HOTARU: Automatic sorting system for large-scale calcium imaging data
HOTARU: Automatic sorting system for large-scale calcium imaging data
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HOTARU：大规模钙成像数据自动排序系统
DOI：
10.1101/2022.04.05.487077
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
T. Fukai
中科院分区：
文献类型：
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作者：
T. Takekawa;Masanori Nomoto;Hirotaka Asai;Noriaki Ohkawa;Reiko Okubo;Khaled Ghandour;Masaaki Sato;Masamichi Ohkura;Junichi Nakai;S. Muramatsu;Y. Hayashi;K. Inokuchi;T. Fukai
目前，钙成像可以长期记录不同状态下的大规模神经元活动。然而，从记录的成像数据中提取神经元动力学仍然很困难。在这项研究中，我们提出了一种改进的基于约束非负矩阵分解（CNMF）的算法，并通过图像处理提出了一种有效的方法，以减少假阳性和假阴性的细胞形状提取。我们还表明，在图像和信号处理期间获得的评价指标可以组合并用于假阳性细胞测定。对于CNMF算法，我们将逐细胞正则化和基线收缩估计相结合，大大提高了算法的稳定性和鲁棒性。将这些方法应用于实际数据，验证了其有效性。该方法简单、快速，能以较低的放电率和信噪比检测到更多的细胞，提高了提取细胞信号的质量。这些进展可以提高下游分析的标准，并有助于神经科学的进步。
Currently, calcium imaging allows long-term recording of large-scale neuronal activity in diverse states. However, it remains difficult to extract neuronal dynamics from recorded imaging data. In this study, we propose an improved constrained nonnegative matrix factorization (CNMF)-based algorithm and an effective method to extract cell shapes with fewer false positives and false negatives through image processing. We also show that the evaluation metrics obtained during image and signal processing can be combined and used for false-positive cell determination. For the CNMF algorithm, we combined cell-by-cell regularization and baseline shrinkage estimation, which greatly improved its stability and robustness. We applied these methods to real data and confirmed their effectiveness. Our method is simpler and faster, detects more cells with lower firing rates and signal-to-noise ratios, and enhances the quality of the extracted cell signals. These advances can improve the standard of downstream analysis and contribute to progress in neuroscience.