Brain segmentation, spatial censoring, and averaging techniques for optical functional connectivity imaging in mice

Brian R. White; Jonah A. Padawer-Curry; Akiva S. Cohen; Daniel J. Licht; Arjun G. Yodh

doi:10.1364/BOE.10.005952

Brain segmentation, spatial censoring, and averaging techniques for optical functional connectivity imaging in mice

Brian R. White, Jonah A. Padawer-Curry, Akiva S. Cohen, Daniel J. Licht, and Arjun G. Yodh

Biomedical Optics Express
Vol. 10,
Issue 11,
pp. 5952-5973
(2019)
•https://doi.org/10.1364/BOE.10.005952

Get Citation
Copy Citation Text
Brian R. White, Jonah A. Padawer-Curry, Akiva S. Cohen, Daniel J. Licht, and Arjun G. Yodh, "Brain segmentation, spatial censoring, and averaging techniques for optical functional connectivity imaging in mice," Biomed. Opt. Express 10, 5952-5973 (2019)

Export Citation
- BibTex
- Endnote (RIS)
- HTML
- Plain Text
Get PDF (8508 KB)
Set citation alerts
Save article

Related Topics
Table of Contents Category
- Diffuse and Fluorescence Tomography
Optics & Photonics Topics
?

The topics in this list come from the OSA Optics and Photonics Topics applied to this article.

About this Article
History
- Original Manuscript: July 31, 2019
- Revised Manuscript: September 5, 2019
- Manuscript Accepted: September 13, 2019
- Published: October 29, 2019

Accessible

Open Access

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.

Full Article | PDF Article

OSA Recommended Articles

Hemodynamic correlation imaging of the mouse brain for application in unilateral neurodegenerative diseases

Seung-ho Paik, Sedef Erdogan, Zephaniah Phillips V, Young-Kyu Kim, Kang-Il Song, Sunghee Estelle Park, Youngwoon Choi, Inchan Youn, and Beop-Min Kim
Biomed. Opt. Express 10(4) 1736-1749 (2019)

Aberration-free multi-plane imaging of neural activity from the mammalian brain using a fast-switching liquid crystal spatial light modulator

Rui Liu, Neil Ball, James Brockill, Leonard Kuan, Daniel Millman, Cassandra White, Arielle Leon, Derric Williams, Shig Nishiwaki, Saskia de Vries, Josh Larkin, David Sullivan, Cliff Slaughterbeck, Colin Farrell, and Peter Saggau
Biomed. Opt. Express 10(10) 5059-5080 (2019)

Depth sensitivity and image reconstruction analysis of dense imaging arrays for mapping brain function with diffuse optical tomography

Hamid Dehghani, Brian R. White, Benjamin W. Zeff, Andrew Tizzard, and Joseph P. Culver
Appl. Opt. 48(10) D137-D143 (2009)

References

View by:
Article Order
|
Year
|
Author
|
Publication

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]
M. D. Fox and M. Greicius, “Clinical applications of resting state functional connectivity,” Front. Syst. Neurosci. 4, 19 (2010).
[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]
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]
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. 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]
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]
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]
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]
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]
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]
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]
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]
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).
[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]
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]
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]
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]
R. A. Alexander, “A note on averaging correlations,” Bull. Psychon. Soc. 28(4), 335–336 (1990).
[Crossref]
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]
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]
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]
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]
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]
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]
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]
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]
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]

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.

Alexander, R. A.

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

Ances, B. M.

Archambault, A. S.

Ayata, C.

Bai, J.

Balbi, M.

Barik, S.

Bauer, A.

Bauer, A. Q.

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.

Baxter, G. A.

Bero, A. W.

Bice, A. R.

Biswal, B.

Boas, D. A.

Bolanos, F.

Boyd, J. D.

Brier, L.

Brier, L. M.

Bruchas, M. R.

Busa, E.

Cao, Q.-J.

Chan, A. W.

Chang, W.-T.

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]

Chen, A. C. N.

Chen, A. W.

Childress, A.

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]

Chuang, K.-H.

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]

Church, J. A.

Cirrito, J. R.

Culican, S. M.

Culver, J.

Culver, J. P.

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]

Dale, A. M.

Dehghani, H.

Detre, J.

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]

Di, X.

Dieterich, M.

Dubis, J. W.

Dunlap, W. P.

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]

Eggebrecht, A. T.

