Browsing by Subject "idiografinen verkostoanalyysi"

This review will address how individual symptomology may be used to model a person’s psychological disorder. The discussed disorders are depression, generalised anxiety disorder and social anxiety disorder, which will be discussed separately and as simultaneous compositions of symptoms. Traditionally mental health disorders have been elucidated by dividing them into separate categories. In 2010s network analysis has risen as an alternative method, where the relationship between symptoms is examined in time and visualised as a network. The network analysis viewpoint suggests that psychological symptoms cause each other and that activation spreads through the network via the interactions between those symptoms. Two disorders that occur simultaneously are thought to constitute separate clusters of symptoms, which are united by bridge symptoms that mediate activation from one cluster to another. This review will focus particularly on idiographic, personalised networks. These personalised models aspire to take into consideration all the variation in a person’s symptomology. These models are formulated from occurring symptoms in several points in time, so it is possible to speculate, although not prove, the direction of an interaction between symptoms. The symptoms that affect several other symptoms strongly spread activation all around them when activated, whereas those symptoms that receive several effects from other symptoms particularly often associate with other symptoms. The application of network models to clinical practice is based on this idea. To demonstrate idiographic modelling, two different cases will be considered. In both examples the person is afflicted with both depression and an anxiety disorder, and for each person an idiographic network model is formed. The observation can be made from these cases that the symptoms described as most central in categorical models are not equal to the symptoms in these networks that most maintain its activation. The idiographic networks of the example cases also do not indicate bridge symptoms, but rather the symptoms form a single cluster and maintain each other across disorder category boundaries. The conclusions made from personalised network analyses cannot be generalised to the population or even the same person in other points in time. In addition, conclusions cannot be made about the validity of DSM categories or the concept of comorbidity. Nevertheless, they open windows to the individual variation and complexity of symptomology, which categorical diagnostic classifications cannot consider. From these observations different study questions and designs can be formulated and novel clinical interventions developed.