ABSTRACT Synthesis Santiago Ramón y Cajal showed that the brain and nervous system consist of cells, like all other living tissues. These cells were called neurons and connections between them, the synapses, are essential to its operation and communication. Everything that happens inside the brain can be described as a network of biochemical reactions and electrical currents between neurons. Since the nineteenth century, the observation and study of diseases or syndromes caused by brain injuries played a major role in the development of neuroscience. For the first time it was possible to establish some correlations between certain areas of the brain and certain higher mental functions such as language or memory. However, this localized model has been exceeded time ago. Today it is assumed that cognitive functions are not located in a specific brain area, but based on the operation of complex functional systems. Thanks to the functional neuroimaging techniques, we can relate a specific task with a certain pattern of brain activation, ie, a set of coactivated areas. One of the greatest challenges in neuroscience today is to consolidate the knowledge of the patterns of brain activity. On the other hand, many neurologic and psychiatric disorders are not due to a focal lesion or alteration of a single brain area. Different disorders such as schizophrenia or autism are understood today as complex disorders of neural connectivity. The study of neural connectivity ‘in vivo’ is one of the main objectives of neuroimaging techniques. The main goal in the near future is that these techniques, which focus on the study of connectivity between different brain areas, can improve the diagnosis directly and indirectly, various neurological and mental treatments. This thesis describes, first, the newly developed techniques that are applied to magnetic resonance imaging to study brain connectivity. In order to study the connectivity, this thesis proposes the development of a methodological framework that encompasses a set of techniques that allow characterization of possible disorders in the functional connectivity. The validation of this methodology has been conducted analyzing images of schizophrenic patients with recurrent auditory hallucinations as primary phenotype. The development of this thesis concludes with a set of results that help the clinician to understand the mental processes that lead to the appearance of this pathology. The results point to an anomalous process of neural connectivity in these patients, as already suggested in the literature. The results of this thesis suggest that some diseases are caused by disfunctions of functional connectivity. This methodological framework is proposed for application to any mental disorder caused by connectivity problems beyond those studied in this work and this opens up many possibilities for application in the broad context of functional neuroimaging. Objectives The main objective of this thesis is to study brain connectivity in both the group of schizophrenic patients with auditory hallucinations and in the control group. Elements of the methodology In order to detect functional connectivity networks in both groups when subjected to auditory stimuli with emotional content, the element of methodology that was used is the Independent Component Analysis (ICA). Subsequently it has been confirmed that cortical synchronization patterns are different between groups. In order to prove this, a detailed study of long-range neuronal synchronization of the signals detected with ICA has been performed. To achieve this goal, a correlation analysis of ICA time series was carried out. Finally, in order to know the direct influence of one brain region in physiological activity that is recorded in other regions, techniques of effective connectivity analysis have been employed, in particular multivariate autoregressive modeling analysis of time series. Achieved results The ICA technique has enabled us to identify functional connectivity networks involved in the detection of auditory stimuli, both emotional and neutral. The causal relationships between the networks detected in both groups are clearly different. In the group of schizophrenics with hallucinations a greater causal disorganization was observed and interactions were more chaotic. Moreover, the methodology provides an important tool for the detailed study of schizophrenia and other diseases associated with functional connectivity problems.