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Record W2338468094 · doi:10.3233/bpl-150016

The Benefits of Exercise on Structural and Functional Plasticity in the Rodent Hippocampus of Different Disease Models

2015· review· en· W2338468094 on OpenAlexaff
Anna Patten, Suk‐Yu Yau, Christine J. Fontaine, Alicia Meconi, Ryan C. Wortman, Brian R. Christie

Bibliographic record

VenueBrain Plasticity · 2015
Typereview
Languageen
FieldNeuroscience
TopicNeurogenesis and neuroplasticity mechanisms
Canadian institutionsUniversity of British ColumbiaUniversity of Victoria
Fundersnot available
KeywordsHippocampal formationNeurogenesisNeuroscienceHippocampusNeuroplasticityDiseaseStroke (engine)Synaptic plasticityMedicinePhysical exercisePsychologyPhysical therapyInternal medicine

Abstract

fetched live from OpenAlex

In this review, the benefits of physical exercise on structural and functional plasticity in the hippocampus are discussed. The evidence is clear that voluntary exercise in rats and mice can lead to increases in hippocampal neurogenesis and enhanced synaptic plasticity which ultimately result in improved performance in hippocampal-dependent tasks. Furthermore, in models of neurological disorders, including fetal alcohol spectrum disorders, traumatic brain injury, stroke, and neurodegenerative disorders including Alzheimer's, Parkinson's and Huntington's disease exercise can also elicit beneficial effects on hippocampal function. Ultimately this review highlights the multiple benefits of exercise on hippocampal function in both the healthy and the diseased 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)0.000
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.093
GPT teacher head0.291
Teacher spread0.198 · 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

Citations62
Published2015
Admission routes1
Has abstractyes

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