Characterizing white matter hemodynamic markers of lesion burden and cognitive function using arterial spin labeling MRI
Bibliographic record
Abstract
Abstract Background White matter lesions (WMLs) have been linked to cognitive decline in Alzheimer’s disease (AD), but physiological mechanisms associated with microvascular dysfunction in white matter remain incompletely understood. The purpose of this study was to investigate associations between arterial spin labeling (ASL) magnetic resonance imaging (MRI) markers of white matter hemodynamics and aging, cognitive function, and WML burden. Method Study design. Participants (n=234) provided written consent for this IRB‐approved cross‐sectional study as part of the Human Connectome Aging Project. Participants underwent a range of health and cognitive assessments, including the Montreal Cognitive Assessment (MoCA), and 3 Tesla MRI. Structural imaging. High‐resolution T1‐weighted images were acquired for co‐registration and were processed with FreeSurfer for calculation of white matter signal abnormalities as a measure of WML burden. Hemodynamic imaging. ASL data were acquired using a labeling duration=1500 ms and five equally spaced post‐labeling delays from 200‐2200 ms with spatial resolution=2.5x2.5x2.5 mm3. ASL images were corrected for distortion and for motion. Cerebral blood flow (CBF) and arterial transit time (ATT) were computed using a cross‐correlation approach and a two‐compartment model. Data analysis. Mean CBF and ATT values were calculated inside white matter masks derived using FreeSurfer for each subject and assessed as a function of age, WML burden, and MoCA score. Result We found an inverse association between white matter CBF and age (p<0.001) and a direct association between white matter ATT and age (p<0.001), where increasing age corresponded to lower CBF and longer ATT. After including age as a covariate, we found a trend for an inverse relationship between white matter CBF and WML burden (p=0.064), with lower CBF indicating higher WML burden (Figure 2). Lastly, we also found a trend for an inverse association between white matter ATT and MoCA score (p=0.097), with longer ATT indicating lower MoCA scores (Figure 3). Conclusion Preliminary results indicate that impaired hemodynamic properties (lower white matter CBF and longer white matter ATT) may be associated with elevated WML burden and poorer cognition. Further longitudinal studies are necessary to determine whether white matter hemodynamic may represent a prospective marker of WML burden and associated 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".