Prospective Investigation of Glutamate Levels and Percentage Gray Matter in the Medial Prefrontal Cortex in Females at Risk for Postpartum Depression
Bibliographic record
Abstract
BACKGROUND: The substantial female hormone fluctuations associated with pregnancy and postpartum have been linked to a greater risk of developing depressive symptoms, particularly in high-risk women (HRW), i.e. those with histories of mood sensitivity to female hormone fluctuations. We have shown that glutamate (Glu) levels in the medial prefrontal cortex (MPFC) decrease during perimenopause, a period of increased risk of developing a major depressive episode. Our team has also demonstrated that percentage gray matter (%GM), another neural correlate of maternal brain health, decreases in the MPFC during pregnancy. OBJECTIVE: To investigate MPFC Glu levels and %GM from late pregnancy up to 7 weeks postpartum in HRW and healthy pregnant women (HPW). METHODS: Single-voxel spectra were acquired from the MPFC of 41 HPW and 22 HRW using 3- Tesla in vivo proton magnetic resonance spectroscopy at five different time points. RESULTS: We observed a statistically significant interaction between time and group for the metabolite Glu, with Glu levels being lower for HRW during pregnancy and early postpartum (p<0.05). MPFC %GM was initially lower during pregnancy and then significantly increased over time in both groups (p<0.01). CONCLUSION: This investigation suggests that the vulnerability towards PPD is associated with unique fluctuations of MPFC Glu levels during pregnancy and early postpartum period. Our results also suggest that the decline in MPFC %GM associated with pregnancy seems to progressively recover over time. Further investigations are needed to determine the specific role that female hormones play on the physiological changes in %GM during pregnancy and postpartum.
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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 | 0.000 |
| 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) | 0.001 | 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".