Analysis of Topological Variability in Mouse Neuronal Populations Based on Fluorescence Microscopy Images

In this work, we processed sets of images obtained by light-sheet fluorescence microscopy method. We selected different cell groups and determined areas occupied by ensembles of cell groups in mouse brain tissue. Recognition of mouse neuronal populations was performed on the basis of visual properties of fluorescence-activated cells. In our study 60 fluorescence microscopy datasets obtained from 23 mice ex vivo were analyzed. Based on data from light-sheet microscopy datasets, we identified visual characteristics of elements in multi-page TIFF files, such as the density of surface fill and its distribution over the study area, the boundaries of distinct objects and object groups, and the boundaries between homogeneous areas. To identify topological properties, we performed operations such as contouring and segmentation, and identification of areas of interest. Individual elements in fluorescence microscopy records were selected based on their brightness in grayscale mode. Frequently occurring patterns formed by individual elements were classified and found in other sets of images: this way we built a training sample and classified the optogenetics data. The presence of training samples was tested for different types of fluorescence microscopy. We selected and constructed six sets of typical samples, with certain topological properties, on the basis of the density at the boundaries, the density inside the boundaries, and the shape type.