Association of pulsatility of lenticulostriate arteries with cognitive decline in elderly adults: a 7T dual‐VENC PC‐MRI study
Bibliographic record
Abstract
Abstract Background Cerebral small vessel disease (cSVD) is a major cause of vascular dementia. Mounting evidence suggests that cSVD pathology is closely linked to vascular dysfunction of lenticulostriate arteries (LSAs). Arterial pulsatility, quantified by pulsatility index (PI), has been used as an indicator of vascular dysfunction. With the advent of ultra‐high field 7T, the velocity and pulsatility of LSAs can be reliably measured. Directly assessing pulsatility of LSAs may offer valuable insight into pathophysiology of cSVD and cognitive impairment. This study aims to investigate relationship between LSA pulsatility and cognitive performance within an aged cohort. Method Twenty‐five elderly participants (13 female: 71 ± 9.2 years) were enrolled in the study with written informed consent. Cognitive assessments were conducted including 19 participants undergoing the Mini‐Mental State Exam (MMSE) and 23 taking Montreal Cognitive Assessment (MoCA). High‐resolution phase‐contrast MRI (PC‐MRI) with dual‐VENC (Venc=20cm/s and 40cm/s) was performed on a Siemens 7T MRI scanner to image velocities of LSAs. PI was calculated by the difference between the peak systolic flow velocity and minimum diastolic flow velocity divided by the mean velocity throughout a cardiac cycle. Pearson correlation was used to evaluate the associations between LSA PI and age, education level, and cognitive measurements. A linear mixed‐effects model was used to test the association between LSA PI and cognitive measurements while correcting for the effects of age, gender, and education. Result Flow velocity curves and PI values of LSAs were successfully extracted and calculated from all 25 participants. Figure 1 shows an example of LSA imaging using PC‐MRI and the acquired velocity curve of LSAs. LSA PI significantly increased with age and lower education levels (p=0.002, 0.03, respectively) (Figure 2). Increased PI was strongly associated with lower MMSE scores with and without corrections for age, gender, and educational level (p=0.048, 0.028) (Figure 3). A similar trend was observed between PI and MOCA scores, although there was no significance (p=0.31). Conclusion This study has demonstrated that LSA pulsatility assessed by 7T high‐resolution PC‐MRI is strongly associated with aging, education, and cognitive performance. These findings suggest the dysfunction of LSA could contribute to the cSVD pathology and result in cognitive impairment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".