Symptom networks in major depressive disorder and treatment response: special focus on TRD
Abstract Background Heterogeneous symptoms in major depression contribute to unsuccessful antidepressant treatment, termed treatment-resistant depression (TRD). Psychometric network modeling conceptualizes depression as interplay of symptoms with potential benefits for treatment; however, a knowledg...
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| Format: | Article |
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Cambridge University Press
2025-01-01
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| Series: | European Psychiatry |
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| Online Access: | https://www.cambridge.org/core/product/identifier/S092493382502454X/type/journal_article |
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| author | Alexander Kautzky Lucie Bartova Markus Dold Daniel Souery Stuart Montgomery Joseph Zohar Julien Mendlewicz Chiara Fabbri Alessandro Serretti Evgenii Tretiakov Dan Rujescu Tibor Harkany Siegfried Kasper |
| author_facet | Alexander Kautzky Lucie Bartova Markus Dold Daniel Souery Stuart Montgomery Joseph Zohar Julien Mendlewicz Chiara Fabbri Alessandro Serretti Evgenii Tretiakov Dan Rujescu Tibor Harkany Siegfried Kasper |
| author_sort | Alexander Kautzky |
| collection | DOAJ |
| description | Abstract
Background
Heterogeneous symptoms in major depression contribute to unsuccessful antidepressant treatment, termed treatment-resistant depression (TRD). Psychometric network modeling conceptualizes depression as interplay of symptoms with potential benefits for treatment; however, a knowledge gap exists regarding networks in TRD.
Methods
Symptoms from 1,385 depressed patients, assessed by the Montgomery-Åsberg-depression rating scale (MADRS) as part of the “TRD-III” cohort of the multinational research consortium “Group for the Studies of Resistant Depression,” were used for Gaussian graphical network modeling. Networks were estimated for two timepoints, pretreatment and posttreatment, after the establishment of outcomes response, non-response, and TRD. Applying the network-comparison test, edge weights, and symptom centrality was assessed by bootstrapping. Applying the network-comparison test, outcome groups were compared cross-sectionally and longitudinally regarding the networks’ global strength, invariance, and centrality.
Results
Pretreatment networks did not differ in global strength, but outcome groups showed distinct symptom connections. For both response and TRD, global strength was reduced posttreatment, leading to significant differences between each pair of networks posttreatment. Sadness, lassitude, inability-to-feel, and pessimistic thoughts ranked most centrally in unfavorable outcomes, while reduced-appetite and suicidal thoughts were more densely connected in response. Connections between central symptoms increased in strength following unsuccessful treatment, particularly regarding links involving pessimistic thoughts in TRD.
Conclusion
Treatment reduced global network strength across outcome groups. However, distinct symptom networks were found in patients showing response to treatment, non-response, and TRD. More easily targetable symptoms such as reduced-appetite were central to networks in patients with response, while pessimistic thoughts may be a key symptom upholding disease burden in TRD.
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| format | Article |
| id | doaj-art-af9a7d16f67f4fa6bf248be8de1e7c54 |
| institution | Matheson Library |
| issn | 0924-9338 1778-3585 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Cambridge University Press |
| record_format | Article |
| series | European Psychiatry |
| spelling | doaj-art-af9a7d16f67f4fa6bf248be8de1e7c542025-07-01T10:06:35ZengCambridge University PressEuropean Psychiatry0924-93381778-35852025-01-016810.1192/j.eurpsy.2025.2454Symptom networks in major depressive disorder and treatment response: special focus on TRDAlexander Kautzky0https://orcid.org/0000-0001-9251-8285Lucie Bartova1https://orcid.org/0000-0002-1769-8025Markus Dold2https://orcid.org/0000-0001-8914-2192Daniel Souery3Stuart Montgomery4https://orcid.org/0000-0002-5983-0063Joseph Zohar5https://orcid.org/0000-0002-6925-9104Julien Mendlewicz6https://orcid.org/0000-0002-6131-2732Chiara Fabbri7Alessandro Serretti8https://orcid.org/0000-0003-4363-3759Evgenii Tretiakov9https://orcid.org/0000-0001-5920-2190Dan Rujescu10https://orcid.org/0000-0002-1432-313XTibor Harkany11https://orcid.org/0000-0002-6637-5900Siegfried Kasper12https://orcid.org/0000-0001-8278-191XDepartment of Psychiatry and Psychotherapy, Clinical Division of General Psychiatry, Medical University of Vienna, Vienna, Austria Department of Clinical Neurosciences, Division of Insurance Medicine, Stockholm, Sweden Comprehensive Center for Clinical Neurosciences and Mental Health, https://ror.org/05n3x4p02 Medical University of Vienna , Vienna, AustriaDepartment of Psychiatry and Psychotherapy, Clinical Division of General Psychiatry, Medical University of Vienna, Vienna, Austria Comprehensive Center for Clinical Neurosciences and Mental Health, https://ror.org/05n3x4p02 Medical University of Vienna , Vienna, AustriaDepartment of Psychiatry and Psychotherapy, Clinical Division of General Psychiatry, Medical University of Vienna, Vienna, Austria Comprehensive Center for Clinical Neurosciences and Mental Health, https://ror.org/05n3x4p02 Medical University of Vienna , Vienna, AustriaLaboratoire de Psychologie Medicale, Université Libre de Bruxelles and Psy Pluriel Centre Europèen de Psychologie Medicale, Brussels, BelgiumImperial College, University of London, London, UKPsychiatric Division, Chaim Sheba Medical Center, Tel Hashomer, IsraelSchool of Medicine, Free University of Brussels, Brussels, BelgiumDepartment of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, ItalyDepartment of Medicine and Surgery, Kore University of Enna, Enna, ItalyDepartment of Molecular Neurosciences, Center for Brain Research, Medical University of Vienna, Vienna, AustriaDepartment of Psychiatry and Psychotherapy, Clinical Division of General Psychiatry, Medical University of Vienna, Vienna, Austria Comprehensive Center for Clinical Neurosciences and Mental Health, https://ror.org/05n3x4p02 Medical University of Vienna , Vienna, AustriaDepartment of Molecular Neurosciences, Center for Brain Research, Medical University of Vienna, Vienna, AustriaDepartment of Psychiatry and Psychotherapy, Clinical Division of General Psychiatry, Medical University of Vienna, Vienna, Austria Comprehensive Center for Clinical Neurosciences and Mental Health, https://ror.org/05n3x4p02 Medical University of Vienna , Vienna, Austria Department of Molecular Neurosciences, Center for Brain Research, Medical University of Vienna, Vienna, AustriaAbstract Background Heterogeneous symptoms in major depression contribute to unsuccessful antidepressant treatment, termed treatment-resistant depression (TRD). Psychometric network modeling conceptualizes depression as interplay of symptoms with potential benefits for treatment; however, a knowledge gap exists regarding networks in TRD. Methods Symptoms from 1,385 depressed patients, assessed by the Montgomery-Åsberg-depression rating scale (MADRS) as part of the “TRD-III” cohort of the multinational research consortium “Group for the Studies of Resistant Depression,” were used for Gaussian graphical network modeling. Networks were estimated for two timepoints, pretreatment and posttreatment, after the establishment of outcomes response, non-response, and TRD. Applying the network-comparison test, edge weights, and symptom centrality was assessed by bootstrapping. Applying the network-comparison test, outcome groups were compared cross-sectionally and longitudinally regarding the networks’ global strength, invariance, and centrality. Results Pretreatment networks did not differ in global strength, but outcome groups showed distinct symptom connections. For both response and TRD, global strength was reduced posttreatment, leading to significant differences between each pair of networks posttreatment. Sadness, lassitude, inability-to-feel, and pessimistic thoughts ranked most centrally in unfavorable outcomes, while reduced-appetite and suicidal thoughts were more densely connected in response. Connections between central symptoms increased in strength following unsuccessful treatment, particularly regarding links involving pessimistic thoughts in TRD. Conclusion Treatment reduced global network strength across outcome groups. However, distinct symptom networks were found in patients showing response to treatment, non-response, and TRD. More easily targetable symptoms such as reduced-appetite were central to networks in patients with response, while pessimistic thoughts may be a key symptom upholding disease burden in TRD. https://www.cambridge.org/core/product/identifier/S092493382502454X/type/journal_articlemajor depressive disordertreatment-resistant depressionantidepressantsnetwork analysissymptom networks |
| spellingShingle | Alexander Kautzky Lucie Bartova Markus Dold Daniel Souery Stuart Montgomery Joseph Zohar Julien Mendlewicz Chiara Fabbri Alessandro Serretti Evgenii Tretiakov Dan Rujescu Tibor Harkany Siegfried Kasper Symptom networks in major depressive disorder and treatment response: special focus on TRD European Psychiatry major depressive disorder treatment-resistant depression antidepressants network analysis symptom networks |
| title | Symptom networks in major depressive disorder and treatment response: special focus on TRD |
| title_full | Symptom networks in major depressive disorder and treatment response: special focus on TRD |
| title_fullStr | Symptom networks in major depressive disorder and treatment response: special focus on TRD |
| title_full_unstemmed | Symptom networks in major depressive disorder and treatment response: special focus on TRD |
| title_short | Symptom networks in major depressive disorder and treatment response: special focus on TRD |
| title_sort | symptom networks in major depressive disorder and treatment response special focus on trd |
| topic | major depressive disorder treatment-resistant depression antidepressants network analysis symptom networks |
| url | https://www.cambridge.org/core/product/identifier/S092493382502454X/type/journal_article |
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