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Deep Brain Stimulation for Treatment-Resistant Depression: Towards a More Personalized Treatment Approach
Major depressive disorder (MDD) affects approximately 4.4% of the world’s population. One third of MDD patients do not respond to routine psychotherapeutic and pharmacotherapeutic treatment and are said to suffer from treatment-resistant depression (TRD). Deep brain stimulation (DBS) is increasingly...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7565181/ https://www.ncbi.nlm.nih.gov/pubmed/32846987 http://dx.doi.org/10.3390/jcm9092729 |
Sumario: | Major depressive disorder (MDD) affects approximately 4.4% of the world’s population. One third of MDD patients do not respond to routine psychotherapeutic and pharmacotherapeutic treatment and are said to suffer from treatment-resistant depression (TRD). Deep brain stimulation (DBS) is increasingly being investigated as a treatment modality for TRD. Although early case studies showed promising results of DBS, open-label trials and placebo-controlled studies have reported inconsistent outcomes. This has raised discussion about the correct interpretation of trial results as well as the criteria for patient selection, the choice of stimulation target, and the optimal stimulation parameters. In this narrative review, we summarize recent studies of the effectiveness of DBS in TRD and address the relation between the targeted brain structures and clinical outcomes. Elaborating upon that, we hypothesize that the effectiveness of DBS in TRD can be increased by a more personalized and symptom-based approach. This may be achieved by using resting-state connectivity mapping for neurophysiological subtyping of TRD, by using individualized tractography to help decisions about stimulation target and electrode placement, and by using a more detailed registration of symptomatic improvements during DBS, for instance by using ‘experience sampling’ methods. |
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