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Exercise, brain, and cognition across the life span

2011· review· en· W2158250395 on OpenAlexafffund
Michelle W. Voss, Lindsay S. Nagamatsu, Teresa Liu‐Ambrose, Arthur F. Kramer

Bibliographic record

VenueJournal of Applied Physiology · 2011
Typereview
Languageen
FieldNeuroscience
TopicFunctional Brain Connectivity Studies
Canadian institutionsVancouver Coastal Health Research InstituteUniversity of British ColumbiaVancouver Coastal Health
FundersNational Institute on AgingCanadian Institutes of Health Research
KeywordsAerobic exerciseCognitionPsychologyNeuroimagingFunctional neuroimagingNeuroscienceCognitive psychologyMedicinePhysical therapy

Abstract

fetched live from OpenAlex

This is a brief review of current evidence for the relationships between physical activity and exercise and the brain and cognition throughout the life span in non-pathological populations. We focus on the effects of both aerobic and resistance training and provide a brief overview of potential neurobiological mechanisms derived from non-human animal models. Whereas research has focused primarily on the benefits of aerobic exercise in youth and young adult populations, there is growing evidence that both aerobic and resistance training are important for maintaining cognitive and brain health in old age. Finally, in these contexts, we point out gaps in the literature and future directions that will help advance the field of exercise neuroscience, including more studies that explicitly examine the effect of exercise type and intensity on cognition, the brain, and clinically significant outcomes. There is also a need for human neuroimaging studies to adopt a more unified multi-modal framework and for greater interaction between human and animal models of exercise effects on brain and cognition across the life span.

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)0.001
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
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.003
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.085
GPT teacher head0.328
Teacher spread0.242 · 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

Citations582
Published2011
Admission routes2
Has abstractyes

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