Brain pathology and cognitive scores prior to onset of late‐life depression
Bibliographic record
Abstract
OBJECTIVES: Understanding the biological changes that occur prior to onset of late-life depression (LLD) is key to its prevention. To investigate potential predictors of LLD, we assessed cognitive scores and neurodegenerative and vascular biomarkers in healthy older adults who later developed depression. METHODS: Longitudinal data from the Alzheimer's Disease Neuroimaging Initiative of 241 cognitively unimpaired and non-depressed older adults aged 56-90 at baseline with at least 4 years of follow-up were included. Participants were classified based on whether they developed an incident depression (n = 96) or not (n = 145). Cognitive measures of memory, executive functioning, and language, and biomarkers proposed to be related to LLD: hippocampal volume, white matter hyperintensity volume (WMH), and cortical and cerebrospinal fluid (CSF) amyloid beta levels, were compared between the incident depression and the never-depressed groups at four time points: at baseline, the visit prior to onset, at onset, and after the onset of depression. RESULTS: In the incident depression group, there was a mild decline in cognitive scores from baseline to the visit before depression onset compared with the never-depressed group. The cognitive differences between the groups became more marked after depression onset. Baseline cortical amyloid burden, CSF amyloid beta levels, and WMH were significant predictors of incident depression. Compared to the non-depressed group, hippocampal volume was not reduced before onset, but was reduced following depression. CONCLUSIONS: Amyloid pathology and WMH can predict future development of LLD in cognitively unimpaired individuals and may be involved in precipitating vulnerability for depression in older adults.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.001 |
| 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".