Neuropathology of mood disorders: do we see the stigmata of inflammation?
Bibliographic record
Abstract
A proportion of cases with mood disorders have elevated inflammatory markers in the blood that conceivably may result from stress, infection and/or autoimmunity. However, it is not yet clear whether depression is a neuroinflammatory disease. Multiple histopathological and molecular abnormalities have been found postmortem but the etiology of these abnormalities is unknown. Here, we take an immunological perspective of this literature. Increases in activated microglia or perivascular macrophages in suicide victims have been reported in the parenchyma. In contrast, astrocytic markers generally are downregulated in mood disorders. Impairment of astrocytic function likely compromises the reuptake of glutamate potentially leading to excitotoxicity. Inflammatory cytokines and microglia/macrophage-derived quinolinic acid (QA) downregulate the excitatory amino acid transporters responsible for this reuptake, while QA has the additional effect of inhibiting astroglial glutamine synthetase, which converts glutamate to glutamine. Given that oligodendroglia are particularly vulnerable to inflammation, it is noteworthy that reductions in numbers or density of oligodendrocyte cells are one of the most prominent findings in depression. Structural and/or functional changes to GABAergic interneurons also are salient in postmortem brain samples, and may conceivably be related to early inflammatory insults. Although the postmortem data are consistent with a neuroimmune etiology in a subgroup of depressed individuals, we do not argue that all depression-associated abnormalities are reflective of a neuroinflammatory process or even that all immunological activity in the brain is deleterious. Rather, we highlight the pervasive role of immune signaling pathways in brain function and provide an alternative perspective on the current postmortem literature.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.003 | 0.012 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".