Shen Tong, DU Mingyu, Johnston Kevin, Lur Gyorgy, Xu Xiangmin, Ombao Hernando, Guindani Michele, Yu Zhaoxia
Department of Statistics, University of California Irvine.
Tong Shen and Mingyu Du contributed equally.
Data Sci Sci. 2024;3(1). doi: 10.1080/26941899.2024.2407770. Epub 2024 Nov 5.
Optical imaging of genetically encoded calcium indicators is a powerful tool to record the activity of a large number of neurons simultaneously over a long period of time from freely behaving animals. However, determining the exact time at which a neuron spikes and estimating the underlying firing rate from calcium fluorescence data remains challenging, especially for calcium imaging data obtained from a longitudinal study. We propose a multi-trial time-varying penalized method to jointly detect spikes and estimate firing rates by robustly integrating evolving neural dynamics across trials. Our simulation study shows that the proposed method performs well in both spike detection and firing rate estimation. We demonstrate the usefulness of our method on calcium fluorescence trace data from two studies, with the first study showing differential firing rate functions between two behaviors and the second study showing evolving firing rate functions across trials due to learning.
对基因编码钙指示剂进行光学成像，是一种强大的工具，可在较长时间内同时记录自由活动动物中大量神经元的活动。然而，确定神经元放电的准确时间，并从钙荧光数据中估计潜在的放电率仍然具有挑战性，特别是对于从纵向研究中获得的钙成像数据。我们提出了一种多试验时变惩罚方法，通过稳健地整合跨试验不断演变的神经动力学，来联合检测放电并估计放电率。我们的模拟研究表明，所提出的方法在放电检测和放电率估计方面均表现良好。我们在两项研究的钙荧光轨迹数据上证明了该方法的有效性，第一项研究显示了两种行为之间不同的放电率函数，第二项研究显示了由于学习导致的跨试验不断演变的放电率函数。
