Fast, scalable, and statistically robust cell extraction from large-scale neural calcium imaging datasets

Abstract

State-of-the-art Ca 2+ imaging studies that monitor large-scale neural dynamics can produce video datasets that tally up to ∼100 TB in size (∼10 days transfer over 1 Gbit/s ethernet). 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 present EXTRACT, an algorithm that is based on robust estimation theory and uses graphical processing units (GPUs) to extract neural dynamics from a typical Ca 2+ video in computing times up to ∼10-times faster than imaging durations. We extensively validated EXTRACT on simulated and experimental data and processed 199 public datasets (∼12 TB) from the Allen Institute in a day. Showcasing its superiority over past cell extraction methods at removing noise contaminants, neural activity traces from EXTRACT allow more accurate decoding of animal behavior. Overall, EXTRACT is a powerful computational tool matched to the present challenges of neural Ca 2+ imaging studies in behaving animals.