Elliott, M.

Ferradal, S. L.

Fischl, B.

Fox, M. D.

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

Fu, B.

Gorges, M.

Greicius, M.

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

Gur, R.

Hakon, J.

Hakonarson, H.

Haselgrove, C.

Hassanpour, M.

Hassanpour, M. S.

Haupt, D.

He, 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]

Hershey, T.

Holtzman, D. M.

Jiang, T.

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]

Jiang, T.-Z.

Kassubek, J.

Kennedy, D.

Killiany, R.

Kim, E.

Klaveness, S.

Kraft, A. W.

Kura, S.

Landsness, E.

Laumann, T. O.

LeDue, J.

LeDue, J. M.

Lee, H.

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.

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]

Li, Z.

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]

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]

Liang, M.

Liu, R.

Long, X.-Y.

Loughead, J.

Lu, 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]

Luders, E.

Ludolph, A. C.

Makris, N.

McVea, D. A.

Mirza, M.

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]

Mitra, A.

Mohajerani, M. H.

Mohsenvand, M.

Montillo, A.

Muller, H.-P.

Murphy, T. H.

Narr, K. L.

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]

Nasrallah, F.

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]

Park, J. J.

Peek, S.

Petersen, S.

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]

Petersen, S. E.

Power, J.

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]

Power, J. D.

Prasad, G.

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]

Qiu, A.

Quattromani, M. J.

Raichle, M. E.

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]

Rasche, V.

Rauch, U.

Rehm, K.

Reimers, M.

Reisman, M. D.

Rex, D. E.

Robichaux-Viehoever, A.

Roselli, F.

Rosen, B.

Rottenberg, D. A.

Ruparel, K.

Rypma, B.

Sakadzic, S.

Salat, D. H.

Satterthwaite, T.

Schlaggar, B.

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]

Schlaggar, B. L.

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]

Shattuck, D. W.

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]

Siegel, J. S.

Silasi, G.

Silver, N. C.

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]

Singh, K.

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]

Snyder, A.

Snyder, A. Z.

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]

Stewart, F. R.

Stolzner, S. E.

Sui, M.-Q.

Tian, L.

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]

Tian, L.-X.

Toga, A. W.

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]

Trinh, T. L. H.

van der Kouwe, A.

Vanni, M. P.

Vogel, A. C.

Wang, Y. T.

Wang, Y.-F.

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.

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.

Wolf, D.

Woods, R. P.

Wright, P.

Wright, P. W.

Wu, G. F.

Wu, X.

Xiao, D.

Xie, H.

Yang, Y.

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.

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.

Zhu, C.-Z.

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.

Zuo, X.-N.

Biomed. Opt. Express (1)

Brain Dev. (1)

Bull. Psychon. Soc. (1)

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

Cereb. Cortex (1)

Exp. Neurol. (1)

Front. Neurol. (1)

Front. Syst. Neurosci. (1)

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

Hum. Brain Mapp. (1)

J. App. Psychol. (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]

J. Neural. Eng. (1)

J. Neurosci. (3)

J. Neurosci. Methods (2)

Magn. Reson. Imaging (2)

Nat. Commun. (1)

Nat. Neurosci. (1)

Nat. Photonics (1)

Nat. Rev. Neurosci. (1)

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]

Neurobiol. Dis. (1)

NeuroImage (9)

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]

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]

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]

Neuron (1)

Neurophotonics (1)

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]

Cited By

OSA participates in Crossref's Cited-By Linking service. Citing articles from OSA journals and other participating publishers are listed here.

Alert me when this article is cited.

Click here to see a list of articles that cite this paper

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.

Download Full Size | PPT Slide | PDF

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).

Download Full Size | PPT Slide | PDF

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.

Download Full Size | PPT Slide | PDF

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).

Download Full Size | PPT Slide | PDF

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).

Download Full Size | PPT Slide | PDF

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.

Download Full Size | PPT Slide | PDF

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.

Download Full Size | PPT Slide | PDF

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.

Download Full Size | PPT Slide | PDF

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.

Download Full Size | PPT Slide | PDF

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.

Download Full Size | PPT Slide | PDF

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.

Download Full Size | PPT Slide | PDF

Tables (2)

Table 1. Definitions of variables.

View Table | View all tables in this article

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

View Table | View all tables in this article

Equations (27)

Equations on this page are rendered with MathJax. Learn more.

