Prognosis and risk models of depression are built from analytical components of the rs-fMRI activity in patients

Field: Neuroscience (Life Sciences)

Depression is the most common type of emotional disorder among the world's population. It is characterized by negative sentiments, the feeling of guilt, low self-esteem, a loss of interest, a high-level process of reflection, and in general by a decrease of the individual's psychic functions. The new non-invasive neuroimaging techniques have increased the ability of studying possible variations in patients' brain activity. In concrete, functional magnetic resonance imaging (fMRI) has become the most important method to study human brain functions in the past two decades, being non-invasive and with no risk for human health. Biswal and others in 1995, and later Lowe and his colleagues in 1998, showed the existence of continous spontaneous activity in the brain's activity at rest. These fluctuations have also been verified in other species like macaques (Vicent JL et l, 2007). Studying the brain's activity at rest (rs-fMRI) by means of neuroimaging techniques has become a powerful tool for the investigation of diseases, since it has demonstrated a better signal to noise ratio concerning task-based approaches on one hand, and since certain patients could have difficulties to perform cognitive, language or motor tasks on the other hand. However, it seems that because of certain inconsistencies found among studies, rs-fMRI techniques would not reach a practical clinical use of a personalised monitoring, prognosis or pre-diagnosis in individuals with depression. In this respect, even if Grecius MD exposed in 2008 the benefits of rs-fMRI techniques, he also commented that the signal to noise ratio remains to be improved to be used in a clinical routine. Grecius suggested to lenghthen the time of the temporal series at rest, and to improve analysis procedures. The aim of this thesis is to elucidate if the existence of certain factors or components in the functional signal at rest could be used at the clinical health level. In order to achieve this, we use rs-fMRI data on two sets of samples. In the first set of samples, composed by 27 patients with major depression (MDD) and 27 individuals as controls, we design descriptors that describe both static and dynamic aspects of the resting-state signal for the construction of prediction models. Conversely, with the second type of samples (48 twins), we analyse the relation between possible genetic and environmental factors which could explain certain depressive components in the activity in resting condition. On the one hand, the results show that depression could simultaneously affect different brain networks located in the prefrontal-limbic area, in the DMN, and between the frontoparietal lobes. Besides, it seems that the alterations in these networks could be explained by both static and dynamic aspects existing in the rest signal. Finally, we achieve the creation of models that would partially explain certain clinical phenomenons present in depressive patients by means of global descriptors in these networks. These network descriptors could be used for personalised monitoring in patients with major depression. On the other hand, using the twin sample, we achieve the construction of a risk model from the amygdalar activity which evaluates the risk or predisposition of an individual from analytical components in the activity at rest. The cerebellum of this sample was also analysed, and the environment was found to be possibly modifying the activity in these regions