Local Atrophy Observed in Subjective Cognitive Decline Varies Based on Questionnaire Employed in ADNI
Bibliographic record
Abstract
Abstract Background: Subjective cognitive decline (SCD) may be associated with increased risk for Alzheimer’s disease. However, neither research nor clinical practices have implemented a universal approach to operationalize SCD. This study was designed to determine whether four different methods of defining SCD influence atrophy differences observed between SCD and normal controls (NC). Methods: We included MRI scans from 273 participants (NC and SCD) from the Alzheimer’s Disease Neuroimaging Initiative. We used four methods to operationalize SCD: Cognitive Change Index (CCI), Everyday Cognition Scale (ECog), Worry, and ECog+Worry. Deformation-based morphometry was performed to examine volumetric change at the lateral ventricles, amygdala, and superior temporal regions (CerebrA atlas; Manera et al., 2020)). A previously validated MRI analysis method (SNIPE) was used for volume and grading of the hippocampus and entorhinal cortex (Coupe et al., 2012). A logistic regression was completed to examine the association between diagnosis and atrophy in SCD and NC. Results: Left hippocampal grading was lower in SCD than NC with the CCI (p=.041) and Worry (p=.021). When using ECog+Worry, smaller left entorhinal volume was observed in SCD than NC (p=.025). Both the right (p=.008) and left (p=.003) superior temporal regions were smaller in SCD than NC, with only the ECog. Conclusion: Although SCD questionnaires are designed to measure the same construct, the results here suggest otherwise. These results suggest that the SCD questionnaire employed will influence whether atrophy is observed in SCD relative to NC. Future research is warranted to better understand how different methodologies result in inconsistent findings.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".