Wide-Field Optical Imaging in Mouse Models of Ischemic Stroke

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Neural Repair

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2616))

Abstract

Functional neuroimaging is a powerful tool for evaluating how local and global brain circuits evolve after focal ischemia and how these changes relate to functional recovery. For example, acutely after stroke, changes in functional brain organization relate to initial deficit and are predictive of recovery potential. During recovery, the reemergence and restoration of connections lost due to stroke correlate with recovery of function. Thus, information gleaned from functional neuroimaging can be used as a proxy for behavior and inform on the efficacy of interventional strategies designed to affect plasticity mechanisms after injury. And because these findings are consistently observed across species, bridge measurements can be made in animal models to enrich findings in human stroke populations. In mice, genetic engineering techniques have provided several new opportunities for extending optical neuroimaging methods to more direct measures of neuronal activity. These developments are especially useful in the context of stroke where neurovascular coupling can be altered, potentially limiting imaging measures based on hemodynamic activity alone. This chapter is designed to give an overview of functional wide-field optical imaging (WFOI) for applications in rodent models of stroke, primarily in the mouse. The goal is to provide a protocol for laboratories that want to incorporate an affordable functional neuroimaging assay into their current research thrusts, but perhaps lack the background knowledge or equipment for develo** a new arm of research in their lab. Within, we offer a comprehensive guide develo** and applying WFOI technology with the hope of facilitating accessibility of neuroimaging technology to other researchers in the stroke field.

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References

  1. Bauer AQ, Kraft AW, Wright PW et al (2014) Optical imaging of disrupted functional connectivity following ischemic stroke in mice. NeuroImage 99:388–401

    Article  Google Scholar 

  2. Carter AR, Astafiev SV, Lang CE et al (2010) Resting interhemispheric functional magnetic resonance imaging connectivity predicts performance after stroke. Ann Neurol 67:365–375

    Google Scholar 

  3. Friston KJ (1994) Functional and effective connectivity in neuroimaging: a synthesis. Hum Brain Mapp 2:56–78

    Article  Google Scholar 

  4. Grefkes C, Fink GR (2011) Reorganization of cerebral networks after stroke: new insights from neuroimaging with connectivity approaches. Brain 134:1264–1276

    Article  Google Scholar 

  5. Raichle ME, Mintun MA (2006) Brain work and brain imaging. Annu Rev Neurosci 29:449–476

    Article  CAS  Google Scholar 

  6. Silasi G, Murphy TH (2014) Removing the brakes on post-stroke plasticity drives recovery from the intact hemisphere and spinal cord. Brain 137:648–650

    Article  Google Scholar 

  7. Silasi G, Murphy TH (2014) Stroke and the connectome: how connectivity guides therapeutic intervention. Neuron 83:1354–1368

    Article  CAS  Google Scholar 

  8. Cramer SC, Riley JD (2008) Neuroplasticity and brain repair after stroke. Curr Opin Neurol 21:76–82

    Article  Google Scholar 

  9. Calautti C, Leroy F, Guincestre JY et al (2001) Sequential activation brain map** after subcortical stroke: changes in hemispheric balance and recovery. Neuroreport 12:3883–3886

    Article  CAS  Google Scholar 

  10. Traversa R, Cicinelli P, Bassi A et al (1997) Map** of motor cortical reorganization after stroke. A brain stimulation study with focal magnetic pulses. Stroke 28:110–117

    Article  CAS  Google Scholar 

  11. Ward NS, Brown MM, Thompson AJ et al (2003) Neural correlates of motor recovery after stroke: a longitudinal fMRI study. Brain 126:2476–2496

    Article  CAS  Google Scholar 

  12. Brown CE, Aminoltejari K, Erb H et al (2009) In vivo voltage-sensitive dye imaging in adult mice reveals that somatosensory maps lost to stroke are replaced over weeks by new structural and functional circuits with prolonged modes of activation within both the peri-infarct zone and distant sites. J Neurosci 29:1719–1734

    Article  CAS  Google Scholar 

  13. Dijkhuizen RM, Ren J, Mandeville JB et al (2001) Functional magnetic resonance imaging of reorganization in rat brain after stroke. Proc Natl Acad Sci U S A 98:12766–12771

    Article  CAS  Google Scholar 

  14. Jaillard A, Martin CD, Garambois K et al (2005) Vicarious function within the human primary motor cortex? A longitudinal fMRI stroke study. Brain J Neurol 128:1122–1138

