White Matter Hyperintensities on Brain MRI are Related to Brain Atrophy and Accelerated Brain Age
Bibliographic record
Abstract
Abstract Background Brain age – an estimate of chronological age‐ derived from structural brain MR neuroimaging may reveal underlying factors driving brain aging. White matter hyperintensities (WMH) are areas of abnormally high signal on FLAIR that frequently reflect chronic small vessel ischemic changes and potentially increased aging. Method Overall, 1,164 healthy participants from four sites (mean chronological age 55.17 ± 12.37 years, 52% women; 48% men; 39% non‐white) were scanned on 1.5T MR machines with a whole‐body protocol. For each participant, a 2D multi‐slice FLAIR image was obtained. A 2D convolutional neural network, trained on data from 120 individuals across three public datasets (MICCAI 2017, ISLES2015, and ISLES2022), was employed to automatically segment WMH from the FLAIR scans. Deep learning with FastSurfer on MPRAGE trained on 134 participants aged 27‐66 and segmented 96 brain regions. Brain age was computed using a regression‐based 3D Simple Fully Convolutional Network trained on in‐house T1‐weighted MRI scans collected from 5,500 healthy individuals (, aged 18 to 89 years). Brain age gap (BAG) was computed by subtracting chronological age from brain age. Partial correlation and regression models evaluated the relationship between WMH normalized to total brain volume (gray matter and white matter), brain age, brain volumes controlling for age, sex, and total intracranial volume. Chronological age was not adjusted for in the brain age models to avoid collinearity. Result Mean brain age was similar to chronological age (mean brain age = 56.04 ± 12.65, mean BAG = 0.69). The median of WMH were 1.4 mL (0.75‐2.51 mL). Increased WMH were related to lower brain volumes in the i) hippocampus (rp= ‐0.13, p = 1.174e‐05) ii) cerebral white matter (rp= ‐0.08, p = .004) iii) thalamus (rp= ‐0.16, p = 1.634e‐07). Additionally, increased WMH was related to larger cerebral ventricle size (rp= 0.28, p = 6.604e‐21). The regression model showed that increased WMH was related to increased brain age (t= 5.92, rp= .42, p <.001) and increased brain age gap (t= 4.07, rp= .12, p <.001). Conclusion Increased WMH are related to brain atrophy – in both Alzheimer's and non‐Alzheimer's affected regions – and are also related to increased brain age and accelerated brain aging.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".