Dataset Information

Reduced thalamic volumes in major depressive disorder.

ABSTRACT: Altered function in the limbic-cortical-striatial-pallidal-thalamic (LCSPT) circuit has been implicated in the pathophysiology of major depressive disorder (MDD). This study evaluated volumetric differences in subcortical volumes between depressed subjects with MDD (N=142), subjects with MDD in remission (N=72), and healthy controls (N=169). Participants underwent magnetic resonance imaging (MRI) scanning, and subcortical volumes were extracted using FMRIB's Integrated Registration and Segmentation Tool (FIRST), University of Oxford, UK. The depressed MDD subjects exhibited significantly smaller volumes in the bilateral thalamus and hippocampus compared to control subjects, and the differences in the bilateral thalamus remained significant after controlling for total intracranial volume. In a smaller subset of healthy controls and depressed MDD subjects matched to the remitted MDD subjects, significant differences in volume were observed across groups in the bilateral thalamus, as well as the right lateralized caudate, hippocampus, and pallidum; these were primarily accounted for by differences between the depressed MDD subjects versus both the remitted and healthy subjects, though none of these changes remained significant after controlling for total intracranial volume (TIV). Volumetric reductions in the thalamus and hippocampus may contribute to dysfunction within subcortical-cortical networks, consistent with previous evidence of metabolic and hemodynamic abnormalities in these regions in MDD.

SUBMITTER: Nugent AC

PROVIDER: S-EPMC3756884 | biostudies-literature | 2013 Sep

REPOSITORIES: biostudies-literature

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Reduced thalamic volumes in major depressive disorder.

Nugent Allison Carol AC Davis Rebecca Marie RM Zarate Carlos Alberto CA Drevets Wayne Curtis WC

Psychiatry research 20130710 3

Altered function in the limbic-cortical-striatial-pallidal-thalamic (LCSPT) circuit has been implicated in the pathophysiology of major depressive disorder (MDD). This study evaluated volumetric differences in subcortical volumes between depressed subjects with MDD (N=142), subjects with MDD in remission (N=72), and healthy controls (N=169). Participants underwent magnetic resonance imaging (MRI) scanning, and subcortical volumes were extracted using FMRIB's Integrated Registration and Segmentat ...[more]

PMID: 23850106

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Project description:Background: Major Depressive Disorder (MDD) is a moderately heritable disorder with a high lifetime prevalence. At present, laboratory blood tests to support MDD diagnosis are not available. Methods: We used a classifier approach on blood gene expression profiles of a unique set of non-medicated subjects (MDD patients and controls) to select genes of which expression is predictive for disease status. To reveal blood gene expression changes related to MDD disease, we applied a powerful ex vivo stimulus to the blood, i.e. incubation with lipopolysaccharide (LPS; 10 ng/ml blood). Results: Based on LPS-stimulated blood gene expression using whole-genome microarrays in 42 subjects (primary cohort; 21 MDD patients (mean age 42.3 years), 21 healthy controls (mean age 41.9 years)), we identified a set of genes (CAPRIN1, CLEC4A, KRT23, MLC1, PLSCR1, PROK2, ZBTB16) that serves as a molecular signature of MDD. These findings were validated for the primary cohort using an independent quantitative PCR method (P = 0.007). The difference between depressive patients and controls was confirmed (P = 0.019) in a replication cohort of 13 patients with MDD (mean age 42.8 years) and 14 controls (mean age 45.6 years). The MDD-signature score comprised of expression levels of 7 genes could discriminate depressive patients from controls with sensitivity of 76.9% and specificity of 71.8%. Conclusions: We show for the first time that molecular analysis of stimulated blood cells can be used as an endophenotype for MDD diagnosis, which is a milestone in establishing biomarkers for neuropsychiatric disorders with moderate heritability in general. Our results may provide a new entry point for following and predicting treatment outcome, as well as prediction of severity and recurrence of MDD. In total, 33 MDD patients and 34 healthy controls were analyzed using basal gene expression in whole blood, and gene expression from whole blood that was stimulated with LPS for 5-6 h, using microarrays. Patients were arbitrarily selected from all patients to serve as primary cohort (nMDD = 21 (MDD01-MDD21); nControls = 21 (Con01-Con21)), or replication cohort (nMDD = 12 (MDD22-MDD35); nControls = 13 (Con22-Con37)) using microarrays. This submission does not include Samples CON21_LPS or CON30_LPS.

Project description:BackgroundMagnetic resonance imaging (MRI) studies have found thalamic abnormalities in major depressive disorder (MDD). Although there are significant differences in the structure and function of the thalamus between MDD patients and healthy controls (HCs) at the group level, it is not clear whether the structural and functional features of the thalamus are suitable for use as diagnostic prediction aids at the individual level. Here, we were to test the predictive value of gray matter density (GMD), gray matter volume (GMV), amplitude of low-frequency fluctuations (ALFF), and fractional amplitude of low-frequency fluctuations (fALFF) in the thalamus using multivariate pattern analysis (MVPA).MethodsSeventy-four MDD patients and 44 HC subjects were recruited. The Gaussian process classifier (GPC) was trained to separate MDD patients from HCs, Gaussian process regression (GPR) was trained to predict depression scores, and Multiple Kernel Learning (MKL) was applied to explore the contribution of each subregion of the thalamus.ResultsThe primary findings were as follows: [1] The balanced accuracy of the GPC trained with thalamic GMD was 96.59% (P < 0.001). The accuracy of the GPC trained with thalamic GMV was 93.18% (P < 0.001). The correlation between Hamilton Depression Scale (HAMD) score targets and predictions in the GPR trained with GMD was 0.90 (P < 0.001, r2 = 0.82), and in the GPR trained with GMV, the correlation between HAMD score targets and predictions was 0.89 (P < 0.001, r2 = 0.79). [2] The models trained with ALFF and fALFF in the thalamus failed to discriminate MDD patients from HC participants. [3] The MKL model showed that the left lateral prefrontal thalamus, the right caudal temporal thalamus, and the right sensory thalamus contribute more to the diagnostic classification.ConclusionsThe results suggested that GMD and GMV, but not functional indicators of the thalamus, have good potential for the individualized diagnosis of MDD. Furthermore, the thalamus shows the heterogeneity in the structural features of thalamic subregions for predicting MDD. To our knowledge, this is the first study to focus on the thalamus for the prediction of MDD using machine learning methods at the individual level.

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Reduced thalamic volumes in major depressive disorder.

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Reduced thalamic volumes in major depressive disorder.

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