IC‐P1‐013: 3D whole‐brain perfusion MRI in APP transgenic mice
Bibliographic record
Abstract
Cerebrovascular dysfunction is increasingly recognized as a major etiologic factor in the pathogenesis of Alzheimer's disease(AD) and is also demonstrated in transgenic(Tg) murine models of AD[1,2]. Current techniques used to evaluate cerebral blood flow(CBF) in murine models, such as laser Doppler flowmetry(LDF), are typically limited to highly localized regions of interest and single time-point studies. Arterial spin labeling(ASL) perfusion MRI uses blood as an endogenous tracer, magnetically labeling it before it enters the imaging volume, allowing for non-invasive, quantitative measurement of CBF. Previous ASL implementations in mice, however, have been limited to single slice experiments to due technical limitations which did not allow for investigation of cerebrovascular function throughout the entire brain. Our objective was to develop and evaluate a novel, 3D ASL perfusion imaging technique in order to generate high resolution, 3D quantitative maps of CBF, with whole-brain coverage, for the longitudinal evaluation of cerebrovascular pathophysiology in the Tg APP mouse model of AD. All MRI experiments were conducted using a Bruker Pharmascan 7T magnet. Arterial spin labeling was achieved using a train of adiabatic inversions in an adaptation of the technique of Moffat[3] to include simultaneous proximal and distal irradiation[4], enabling the extension to a 3D experiment. Tag/control images were acquired from 5 mice, anesthetized under isoflurane, with a TrueFISP readout with FOV=1x2x1.5cm, spatial resolution of 156x156x625μm, and scan time of 1h40min. Figure 1 shows a representative control scan and CBF map. CBF values were within ranges previously reported in mice[5]. Further, the significantly improved anatomical contrast in the imaging volumes generated by this technique allowed for the generation of population-based maps and voxel-wise statistical analyses. By extending ASL techniques across the entire brain, we were able, for the first time, to assess alterations in CBF quantitatively across different regions, which is critical for investigating a diffuse pathological process such as AD. Whole-brain ASL MRI also allows for generation of population-based volumes and powerful, exploratory, statistical analyses. Longitudinal analysis of CBF will serve to better understand cerebrovascular progression of the Tg APP phenotype, as well as the impact of potential AD therapeutic agents.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".