The Relationship between Neuropsychiatric Symptoms and Default-Mode Network Connectivity in Alzheimer’s Disease
Bibliographic record
Abstract
OBJECTIVE: Neuropsychiatric symptoms of dementia are prevalent and extremely burdening for the patient and caregivers, but the underlying mechanism of these symptoms has not been investigated. This study aimed to investigate the relationship between neuropsychiatric symptoms and default-mode functional connectivity in Alzheimer's disease. METHODS: Neuropsychiatric symptoms were assessed using the Neuropsychiatric Inventory. Functional magnetic resonance imaging was conducted on seventy patients with Alzheimer's disease during rest. We conducted a seed-based functional connectivity analysis to identify anterior and posterior default-mode networks (DMN). Seeds were the medial prefrontal cortex (Montreal Neurological Institute 12, 51, 36; seed radius=3 mm) for the anterior DMN and the precuneus (Montreal Neurological Institute -6, -63, 27; seed radius=3 mm) for the posterior DMN We then correlated the scores on neuropsychiatric inventory syndromes (apathy, hyperactivity, affective, and psychosis syndrome) with maps of connectivity in the default-mode network. RESULTS: There was a significant correlation between decreased connectivity in the medial prefrontal cortex of the anterior defaultmode network and hyperactivity (agitation, irritability, aberrant motor behavior, euphoria, and disinhibition) syndrome (p<0.05, family wise error cluster-level corrected). CONCLUSION: Our study demonstrated that hyperactivity syndrome is related to hypoconnected default-mode network in Alzheimer's disease. This finding suggests that specific network alterations are associated with certain neuropsychiatric syndromes.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".