Miniscope Pipeline: The Construction and Implementation of Open-Source Resources to Derive Neural Signal and Behavior from High-Dimensional Video Data
Ivy Chen ’21, Eden Forbes ’21, Abigail Jenkins ’22, Hero Liu ’22, and Professor Josh de Leeuw (Cognitive Science), Professor Hadley Bergstrom (Psychological Science), Professor Bojana Zupan (Psychological Science), Professor Lori Newman (Psychological Science)
In trying to identify and understand what the neural underpinnings are for an observed behavior, one of the critical challenges is recording neural activity in real time and space. Here at Vassar, we have recently developed a miniaturized microscope (i.e. Miniscope) that allows for imaging of the brain in a free-moving animal at single cell resolution. However, this process produces video data that is challenging to analyze due to both the noisiness of the data and the massive amount of data produced. We constructed a computational architecture to address these challenges and yield useful data from the Miniscope in a comprehensive and efficient manner. Furthermore, we implemented other analysis tools to analyze behavioral video data and in turn allow for the synthesis of a recorded behavior and its corresponding neural activity. Our architecture is derived from a series of open-source packages in Python, most importantly the Minian package from the Denise Cai lab at Mount Sinai and DeepLabCut. Additionally, the pipeline is configured to run on Vassar’s computer cluster, Hopper, to allow for efficient processing available to the whole college. In sum, the set of analytic tools amalgamated here allows us to derive neural signal and behavior from high-dimensional video data and opens the door for the addition of further tools to better decode the relationships between brain activity and behavior.