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Bonnet U. Ten years of maintenance treatment of severe melancholic depression in an adult woman including discontinuation experiences. FORTSCHRITTE DER NEUROLOGIE-PSYCHIATRIE 2024. [PMID: 38901434 DOI: 10.1055/a-2332-6107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/22/2024]
Abstract
BACKGROUND There are only few publications on long-term treatments for major depressive disorder (MDD) lasting 5 years or longer. Most clinical controlled trials lasted no longer than 2 years and some recent studies suggested an advantage of cognitive behavioral therapy (CBT) over antidepressants in relapse prevention of MDD. METHODS Exclusively outpatient "real world" treatment of severe melancholia, prospectively documented over 10 years with different serial treatment strategies, discontinuation phenomena and complications. METHODS Compared to CBT, agomelatine, mirtazapine, bupropion and high-dose milnacipran, high-dose venlafaxine (extended-release form, XR) was effective, even sustainably. Asymptomatic premature ventricular contractions (PVCs) were found at the beginning of the treatment of the MDD, which initially led to the discontinuation of high-dose venlafaxine (300 mg daily). Even the various treatment strategies mentioned above were unable to compensate for or prevent the subsequent severe deterioration in MDD (2 rebounds, 1 recurrence). Only the renewed use of high-dose venlafaxine was successful. PVC no longer occurred and the treatment was also well tolerated over the years, with venlafaxine serum levels at times exceeding 5 times the recommended upper therapeutic reference level (known bupropion-venlafaxine interaction, otherwise 2.5 to 3-fold increase with high-dose venlafaxine alone). During dose reduction or after gradual discontinuation of high-dose venlafaxine, rather mild withdrawal symptoms occurred, but as described above, also two severe rebounds and one severe recurrence happened. DISCUSSION This long-term observation supports critical reflections on the discontinuation of successful long-term treatment with antidepressants in severe MDD, even if it should be under "the protection" of CBT. The PVC seemed to be more related to the duration of the severe major depressive episode than to the venlafaxine treatment itself. A particular prospective observation of this longitudinal case study is that relapses (in the sense of rebounds) during or after previous venlafaxine tapering seemed to herald the recurrence after complete recovery. Remarkably, neither relapses nor recurrence could be prevented by CBT. CONCLUSION In this case, high-dose venlafaxine has a particular relapse-preventive (and "recurrence-preventive") effect with good long-term tolerability.
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Affiliation(s)
- Udo Bonnet
- Department of Mental Health, Evangelisches Krankenhaus Castrop-Rauxel, Academic Teaching Hospital of the University of Duisburg-Essen, D-44577 Castrop-Rauxel, Germany
- LVR-Hospital Essen, Department of Psychiatry and Psychotherapy, Faculty of Medicine, University of Duisburg-Essen, D-45147 Essen, Germany
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Borgers T, Enneking V, Klug M, Garbe J, Meinert H, Wulle M, König P, Zwiky E, Herrmann R, Selle J, Dohm K, Kraus A, Grotegerd D, Repple J, Opel N, Leehr EJ, Gruber M, Goltermann J, Meinert S, Bauer J, Heindel W, Kavakbasi E, Baune BT, Dannlowski U, Redlich R. Long-term effects of electroconvulsive therapy on brain structure in major depression. Psychol Med 2024; 54:940-950. [PMID: 37681274 DOI: 10.1017/s0033291723002647] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 09/09/2023]
Abstract
BACKGROUND Magnetic resonance imaging (MRI) studies on major depressive disorder (MDD) have predominantly found short-term electroconvulsive therapy (ECT)-related gray matter volume (GMV) increases, but research on the long-term stability of such changes is missing. Our aim was to investigate long-term GMV changes over a 2-year period after ECT administration and their associations with clinical outcome. METHODS In this nonrandomized longitudinal study, patients with MDD undergoing ECT (n = 17) are assessed three times by structural MRI: Before ECT (t0), after ECT (t1) and 2 years later (t2). A healthy (n = 21) and MDD non-ECT (n = 33) control group are also measured three times within an equivalent time interval. A 3(group) × 3(time) ANOVA on whole-brain level and correlation analyses with clinical outcome variables is performed. RESULTS Analyses yield a significant group × time interaction (pFWE < 0.001) resulting from significant volume increases from t0 to t1 and decreases from t1 to t2 in the ECT group, e.g., in limbic areas. There are no effects of time in both control groups. Volume increases from t0 to t1 correlate with immediate and delayed symptom increase, while volume decreases from t1 to t2 correlate with long-term depressive outcome (all p ⩽ 0.049). CONCLUSIONS Volume increases induced by ECT appear to be a transient phenomenon as volume strongly decreased 2 years after ECT. Short-term volume increases are associated with less symptom improvement suggesting that the antidepressant effect of ECT is not due to volume changes. Larger volume decreases are associated with poorer long-term outcome highlighting the interplay between disease progression and structural changes.
