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Record W2940631805 · doi:10.3390/jcm8050578

The Beneficial Effect of Acute Exercise on Motor Memory Consolidation is Modulated by Dopaminergic Gene Profile

2019· article· en· W2940631805 on OpenAlexaff
Lasse Christiansen, Richard Thomas, Mikkel Malling Beck, Jessica Pingel, Jeppe Dyrberg Andersen, Cameron S. Mang, Mads Alexander Just Madsen, Marc Roig, Jesper Lundbye‐Jensen

Bibliographic record

VenueJournal of Clinical Medicine · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicZebrafish Biomedical Research Applications
Canadian institutionsMcGill UniversityCentre for Interdisciplinary Research in RehabilitationJewish Rehabilitation HospitalUniversity of Regina
FundersNordea-fondenSundhed og Sygdom, Det Frie Forskningsråd
KeywordsDopaminergicDopamineMemory consolidationMedicineNeuroscienceConsolidation (business)Psychology

Abstract

fetched live from OpenAlex

When aerobic exercise is performed following skilled motor practice, it can enhance motor memory consolidation. Previous studies have suggested that dopamine may play a role in motor memory consolidation, but whether it is involved in the exercise effects on consolidation is unknown. Hence, we aimed to investigate the influence of dopaminergic pathways on the exercise-induced modulation of motor memory consolidation. We compared the effect of acute exercise on motor memory consolidation between the genotypes that are known to affect dopaminergic transmission and learning. By combining cluster analyses and fitting linear models with and without included polymorphisms, we provide preliminary evidence that exercise benefits the carriers of alleles that are associated with low synaptic dopamine content. In line with previous reports, our findings implicate dopamine as a modulator of the exercise-induced effects on motor memory consolidation, and suggest exercise as a potential clinical tool to counteract low endogenous dopamine bioavailability. Further experiments are needed to establish causal relations.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.269

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.014
GPT teacher head0.371
Teacher spread0.357 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations20
Published2019
Admission routes1
Has abstractyes

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