VolPy: automated and scalable analysis pipelines for voltage imaging datasets

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.

File names contain 'L1'(mouse cortex) or 'TEG'(zebrafish tegmental area) are from the following paper:

Abdelfattah AS, Kawashima T, Singh A, Novak O, Liu H, Shuai Y, et al. Bright and photostable chemigenetic indicators for extended in vivo voltage imaging.Science. 2019;365(6454):699–704. doi:10.1126/science.aav6416.

File names contain 'HPC'(mouse hippocampus) are from the following paper:

Adam Y, Kim JJ, Lou S, Zhao Y, Xie ME, Brinks D, et al. Voltage imaging and optogenetics reveal behaviour-dependent changes in hippocampal dynamics.Nature. 2019;569(7756):413.