Association between white-matter lesion volume and heart-rate variability: A comparison of T1, T1-T2 and T1-FLAIR-based segmentations using FreeSurfer SAMSEG
Bibliographic record
Abstract
ABSTRACT Background Heart disease is a crucial risk factor for brain degeneration, but the exact mechanisms are still under investigation. Notably, a growth in white-matter lesion (WML) volume, a hallmark of cerebral small-vessel disease (cSVD) with connections to progressive neurodegeneration and cognitive decline, has been linked to cardiovascular disease. As such, measures of cardiovascular function such as heart rate variability may represent early and accessible predictors of WML development. Methods This study used data from healthy adults drawn from the Leipzig Study for Mind-Body-Emotion Interactions (LEMON) dataset to examine heart function through measures of heart rate variability (HRV), as well as their relationship to cSVD as quantified through WML volumes, using FreeSurfer SAMSEG. WML volumes were segmented using 3T T1+FLAIR, T1+T2 or T1 images alone. Results Results indicate that both young and old adults exhibited WMLs, and that lower HRV was associated with higher WML volume. This was particularly strong for high-frequency HRV across both young and older adults, but it was low-frequency HRV that was related to WML volume in an age-specific manner. Moreover, we showed that the WML-HRV relationship that is apparent using T1+FLAIR or T1+T2 based WML volumes could be observed even with T1w-based WML estimation alone. Conclusions These results suggest HRV not only as an avenue through which cardiovascular risk translates into brain degeneration, but also as a potential early and readily measurable non-invasive predictor of later WML development and cognitive impairment. Research Perspective - Differences in heart rate variability show consistent associations with the volume of white-matter lesions in the brain, with low-frequency heart rate variability uniquely sensitive to age effects on lesion volume. - Associations between heart rate variability and white matter lesion volume are detectable using T1-weighted volume estimation in absence of more intensive T1-weighted + T2-weighted or T1-weighted + FLAIR lesion estimations. - The neuroanatomical basis for this relationship, particularly the difference in high- and low-frequency heart rate variability associations, will require incorporating lifestyle, hormonal, and genetic factors, as well as targeted examination of high-versus low-frequency heart rate variability bands.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".