Efficient and accurate extraction of in vivo calcium signals from microendoscopic video data

In vivo calcium imaging through microendoscopic lenses enables imaging of previously inaccessible neuronal populations deep within the brains of freely moving animals. However, it is computationally challenging to extract single-neuronal activity from microendoscopic data, because of the very large background fluctuations and high spatial overlaps intrinsic to this recording modality. Here, we describe a new constrained matrix factorization approach to accurately separate the background and then demix and denoise the neuronal signals of interest. We compared the proposed method against previous independent components analysis and constrained nonnegative matrix factorization approaches. On both simulated and experimental data recorded from mice, our method substantially improved the quality of extracted cellular signals and detected more well-isolated neural signals, especially in noisy data regimes. These advances can in turn significantly enhance the statistical power of downstream analyses, and ultimately improve scientific conclusions derived from microendoscopic data.

借助微型内窥镜镜头开展的在体钙成像（in vivo calcium imaging）技术，可对自由活动动物大脑深处此前难以触及的神经元群体开展成像观测。然而，受该记录模式固有大幅背景波动与高度空间重叠特性的限制，从微型内窥镜成像数据中提取单神经元活动（single-neuronal activity）在计算层面极具挑战性。本文提出一种全新的约束矩阵分解（constrained matrix factorization）方法，能够精准分离背景信号，随后对目标神经元信号进行解混合与降噪处理。我们将所提方法与既往的独立成分分析（independent components analysis）、约束非负矩阵分解（constrained nonnegative matrix factorization）方法进行了对比。在小鼠的模拟数据与实验记录数据中，本方法均显著提升了提取的细胞信号质量，检测到更多分离效果优异的神经信号，尤其在高噪声数据场景中表现突出。这些进展可进一步显著增强下游分析的统计效力，并最终优化基于微型内窥镜成像数据得出的科学结论。