Joint Department of Biomedical Engineering at University of North Carolina at Chapel Hill and North Carolina State University, Chapel Hill, North Carolina, United States of America.
Flatiron Institute, Simons Foundation, New York, New York, United States of America.
PLoS Comput Biol. 2021 Apr 14;17(4):e1008806. doi: 10.1371/journal.pcbi.1008806. eCollection 2021 Apr.
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.
电压成像使我们能够在亚毫秒和亚细胞尺度上监测神经活动，以前所未有的时空分辨率解锁了对亚阈值活动、同步和网络动力学的研究。然而，高数据速率（>800MB/s）和低信噪比为分析此类数据集带来了瓶颈。在这里，我们展示了 VolPy，这是一种用于预处理电压成像数据集的自动化和可扩展的流水线。VolPy 具有运动校正、内存映射、自动分割、去噪和尖峰提取功能，所有这些功能都构建在一个高度并行化、模块化和可扩展的框架上，该框架针对内存和速度进行了优化。为了帮助自动分割，我们引入了一个由 24 个来自不同准备、脑区和电压指示剂的手动注释数据集组成的语料库。我们将 VolPy 与地面真实分割、模拟和电生理记录进行基准测试，并比较其在检测尖峰方面的性能与现有算法的性能。我们的结果表明，VolPy 在提取尖峰方面的性能和可扩展性均达到了最新水平。
