DEveloping BBB-ASL as non-Invasive Early biomarker of Alzheimer's Disease (DEBBIE-AD): Study design
Bibliographic record
Abstract
Arterial spin labeling (ASL) MRI, a non-invasive technique for imaging perfusion, now allows studying BBB permeability. The DEveloping BBB-ASL as a non-Invasive Early biomarker of Alzheimer's Disease (DEBBIE-AD) multi-cohort study integrates this modified BBB-ASL technique in several healthy and diseased populations (Table 1) to study methodological and clinical research questions (Table 2) on the ability of BBB-ASL as an early AD biomarker. DEBBIE-AD will enroll various cohorts with subjective cognitive decline, mild cognitive impairment, and AD dementia, as well as age-matched healthy controls, at seven sites (Table 1). Our newly developed BBB-ASL sequence — implemented with the vendor-independent MRI framework gammaSTAR — will be added to multiple MRI protocols. The BBB-ASL sequence combines time-encoded multi-post labeling delay pseudo-continuous ASL with a multi-echo 3D GRASE readout, allowing estimating CBF, ATT, and the BBB time of exchange (Tex). Data analyses will be conducted using ExploreASL. Beyond MRI standard sequences, including T1w, T2w, FLAIR, DWI, the DEBBIE clinical outcomes include amyloid-PET and blood and CSF fluid biomarkers (Table 1). Preliminary testing of the BBB-ASL has been conducted on 3T systems (different Siemens Heathineers scanners) in different cohorts at multiple sites. Data processing with ExploreASL includes FSL-FABBER4 for quantification, allowing harmonized image processing. An example of the mean and standard deviation Tex maps of two DEBBIE cohorts is shown in Figure 1 to illustrate the similarities of the Tex patterns from two cohorts of similar-aged healthy adults from different sites. The DEBBIE-AD study aims to provide evidence on the ability of BBB-ASL to measure BBB permeability and demonstrate its utility in AD-related pathologies. The presented sequence may provide novel and unique insights into the staging of BBB permeability changes in groups at greater risk of developing AD, which may, in turn, provide new targets for treatment.
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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 | 0.013 | 0.005 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".