Painting by Lesions: Functional Networks Affected by White Matter Lesions Are Associated with Poorer Cognition
Bibliographic record
Abstract
Abstract Cerebrovascular disease (CvD) is the second most common cause of dementia. Its associated pathology, such as white matter lesions (WML), is associated with reduced cognition. Due to the high variability, the relevance of WML location remains unknown. We hypothesised that although the location of WMLs may appear sporadic, they may actually lie within common functional networks. We used novel imaging methods to map the location of WMLs in a clinical sample with the functional connectivity associated with the same location in the human connectome. This identified the functional networks containing the largest WML load (>50%) in older adults with CvD. We then analyzed the association between level of disruption to these networks and measures of global cognition and executive functions. Included in this study were 164 older adults (>55 years old) with CvD. Cognition was assessed using the: 1) Montreal Cognitive Assessment (MoCA); 2) Stroop Colour Word Test; 3) Trail Making Tests; and 4) Digit Symbol Substitution Test. Our results found that the visual network and ventral attention network (VAN) surpassed the 50% overlap threshold with 85% and 66% overlap respectively. Additionally, after controlling for multiple comparisons and age, the level of disruption to the VAN was significantly associated with poorer global cognition, as measured by the MoCA (p=.001). These novel findings identify the functional networks most affected by the presence of WMLs in older adults with CvD and suggest that the disruption to the VAN caused by WML load may underlie the deficits seen in cognition in this population.
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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 | 0.002 |
| 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 | 0.001 |
| 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".