A database and deep learning toolbox for noise-optimized, generalized spike inference from calcium imaging.
Journal
Nature neuroscience
ISSN: 1546-1726
Titre abrégé: Nat Neurosci
Pays: United States
ID NLM: 9809671
Informations de publication
Date de publication:
09 2021
09 2021
Historique:
received:
14
08
2020
accepted:
23
06
2021
pubmed:
4
8
2021
medline:
18
9
2021
entrez:
3
8
2021
Statut:
ppublish
Résumé
Inference of action potentials ('spikes') from neuronal calcium signals is complicated by the scarcity of simultaneous measurements of action potentials and calcium signals ('ground truth'). In this study, we compiled a large, diverse ground truth database from publicly available and newly performed recordings in zebrafish and mice covering a broad range of calcium indicators, cell types and signal-to-noise ratios, comprising a total of more than 35 recording hours from 298 neurons. We developed an algorithm for spike inference (termed CASCADE) that is based on supervised deep networks, takes advantage of the ground truth database, infers absolute spike rates and outperforms existing model-based algorithms. To optimize performance for unseen imaging data, CASCADE retrains itself by resampling ground truth data to match the respective sampling rate and noise level; therefore, no parameters need to be adjusted by the user. In addition, we developed systematic performance assessments for unseen data, openly released a resource toolbox and provide a user-friendly cloud-based implementation.
Identifiants
pubmed: 34341584
doi: 10.1038/s41593-021-00895-5
pii: 10.1038/s41593-021-00895-5
pmc: PMC7611618
mid: EMS128604
doi:
Substances chimiques
Calcium
SY7Q814VUP
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
1324-1337Subventions
Organisme : European Research Council
ID : 670757
Pays : International
Organisme : NIMH NIH HHS
ID : R01 MH112750
Pays : United States
Organisme : Swiss National Science Foundation
ID : 152833
Pays : Switzerland
Organisme : Swiss National Science Foundation
ID : 127091
Pays : Switzerland
Organisme : NIMH NIH HHS
ID : R01 MH121848
Pays : United States
Organisme : European Research Council
ID : 742576
Pays : International
Commentaires et corrections
Type : CommentIn
Informations de copyright
© 2021. The Author(s), under exclusive licence to Springer Nature America, Inc.
Références
Göbel, W. & Helmchen, F. In vivo calcium imaging of neural network function. Physiology 22, 358–365 (2007).
pubmed: 18073408
doi: 10.1152/physiol.00032.2007
Harris, K. D., Quiroga, R. Q., Freeman, J. & Smith, S. L. Improving data quality in neuronal population recordings. Nat. Neurosci. 19, 1165–1174 (2016).
pubmed: 27571195
pmcid: 5244825
doi: 10.1038/nn.4365
Rose, T., Goltstein, P. M., Portugues, R. & Griesbeck, O. Putting a finishing touch on GECIs. Front. Mol. Neurosci. 7, 88 (2014).
pubmed: 25477779
pmcid: 4235368
doi: 10.3389/fnmol.2014.00088
Sabatini, B. L. The impact of reporter kinetics on the interpretation of data gathered with fluorescent reporters. Preprint at https://www.biorxiv.org/content/10.1101/834895v1 (2019).
Wei, Z. et al. A comparison of neuronal population dynamics measured with calcium imaging and electrophysiology. PLoS Comput. Biol. 16, e1008198 (2020).
pubmed: 32931495
pmcid: 7518847
doi: 10.1371/journal.pcbi.1008198
Ali, F. & Kwan, A. C. Interpreting in vivo calcium signals from neuronal cell bodies, axons, and dendrites: a review. Neurophotonics 7, 011402 (2020).
pubmed: 31372367
Yaksi, E. & Friedrich, R. W. Reconstruction of firing rate changes across neuronal populations by temporally deconvolved Ca
pubmed: 16628208
doi: 10.1038/nmeth874
Greenberg, D. S., Houweling, A. R. & Kerr, J. N. D. Population imaging of ongoing neuronal activity in the visual cortex of awake rats. Nat. Neurosci. 11, 749–751 (2008).
pubmed: 18552841
doi: 10.1038/nn.2140
Vogelstein, J. T. et al. Spike inference from calcium imaging using sequential Monte Carlo methods. Biophys. J. 97, 636–655 (2009).
