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At the Intersection of Network Science and Psychology

Tuesday, February 19, 2019, 2:00 pm – 3:00 pm

ABSTRACT | Srebrenka Letina, a third-year Ph.D. student at the Department of Network and Data Science, will present the work on her Ph.D. dissertation that has an overarching goal to investigate how network science theory and methods can provide new insights in psychological sciences. This goal is decomposed in three narrower research lines that focus on the following questions: i) How can we better understand the association between the individual psychological attributes and the structure of her/his social network?; ii) How can network theory and methods help us gain an understanding of the relationship between well-studied psychological concepts that describe individual differences in personality, interests, values, and intelligence?; and iii) How can network science contribute to the understanding of co-occurrence of mental disorders? The last question will be discussed in more details, starting with the characteristics of the classification system used currently in the diagnosis of mental disorders and the possible causes of high comorbidity, as well as the potential importance of studying the comorbidity patterns. Although network science methods and theory have been widely used in studying comorbidity of all medical illnesses, here the focus will be explicitly on the diagnosis of mental disorders that are conceptually and measurement-wise different from somatic disorders. The network framework enables tackling some long-standing questions about both the nature and the classification of mental disorders. Specifically: what is the structure of comorbidity of mental disorders, how can the hierarchical structure be uncovered, which disorders are central in the comorbidity network, how can the network analysis of comorbidity help the understanding of differences in the co-occurrence patterns related with basic sociodemographic attributes (e.g. gender and age), how can potential problems and therefore suggestions for improvement of the classification system be identified via network analysis.

BIO | Srebrenka's general research interest lies at the intersection of network science, computational social sciences and psychological sciences (social, cognitive, organizational psychology, community psychology, etc.) and their joint applications in interdisciplinary research areas.