Shift in the brain network of emotional regulation in midlife women
Bibliographic record
Abstract
In Brief Objective: The menopausal transition is marked by hormonal changes and is quite often accompanied by cognitive and emotional complaints. Recent data also suggest a heightened risk for depression. Little is known about the changes in emotional regulation that might contribute to the increased risk of depression in this population. The aim of this study was to examine the brain correlates of emotional regulation in healthy, nondepressed midlife women. Methods: Functional magnetic resonance imaging was obtained in response to a standardized emotional regulation task. Levels of congruency were set and brain activation was measured during high- and low-conflict-resolution trials. Results: Fourteen women aged 40 to 60 years were enrolled into the study, and 11 were included in the final analyses. Activity associated with resolution of emotional conflict was observed in the dorsolateral prefrontal cortex (P < 0.05). No regions were engaged in the generation/monitoring of emotional conflict. Moreover, there was a significant deactivation of the amygdala in response to fearful faces (P < 0.05). Conclusions: Unlike similar studies in younger populations, these results suggest a more significant engagement of the dorsolateral prefrontal cortex and less amygdala activation in emotional regulation in midlife women. These findings are, however, consistent with previous studies in older populations. We hypothesize that a shift in emotional regulation circuitry might therefore occur in women during the menopausal transition and possibly contribute to the occurrence of mood and anxiety symptoms in women during/after this period in life. This study investigated brain correlates of emotional regulation in healthy peri- and postmenopausal women using functional magnetic resonance imaging. The results suggest that a shift in the brain network involved with emotional regulation in women may occur during the menopausal transition, a finding that might have potential clinical and therapeutic implications.
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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".