Neuroplasticity and the next wave of antidepressant strategies
Bibliographic record
Abstract
Depression is a common chronic psychiatric disorder that is also often co-morbid with numerous neurological and immune diseases. Accumulating evidence indicates that disturbances of neuroplasticity occur with depression, including reductions of hippocampal neurogenesis and cortical synaptogenesis. Improper trophic support stemming from stressor-induced reductions of growth factors, most notably brain derived neurotrophic factor (BDNF), likely drives such aberrant neuroplasticity. We posit that psychological and immune stressors can interact upon a vulnerable genetic background to promote depression by disturbing BDNF and neuroplastic processes. Furthermore, the chronic and commonly relapsing nature of depression is suggested to stem from "faulty wiring" of emotional circuits driven by neuroplastic aberrations. The present review considers depression in such terms and attempts to integrate the available evidence indicating that the efficacy of current and "next wave" antidepressant treatments, whether used alone or in combination, is at least partially tied to their ability to modulate neuroplasticity. We particularly focus on the N-methyl-D-aspartate (NMDA) antagonist, ketamine, which already has well documented rapid antidepressant effects, and the trophic cytokine, erythropoietin (EPO), which we propose as a potential adjunctive antidepressant agent.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".