Denoising Two-Photon Calcium Imaging Data
Two-photon calcium imaging is now an important tool for in vivo imaging of biological systems. By enabling neuronal population imaging with subcellular resolution, this modality offers an approach for gaining a fundamental understanding of brain anatomy and physiology. Proper analysis of calcium ima...
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| Format: | Article |
| Language: | en_US |
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Public Library of Science
2011
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| Online Access: | http://hdl.handle.net/1721.1/65395 https://orcid.org/0000-0003-2668-7819 https://orcid.org/0000-0003-2442-5671 https://orcid.org/0000-0002-7260-7560 |
| _version_ | 1826211664823844864 |
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| author | Malik, Wasim Qamar Schummers, James Sur, Mriganka Brown, Emery N. |
| author2 | Harvard University--MIT Division of Health Sciences and Technology |
| author_facet | Harvard University--MIT Division of Health Sciences and Technology Malik, Wasim Qamar Schummers, James Sur, Mriganka Brown, Emery N. |
| author_sort | Malik, Wasim Qamar |
| collection | MIT |
| description | Two-photon calcium imaging is now an important tool for in vivo imaging of biological systems. By enabling neuronal population imaging with subcellular resolution, this modality offers an approach for gaining a fundamental understanding of brain anatomy and physiology. Proper analysis of calcium imaging data requires denoising, that is separating the signal from complex physiological noise. To analyze two-photon brain imaging data, we present a signal plus colored noise model in which the signal is represented as harmonic regression and the correlated noise is represented as an order autoregressive process. We provide an efficient cyclic descent algorithm to compute approximate maximum likelihood parameter estimates by combing a weighted least-squares procedure with the Burg algorithm. We use Akaike information criterion to guide selection of the harmonic regression and the autoregressive model orders. Our flexible yet parsimonious modeling approach reliably separates stimulus-evoked fluorescence response from background activity and noise, assesses goodness of fit, and estimates confidence intervals and signal-to-noise ratio. This refined separation leads to appreciably enhanced image contrast for individual cells including clear delineation of subcellular details and network activity. The application of our approach to in vivo imaging data recorded in the ferret primary visual cortex demonstrates that our method yields substantially denoised signal estimates. We also provide a general Volterra series framework for deriving this and other signal plus correlated noise models for imaging. This approach to analyzing two-photon calcium imaging data may be readily adapted to other computational biology problems which apply correlated noise models. |
| first_indexed | 2024-09-23T15:09:34Z |
| format | Article |
| id | mit-1721.1/65395 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T15:09:34Z |
| publishDate | 2011 |
| publisher | Public Library of Science |
| record_format | dspace |
| spelling | mit-1721.1/653952022-09-29T13:05:32Z Denoising Two-Photon Calcium Imaging Data Malik, Wasim Qamar Schummers, James Sur, Mriganka Brown, Emery N. Harvard University--MIT Division of Health Sciences and Technology Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences Picower Institute for Learning and Memory Brown, Emery N. Brown, Emery N. Malik, Wasim Qamar Schummers, James Sur, Mriganka Two-photon calcium imaging is now an important tool for in vivo imaging of biological systems. By enabling neuronal population imaging with subcellular resolution, this modality offers an approach for gaining a fundamental understanding of brain anatomy and physiology. Proper analysis of calcium imaging data requires denoising, that is separating the signal from complex physiological noise. To analyze two-photon brain imaging data, we present a signal plus colored noise model in which the signal is represented as harmonic regression and the correlated noise is represented as an order autoregressive process. We provide an efficient cyclic descent algorithm to compute approximate maximum likelihood parameter estimates by combing a weighted least-squares procedure with the Burg algorithm. We use Akaike information criterion to guide selection of the harmonic regression and the autoregressive model orders. Our flexible yet parsimonious modeling approach reliably separates stimulus-evoked fluorescence response from background activity and noise, assesses goodness of fit, and estimates confidence intervals and signal-to-noise ratio. This refined separation leads to appreciably enhanced image contrast for individual cells including clear delineation of subcellular details and network activity. The application of our approach to in vivo imaging data recorded in the ferret primary visual cortex demonstrates that our method yields substantially denoised signal estimates. We also provide a general Volterra series framework for deriving this and other signal plus correlated noise models for imaging. This approach to analyzing two-photon calcium imaging data may be readily adapted to other computational biology problems which apply correlated noise models. National Institutes of Health (U.S.) (DP1 OD003646-01) National Institutes of Health (U.S.) (R01EB006385-01) National Institutes of Health (U.S.) (EY07023) National Institutes of Health (U.S.) (EY017098) 2011-08-26T14:51:34Z 2011-08-26T14:51:34Z 2011-06 2011-02 Article http://purl.org/eprint/type/JournalArticle 1932-6203 http://hdl.handle.net/1721.1/65395 Malik, Wasim Q. et al. “Denoising Two-Photon Calcium Imaging Data.” Ed. Matjaz Perc. PLoS ONE 6.6 (2011) : e20490. https://orcid.org/0000-0003-2668-7819 https://orcid.org/0000-0003-2442-5671 https://orcid.org/0000-0002-7260-7560 en_US http://dx.doi.org/10.1371/journal.pone.0020490 PLoS ONE Creative Commons Attribution http://creativecommons.org/licenses/by/2.5/ application/pdf Public Library of Science PLoS |
| spellingShingle | Malik, Wasim Qamar Schummers, James Sur, Mriganka Brown, Emery N. Denoising Two-Photon Calcium Imaging Data |
| title | Denoising Two-Photon Calcium Imaging Data |
| title_full | Denoising Two-Photon Calcium Imaging Data |
| title_fullStr | Denoising Two-Photon Calcium Imaging Data |
| title_full_unstemmed | Denoising Two-Photon Calcium Imaging Data |
| title_short | Denoising Two-Photon Calcium Imaging Data |
| title_sort | denoising two photon calcium imaging data |
| url | http://hdl.handle.net/1721.1/65395 https://orcid.org/0000-0003-2668-7819 https://orcid.org/0000-0003-2442-5671 https://orcid.org/0000-0002-7260-7560 |
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