Brain grey matter volume alterations in late-life depression
Bibliographic record
Abstract
BACKGROUND: Voxel-based morphometry (VBM) studies have demonstrated that grey matter abnormalities are involved in the pathophysiology of late-life depression (LLD), but the findings are inconsistent and have not been quantitatively reviewed. The aim of the present study was to conduct a meta-analysis that integrated the reported VBM studies, to determine consistent grey matter alterations in individuals with LLD. METHODS: A systematic search was conducted to identify VBM studies that compared patients with LLD and healthy controls. We performed a meta-analysis using the effect size signed differential mapping method to quantitatively estimate regional grey matter abnormalities in patients with LLD. RESULTS: We included 9 studies with 11 data sets comprising 292 patients with LLD and 278 healthy controls in our meta-analysis. The pooled and subgroup meta-analyses showed robust grey matter reductions in the right lentiform nucleus extending into the parahippocampus, the hippocampus and the amygdala, the bilateral medial frontal gyrus and the right subcallosal gyrus as well as a grey matter increase in the right lingual gyrus. Meta-regression analyses showed that mean age and the percentage of female patients with LLD were not significantly related to grey matter changes. LIMITATIONS: The analysis techniques, patient characteristics and clinical variables of the studies included were heterogeneous, and most participants were medicated. CONCLUSION: The present meta-analysis is, to our knowledge, the first to overcome previous inconsistencies in the VBM studies of LLD and provide robust evidence for grey matter alterations within fronto-striatal-limbic networks, thereby implicating them in the pathophysiology of LLD. The mean age and the percentage of female patients with LLD did not appear to have a measurable impact on grey matter changes, although we cannot rule out the contributory effects of medication.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.010 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".