The neuropsychological profile of vascular cognitive impairment not demented: A meta‐analysis
Bibliographic record
Abstract
The most common cause of vascular cognitive impairment not demented (VCIND) is cerebral small vessel disease leading to diffuse subcortical white matter lesions. While many studies indicate that the core cognitive features of VCIND are executive dysfunction and impaired processing speed, this finding is not always consistent, and may be partially dependent on the comparison group applied. Hence, we undertook two systematic meta-analytic reviews on neuropsychological test performance across eight cognitive domains: between VCIND and healthy controls (data from 27 studies), and between VCIND and non-vascular mild cognitive impairment (nv-MCI; data from 20 studies). Our quantitative synthesis of the research literature demonstrates that individuals with VCIND show weaknesses across all cognitive domains relative to healthy controls, with the greatest impairment in the domain of processing speed (Md = -1.36), and the least affected being working memory (Md = -.48) and visuospatial construction (Md = -.63). When compared directly with nv-MCI, individuals with VCIND had significantly greater deficits in processing speed (Md = -.55) and executive functioning (Md = -.40), while those with nv-MCI exhibited a greater relative deficit in delayed memory (Md = .41). Our analyses indicate that disruption to subcortical white matter tracts impairs more cognitive processes than is typically thought to be directly related to the fronto-subcortical network. The data also suggest that differing brain aetiologies can be responsible for similar cognitive profiles. Although the findings do not evince diagnostic value, they allude to the interconnectivity of disparate cognitive processes and call for further research on the behavioural outcome of network disruption.
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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 | 0.013 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.045 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".