Charting the impact of ovarian hormone depletion on brain structure in the ovariectomized mouse model of menopause
Bibliographic record
Abstract
Menopause, whether resulting from natural endocrine aging or clinical interventions, is a universal transition marking the end of reproductive capacity in women.While a normal biological process, menopause has been associated with neurological symptoms, diminished quality of life, and increased risk of neurodegenerative diseases such as Alzheimer's disease.Yet, the mechanisms by which ovarian hormone depletion impacts the brain remain poorly understood, in part because of the difficulty in disentangling menopause-specific effects from confounding variables like chronological aging and exogenous hormone use, and in part due to methodological inconsistencies across studies.Existing neuroimaging studies in women report highly heterogeneous findings, limiting our understanding of how menopause influences brain structure and whether these changes reflect vulnerability or resilience.To address these gaps, we employed a well-powered, longitudinal, whole-brain voxel-wise analysis in a controlled preclinical model.Using the bilateral ovariectomized (BLO) mouse model of late human menopause, we longitudinally investigated structural brain changes across four timepoints using T1-weighted magnetic resonance imaging (100 μm³ voxels, 7T Bruker scanner).Age-matched, sham-operated female mice served as controls (SHAM) (n=90).Circulating pituitary gonadotropin hormone levels (luteinizing hormone and follicle-stimulating hormone) were measured using enzyme-linked immunosorbent assay (ELISA) to confirm the endocrine status of the animals.We found that BLO mice exhibited increased brain volumes between 30 and 60 days post-ovariectomy, particularly in hormone-sensitive regions such as the cerebral cortex, hippocampus, hypothalamus, and association cortices.By 90 days, brain volumes in these regions returned to levels comparable to the SHAM group, suggesting the engagement of endogenous compensatory mechanisms and structural adaptation.BLO mice exhibited persistently elevated gonadotropin levels irrespective of time since ovariectomy.Brains, Healthy Lives fellowship and a scholarship from le Réseau de Bio-Imagerie du Québec.First and foremost, I extend my deepest gratitude to my supervisor, Dr. Mallar Chakravarty.When I joined your lab, I hoped to gain experience in animal work and computational science, but you taught me far more than that.You taught me to think critically, to question assumptions, and, above all, to be self-reflective.When you first interviewed me, I asked you what you considered your greatest strength as a supervisor, and you modestly replied that you are not a micromanager.But over time, I discovered that your strengths go well beyond that: your patience, understanding, and perspective have shaped how I approach not just research, but learning itself.You showed me that the feeling of being behind is often what drives us forward, and you encouraged me to trust my abilities and my voice as a young researcher.You have created a collaborative and supportive environment at the Cobra Lab, where new ideas and growth are always encouraged.Thank you for guiding me through this chapter of my journey, and for continuing to inspire me as I grow both personally and professionally.I look forward to continuing to work with you.I am equally grateful to all the members of the Cobra Lab for fostering such a generous and collaborative atmosphere.I have been consistently inspired by everyone's willingness to lend a hand and go above and beyond to support one another, a quality I will strive to carry with me throughout my career.In particular, I would like to thank Dr. Stephanie Tullo for mentoring me with kindness and patience, for sharing your expertise, and for guiding me through the challenges of data analysis and presentation.Thank you to Medhinee Malvankar for being an invaluable source of support during the planning and execution of my project, List of
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".