Abnormal functional connectivity density in patients with ischemic white matter lesions
Bibliographic record
Abstract
White matter lesions (WMLs) are frequently detected in elderly people. Previous structural and functional studies have demonstrated that WMLs are associated with cognitive and motor decline. However, the underlying mechanism of how WMLs lead to cognitive decline and motor disturbance remains unclear. We used functional connectivity density mapping (FCDM) to investigate changes in brain functional connectivity in 16 patients with ischemic WMLs and 13 controls. Both short- and long-range FCD maps were computed, and group comparisons were performed between the 2 groups. A correlation analysis was further performed between regions with altered FCD and cognitive test scores (Mini-Mental State Examination [MMSE] and Montreal Cognitive Assessment [MoCA]) in the patient group. We found that patients with ischemic WMLs showed reduced short-range FCD in the temporal cortex, primary motor cortex, and subcortical region, which may account for inadequate top-down attention, impaired motor, memory, and executive function associated with WMLs. The positive correlation between primary motor cortex and MoCA scores may provide evidence for the influences of cognitive function on behavioral performance. The inferior parietal cortex exhibited increased short-range FCD, reflecting a hyper bottom-up attention to compensate for the inadequate top-down attention for language comprehension and information retrieval in patients with WMLs. Moreover, the prefrontal and primary motor cortex showed increased long-range FCD and the former positively correlated with MoCA scores, which may suggest a strategy of cortical functional reorganization to compensate for motor and executive deficits. Our findings provide new insights into how WMLs cause cognitive and motor decline from cortical functional connectivity perspective.
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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) | 0.001 | 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".