BOLD fMRI in the White Matter as a Marker of Aging and Small Vessel Disease
Bibliographic record
Abstract
PURPOSE: Determine whether white matter signal fluctuation on T2* weighted BOLD contrast images are associated with aging and cerebral small vessel disease (SVD). METHODOLOGY: Resting state BOLD data were collected with a 250 ms repetition time (TR) to achieve unaliased, ungated cardiac sampled BOLD (cs-BOLD) images on 11 young adult controls, 10 healthy older adult controls and 7 adults with extensive white matter hyperintensities (WMH) from SVD. Tissue classes (WM and GM) were segmented on T1 images. WMH were identified on FLAIR images in the SVD group. Raw physiological noise (σphysio) and cardiac pulsatility (i.e. fluctuations at the cardiac frequency) were calculated voxel wise and group differences were tested by ANOVA. It was also possible to calculate σphysio in 2s TR cardiac aliased whole-brain BOLD (wb-BOLD) data (N = 84) obtained from the International Consortium for Brain Mapping. RESULTS: CS-BOLD metrics showed an aging and SVD effects (p<0.0005). Covariates such as thermal noise, WM volume and partial volume did not influence the significant aging effect seen on the cardiac pulsatility metric (p<0.017) but did influence the σphysio (p = 0.184). As a verification of the cs-BOLD findings, the wb-BOLD also showed a linear aging effect of σphysio in WM. In the SVD adults, cardiac pulsatility and σphysio were lower in WMH regions compared to normal appearing white matter (NAWM) regions (p<0.0013 and p<0.002, respectively). Cardiac pulsatility was better able to distinguish WMH regions from NAWM than σphysio as measured by effect size (Cohen's d 2.2 and 0.88, respectively). CONCLUSION: NAWM was found to have graded increases in cardiac pulsations due to age and SVD, independently. Within SVD participants, WMH lesions had reduced physiological noise compared to NAWM. Cardiac pulsatility in resting BOLD data may provide a complementary dynamic measure of WM integrity to add to static FLAIR anatomical images.
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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 | 0.001 |
| 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) | 0.001 | 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".