VolPy: automated and scalable analysis pipelines for voltage imaging datasets

Cai, Changjia; Friedrich, Johannes; Pnevmatikakis, Eftychios A.; Podgorski, Kaspar; Giovannucci, Andrea

doi:10.1101/2020.01.02.892323

“…An independent voltage imaging spike detection pipeline, VolPy, was recently introduced by Cai et al (2020). This pipeline is largely complementary to ours, because it focuses on extracting spike times, rather than accuracy of subthreshold dynamics.…”

Section: Discussionmentioning

confidence: 99%

“…Although our method does not include a spike-finding step, such algorithms are available in the literature and can be appended to our pipeline. Specifically, the SpikePursuit algorithm (Abdelfattah et al, 2019) and the improved version incorporated in the VolPy package (Cai et al, 2020) have been optimized for fluorescent voltage recordings.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

High-fidelity estimates of spikes and subthreshold waveforms from 1-photon voltage imaging in vivo

Xie

¹

,

Adam

²

,

Fan

³

et al. 2021

View full text Add to dashboard Cite

The ability to probe the membrane potential of multiple genetically defined neurons simultaneously would have a profound impact on neuroscience research. Genetically encoded voltage indicators are a promising tool for this purpose, and recent developments have achieved a high signal-to-noise ratio in vivo with 1photon fluorescence imaging. However, these recordings exhibit several sources of noise and signal extraction remains a challenge. We present an improved signal extraction pipeline, spike-guided penalized matrix decomposition-nonnegative matrix factorization (SGPMD-NMF), which resolves supra-and subthreshold voltages in vivo. The method incorporates biophysical and optical constraints. We validate the pipeline with simultaneous patch-clamp and optical recordings from mouse layer 1 in vivo and with simulated and composite datasets with realistic noise. We demonstrate applications to mouse hippocampus expressing paQuasAr3-s or SomArchon1, mouse cortex expressing SomArchon1 or Voltron, and zebrafish spines expressing zArchon1.

show abstract

“…An independent voltage imaging spike detection pipeline, VolPy was recently introduced by (Cai et al, 2020). This pipeline is largely complementary to ours, as it focuses on extracting spike times, rather than accuracy of sub-threshold dynamics.…”

Section: Discussionmentioning

confidence: 99%

High fidelity estimates of spikes and subthreshold waveforms from 1-photon voltage imagingin vivo

Xie

¹

,

Adam

²

,

Fan

³

et al. 2020

Preprint

View full text Add to dashboard Cite

The ability to probe the membrane potential of multiple genetically defined neurons simultaneously would have a profound impact on neuroscience research. Genetically encoded voltage indicators are a promising tool for this purpose, and recent developments have achieved high signal to noise ratio in vivo with 1-photon fluorescence imaging. However, these recordings exhibit several sources of noise that present analysis challenges, namely light scattering, out-offocus sources, motion, and blood flow. We present a novel signal extraction methodology, Spike-Guided Penalized Matrix Decomposition-Nonnegative Matrix Factorization (SGPMD-NMF), which resolves supra-and sub-threshold voltages with high fidelity, even in the presence of correlated noise. The method incorporates biophysical constraints (shared soma profiles for spiking and subthreshold dynamics) and optical constraints (smoother spatial profiles from defocused vs. in-focus sources) to cleave signal from background. We validated the pipeline using simulated and composite datasets with realistic noise properties. We demonstrate applications to mouse hippocampus expressing paQuasAr3-s or SomArchon, mouse cortex expressing SomArchon or Voltron, and zebrafish spine expressing zArchon1.Recently, a joint penalized matrix decomposition (PMD) and non-negative matrix factorization (NMF) approach has been proposed to denoise and demix voltage imaging data (Buchanan et al., 2019). This method can extract cell signals that have high signal to noise ratio (SNR) from in vitro voltage imaging movies, where motion artifacts, blood flow, light scattering, and temporally-varying background can all be ignored.

show abstract

“…The code and datasets are available at the master branch of the repository https://github.com/ flatironinstitute/CaImAn and at the Zenodo repository [38]. Data used in figures are included in S1 Data.…”

Section: Availabilitymentioning

confidence: 99%

VolPy: Automated and scalable analysis pipelines for voltage imaging datasets

Cai

¹

,

Friedrich

²

,

Singh

³

et al. 2021

Self Cite

View full text Add to dashboard Cite

Voltage imaging enables monitoring neural activity at sub-millisecond and sub-cellular scale, unlocking the study of subthreshold activity, synchrony, and network dynamics with unprecedented spatio-temporal resolution. However, high data rates (>800MB/s) and low signal-to-noise ratios create bottlenecks for analyzing such datasets. Here we present VolPy, an automated and scalable pipeline to pre-process voltage imaging datasets. VolPy features motion correction, memory mapping, automated segmentation, denoising and spike extraction, all built on a highly parallelizable, modular, and extensible framework optimized for memory and speed. To aid automated segmentation, we introduce a corpus of 24 manually annotated datasets from different preparations, brain areas and voltage indicators. We benchmark VolPy against ground truth segmentation, simulations and electrophysiology recordings, and we compare its performance with existing algorithms in detecting spikes. Our results indicate that VolPy’s performance in spike extraction and scalability are state-of-the-art.

show abstract

VolPy: automated and scalable analysis pipelines for voltage imaging datasets

Cited by 10 publications

References 23 publications

High-fidelity estimates of spikes and subthreshold waveforms from 1-photon voltage imaging in vivo

High-fidelity estimates of spikes and subthreshold waveforms from 1-photon voltage imaging in vivo

High fidelity estimates of spikes and subthreshold waveforms from 1-photon voltage imagingin vivo

VolPy: Automated and scalable analysis pipelines for voltage imaging datasets

Contact Info

Product

Resources

About