Neuropathological relationship between major depression and dementia: A hypothetical model and review
Bibliographic record
Abstract
Major depression (MDD) is a chronic psychiatric condition in which patients often show increasing cognitive impairment with recurring episodes. Neurodegeneration may play an important component in the pathogenesis of MDD associated with cognitive complaints. In agreement with this, patients with MDD show decreased brain volumes in areas implicated in emotional regulation and cognition, neuronal and glial cell death as well as activation of various pathways that can contribute to cell death. Therefore, the aim of this review is to provide an integrative overview of potential contributing factors to neurodegeneration in MDD. Studies have reported increased neuronal and glial cell death in the frontal cortex, amygdala, and hippocampus of patients with MDD. This may be due to decreased neurogenesis from lower levels of brain-derived neurotrophic factor (BDNF), excitotoxicity from increased glutamate signaling, and lower levels of gamma-aminobutyric acid (GABA) signaling. In addition, mitochondrial dysfunction and oxidative stress are found in similar brain areas where evidence of excitotoxicity has been reported. Also, levels of antioxidant enzymes were reported to be increased in patients with MDD. Inflammation may also be a contributing factor, as levels of inflammatory cytokines were reported to be increased in the prefrontal cortex of patients with MDD. While preliminary, studies have also reported neuropathological alterations in patients with MDD. Together, these studies suggest that lower BDNF levels, mitochondrial dysfunction, oxidative stress, inflammation and excitotoxicity may be contributing to neuronal and glial cell death in MDD, leading to decreased brain volume and cognitive dysfunction with multiple recurrent episodes. This highlights the need to identify specific pathways involved in neurodegeneration in MDD, which may elucidate targets that can be treated to ameliorate the effects of disease progression in this disorder.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".