Assessment of brain oxygen extraction in aging and Alzheimer’s disease with MRI images
Bibliographic record
Abstract
Abstract Background There is a growing body of evidence suggesting changes in blood flow and metabolism as a trigger of the cascade of events leading to Alzheimer’s disease (AD). Oxygen extraction fraction (OEF) MRI images provide valuable information about the brain’s metabolism and blood flow in stroke, brain injury, and recently neurodegenerative diseases. However, OEF is a research tool, and its clinical utility is still being evaluated. Here, we aimed to assess brain oxygenation in aging and AD using OEF images. We test the hypothesis that OEF reduction is associated with the clinical stages of AD. Method A cohort of 310 subjects with cognitively unimpaired (CU) (n = 182), mild cognitive impairment (MCI) (n = 80), and AD (n = 48), were recruited. All participants received 3D gradient‐recalled echo sequence MRI. All OEF images were constructed by QSM+qBOLD model with CCTV (Temporal clustering, tissue composition, and total variation) (Figure1). Eighty‐two various brain regions of interest (ROI) and 6 Braak ROI were investigated in the current study. Result Our results demonstrated that the OEF mean value in almost all of the brain ROI encompassed the lowest amount of OEF in AD compared with MCI and CU. In most parts of the brain, the OEF mean value was AD Conclusion OEF is an affordable and relevant non‐invasive MR approach to assess brain metabolism during disease progression or therapeutic interventions. OEF decreased in symptomatic cases. Further studies should explore the mechanisms underlying increased OEF in MCI. Our results support the potential of OEF to improve our understanding of metabolic changes associated with AD pathophysiology.
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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 | 0.001 |
| 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) | 0.001 | 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".