Diverse tau pathologies in late‐life mood disorders revealed by PET and autopsy assays
Bibliographic record
Abstract
INTRODUCTION: Late-life mood disorders (LLMDs) may represent prodromal manifestations of neurodegenerative dementia; however, the neuropathological basis of LLMDs, including depression and bipolar disorder, remains unclear. We aimed to investigate the involvement of Alzheimer's disease (AD) and non-AD tau pathologies in LLMD participants. METHODS: C-Pittsburgh compound B. Additionally, we conducted a clinicopathological correlation analysis in 208 autopsy cases, including various neurodegenerative diseases. RESULTS: LLMD participants were more likely to be tau PET and Aβ PET positive than HCs. The PET results were supported by the post mortem results that showed a higher likelihood of diverse tauopathies in patients with late-life mania or depression than those without. DISCUSSION: Our PET and autopsy assays suggest that AD and diverse non-AD tau pathologies might underlie the neuropathological basis of some LLMD cases. HIGHLIGHTS: Late-life mood disorders (LLMDs) may represent prodromal states of dementia. The neuropathological basis of LLMDs remains unclear. LLMD subjects were highly likely to be tau positron emission tomography (PET) positive. Brain bank data supported our PET results. Alzheimer's disease (AD) and diverse non-AD tau pathologies can contribute to LLMDs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".