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Detecting and correcting false transients in calcium imaging

Nature Methods volume 19, pages 470–478 (2022)Cite this article

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Abstract

Population recordings of calcium activity are a major source of insight into neural function. Large datasets require automated processing, but this can introduce errors that are difficult to detect. Here we show that popular time course-estimation algorithms often contain substantial misattribution errors affecting 10–20% of transients. Misattribution, in which fluorescence is ascribed to the wrong cell, arises when overlapping cells and processes are imperfectly defined or not identified. To diagnose misattribution, we develop metrics and visualization tools for evaluating large datasets. To correct time courses, we introduce a robust estimator that explicitly accounts for contaminating signals. In one hippocampal dataset, removing contamination reduced the number of place cells by 15%, and 19% of place fields shifted by over 10 cm. Our methods are compatible with other cell-finding techniques, empowering users to diagnose and correct a potentially widespread problem that could alter scientific conclusions.

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Fig. 1: False transients occur and can interfere with scientific conclusions.
Fig. 2: Quantitative metrics for evaluating transients.
Fig. 3: Geometric intuition for the proposed solution.
Fig. 4: Explicit model of unexplained fluorescence.
Fig. 5: SEUDO performance on a full population of sources.
Fig. 6: SEUDO can correct errors affecting scientific results.

Data availability

The data that support the findings of this study, including the CA1 raw imaging data, identified source profiles and time traces from automated cell-finding algorithms and manual annotations of transients, are available on the Open Science Framework at https://osf.io/zt54q/.

Code availability

All algorithms described are available in a MATLAB-based software suite. The software also includes several graphical user interfaces (GUIs) to facilitate visualization and parameter optimization. For example, one GUI displays transients for manual classification (Extended Data Fig. 2). Users can also interact with this GUI to specify which transients are true, false or mixed or identify artifact sources. Other GUIs (not shown) facilitate comparing different sets of parameters to optimize performance. All software is available under the MIT license and can be downloaded from https://github.com/adamshch/SEUDO.

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Acknowledgements

We thank L. Meshulam, P.D. Rich, A. Giovannucci and E. Pnevmatikakis for helpful comments on the manuscript and M. Lewis for developing the SEUDO acronym. J.L.G. was funded by NIH NRSA 1F32NS077840-01A1. A.S.C. was supported by the NIH BRAIN Initiative (R01 MH115750). J.W.P. was supported by grants from the Simons Collaboration on the Global Brain (SCGB AWD543027), the NIH BRAIN Initiative (R01 MH115750) and a U19 NIH–NINDS BRAIN Initiative award (5U19NS104648). D.W.T. was supported by grants from the Simons Collaboration on the Global Brain (SCGB 328057) and a U19 NIH–NINDS BRAIN Initiative award (5U19NS104648).

Author information

Authors and Affiliations

  1. Department of Biology, Swarthmore College, Swarthmore, PA, USA

    Jeffrey L. Gauthier

  2. Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA

    Sue Ann Koay, Edward H. Nieh, David W. Tank & Jonathan W. Pillow

  3. Bezos Center for Neural Circuit Dynamics, Princeton University, Princeton, NJ, USA

    David W. Tank

  4. Department of Molecular Biology, Princeton University, Princeton, NJ, USA

    David W. Tank

  5. Department of Psychology, Princeton University, Princeton, NJ, USA

    Jonathan W. Pillow

  6. Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA

    Adam S. Charles

  7. Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD, USA

    Adam S. Charles

  8. Center for Imaging Science, Johns Hopkins University, Baltimore, MD, USA

    Adam S. Charles

  9. Mathematical Institute for Data Science, Johns Hopkins University, Baltimore, MD, USA

    Adam S. Charles

Authors
  1. Jeffrey L. Gauthier
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  4. David W. Tank
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Contributions

J.L.G. and A.S.C. developed and applied analysis methods; J.L.G., S.A.K. and E.H.N. performed imaging experiments; all authors wrote the paper.

Corresponding authors

Correspondence to David W. Tank or Adam S. Charles.

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The authors declare no competing interests.

Peer review

Peer review information

Nature Methods thanks Philipp Berens and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Nina Vogt, in collaboration with the Nature Methods team.

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Extended data

Extended Data Fig. 1 Example classified transient profiles for a single source from mouse CA1 found using CNMF.

Each panel is titled with the Pearson correlation between the source profile and transient profile.

Extended Data Fig. 2 User interface for manual classification of transients.

For detailed description, see software documentation in the GIT repository https://github.com/adamshch/SEUDO.

Extended Data Fig. 3 Comparison of manual annotation for two graders on the same 1,834 transients.

Confusion matrices showing the number of transients graded in different ways by each reviewer (left) and the total percentage of transients in each bin (right).

Extended Data Fig. 4 Algorithm to classify transients as true or false using the spatial Ljung-Box quartile test instead of the correlation metric.

A: Schematic for how the transient profile residual autocorrelation is computed. B: Two example transients, shown to illustrate difference in residual for true and false transients. C: Classification of four example transients using the LBQ test. D: Results of the LBQ test on transients classified by human expert. Left: Results of the test applied to true and false transients for various values of α. Right: Results of the test applied to all four transient types.

Extended Data Fig. 5 Effect of SEUDO parameters.

A: Activity estimated by SEUDO and least squares for one true transient (top row) and one false transient (bottom row) using several types of estimation (column labels). Images and traces show estimated amplitude of the source profile (green) and sum of fitted Gaussian kernels (magenta). B: Sum of activity ascribed to the source profile (green) and Gaussian kernels (magenta) for the true transient (left) and the false transient (right). Each subplot shows results for one set of parameters. Roman numerals indicate parameter regimes shown in A.

Extended Data Fig. 6 Performance of SEUDO as σ2 was varied over three orders of magnitude, for the same sources and quantified in the same way as in Fig. 5c.

Each subplot shows performance for the value of σ2 indicated in the title and the shown values of λ (green points). Also plotted for comparison are the collection of points taken from all subplots (gray points).

Extended Data Fig. 7 Removing false transients can impact global summaries of activity.

Here, time courses were sorted into 5 clusters using K-means (best clustering over 50 random seeds). A-B: SEUDO time courses were more highly consolidated (that is, fewer clusters contained more of the neurons) as compared to the time-traces with contamination. C: A confusion matrix depicts that a small number of cluster relabelings accounted for much of the changes in SEUDO time courses.

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Supplementary Figs. 1–18 and Tables 1–3

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Gauthier, J.L., Koay, S.A., Nieh, E.H. et al. Detecting and correcting false transients in calcium imaging. Nat Methods 19, 470–478 (2022). https://doi.org/10.1038/s41592-022-01422-5

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