    Article  Google Scholar 

  15. Nudo RJ, Wise BM, Sifuentes F et al (1996) Neural substrates for the effects of rehabilitative training on motor recovery after ischemic infarct. Science 272:1791–1794

    Article  CAS  Google Scholar 

  16. Wei L, Erinjeri JP, Rovainen CM et al (2001) Collateral growth and angiogenesis around cortical stroke. Stroke 32:2179–2184

    Article  CAS  Google Scholar 

  17. Gonzalez CL, Kolb B (2003) A comparison of different models of stroke on behaviour and brain morphology. Eur J Neurosci 18:1950–1962

    Article  CAS  Google Scholar 

  18. Felleman DJ, Van Essen DC (1991) Distributed hierarchical processing in the primate cerebral cortex. Cereb Cortex 1:1–47

    Article  CAS  Google Scholar 

  19. Posner MI, Petersen SE, Fox PT et al (1988) Localization of cognitive operations in the human brain. Science 240:1627–1631

    Article  CAS  Google Scholar 

  20. Carter AR, Patel KR, Astafiev SV et al (2012) Upstream dysfunction of somatomotor functional connectivity after corticospinal damage in stroke. Neurorehabil Neural Repair 26:7–19

    Article  Google Scholar 

  21. He BJ, Snyder AZ, Vincent JL et al (2007) Breakdown of functional connectivity in frontoparietal networks underlies behavioral deficits in spatial neglect. Neuron 53:905–918

    Article  CAS  Google Scholar 

  22. Van Meer MPA, Van Der Marel K, Wang K et al (2010) Recovery of sensorimotor function after experimental stroke correlates with restoration of resting-state interhemispheric functional connectivity. J Neurosci 30:3964–3972

    Article  Google Scholar 

  23. Hakon J, Quattromani MJ, Sjolund C et al (2018) Multisensory stimulation improves functional recovery and resting-state functional connectivity in the mouse brain after stroke. Neuroimage Clin 17:717–730

    Article  Google Scholar 

  24. Kraft AW, Bauer AQ, Smith KP et al (2015) Sensory deprivation following cortical focal ischemia facilitates remap** and accelerates behavioral recovery. In: BRAIN: international symposium on cerebral blood flow, metabolism and function, Vancouver

    Google Scholar 

  25. Quattromani MJ, Hakon J, Rauch U et al (2018) Changes in resting-state functional connectivity after stroke in a mouse brain lacking extracellular matrix components. Neurobiol Dis 112:91–105

    Article  CAS  Google Scholar 

  26. Dijkhuizen Rm SA, Mandeville JB, Wu O, Halpern EF, Finklestein SP, Rosen BR, Lo E (2003) Correlation between brain reorganization, ischemic damage, and neurologic status after transient focal cerebral ischemia in rats: a functional magnetic resonance imaging study. J Neurosci 23:510–517

    Article  Google Scholar 

  27. Harrison TC, Silasi G, Boyd JD et al (2013) Displacement of sensory maps and disorganization of motor cortex after targeted stroke in mice. Stroke 44:2300–2306

    Article  Google Scholar 

  28. Winship IR, Murphy TH (2008) In vivo calcium imaging reveals functional rewiring of single somatosensory neurons after stroke. J Neurosci 28:6592–6606

    Article  CAS  Google Scholar 

  29. Grinvald A, Lieke E, Frostig RD et al (1986) Functional architecture of cortex revealed by optical imaging of intrinsic signals. Nature 324:361–364

    Article  CAS  Google Scholar 

  30. Malonek D, Grinvald A (1996) Interactions between electrical activity and cortical microcirculation revealed by imaging spectroscopy: implications for functional brain map**. Science 272:551–554

    Article  CAS  Google Scholar 

  31. Frostig RD, Lieke EE, Ts’o DY et al (1990) Cortical functional architecture and local coupling between neuronal activity and the microcirculation revealed by in vivo high-resolution optical imaging of intrinsic signals. Proc Natl Acad Sci 87:6082–6086

    Article  CAS  Google Scholar 

  32. Berwick J, Johnston D, Jones M et al (2005) Neurovascular coupling investigated with two-dimensional optical imaging spectroscopy in rat whisker barrel cortex. Eur J Neurosci 22:1655–1666

