Application of calibrated fMRI in Alzheimer's disease
Bibliographic record
Abstract
Calibrated fMRI based on arterial spin-labeling (ASL) and blood oxygen-dependent contrast (BOLD), combined with periods of hypercapnia and hyperoxia, can provide information on cerebrovascular reactivity (CVR), resting blood flow (CBF), oxygen extraction fraction (OEF), and resting oxidative metabolism (CMRO2). Vascular and metabolic integrity are believed to be affected in Alzheimer's disease (AD), thus, the use of calibrated fMRI in AD may help understand the disease and monitor therapeutic responses in future clinical trials. In the present work, we applied a calibrated fMRI approach referred to as Quantitative O2 (QUO2) in a cohort of probable AD dementia and age-matched control participants. The resulting CBF, OEF and CMRO2 values fell within the range from previous studies using positron emission tomography (PET) with 15O labeling. Moreover, the typical parietotemporal pattern of hypoperfusion and hypometabolism in AD was observed, especially in the precuneus, a particularly vulnerable region. We detected no deficit in frontal CBF, nor in whole grey matter CVR, which supports the hypothesis that the effects observed were associated specifically with AD rather than generalized vascular disease. Some key pitfalls affecting both ASL and BOLD methods were encountered, such as prolonged arterial transit times (particularly in the occipital lobe), the presence of susceptibility artifacts obscuring medial temporal regions, and the challenges associated with the hypercapnic manipulation in AD patients and elderly participants. The present results are encouraging and demonstrate the promise of calibrated fMRI measurements as potential biomarkers in AD. Although CMRO2 can be imaged with 15O PET, the QUO2 method uses more widely available imaging infrastructure, avoids exposure to ionizing radiation, and integrates with other MRI-based measures of brain structure and function.
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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 | 0.000 | 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) | 0.001 | 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".