Modèle en réseau et troubles mentaux : application et intérêts dans la dépression post-AVC

In contrast to the classic models in psychopathology, the network model considers that the temporal interactions between symptoms are the causes of their occurrence. This model could also be particularly suitable for understanding the processes involved in post-stroke depression. The aim of this paper is to perform a network analysis in order to describe the temporal dynamic of the links existing between depression symptoms during the acute phase after stroke. Twenty-five patients (64% male, mean age 58.1±14.9 years old) hospitalized for a minor stroke (no neurocognitive or motor impairment) were involved in an Ecological Momentary Assessment methodology-based study. They used a smartphone application in order to complete four brief questionnaires each day during the week after hospital discharge. The questionnaire included 7-point Likert scales to measure the severity of the following depressive symptoms: sadness, anhedonia, fatigue, diminished concentration ability, negative thoughts on oneself, pessimism. We used Multilevel Vector Autoregressive analysis to describe the temporal links between those symptoms. We used the software R 3.6.0 with the mlVAR package. The p-value was set at .05. The results show two independent symptoms networks. The first one involves the anhedonia, fatigue, negative thoughts on oneself and sadness. It shows that: anhedonia predicts the activation of later fatigue (β=0.135, P=0.037) and later negative thoughts (β=0.152, P=0.019); negative thoughts predict later negative thoughts (β=0.143, P=0.028) and later sadness (β=0.171, P=0.021); fatigue predicts later fatigue (β=0.261, P