GDspike: An accurate spike estimation algorithm from noisy calcium fluorescence signals

Sebastian, Jilt; Kumar, Mari Ganesh; Sreekar, Y. S.; Rikhye, Rajeev; Sur, Mriganka; Murthy, Hema A.

doi:10.1109/icassp.2017.7952315

Cited by 9 publications

(6 citation statements)

References 18 publications

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“…Many calcium deconvolution algorithms have recently been described ( Vogelstein et al, 2010 ; Andilla and Hamprecht, 2014 ; Reynolds et al, 2015 ; Deneux et al, 2016 ; Theis et al, 2016 ; Friedrich et al, 2017 ; Jewell and Witten, 2017 ; Sebastian et al, 2017 ; Kazemipour et al, 2018 ), some of which have provided their code publicly. However, little effort has been made to compare the performance of these algorithms with each other, with the notable exception of Theis et al (2016 ) who concluded that supervised algorithms, trained on available ground truth data, perform better than the more routinely used unsupervised algorithms.…”

Section: Resultsmentioning

confidence: 99%

“…Nevertheless, calcium-sensitive fluorescence signals are an indirect readout of cellular activity. Therefore, accurate and well-calibrated data processing methods will be required to make optimal use of this activity ( Vogelstein et al, 2010 ; Andilla and Hamprecht, 2014 ; Reynolds et al, 2015 ; Deneux et al, 2016 ; Theis et al, 2016 ; Friedrich et al, 2017 ; Jewell and Witten, 2017 ; Sebastian et al, 2017 ; Kazemipour et al, 2018 ). One important problem is developing methods for spike detection: inferring the times of action potentials from the fluorescence traces.…”

Section: Introductionmentioning

confidence: 99%

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Robustness of Spike Deconvolution for Neuronal Calcium Imaging

2018

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Calcium imaging is a powerful method to record the activity of neural populations in many species, but inferring spike times from calcium signals is a challenging problem. We compared multiple approaches using multiple datasets with ground truth electrophysiology and found that simple non-negative deconvolution (NND) outperformed all other algorithms on out-of-sample test data. We introduce a novel benchmark applicable to recordings without electrophysiological ground truth, based on the correlation of responses to two stimulus repeats, and used this to show that unconstrained NND also outperformed the other algorithms when run on “zoomed out” datasets of ∼10,000 cell recordings from the visual cortex of mice of either sex. Finally, we show that NND-based methods match the performance of a supervised method based on convolutional neural networks while avoiding some of the biases of such methods, and at much faster running times. We therefore recommend that spikes be inferred from calcium traces using simple NND because of its simplicity, efficiency, and accuracy.SIGNIFICANCE STATEMENT The experimental method that currently allows for recordings of the largest numbers of cells simultaneously is two-photon calcium imaging. However, use of this powerful method requires that neuronal firing times be inferred correctly from the large resulting datasets. Previous studies have claimed that complex supervised learning algorithms outperform simple deconvolution methods at this task. Unfortunately, these studies suffered from several problems and biases. When we repeated the analysis, using the same data and correcting these problems, we found that simpler spike inference methods perform better. Even more importantly, we found that supervised learning methods can introduce artifactual structure into spike trains, which can in turn lead to erroneous scientific conclusions. Of the algorithms we evaluated, we found that an extremely simple method performed best in all circumstances tested, was much faster to run, and was insensitive to parameter choices, making incorrect scientific conclusions much less likely.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robustness of Spike Deconvolution for Neuronal Calcium Imaging

2018

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“…Because of the slower timescale of calcium indicator responses relative to action potential-related voltage changes, deconvolution of the calcium signal to the best estimate of spiking activity is required 32,33 . Several calcium deconvolution algorithms have been developed to address this non-trivial issue [63][64][65][66] . We chose to use a non-negative deconvolution algorithm 66,67 to estimate the timing and the number of deconvolved calcium events in our recordings (Movie 1, Supplementary Figure 1) because it outperformed all other algorithms in a dataset of mutual calcium imaging and ground-truth electrophysiology recordings 68 .…”

Section: Cranial Window Surgery and Viral Transduction Of Genetically...mentioning

confidence: 99%

Hyperactive somatostatin interneurons near amyloid plaque and cell-type-specific firing deficits in a mouse model of Alzheimer’s disease