pubmed: 19619479
pmcid: 2711341
doi: 10.1016/j.bpj.2008.08.005
Vogelstein, J. T. et al. Fast nonnegative deconvolution for spike train inference from population calcium imaging. J. Neurophysiol. 104, 3691–3704 (2010).
pubmed: 20554834
pmcid: 3007657
doi: 10.1152/jn.01073.2009
Lütcke, H., Gerhard, F., Zenke, F., Gerstner, W. & Helmchen, F. Inference of neuronal network spike dynamics and topology from calcium imaging data. Front. Neural Circuits 7, 201 (2013).
pubmed: 24399936
pmcid: 3871709
doi: 10.3389/fncir.2013.00201
Deneux, T. et al. Accurate spike estimation from noisy calcium signals for ultrafast three-dimensional imaging of large neuronal populations in vivo. Nat. Commun. 7, 1–17 (2016).
doi: 10.1038/ncomms12190
Greenberg, D. S. et al. Accurate action potential inference from a calcium sensor protein through biophysical modeling. Preprint at https://www.biorxiv.org/content/10.1101/479055v1 (2018).
Pachitariu, M., Stringer, C. & Harris, K. D. Robustness of spike deconvolution for neuronal calcium imaging. J. Neurosci. 38, 7976–7985 (2018).
pubmed: 30082416
pmcid: 6136155
doi: 10.1523/JNEUROSCI.3339-17.2018
Friedrich, J., Zhou, P. & Paninski, L. Fast online deconvolution of calcium imaging data. PLoS Comput. Biol. 13, e1005423 (2017).
pubmed: 28291787
pmcid: 5370160
doi: 10.1371/journal.pcbi.1005423
Berens, P. et al. Community-based benchmarking improves spike rate inference from two-photon calcium imaging data. PLoS Comput. Biol. 14, e1006157 (2018).
pubmed: 29782491
pmcid: 5997358
doi: 10.1371/journal.pcbi.1006157
Jewell, S. & Witten, D. Exact spike inference via l
pubmed: 30627301
pmcid: 6322847
doi: 10.1214/18-AOAS1162
Sasaki, T., Takahashi, N., Matsuki, N. & Ikegaya, Y. Fast and accurate detection of action potentials from somatic calcium fluctuations. J. Neurophysiol. 100, 1668–1676 (2008).
pubmed: 18596182
doi: 10.1152/jn.00084.2008
Theis, L. et al. Benchmarking spike rate inference in population calcium imaging. Neuron 90, 471–482 (2016).
pubmed: 27151639
pmcid: 4888799
doi: 10.1016/j.neuron.2016.04.014
Sebastian, J., Sur, M., Murthy, H. A. & Magimai-Doss, M. Signal-to-signal neural networks for improved spike estimation from calcium imaging data. PLoS Comput. Biol. 17, e1007921 (2021).
pubmed: 33647015
pmcid: 7951974
doi: 10.1371/journal.pcbi.1007921
Hoang, H. et al. Improved hyperacuity estimation of spike timing from calcium imaging. Sci. Rep. 10, 17844 (2020).
pubmed: 33082425
pmcid: 7576127
doi: 10.1038/s41598-020-74672-y
Éltes, T., Szoboszlay, M., Kerti-Szigeti, K. & Nusser, Z. Improved spike inference accuracy by estimating the peak amplitude of unitary [Ca2
pubmed: 31006863
doi: 10.1113/JP277681
Evans, M. H., Petersen, R. S. & Humphries, M. D. On the use of calcium deconvolution algorithms in practical contexts. Preprint at https://www.biorxiv.org/content/10.1101/871137v1 (2019).
Zhu, P., Fajardo, O., Shum, J., Zhang Schärer, Y.-P. & Friedrich, R. W. High-resolution optical control of spatiotemporal neuronal activity patterns in zebrafish using a digital micromirror device. Nat. Protoc. 7, 1410–1425 (2012).
pubmed: 22743832
doi: 10.1038/nprot.2012.072
Schoenfeld, G., Carta, S., Rupprecht, P., Ayaz, A. & Helmchen, F. In vivo calcium imaging of CA3 pyramidal neuron populations in adult mouse hippocampus. Preprint at https://www.biorxiv.org/content/10.1101/2021.01.21.427642v1 (2021).
