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Neuroimaging-based biomarkers for treatment selection in major depressive disorder

The use of neuroimaging approaches to identify likely treatment outcomes in patients with major depressive disorder is developing rapidly. Emerging work suggests that resting state pretreatment metabolic activity in the fronto-insular cortex may distinguish between patients likely to respond to psyc...

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Detalles Bibliográficos
Autores principales: Dunlop, Boadie W., Mayberg, Helen S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Les Laboratoires Servier 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4336918/
https://www.ncbi.nlm.nih.gov/pubmed/25733953
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author Dunlop, Boadie W.
Mayberg, Helen S.
author_facet Dunlop, Boadie W.
Mayberg, Helen S.
author_sort Dunlop, Boadie W.
collection PubMed
description The use of neuroimaging approaches to identify likely treatment outcomes in patients with major depressive disorder is developing rapidly. Emerging work suggests that resting state pretreatment metabolic activity in the fronto-insular cortex may distinguish between patients likely to respond to psychotherapy or medication and may function as a treatment-selection biomarker. In contrast, high metabolic activity in the subgenual anterior cingulate cortex may be predictive of poor outcomes to both medication and psychotherapy, suggesting that nonstandard treatments may be pursued earlier in the treatment course. Although these findings will require replication before clinical adoption, they provide preliminary support for the concept that brain states can be measured and applied to the selection of a specific treatment most likely to be beneficial for an individual patient.
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spelling pubmed-43369182015-03-02 Neuroimaging-based biomarkers for treatment selection in major depressive disorder Dunlop, Boadie W. Mayberg, Helen S. Dialogues Clin Neurosci Translational Research The use of neuroimaging approaches to identify likely treatment outcomes in patients with major depressive disorder is developing rapidly. Emerging work suggests that resting state pretreatment metabolic activity in the fronto-insular cortex may distinguish between patients likely to respond to psychotherapy or medication and may function as a treatment-selection biomarker. In contrast, high metabolic activity in the subgenual anterior cingulate cortex may be predictive of poor outcomes to both medication and psychotherapy, suggesting that nonstandard treatments may be pursued earlier in the treatment course. Although these findings will require replication before clinical adoption, they provide preliminary support for the concept that brain states can be measured and applied to the selection of a specific treatment most likely to be beneficial for an individual patient. Les Laboratoires Servier 2014-12 /pmc/articles/PMC4336918/ /pubmed/25733953 Text en Copyright: © 2014 Institut la Conférence Hippocrate - Servier Research Group http://creativecommons.org/licenses/by-nc-nd/3.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by-nc-nd/3.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Translational Research
Dunlop, Boadie W.
Mayberg, Helen S.
Neuroimaging-based biomarkers for treatment selection in major depressive disorder
title Neuroimaging-based biomarkers for treatment selection in major depressive disorder
title_full Neuroimaging-based biomarkers for treatment selection in major depressive disorder
title_fullStr Neuroimaging-based biomarkers for treatment selection in major depressive disorder
title_full_unstemmed Neuroimaging-based biomarkers for treatment selection in major depressive disorder
title_short Neuroimaging-based biomarkers for treatment selection in major depressive disorder
title_sort neuroimaging-based biomarkers for treatment selection in major depressive disorder
topic Translational Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4336918/
https://www.ncbi.nlm.nih.gov/pubmed/25733953
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