Imaging Characteristics for Predicting Cognitive Impairment in Patients With Cerebral Autosomal Dominant Arteriopathy With Subcortical Infarcts and Leukoencephalopathy
Bibliographic record
Abstract
Objectives Patients with cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) show various clinical symptoms, including migraine, recurrent stroke, and cognitive impairment. We investigated the associations between magnetic resonance imaging (MRI) markers of small vessel disease and neuropsychological tests and identified the MRI characteristics for predicting cognitive impairment in patients with CADASIL. Methods Subjects included 60 CADASIL patients diagnosed with genetic tests and registered in the Japanese CADASIL REDCap database between June 2016 and December 2020. Patient information including clinical data, modified Rankin Scale (mRS); MRI findings of small vessel disease including periventricular and deep white matter lesions (WML), lacunar infarcts, and cerebral microbleeds (CMBs); and neuropsychological tests, including the Japanese version of the Mini-Mental State Examination (MMSE), the Japanese version of the Montreal Cognitive Assessment (MoCA-J), and the Frontal Assessment Battery (FAB), were evaluated. Results Data from 44 CADASIL patients were eligible for this study, compared between patients with and without dementia. Regarding the neuroimaging findings, the Fazekas score of periventricular and deep WML was higher in patients with dementia (periventricular, p = 0.003; deep, p = 0.009). The number of lacunar infarcts was higher in patients with dementia ( p = 0.001). The standardized partial regression coefficient (SPRC) in MoCA-J was 0.826 (95% CI, 0.723–0.942; p = 0.005) for the number of CMBs. The SPRC in MMSE was 0.826 (95% CI, 0.719–0.949; p = 0.007) for the number of CMBs. The SPRC for FAB decreased significantly to 0.728 (95% CI, 0.551–0.960; p = 0.024) for the number of lacunar infarcts. Receiver operating characteristic (ROC) curves for dementia showed that in the number of lacunar infarcts, a cut-off score of 5.5 showed 90.9% sensitivity and 61.1% specificity. For the number of CMBs, a cut-off score of 18.5 showed 45.5% sensitivity and 100% specificity. Conclusion The characteristic MRI findings were that CADASIL patients with dementia had severe WML, both periventricular and deep, and a larger number of lacunar infarcts than those without dementia. The risk of dementia may be associated with ≥ 6 lacunar infarcts, ≥19 CMBs, or a Fazekas scale score of 3 in periventricular and deep WML.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".