NATURALISTIC COURSE OF MAJOR DEPRESSIVE DISORDER PREDICTED BY CLINICAL AND STRUCTURAL NEUROIMAGING DATA: A 5-YEAR FOLLOW-UP: Research Article: Prediction of Long-Term Depressive Outcomes
Open accessAbstract
BACKGROUND: Despite its high recurrence rate, major depression disorder (MDD) still lacks neurobiological markers to optimize treatment selection. The aim of this study was to examine the prognostic potential of clinical and structural magnetic resonance imaging (sMRI) in the long-term MDD clinical outcomes (COs). METHODS: Forty-nine MDD patients were grouped into one of four different CO categories according to their trajectory: recovery, partial remission, remission recurrence, and chronic depression. Regression models including baseline demographic, clinical, and sMRI data were used for predicting patients' COs and symptom severity 5 years later. RESULTS: The model including only clinical data explained 32.4% of the variance in COs and 55% in HDRS, whereas the model combining clinical and sMRI data increased up to 52/68%, respectively. A bigger volume of right anterior cingulate gyrus was the variable that best predicted COs. CONCLUSIONS: The findings suggest that the addition of sMRI brain data to clinical information in depressive patients can significantly improve the prediction of their COs. The dorsal part of the right anterior cingulate gyrus may act as a potential biomarker of long-term clinical trajectories.
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In a 5-year naturalistic prospective study, gray matter volume in the right dorsal anterior cingulate cortex and right inferior frontal gyrus contributed to disease trajectory predictions; larger volume in these areas predicted better clinical outcome and lower depressive symptoms over 5 years.
“study, GMV in the right dACC and right inferior frontal gyrus contributed to disease trajectory predictions; specifically, larger volume in these areas predicted better clinical outcome and lower depressive symptoms over 5 years [83].”
From Prefrontal cortex and depression (2022) · cites this paper as [83]
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Experimental — accuracy not yet measuredSmaller hippocampal and medial prefrontal cortex volumes are consistently linked to non-remission or treatment resistance in major depressive disorder.
“and non-remission/treatment resistance (28,31–38).”
From The Impact of Stress and Major Depressive Disorder on Hippocampal and Medial Prefrontal Cortex Morphology (2019) · cites this paper as [37]
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- Authors
- Serra-Blasco, M., de Diego-Adeliño, J., Vives-Gilabert, Y., Trujols, J., Puigdemont, D., Carceller-Sindreu, M., Pérez, V., Álvarez, E., & Portella, M. J.
- Affiliations
- Universitat Autònoma de Barcelona (ES)
- Department of Psychiatry, Hospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau (IIB Sant Pau); Universitat Autònoma de Barcelona (UAB); Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM); Barcelona Catalonia Spain
- INNDACYT; Barcelona Spain
- Venue
- Depression and Anxiety
- Publisher
- Wiley
- Published
- 2016-05-09
- Volume
- 33
- Issue
- 11
- Pages
- 1055-1064
- ISSN
- 1091-4269 (ISSN-L)
- OpenAlex
- W2359286288
- Cited by
- 21
- References
- 40
- Citations by year
- 2026: 3 · 2023: 1 · 2022: 2 · 2021: 3 · 2020: 4 · 2019: 2 · 2018: 5 · 2017: 1
- FWCI
- 2.16
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Depression and Anxiety (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Fondo de Investigación Sanitaria (PI 10/00372, PI 13/01057)
- Agència de Gestió d'Ajuts Universitaris i de Recerca of the Generalitat de Catalunya (FI-DGR-2012)
- Instituto Carlos III and European Regional Development Fund (ERDF) (CP10-00393)
- Topics
- Health Sciences › Medicine › Pharmacology › Treatment of Major Depression
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Anxiety, Depression, Psychometrics, Treatment, Cognitive Processes
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Keywords
- major depressive disorder, long-term clinical outcomes, prognostic factors, structural MRI, chronic depression, clinical trajectories, symptom severity prediction, neuroimaging
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