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Affiliation(s)
- Tiana Borgers
- Institute for Translational Psychiatry, University of Münster, Albert-Schweitzer-Campus 1, Building A9a, 48149 Münster, Germany
| | - Verena Enneking
- Institute for Translational Psychiatry, University of Münster, Albert-Schweitzer-Campus 1, Building A9a, 48149 Münster, Germany
| | - Melissa Klug
- Institute for Translational Psychiatry, University of Münster, Albert-Schweitzer-Campus 1, Building A9a, 48149 Münster, Germany
| | - Jasper Garbe
- Institute for Translational Psychiatry, University of Münster, Albert-Schweitzer-Campus 1, Building A9a, 48149 Münster, Germany
| | - Hannah Meinert
- Institute for Translational Psychiatry, University of Münster, Albert-Schweitzer-Campus 1, Building A9a, 48149 Münster, Germany
| | - Marius Wulle
- Institute for Translational Psychiatry, University of Münster, Albert-Schweitzer-Campus 1, Building A9a, 48149 Münster, Germany
| | - Philine König
- Department of Psychology, University of Halle, Emil-Abderhalden-Straße 26, 06108 Halle, Germany
| | - Esther Zwiky
- Department of Psychology, University of Halle, Emil-Abderhalden-Straße 26, 06108 Halle, Germany
| | - Rebekka Herrmann
- Department of Psychology, University of Halle, Emil-Abderhalden-Straße 26, 06108 Halle, Germany
| | - Janine Selle
- Department of Psychology, University of Halle, Emil-Abderhalden-Straße 26, 06108 Halle, Germany
- Deutsches Zentrum für Psychische Gesundheit, German Center of Mental Health, Site Halle, MLU Halle, Halle, Germany
| | - Katharina Dohm
- Institute for Translational Psychiatry, University of Münster, Albert-Schweitzer-Campus 1, Building A9a, 48149 Münster, Germany
| | - Anna Kraus
- Institute for Translational Psychiatry, University of Münster, Albert-Schweitzer-Campus 1, Building A9a, 48149 Münster, Germany
| | - Dominik Grotegerd
- Institute for Translational Psychiatry, University of Münster, Albert-Schweitzer-Campus 1, Building A9a, 48149 Münster, Germany
| | - Jonathan Repple
- Institute for Translational Psychiatry, University of Münster, Albert-Schweitzer-Campus 1, Building A9a, 48149 Münster, Germany
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt, Heinrich-Hoffmann-Strasse 10, 60528 Frankfurt am Main, Germany
| | - Nils Opel
- Institute for Translational Psychiatry, University of Münster, Albert-Schweitzer-Campus 1, Building A9a, 48149 Münster, Germany
- Department of Psychiatry and Psychotherapy, University Hospital Jena, Philosophenweg 3, 07743 Jena, Germany
| | - Elisabeth J Leehr
- Institute for Translational Psychiatry, University of Münster, Albert-Schweitzer-Campus 1, Building A9a, 48149 Münster, Germany
| | - Marius Gruber
- Institute for Translational Psychiatry, University of Münster, Albert-Schweitzer-Campus 1, Building A9a, 48149 Münster, Germany
| | - Janik Goltermann
- Institute for Translational Psychiatry, University of Münster, Albert-Schweitzer-Campus 1, Building A9a, 48149 Münster, Germany
| | - Susanne Meinert
- Institute for Translational Psychiatry, University of Münster, Albert-Schweitzer-Campus 1, Building A9a, 48149 Münster, Germany
- Institute for Translational Neuroscience, University of Münster, Albert-Schweitzer-Campus 1, Building A9a, 48149 Münster, Germany
| | - Jochen Bauer
- Department of Clinical Radiology, University of Münster, Albert-Schweitzer-Campus 1, Building A16, 48149 Münster, Germany
| | - Walter Heindel
- Department of Clinical Radiology, University of Münster, Albert-Schweitzer-Campus 1, Building A16, 48149 Münster, Germany
