Literature DB >> 19931620

Structural and cognitive deficits in remitting and non-remitting recurrent depression: a voxel-based morphometric study.

Cheng-Ta Li1, Ching-Po Lin, Kun-Hsien Chou, I-Yun Chen, Jen-Chuen Hsieh, Chia-Liang Wu, Wei-Chen Lin, Tung-Ping Su.   

Abstract

Remission is the optimal outcome for major depressive disorder (MDD), but many patients do not improve appreciably despite treatment with medication. Treatment-resistant patients may experience deterioration in cognitive functions. Research has reported structural abnormalities in certain brain areas that may contribute to a poor clinical response. We hypothesize that there will be structural differences between patients able to achieve remission and those responding poorly to antidepressants. In the first voxel-based morphometric (VBM) study comparing remitting with non-remitting MDD, we investigated gray matter volume (GMV) differences between depressives to determine which structural abnormalities existed, and correlated these with diminished cognitive functioning. Of 44 adults with recurrent MDD, 19 had full remissions and 25 were non-remitters after a 6-week trial with antidepressant treatment. Remission was defined by 17-item Hamilton Depression Rating Scale scores of </=7 for at least 2 weeks. VBM and neuropsychological studies were conducted on all patients and 25 healthy controls. The patients who remitted revealed milder visual attention deficits than did controls. This correlated with reduced GMV in the left postcentral gyrus (Brodmann area, or BA, 3) and the bilateral medial/superior frontal gyrus (BA 6). The non-remitting patients had reduced GMV in the left dorsolateral prefrontal cortex (DLPFC, BA 9), and impaired acoustic and visual attention associated with GMV differences in several cortical regions, thalamus and amygdala/parahippocampal gyrus. These findings indicated that patients whose MDD remitted were cognitively and morphologically different from non-remitters. Voxel-based structural deficits in the left DLPFC may characterize a subgroup of people with recurrent MDD who respond poorly to antidepressants. Copyright (c) 2009 Elsevier Inc. All rights reserved.

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Year:  2009        PMID: 19931620     DOI: 10.1016/j.neuroimage.2009.11.021

Source DB:  PubMed          Journal:  Neuroimage        ISSN: 1053-8119            Impact factor:   6.556


  59 in total

Review 1.  Frontocingulate dysfunction in depression: toward biomarkers of treatment response.

Authors:  Diego A Pizzagalli
Journal:  Neuropsychopharmacology       Date:  2010-09-22       Impact factor: 7.853

2.  Prefrontal cortical abnormalities in currently depressed versus currently remitted patients with major depressive disorder.

Authors:  Giacomo Salvadore; Allison C Nugent; Herve Lemaitre; David A Luckenbaugh; Ruth Tinsley; Dara M Cannon; Alexander Neumeister; Carlos A Zarate; Wayne C Drevets
Journal:  Neuroimage       Date:  2010-11-10       Impact factor: 6.556

Review 3.  A Meta-Analysis of Executive Dysfunction and Antidepressant Treatment Response in Late-Life Depression.

Authors:  Monique A Pimontel; David Rindskopf; Bret R Rutherford; Patrick J Brown; Steven P Roose; Joel R Sneed
Journal:  Am J Geriatr Psychiatry       Date:  2015-05-21       Impact factor: 4.105

4.  High dosage of hypnotics predicts subsequent sleep-related breathing disorders and is associated with worse outcomes for depression.

Authors:  Cheng-Ta Li; Ya-Mei Bai; Ying-Chiao Lee; Wei-Chung Mao; Mu-Hong Chen; Pei-Chi Tu; Ying-Sheue Chen; Tzeng-Ji Chen; Wen-Hang Chang; Tung-Ping Su
Journal:  Sleep       Date:  2014-04-01       Impact factor: 5.849

Review 5.  Neural mechanisms of the cognitive model of depression.

Authors:  Seth G Disner; Christopher G Beevers; Emily A P Haigh; Aaron T Beck
Journal:  Nat Rev Neurosci       Date:  2011-07-06       Impact factor: 34.870

6.  Association between change in brain gray matter volume, cognition, and depression severity: Pre- and post- antidepressant pharmacotherapy for late-life depression.

Authors:  K Droppa; H T Karim; D L Tudorascu; J F Karp; C F Reynolds; H J Aizenstein; M A Butters
Journal:  J Psychiatr Res       Date:  2017-08-08       Impact factor: 4.791

Review 7.  Progress in Elucidating Biomarkers of Antidepressant Pharmacological Treatment Response: A Systematic Review and Meta-analysis of the Last 15 Years.

Authors:  G Voegeli; M L Cléry-Melin; N Ramoz; P Gorwood
Journal:  Drugs       Date:  2017-12       Impact factor: 9.546

8.  Changes of grey matter volume in first-episode drug-naive adult major depressive disorder patients with different age-onset.

Authors:  Zonglin Shen; Yuqi Cheng; Shuran Yang; Nan Dai; Jing Ye; Xiaoyan Liu; Jin Lu; Na Li; Fang Liu; Yi Lu; Xuejin Sun; Xiufeng Xu
Journal:  Neuroimage Clin       Date:  2016-08-24       Impact factor: 4.881

Review 9.  Towards automated detection of depression from brain structural magnetic resonance images.

Authors:  Kuryati Kipli; Abbas Z Kouzani; Lana J Williams
Journal:  Neuroradiology       Date:  2013-01-22       Impact factor: 2.804

10.  A face a mother could love: depression-related maternal neural responses to infant emotion faces.

Authors:  Heidemarie K Laurent; Jennifer C Ablow
Journal:  Soc Neurosci       Date:  2013-01-21       Impact factor: 2.083

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