( x + Δ x , y + Δ y ) .
B G 2 ( x , y ) = { 1  if  B G ( x , y ) = 1  or  P ( x , y ) 10 ; 0  if  B G ( x , y ) = 0  and  P ( x , y ) < 10.
S M ( t ) = x = 1 X y = 1 Y Δ μ a , M ( x , y , t ) × B M ( x , y ) x = 1 X y = 1 Y B M ( x , y ) ,
S G ( t ) = x = 1 X y = 1 Y Δ μ a , G ( x , y , t ) × B G 2 ( x , y ) x = 1 X y = 1 Y B G 2 ( x , y ) .
β i ( x , y ) = t = 1 T S i ( t ) × Δ μ a , i ( x , y , t ) ;
Δ μ a , i ( x , y , t ) = Δ μ a , i ( x , y , t ) β i ( x , y ) × S i ( t ) ;
R ( n , m ) = ρ ( Δ μ a ( n , t ) , Δ μ a ( m , t ) = 1 T t = 1 T Δ μ a ( n , t ) × Δ μ a ( m , t ) .
M ( n , m ) = { 1  if  B ( n ) = 1  and  B ( m ) = 1 ; 0  if  B ( n ) = 0  or  B ( m ) = 0.
M I ( n , m ) = s = 1 S M s ( n , m ) = { 1  if all of  M s ( n , m ) = 1 ; 0  if any of  M s ( n , m ) = 0 .
F I ( n , m ) = s = 1 S F s ( n , m ) S × M I ( n , m ) .
M C ( n , m ) = { 1  if any of  M s ( n , m ) = 1 ; 0  if all of  M s ( n , m ) = 0 .
F C ( n , m ) = s = 1 S F s ( n , m ) × M s ( n , m ) s = 1 S M s ( n , m ) .
N ( x , y ) = s = 1 S M s ( n , m ) .

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