Bethge, P. et al. An R-CaMP1.07 reporter mouse for cell-type-specific expression of a sensitive red fluorescent calcium indicator. PLoS ONE 12, e0179460 (2017).
pubmed: 28640817
pmcid: 5480891
doi: 10.1371/journal.pone.0179460
Tada, M., Takeuchi, A., Hashizume, M., Kitamura, K. & Kano, M. A highly sensitive fluorescent indicator dye for calcium imaging of neural activity in vitro and in vivo. Eur. J. Neurosci. 39, 1720–1728 (2014).
pubmed: 24405482
pmcid: 4232931
doi: 10.1111/ejn.12476
Khan, A. G. et al. Distinct learning-induced changes in stimulus selectivity and interactions of GABAergic interneuron classes in visual cortex. Nat. Neurosci. 21, 851–859 (2018).
pubmed: 29786081
doi: 10.1038/s41593-018-0143-z
Kwan, A. C. & Dan, Y. Dissection of cortical microcircuits by single-neuron stimulation in vivo. Curr. Biol. CB 22, 1459–1467 (2012).
pubmed: 22748320
doi: 10.1016/j.cub.2012.06.007
Huang, L. et al. Relationship between simultaneously recorded spiking activity and fluorescence signal in GCaMP6 transgenic mice. Elife 10, e51675 (2021).
pubmed: 33683198
pmcid: 8060029
doi: 10.7554/eLife.51675
Ledochowitsch, P. et al. On the correspondence of electrical and optical physiology in in vivo population-scale two-photon calcium imaging. Preprint at https://www.biorxiv.org/content/10.1101/800102v1 (2019).
Dana, H. et al. Sensitive red protein calcium indicators for imaging neural activity. eLife 5, e12727 (2016).
pubmed: 27011354
pmcid: 4846379
doi: 10.7554/eLife.12727
Akerboom, J. et al. Optimization of a GCaMP calcium indicator for neural activity imaging. J. Neurosci. 32, 13819–13840 (2012).
pubmed: 23035093
pmcid: 3482105
doi: 10.1523/JNEUROSCI.2601-12.2012
Chen, T.-W. et al. Ultrasensitive fluorescent proteins for imaging neuronal activity. Nature 499, 295–300 (2013).
pubmed: 23868258
pmcid: 3777791
doi: 10.1038/nature12354
Russakovsky, O. et al. ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. 115, 211–252 (2015).
doi: 10.1007/s11263-015-0816-y
Mathis, A. et al. DeepLabCut: markerless pose estimation of user-defined body parts with deep learning. Nat. Neurosci. 21, 1281–1289 (2018).
pubmed: 30127430
doi: 10.1038/s41593-018-0209-y
Deng, J. et al. ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition https://ieeexplore.ieee.org/document/5206848 (2009).
Giovannucci, A. et al. CaImAn an open source tool for scalable calcium imaging data analysis. eLife 8, e38173 (2019).
pubmed: 30652683
pmcid: 6342523
doi: 10.7554/eLife.38173
Keemink, S. W. et al. FISSA: a neuropil decontamination toolbox for calcium imaging signals. Sci. Rep. 8, 3493 (2018).
pubmed: 29472547
pmcid: 5823956
doi: 10.1038/s41598-018-21640-2
Charles, A. S., Song, A., Gauthier, J. L., Pillow, J. W. & Tank, D. W. Neural anatomy and optical microscopy (NAOMi) simulation for evaluating calcium imaging methods. J. Neurosci. Methods 358, 109173 (2019).
Pachitariu, M. et al. Suite2p: beyond 10,000 neurons with standard two-photon microscopy. Preprint at https://www.biorxiv.org/content/10.1101/061507v2 (2017).
Jewell, S., Hocking, T. D., Fearnhead, P. & Witten, D. Fast nonconvex deconvolution of calcium imaging data. Biostatistics 21, 709–726 (2019).
