Associations between brain and cognitive resilience, tau load and extent in Alzheimer’s disease
Bibliographic record
Abstract
BACKGROUND: Brain and cognitive resilience (BR, CR) reflect the capacity to maintain structural integrity and cognitive function despite pathological tau deposition in Alzheimer's disease (AD). Tau pathology can be characterized in terms of spatial extent of tauopathy (SEOT) or load using standardized uptake value ratio (SUVR). The aim was to compare SEOT and SUVR in their association with BR and CR. To replicate findings from Ossenkoppele et al. (2020) using MK-6240 PET imaging and evaluate demographic, genetic, and imaging factors associated with BR and CR. The objective of this study is to assess the value of SEOT metrics in resilience models and compare their predictive power to standardized uptake value ratio (SUVR) and to evaluate cross sectional interactions between tau pathology, cognitive resilience, and cognitive decline. METHOD: F]MK6240 and cognitive assessments (MMSE). SEOT was quantified as the proportion of voxels considered as abnormal relative to young controls. We used Participants recruited from TRIAD cohort, including individuals with mild cognitive impairment (MCI) or AD, positive amyloid-β biomarkers, MK-6240 PET imaging data. RESULT: Higher Whole Cortex MK SUVR is associated with lower MMSE scores, showing increased tau pathology correlates with cognitive decline. MCI patients maintain higher MMSE scores despite some tau accumulation, while AD patients show greater variability and decline. The negative trend suggests tau deposition contributes to cognitive impairment, but other factors may also play a role. 2. Whole Cortex MK SUVR vs. MMSE the negative correlation between Whole Cortex SEOT and MMSE appears stronger, with a more pronounced decline in cognitive function (MMSE scores) as SEOT increases, suggesting SEOT may be a more sensitive marker of disease progression in AD patients. CONCLUSION: Whole Cortex SEOT exhibits a stronger negative correlation with MMSE compared to Whole Cortex MK-6240 SUVR, indicating that SEOT may serve as a more sensitive marker of cognitive decline in Alzheimer's disease and mild cognitive impairment. Further research is needed to validate SEOT's potential as a diagnostic or prognostic biomarker in neurodegenerative conditions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".