Dementia is Associated with a Syndrome of Global Neuropsychiatric Disturbance
Bibliographic record
Abstract
Abstract Objective Global factors have been identified in measures of cognitive performance (i.e., Spearman’s g ) and psychopathology (i.e., “General Psychopathology”, “ p ”). Dementia is also strongly determined by the latent phenotype “δ”, derived from g . We wondered if the Behavior and Psychological Symptoms of Dementia (BPSD) might arise from an association between δ and p . Methods δ and p were constructed by confirmatory factor analyses in data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). δ and orthogonal factors representing “domain-specific” variance in memory (MEM) and executive function (EF) were regressed onto p and orthogonal factors representing “domain-specific” variance in positive (+) and negative (-) symptoms rated by the Neuropsychiatric Inventory Nursing Home Questionnaire (NPI-Q) by multiple regression in a structural equation model (SAM) framework. Results Model fit was excellent (CFI = 0.98, RMSEA = 0.03). δ was strongly associated with p , (+) and (-) and strongly associated with p (r = −0.57, p<0.001). All three associations were inverse (adverse). Independently of δ, MEM was uniquely associated with (+), while ECF was associated with (-). Both associations were moderately strong. ECF was also weakly associated with p . Conclusions Dementia severity (δ) derived from general intelligence ( g ) is specifically associated with general psychopathology ( p ). This is p ’s first demonstration in an elderly sample and the first to distinguish the global behavioral and psychological symptoms specific to dementia (BPSSD) from behavioral disturbances arising by way of non-dementing, albeit likely disease-specific, processes affecting domain-specific cognitive and behavioral constructs. Our findings call into question the utility of proposed regional interventions in BPSSD, and point to the need to explore global interventions against dementia-specific behavioral features.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".