MétaCan
Menu
← Back to cohort
Record W4380884160 · doi:10.1002/alz.063700

Early structural brain markers for Alzheimer’s Disease in women: Brain‐behaviour relationships after ovarian removal

2023· article· en· W4380884160 on OpenAlexaffabout
Alana Brown, Laura Gravelsins, Anne Almey, Nicole Gervais, Annie Duchesne, Jenny Rieck, Rebekah Reuben, Laurice Karkaby, Mateja Perović, Suzanne Tyson Witt, Elisabeth Classon, Nina Lykke, Elvar Theodorsson, Jan Ernerudh, Elisabeth Åvall Lundqvist, Preben Kjølhede, India Morrison, Giovanni Novembre, Maria Engström, Marcus Q. Bernardini, William D. Foulkes, Natasha Rajah, Cheryl L. Grady, Gillian Einstein

Bibliographic record

VenueAlzheimer s & Dementia · 2023
Typearticle
Languageen
FieldMedicine
TopicMenopause: Health Impacts and Treatments
Canadian institutionsDouglas Mental Health University InstituteMcGill UniversityPrincess Margaret Cancer CentreWestern UniversityUniversity of Northern British ColumbiaBaycrest HospitalUniversity of Toronto
Fundersnot available
KeywordsMagnetic resonance imagingPsychologyInternal medicineMedicineVoxelEndocrinologyGynecologyOncologyRadiology

Abstract

fetched live from OpenAlex

Abstract Background Women with bilateral salpingo‐oophorectomy (BSO; removal of ovaries and fallopian tubes) prior to age 50 have increased Alzheimer’s disease (AD) risk (Rocca et al., 2007), but the neural mechanisms for this are unclear. Abilities involving manipulation/maintenance aspects of working memory (WM) decline post‐BSO, and this effect may be reduced by estradiol‐based hormone therapy (ET; Gervais et al., 2020). Considering decline in manipulation/maintenance aspects of WM in AD (Germano & Kinsella, 2005), we aimed to understand the relationships between brain structure and WM after BSO. Method Women with BSO taking ET (BSO+ET: M±SDage = 44.92±5.11, n = 26) or not taking ET (BSO: M±SDage = 45.62±5.04, n = 26) were compared to age‐matched premenopausal controls (AMC: M±SDage = 43.71±2.92, n = 42) recruited from Toronto, Montreal, and Linköping. T1‐weighted structural magnetic resonance imaging scans were acquired. Voxel‐based morphometry measures of gray matter volume were obtained (CIVET pipeline, Ad‐Dab’bagh et al., 2005). Digit Span Backward (DSB) and Forward (DSF) subtests (Wechsler, 1945) were administered to assess manipulation/maintenance and updating/maintenance aspects of WM, respectively. Multivariate Behaviour Partial Least Squares (McIntosh & Lobaugh, 2004) was used to compare the correlation between performance (DSB and DSF) and volume between groups. Result There were no group differences in DSB/DSF total span performance. AMC did not show a significant relationship between volume and WM (Figure1A). For BSO and BSO+ET, gray matter volume in key WM regions, including bilateral supplementary motor area, inferior temporal gyrus, superior and inferior frontal gyri, left precuneus, and right cuneus, was negatively associated with DSB performance (Figure1B warm colours), while hippocampal volume was positively associated with DSB performance (Figure1B cold colours). These relationships were strong for BSO (R = ‐0.78), while BSO+ET (R = ‐0.37; moderate correlation) showed an intermediate phenotype between BSO and AMC (R = 0.14; small correlation). Conclusion Manipulation/maintenance aspects of WM and regional volume relationships depended on menopause status and ET. Without ET after BSO, successful WM performance correlated more strongly with hippocampal volume, which is usually the case for high memory load WM tasks (Geva et al., 2016). Even when memory changes are undetected by standard neuropsychological tests, brain‐behaviour relationships may prove a useful tool for assessing early changes in individuals at increased AD risk.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.055
GPT teacher head0.330
Teacher spread0.275 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueAlzheimer s & Dementia→Same topicMenopause: Health Impacts and Treatments→French-language works237,207→