ArticleNature methods2023
FIOLA: an accelerated pipeline for fluorescence imaging online analysis.
Article in Nature methods, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Article
- Voltage imaging as a window into neural computation.Neurophotonics · 2026Article
- Combinatorial responsiveness of chemosensory neurons in mouse explants revealed by DynamicNeuroTracker.Cell reports methods · 2025Article
- A software platform for real-time and adaptive neuroscience experiments.Nature communications · 2025Article
- A real-time all-optical interface for dynamic perturbation of neural activity during behavior.Cell reports methods · 2025Article
- Imaging neuronal voltage beyond the scattering limit.Nature methods · 2025Article
- A sensitive soma-localized red fluorescent calcium indicator for in vivo imaging of neuronal populations at single-cell resolution.PLoS biology · 2025Article
- Scanless two-photon voltage imaging.Nature communications · 2024Article
- Review of data processing of functional optical microscopy for neuroscience.Neurophotonics · 2022Review
- Two-photon calcium imaging of neuronal activity.Nature reviews. Methods primers · 2022Article
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6 authors.
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Abstract
Optical microscopy methods such as calcium and voltage imaging enable fast activity readout of large neuronal populations using light. However, the lack of corresponding advances in online algorithms has slowed progress in retrieving information about neural activity during or shortly after an experiment. This gap not only prevents the execution of real-time closed-loop experiments, but also hampers fast experiment-analysis-theory turnover for high-throughput imaging modalities. Reliable extraction of neural activity from fluorescence imaging frames at speeds compatible with indicator dynamics and imaging modalities poses a challenge. We therefore developed FIOLA, a framework for fluorescence imaging online analysis that extracts neuronal activity from calcium and voltage imaging movies at speeds one order of magnitude faster than state-of-the-art methods. FIOLA exploits algorithms optimized for parallel processing on GPUs and CPUs. We demonstrate reliable and scalable performance of FIOLA on both simulated and real calcium and voltage imaging datasets. Finally, we present an online experimental scenario to provide guidance in setting FIOLA parameters and to highlight the trade-offs of our approach.
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