Dataset Information

Meta-analysis of emotion recognition deficits in major depressive disorder.

ABSTRACT:

Background

Many studies have explored associations between depression and facial emotion recognition (ER). However, these studies have used various paradigms and multiple stimulus sets, rendering comparisons difficult. Few studies have attempted to determine the magnitude of any effect and whether studies are properly powered to detect it. We conducted a meta-analysis to synthesize the findings across studies on ER in depressed individuals compared to controls.

Method

Studies of ER that included depressed and control samples and published before June 2013 were identified in PubMed and Web of Science. Studies using schematic faces, neuroimaging studies and drug treatment studies were excluded.

Results

Meta-analysis of k = 22 independent samples indicated impaired recognition of emotion [k = 22, g = -0.16, 95% confidence interval (CI) -0.25 to -0.07, p < 0.001]. Critically, this was observed for anger, disgust, fear, happiness and surprise (k's = 7-22, g's = -0.42 to -0.17, p's < 0.08), but not sadness (k = 21, g = -0.09, 95% CI -0.23 to +0.06, p = 0.23). Study-level characteristics did not appear to be associated with the observed effect. Power analysis indicated that a sample of approximately 615 cases and 615 controls would be required to detect this association with 80% power at an alpha level of 0.05.

Conclusions

These findings suggest that the ER impairment reported in the depression literature exists across all basic emotions except sadness. The effect size, however, is small, and previous studies have been underpowered.

SUBMITTER: Dalili MN

PROVIDER: S-EPMC4712476 | biostudies-literature | 2015 Apr

REPOSITORIES: biostudies-literature

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Publications

Meta-analysis of emotion recognition deficits in major depressive disorder.

Dalili M N MN Penton-Voak I S IS Harmer C J CJ Munafò M R MR

Psychological medicine 20141114 6

<h4>Background</h4>Many studies have explored associations between depression and facial emotion recognition (ER). However, these studies have used various paradigms and multiple stimulus sets, rendering comparisons difficult. Few studies have attempted to determine the magnitude of any effect and whether studies are properly powered to detect it. We conducted a meta-analysis to synthesize the findings across studies on ER in depressed individuals compared to controls.<h4>Method</h4>Studies of E ...[more]

PMID: 25395075

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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.

Dataset Information

Meta-analysis of emotion recognition deficits in major depressive disorder.

Background

Method

Results

Conclusions

Publications

Meta-analysis of emotion recognition deficits in major depressive disorder.

Similar Datasets

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