Cardiac‐Related Pulsatility in the Insula Is Directly Associated With Middle Cerebral Artery Pulsatility Index
Bibliographic record
Abstract
BACKGROUND: Arterial stiffness in large arteries is a risk factor for cerebral small vessel disease and neurodegeneration. The challenge of accessing intracranial pulsatility noninvasively is one reason few studies provide empirical insight on the relationship between large artery and tissue pulsatility in the human brain. PURPOSE: To investigate the association between the functional magnetic resonance imaging (fMRI)-derived cardiac-related pulsatility in the insular cortex and the ultrasound-derived pulsatility index in the middle cerebral artery (MCA-PI). STUDY TYPE: Cross-sectional. POPULATION: Younger adults (11; 25 ± 4 years) and older adults with and without cardiovascular risk factors (44; 70 ± 6 years). FIELD STRENGTH/SEQUENCE: *-weighted blood oxygenation level-dependent (BOLD) sequences at 3T. ASSESSMENT: MCA-PI and cardiac-related pulsatility were assessed at rest by transcranial Doppler ultrasound and BOLD fMRI, respectively. STATISTICAL TESTS: Multivariate analyses of covariance between MCA-PI and cardiac-related pulsatility. Analysis of variance was used to assess regional differences. RESULTS: MCA-PI was associated with cardiac-related insular pulsatility (P = 0.037), but not whole-brain pulsatility (P = 0.81). Left insular pulsatility was higher than right insular pulsatility (P < 0.01) and was associated with diastolic blood pressure (P = 0.028). DATA CONCLUSION: We show a correlation between ultrasound and fMRI measures of cerebrovascular pulsatility. This association provides insight into the transmission of pulsatile energy from large basal arteries at the Circle of Willis to downstream cerebrovascular beds and has implications for the utility of cardiac-related pulsatility as a potential marker for cerebral small vessel disease. LEVEL OF EVIDENCE: 4 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2020;51:1454-1462.
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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 | 0.002 |
| 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) | 0.003 | 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".