Association of Cerebral Small Vessel Disease Burden with Neuropsychiatric Symptoms in Non-Demented Elderly: A Longitudinal Study
Bibliographic record
Abstract
BACKGROUND: The links between cerebral small vessel disease (CSVD) burden and neuropsychiatric symptoms (NPS) have not been fully studied. OBJECTIVE: We aimed to explore the associations of the CSVD burden with Neuropsychiatric Inventory (NPI) total scores and its subsyndromes in the elderly without dementia. METHODS: We investigated 630 non-demented participants from the Alzheimer's Disease Neuroimaging Initiative. All of them had NPI assessments and 3 Tesla MRI scans at baseline and 616 had longitudinal NPI assessments during the follow-up. Linear mixed-effects models were used to investigate the cross-sectional and longitudinal associations of CSVD burden with NPI total scores and its subsyndromes. RESULTS: Higher CSVD burden longitudinally predicted more serious neuropsychiatric symptoms, including NPS (p < 0.0001), hyperactivity (p = 0.0006), affective symptoms (p = 0.0091), and apathy (p < 0.0001) in the total participants. Lacunar infarcts (LIs), white matter hyperactivities (WMHs), and cerebral microbleeds (CMBs) might play important roles in the occurrence of NPS, since they were longitudinally associated with specific neuropsychiatric subsyndromes. LIs contributed to hyperactivity (p = 0.0092), psychosis (p = 0.0402), affective symptoms (p = 0.0156), and apathy (p < 0.0001). WMHs were associated with hyperactivity (p = 0.0377) and apathy (p = 0.0343). However, CMBs were only related to apathy (p = 0.0141). CONCLUSION: CSVD burden was associated with multiple neuropsychiatric symptoms, suggesting the importance of monitoring and controlling vascular risk factors. Different markers of CSVD were associated with specific subsyndromes of NPS, suggesting that different markers tended to occur in different encephalic regions.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".