Association of Stress Hormone System, Epigenetics and Imaging
Bibliographic record
Abstract
Introduction Major depressive disorder (MDD) is a common psychiatric condition, affecting up to 350 million people worldwide. Its pathogenesis seems to involve dysregulation of the hypothalamic-pituitary (HPA) axis and inflammation as key elements of the condition. Stressful life events and in particular early life adversity seem to play an important role as risk factors for MDD. Epigenetic, which has been found to impact in the transcription of genes, seem to be associated with brain structure and function. Aim of the research was to provide an overview about neuroimaging (epi)-genetics in MDD. Methods Functional MRI, epigenetic and genetic information was obtained in a cohort of patients with MDD and healthy controls. Associations between, early life adversity, methylation of FKBP5 and SLC6A4, genetic variants and brain function and connectivity have been analysed. Results Higher methylation of SLC6A4 gene was associated with higher BOLD response during emotion processing and lower BOLD response during higher order cognitive processes. Healthy participants with higher SLC6A4 methylation involved prefrontal cortical regions to a greater extent than the participants with lower SLC6A4 methylation, when trying to switch attention away from negative emotional stimuli (Frodl et al., 2015). Moreover, FKBP5 methylation was association with HPA axis functioning and amygdala brain function in patients with MDD. FKBP5 methylation also was related to grey matter volume. Conclusions Our study provides further support to the hypothesis that DNA methylation plays a role. Particular peripheral DNA methylation states of MDD candidate genes are associated with brain function during emotion processing in patients with MDD. Disclosure of interest The authors have not supplied their declaration of competing interest.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".