The pathogenesis of cerebral small vessel disease and vascular cognitive impairment
Bibliographic record
Abstract
Cerebral small vessel disease (cSVD) is the broadly accepted term nowadays to designate a heterogeneous group of diseases caused by in situ damage of small brain vessels commonly related to aging, hypertension, or genetic factors. Cardinal neuroimaging features include small (<20 mm) infarcts or lacunes, cerebral microbleeds, white matter hyperintensities, enlarged perivascular spaces, and brain atrophy. Overall, cSVD represents one of the major problems facing global society today, causing a quarter of all ischemic strokes and the vast majority of spontaneous hemorrhages and accounting for 20% or more of all dementias. Yet mechanisms of cSVD are still incompletely understood, and we have no effective proven treatments other than risk factor modification. Recently, major progress in understanding the underlying disease mechanisms has occurred thanks to novel approaches including advanced molecular, genetic, and imaging tools. Here, we provide a comprehensive and critical appraisal of the biggest advances in our understanding of how cSVD affects the structure and function of small brain vessels, causes brain lesions, and alters cognition. To set the stage, we begin by reviewing the molecular anatomy and physiology of healthy small brain vessels and report on the milestones from the medical literature, starting in the 1850s, that have laid the foundation for the "modern" definition of cSVD. We conclude by discussing the framework for clinical interventions that will emerge from these novel insights. We also highlight the outstanding questions to address and challenges to tackle to move the field forward.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".