Formalizing Methods and Analysis of Brain Dynamic Communities from Fluorescence Brain Imaging

In the recent years the research has seen an enormous e ort in modeling, analyzing and describing brain dynamics. This work aims to give a contribute to this eld, by validating a pipeline to identify dynamic communities in mouse brain imaging data. Understanding the dynamic aspect of brain functioning has been for long ignored both for computational reason and for the lack of algorithms able to perform such type of analysis. Identifying dynamic communities represents a fundamental step in characterizing the behavior of the brain, in order to understand functional groups of neurons and their interactions. After validating the robustness and signi cance of the pipeline, the statistics collected from the brains are used to train a machine learning algorithm which is able to classify a young brain from a old one with an accuracy of 92%. A biological hypothesis for this di erence is formulated and tested through the injection of a drug that slows down synapses, and the new machine learning algorithm reaches an accuracy of 82% validating this hypothesis. This last results sheds lights on the possibility to use this pipeline as the source of input data for a classi cation algorithm that could di erentiate between di erent types of brains as well as illnesses (e.g. Alzheimer) and at the same time o ers insights on the reasons why a certain di erence exists.