    Article  CAS  Google Scholar 

  33. Devor A, Dunn AK, Andermann ML et al (2003) Coupling of total hemoglobin concentration, oxygenation, and neural activity in rat somatosensory cortex. Neuron 39:353–359

    Article  CAS  Google Scholar 

  34. Jones M, Berwick J, Johnston D et al (2001) Concurrent optical imaging spectroscopy and laser-Doppler flowmetry: the relationship between blood flow, oxygenation, and volume in rodent barrel cortex. NeuroImage 13:1002–1015

    Article  CAS  Google Scholar 

  35. Sheth SA, Nemoto M, Guiou M et al (2004) Columnar specificity of microvascular oxygenation and volume responses: implications for functional brain map**. J Neurosci 24:634–641

    Article  CAS  Google Scholar 

  36. Iadecola C (2017) The neurovascular unit coming of age: a journey through neurovascular coupling in health and disease. Neuron 96:17–42

    Article  CAS  Google Scholar 

  37. Ekstrom A (2010) How and when the fMRI BOLD signal relates to underlying neural activity: the danger in dissociation. Brain Res Rev 62:233–244

    Article  Google Scholar 

  38. Hillman EM (2014) Coupling mechanism and significance of the BOLD signal: a status report. Annu Rev Neurosci 37:161–181

    Article  CAS  Google Scholar 

  39. Logothetis NK, Wandell BA (2004) Interpreting the BOLD signal. Annu Rev Physiol 66:735–769

    Article  CAS  Google Scholar 

  40. Nair DG (2005) About being BOLD. Brain Res Rev 50:229–243

    Article  Google Scholar 

  41. Ma Y, Shaik MA, Kim SH et al (2016) Wide-field optical map** of neural activity and brain haemodynamics: considerations and novel approaches. Philos Trans R Soc Lond Ser B Biol Sci 371:20150360

    Article  Google Scholar 

  42. Grienberger C, Konnerth A (2012) Imaging calcium in neurons. Neuron 73:862–885

    Article  CAS  Google Scholar 

  43. Hendel T, Mank M, Schnell B et al (2008) Fluorescence changes of genetic calcium indicators and OGB-1 correlated with neural activity and calcium in vivo and in vitro. J Neurosci 28:7399–7411

    Article  CAS  Google Scholar 

  44. Oh J, Lee C, Kaang BK (2019) Imaging and analysis of genetically encoded calcium indicators linking neural circuits and behaviors. Korean J Physiol Pharmacol 23:237–249

    Article  CAS  Google Scholar 

  45. Tian L, Hires SA, Looger LL (2012) Imaging neuronal activity with genetically encoded calcium indicators. Cold Spring Harb Protoc 2012:647–656

    Article  Google Scholar 

  46. Tian L, Hires SA, Mao T et al (2009) Imaging neural activity in worms, flies and mice with improved GCaMP calcium indicators. Nat Methods 6:875–881

    Article  CAS  Google Scholar 

  47. Akemann W, Mutoh H, Perron A et al (2012) Imaging neural circuit dynamics with a voltage-sensitive fluorescent protein. J Neurophysiol 108:2323–2337

    Article  CAS  Google Scholar 

  48. Broussard GJ, Liang R, Tian L (2014) Monitoring activity in neural circuits with genetically encoded indicators. Front Mol Neurosci 7:97

    Article  Google Scholar 

  49. Inagaki S, Nagai T (2016) Current progress in genetically encoded voltage indicators for neural activity recording. Curr Opin Chem Biol 33:95–100

    Article  CAS  Google Scholar 

  50. Momose-Sato Y, Sato K, Arai Y et al (1999) Evaluation of voltage-sensitive dyes for long-term recording of neural activity in the hippocampus. J Membr Biol 172:145–157

    Article  CAS  Google Scholar 

  51. Yang HH, St-Pierre F (2016) Genetically encoded voltage indicators: opportunities and challenges. J Neurosci 36:9977–9989

    Article  CAS  Google Scholar 

  52. Dana H, Chen TW, Hu A et al (2014) Thy1-GCaMP6 transgenic mice for neuronal population imaging in vivo. PLoS One 9:e108697

    Article  Google Scholar 

  53. Dana H, Mohar B, Sun Y et al (2016) Sensitive red protein calcium indicators for imaging neural activity. elife 5:e12727

