Neuroimaging in vascular cognitive impairment: a state-of-the-art review
Bibliographic record
Abstract
Imaging is critical in the diagnosis and treatment of dementia, particularly in vascular cognitive impairment, due to the visualization of ischemic and hemorrhagic injury of gray and white matter. Magnetic resonance imaging (MRI) and positron emission tomography (PET) provide structural and functional information. Clinical MRI is both generally available and versatile - T2-weighted images show infarcts, FLAIR shows white matter changes and lacunar infarcts, and susceptibility-weighted images reveal microbleeds. Diffusion MRI adds another dimension by showing graded damage to white matter, making it more sensitive to white matter injury than FLAIR. Regions of neuroinflammatory disruption of the blood-brain barrier with increased permeability can be quantified and visualized with dynamic contrast-enhanced MRI. PET shows metabolism of glucose and accumulation of amyloid and tau, which is useful in showing abnormal metabolism in Alzheimer's disease. Combining MRI and PET allows identification of patients with mixed dementia, with MRI showing white matter injury and PET demonstrating regional impairment of glucose metabolism and deposition of amyloid. Excellent anatomical detail can be observed with 7.0-Tesla MRI. Imaging is the optimal method to follow the effect of treatments since changes in MRI scans are seen prior to those in cognition. This review describes the role of various imaging modalities in the diagnosis and treatment of vascular cognitive impairment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 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 teacher head, 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".