Two cases report on the relationship between white matter hyperintensity volume and cognitive dysfunction in cerebral small vessel disease based on magnetic resonance imaging
Bibliographic record
Abstract
RATIONALE: With the development of magnetic resonance imaging (MRI) technology, most of the research tends to find that there is a significant positive correlation between white matter hyperintensities (WMHs) and cognitive dysfunction in cerebral small vessel vascular disease. In this paper, we report 2 cases of cerebral small vessel disease with significant differences in cognitive function and analyze them by multidimensional assessment using imaging technology so as to provide a methodological reference for identifying and diagnosing the causes of differences in cognitive function in cerebral small vessel disease patients. PATIENT CONCERNS: Patient 1 was a 64-year-old middle-aged man who presented 10 years ago with slow reaction time, memory loss, and loss of self-care ability, and MRI suggested multiple ischemic infarct foci with cerebral white matter changes. Patient 2 was a 69-year-old middle-aged woman, who did not have any significant abnormalities in cognitive function, and imaging suggested multiple ischemic foci, infarct foci, and cerebral white matter degeneration. DIAGNOSIS: MRI showed a large fusion of high signal in the cerebral white matter in both patients, which belonged to the category of cerebral small vessel disease according to the Fazekas classification of grade 3. INTERVENTIONS: We used imaging techniques to compare the 2 MRI brain white matter high signals in a multidimensional manner and further compared the differences in cognitive functioning between the 2 in terms of brain age, brain functional networks, focal loading of white matter fiber tracts, and neuropsychological scales. OUTCOMES: Brain age difference was assessed by whole-brain level and brain function network, white matter fiber bundle lesion load, and Montreal Cognitive Assessment and Mini-Mental State Examination scale scores; the results suggested that patient 1 had relatively poor cognitive function. LESSONS: In this paper, we concluded that the volume of high white matter signal in WMH is not positively correlated with the severity of cognitive impairment. In addition to cerebral WMHs, we believe that alterations in cerebral network connectivity and white matter microstructure may be the neuroimaging basis of cognitive decline in patients with WMH, which may provide a new idea for the early diagnosis of cognitive function in patients with cerebral small vessel disease.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".