Cortical Thinning in Vascular Mild Cognitive Impairment and Vascular Dementia of Subcortical Type
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Amnestic mild cognitive impairment (MCI) is known to be a preclinical stage of Alzheimer's disease (AD). Similarly, MCI associated with small-vessel disease (svMCI), might be a forme froste of subcortical vascular dementia (SVaD). Patterns of cortical thinning in addition to the ischemia rating on MRI may further elucidate the clinical characteristics and pathogenesis of SVaD and svMCI. We tried to determine if svMCI differs from SVaD in the distribution of cortical atrophy, which may help understand the hierarchy between svMCI and SVaD and possibly also how svMCI evolves into SVaD. METHODS: Twenty patients with SVaD, 34 patients with svMCI, 115 patients with AD, and 96 individuals with normal-cognition (NC) were imaged with magnetic resonance imaging (MRI) including 3-dimensional volumetric images for cortical thickness analysis across the entire brain. RESULTS: Compared to NC, svMCI patients showed cortical thinning in inferior frontal and orbitofrontal gyri, anterior cingulate, insula, superior temporal gyrus, and lingual gyrus, while cortical thinning in SVaD patients involved all these areas plus dorsolateral prefrontal and temporal cortices. CONCLUSION: Our findings suggest the presence of hierarchy between svMCI and SVaD, and that the cognitive decline from svMCI to SVaD is associated with lesions in dorsolateral prefrontal and temporal cortices.
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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 | 0.002 | 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".