Algamal

Russ

Miller

et al. 2022

Preprint

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Alzheimer’s disease (AD) is characterized by synaptic loss and neuronal network dysfunction. These network deficits are mediated by early alterations in neuronal firing rates that coincide with amyloid plaque accumulation. Mounting evidence supports that inhibitory networks are impaired in AD, but the mechanisms driving these inhibitory deficits are poorly understood. Here we use in vivo multiphoton calcium imaging to determine the relationship between amyloid accumulation and the spontaneous activity of excitatory neurons and inhibitory interneurons in an APP/PS1 mouse model of Alzheimer’s disease. We show that somatostatin-expressing (SOM) interneurons are hyperactive, while parvalbumin-expressing interneurons are hypoactive in APP/PS1 mice. Only SOM interneuron hyperactivity correlated with proximity to amyloid plaque. These inhibitory deficits were accompanied by decreased excitatory neurons activity and decreased pairwise activity correlations in APP/PS1 mice. Our study identifies cell-specific interneuronal firing deficits driven by amyloid pathology in APP/PS1 mice and provides new insights for targeting inhibitory circuits in Alzheimer’s disease.

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“…The other problems are the nonlinearity, the slow dynamics and the low signal-to-noise ratio (SNR) of the calcium (Ca) responses 7–10 . Many algorithms have been proposed to reconstruct spike trains from Ca imaging data, including conventional thresholding 11 , deconvolution 12–15 , template matching 16–20 , Bayes inference 21–23 and machine learning 24,25 , to overcome these problems. Few of them, however, have addressed the two challenging goals simultaneously: reliable spike detection and spike time estimation with high temporal precision in the presence of the nonlinearity, slow dynamics and low SNR of the Ca responses 26 .…”

Section: Introductionmentioning

confidence: 99%

Precise hyperacuity estimation of spike timing from calcium imaging

Hoang

Sato

Shinomoto

et al. 2019

Preprint

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25Two-photon imaging is a major recording technique in neuroscience, but it suffers 26 from several limitations, including a low sampling rate, the nonlinearity of calcium 27 responses, the slow dynamics of calcium dyes and a low signal-to-noise ratio, all of which 28 impose a severe limitation on the application of two-photon imaging in elucidating 29 neuronal dynamics with high temporal resolution. Here, we developed a hyperacuity 30 algorithm (HA_time) based on an approach combining a generative model and machine 31 learning to improve spike detection and the precision of spike time inference. First, 32 Bayesian inference estimates the calcium spike model by assuming the constancy of the 33 spike shape and size. A support vector machine employs this information and detects 34 spikes with higher temporal precision than the sampling rate. Compared with conventional 35 thresholding, HA_time improved the precision of spike time estimation up to 20-fold for 36 simulated calcium data. Furthermore, the benchmark analysis of experimental data from 37 different brain regions and simulation of a broader range of experimental conditions 38 showed that our algorithm was among the best in a class of hyperacuity algorithms. We 39 encourage experimenters to use the proposed algorithm to precisely estimate hyperacuity 40 spike times from two-photon imaging. 42Recently, two-photon imaging has been one of the major means of recording 43 multineuronal activities in neuroscience to obtain the precise morphology and location of 44 the target neurons because of its high spatial resolution 1-6 . However, its utility is still 45 constrained by its relatively low temporal resolution due to the mechanical scanning of 46 two-photon rays. The other problems are the nonlinearity, the slow dynamics and the low 47 signal-to-noise ratio (SNR) of the calcium (Ca) responses 7-10 . Many algorithms have been 48 proposed to reconstruct spike trains from Ca imaging data, including conventional 49 thresholding 11 , deconvolution 12-15 , template matching 16-20 , Bayes inference 21-23 and 50 machine learning 24,25 , to overcome these problems. Few of them, however, have 51 addressed the two challenging goals simultaneously: reliable spike detection and spike 52 time estimation with high temporal precision in the presence of the nonlinearity, slow 53 dynamics and low SNR of the Ca responses 26 . For the former goal, the spike dynamics 54 of the target neurons and/or kinematics of the Ca responses may vary dramatically across 55 brain regions and different Ca dyes. For the latter goal, a trade-off between the number 56 of recorded neurons and temporal resolution exists. The slow kinematics and the low 57 SNR of the currently available Ca dyes may also limit the temporal precision of the 58 information conveyed by the Ca responses. These factors impair reliable spike detection 59 as well as precise spike time estimation for high-frequency firing that is frequently 60 encountered in cortical cells 27-29 . 61 Here, we propose an approach combining a g...

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GDspike: An accurate spike estimation algorithm from noisy calcium fluorescence signals

Cited by 9 publications

References 18 publications

Robustness of Spike Deconvolution for Neuronal Calcium Imaging

Robustness of Spike Deconvolution for Neuronal Calcium Imaging

Hyperactive somatostatin interneurons near amyloid plaque and cell-type-specific firing deficits in a mouse model of Alzheimer’s disease

Precise hyperacuity estimation of spike timing from calcium imaging

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