Risk Factors, Neuroanatomical Correlates, and Outcome of Neuropsychiatric Symptoms in Alzheimer’s Disease
Bibliographic record
Abstract
BACKGROUND: An integrative model of neuropsychiatric symptoms (NPS) in Alzheimer's disease (AD) is lacking. OBJECTIVE: In this study, we investigated the risk factors, anatomy, biology, and outcomes of NPS in AD. METHODS: 181 subjects were included from the Alzheimer's Disease Neuroimaging Study (ADNI). NPS were assessed with the Neuropsychiatric Inventory Questionnaire at baseline and 6 months. NPI >3 was used as a threshold for NPS positivity. Three NPS courses were characterized: 1) minimal/absent (negative at 0 and 6 months, n = 77); 2) fluctuating (positive only at one time point, n = 53); 3) persistent (positive at both time points, n = 51). We examined the association between NPS course and family history of dementia, personal history of psychiatric disorders, cerebrospinal fluid biomarkers, atrophy patterns, as well as longitudinal cognitive and functional measures at 12 and 24 months (MMSE, CDR-SOB, FAQ). RESULTS: AD subjects with absent, fluctuating, or persistent NPS had similar CSF amyloid-β and tau levels. AD subjects with minimal/absent NPS had less personal history of psychiatric disorders (35%) than those with fluctuating (57%; p = 0.015) or persistent NPS (47%, not significant). At 24 months, AD subjects with persistent NPS had worse cognitive (MMSE; p = 0.05) and functional (CDR-SOB; p = 0.016) outcomes. Dorsolateral prefrontal atrophy was seen in persistent NPS, but not in fluctuating NPS. CONCLUSIONS: Our results suggest that individuals with personal history of psychiatric disorders might be more vulnerable to develop NPS throughout the course of AD. The worst cognitive and functional outcomes associated with NPS in AD underscores the importance of monitoring NPS early in the disease course.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".