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Brain imaging predictors and the international study to predict optimized treatment for depression: study protocol for a randomized controlled trial
BACKGROUND: Approximately 50% of patients with major depressive disorder (MDD) do not respond optimally to antidepressant treatments. Given this is a large proportion of the patient population, pretreatment tests that predict which patients will respond to which types of treatment could save time, m...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3729660/ https://www.ncbi.nlm.nih.gov/pubmed/23866851 http://dx.doi.org/10.1186/1745-6215-14-224 |
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author | Grieve, Stuart M Korgaonkar, Mayuresh S Etkin, Amit Harris, Anthony Koslow, Stephen H Wisniewski, Stephen Schatzberg, Alan F Nemeroff, Charles B Gordon, Evian Williams, Leanne M |
author_facet | Grieve, Stuart M Korgaonkar, Mayuresh S Etkin, Amit Harris, Anthony Koslow, Stephen H Wisniewski, Stephen Schatzberg, Alan F Nemeroff, Charles B Gordon, Evian Williams, Leanne M |
author_sort | Grieve, Stuart M |
collection | PubMed |
description | BACKGROUND: Approximately 50% of patients with major depressive disorder (MDD) do not respond optimally to antidepressant treatments. Given this is a large proportion of the patient population, pretreatment tests that predict which patients will respond to which types of treatment could save time, money and patient burden. Brain imaging offers a means to identify treatment predictors that are grounded in the neurobiology of the treatment and the pathophysiology of MDD. METHODS/DESIGN: The international Study to Predict Optimized Treatment in Depression is a multi-center, parallel model, randomized clinical trial with an embedded imaging sub-study to identify such predictors. We focus on brain circuits implicated in major depressive disorder and its treatment. In the full trial, depressed participants are randomized to receive escitalopram, sertraline or venlafaxine-XR (open-label). They are assessed using standardized multiple clinical, cognitive-emotional behavioral, electroencephalographic and genetic measures at baseline and at eight weeks post-treatment. Overall, 2,016 depressed participants (18 to 65 years old) will enter the study, of whom a target of 10% will be recruited into the brain imaging sub-study (approximately 67 participants in each treatment arm) and 67 controls. The imaging sub-study is conducted at the University of Sydney and at Stanford University. Structural studies include high-resolution three-dimensional T1-weighted, diffusion tensor and T2/Proton Density scans. Functional studies include standardized functional magnetic resonance imaging (MRI) with three cognitive tasks (auditory oddball, a continuous performance task, and Go-NoGo) and two emotion tasks (unmasked conscious and masked non-conscious emotion processing tasks). After eight weeks of treatment, the functional MRI is repeated with the above tasks. We will establish the methods in the first 30 patients. Then we will identify predictors in the first half (n = 102), test the findings in the second half, and then extend the analyses to the total sample. TRIAL REGISTRATION: International Study to Predict Optimized Treatment - in Depression (iSPOT-D). ClinicalTrials.gov, NCT00693849. |
format | Online Article Text |
id | pubmed-3729660 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-37296602013-08-01 Brain imaging predictors and the international study to predict optimized treatment for depression: study protocol for a randomized controlled trial Grieve, Stuart M Korgaonkar, Mayuresh S Etkin, Amit Harris, Anthony Koslow, Stephen H Wisniewski, Stephen Schatzberg, Alan F Nemeroff, Charles B Gordon, Evian Williams, Leanne M Trials Study Protocol BACKGROUND: Approximately 50% of patients with major depressive disorder (MDD) do not respond optimally to antidepressant treatments. Given this is a large proportion of the patient population, pretreatment tests that predict which patients will respond to which types of treatment could save time, money and patient burden. Brain imaging offers a means to identify treatment predictors that are grounded in the neurobiology of the treatment and the pathophysiology of MDD. METHODS/DESIGN: The international Study to Predict Optimized Treatment in Depression is a multi-center, parallel model, randomized clinical trial with an embedded imaging sub-study to identify such predictors. We focus on brain circuits implicated in major depressive disorder and its treatment. In the full trial, depressed participants are randomized to receive escitalopram, sertraline or venlafaxine-XR (open-label). They are assessed using standardized multiple clinical, cognitive-emotional behavioral, electroencephalographic and genetic measures at baseline and at eight weeks post-treatment. Overall, 2,016 depressed participants (18 to 65 years old) will enter the study, of whom a target of 10% will be recruited into the brain imaging sub-study (approximately 67 participants in each treatment arm) and 67 controls. The imaging sub-study is conducted at the University of Sydney and at Stanford University. Structural studies include high-resolution three-dimensional T1-weighted, diffusion tensor and T2/Proton Density scans. Functional studies include standardized functional magnetic resonance imaging (MRI) with three cognitive tasks (auditory oddball, a continuous performance task, and Go-NoGo) and two emotion tasks (unmasked conscious and masked non-conscious emotion processing tasks). After eight weeks of treatment, the functional MRI is repeated with the above tasks. We will establish the methods in the first 30 patients. Then we will identify predictors in the first half (n = 102), test the findings in the second half, and then extend the analyses to the total sample. TRIAL REGISTRATION: International Study to Predict Optimized Treatment - in Depression (iSPOT-D). ClinicalTrials.gov, NCT00693849. BioMed Central 2013-07-18 /pmc/articles/PMC3729660/ /pubmed/23866851 http://dx.doi.org/10.1186/1745-6215-14-224 Text en Copyright © 2013 Grieve et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Study Protocol Grieve, Stuart M Korgaonkar, Mayuresh S Etkin, Amit Harris, Anthony Koslow, Stephen H Wisniewski, Stephen Schatzberg, Alan F Nemeroff, Charles B Gordon, Evian Williams, Leanne M Brain imaging predictors and the international study to predict optimized treatment for depression: study protocol for a randomized controlled trial |
title | Brain imaging predictors and the international study to predict optimized treatment for depression: study protocol for a randomized controlled trial |
title_full | Brain imaging predictors and the international study to predict optimized treatment for depression: study protocol for a randomized controlled trial |
title_fullStr | Brain imaging predictors and the international study to predict optimized treatment for depression: study protocol for a randomized controlled trial |
title_full_unstemmed | Brain imaging predictors and the international study to predict optimized treatment for depression: study protocol for a randomized controlled trial |
title_short | Brain imaging predictors and the international study to predict optimized treatment for depression: study protocol for a randomized controlled trial |
title_sort | brain imaging predictors and the international study to predict optimized treatment for depression: study protocol for a randomized controlled trial |
topic | Study Protocol |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3729660/ https://www.ncbi.nlm.nih.gov/pubmed/23866851 http://dx.doi.org/10.1186/1745-6215-14-224 |
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