Dataset Information

Brain SPECT perfusion and PET metabolism as discordant biomarkers in major depressive disorder.

ABSTRACT:

Background

Brain SPECT perfusion and PET metabolism have been, most often interchangeably, proposed to study the underlying pathological process in major depressive disorder (MDD). The objective of this study was to specify similarities and inconsistencies between these two biomarkers according to global characteristics of the disease. We conducted a retrospective study in 16 patients suffering from treatment-resistant MDD who underwent, during the same current episode, a cerebral perfusion SPECT with ^99mTc-HMPAO and a metabolic PET with ¹⁸F-FDG. Whole-brain voxel-based SPM(T) maps were generated in correlation with the number of depressive episodes and in correlation with the depression duration, separately for the two exams (p-voxel < 0.001 uncorrected, k > 20).

Results

No significant correlations were found between brain metabolism and either the number of depressive episodes or the duration of the disease, even at an uncorrected p-voxel < 0.005. On the other hand, the increased number of depressive episodes was correlated with decreased perfusion of the right middle frontal cortex, the right anterior cingulum cortex, the right insula, the right medial temporal cortex and the left precuneus. The increased depression duration was correlated with decreased perfusion of the right anterior cingulum cortex.

Conclusions

This preliminary study demonstrates more significant results with brain perfusion compared with glucose metabolism in treatment-resistant MDD, highlighting the value of brain SPECT despite less favourable instrumentation detection compared to PET.

SUBMITTER: Tastevin M

PROVIDER: S-EPMC7544792 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Brain SPECT perfusion and PET metabolism as discordant biomarkers in major depressive disorder.

Tastevin Maud M Boyer Laurent L Korchia Theo T Fond Guillaume G Lançon Christophe C Richieri Raphaëlle R Guedj Eric E

EJNMMI research 20201008 1

<h4>Background</h4>Brain SPECT perfusion and PET metabolism have been, most often interchangeably, proposed to study the underlying pathological process in major depressive disorder (MDD). The objective of this study was to specify similarities and inconsistencies between these two biomarkers according to global characteristics of the disease. We conducted a retrospective study in 16 patients suffering from treatment-resistant MDD who underwent, during the same current episode, a cerebral perfus ...[more]

PMID: 33030615

Similar Datasets

Project description:Although monozygotic (MZ) twins share the majority of their genetic makeup, they can be phenotypically discordant on several traits and diseases. DNA methylation is an epigenetic mechanism that can be influenced by genetic, environmental and stochastic events and may have an important impact on individual variability. In this study we explored epigenetic differences in peripheral blood samples in three MZ twin studies on major depressive disorder (MDD). Epigenetic data for twin pairs were collected as part of a previous study using 8.1-K-CpG microarrays tagging DNA modification in white blood cells from MZ twins discordant for MDD. Data originated from three geographical regions: UK, Australia and the Netherlands. Ninety-seven MZ pairs (194 individuals) discordant for MDD were included. Different methods to address non independently-and-identically distributed (non-i.i.d.) data were evaluated. Machine-learning methods with feature selection centered on support vector machine and random forest were used to build a classifier to predict cases and controls based on epivariations. The most informative variants were mapped to genes and carried forward for network analysis. A mixture approach using principal component analysis (PCA) and Bayes methods allowed to combine the three studies and to leverage the increased predictive power provided by the larger sample. A machine-learning algorithm with feature reduction classified affected from non-affected twins above chance levels in an independent training-testing design. Network analysis revealed gene networks centered on the PPAR-γ (NR1C3) and C-MYC gene hubs interacting through the AP-1 (c-Jun) transcription factor. PPAR-γ (NR1C3) is a drug target for pioglitazone, which has been shown to reduce depression symptoms in patients with MDD. Using a data-driven approach we were able to overcome challenges of non-i.i.d. data when combining epigenetic studies from MZ twins discordant for MDD. Individually, the studies yielded negative results but when combined classification of the disease state from blood epigenome alone was possible. Network analysis revealed genes and gene networks that support the inflammation hypothesis of MDD.

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

Brain SPECT perfusion and PET metabolism as discordant biomarkers in major depressive disorder.

Background

Results

Conclusions

Publications

Brain SPECT perfusion and PET metabolism as discordant biomarkers in major depressive disorder.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets