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Calcium Imaging and the Curse of Negativity

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Calcium Imaging and the Curse of Negativity. / Vanwalleghem, Gilles; Constantin, Lena; Scott, Ethan K.

In: Frontiers in Neural Circuits, Vol. 14, 607391, 01.2021.

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

Harvard

Vanwalleghem, G, Constantin, L & Scott, EK 2021, 'Calcium Imaging and the Curse of Negativity', Frontiers in Neural Circuits, vol. 14, 607391. https://doi.org/10.3389/fncir.2020.607391

APA

Vanwalleghem, G., Constantin, L., & Scott, E. K. (2021). Calcium Imaging and the Curse of Negativity. Frontiers in Neural Circuits, 14, [607391]. https://doi.org/10.3389/fncir.2020.607391

CBE

Vanwalleghem G, Constantin L, Scott EK. 2021. Calcium Imaging and the Curse of Negativity. Frontiers in Neural Circuits. 14:Article 607391. https://doi.org/10.3389/fncir.2020.607391

MLA

Vanwalleghem, Gilles, Lena Constantin and Ethan K. Scott. "Calcium Imaging and the Curse of Negativity". Frontiers in Neural Circuits. 2021. 14. https://doi.org/10.3389/fncir.2020.607391

Vancouver

Vanwalleghem G, Constantin L, Scott EK. Calcium Imaging and the Curse of Negativity. Frontiers in Neural Circuits. 2021 Jan;14. 607391. https://doi.org/10.3389/fncir.2020.607391

Author

Vanwalleghem, Gilles ; Constantin, Lena ; Scott, Ethan K. / Calcium Imaging and the Curse of Negativity. In: Frontiers in Neural Circuits. 2021 ; Vol. 14.

Bibtex

@article{1f49904f3a054b28a696a29e8ccea8df,
title = "Calcium Imaging and the Curse of Negativity",
abstract = "The imaging of neuronal activity using calcium indicators has become a staple of modern neuroscience. However, without ground truths, there is a real risk of missing a significant portion of the real responses. Here, we show that a common assumption, the non-negativity of the neuronal responses as detected by calcium indicators, biases all levels of the frequently used analytical methods for these data. From the extraction of meaningful fluorescence changes to spike inference and the analysis of inferred spikes, each step risks missing real responses because of the assumption of non-negativity. We first show that negative deviations from baseline can exist in calcium imaging of neuronal activity. Then, we use simulated data to test three popular algorithms for image analysis, CaImAn, suite2p, and CellSort, finding that suite2p may be the best suited to large datasets. We also tested the spike inference algorithms included in CaImAn, suite2p, and Cellsort, as well as the dedicated inference algorithms MLspike and CASCADE, and found each to have limitations in dealing with inhibited neurons. Among these spike inference algorithms, FOOPSI, from CaImAn, performed the best on inhibited neurons, but even this algorithm inferred spurious spikes upon the return of the fluorescence signal to baseline. As such, new approaches will be needed before spikes can be sensitively and accurately inferred from calcium data in inhibited neurons. We further suggest avoiding data analysis approaches that, by assuming non-negativity, ignore inhibited responses. Instead, we suggest a first exploratory step, using k-means or PCA for example, to detect whether meaningful negative deviations are present. Taking these steps will ensure that inhibition, as well as excitation, is detected in calcium imaging datasets.",
keywords = "baseline fluorescence, calcium imaging, cerebellar circuitry, data analysis, GCaMP, segmentation, spike inference, zebrafish",
author = "Gilles Vanwalleghem and Lena Constantin and Scott, {Ethan K.}",
note = "Funding Information: Support was provided by NHMRC Project Grants APP1066887 and APP1165173, a Simons Foundation Pilot Award (399432), a Simons Foundation Research Award (625793), and two ARC Discovery Project Grants (DP140102036 and DP110103612) to ES, and the Australian National Fabrication Facility (ANFF), QLD node. The research reported in this publication was supported by the National Institute of Neurological Disorders and Stroke of the National Institutes of Health under Award Number R01NS118406 to ES. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. GV was supported by an EMBO Long-term Fellowship. Funding Information: We thank Itia A. Favre-Bulle for data and the Vulcans who remind us ?Challenge your preconceptions, or they will challenge you?. We thank Carsen Stringer, Peter Rupprecht, and Eftichyos A. Pnevmatikakis for helpful discussions and comments on the manuscript. Funding. Support was provided by NHMRC Project Grants APP1066887 and APP1165173, a Simons Foundation Pilot Award (399432), a Simons Foundation Research Award (625793), and two ARC Discovery Project Grants (DP140102036 and DP110103612) to ES, and the Australian National Fabrication Facility (ANFF), QLD node. The research reported in this publication was supported by the National Institute of Neurological Disorders and Stroke of the National Institutes of Health under Award Number R01NS118406 to ES. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. GV was supported by an EMBO Long-term Fellowship. Publisher Copyright: {\textcopyright} Copyright {\textcopyright} 2021 Vanwalleghem, Constantin and Scott.",
year = "2021",
month = jan,
doi = "10.3389/fncir.2020.607391",
language = "English",
volume = "14",
journal = "Frontiers in Neural Circuits",
issn = "1662-5110",
publisher = "Frontiers Research Foundation",

}

RIS

TY - JOUR

T1 - Calcium Imaging and the Curse of Negativity

AU - Vanwalleghem, Gilles

AU - Constantin, Lena

AU - Scott, Ethan K.

N1 - Funding Information: Support was provided by NHMRC Project Grants APP1066887 and APP1165173, a Simons Foundation Pilot Award (399432), a Simons Foundation Research Award (625793), and two ARC Discovery Project Grants (DP140102036 and DP110103612) to ES, and the Australian National Fabrication Facility (ANFF), QLD node. The research reported in this publication was supported by the National Institute of Neurological Disorders and Stroke of the National Institutes of Health under Award Number R01NS118406 to ES. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. GV was supported by an EMBO Long-term Fellowship. Funding Information: We thank Itia A. Favre-Bulle for data and the Vulcans who remind us ?Challenge your preconceptions, or they will challenge you?. We thank Carsen Stringer, Peter Rupprecht, and Eftichyos A. Pnevmatikakis for helpful discussions and comments on the manuscript. Funding. Support was provided by NHMRC Project Grants APP1066887 and APP1165173, a Simons Foundation Pilot Award (399432), a Simons Foundation Research Award (625793), and two ARC Discovery Project Grants (DP140102036 and DP110103612) to ES, and the Australian National Fabrication Facility (ANFF), QLD node. The research reported in this publication was supported by the National Institute of Neurological Disorders and Stroke of the National Institutes of Health under Award Number R01NS118406 to ES. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. GV was supported by an EMBO Long-term Fellowship. Publisher Copyright: © Copyright © 2021 Vanwalleghem, Constantin and Scott.

PY - 2021/1

Y1 - 2021/1

N2 - The imaging of neuronal activity using calcium indicators has become a staple of modern neuroscience. However, without ground truths, there is a real risk of missing a significant portion of the real responses. Here, we show that a common assumption, the non-negativity of the neuronal responses as detected by calcium indicators, biases all levels of the frequently used analytical methods for these data. From the extraction of meaningful fluorescence changes to spike inference and the analysis of inferred spikes, each step risks missing real responses because of the assumption of non-negativity. We first show that negative deviations from baseline can exist in calcium imaging of neuronal activity. Then, we use simulated data to test three popular algorithms for image analysis, CaImAn, suite2p, and CellSort, finding that suite2p may be the best suited to large datasets. We also tested the spike inference algorithms included in CaImAn, suite2p, and Cellsort, as well as the dedicated inference algorithms MLspike and CASCADE, and found each to have limitations in dealing with inhibited neurons. Among these spike inference algorithms, FOOPSI, from CaImAn, performed the best on inhibited neurons, but even this algorithm inferred spurious spikes upon the return of the fluorescence signal to baseline. As such, new approaches will be needed before spikes can be sensitively and accurately inferred from calcium data in inhibited neurons. We further suggest avoiding data analysis approaches that, by assuming non-negativity, ignore inhibited responses. Instead, we suggest a first exploratory step, using k-means or PCA for example, to detect whether meaningful negative deviations are present. Taking these steps will ensure that inhibition, as well as excitation, is detected in calcium imaging datasets.

AB - The imaging of neuronal activity using calcium indicators has become a staple of modern neuroscience. However, without ground truths, there is a real risk of missing a significant portion of the real responses. Here, we show that a common assumption, the non-negativity of the neuronal responses as detected by calcium indicators, biases all levels of the frequently used analytical methods for these data. From the extraction of meaningful fluorescence changes to spike inference and the analysis of inferred spikes, each step risks missing real responses because of the assumption of non-negativity. We first show that negative deviations from baseline can exist in calcium imaging of neuronal activity. Then, we use simulated data to test three popular algorithms for image analysis, CaImAn, suite2p, and CellSort, finding that suite2p may be the best suited to large datasets. We also tested the spike inference algorithms included in CaImAn, suite2p, and Cellsort, as well as the dedicated inference algorithms MLspike and CASCADE, and found each to have limitations in dealing with inhibited neurons. Among these spike inference algorithms, FOOPSI, from CaImAn, performed the best on inhibited neurons, but even this algorithm inferred spurious spikes upon the return of the fluorescence signal to baseline. As such, new approaches will be needed before spikes can be sensitively and accurately inferred from calcium data in inhibited neurons. We further suggest avoiding data analysis approaches that, by assuming non-negativity, ignore inhibited responses. Instead, we suggest a first exploratory step, using k-means or PCA for example, to detect whether meaningful negative deviations are present. Taking these steps will ensure that inhibition, as well as excitation, is detected in calcium imaging datasets.

KW - baseline fluorescence

KW - calcium imaging

KW - cerebellar circuitry

KW - data analysis

KW - GCaMP

KW - segmentation

KW - spike inference

KW - zebrafish

UR - http://www.scopus.com/inward/record.url?scp=85099729425&partnerID=8YFLogxK

U2 - 10.3389/fncir.2020.607391

DO - 10.3389/fncir.2020.607391

M3 - Journal article

C2 - 33488363

AN - SCOPUS:85099729425

VL - 14

JO - Frontiers in Neural Circuits

JF - Frontiers in Neural Circuits

SN - 1662-5110

M1 - 607391

ER -