Cerebral white matter lesions and regional blood flow are associated with reduced cognitive function in early-stage cognitive impairment
Bibliographic record
Abstract
Background Differences in the extent of cerebral white matter lesions (WML) and regional cerebral blood flow (rCBF) in early-stage cognitive impairment (ESCI) contribute to the prognosis of cognitive decline; however, it is unclear precisely how WML and rCBF affect cognitive decline in ESCI. Objective We examined the association between WML, rCBF, and cognitive impairment in the ESCI, using path analysis to clarify how these variables affect each other. Methods Eighty-three patients who consulted our memory clinic regarding memory loss were included in this study based on the Clinical Dementia Rating. Participants underwent the Mini-Mental State Examination (MMSE), brain magnetic resonance imaging (MRI) for voxel-based morphometry analysis, and brain perfusion single-photon emission computed tomography (SPECT) for rCBF evaluation in cortical regions, using 3D stereotactic surface projection (3D-SSP) analysis. Results Path analysis was performed on the MRI voxel-based morphometry and SPECT 3D-SSP data, showing a significant correlation between both and MMSE scores. In the most suitable model (GFI = 0.957), correlations were observed between lateral ventricular (LV-V) and periventricular WML (PvWML-V) volumes [standardized coefficient (SC) = 0.326, p = 0.005], LV-V and rCBF of the anterior cingulate gyrus (ACG-rCBF; SC = 0.395, p < 0.0001), and ACG-rCBF and PvWML-V (SC = 0.231, p = 0.041). Furthermore, a direct relationship between PvWML-V and MMSE scores was identified (SC = −0.238, p = 0.026). Conclusion Significant interrelationships were observed among the LV-V, PvWML-V, and ACG-rCBF that directly affected the MMSE score in the ESCI. The mechanisms behind these interactions and the impact of PvWML-V on cognitive function require further investigation.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".