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maskNMF: A denoise-sparsen-detect approach for extracting neural signals from dense imaging data

A number of calcium imaging methods have been developed to monitor the activity of large populations of neurons. One particularly promising approach, Bessel imaging, captures neural activity from a volume by projecting within the imaged volume onto a single imaging plane, therefore effectively mixin...

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Autores principales: Pasarkar, Amol, Kinsella, Ian, Zhou, Pengcheng, Wu, Melissa, Pan, Daisong, Fan, Jiang Lan, Wang, Zhen, Abdeladim, Lamiae, Peterka, Darcy S., Adesnik, Hillel, Ji, Na, Paninski, Liam
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cold Spring Harbor Laboratory 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10515957/
https://www.ncbi.nlm.nih.gov/pubmed/37745388
http://dx.doi.org/10.1101/2023.09.14.557777
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author Pasarkar, Amol
Kinsella, Ian
Zhou, Pengcheng
Wu, Melissa
Pan, Daisong
Fan, Jiang Lan
Wang, Zhen
Abdeladim, Lamiae
Peterka, Darcy S.
Adesnik, Hillel
Ji, Na
Paninski, Liam
author_facet Pasarkar, Amol
Kinsella, Ian
Zhou, Pengcheng
Wu, Melissa
Pan, Daisong
Fan, Jiang Lan
Wang, Zhen
Abdeladim, Lamiae
Peterka, Darcy S.
Adesnik, Hillel
Ji, Na
Paninski, Liam
author_sort Pasarkar, Amol
collection PubMed
description A number of calcium imaging methods have been developed to monitor the activity of large populations of neurons. One particularly promising approach, Bessel imaging, captures neural activity from a volume by projecting within the imaged volume onto a single imaging plane, therefore effectively mixing signals and increasing the number of neurons imaged per pixel. These signals must then be computationally demixed to recover the desired neural activity. Unfortunately, currently-available demixing methods can perform poorly in the regime of high imaging density (i.e., many neurons per pixel). In this work we introduce a new pipeline (maskNMF) for demixing dense calcium imaging data. The main idea is to first denoise and temporally sparsen the observed video; this enhances signal strength and reduces spatial overlap significantly. Next we detect neurons in the sparsened video using a neural network trained on a library of neural shapes. These shapes are derived from segmented electron microscopy images input into a Bessel imaging model; therefore no manual selection of “good” neural shapes from the functional data is required here. After cells are detected, we use a constrained non-negative matrix factorization approach to demix the activity, using the detected cells’ shapes to initialize the factorization. We test the resulting pipeline on both simulated and real datasets and find that it is able to achieve accurate demixing on denser data than was previously feasible, therefore enabling faithful imaging of larger neural populations. The method also provides good results on more “standard” two-photon imaging data. Finally, because much of the pipeline operates on a significantly compressed version of the raw data and is highly parallelizable, the algorithm is fast, processing large datasets faster than real time.
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spelling pubmed-105159572023-09-23 maskNMF: A denoise-sparsen-detect approach for extracting neural signals from dense imaging data Pasarkar, Amol Kinsella, Ian Zhou, Pengcheng Wu, Melissa Pan, Daisong Fan, Jiang Lan Wang, Zhen Abdeladim, Lamiae Peterka, Darcy S. Adesnik, Hillel Ji, Na Paninski, Liam bioRxiv Article A number of calcium imaging methods have been developed to monitor the activity of large populations of neurons. One particularly promising approach, Bessel imaging, captures neural activity from a volume by projecting within the imaged volume onto a single imaging plane, therefore effectively mixing signals and increasing the number of neurons imaged per pixel. These signals must then be computationally demixed to recover the desired neural activity. Unfortunately, currently-available demixing methods can perform poorly in the regime of high imaging density (i.e., many neurons per pixel). In this work we introduce a new pipeline (maskNMF) for demixing dense calcium imaging data. The main idea is to first denoise and temporally sparsen the observed video; this enhances signal strength and reduces spatial overlap significantly. Next we detect neurons in the sparsened video using a neural network trained on a library of neural shapes. These shapes are derived from segmented electron microscopy images input into a Bessel imaging model; therefore no manual selection of “good” neural shapes from the functional data is required here. After cells are detected, we use a constrained non-negative matrix factorization approach to demix the activity, using the detected cells’ shapes to initialize the factorization. We test the resulting pipeline on both simulated and real datasets and find that it is able to achieve accurate demixing on denser data than was previously feasible, therefore enabling faithful imaging of larger neural populations. The method also provides good results on more “standard” two-photon imaging data. Finally, because much of the pipeline operates on a significantly compressed version of the raw data and is highly parallelizable, the algorithm is fast, processing large datasets faster than real time. Cold Spring Harbor Laboratory 2023-09-15 /pmc/articles/PMC10515957/ /pubmed/37745388 http://dx.doi.org/10.1101/2023.09.14.557777 Text en https://creativecommons.org/licenses/by/4.0/This work is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/) , which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use.
spellingShingle Article
Pasarkar, Amol
Kinsella, Ian
Zhou, Pengcheng
Wu, Melissa
Pan, Daisong
Fan, Jiang Lan
Wang, Zhen
Abdeladim, Lamiae
Peterka, Darcy S.
Adesnik, Hillel
Ji, Na
Paninski, Liam
maskNMF: A denoise-sparsen-detect approach for extracting neural signals from dense imaging data
title maskNMF: A denoise-sparsen-detect approach for extracting neural signals from dense imaging data
title_full maskNMF: A denoise-sparsen-detect approach for extracting neural signals from dense imaging data
title_fullStr maskNMF: A denoise-sparsen-detect approach for extracting neural signals from dense imaging data
title_full_unstemmed maskNMF: A denoise-sparsen-detect approach for extracting neural signals from dense imaging data
title_short maskNMF: A denoise-sparsen-detect approach for extracting neural signals from dense imaging data
title_sort masknmf: a denoise-sparsen-detect approach for extracting neural signals from dense imaging data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10515957/
https://www.ncbi.nlm.nih.gov/pubmed/37745388
http://dx.doi.org/10.1101/2023.09.14.557777
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