Study of Predicting Longitudinal Cognitive Changes with “AN” Imaging Biomarkers in Subjective Cognitive Decline
Bibliographic record
Abstract
BACKGROUND: To explore whether baseline "AN" features in SCD, including brain Aβ deposition, hippocampal volume, and glucose metabolism in the hippocampus and parahippocampal gyrus, can predict longitudinal cognitive changes, thus identifying imaging biomarkers for SCD progression. METHOD: This study included 95 subjects with follow-up (59 SCD patients and 36 normal controls). Baseline imaging included 18F-Florbetapir PET/CT (brain Aβ), 18F-FDG PET/CT (hippocampal and parahippocampal glucose metabolism), and T1-weighted MRI (hippocampal volume). Cognitive function was assessed with neuropsychological tests at baseline and after two years. Cognitive change rates (Δchange = (follow-up - baseline) / baseline) were used as the primary outcome. A general linear model analyzed the relationship between baseline "AN" features and cognitive change rates, with Bonferroni correction for multiple comparisons. RESULT: Compared to the NC group, SCD subjects showed a trend toward accelerated decline in executive function (Shape Trail Test (STT)-A score, β= -0.04, p = 0.083). Baseline whole-brain Aβ deposition predicted longitudinal changes in the Boston Naming Test (BNT, β= -0.38, p = 0.00435) and STT-A score (β= 0.25, p = 0.053). Baseline hippocampal volume predicted changes in Montreal Cognitive Assessment-Basic (MoCA-B, β= 0.43, p = 0.00449). Baseline FDG SUVr in the left parahippocampal gyrus predicted MoCA-B (β= 0.36, p = 0.021) and STT-B scores (β= -0.38, p = 0.0223). FDG SUVr in the right hippocampus predicted changes in BNT scores (β= 0.46, p = 0.00407). CONCLUSION: The 2-year follow-up indicated that SCD subjects experienced accelerated executive function decline. Baseline "A" and "N" imaging features predicted longitudinal cognitive changes in language, executive, and overall cognitive function in SCD. This study identifies baseline "AN" features as key imaging biomarkers for SCD progression.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".