Rupprecht, P., Prendergast, A., Wyart, C. & Friedrich, R. W. Remote z-scanning with a macroscopic voice coil motor for fast 3D multiphoton laser scanning microscopy. Biomed. Opt. Express 7, 1656–1671 (2016).
pubmed: 27231612
pmcid: 4871072
doi: 10.1364/BOE.7.001656
Blumhagen, F. et al. Neuronal filtering of multiplexed odour representations. Nature 479, 493–498 (2011).
pubmed: 22080956
doi: 10.1038/nature10633
Rupprecht, P. & Friedrich, R. W. Precise synaptic balance in the zebrafish homolog of olfactory cortex. Neuron 100, 669–683.e5 (2018).
pubmed: 30318416
doi: 10.1016/j.neuron.2018.09.013
Mackevicius, E. L. et al. Unsupervised discovery of temporal sequences in high-dimensional datasets, with applications to neuroscience. eLife 8, e38471 (2019).
pubmed: 30719973
pmcid: 6363393
doi: 10.7554/eLife.38471
de Vries, S. E. J. et al. A large-scale standardized physiological survey reveals functional organization of the mouse visual cortex. Nat. Neurosci. 23, 138–151 (2020).
pubmed: 31844315
doi: 10.1038/s41593-019-0550-9
Lin, I.-C., Okun, M., Carandini, M. & Harris, K. D. The nature of shared cortical variability. Neuron 87, 644–656 (2015).
pubmed: 26212710
pmcid: 4534383
doi: 10.1016/j.neuron.2015.06.035
Kaifosh, P., Zaremba, J. D., Danielson, N. B. & Losonczy, A. SIMA: Python software for analysis of dynamic fluorescence imaging data. Front. Neuroinformatics 8, 80 (2014).
doi: 10.3389/fninf.2014.00080
Siegle, J. H. et al. Reconciling functional differences in populations of neurons recorded with two-photon imaging and electrophysiology. Preprint at https://www.biorxiv.org/content/10.1101/2020.08.10.244723v1.full (2020).
Vanwalleghem, G., Constantin, L. & Scott, E. K. Calcium imaging and the curse of negativity. Front. Neural Circuits 14, 607391 (2021).
pubmed: 33488363
pmcid: 7815594
doi: 10.3389/fncir.2020.607391
Kay, K. et al. Constant sub-second cycling between representations of possible futures in the hippocampus. Cell 180, 552–567 (2020).
pubmed: 32004462
pmcid: 7126188
doi: 10.1016/j.cell.2020.01.014
van der, Bourg,A. et al. Temporal refinement of sensory-evoked activity across layers in developing mouse barrel cortex. Eur. J. Neurosci. 50, 2955–2969 (2019).
doi: 10.1111/ejn.14413
Pégard, N. C. et al. Three-dimensional scanless holographic optogenetics with temporal focusing (3D-SHOT). Nat. Commun. 8, 1228 (2017).
pubmed: 29089483
pmcid: 5663714
doi: 10.1038/s41467-017-01031-3
Packer, A. M., Russell, L. E., Dalgleish, H. W. P. & Häusser, M. Simultaneous all-optical manipulation and recording of neural circuit activity with cellular resolution in vivo. Nat. Methods 12, 140–146 (2015).
pubmed: 25532138
doi: 10.1038/nmeth.3217
Griffiths, V. A. et al. Real-time 3D movement correction for two-photon imaging in behaving animals. Nat. Methods 17, 741–748 (2020).
Inoue, M. et al. Rational engineering of XCaMPs, a multicolor GECI suite for in vivo imaging of complex brain circuit dynamics. Cell 177, 1346–1360 (2019).
pubmed: 31080068
doi: 10.1016/j.cell.2019.04.007
Frank, T., Mönig, N. R., Satou, C., Higashijima, S. & Friedrich, R. W. Associative conditioning remaps odor representations and modifies inhibition in a higher olfactory brain area. Nat. Neurosci. 22, 1844–1856 (2019).
pubmed: 31591559
pmcid: 6858881
doi: 10.1038/s41593-019-0495-z
Kitamura, K., Judkewitz, B., Kano, M., Denk, W. & Häusser, M. Targeted patch-clamp recordings and single-cell electroporation of unlabeled neurons in vivo. Nat. Methods 5, 61–67 (2008).
pubmed: 18157136
doi: 10.1038/nmeth1150
Perkins, K. L. Cell-attached voltage-clamp and current-clamp recording and stimulation techniques in brain slices. J. Neurosci. Methods 154, 1–18 (2006).
