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Record W1974065417 · doi:10.1155/2014/403120

Physical Exercise-Induced Adult Neurogenesis: A Good Strategy to Prevent Cognitive Decline in Neurodegenerative Diseases?

2014· review· en· W1974065417 on OpenAlexafffund
Suk‐Yu Yau, Joana Gil‐Mohapel, Brian R. Christie, Kwok‐Fai So

Bibliographic record

VenueBioMed Research International · 2014
Typereview
Languageen
FieldNeuroscience
TopicNeurogenesis and neuroplasticity mechanisms
Canadian institutionsUniversity of Victoria
FundersCiência sem FronteirasNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchUniversity of Hong KongFundamental Research Funds for the Central UniversitiesMichael Smith Health Research BCCanada Foundation for Innovation
KeywordsNeurogenesisHippocampal formationCognitive declineNeuroscienceCognitionPopulationPsychologyHippocampusDementiaNeuroplasticityMedicineDiseaseInternal medicine

Abstract

fetched live from OpenAlex

Cumulative evidence has indicated that there is an important role for adult hippocampal neurogenesis in cognitive function. With the increasing prevalence of cognitive decline associated with neurodegenerative diseases among the ageing population, physical exercise, a potent enhancer of adult hippocampal neurogenesis, has emerged as a potential preventative strategy/treatment to reduce cognitive decline. Here we review the functional role of adult hippocampal neurogenesis in learning and memory, and how this form of structural plasticity is altered in neurodegenerative diseases known to involve cognitive impairment. We further discuss how physical exercise may contribute to cognitive improvement in the ageing brain by preserving adult neurogenesis, and review the recent approaches for measuring changes in neurogenesis in the live human brain.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.002

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.

Opus teacher head0.167
GPT teacher head0.457
Teacher spread0.290 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations114
Published2014
Admission routes2
Has abstractyes

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