Chronic, cortex-wide imaging of specific cell populations during behavior

Joao Couto, Simon Musall, Xiaonan R. Sun, Anup Khanal, Steven Gluf, Shreya Saxena, Ian Kinsella, Taiga Abe, John P. Cunningham, Liam Paninski, Anne K. Churchland

Published: 2021-06-01 DOI: 10.1038/s41596-021-00527-z

neuronal activity

wide-field imaging

macroscope setup

chronic imaging

data analysis

AI 解读

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Heterogeneity（异质性）

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Measurements of neuronal activity across brain areas are important for understanding the neural correlates of cognitive and motor processes such as attention, decision-making and action selection. However, techniques that allow cellular resolution measure
Introduction
Materials
Procedure
Troubleshooting
Timing
Anticipated results
References
ACKNOWLEDGMENTS
ACKNOWLEDGMENTS

Stringer, C. et al. Spontaneous behaviors drive multidimensional, brainwide activity. Science 364, 255 (2019).
Vanni, M. P., Chan, A. W., Balbi, M., Silasi, G. & Murphy, T. H. Mesoscale mapping of mouse cortex reveals frequency-dependent cycling between distinct macroscale functional modules. J. Neurosci. 37, 7513–7533 (2017).
Clancy, K. B., Orsolic, I. & Mrsic-Flogel, T. D. Locomotion-dependent remapping of distributed cortical networks. Nat. Neurosci. 22, 778–786 (2019).
Musall, S., Kaufman, M. T., Juavinett, A. L., Gluf, S. & Churchland, A. K. Single-trial neural dynamics are dominated by richly varied movements. Nat. Neurosci. 22, 1677–1686 (2019).
Allen, W. E. et al. Global representations of goal-directed behavior in distinct cell types of mouse neocortex. Neuron 94, 891–907.e6 (2017).
Pinto, L. et al. Task-dependent changes in the large-scale dynamics and necessity of cortical regions. Neuron 104, 810–824.e9 (2019).
Zatka-Haas, P., Steinmetz, N. A., Carandini, M. & Harris, K. D. A perceptual decision requires sensory but not action coding in mouse cortex. Preprint at bioRxiv http://doi.org/10.1101/501627 (2020).
Salkoff, D. B., Zagha, E., McCarthy, E. & McCormick, D. A. Movement and performance explain widespread cortical activity in a visual detection task. Cereb. Cortex 30, 421–437 (2020).
Grinvald, A., Lieke, E., Frostig, R. D., Gilbert, C. D. & Wiesel, T. N. Functional architecture of cortex revealed by optical imaging of intrinsic signals. Nature 324, 361–364 (1986).
Frostig, R. D., Lieke, E. E., Ts’o, D. Y. & Grinvald, A. 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. USA 87, 6082–6086 (1990).
Shmuel, A. & Grinvald, A. Functional organization for direction of motion and its relationship to orientation maps in cat area 18. J. Neurosci. 16, 6945–6964 (1996).
Garrett, M. E., Nauhaus, I., Marshel, J. H. & Callaway, E. M. Topography and areal organization of mouse visual cortex. J. Neurosci. 34, 12587–12600 (2014).
Andermann, M. L., Kerlin, A. M., Roumis, D. K., Glickfeld, L. L. & Reid, R. C. Functional specialization of mouse higher visual cortical areas. Neuron 72, 1025–1039 (2011).
Juavinett, A. L., Nauhaus, I., Garrett, M. E., Zhuang, J. & Callaway, E. M. Automated identification of mouse visual areas with intrinsic signal imaging. Nat. Protoc. 12, 32–43 (2017).
Ferezou, I. et al. Spatiotemporal dynamics of cortical sensorimotor integration in behaving mice. Neuron 56, 907–923 (2007).
Akemann, W., Mutoh, H., Perron, A., Rossier, J. & Knöpfel, T. Imaging brain electric signals with genetically targeted voltage-sensitive fluorescent proteins. Nat. Methods 7, 643–649 (2010).
Wekselblatt, J. B., Flister, E. D., Piscopo, D. M. & Niell, C. M. Large-scale imaging of cortical dynamics during sensory perception and behavior. J. Neurophysiol. 115, 2852–2866 (2016).
Gilad, A., Gallero-Salas, Y., Groos, D. & Helmchen, F. Behavioral strategy determines frontal or posterior of short-term memory in neocortex. Neuron 99, 814–828.e7 (2018).
Guo, Z. V. et al. Flow of cortical activity underlying a tactile decision in mice. Neuron 81, 179–194 (2014).
Silasi, G., Xiao, D., Vanni, M. P., Chen, A. C. N. & Murphy, T. H. Intact skull chronic windows for mesoscopic wide-field imaging in awake mice. J Neurosci Methods 267, 141–149 (2016).
Cunningham, J. P. & Yu, B. M. Dimensionality reduction for large-scale neural recordings. Nat. Neurosci. 17, 1500–1509 (2014).
Buchanan, E. K. et al. Penalized matrix decomposition for denoising, compression, and improved demixing of functional imaging data. Preprint at http://arxiv.org/abs/1807.06203 (2018).
