Spike Detection Using FRI Methods and Protein Calcium Sensors: Performance Analysis and Comparisons

Reynolds, Stephanie; Onativia, Jon; Copeland, Caroline S.; Schultz, Simon R.; Dragotti, Pier Luigi

doi:10.1101/029124

Cited by 6 publications

(8 citation statements)

References 14 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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“…The CRB is commonly used as a benchmark for algorithm performance in parameter estimation problems. In the context of calcium imaging, it has been previously used to evaluate detectability of spikes under different imaging modalities (Reynolds, Oñativia, Copeland, Schultz, & Dragotti, 2015;Schuck et al, 2018). In this case, the CRB reports the minimum uncertainty achievable by any unbiased estimator when estimating the location of one spike.…”

Section: Appendix: Further Analytical Resultsmentioning

confidence: 99%

CosMIC: A Consistent Metric for Spike Inference from Calcium Imaging

et al. 2018

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In recent years, the development of algorithms to detect neuronal spiking activity from two-photon calcium imaging data has received much attention, yet few researchers have examined the metrics used to assess the similarity of detected spike trains with the ground truth. We highlight the limitations of the two most commonly used metrics, the spike train correlation and success rate, and propose an alternative, which we refer to as CosMIC. Rather than operating on the true and estimated spike trains directly, the proposed metric assesses the similarity of the pulse trains obtained from convolution of the spike trains with a smoothing pulse. The pulse width, which is derived from the statistics of the imaging data, reflects the temporal tolerance of the metric. The final metric score is the size of the commonalities of the pulse trains as a fraction of their average size. Viewed through the lens of set theory, CosMIC resembles a continuous Sørensen-Dice coefficient-an index commonly used to assess the similarity of discrete, presence/absence data. We demonstrate the ability of the proposed metric to discriminate the precision and recall of spike train estimates. Unlike the spike train correlation, which appears to reward overestimation, the proposed metric score is maximized when the correct number of spikes have been detected. Furthermore, we show that CosMIC is more sensitive to the temporal precision of estimates than the success rate.

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“…We also hope to further increase scan speeds by applying optimal control theory to our 3D algorithms to find kinematically optimal modulations of the driving signals applied to GSs and ETL. The highest scanning speed that we can achieve whilst maintaining high enough sampling for good spike detection accuracy [10], [11] remains to be seen. One possible problem we anticipate when we implement high speed scanning in hardware is drift in focal spot position caused by heating of the ETL and GSs.…”

Section: B Sampling Frequency and Calcium Transient Acquisitionmentioning

confidence: 97%

Rapid three dimensional two photon neural population scanning

Schuck

Quicke

Copeland

et al. 2015

2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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Abstract-Recording the activity of neural populations at high sampling rates is a fundamental requirement for understanding computation in neural circuits. Two photon microscopy provides one promising approach towards this. However, neural circuits are three dimensional, and functional imaging in two dimensions fails to capture the 3D nature of neural dynamics. Electrically tunable lenses (ETLs) provide a simple and cheap method to extend laser scanning microscopy into the relatively unexploited third dimension. We have therefore incorporated them into our Adaptive Spiral Scanning (SSA) algorithm, which calculates kinematically efficient scanning strategies using radially modulated spiral paths. We characterised the response of the ETL, incorporated its dynamics using MATLAB models of the SSA algorithm and tested the models on populations of Izhikevich neurons of varying size and density. From this, we show that our algorithms can theoretically at least achieve sampling rates of 36.2Hz compared to 21.6Hz previously reported for 3D scanning techniques.

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Spike Detection Using FRI Methods and Protein Calcium Sensors: Performance Analysis and Comparisons

Cited by 6 publications

References 14 publications

Robustness of Spike Deconvolution for Neuronal Calcium Imaging

Robustness of Spike Deconvolution for Neuronal Calcium Imaging

CosMIC: A Consistent Metric for Spike Inference from Calcium Imaging

Rapid three dimensional two photon neural population scanning

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