Simultaneous Denoising, Deconvolution, and Demixing of Calcium Imaging Data

Pnevmatikakis, Eftychios A.; Soudry, Daniel; Gao, Yuan‐Jun; Machado, Timothy A.; Merel, Josh; Pfau, David; Reardon, Thomas R.; Mu, Yu; Lacefield, Clay; Yang, Wankou; Ahrens, Misha B.; Bruno, Randy M.; Jessell, Thomas M.; Peterka, Darcy S.; Paninski, Liam

doi:10.1016/j.neuron.2015.11.037

Cited by 996 publications

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“…We implicitly evolve an active contour via the level set function. The evolution of the level set 13 function is driven by a local model of the imaging data temporal activity. The data model includes no 14 assumptions on a cell's morphology or stereotypical temporal activity.…”

Section: Introductionmentioning

confidence: 99%

“…When we have an image not a video (i.e. I(x) and f are one-dimensional) this dissimilarity met- 13 ric reduces to the fitting term introduced by Chan and Vese (2001). For datasets in which the fluorescence 14 expression level varies significantly throughout cells and, as a consequence, pixels in the same cell exhibit 15 the same pattern of activity at different magnitudes, we use a measure based on the correlation, such that 16 (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.…”

mentioning

confidence: 99%

“…http://dx.doi.org/10.1101/190348 doi: bioRxiv preprint first posted online Sep. 18, 2017; In particular, we assume that a pixel in multiple cells would have a time course well fit by the sum of the 1 interior time courses for each cell. The external energy in the case of multiple cells is thus by a function φ i , where φ i is positive for all pixels in the cell interior, negative for those in the narrowband 13 and zero for all pixels on the boundary (see Fig. 1C).…”

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confidence: 99%

“…To attain segmentation results on the real datasets presented in this paper, 12 we use λ = 150 (Section 3.1), λ = 50 (Section 3.3), λ = 25 (Section 3.4) and λ = 10 (Section 3.5). 13 …”

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confidence: 99%

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ABLE: an Activity-Based Level Set Segmentation Algorithm for Two-Photon Calcium Imaging Data

Reynolds

Abrahamsson

Schuck

et al. 2017

Preprint

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We present an algorithm for detecting the location of cells from two-photon calcium imaging data. In our 1 framework, multiple coupled active contours evolve, guided by a model-based cost function, to identify cell 2 boundaries. An active contour seeks to partition a local region into two subregions, a cell interior and ex-3 terior, in which all pixels have maximally 'similar' time courses. This simple, local model allows contours to 4 be evolved predominantly independently. When contours are sufficiently close, their evolution is coupled, 5 in a manner that permits overlap. We illustrate the ability of the proposed method to demix overlapping 6 cells on real data. The proposed framework is flexible, incorporating no prior information regarding a cell's 7 morphology or stereotypical temporal activity, which enables the detection of cells with diverse properties. 8 We demonstrate algorithm performance on a challenging mouse in vitro dataset, containing synchronously 9 spiking cells, and a manually labelled mouse in vivo dataset, on which ABLE achieves a 67.5% success 10 rate. We developed a versatile algorithm based on a popular image segmentation approach (the Level Set 18 Method) and demonstrated its capability to overcome these challenges. We include no assumptions on 19 cell shape or stereotypical temporal activity. This lends our framework the flexibility to be applied to new 20 datasets with minimal adjustment.

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Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

“…To attain segmentation results on the real datasets presented in this paper, 12 we use λ = 150 (Section 3.1), λ = 50 (Section 3.3), λ = 25 (Section 3.4) and λ = 10 (Section 3.5). 13 …”

mentioning

confidence: 99%

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ABLE: an Activity-Based Level Set Segmentation Algorithm for Two-Photon Calcium Imaging Data

Reynolds

Abrahamsson

Schuck

et al. 2017

Preprint

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“…Past studies have approached separately those studies [141][142][143][144]. Very few methods cover these three problems but, Pnevmatikakis et al [145] is such a method and has the advantages of dealing with big datasets by running computations in the cloud, giving clean results by discarding artefacts. It also is one of the top performers in the neurofinder competition.…”

