Glutamatergic neurometabolite levels in major depressive disorder: a systematic review and meta-analysis of proton magnetic resonance spectroscopy studies
Bibliographic record
Abstract
Abstract Alterations in glutamatergic neurotransmission are implicated in the pathophysiology of depression, and the glutamatergic system represents a treatment target for depression. To summarize the nature of glutamatergic alterations in patients with depression, we conducted a meta-analysis of proton magnetic resonance (1H-MRS) spectroscopy studies examining levels of glutamate. We used the search terms: depress* AND (MRS OR “magnetic resonance spectroscopy”). The search was performed with MEDLINE, Embase, and PsycINFO. The inclusion criteria were 1H-MRS studies comparing levels of glutamate + glutamine (Glx), glutamate, or glutamine between patients with depression and healthy controls. Standardized mean differences (SMD) were calculated to assess group differences in the levels of glutamatergic neurometabolites. Forty-nine studies met the eligibility criteria, which included 1180 patients and 1066 healthy controls. There were significant decreases in Glx within the medial frontal cortex (SMD = −0.38; 95% CI, −0.69 to −0.07) in patients with depression compared with controls. Subanalyses revealed that there was a significant decrease in Glx in the medial frontal cortex in medicated patients with depression (SMD = −0.50; 95% CI, −0.80 to −0.20), but not in unmedicated patients (SMD = −0.27; 95% CI, −0.76 to 0.21) compared with controls. Overall, decreased levels of glutamatergic metabolites in the medial frontal cortex are linked with the pathophysiology of depression. These findings are in line with the hypothesis that depression may be associated with abnormal glutamatergic neurotransmission.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.022 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".