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Dimensionality reduction of calcium-imaged neuronal population activity

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Abstract

Calcium imaging has been widely adopted for its ability to record from large neuronal populations. To summarize the time course of neural activity, dimensionality-reduction methods, which have been applied extensively to population spiking activity, may be particularly useful. However, it is unclear whether the dimensionality-reduction methods applied to spiking activity are appropriate for calcium imaging. We thus carried out a systematic study of design choices based on standard dimensionality-reduction methods. We have also developed a method to perform deconvolution and dimensionality reduction simultaneously (calcium imaging linear dynamical system, CILDS). CILDS most accurately recovered the single-trial, low-dimensional time courses from simulated calcium imaging data. CILDS also outperformed the other methods on calcium imaging recordings from larval zebrafish and mice. More broadly, this study represents a foundation for summarizing calcium-imaging recordings of large neuronal populations using dimensionality reduction in diverse experimental settings.

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Fig. 1: Dimensionality reduction of calcium imaging recordings.
Fig. 2: Comparison of three classes of dimensionality reduction methods.
Fig. 3: Accuracy of latent-variable recovery in simulation.
Fig. 4: Comparison of CILDS and methods that do not include a latent dynamical model.
Fig. 5: Performance comparison on larval-zebrafish DRN recordings.
Fig. 6: Performance comparison on mice V1 recordings.

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Data availability

Larval zebrafish DRN data are available at https://doi.org/10.6084/m9.figshare.21646682 (ref. 65). Mouse V1 data are available at https://doi.org/10.12751/g-node.wc3f8g (ref. 66) Source data are provided with this paper.

Code availability

A MATLAB implementation of CILDS is available on GitHub at https://github.com/kohth/cilds and on Zenodo at https://doi.org/10.5281/zenodo.7388544 (refs. 67)

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Acknowledgements

We thank K. P. Nguyen for the animal illustrations. This work was supported by the Agency for Science, Technology and Research (A*STAR) Singapore (T.H.K.), Howard Hughes Medical Institute (W.E.B., T.K., Z.W. and M.B.A.), Simons Foundation Simons Collaboration on the Global Brain Award 542943 (M.B.A.) and 543065 (B.M.Y.), the Shurl and Kay Curci Foundation (S.M.C. and S.J.K.), NIH R01 HD071686 (S.M.C. and B.M.Y.), NIH R01 EY024678 (S.J.K.), NSF NCS BCS1533672 (S.M.C. and B.M.Y.), NSF CAREER award IOS1553252 (S.M.C.), NSF NCS DRL2124066 (B.M.Y. and S.M.C.), NSF NCS BCS1734916 (B.M.Y.), NIH CRCNS R01 NS105318 (B.M.Y.), NIH CRCNS R01 MH118929 (B.M.Y.) and NIH R01 EB026953 (B.M.Y.).

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Author notes

  1. These authors contributed equally: Steven M. Chase, Byron M. Yu.

Authors and Affiliations

  1. Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA

    Tze Hui Koh, Brian B. Jeon, Ranjani Srinivasan, Steven M. Chase & Byron M. Yu

  2. Center for the Neural Basis of Cognition, Pittsburgh, PA, USA

    Tze Hui Koh, William E. Bishop & Brian B. Jeon

  3. Department of Machine Learning, Carnegie Mellon University, Pittsburgh, PA, USA

    William E. Bishop

  4. Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA

    William E. Bishop, Takashi Kawashima, Ziqiang Wei & Misha B. Ahrens

  5. Department of Brain Sciences, Weizmann Institute of Science, Rehovot, Israel

    Takashi Kawashima

  6. Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA

    Ranjani Srinivasan

  7. Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China

    Yu Mu

  8. Carnegie Mellon Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA

    Sandra J. Kuhlman, Steven M. Chase & Byron M. Yu

  9. Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA, USA

    Sandra J. Kuhlman

  10. Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA

    Byron M. Yu

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Contributions

T.H.K., W.E.B., S.M.C. and B.M.Y. designed the dimensionality-reduction and analysis methods. T.H.K. derived and implemented the dimensionality-reduction methods. T.H.K. performed the analyses, based on earlier analyses by R.S., T.K. and M.B.A. contributed to evaluation of the methods. T.K., B.B.J., Y.M., S.J.K. and M.B.A. designed the animal experiments. T.K. and Y.M. performed the larval zebrafish experiments, and B.B.J. performed the mouse experiments. Z.W. processed and segmented the 66-Hz single-plane larval zebrafish recordings. T.H.K., W.E.B., M.B.A., S.M.C. and B.M.Y. wrote the manuscript. All authors discussed the results and commented on the manuscript.

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Correspondence to Misha B. Ahrens, Steven M. Chase or Byron M. Yu.

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Nature Computational Science thanks Germán Sumbre and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Ananya Rastogi, in collaboration with the Nature Computational Science team.

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Koh, T.H., Bishop, W.E., Kawashima, T. et al. Dimensionality reduction of calcium-imaged neuronal population activity. Nat Comput Sci 3, 71–85 (2023). https://doi.org/10.1038/s43588-022-00390-2

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