Literature DB >> 27137427

Prediction of nonremission to antidepressant therapy using diffusion tensor imaging.

Stuart M Grieve1, Mayuresh S Korgaonkar, Evian Gordon, Leanne M Williams, A John Rush.   

Abstract

OBJECTIVE: Over 50% of outpatients with nonpsychotic major depressive disorder (MDD) do not achieve remission with any single antidepressant medication (ADM). There are currently no clinically useful pretreatment measures that inform the decision to prescribe or select ADMs. This report examines whether a biomarker based on diffusion tensor imaging (DTI) measures of brain connectivity can identify a subset of nonremitting patients with a sufficiently high degree of specificity that use of a medication that is likely to fail could be avoided.
METHODS: MDD outpatients recruited from community and primary-care settings underwent pretreatment magnetic resonance imaging as part of the international Study to Predict Optimized Treatment in Depression (conducted December 2008-June 2014). DSM-IV criteria and a 17-item Hamilton Depression Rating Scale (HDRS17) score ≥ 16 confirmed the primary diagnosis of nonpsychotic MDD. Data from the first cohort of MDD patients (n = 74) were used to calculate fractional anisotropy measures of the stria terminalis and cingulate portion of the cingulate bundle (CgC). On the basis of our previous data, we hypothesized that nonremission might be predicted using a ratio of these 2 values. Remission was defined as an HDRS17 score of ≤ 7 following 8 weeks of open-label treatment with escitalopram, sertraline, or venlafaxine extended-release, randomized across participants. The second study cohort (n = 83) was used for replication.
RESULTS: Thirty-four percent of all participants achieved remission. A value > 1.0 for the ratio of the fractional anisotropy of the stria terminalis over the CgC identified 38% of the nonremitting participants with an accuracy of 88% (test cohort; odds ratio [OR] = 9.6; 95% CI, 2.0-45.9); 24% with an accuracy of 83% (replication cohort; OR = 1.8; 95% CI, 0.5-6.9) and 29% with an accuracy of 86% (pooled data; OR = 4.0; 95% CI, 1.5-11.1). Treatment moderation analysis showed greater specificity for escitalopram and sertraline (χ(2) = 8.07; P = .003).
CONCLUSIONS: To our knowledge, this simple DTI-derived metric represents the first brain biomarker to reliably identify nonremitting patients in MDD. The test identifies a meaningful proportion of nonremitters, has high specificity, and may assist in managing the antidepressant treatment of depression. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT00693849. © Copyright 2016 Physicians Postgraduate Press, Inc.

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Year:  2016        PMID: 27137427     DOI: 10.4088/JCP.14m09577

Source DB:  PubMed          Journal:  J Clin Psychiatry        ISSN: 0160-6689            Impact factor:   4.384


  11 in total

1.  High-resolution diffusion imaging: ready to become more than just a research tool in psychiatry?

Authors:  S M Grieve; J J Maller
Journal:  Mol Psychiatry       Date:  2016-10-11       Impact factor: 15.992

2.  Examining raphe-amygdala structural connectivity as a biological predictor of SSRI response.

Authors:  Rajapillai L I Pillai; Chuan Huang; Andrew LaBella; Mengru Zhang; Jie Yang; Madhukar Trivedi; Myrna Weissman; Patrick McGrath; Maurizio Fava; Benji Kurian; Crystal Cooper; Melvin McInnis; Maria A Oquendo; Diego A Pizzagalli; Ramin V Parsey; Christine DeLorenzo
Journal:  J Affect Disord       Date:  2019-05-28       Impact factor: 4.839

3.  Accurately identifying patients who are excellent candidates or unsuitable for a medication: a novel approach.

Authors:  A John Rush; Madhukar H Trivedi; Charles South; Thomas J Carmody; Manish K Jha
Journal:  Neuropsychiatr Dis Treat       Date:  2017-12-15       Impact factor: 2.570

4.  Promising Neuroimaging Biomarkers in Depression.

Authors:  Chien-Han Lai
Journal:  Psychiatry Investig       Date:  2019-09-23       Impact factor: 2.505

5.  Magnetic resonance imaging for individual prediction of treatment response in major depressive disorder: a systematic review and meta-analysis.

Authors:  Sem E Cohen; Jasper B Zantvoord; Babet N Wezenberg; Claudi L H Bockting; Guido A van Wingen
Journal:  Transl Psychiatry       Date:  2021-03-15       Impact factor: 6.222

6.  Toward personalised diffusion MRI in psychiatry: improved delineation of fibre bundles with the highest-ever angular resolution in vivo tractography.

Authors:  Fraser Callaghan; Jerome J Maller; Thomas Welton; Matthew J Middione; Ajit Shankaranarayanan; Stuart M Grieve
Journal:  Transl Psychiatry       Date:  2018-04-25       Impact factor: 6.222

Review 7.  Neuroimaging Biomarkers for Predicting Treatment Response and Recurrence of Major Depressive Disorder.

Authors:  Seung-Gul Kang; Seo-Eun Cho
Journal:  Int J Mol Sci       Date:  2020-03-20       Impact factor: 5.923

8.  Changes in White Matter Microstructure After Electroconvulsive Therapy for Treatment-Resistant Depression.

Authors:  Gregor Gryglewski; René Seiger; Pia Baldinger-Melich; Jakob Unterholzner; Benjamin Spurny; Thomas Vanicek; Andreas Hahn; Siegfried Kasper; Richard Frey; Rupert Lanzenberger
Journal:  Int J Neuropsychopharmacol       Date:  2020-03-10       Impact factor: 5.176

Review 9.  Magnetic resonance diffusion tensor imaging in psychiatry: a narrative review of its potential role in diagnosis.

Authors:  Piotr Podwalski; Krzysztof Szczygieł; Ernest Tyburski; Leszek Sagan; Błażej Misiak; Jerzy Samochowiec
Journal:  Pharmacol Rep       Date:  2020-10-30       Impact factor: 3.024

10.  Distinct Features of Cerebral Blood Flow and Spontaneous Neural Activity as Integrated Predictors of Early Response to Antidepressants.

Authors:  Zhenghua Hou; Tong Li; Xiaofu He; Yuqun Zhang; Huanxin Chen; Wenhao Jiang; Yingying Yin; Yonggui Yuan
Journal:  Front Psychiatry       Date:  2022-01-18       Impact factor: 4.157

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