Prefrontal Aβ pathology influencing the pathway from apathy to cognitive decline in non-dementia elderly
Bibliographic record
Abstract
Abstract The purpose of this study is to investigate the complex connection between apathy and cognitive decline that remains unclear. A total of 1057 non-dementia elderly from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database received up to 13 years of follow-up and were divided into an apathy negative (−) group of 943 participants and an apathy positive (+) group of 114 participants through the Neuropsychiatric Inventory (NPI)-apathy subitem. Cerebrospinal fluid (CSF) AD biomarkers and amyloid β (Aβ) PET were measured, and their longitudinal changes were assessed using linear mixed-effects models. Risk factors for cognitive decline and apathy conversion were explored through the Cox proportional hazards model. Mediation effects of Aβ pathology on cognition were investigated using the causal mediation analysis. Apathy syndrome was associated with faster impairment of cognition and elevation of the Aβ burden. The effects of apathy on cognitive function and life quality were mediated by Aβ pathology, including CSF Aβ 42 /total tau ratio, and Aβ deposition in the prefrontal regions. Apathy syndrome was the risk factor for cognitive deterioration; meanwhile, frontal Aβ burden was the risk factor for apathy conversion. Apathy syndrome is an early manifestation of cognitive decline and there are bidirectional roles between apathy syndrome and Aβ pathology. Prefrontal Aβ pathology influenced the pathway from apathy to cognitive decline.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".