    Article  Google Scholar 

  54. Dana H, Novak O, Guardado-Montesino M et al (2018) Thy1 transgenic mice expressing the red fluorescent calcium indicator jRGECO1a for neuronal population imaging in vivo. PLoS One 13:e0205444

    Article  Google Scholar 

  55. Dana H, Sun Y, Mohar B et al (2019) High-performance calcium sensors for imaging activity in neuronal populations and microcompartments. Nat Methods 16:649–657

    Article  CAS  Google Scholar 

  56. Fosque BF, Sun Y, Dana H et al (2015) Neural circuits. Labeling of active neural circuits in vivo with designed calcium integrators. Science 347:755–760

    Article  CAS  Google Scholar 

  57. Shen Y, Dana H, Abdelfattah AS et al (2018) A genetically encoded Ca(2+) indicator based on circularly permutated sea anemone red fluorescent protein eqFP578. BMC Biol 16:9

    Article  Google Scholar 

  58. Bauer AQ, Kraft AW, Baxter GA et al (2018) Effective connectivity measured using Optogenetically evoked hemodynamic signals exhibits topography distinct from resting state functional connectivity in the mouse. Cerebral Cortex (New York, NY : 1991) 28:370–386

    Google Scholar 

  59. Brier LM, Landsness EC, Snyder AZ et al (2019) Separability of calcium slow waves and functional connectivity during wake, sleep, and anesthesia. Neurophotonics 6:035002

    Article  CAS  Google Scholar 

  60. Corlu A, Durduran T, Choe R et al (2003) Uniqueness and wavelength optimization in continuous-wave multispectral diffuse optical tomography. Opt Lett 28:2339–2341

    Article  Google Scholar 

  61. Kraft AW, Bauer AQ, Culver JP et al (2018) Sensory deprivation after focal ischemia in mice accelerates brain remap** and improves functional recovery through Arc-dependent synaptic plasticity. Sci Transl Med 10:eaag1328

    Article  Google Scholar 

  62. Ma Y, Shaik MA, Kozberg MG et al (2016) Resting-state hemodynamics are spatiotemporally coupled to synchronized and symmetric neural activity in excitatory neurons. Proc Natl Acad Sci U S A 113:E8463–E8471

    Article  CAS  Google Scholar 

  63. Silasi G, **ao D, Vanni MP et al (2016) Intact skull chronic windows for mesoscopic wide-field imaging in awake mice. J Neurosci Methods 267:141–149

    Article  Google Scholar 

  64. Wright PW, Brier LM, Bauer AQ et al (2017) Functional connectivity structure of cortical calcium dynamics in anesthetized and awake mice. PLoS One 12:e0185759

    Article  Google Scholar 

  65. Macknik SL, Alexander RG, Caballero O et al (2019) Advanced circuit and cellular imaging methods in nonhuman primates. J Neurosci 39:8267–8274

    Article  CAS  Google Scholar 

  66. Park K, Liyanage AC, Koretsky AP et al (2021) Optical imaging of stimulation-evoked cortical activity using GCaMP6f and jRGECO1a. Quant Imaging Med Surg 11:998

    Article  Google Scholar 

  67. Ye L, Haroon MA, Salinas A et al (2017) Comparison of GCaMP3 and GCaMP6f for studying astrocyte Ca2+ dynamics in the awake mouse brain. PLoS One 12:e0181113

    Article  Google Scholar 

  68. Akerboom J, Carreras Calderón N, Tian L et al (2013) Genetically encoded calcium indicators for multi-color neural activity imaging and combination with optogenetics. Front Mol Neurosci 6:2

    Article  CAS  Google Scholar 

  69. Hires SA, Tian L, Looger LL (2008) Reporting neural activity with genetically encoded calcium indicators. Brain Cell Biol 36:69–86

    Article  CAS  Google Scholar 

  70. Mank M, Santos AF, Direnberger S et al (2008) A genetically encoded calcium indicator for chronic in vivo two-photon imaging. Nat Methods 5:805–811

    Article  CAS  Google Scholar 

  71. Mao T, O’connor DH, Scheuss V et al (2008) Characterization and subcellular targeting of GCaMP-type genetically-encoded calcium indicators. PLoS One 3:e1796

    Article  Google Scholar 

  72. Tian L, Hires SA, Looger LL (2012) Imaging neuronal activity with genetically encoded calcium indicators. Cold Spring Harbor Protocols 2012:pdb top069609