B_{S A T} (x, y) = {\begin{cases} 1 if Φ (x, y, t) < 2^{14} for all t; \\ 0 if Φ (x, y, t) \geq 2^{14} for any t . \end{cases}

M (x, y) = \frac{1}{T} \sum_{t = 1}^{T} Φ (x, y, t) .

Φ (x, y, t) = a_{1} (x, y) \times t + a_{0} (x, y) .

Φ^{'} (x, y, t) = Φ (x, y, t) - [a_{1} (x, y) \times t + a_{0} (x, y)] + M (x, y) .

S (x, y) = \sqrt{\frac{1}{T - 1} \sum_{t = 1}^{T} {[Φ^{'} (x, y, t) - M (x, y)]}^{2}} .

S (x, y) = b_{1} \sqrt{M (x, y)} + b_{0} .

B_{S N R} (x, y) = {\begin{cases} 1 if S (x, y) \leq λ_{1} b_{1} \sqrt{M (x, y)} + b_{0}; \\ 0 if S (x, y) > λ_{1} b_{1} \sqrt{M (x, y)} + b_{0} . \end{cases}

B_{L C} (x, y) = {\begin{cases} 1 if all r > λ_{2}; \\ 0 if any r < λ_{2} . \end{cases}

B_{M} (x, y) = {\begin{cases} 1 if within manual segmentation; \\ 0 if outside manual segmentation . \end{cases}

B_{0} (x, y) = B_{S A T} (x, y) \times B_{S N R} (x, y) \times B_{L C} (x, y) .

B_{G} (x, y) = {\begin{cases} 1 if B_{0} (x, y) = 1 and within manual segmentation; \\ 0 if B_{0} (x, y) = 0 or outside manual segmentation . \end{cases}

Δ μ_{a} (x, y, t) = - \frac{1}{L} \ln (\frac{Φ (x, y, t)}{M (x, y)}),

Δ μ_{a, M}^{'} (x, y, t) = \sum_{Δ x = - 2}^{2} \sum_{Δ y = - 2}^{2} Δ μ_{a} (x + Δ x_{,} y + Δ x, t) \times g (Δ x, Δ y) .

Δ μ_{a, G}^{'} (x, y, t) = \frac{\sum_{Δ x = - 2}^{2} \sum_{Δ y = - 2}^{2} Δ μ_{a} (x + Δ x_{,} y + Δ x, t) \times g (Δ x, Δ y) \times B_{G} (x + Δ x, y + Δ y)}{\sum_{Δ x = - 2}^{2} \sum_{Δ y = - 2}^{2} g (Δ x, Δ y) \times B_{G} (x + Δ x, y + Δ y)} .

P (x, y) = \sum_{Δ x = - 2}^{2} \sum_{Δ y = - 2}^{2} B_{G} (x + Δ x, y + Δ y) .

B_{G 2} (x, y) = {\begin{cases} 1 if B_{G} (x, y) = 1 or P (x, y) \geq 10; \\ 0 if B_{G} (x, y) = 0 and P (x, y) < 10. \end{cases}

S_{M} (t) = \frac{\sum_{x = 1}^{X} \sum_{y = 1}^{Y} Δ μ_{a, M}^{'} (x, y, t) \times B_{M} (x, y)}{\sum_{x = 1}^{X} \sum_{y = 1}^{Y} B_{M} (x, y)},

S_{G} (t) = \frac{\sum_{x = 1}^{X} \sum_{y = 1}^{Y} Δ μ_{a, G}^{'} (x, y, t) \times B_{G 2} (x, y)}{\sum_{x = 1}^{X} \sum_{y = 1}^{Y} B_{G 2} (x, y)} .

β_{i} (x, y) = \sum_{t = 1}^{T} S_{i} (t) \times 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) \times S_{i} (t);

R (n, m) = ρ (Δ μ_{a} (n, t), Δ μ_{a} (m, t) = \frac{1}{T} \sum_{t = 1}^{T} Δ μ_{a} (n, t) \times Δ μ_{a} (m, t) .

M (n, m) = {\begin{cases} 1 if B (n) = 1 and B (m) = 1; \\ 0 if B (n) = 0 or B (m) = 0. \end{cases}

M_{I} (n, m) = \prod_{s = 1}^{S} M_{s} (n, m) = {\begin{cases} 1 if all of M_{s} (n, m) = 1; \\ 0 if any of M_{s} (n, m) = 0 . \end{cases}

F_{I} (n, m) = \frac{\sum_{s = 1}^{S} F_{s} (n, m)}{S} \times M_{I} (n, m) .

