During deep sleep and under anaesthesia spontaneous patterns of cortical activation frequently take the form of slow travelling waves. Slow wave sleep is an important cognitive state especially because of its relevance for memory consolidation. However, despite extensive research the exact mechanisms are still ill-understood. Novel methods such as high speed widefield imaging of GCamP activity offer new potentials. Here we show how data recorded from transgenic mice under anesthesia can be processed to analyze sources, sinks and patterns of flow. To make the best possible use of the data novel means of data processing are necessary. Therefore, we (1) give a an brief account on processes that play a role in generating slow waves and demonstrate (2) a novel approach to characterize its patterns in GCamP recordings. While slow waves are highly variable, it shows that some are surprisingly similar. To enable quantitative means of analysis and examine the structure of such prototypical events we propose a novel approach for the characterization of slow waves: The Helmholtz-Decomposition of gradient-based Dense Optical Flow of the pixeldense GCamP contrast (df/f). It allows to detect the sources and sinks of activation and discern them from global patterns of neural flow. Aggregated features can be analyzed with variational autoencoders. The results unravel regularities between slow waves and shows how they relate to the experimental conditions. The approach reveals a complex topology of different features in latent slow wave space and identifies prototypical examples for each stage.

用高速广域成像分析 GCamP 活动的数据，从转基因小鼠的麻醉中提取出潜在的慢波源与汇，并通过梯度基础密集光流的赫姆霍兹分解来表征慢波的模式，揭示了慢波与实验条件的关系。

Helmholtz分解和光流：一种表征GCamP记录的新方法