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Record W2067668823 · doi:10.1111/ejn.12207

Aging and neurogenesis in the adult forebrain: what we have learned and where we should go from here

2013· review· en· W2067668823 on OpenAlexafffund
Laura K. Hamilton, Sandra E. Joppé, Loїc M. Cochard, Karl J. L. Fernandes

Bibliographic record

VenueEuropean Journal of Neuroscience · 2013
Typereview
Languageen
FieldNeuroscience
TopicNeurogenesis and neuroplasticity mechanisms
Canadian institutionsUniversité de Montréal
FundersCanadian Institutes of Health ResearchCanada Research ChairsAlzheimer Society
KeywordsNeurogenesisSubventricular zoneNeuroscienceForebrainNeural stem cellAging brainPsychologyCognitive declineNeuroplasticityBiologyDiseaseStem cellCognitionMedicineCentral nervous systemDementiaPathology

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.964
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.163
GPT teacher head0.324
Teacher spread0.161 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations62
Published2013
Admission routes2
Has abstractyes

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