Saxena, S. et al. Localized semi-nonnegative matrix factorization (LocaNMF) of widefield calcium imaging data. PLoS Comput. Biol. 16, e1007791 (2020).
Kim, E. J., Juavinett, A. L., Kyubwa, E. M., Jacobs, M. W. & Callaway, E. M. Three types of cortical layer 5 neurons that differ in brain-wide connectivity and function. Neuron 88, 1253–1267 (2015).
Matho, K. S. et al. Genetic dissection of glutamatergic neuron subpopulations and developmental trajectories in the cerebral cortex. Preprint at bioRxiv http://doi.org/10.1101/2020.04.22.054064 (2020).
Murphy, T. H. et al. High-throughput automated home-cage mesoscopic functional imaging of mouse cortex. Nat. Commun. 7, 11611 (2016).
Murphy, T. H. et al. Automated task training and longitudinal monitoring of mouse mesoscale cortical circuits using home cages. eLife 9, e55964 (2020).
Cramer, J. V. et al. In vivo widefield calcium imaging of the mouse cortex for analysis of network connectivity in health and brain disease. NeuroImage 199, 570–584 (2019).
Shimaoka, D., Harris, K. D. & Carandini, M. Effects of arousal on mouse sensory cortex depend on modality. Cell Rep. 25, 3230 (2018).
Dana, H. et al. Sensitive red protein calcium indicators for imaging neural activity. eLife 5, e12727 (2016).
Xiao, D. et al. Mapping cortical mesoscopic networks of single spiking cortical or sub-cortical neurons. eLife 6, e19976 (2017).
Deffieux, T., Demene, C., Pernot, M. & Tanter, M. Functional ultrasound neuroimaging: a review of the preclinical and clinical state of the art. Curr. Opin. Neurobiol. 50, 128–135 (2018).
Lake, E. M. R. et al. Simultaneous cortex-wide fluorescence Ca 2+ imaging and whole-brain fMRI. Nat. Methods 17, 1262–1271 (2020).
Shemesh, O. A. et al. Precision calcium imaging of dense neural populations via a cell body-targeted calcium indicator. Preprint at bioRxiv http://doi.org/10.1101/773069 (2019).
Abe, T., Kinsella, I., Saxena, S., Paninski, L. & Cunningham, J. P. Neuroscience cloud analysis as a service. Preprint at bioRxiv http://doi.org/10.1101/2020.06.11.146746 (2020).
Ratzlaff, E. H. & Grinvald, A. A tandem-lens epifluorescence macroscope: hundred-fold brightness advantage for wide-field imaging. J. Neurosci. Methods 36, 127–137 (1991).
Chen, T.-W. et al. Ultrasensitive fluorescent proteins for imaging neuronal activity. Nature 499, 295–300 (2013).
Ma, Y. et al. Wide-field optical mapping of neural activity and brain haemodynamics: considerations and novel approaches. Phil. Trans. R. Soc. B 371, 20150360 (2016).
Musall, S., Haiss, F., Weber, B. & von der Behrens, W. Deviant processing in the primary somatosensory cortex. Cereb. Cortex 27, 863–876 (2015).
Lerner, T. N. et al. Intact-brain analyses reveal distinct information carried by SNc dopamine subcircuits. Cell 162, 635–647 (2015).
Valley, M. T. et al. Separation of hemodynamic signals from GCaMP fluorescence measured with wide-field imaging. J. Neurophysiol. 123, 356–366 (2019).
Vanni, M. P. & Murphy, T. H. Mesoscale transcranial spontaneous activity mapping in GCaMP3 transgenic mice reveals extensive reciprocal connections between areas of somatomotor cortex. J. Neurosci. 34, 15931–15946 (2014).
Xiao, D. et al. Mapping cortical mesoscopic networks of single spiking cortical or sub-cortical neurons. eLife. 6, e19976 (2017).
Kim, T. H. et al. Long-term optical access to an estimated one million neurons in the live mouse cortex. Cell Rep. 17, 3385–3394 (2016).
Ghanbari, L. et al. Cortex-wide neural interfacing via transparent polymer skulls. Nat. Commun. 10, 1500 (2019).
Steinmetz, N. A., Zatka-Haas, P., Carandini, M. & Harris, K. D. Distributed coding of choice, action and engagement across the mouse brain. Nature 576, 266–273 (2019).
Steinmetz, N. A. et al. Aberrant cortical activity in multiple GCaMP6-expressing transgenic mouse lines. eNeuro http://doi.org/10.1523/ENEURO.0207-17.2017 (2017).
Oh, S. W. et al. A mesoscale connectome of the mouse brain. Nature 508, 207–214 (2014).
Gilad, A. & Helmchen, F. Spatiotemporal refinement of signal flow through association cortex during learning. Nat. Commun. 11, 1–14 (2020).
Montgomery, M. K. et al. Glioma-induced alterations in neuronal activity and neurovascular coupling during disease progression. Cell Rep. 31, 107500 (2020).

Chronic, cortex-wide imaging of specific cell populations during behavior

Extended

Extended Data Fig. 1 Photobleaching due to repeated or high-power imaging.

Extended Data Fig. 2 Steps for setup calibration.

Supplementary information

Reporting Summary

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