M_{C} (n, m) = {\begin{cases} 1 if any of M_{s} (n, m) = 1; \\ 0 if all of M_{s} (n, m) = 0 . \end{cases}

F_{C} (n, m) = \frac{\sum_{s = 1}^{S} F_{s} (n, m) \times M_{s} (n, m)}{\sum_{s = 1}^{S} M_{s} (n, m)} .

N (x, y) = \sum_{s = 1}^{S} M_{s} (n, m) .

$\Phi (x,y,t)$ $Φ (x, y, t)$	Raw images of light intensity over time as captured by the camera.
$\Phi '(x,y,t)$ $Φ^{'} (x, y, t)$	Detrended images of light intensity over time.
$M(x,y)$ $M (x, y)$	Mean value of each pixel’s time course.
$S(x,y)$ $S (x, y)$	Standard deviation of each pixel’s time course (after detrending).
$B_{SAT}(x,y)$ $B_{S A T} (x, y)$	Boolean pixel mask for saturation,
	where included pixels have a value of 1 and saturated pixels 0.
$B_{SNR}(x,y)$ $B_{S N R} (x, y)$	Boolean pixel mask for signal-to-noise,
	where included pixels have a value of 1 and low SNR pixels 0.
$B_{LC}(x,y)$ $B_{L C} (x, y)$	Boolean pixel mask for local correlation,
	where included pixels have a value of 1 and low local correlation pixels 0.
$B_0(x,y)$ $B_{0} (x, y)$	Combination Boolean mask using the three masks:
	$B_{SAT}(x,y)$ $B_{S A T} (x, y)$ , $B_{SNR}(x,y)$ $B_{S N R} (x, y)$ , and $B_{LC}(x,y)$ $B_{L C} (x, y)$ .
$B_M(x,y)$ $B_{M} (x, y)$	Manual segmentation.
$B_G(x,y)$ $B_{G} (x, y)$	Guided segmentation.
$\lambda _1$ $λ_{1}$	Masking parameter for SNR mask.
$\lambda _2$ $λ_{2}$	Masking parameter for correlation mask.
$\Delta \mu _a(x,y,t)$ $Δ μ_{a} (x, y, t)$	Images of changes in the absorption coefficient over time.
$L$ $L$	Optical pathlength.
$g$ $g$	Spatial Gaussian smoothing kernel.
$\Delta \mu '_{a,i}(x,y,t)$ $Δ μ_{a, i}^{'} (x, y, t)$	Spatially-smoothed images of changes in the absorption coefficient over time.$^{a}$ $^{a}$
$P(x,y)$ $P (x, y)$	Number of pixels included in each pixel’s interpolation after smoothing.
$B_{G2}(x,y)$ $B_{G 2} (x, y)$	Guided segmentation interpolated by smoothing.
$S_i(t)$ $S_{i} (t)$	Global signal averaged over the segmented brain.$^{a}$ $^{a}$
$\beta _i(x,y)$ $β_{i} (x, y)$	Regression coefficient at each pixel for the global signal.$^{a}$ $^{a}$
$\Delta \mu ''_{a,i}(x,y,t)$ $Δ μ_{a, i}^{″} (x, y, t)$	Images of changes in the absorption coefficient over time
	after spatial smoothing and global signal regression.$^{a}$ $^{a}$
$R(n,m)$ $R (n, m)$	Matrix of correlation coefficients.
$M(n,m)$ $M (n, m)$	Boolean mask for correlation coefficient matrix.
$F(n,m)$ $F (n, m)$	Matrix of Fisher-transformed correlation coefficients.