    Google Scholar 

  73. De Ryck M, Van Reempts J, Duytschaever H et al (1992) Neocortical localization of tactile/proprioceptive limb placing reactions in the rat. Brain Res 573:44–60

    Article  Google Scholar 

  74. Johansson BB, Ohlsson A-L (1996) Environment, social interaction, and physical activity as determinants of functional outcome after cerebral infarction in the rat. Exp Neurol 139:322–327

    Article  CAS  Google Scholar 

  75. Madinier A, Quattromani MJ, Sjölund C et al (2014) Enriched housing enhances recovery of limb placement ability and reduces aggrecan-containing perineuronal nets in the rat somatosensory cortex after experimental stroke. PLoS One 9:e93121

    Article  Google Scholar 

  76. Lay CC, Frostig RD (2014) Complete protection from impending stroke following permanent middle cerebral artery occlusion in awake, behaving rats. Eur J Neurosci 40:3413–3421

    Article  Google Scholar 

  77. Bero AW, Bauer AQ, Stewart FR et al (2012) Bidirectional relationship between functional connectivity and amyloid-beta deposition in mouse brain. J Neurosci 32:4334–4340

    Article  CAS  Google Scholar 

  78. Power JD, Schlaggar BL, Petersen SE (2015) Recent progress and outstanding issues in motion correction in resting state fMRI. NeuroImage 105:536–551

    Article  Google Scholar 

  79. Bergonzi KM, Bauer AQ, Wright PW et al (2015) Map** functional connectivity using cerebral blood flow in the mouse brain. J Cereb Blood Flow Metab 35:367–370

    Article  Google Scholar 

  80. Goldey GJ, Roumis DK, Glickfeld LL et al (2014) Removable cranial windows for long-term imaging in awake mice. Nat Protoc 9:2515–2538

    Article  CAS  Google Scholar 

  81. Ren C, Komiyama T (2021) Wide-field calcium imaging of cortex-wide activity in awake, head-fixed mice. STAR Protoc 2:100973

    Article  CAS  Google Scholar 

  82. Mightex In:Mightex, p Commercial Optical Imaging Hardware.

    Google Scholar 

  83. Scimedia In:SciMedia pCommercial Optical Imaging Hardware.

    Google Scholar 

  84. Stopczynski A, Stahlhut C, Larsen JE et al (2014) The smartphone brain scanner: a portable real-time neuroimaging system. PLoS One 9:e86733

    Article  Google Scholar 

  85. Zhang X, Landsness EC, Chen W et al (2022) Automated sleep state classification of wide-field calcium imaging data via multiplex visibility graphs and deep learning. J Neurosci Methods 366:109421

    Article  CAS  Google Scholar 

  86. Desjardins M, Kılıç K, Thunemann M et al (2019) Awake mouse imaging: from two-photon microscopy to blood oxygen level–dependent functional magnetic resonance imaging. Biol Psychiatry Cogn Neurosci Neuroimag 4:533–542

    Google Scholar 

  87. Dinh TNA, Jung WB, Shim H-J et al (2021) Characteristics of fMRI responses to visual stimulation in anesthetized vs. awake mice. NeuroImage 226:117542

    Article  CAS  Google Scholar 

  88. Drew PJ, Shih AY, Driscoll JD et al (2010) Chronic optical access through a polished and reinforced thinned skull. Nat Methods 7:981–984

    Article  CAS  Google Scholar 

  89. Gao Y-R, Drew PJ (2016) Effects of voluntary locomotion and calcitonin gene-related peptide on the dynamics of single dural vessels in awake mice. J Neurosci 36:2503–2516

    Article  CAS  Google Scholar 

  90. Henckens MJ, Van Der Marel K, Van Der Toorn A et al (2015) Stress-induced alterations in large-scale functional networks of the rodent brain. NeuroImage 105:312–322

    Article  Google Scholar 

  91. Krawchuk MB, Ruff CF, Yang X et al (2020) Optogenetic assessment of VIP, PV, SOM and NOS inhibitory neuron activity and cerebral blood flow regulation in mouse somato-sensory cortex. J Cereb Blood Flow Metab 40:1427–1440

    Article  CAS  Google Scholar 

  92. Mandino F, Cerri DH, Garin CM et al (2019) Animal functional magnetic resonance imaging: trends and path toward standardization. Front Neuroinform 13:78

