Perfusion and cerebrovascular reactivity characterization in Alzheimer’s disease and vascular dementia
Bibliographic record
Abstract
Abstract Background Cerebrovascular changes are often reported in normal aging, Alzheimer’s disease (AD), and vascular dementia (VaD). Cerebral perfusion and cerebrovascular reactivity (CVR) both decrease with dementia compared to healthy aging; as these changes occur prior to symptomatic onset and in distinct brain regions, perfusion and CVR may act as complementary biomarkers of early cerebrovascular changes. These biomarkers can be measured using MRI methods, yielding macrovascular measures of perfusion and CVR. We recently developed a more advanced method capable of measuring microvascular‐specific measures of perfusion and CVR. Here, we characterized the cerebrovascular profiles of AD and VaD using complementary perfusion and CVR biomarkers representing both macrovascular and microvascular regimes. Method MRI data were acquired at 3T (Ingenia, Philips) in three cohorts: non‐cognitively impaired cohort (HC), AD, and VaD (Table 1). Perfusion data were acquired with a multi‐echo, multi‐contrast (SAGE) acquisition (5 echoes, 7.7/26/56/74/92 ms), before, during, and after injection of gadolinium‐based contrast agent. Acquisition parameters include: repetition time (TR) = 1.5 s, voxel size = 2.75×2.75×5 mm, 200 volumes, acquisition time = 5 min. SAGE functional MRI (fMRI) data were acquired in the same cohort with the same TEs and the following acquisition parameters: TR = 3.0 s, voxel size = 3 mm3, 160 volumes, acquisition time = 8 min. SAGE data underwent standard pre‐processing. Macro‐ and microvascular cerebral blood flow (CBF), relative cerebral blood volume (rCBV), and relative CVR (rCVR) were calculated using advanced analysis pipelines. The Montreal Cognitive Assessment (MoCA) was administered prior to MRI acquisition. Result As expected, microvascular perfusion and rCVR were lower than the corresponding macrovascular metrics for all groups. Macrovascular and microvascular CBF were lower for AD and VaD compared to HC, while there was no difference in CBF between AD and VaD (Figures 1,2). Similar trends were observed for rCBV. There were no differences in macrovascular rCVR between groups; microvascular rCVR was lower for AD and VaD compared to controls. Conclusion Macrovascular and microvascular perfusion decreases with AD and VaD. Enrollment is ongoing, and future directions include analysis of perfusion metrics within cortical and subcortical regions and correlation of neuroimaging findings with cognitive testing.
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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 | 0.000 | 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) | 0.001 | 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".