Arterial Spin-Labeling Parameters Influence Signal Variability and Estimated Regional Relative Cerebral Blood Flow in Normal Aging and Mild Cognitive Impairment: FAIR versus PICORE Techniques
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Arterial spin-labeling is a noninvasive method to map cerebral blood flow, which might be useful for early diagnosis of neurodegenerative diseases. We directly compared 2 arterial spin-labeling techniques in healthy elderly controls and individuals with mild cognitive impairment. MATERIALS AND METHODS: This prospective study was approved by the local ethics committee and included 198 consecutive healthy controls (mean age, 73.65 ± 4.02 years) and 43 subjects with mild cognitive impairment (mean age, 73.38 ± 5.85 years). Two pulsed arterial spin-labeling sequences were performed at 3T: proximal inversion with a control for off-resonance effects (PICORE) and flow-sensitive alternating inversion recovery technique (FAIR). Relative cerebral blood flow maps were calculated by using commercial software and standard parameters. Data analysis included spatial normalization of gray matter-corrected relative CBF maps, whole-brain average, and voxelwise comparison of both arterial spin-labeling sequences. RESULTS: Overall, FAIR yielded higher relative CBF values compared with PICORE (controls, 32.7 ± 7.1 versus 30.0 ± 13.1 mL/min/100 g, P = .05; mild cognitive impairment, 29.8 ± 5.4 versus 26.2 ± 8.6 mL/min/100 g, P < .05; all, 32.2 ± 6.8 versus 29.3 ± 12.3 mL/min/100 g, P < .05). FAIR had lower variability (controls, 36.2% versus 68.8%, P < .00001; mild cognitive impairment, 18.9% versus 22.9%, P < .0001; all, 34.4% versus 64.9% P < .00001). The detailed voxelwise analysis revealed a higher signal for FAIR, notably in both convexities, while PICORE had higher signal predominantly in deep cerebral regions. CONCLUSIONS: Overall, FAIR had higher estimated relative CBF and lower interindividual variability than PICORE. In more detail, there were regional differences between both arterial spin-labeling sequences. In summary, these results highlight the need to calibrate arterial spin-labeling sequences.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| 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) | 0.000 | 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".