Resumen de Computational and modeling approaches to multi-scale anatomical description of neuronal circuitry
During the last century the nervous system has been mainly studied from a reductionistic approach, based on the hypothesis that understanding in depth single neurons or limited neuronal populations would lead to general conclusions on brain function. However, to what extent anatomical details of single neurons can affect the wiring of the networks they form is a largely overlooked question. Intellectual disability provides an excellent opportunity to explore the relevance of fine structural details, because many disorders show specific architectural alterations that correlate with cognitive performance.
In this Thesis, I aimed to study how the network topology of neuronal circuits is affected by dendritic architectural features in a mouse model of intellectual disability, namely Down's syndrome, and upon the rewiring effect of pro-cognitive treatment. I did so from three points of view: 1. The exploration of a 2D minimal computational model of cortical layer II/III parameterized by experimental data on dendritic tree architecture of healthy mice and two Down syndrome mouse models.
2. The study of within-region morphological variations of hippocampal CA1 pyramidal neurons and their dependency of spatial embedding in healthy mice and a Down syndrome mouse model.
3. The development of an experimental and computational framework for whole brain multiscale analysis and modeling.
My work revealed that the dendritic tree architecture and the distribution of synaptic contacts have significant implications on how optimal single neurons are for information processing efficiency and storage capacity, and suggests that those single-neuron features permeate to the network level, determining the computational capacities of neural ensembles.
Also, I found position-dependent neuromorphological inhomogeneities in CA1 pyramids along with variations of neuronal cell density, suggesting that intrinsic properties of CA1 can vary across its extension. Those inhomogeneities were different in healthy and TgDyrk1A mice, possibly affecting emergent functional aspects.
In my Thesis I faced challenges to bridge structural descriptions at the cellular and networks scales and to study morphological inhomogeneities in cell populations. To solve those challenges, I developed computational tools for analyzing and modeling the anatomy of neuronal circuits while bridging the microscopic and mesoscopic scales. The analysis tools I developed and validated allow population-based analyses of cellular and dendritic density. Those, combined with the optimized CLARITY whole-brain clearing technique I implemented, will allow tackling the relationship between neuromorphology and the computational capacities of neural ensembles from a systems perspective.
