Aging and neurogenesis in the adult forebrain: what we have learned and where we should go from here
Bibliographic record
Abstract
In the brains of adult vertebrates, including humans, neurogenesis occurs in restricted niches where it maintains cellular turnover and cognitive plasticity. In virtually all species, however, aging is associated with a significant decline in adult neurogenesis. Moreover, an acceleration of neurogenic defects is observed in models of Alzheimer's disease and other neurodegenerative diseases, suggesting an involvement in aging- and disease-associated cognitive deficits. To gain insights into when, how and why adult neurogenesis decreases in the aging brain, we critically reviewed the scientific literature on aging of the rodent subventricular zone, the neurogenic niche of the adult forebrain. Our analysis revealed that deficits in the neurogenic pathway are largely established by middle age, but that there remains striking ambiguity in the underlying mechanisms, especially at the level of stem and progenitor cells. We identify and discuss several challenging issues that have contributed to these key gaps in our current knowledge. In the future, addressing these issues should help untangle the interactions between neurogenesis, aging and aging-associated diseases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".