Lower Hippocampal Volume in Patients Suffering From Depression: A Meta-Analysis
Bibliographic record
Abstract
OBJECTIVE: A number of studies have used magnetic resonance imaging to examine volumetric differences in temporal structures in subjects suffering from major depressive disorder. Studies have reported lower hippocampal and amygdala volume, but results have been inconsistent. The authors were interested, therefore, in examining these studies in the aggregate in order to determine whether hippocampal volume is lower in major depressive disorder. They also examined factors that may contribute to the disparate results in the literature. METHOD: A meta-analysis was conducted of studies that used magnetic resonance imaging to assess the volume of the hippocampus and related structures in patients with major depressive disorder. RESULTS: Patients were seen to have lower hippocampal volume relative to comparison subjects, detectable if the hippocampus was measured as a discrete structure. CONCLUSIONS: Although the effect of major depressive disorder on amygdala volume remains to be conclusively established, inclusion of the amygdala with the hippocampus appears to have decreased the likelihood of detecting volumetric differences in either structure. Slice thickness or other scan parameters did not account for a substantive amount of the variance in results, whereas clinical variables of the populations studied, such as duration of illness or presence of abuse, may account for much of the discrepancy between findings.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.010 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".