Resumen de Self-organizing cultured neural networks. Image analysis techniques for longitudinal tracking and modeling of the underlying network structure
This thesis analyzes the morphological evolution of assemblies of living neurons, as they self-organize from collections of separated cells into elaborated, clustered, networks.
In particular, the contribution consists in the design and implementation of a graph-based unsupervised segmentation algorithm with a very low computational cost. The processing automatically retrieves the whole network structure from large scale phase-contrast images taken at high resolution throughout the entire life of a cultured neuronal network. The network structure is represented by a mathematical object (a matrix) in which nodes are identified neurons or neurons¿ clusters, and links are the reconstructed connections between them. The algorithm is also able to extract all other relevant morphological information characterizing neurons and neurites. More importantly and at variance with other segmentation methods that require fluorescence imaging from immunocytochemistry techniques, our measures are non invasive and entitle us to carry out a fully longitudinal analysis during the maturation of a single culture.
In turn, a systematic statistical analysis of a group of topological observables grants us the possibility of quantifying and tracking the progression of the main networks characteristics during the self-organization process of the culture. Our results point to the existence of a particular state corresponding to a small-world network configuration, in which several relevant graphs¿ micro- and meso-scale properties emerge.
Finally, we identify the main physical processes taking place during the cultures morphological transformations, and embed them into a simplified growth model that quantitatively reproduces the overall set of experimental observations.
