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Predictors of functional outcome in patients with major depression and bipolar disorder: A dynamic network approach to identify distinct patterns of interacting symptoms

The purpose of this study is to use a dynamic network approach as an innovative way to identify distinct patterns of interacting symptoms in patients with Major Depressive Disorder (MDD) and patients with Bipolar Type I Disorder (BD). More precisely, the hypothesis will be testing that the phenotype...

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Autores principales: Platania, Giuseppe Alessio, Savia Guerrera, Claudia, Sarti, Pierfrancesco, Varrasi, Simone, Pirrone, Concetta, Popovic, Dina, Ventimiglia, Andrea, De Vivo, Simona, Cantarella, Rita Anna, Tascedda, Fabio, Drago, Filippo, Di Nuovo, Santo, Colliva, Chiara, Caraci, Filippo, Castellano, Sabrina, Blom, Johanna M. C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9931103/
https://www.ncbi.nlm.nih.gov/pubmed/36791083
http://dx.doi.org/10.1371/journal.pone.0276822
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author Platania, Giuseppe Alessio
Savia Guerrera, Claudia
Sarti, Pierfrancesco
Varrasi, Simone
Pirrone, Concetta
Popovic, Dina
Ventimiglia, Andrea
De Vivo, Simona
Cantarella, Rita Anna
Tascedda, Fabio
Drago, Filippo
Di Nuovo, Santo
Colliva, Chiara
Caraci, Filippo
Castellano, Sabrina
Blom, Johanna M. C.
author_facet Platania, Giuseppe Alessio
Savia Guerrera, Claudia
Sarti, Pierfrancesco
Varrasi, Simone
Pirrone, Concetta
Popovic, Dina
Ventimiglia, Andrea
De Vivo, Simona
Cantarella, Rita Anna
Tascedda, Fabio
Drago, Filippo
Di Nuovo, Santo
Colliva, Chiara
Caraci, Filippo
Castellano, Sabrina
Blom, Johanna M. C.
author_sort Platania, Giuseppe Alessio
collection PubMed
description The purpose of this study is to use a dynamic network approach as an innovative way to identify distinct patterns of interacting symptoms in patients with Major Depressive Disorder (MDD) and patients with Bipolar Type I Disorder (BD). More precisely, the hypothesis will be testing that the phenotype of patients is driven by disease specific connectivity and interdependencies among various domains of functioning even in the presence of underlying common mechanisms. In a prospective observational cohort study, hundred-forty-three patients were recruited at the Psychiatric Clinic “Villa dei Gerani” (Catania, Italy), 87 patients with MDD and 56 with BD with a depressive episode. Two nested sub-groups were treated for a twelve-week period, which allowed us to explore differences in the pattern of symptom distribution (central vs. peripheral) and their connectedness (strong vs weak) before (T0) and after (T1) treatment. All patients underwent a complete neuropsychological evaluation at baseline (T0) and at T1. A network structure was computed for MDD and BD patients at T0 and T1 from a covariance matrix of 17 items belonging to three domains–neurocognitive, psychosocial, and mood-related (affective) to identify what symptoms were driving the networks. Clinically relevant differences were observed between MDD and BD, at T0 and after 12 weeks of pharmacological treatment. At time T0, MDD patients displayed an affective domain strongly connected with the nodes of psychosocial functioning, while direct connectivity of the affective domain with the neurocognitive cluster was absent. The network of patients with BD, in contrast, revealed a cluster of highly interconnected psychosocial nodes but was guided by neurocognitive functions. The nodes related to the affective domain in MDD are less connected and placed in the periphery of the networks, whereas in BD they are more connected with psychosocial and neurocognitive nodes. Noteworthy is that, from T0 to T1 the “Betweenness” centrality measure was lower in both disorders which means that fewer “shortest paths” between nodes pass through the affective domain. Moreover, fewer edges were connected directly with the nodes in this domain. In MDD patients, pharmacological treatment primarily affected executive functions which seem to improve with treatment. In contrast, in patients with BD, treatment resulted in improvement of overall connectivity and centrality of the affective domain, which seems then to affect and direct the overall network. Though different network structures were observed for MDD and BD patients, data suggest that treatment should include tailored cognitive therapy, because improvement in this central domain appeared to be fundamental for better outcomes in other domains. In sum, the advantage of network analysis is that it helps to predict the trajectory of future phenotype related disease manifestations. In turn, this allows new insights in how to balance therapeutic interventions, involving different fields of function and combining pharmacological and non-pharmacological treatment modalities.
