Abstract WP494: Neuroimaging Correlates of Apathy in Late-Onset Depression
Bibliographic record
Abstract
Apathy is prevalent in older depressed patients and considered to be a predictor of increased dementia risk. Neuroimaging characteristics of late-life depression are being discussed. However, it is unclear whether apathetic (ApD) and nonapathetic depression (D) have different neuroimaging correlates. The objective of this study was to examine structural and functional bases of ApD by using morphometric and functional connectivity MRI analyses. We enrolled 45 consecutive patients with late-onset depression (85% female; mean age=66 (4) years, mean education=14 (2) years) and 22 age and gender-matched healthy elderly. Patients were divided into ApD (n=26) and D (n=19) groups based on Apathy Scale scores. All the participants underwent 1.5 T structural MRI and resting-state fMRI. Fazekas scale was used to quantify white matter hyperintensities. Demographic data, Hamilton Depression Rating Scale (HAMD), Apathy Scale, and MoCA scores for the three groups were compared. Association between apathy, depression and neuroimaging characteristics was assessed using regression analysis with demographic and cognitive variables included as covariates. ApD patients demonstrated nonsignificantly higher HAMD and Fazekas scores and lower MoCA scores compared to D patients. The latter group showed similar Fazekas and slightly lower MoCA scores vs healthy elderly. After controlling for covariates, apathy was significantly associated with volumes of nucleus caudatus and putamen on the right as well as functional connectivity between anterior cingulate and parahippocampal gyrus. Depression correlated with the volumes of the cerebral and cerebellum cortices as well as functional connectivity of salience resting state network. Our study demonstrated an association between volumes of basal ganglia, functional connectivity of anterior cingulate and apathy in late-onset depression.
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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) | 0.002 | 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".