Brain structure in subjective cognitive decline: A replication study
Bibliographic record
Abstract
Abstract Background Alzheimer’s disease (AD) is an incurable neurodegenerative disorder, which disproportionately affects women (Erol et al., 2015). Given that the current treatments for AD aim to prevent symptom progression, the primary objective of emerging research is to identify pre‐clinical biological markers. Investigating such biomarkers would be appropriate in those experiencing subjective cognitive decline (SCD). Individuals with SCD are thought to be the earliest along the cognitive continuum between healthy aging and AD, as their reported change in cognition is not yet measurable using standard neuropsychological assessment measures. Previous research has found differences to exist in brain function, but not structure between healthy controls and those with SCD (Parker et al., 2020). The current study represents a preliminary analysis of structural brain images in healthy women compared to women with SCD; no structural differences between groups were hypothesized. Method The 3T T1 anatomical magnetic resonance images (MRI) used in this analysis was a subset of participants obtained from the Women’s Healthy Ageing Project collected in 2012 (N = 170). This subset included 30 healthy women (mean age = 71.4; SD = 3.12) and 30 women with SCD (mean age = 70.5; SD = 2.23). Voxel‐based morphometry (VBM) analyses were conducted using FMRIB’s Software Library to examine cross sectional differences between healthy women and women with SCD. Result Whole brain VBM analyses did not reveal any significant differences in grey matter tissue density between healthy women and women with SCD at p < 0.05 level with threshold free cluster enhancement (corrected for multiple comparisons). Conclusion Although the current findings did not reveal differences in grey matter between healthy women and women with SCD, this result replicates our previous findings and other reports in the literature that structural differences may not be detectable between healthy individuals and those with SCD. Follow‐up work will include investigation of differences in functional connectivity between these two groups using resting‐state functional MRI with the total sample collected in 2012 (N = 170). Identifying changes in brain function prior to structural atrophy and measurable decline on neuropsychological measures would represent a major advance in AD biomarker research.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".