    Article  Google Scholar 

  93. Martin C, Martindale J, Berwick J et al (2006) Investigating neural-hemodynamic coupling and the hemodynamic response function in the awake rat. NeuroImage 32:33–48

    Article  Google Scholar 

  94. Michelson NJ, Vazquez AL, Eles JR et al (2018) Multi-scale, multi-modal analysis uncovers complex relationship at the brain tissue-implant neural interface: new emphasis on the biological interface. J Neural Eng 15:033001

    Article  Google Scholar 

  95. Russo G, Helluy X, Behroozi M et al (2021) Gradual restraint habituation for awake functional magnetic resonance imaging combined with a sparse imaging paradigm reduces motion artifacts and stress levels in rodents. Front Neurosci-Switz 15:805679

    Article  Google Scholar 

  96. Şencan İ, Esipova T, Kılıç K et al (2020) Optical measurement of microvascular oxygenation and blood flow responses in awake mouse cortex during functional activation. J Cereb Blood Flow Metab. 0271678X20928011

    Google Scholar 

  97. Shih AY, Drew PJ, Kleinfeld D (2014) Imaging vasodynamics in the awake mouse brain with two-photon microscopy. In: Neurovascular coupling methods. Springer, pp 55–73

    Chapter  Google Scholar 

  98. Sunil S, Erdener SE, Lee BS et al (2020) Awake chronic mouse model of targeted pial vessel occlusion via photothrombosis. Neurophotonics 7:015005

    Article  Google Scholar 

  99. Turner KL, Gheres KW, Proctor EA et al (2020) Neurovascular coupling and bilateral connectivity during NREM and REM sleep. elife 9:e62071

    Article  CAS  Google Scholar 

  100. Yang Q, Vazquez AL, Cui XT (2021) Long-term in vivo two-photon imaging of the neuroinflammatory response to intracortical implants and micro-vessel disruptions in awake mice. Biomaterials 276:121060

    Article  CAS  Google Scholar 

  101. Siegel JS, Ramsey LE, Snyder AZ et al (2016) Disruptions of network connectivity predict impairment in multiple behavioral domains after stroke. Proc Natl Acad Sci U S A 113:E4367–E4376

    Article  CAS  Google Scholar 

  102. Biswal B, Yetkin FZ, Haughton VM et al (1995) Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn Reson Med 34:537–541

    Article  CAS  Google Scholar 

  103. Raichle ME (2015) The restless brain: how intrinsic activity organizes brain function. Philos Trans R Soc Lond Ser B Biol Sci 370:20140172

    Article  Google Scholar 

  104. Beckmann CF, Deluca M, Devlin JT et al (2005) Investigations into resting-state connectivity using independent component analysis. Phil Trans R Soc London Series B Biol Sci 360:1001–1013

    Article  Google Scholar 

  105. Seeley WW, Menon V, Schatzberg AF et al (2007) Dissociable intrinsic connectivity networks for salience processing and executive control. J Neurosci 27:2349–2356

    Article  CAS  Google Scholar 

  106. Eggebrecht AT, Ferradal SL, Robichaux-Viehoever A et al (2014) Map** distributed brain function and networks with diffuse optical tomography. Nat Photonics 8:448–454

    Article  CAS  Google Scholar 

  107. Shehzad Z, Kelly AM, Reiss PT et al (2009) The resting brain: unconstrained yet reliable. Cereb Cortex 19:2209–2229

    Article  Google Scholar 

  108. Laumann TO, Gordon EM, Adeyemo B et al (2015) Functional system and areal organization of a highly sampled individual human brain. Neuron 87:657–670

    Article  CAS  Google Scholar 

  109. Power JD, Cohen AL, Nelson SM et al (2011) Functional network organization of the human brain. Neuron 72:665–678

    Article  CAS  Google Scholar 

  110. Smith SM, Fox PT, Miller KL et al (2009) Correspondence of the brain’s functional architecture during activation and rest. Proc Natl Acad Sci 106:13040–13045

    Article  CAS  Google Scholar 

  111. Lim DH, Ledue JM, Mohajerani MH et al (2014) Optogenetic map** after stroke reveals network-wide scaling of functional connections and heterogeneous recovery of the peri-infarct. J Neurosci 34:16455–16466

    Article  CAS  Google Scholar 

  112. Assenza G, Zappasodi F, Pasqualetti P et al (2013) A contralesional EEG power increase mediated by interhemispheric disconnection provides negative prognosis in acute stroke. Restor Neurol Neurosci 31:177–188

