Regional cerebral blood flow decline can predict atrophy in Alzheimer’s disease spectrum
Bibliographic record
Abstract
Abstract Alzheimer’s disease (AD) is a neurodegenerative disease characterized by symptoms such as dementia, personality changes, and executive dysfunction. Brain atrophy and structural changes based on the MRI play an important role as a valid biomarker of AD and can support the clinical diagnosis of AD. The decline in the regional cerebral blood flow (rCBF) is believed to be among the first changes in Alzheimer’s continuum. The reason for this reduction in cerebral blood flow is not fully understood yet. Previous studies revealed the association between amyloid-beta and rCBF pattern and suggested that reduced rCBF is an early consequence of neural death and is prior to the considerable grey matter loss. In this study we investigated the association between rCBF and brain structural changes in three different groups of subjects consisted of control (CN), MCI, and AD groups. Our findings revealed a significant correlation between rCBF and structural changes including cortical volume, subcortical volume, surface area, and thickness in all groups after adjusting for age, sex, and APOE genotyping status. As our investigation, cerebral blood flow as measured by ASL-MRI might independent from Aβ and tau accumulation predict future structural changes and causes neurodegeneration in relation to AD development.
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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 | 0.001 |
| 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 | 0.000 | 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".