Accurate spike estimation from noisy calcium signals for ultrafast three-dimensional imaging of large neuronal populations in vivo

Thomas Deneux, Attila Kaszas, Gergely Szalay, Gergely Katona, Tamás Lakner, Amiram Grinvald, Balázs Rózsa and Ivo Vanzetta ()
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Thomas Deneux: Institut de Neurosciences de la Timone (INT), CNRS and Aix-Marseille Université
Attila Kaszas: Aix Marseille Université, Institut de Neuroscience des Systèmes
Gergely Szalay: Two-Photon Imaging Center, Institute of Experimental Medicine, Hungarian Academy of Sciences
Gergely Katona: Two-Photon Imaging Center, Institute of Experimental Medicine, Hungarian Academy of Sciences
Tamás Lakner: Institut de Neurosciences de la Timone (INT), CNRS and Aix-Marseille Université
Amiram Grinvald: Weizmann Institute of Science
Balázs Rózsa: Two-Photon Imaging Center, Institute of Experimental Medicine, Hungarian Academy of Sciences
Ivo Vanzetta: Institut de Neurosciences de la Timone (INT), CNRS and Aix-Marseille Université

Nature Communications, 2016, vol. 7, issue 1, 1-17

Abstract: Abstract Extracting neuronal spiking activity from large-scale two-photon recordings remains challenging, especially in mammals in vivo, where large noises often contaminate the signals. We propose a method, MLspike, which returns the most likely spike train underlying the measured calcium fluorescence. It relies on a physiological model including baseline fluctuations and distinct nonlinearities for synthetic and genetically encoded indicators. Model parameters can be either provided by the user or estimated from the data themselves. MLspike is computationally efficient thanks to its original discretization of probability representations; moreover, it can also return spike probabilities or samples. Benchmarked on extensive simulations and real data from seven different preparations, it outperformed state-of-the-art algorithms. Combined with the finding obtained from systematic data investigation (noise level, spiking rate and so on) that photonic noise is not necessarily the main limiting factor, our method allows spike extraction from large-scale recordings, as demonstrated on acousto-optical three-dimensional recordings of over 1,000 neurons in vivo.

Date: 2016
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DOI: 10.1038/ncomms12190

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