| Title: |
Network model of mental disorders: application and interest in post-stroke depression ; Modèle en réseau et troubles mentaux : application et intérêts dans la dépression post-AVC |
| Authors: |
Vansimayes, Camille; Zuber, Mathieu; Pitrat, Benjamin; Farhat, Wassim; Join-Lambert, Claire; Tamazyan, Ruben; Bungener, Catherine |
| Contributors: |
Département Management, Marketing et Stratégie (IMT-BS - MMS); Télécom Ecole de Management (TEM)-Institut Mines-Télécom Business School (IMT-BS); Institut Mines-Télécom Paris (IMT)-Institut Mines-Télécom Paris (IMT); Laboratoire en Innovation, Technologies, Economie et Management (EA 7363) (LITEM); Université d'Évry-Val-d'Essonne (UEVE)-Université Paris-Saclay-Institut Mines-Télécom Business School (IMT-BS); Laboratoire de Psychopathologie et Processus de Santé (LPPS (URP_4057)); Université Paris Cité (UPCité); Centre hospitalier Saint-Joseph Paris; Groupe Hospitalier Paris Saint-Joseph (hpsj); Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP); Service psychiatrique de l'enfant et de l'adolescent CHU Hôpital Robert Debré; Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpital Robert Debré; Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP); LITEM-NPR |
| Source: |
ISSN: 0013-7006 ; L'Encéphale ; https://hal.science/hal-03145132 ; L'Encéphale, 2021, 47 (4), pp.334-340. ⟨10.1016/j.encep.2020.08.007⟩. |
| Publisher Information: |
CCSD; Elsevier Masson |
| Publication Year: |
2021 |
| Subject Terms: |
Ecological Momentary Assessment; Sustainable Development Goals; Stroke; Depression; Network analysis; Analyse en réseau; Évaluation écologique instantanée; Dépression; Accident vasculaire cérébral; Objectifs de Développement Durable; [SDV.MHEP.PSM]Life Sciences [q-bio]/Human health and pathology/Psychiatrics and mental health |
| Description: |
International audience ; 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 < 0.000). Pessimism and diminished concentration ability compose the second network, and the results show that pessimism predicts later pessimism (β = 0.215, P = 0.012) and later diminished concentration ability (β = 0.178, P = 0.045). On the one hand, anhedonia thus plays an important role in the initial and progressive activation of the other symptoms of its ... |
| Document Type: |
article in journal/newspaper |
| Language: |
French |
| DOI: |
10.1016/j.encep.2020.08.007 |
| Availability: |
https://hal.science/hal-03145132; https://hal.science/hal-03145132v1/document; https://hal.science/hal-03145132v1/file/S0013700620302359.pdf; https://doi.org/10.1016/j.encep.2020.08.007 |
| Rights: |
https://creativecommons.org/licenses/by-nc/4.0/ ; info:eu-repo/semantics/OpenAccess |
| Accession Number: |
edsbas.F801C184 |
| Database: |
BASE |