Fast online deconvolution of calcium imaging data

• Published in: PLoS computational biology Vol. 13; no. 3; p. e1005423
• Main Authors: Friedrich, Johannes, Zhou, Pengcheng, Paninski, Liam
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.03.2017; Public Library of Science (PLoS)
• Subjects: Algorithms; Animals; Biology and Life Sciences; Brain; Brain research; Calcium; Calcium - metabolism; Calcium imaging; Calcium Signaling - physiology; Computational neuroscience; Copyright; Data Interpretation, Statistical; Deconvolution; Diagnostic imaging; Embedded systems; Firing pattern; Fluorescence; Humans; Image Interpretation, Computer-Assisted - methods; Image processing; Imaging; Internet; Linear programming; Medicine and Health Sciences; Methods; Microscopy; Microscopy, Fluorescence - methods; Molecular Imaging - methods; Neuroimaging; Neurons; Neurons - cytology; Neurons - physiology; Neurosciences; Physical Sciences; Real time; Reproducibility of Results; Research and Analysis Methods; Sensitivity and Specificity; Solvers; Voltage-Sensitive Dye Imaging - methods; Zebrafish; United States > US
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1005423

Fluorescent calcium indicators are a popular means for observing the spiking activity of large neuronal populations, but extracting the activity of each neuron from raw fluorescence calcium imaging data is a nontrivial problem. We present a fast online active set method to solve this sparse non-negative deconvolution problem. Importantly, the algorithm 3progresses through each time series sequentially from beginning to end, thus enabling real-time online estimation of neural activity during the imaging session. Our algorithm is a generalization of the pool adjacent violators algorithm (PAVA) for isotonic regression and inherits its linear-time computational complexity. We gain remarkable increases in processing speed: more than one order of magnitude compared to currently employed state of the art convex solvers relying on interior point methods. Unlike these approaches, our method can exploit warm starts; therefore optimizing model hyperparameters only requires a handful of passes through the data. A minor modification can further improve the quality of activity inference by imposing a constraint on the minimum spike size. The algorithm enables real-time simultaneous deconvolution of O(105) traces of whole-brain larval zebrafish imaging data on a laptop.