Damage to white matter networks resulting from small vessel disease and the effects on cognitive function
Bibliographic record
Abstract
White matter hyperintensities (WMHs) are prevalent in older age and are associated with cognitive decline. The location and extent of WMHs likely influences the relationship with behavior. Identifying which tracts are more likely impacted by WMHs might enable better understanding of which behaviors are affected. Therefore, this study aimed to i) identify which white matter tracts are most affected by WMHs, and ii) identify tracts where the presence of WMHs is associated with poorer cognitive scores. Participants (N = 212, 20-80 years) completed the Montreal Cognitive Assessment (MoCA). WMHs were manually delineated on FLAIR scans. In DSIStudio, we used the Human Connectome Project 1065FIB template to track how many fibers of each white matter tract intersected each participant's WMH map. Values obtained represent disconnection associated with WMHs. These scores were correlated with age, MoCA total and memory index scores. There was significantly more disconnection with older age in the right arcuate fasciculus, extreme capsule, frontal aslant tract, bilateral inferior, middle and superior longitudinal fasciculi, and the corpus callosum. Disconnection associated with WMHs in the right superior longitudinal fasciculus was significantly associated with a lower MoCA scores. Finally, disconnection in the right extreme capsule, middle and superior longitudinal fascicli, and bilateral frontal aslant tracts were significantly associated with lower MoCA memory index scores. This study highlights the prevalence of WMHs across the lifespan and demonstrates a clear relationship between tract-specific WMHs and cognition. Age-related white-matter degeneration was pronounced in many association fibers, particularly in the right hemisphere. These data suggest age related disruption of specific white matter tracts represents a clear and present risk factor for global cognition and memory as we age.
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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 | 0.001 | 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".