Optical Neuroimage Studio (OptiNiSt): intuitive, scalable, extendable framework for optical neuroimage data analysis

Advancements in calcium indicators and optical techniques have made optical neural recording a common tool in neuroscience. As the volume of optical neural recording data grows, streamlining the data analysis pipelines for image preprocessing, signal extraction, and subsequent neural activity analyses becomes essential. There are a number of challenges in optical neural data analysis. 1) The quality of original and processed data needs to be carefully examined at each step. 2) As there are numerous image preprocessing, cell extraction, and activity analysis algorithms, each with pros and cons, experimenters need to implement or install them to compare and select optimal methods and parameters for each step of processing. 3) To ensure the reproducibility of the research, each analysis step needs to be recorded in a systematic way. 4) For data sharing and meta-analyses, adoption of standard data formats and processing protocols is required. To address these challenges, we developed Optical Neuroimage Studio (OptiNiSt) ( https://github.com/oist/optinist ), a framework for intuitively creating calcium data analysis pipelines that are scalable, extendable, and reproducible. OptiNiSt includes the following features. 1) Researchers can easily create analysis pipelines by selecting multiple processing modules, tuning their parameters, and visualizing the results at each step through a graphic user interface in a web browser. 2) In addition to common analytical tools that are pre-installed, new analysis algorithms in Python can be easily added. 3) Once a processing pipeline is designed, the entire workflow with its modules and parameters are stored in a YAML file, which makes the pipeline reproducible and deployable on high-performance computing clusters. 4) OptiNiSt can read image data in a variety of file formats and store the analysis results in NWB (Neurodata Without Borders), a standard data format for data sharing. We expect that this framework will be helpful in standardizing optical neural data analysis protocols.