New insights into cerebral small vessel disease and vascular cognitive impairment from MRI
Bibliographic record
Abstract
PURPOSE OF REVIEW: We review recent MRI research that addresses two important challenges in cerebral small vessel disease (SVD) research: early diagnosis, and linking SVD with cognitive impairment. First, we review studies of MRI measurements of blood flow and blood-brain barrier integrity. Second, we review MRI studies identifying neuroimaging correlates of SVD-related cognitive dysfunction, focusing on brain connectivity and white matter microarchitecture. This research is placed in context through discussion of recent recommendations for management of incidentally discovered SVD, and neuroimaging biomarker use in clinical trials. RECENT FINDINGS: Cerebral perfusion, cerebrovascular reactivity (CVR), blood-brain barrier permeability, and white matter microarchitecture are measurable using MRI, and are altered in SVD. Lower cerebral blood flow predicts a higher future risk for dementia, whereas decreased CVR occurs at early stages of SVD and is associated with future white matter hyperintensity growth. Two new approaches to analyzing diffusion tensor imaging (DTI) data in SVD patients have emerged: graph theory-based analysis of networks of DTI connectivity between cortical nodes, and analysis of histograms of mean diffusivity of the hemispheric white matter. SUMMARY: New, advanced quantitative neuroimaging techniques are not ready for routine radiological practice but are already being employed as monitoring biomarkers in the newest generation of trials for SVD.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".