The Neural Plasticity Theory of Depression: Assessing the Roles of Adult Neurogenesis and PSA‐NCAM within the Hippocampus
Bibliographic record
Abstract
Depression is a devastating and prevalent disease, with profound effects on neural structure and function; however the etiology and neuropathology of depression remain poorly understood. Though antidepressant drugs exist, they are not ideal, as only a segment of patients are effectively treated, therapeutic onset is delayed, and the exact mechanism of these drugs remains to be elucidated. Several theories of depression do exist, including modulation of monoaminergic neurotransmission, alterations in neurotrophic factors, and the upregulation of adult hippocampal neurogenesis, and are briefly mentioned in the review. However none of these theories sufficiently explains the pathology and treatment of depression unto itself. Recently, neural plasticity theories of depression have postulated that multiple aspects of brain plasticity, beyond neurogenesis, may bridge the prevailing theories. The term "neural plasticity" encompasses an array of mechanisms, from the birth, survival, migration, and integration of new neurons to neurite outgrowth, synaptogenesis, and the modulation of mature synapses. This review critically assesses the role of adult hippocampal neurogenesis and the cell adhesion molecule, PSA-NCAM (which is known to be involved in many facets of neural plasticity), in depression and antidepressant treatment.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".