pubmed: 16554092
pmcid: 2373773
doi: 10.1016/j.jneumeth.2006.02.010
Pologruto, T. A., Sabatini, B. L. & Svoboda, K. ScanImage: flexible software for operating laser scanning microscopes. Biomed. Eng. Online 2, 13 (2003).
pubmed: 12801419
pmcid: 161784
doi: 10.1186/1475-925X-2-13
Suter, B. A. et al. Ephus: multipurpose data acquisition software for neuroscience experiments. Front. Neural Circuits 4, 100 (2010).
pubmed: 21960959
pmcid: 3176413
doi: 10.3389/fncir.2010.00100
Huang, K.-H. et al. A virtual reality system to analyze neural activity and behavior in adult zebrafish. Nat. Methods 17, 343–351 (2020).
pubmed: 32123394
pmcid: 7100911
doi: 10.1038/s41592-020-0759-2
Langer, D. et al. HelioScan: a software framework for controlling in vivo microscopy setups with high hardware flexibility, functional diversity and extendibility. J. Neurosci. Methods 215, 38–52 (2013).
pubmed: 23416135
doi: 10.1016/j.jneumeth.2013.02.006
Pecka, M., Han, Y., Sader, E. & Mrsic-Flogel, T. D. Experience-dependent specialization of receptive field surround for selective coding of natural scenes. Neuron 84, 457–469 (2014).
pubmed: 25263755
pmcid: 4210638
doi: 10.1016/j.neuron.2014.09.010
Pernía-Andrade, A. J. et al. A deconvolution-based method with high sensitivity and temporal resolution for detection of spontaneous synaptic currents in vitro and in vivo. Biophys. J. 103, 1429–1439 (2012).
pubmed: 23062335
pmcid: 3471482
doi: 10.1016/j.bpj.2012.08.039
Guzman, S. J., Schlögl, A. & Schmidt-Hieber, C. Stimfit: quantifying electrophysiological data with Python. Front. Neuroinformatics 8, 16 (2014).
doi: 10.3389/fninf.2014.00016
GENIE project, Janelia Farm Campus, HHMI & Svoboda, K. Simultaneous imaging and loose-seal cell-attached electrical recordings from neurons expressing a variety of genetically encoded calcium indicators. https://crcns.org/data-sets/methods/cai-1/about-cai-1 (2015).
Boaz, M., Dana, H., Kim, D. S., Svoboda, K. & GENIE project, Janelia Farm Campus, HHMI. jRGECO1a and jRCaMP1a characterization in the intact mouse visual cortex, using AAV-based gene transfer, 2-photon imaging and loose-seal cell attached recordings. https://crcns.org/data-sets/methods/cai-2/about-cai-2 (2016).
Reynolds, S., Abrahamsson, T., Sjöström, P. J., Schultz, S. R. & Dragotti, P. L. CosMIC: a consistent metric for spike inference from calcium imaging. Neural Comput. 30, 2726–2756 (2018).
pubmed: 30021084
doi: 10.1162/neco_a_01114
Ioffe, S. & Szegedy, C. Batch normalization: accelerating deep network training by reducing internal covariate shift. In International conference on machine learning. 448-456 (PMLR, 2015).
Hochreiter, S. & Schmidhuber, J. Long short-term memory. Neural Comput. 9, 1735–1780 (1997).
pubmed: 9377276
doi: 10.1162/neco.1997.9.8.1735
Gers, F. A., Schmidhuber, J. & Cummins, F. Learning to forget: continual prediction with LSTM. Neural Comput. 12, 2451–2471 (1999).
Schuster, M. & Paliwal, K. Bidirectional recurrent neural networks. Signal Process. IEEE Trans. 45, 2673–2681 (1997).
doi: 10.1109/78.650093
Graves A., Fernández S., Schmidhuber J. Bidirectional LSTM Networks for Improved Phoneme Classification and Recognition. Duch W., Kacprzyk J., Oja E., Zadrożny S. (eds) Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005. Lecture Notes in Computer Science, vol 3697. (Springer, Berlin, Heidelberg, 2005).
Eden, U. T. & Kramer, M. A. Drawing inferences from Fano factor calculations. J. Neurosci. Methods 190, 149–152 (2010).
pubmed: 20416340
doi: 10.1016/j.jneumeth.2010.04.012