	Intra-rater
	Manual	Guided	$P$ $P$ -value
Dice Coefficient	0.96 (0.94–0.97)	0.98 (0.97–0.99)	0.0003
Jaccard Coefficient	0.92 (0.89–0.94)	0.97 (0.95–0.98)	0.0002
Boundary F1 Score	0.68 (0.49–0.76)	0.96 (0.92–0.99)	$<0.0001$ $< 0.0001$
	Inter-rater
	Manual	Guided	$P$ $P$ -value
Dice Coefficient	0.92 (0.89–0.95)	0.96 (0.94–0.97)	$<0.0001$ $< 0.0001$
Jaccard Coefficient	0.84 (0.81–0.90)	0.92 (0.89–0.93)	$<0.0001$ $< 0.0001$
Boundary F1 Score	0.37 (0.19–0.55)	0.91 (0.82–0.94)	$<0.0001$ $< 0.0001$

$\Phi (x,y,t)$ $Φ (x, y, t)$	Raw images of light intensity over time as captured by the camera.
$\Phi '(x,y,t)$ $Φ^{'} (x, y, t)$	Detrended images of light intensity over time.
$M(x,y)$ $M (x, y)$	Mean value of each pixel’s time course.
$S(x,y)$ $S (x, y)$	Standard deviation of each pixel’s time course (after detrending).
$B_{SAT}(x,y)$ $B_{S A T} (x, y)$	Boolean pixel mask for saturation,
	where included pixels have a value of 1 and saturated pixels 0.
$B_{SNR}(x,y)$ $B_{S N R} (x, y)$	Boolean pixel mask for signal-to-noise,
	where included pixels have a value of 1 and low SNR pixels 0.
$B_{LC}(x,y)$ $B_{L C} (x, y)$	Boolean pixel mask for local correlation,
	where included pixels have a value of 1 and low local correlation pixels 0.
$B_0(x,y)$ $B_{0} (x, y)$	Combination Boolean mask using the three masks:
	$B_{SAT}(x,y)$ $B_{S A T} (x, y)$ , $B_{SNR}(x,y)$ $B_{S N R} (x, y)$ , and $B_{LC}(x,y)$ $B_{L C} (x, y)$ .
$B_M(x,y)$ $B_{M} (x, y)$	Manual segmentation.
$B_G(x,y)$ $B_{G} (x, y)$	Guided segmentation.
$\lambda _1$ $λ_{1}$	Masking parameter for SNR mask.
$\lambda _2$ $λ_{2}$	Masking parameter for correlation mask.
$\Delta \mu _a(x,y,t)$ $Δ μ_{a} (x, y, t)$	Images of changes in the absorption coefficient over time.
$L$ $L$	Optical pathlength.
$g$ $g$	Spatial Gaussian smoothing kernel.
$\Delta \mu '_{a,i}(x,y,t)$ $Δ μ_{a, i}^{'} (x, y, t)$	Spatially-smoothed images of changes in the absorption coefficient over time.$^{a}$ $^{a}$
$P(x,y)$ $P (x, y)$	Number of pixels included in each pixel’s interpolation after smoothing.
$B_{G2}(x,y)$ $B_{G 2} (x, y)$	Guided segmentation interpolated by smoothing.
$S_i(t)$ $S_{i} (t)$	Global signal averaged over the segmented brain.$^{a}$ $^{a}$
$\beta _i(x,y)$ $β_{i} (x, y)$	Regression coefficient at each pixel for the global signal.$^{a}$ $^{a}$
$\Delta \mu ''_{a,i}(x,y,t)$ $Δ μ_{a, i}^{″} (x, y, t)$	Images of changes in the absorption coefficient over time
	after spatial smoothing and global signal regression.$^{a}$ $^{a}$
$R(n,m)$ $R (n, m)$	Matrix of correlation coefficients.
$M(n,m)$ $M (n, m)$	Boolean mask for correlation coefficient matrix.
$F(n,m)$ $F (n, m)$	Matrix of Fisher-transformed correlation coefficients.

	Intra-rater
	Manual	Guided	$P$ $P$ -value
Dice Coefficient	0.96 (0.94–0.97)	0.98 (0.97–0.99)	0.0003
Jaccard Coefficient	0.92 (0.89–0.94)	0.97 (0.95–0.98)	0.0002
Boundary F1 Score	0.68 (0.49–0.76)	0.96 (0.92–0.99)	$<0.0001$ $< 0.0001$
	Inter-rater
	Manual	Guided	$P$ $P$ -value
Dice Coefficient	0.92 (0.89–0.95)	0.96 (0.94–0.97)	$<0.0001$ $< 0.0001$
Jaccard Coefficient	0.84 (0.81–0.90)	0.92 (0.89–0.93)	$<0.0001$ $< 0.0001$
Boundary F1 Score	0.37 (0.19–0.55)	0.91 (0.82–0.94)	$<0.0001$ $< 0.0001$