Fiber photometry is a cutting-edge technique that captures real-time in-vivo brain activity in laboratory animals. Often aligned with video-tracked behavioral data, fiber photometry allows scientists to directly pair observed behavior and brain signaling. While fiber photometry is useful for novel behavioral experiments, each experimental step (data collection, processing, analysis) introduces the possibility of error. As such, ensuring research is reproducible is critical, but the size of the data sets (up to 0.25 gigabytes) makes quality control a cumbersome task.
JMP Graph Builder provides an excellent interface for dynamically and efficiently identifying quality control issues. In a project using fiber photometry to understand how norepinephrine signaling accompanies fear behavior in rodents, we used JMP to detect misalignment between the Ethovision XT-tracked behavior data and the fiber photometry data, to provide easy identification of behavioral tracking disruptions and to ensure that expected patterns were present. These quality control checks allowed for timely understanding of, intervention to, and correction to the data set, thus promoting research integrity. Additionally, the quality control plots gave scientists a novel and insightful way to understand their experiments.
Graph Builder provided data quality checks for a sound analysis via JMP’s modeling platform. The scientists were also able to use JMP scripts and were motivated to learn how to use JMP themselves from this process. In this talk, we showcase the problem of quality control for large, complicated data sets, as exemplified through fiber photometry, and how JMP’s graphing capabilities allowed us to ensure quality data and reproducible research.
