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The main objective of this Ph.D. Thesis is the study of functional brain connectivity in vivo combining functional magnetic resonance imaging with electrical brain microstimulation in experimental animals. This Ph.D. Thesis is part of a multidisciplinary team result of close collaboration between the Center for Biomaterials and Tissue Engineering of Universitat Politècnica de València and the Neuroscience Institute of Alicante of the National Research Council in Spain–Universidad Miguel Hernández. The study of functional connectivity is of great interest in current basic neurobiology and a fundamental part of pre-clinical research of psychiatric illness. Indeed, it is the mechanism on which are based two of the basic principles of neurobiology: functional specialization and integration. The nervous system implements a highly distributed processing of information, with specialized modules on specific aspects of processing that are combined together in an integral form. For example, the multisensorial experience of a daily episode is experienced as a single (or integrated) perception. In this context, the efficient connectivity between functional modules, or functional connectivity, is an indispensable requirement. In order to be efficient, this connectivity must adapt to the needs of the system in an enviroment –the medium in which the body develops– which is in continuous change. Therefore, the functional configuration is a dynamic characteristic determined by synaptic plasticity mechanisms, adaptation and modulation, defining the flow of information in the system. The great similarity between some brain models (such as monkeys or rats) and the human brain, makes conclusions drawn from animal experiments relatively easy to extrapolate. The analysis methods for studying functional connectivity presented here have been applied to the specific case of functional magnetic resonance imaging of rat brains that are stimulated electrically. In order to detect brain networks that are functionally connected, cerebral hemodynamical signal correlation techniques have been used as indicators of connectivity, either from the standard analysis of the cross-correlation or from multivariate measures for detecting patterns of variance/covariance. The first six chapters of the doctoral dissertation introduce the theoretical and knowledge foundations that enable the design and development of all those methods that are presented as tools for our study of functional connectivity. In this first part of the Doctoral Thesis the following key concepts are shown: 1. That modern neuroscience is postulated on the basis of neuron doctrine—a theory that describes the human brain as a system composed of billions of discrete elements specialized in very precise operations (called neurons), that are connected (via a small electrochemical connections called synapses) with other elements or similar neurons forming small-world networks that respond specifically against certain stimuli families. 2. That one of the most efficient ways to study the functional connectivity, nowadays, is through functional magnetic resonance imaging (or fMRI) and the general linear model, which allows the construction of statistical parametric maps that locate the brain regions involved in a cognitive task or specific sensory perception. The statistical parametric maps were constructed based on the Fick principle and the blood-oxygen-leveldependent signal or BOLD, which predicts with a high degree of confidence the underlying neural activity [1]. 3. That constructing statistical parametric maps to reveal the global neuronal activity in an effective way involves a set of operations prior to parametric analysis, such as: pre-processing of functional images, modeling

Estudio de la conectividad funcional en cerebros de animales de experimentación a partir del análisis de imágenes de resonancia magnética mediante técnicas de clasificación no supervisada