Regional atrophy and cognitive decline depend on definition of subjective cognitive decline
Bibliographic record
Abstract
Abstract Background People with subjective cognitive decline (SCD) may be at increased risk for Alzheimer’s disease (AD). However, not all studies have observed this increased risk. Inconsistencies may be related to different methods used to define SCD. The current project examined whether four methods of defining SCD (applied to the same sample) results in different patterns of atrophy and future cognitive decline between cognitively normal older adults with (SCD+) and without SCD (SCD-). Methods MRI scans and questionnaire data for 273 cognitively normal older adults from Alzheimer’s Disease Neuroimaging Initiative were examined. To operationalize SCD we used four common methods: Cognitive Change Index (CCI), Everyday Cognition Scale (ECog), ECog + Worry, and Worry only. A previously validated MRI analysis method (SNIPE) was used to measure hippocampal volume and grading . Deformation-based morphometry was performed to examine volume at regions known to be vulnerable to AD. Logistic regressions were completed to determine whether diagnostic method was associated with volume differences between SCD- and SCD+. Linear mixed effects models were performed to examine the relationship between the definitions of SCD and future cognitive decline. Results Results varied between the four methods of defining SCD. Left hippocampal grading was lower in SCD+ than SCD-when using the CCI ( p =.041) and Worry ( p =.021) definitions. The right ( p= .008) and left ( p= .003) superior temporal regions were smaller in SCD+ than SCD-, but only with the ECog. SCD+ was associated with greater future cognitive decline measured by Alzheimer’s Disease Assessment Scale, but only with the CCI definition. In contrast, only the ECog definition of SCD was associated with future decline on the Montreal Cognitive Assessment. Conclusion The current findings suggest that the various methods used to differentiate between SCD- and SCD+ influence whether volume differences and findings of cognitive decline are observed between groups in this retrospective analysis.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".