PT  - JOURNAL ARTICLE
AU  - Hakan Inan
AU  - Claudia Schmuckermair
AU  - Tugce Tasci
AU  - Biafra O. Ahanonu
AU  - Oscar Hernandez
AU  - Jérôme Lecoq
AU  - Fatih Dinç
AU  - Mark J. Wagner
AU  - Murat A. Erdogdu
AU  - Mark J. Schnitzer
TI  - Fast and statistically robust cell extraction from large-scale neural calcium imaging datasets
AID  - 10.1101/2021.03.24.436279
DP  - 2021 Jan 01
TA  - bioRxiv
PG  - 2021.03.24.436279
4099  - http://biorxiv.org/content/early/2021/03/27/2021.03.24.436279.short
4100  - http://biorxiv.org/content/early/2021/03/27/2021.03.24.436279.full
AB  - State-of-the-art Ca2+ imaging studies that monitor large-scale neural dynamics can produce video datasets ~10 terabytes or more in total size, roughly comparable to ~10,000 Hollywood films. Processing such data volumes requires automated, general-purpose and fast computational methods for cell identification that are robust to a wide variety of noise sources. We introduce EXTRACT, an algorithm that is based on robust estimation theory and uses graphical processing units (GPUs) to extract neural dynamics in computing times up to 10-times faster than imaging durations. We validated EXTRACT on simulated and experimental data and processed 94 public datasets from the Allen Institute Brain Observatory in one day. Showcasing its superiority over past cell-sorting methods at removing noise contaminants, neural activity traces from EXTRACT allow more accurate decoding of animal behavior. Overall, EXTRACT provides neuroscientists with a powerful computational tool matched to the present challenges of neural Ca2+ imaging studies in behaving animals.Competing Interest StatementThe authors have declared no competing interest.