    CAS  Google Scholar 

  113. Fanciullacci C, Bertolucci F, Lamola G et al (2017) Delta power is higher and more symmetrical in ischemic stroke patients with cortical involvement. Front Hum Neurosci 11:385

    Article  Google Scholar 

  114. Ramanathan DS, Guo L, Gulati T et al (2018) Low-frequency cortical activity is a neuromodulatory target that tracks recovery after stroke. Nat Med 24:1257–1267

    Article  CAS  Google Scholar 

  115. Biswal B, Zerrin Yetkin F, Haughton VM et al (1995) Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn Reson Med 34:537–541

    Article  CAS  Google Scholar 

  116. Fox MD, Raichle ME (2007) Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat Rev Neurosci 8:700–711

    Article  CAS  Google Scholar 

  117. White BR, Snyder AZ, Cohen AL et al (2009) Resting-state functional connectivity in the human brain revealed with diffuse optical tomography. NeuroImage 47:148–156

    Article  Google Scholar 

  118. Buxton RB, Uludag K, Dubowitz DJ et al (2004) Modeling the hemodynamic response to brain activation. NeuroImage 23(Suppl 1):S220–S233

    Article  Google Scholar 

  119. Ciuciu P, Poline JB, Marrelec G et al (2003) Unsupervised robust nonparametric estimation of the hemodynamic response function for any fMRI experiment. IEEE Trans Med Imaging 22:1235–1251

    Article  Google Scholar 

  120. Gossl C, Fahrmeir L, Auer DP (2001) Bayesian modeling of the hemodynamic response function in BOLD fMRI. NeuroImage 14:140–148

    Article  CAS  Google Scholar 

  121. Hossein-Zadeh GA, Ardekani BA, Soltanian-Zadeh H (2003) A signal subspace approach for modeling the hemodynamic response function in fMRI. Magn Reson Imaging 21:835–843

    Article  Google Scholar 

  122. Kennerley AJ, Berwick J, Martindale J et al (2005) Concurrent fMRI and optical measures for the investigation of the hemodynamic response function. Magn Reson Med 54:354–365

    Article  Google Scholar 

  123. Lindquist MA, Wager TD (2007) Validity and power in hemodynamic response modeling: a comparison study and a new approach. Hum Brain Mapp 28:764–784

    Article  Google Scholar 

  124. Rajapakse JC, Kruggel F, Maisog JM et al (1998) Modeling hemodynamic response for analysis of functional MRI time-series. Hum Brain Mapp 6:283–300

    Article  CAS  Google Scholar 

  125. Uga M, Dan I, Sano T et al (2014) Optimizing the general linear model for functional near-infrared spectroscopy: an adaptive hemodynamic response function approach. Neurophotonics 1:015004

    Article  Google Scholar 

  126. Marschner JA, Schafer H, Holderied A et al (2016) Optimizing mouse surgery with online rectal temperature monitoring and preoperative heat supply. Effects on post-ischemic acute kidney injury. PLoS One 11:e0149489

    Article  Google Scholar 

  127. Valley MT, Moore MG, Zhuang J et al (2020) Separation of hemodynamic signals from GCaMP fluorescence measured with wide-field imaging. J Neurophysiol 123:356–366

    Article  CAS  Google Scholar 

  128. Ma Y, Shaik MA, Kozberg MG et al (2016) Resting-state hemodynamics are spatiotemporally coupled to synchronized and symmetric neural activity in excitatory neurons. Proc Natl Acad Sci 113:E8463–E8471

    Article  CAS  Google Scholar 

  129. Fox MD, Snyder AZ, Vincent JL et al (2005) The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc Natl Acad Sci U S A 102:9673–9678

    Article  CAS  Google Scholar 

  130. Fox MD, Zhang D, Snyder AZ et al (2009) The global signal and observed anticorrelated resting state brain networks. J Neurophysiol 101:3270–3283

    Article  Google Scholar 

  131. Kura S, ** the resting state functional connectivity in the rodent cortex. J Neural Eng 15:035003

    Article  Google Scholar 

  132. White BR, Padawer-Curry JA, Ko T et al (2021) Wavelength censoring for spectroscopy in optical functional neuroimaging. Phys Med Biol 66:065026

    Article  CAS  Google Scholar 

  133. Brendel B, Nielsen T (2009) Selection of optimal wavelengths for spectral reconstruction in diffuse optical tomography. J Biomed Opt 14:034041

