The influence of 5-HTTLPR and Val66Met polymorphisms on cortical thickness and volume in limbic and paralimbic regions in depression: a preliminary study
Bibliographic record
Abstract
BACKGROUND: Structural brain abnormalities have been investigated in multi-genetic and complex disorders such as major depressive disorder (MDD). Among the various candidate genes implicated in MDD, the brain-derived neurotrophic factor (BDNF) Val66Met polymorphism and 5-HT transporter gene linked polymorphism (5-HTTLPR) have garnered the most attention due to their putative roles in neural plasticity and antidepressant response. However, relatively few studies have assessed the influence of these polymorphysims on cortical thickness or brain volume in para-limbic and limbic regions in MDD, which was the aim of this study. METHODS: Forty-three adults with MDD and 15 healthy controls (HC) underwent structural magnetic resonance imaging (MRI). Cortical thickness was assessed in frontal, cingulate and temporal regions. Volumetric measures were carried out in the thalamus, caudate, putamen, pallidum, hippocampus and amygdala. Participants were genotyped to determine their 5-HTTLPR (tri-allelic) and Val66Met polymorphisms. RESULTS: In the combined sample (MDD + HC), smaller right pallidum volumes were found in LA/S (LA/S & LA/LG) heterozygotes compared to S/S (S/S, LG/S & LG/LG) homozygotes, though the effect was modest. In the MDD group, larger left thalamus and putamen volumes were observed in LA/LA homozygotes. No Val66Met or 5-HTTLPR genotype effects existed on cortical thickness and no main effects of the Val66Met polymorphism were observed. CONCLUSION: Our preliminary results suggest that the 5-HTTLPR polymorphism is associated with morphometric changes in regions known to play an important role in emotional and reward processing in depression. A larger sample size is required to replicate these findings and to potentially reveal subtle morphometric changes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".