| | - Erhan Kavakbasi
- Department of Psychiatry, University Hospital Münster, University of Münster, Albert-Schweitzer-Campus 1, Building A9, 48149 Münster, Germany
| | - Bernhard T Baune
- Department of Psychiatry, University Hospital Münster, University of Münster, Albert-Schweitzer-Campus 1, Building A9, 48149 Münster, Germany
- Department of Psychiatry, University of Melbourne, Victoria, Australia
- The Florey Institute of Neuroscience and Mental Health, Melbourne, Australia
| | - Udo Dannlowski
- Institute for Translational Psychiatry, University of Münster, Albert-Schweitzer-Campus 1, Building A9a, 48149 Münster, Germany
| | - Ronny Redlich
- Institute for Translational Psychiatry, University of Münster, Albert-Schweitzer-Campus 1, Building A9a, 48149 Münster, Germany
- Department of Psychology, University of Halle, Emil-Abderhalden-Straße 26, 06108 Halle, Germany
- Deutsches Zentrum für Psychische Gesundheit, German Center of Mental Health, Site Halle, MLU Halle, Halle, Germany
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Yasin S, Othmani A, Raza I, Hussain SA. Machine learning based approaches for clinical and non-clinical depression recognition and depression relapse prediction using audiovisual and EEG modalities: A comprehensive review. Comput Biol Med 2023; 159:106741. [PMID: 37105109 DOI: 10.1016/j.compbiomed.2023.106741] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 02/25/2023] [Accepted: 03/02/2023] [Indexed: 03/07/2023]
Abstract
Mental disorders are rapidly increasing each year and have become a major challenge affecting the social and financial well-being of individuals. There is a need for phenotypic characterization of psychiatric disorders with biomarkers to provide a rich signature for Major Depressive Disorder, improving the understanding of the pathophysiological mechanisms underlying these mental disorders. This comprehensive review focuses on depression and relapse detection modalities such as self-questionnaires, audiovisuals, and EEG, highlighting noteworthy publications in the last ten years. The article concentrates on the literature that adopts machine learning by audiovisual and EEG signals. It also outlines preprocessing, feature extraction, and public datasets for depression detection. The review concludes with recommendations that will help improve the reliability of developed models and the determinism of computational intelligence-based systems in psychiatry. To the best of our knowledge, this survey is the first comprehensive review on depression and relapse prediction by self-questionnaires, audiovisual, and EEG-based approaches. The findings of this review will serve as a useful and structured starting point for researchers studying clinical and non-clinical depression recognition and relapse through machine learning-based approaches.
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Affiliation(s)
- Sana Yasin
- Department of Computer Science, COMSATS University Islamabad, Lahore Campus Lahore, Pakistan; Department of Computer Science, University of Okara, Okara, Pakistan.
| | - Alice Othmani
- Université Paris-Est Créteil (UPEC), LISSI, Vitry sur Seine, 94400, France.
| | - Imran Raza
- Department of Computer Science, COMSATS University Islamabad, Lahore Campus Lahore, Pakistan.
| | - Syed Asad Hussain
- Department of Computer Science, COMSATS University Islamabad, Lahore Campus Lahore, Pakistan.
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