    Article  Google Scholar 

  134. Pouratian N, Cannestra AF, Martin NA et al (2002) Intraoperative optical intrinsic signal imaging: a clinical tool for functional brain map**. Neurosurg Focus 13:e1

    Article  Google Scholar 

  135. White BR, Bauer AQ, Snyder AZ et al (2011) Imaging of functional connectivity in the mouse brain. PLoS One 6:e16322

    Article  CAS  Google Scholar 

  136. Cramer JV, Gesierich B, Roth S et al (2019) In vivo widefield calcium imaging of the mouse cortex for analysis of network connectivity in health and brain disease. NeuroImage 199:570–584

    Article  CAS  Google Scholar 

  137. Harrison TC, Sigler A, Murphy TH (2009) Simple and cost-effective hardware and software for functional brain map** using intrinsic optical signal imaging. J Neurosci Methods 182:211–218

    Article  Google Scholar 

  138. Mcvey BFP, Prabakar S, Gooding JJ et al (2017) Solution synthesis, surface passivation, optical properties, biomedical applications, and cytotoxicity of silicon and germanium nanocrystals. ChemPlusChem 82:60–73

    Article  CAS  Google Scholar 

  139. Grinvald A, Frostig RD, Lieke E et al (1988) Optical imaging of neuronal activity. Physiol Rev 68:1285–1366

    Article  CAS  Google Scholar 

  140. Morone KA, Neimat JS, Roe AW et al (2017) Review of functional and clinical relevance of intrinsic signal optical imaging in human brain map**. Neurophotonics 4:031220

    Article  Google Scholar 

  141. Juavinett AL, Nauhaus I, Garrett ME et al (2017) Automated identification of mouse visual areas with intrinsic signal imaging. Nat Protoc 12:32–43

    Article  CAS  Google Scholar 

  142. Padawer-Curry JA, Jahnavi J, Breimann JS et al (2021) Variability in atlas registration of optical intrinsic signal imaging and its effect on functional connectivity analysis. J Opt Soc Am A 38:245–252

    Google Scholar 

  143. **ao D, Forys BJ, Vanni MP et al (2021) MesoNet allows automated scaling and segmentation of mouse mesoscale cortical maps using machine learning. Nat Commun 12:1–13

    Article  Google Scholar 

  144. White BR, Padawer-Curry JA, Cohen AS et al (2019) Brain segmentation, spatial censoring, and averaging techniques for optical functional connectivity imaging in mice. Biomed Opt Express 10(11):5952–5973

    Google Scholar 

  145. Boas D, Strangman G, Culver J et al (2003) Can the cerebral metabolic rate of oxygen be estimated with near-infrared spectroscopy? Phys Med Biol 48:2405

    Article  CAS  Google Scholar 

  146. Schweiger M, Arridge SR, Delpy DT (1993) Application of the finite-element method for the forward and inverse models in optical tomography. J Math Imag Vision 3:263–283

    Article  Google Scholar 

  147. Prahl S (1999) Tabulated molar extinction coefficient for hemoglobin in water. http://omlc.ogi.edu/spectra/hemoglobin/summary.html

  148. Vanni MP, Chan AW, Balbi M et al (2017) Mesoscale map** of mouse cortex reveals frequency-dependent cycling between distinct macroscale functional modules. J Neurosci 37:7513–7533

    Article  CAS  Google Scholar 

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Acknowledgments

We would like to thank past and present contributors to these projects (in alphabetical order): Annie Bice, Joseph Culver, Ernesto Gonzales, Andrew Kraft, Ron Perez, Zachary Rosenthal, Karen Smith, Avi Snyder, Brian White, Patrick Wright, and ** Yan. This work was supported by National Institute of Health grants: R01-NS102870 (AQB), R37NS110699 (JML), R01NS084028 (JML), F31 NS122499-01A1 (RMB), and Imaging Science Pathway T32 Fellowship (JAP-C).

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Correspondence to Adam Q. Bauer .

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Padawer-Curry, J.A., Bowen, R.M., Jarang, A., Wang, X., Lee, JM., Bauer, A.Q. (2023). Wide-Field Optical Imaging in Mouse Models of Ischemic Stroke. In: Karamyan, V.T., Stowe, A.M. (eds) Neural Repair. Methods in Molecular Biology, vol 2616. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2926-0_11

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