Section: Preliminary Resultsmentioning

confidence: 99%

Imaging, manipulation and optogenetics in zebrafish

Favre-bulle¹

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This thesis investigates the advantages and limits of using laser light for illumination and micromanipulation of the nervous system deep in-vivo in zebrafish. Further interest in the study neuronal pathways and functions in zebrafish led to employ laser micro-manipulation for the stimulation of the systems of interest and the development of optogenetics system and light sheet microscopy for analyses of the processes. First I present the investigation of the interaction of light and brain tissue as we aim to quantify light scattered in zebrafish brain tissue with depth. This is of great importance for optogenetics as it uses light to control (turn on or off) neurons and the unwanted/scattered light out of the aimed region could significantly affect results of experiments. The measurements and model developed to predict light propagation through brain tissue and how much a focussed beam broadens and alters with depth are presented in details. Results show that illumination of individual neurons is possible at depth in the zebrafish brain, despite scattering that results from shallower neural tissue. This means that the approach presented allows for optogenetics manipulation of single neurons to be performed without optical correction for scattering. Secondly, I construct an optogenetics system where we employ advanced optics and novel algorithm to deliver versatile two dimensional illumination that can be used at any depth in the fish. I present an example of a problem that I have solved, and resulting optogenetics results that were obtained, using this new technology. In chapter 5, I was responsible for the improvements implemented on the optogenetics system and the different tests to show its performances. Lucy Heap has performed multiple experiments on that system and I present one example she performed and the results she got.In chapter 7, I performed the manipulation of free otolith in water. I with Alex Stilgoe performed scanning experiments of otolith on an optical trapping system that Alex Stilgoe built.We both performed the analysis of the experimental results. Alex Stilgoe performed the force modelisation with Ray optics methods and we compared it with experimental results. Statement of parts of the thesis submitted to qualify for the award of another degreeNone. I have special thanks to Shu, Swaantje and Lucy for our philosophical discussions, the sharing of our experiences in the good and bad moments of our PhDs and their support.Finally I have some friends that I would like to thank for their great support without necessarily realising it: Mari-wenn, Estelle and Arnaud.8 ABSTRACT

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Selective Modulation of Fear Memory in Non‐Rapid Eye Movement Sleep

Zheng,

Huang,

et al. 2024

Advanced Science

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Sleep stabilizes memories for their consolidation, but how to modify specific fear memory during sleep remains unclear. Here, it is reported that using targeted memory reactivation (TMR) to reactivate prior fear learning experience in non‐slow wave sleep (NS) inhibits fear memory consolidation, while TMR during slow wave sleep (SWS) enhances fear memory in mice. Replaying conditioned stimulus (CS) during sleep affects sleep spindle occurrence, leading to the reduction or enhancement of slow oscillation‐spindle (SO‐spindle) coupling in NS and SWS, respectively. Optogenetic inhibition of pyramidal neurons in the frontal association cortex (FrA) during TMR abolishes the behavioral effects of NS‐TMR and SWS‐TMR by modulating SO‐spindle coupling. Notably, calcium imaging of the L2/3 pyramidal neurons in the FrA shows that CS during SWS selectively enhances the activity of neurons previously activated during fear conditioning (FC+ neurons), which significantly correlates with CS‐elicited spindle power spectrum density. Intriguingly, these TMR‐induced calcium activity changes of FC+ neurons further correlate with mice freezing behavior, suggesting their contributions to the consolidation of fear memories. The findings indicate that TMR can selectively weaken or strengthen fear memory, in correlation with modulating SO‐spindle coupling and the reactivation of FC+ neurons during substages of non‐rapid eye movement (NREM) sleep.

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Simultaneous Denoising, Deconvolution, and Demixing of Calcium Imaging Data

Cited by 996 publications

References 21 publications

ABLE: an Activity-Based Level Set Segmentation Algorithm for Two-Photon Calcium Imaging Data

ABLE: an Activity-Based Level Set Segmentation Algorithm for Two-Photon Calcium Imaging Data

Imaging, manipulation and optogenetics in zebrafish

Selective Modulation of Fear Memory in Non‐Rapid Eye Movement Sleep

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