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spelling pubmed-99311032023-02-16 Predictors of functional outcome in patients with major depression and bipolar disorder: A dynamic network approach to identify distinct patterns of interacting symptoms Platania, Giuseppe Alessio Savia Guerrera, Claudia Sarti, Pierfrancesco Varrasi, Simone Pirrone, Concetta Popovic, Dina Ventimiglia, Andrea De Vivo, Simona Cantarella, Rita Anna Tascedda, Fabio Drago, Filippo Di Nuovo, Santo Colliva, Chiara Caraci, Filippo Castellano, Sabrina Blom, Johanna M. C. PLoS One Research Article The purpose of this study is to use a dynamic network approach as an innovative way to identify distinct patterns of interacting symptoms in patients with Major Depressive Disorder (MDD) and patients with Bipolar Type I Disorder (BD). More precisely, the hypothesis will be testing that the phenotype of patients is driven by disease specific connectivity and interdependencies among various domains of functioning even in the presence of underlying common mechanisms. In a prospective observational cohort study, hundred-forty-three patients were recruited at the Psychiatric Clinic “Villa dei Gerani” (Catania, Italy), 87 patients with MDD and 56 with BD with a depressive episode. Two nested sub-groups were treated for a twelve-week period, which allowed us to explore differences in the pattern of symptom distribution (central vs. peripheral) and their connectedness (strong vs weak) before (T0) and after (T1) treatment. All patients underwent a complete neuropsychological evaluation at baseline (T0) and at T1. A network structure was computed for MDD and BD patients at T0 and T1 from a covariance matrix of 17 items belonging to three domains–neurocognitive, psychosocial, and mood-related (affective) to identify what symptoms were driving the networks. Clinically relevant differences were observed between MDD and BD, at T0 and after 12 weeks of pharmacological treatment. At time T0, MDD patients displayed an affective domain strongly connected with the nodes of psychosocial functioning, while direct connectivity of the affective domain with the neurocognitive cluster was absent. The network of patients with BD, in contrast, revealed a cluster of highly interconnected psychosocial nodes but was guided by neurocognitive functions. The nodes related to the affective domain in MDD are less connected and placed in the periphery of the networks, whereas in BD they are more connected with psychosocial and neurocognitive nodes. Noteworthy is that, from T0 to T1 the “Betweenness” centrality measure was lower in both disorders which means that fewer “shortest paths” between nodes pass through the affective domain. Moreover, fewer edges were connected directly with the nodes in this domain. In MDD patients, pharmacological treatment primarily affected executive functions which seem to improve with treatment. In contrast, in patients with BD, treatment resulted in improvement of overall connectivity and centrality of the affective domain, which seems then to affect and direct the overall network. Though different network structures were observed for MDD and BD patients, data suggest that treatment should include tailored cognitive therapy, because improvement in this central domain appeared to be fundamental for better outcomes in other domains. In sum, the advantage of network analysis is that it helps to predict the trajectory of future phenotype related disease manifestations. In turn, this allows new insights in how to balance therapeutic interventions, involving different fields of function and combining pharmacological and non-pharmacological treatment modalities. Public Library of Science 2023-02-15 /pmc/articles/PMC9931103/ /pubmed/36791083 http://dx.doi.org/10.1371/journal.pone.0276822 Text en © 2023 Platania et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Platania, Giuseppe Alessio
Savia Guerrera, Claudia
Sarti, Pierfrancesco
Varrasi, Simone
Pirrone, Concetta
Popovic, Dina
Ventimiglia, Andrea
De Vivo, Simona
Cantarella, Rita Anna
Tascedda, Fabio
Drago, Filippo
Di Nuovo, Santo
Colliva, Chiara
Caraci, Filippo
Castellano, Sabrina
Blom, Johanna M. C.
Predictors of functional outcome in patients with major depression and bipolar disorder: A dynamic network approach to identify distinct patterns of interacting symptoms
title Predictors of functional outcome in patients with major depression and bipolar disorder: A dynamic network approach to identify distinct patterns of interacting symptoms
title_full Predictors of functional outcome in patients with major depression and bipolar disorder: A dynamic network approach to identify distinct patterns of interacting symptoms
title_fullStr Predictors of functional outcome in patients with major depression and bipolar disorder: A dynamic network approach to identify distinct patterns of interacting symptoms
title_full_unstemmed Predictors of functional outcome in patients with major depression and bipolar disorder: A dynamic network approach to identify distinct patterns of interacting symptoms
title_short Predictors of functional outcome in patients with major depression and bipolar disorder: A dynamic network approach to identify distinct patterns of interacting symptoms
title_sort predictors of functional outcome in patients with major depression and bipolar disorder: a dynamic network approach to identify distinct patterns of interacting symptoms
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9931103/
https://www.ncbi.nlm.nih.gov/pubmed/36791083
http://dx.doi.org/10.1371/journal.pone.0276822
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