Differential effects of confluent and non‐confluent white matter hyperintensities on functional connectivity in mild cognitive impairment
Bibliographic record
Abstract
Abstract Background Emerging evidence suggests that confluent WMH results in greater cognitive impairment compared to non‐confluent WMH. However, the mechanism linking confluent WMH and early cognitive impairment is not clearly understood. We studied the effects of confluent and non‐confluent WMH on whole‐brain functional connectivity (FC) across 164 regions of interest (ROI) in subjects with Mild Cognitive Impairment (MCI). Method Sixty‐three MCI subjects with T1‐weighted MRI, T2‐weighted MRI, resting‐state functional MRI (rs‐fMRI) and neuropsychological data were studied. Subjects were classified as confluent WMH (C‐WMH) or non‐confluent WMH (NC‐WMH) using the Staal’s criteria on the Fazekas WMH scale. Group‐level ROI‐to‐ROI FC trends and differences at differential WMH subtypes were computed using standard rs‐fMRI analysis. Result Subjects with C‐WMH exhibited increased inter‐regional FC in the fronto‐parietal, fronto‐occipital, parieto‐occipital, and temporo‐parietal regions of the salience, dorsal‐attention, default‐mode, and visual networks. Increased intra‐regional FC in the frontal lobe was also observed in C‐WMH. In contrast, only intra‐regional FC changes were seen in the frontal and temporal lobes in NC‐WMH. Performance on the Montreal Cognitive Assessment correlated positively with increased inter‐regional FC only in the C‐WMH group, suggesting that increased connectivity was associated with better global cognition in MCI subjects with C‐WMH. Conclusion C‐WMH in subjects with MCI is associated with widespread increased inter‐regional FC changes which correlates with better performance in global cognition. These findings provide novel insights into divergent functional alterations related to the confluence of WMH in MCI.
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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 | 0.000 | 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".