Transtorno depressivo maior como um sistema complexo e dinâmico: um estudo com análise de redes

Abstract

Major Depressive Disorder (MDD) is a highly prevalent psychiatric condition, affecting millions of people worldwide. The complexity of this disorder is evident both in the diversity of its symptoms and in the difficulty of understanding its pathogenesis. Currently, the DSM and ICD are widely used manuals for diagnosing mental disorders, including MDD, based on a biomedical model that is constantly being updated. However, the literature points to substantial limitations of these manuals, such as the lack of consideration of the causes and etiologies of diagnoses, inconsistencies in the conceptualization of certain conditions, low inter-rater reliability, significant comorbidities, and the inadequacy of a categorical model to describe the severity of mental health problems. The general objective of the dissertation is to investigate the trajectory of MDD through network analysis. To this end, the present work is organized into three studies/parts, with complementary themes but independent structures. Study 1 presents an introductory discussion of the problems associated with the current psychopathological model, exploring existing possibilities for its replacement in the field of depression. This study contributed to the justification for the choice of the theoretical and methodological paradigm of the other studies, which are based on the use of Network Analysis (NA) as a methodological framework to consider psychopathology as a complex and dynamic system. In Study 2, a bibliometric review was conducted, followed by a systematic literature review on the application of NA to investigate depression and its correlates, using the PRISMA protocol (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) as the basis for the selection and analysis of articles. Study 3 consists of an empirical single-subject study with an individual diagnosed with MDD undergoing psychotherapeutic intervention, aiming to observe whether the hypotheses proposed by Cramer and colleagues (2016)—that depressive individuals exhibit more interconnected symptoms in a network than non-depressive individuals—are confirmed when applied to a case followed over time. For this purpose, a Bayesian network analysis was performed. When taken together, the results of the developed studies indicate that: 1) The current literature demonstrates a need to find new ways to address the existing problems in the prevailing psychopathology model, and network analysis emerges as a possibility to solve some of these issues; 2) The bibliometric review revealed an increase in the number of studies employing network analysis focused on depression worldwide, with a total of 391 studies conducted since the 2000s. In Brazil, however, only 9 studies were found, with promising but still incipient results; 3) The hypothesis of Cramer and colleagues (2016) was corroborated by the case study data. Although it is not possible to generalize this result, it points to a potential path for more complex analyses focused on single subjects, aiming for a more specific and individualized understanding.