White matter hyperintensity load varies depending on subjective cognitive decline criteria
Bibliographic record
Abstract
Background Increased age and cognitive impairment is associated with an increase in cerebrovascular pathology often measured as white matter hyperintensities (WMHs) on MRI. Whether WMH burden differs between cognitively unimpaired older adults with subjective cognitive decline (SCD+) and without subjective cognitive decline (SCD–) remains conflicting, and could be related to the methods used to identify SCD. Our goal was to examine if four common SCD classification methods are associated with different WMH accumulation patterns between SCD+ and SCD-. Methods A total of 535 cognitively unimpaired older adults with 1353 time points from the Alzheimer’s Disease Neuroimaging Initiative were included in this study. SCD was operationalized using four different methods: Cognitive Change Index (CCI), Everyday Cognition Scale (ECog), ECog+Worry, and Worry. Linear mixed-effects models were used to investigate the associations between SCD and overall and regional WMH burden. Results Overall temporal WMH burden differences were only observed with the Worry questionnaire. Higher WMH burden change over time was observed in SCD+ compared to SCD– in the temporal and parietal regions using the CCI (temporal, p =.01; parietal p =.03) and ECog (temporal, p =.03; parietal p =.01). For both the ECog+Worry and Worry questionnaire, change in WMH burden over time was increased in SCD+ compared to SCD- for overall, frontal, temporal, and parietal WMH burden ( p <.05). Conclusion These results show that WMH burden differs between SCD+ and SCD– depending on the questionnaire and the approach (regional/global) used to measure WMHs. The various methods used to define SCD may reflect different types of underlying pathologies.
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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 | 0.001 | 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) | 0.002 | 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".