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Record W3184630054 · doi:10.3389/fnhum.2021.681634

Effect of Aerobic Exercise on White Matter Tract Microstructure in Young and Middle-Aged Healthy Adults

2021· article· en· W3184630054 on OpenAlexfundno aff
David Predovan, Yunglin Gazes, Seonjoo Lee, Peipei Li, Richard P. Sloan, Yaakov Stern

Bibliographic record

VenueFrontiers in Human Neuroscience · 2021
Typearticle
Languageen
FieldMedicine
TopicAdvanced Neuroimaging Techniques and Applications
Canadian institutionsnot available
FundersFonds de Recherche du Québec - SantéNational Institute on AgingNational Institutes of HealthRéseau québécois de recherche sur le vieillissement
KeywordsFractional anisotropyCardiorespiratory fitnessAerobic exerciseWhite matterFasciculusMedicinePsychologyPhysical therapyInternal medicineMagnetic resonance imaging

Abstract

fetched live from OpenAlex

Recent evidence suggests that being physically active can mitigate age-related white matter (WM) changes. In a randomized clinical trial, the effect of 6-month aerobic exercise (AE) or stretching/toning interventions on measures of WM microstructure (WMM) was assessed in a sample of 74 adults aged 20–67 years. Major WM pathways were reconstructed. No significant group-level change in WM tract microstructure following an AE training was observed. Without adjustment for multiple comparisons, an increase in fractional anisotropy (FA) and a decrease in mean diffusivity (MD) of the uncinate fasciculus were observed post-intervention in the AE group in comparison with the stretching group. In the AE group, a significant increase in cardiorespiratory fitness was measured but did not correlate with FA and MD change. The present results of this study are in accordance with similar studies in healthy adults that did not show significant benefit on WMM after participating in an AE program. Clinical Trial Registration: Clinicaltrials.gov identifier, NCT01179958.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.019
GPT teacher head0.308
Teacher spread0.289 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations13
Published2021
Admission routes1
Has abstractyes

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