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Cited by 1,066Motor Control and AdaptationMemory and Neural MechanismsNeuroscience and Music Perception

Consolidation alters motor sequence-specific distributed representations

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Pinsard, Basile, Boutin, Arnaud, Gabitov, Ella, Lungu, Ovidiu, Benali, Habib, Doyon, Julien·2019·eLife
DOI 10.7554/elife.39324PMC PMC6461441

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Abstract

Functional magnetic resonance imaging (fMRI) studies investigating the acquisition of sequential motor skills in humans have revealed learning-related functional reorganizations of the cortico-striatal and cortico-cerebellar motor systems accompanied with an initial hippocampal contribution. Yet, the functional significance of these activity-level changes remains ambiguous as they convey the evolution of both sequence-specific knowledge and unspecific task ability. Moreover, these changes do not specifically assess the occurrence of learning-related plasticity. To address these issues, we investigated local circuits tuning to sequence-specific information using multivariate distances between patterns evoked by consolidated or newly acquired motor sequences production. The results reveal that representations in dorsolateral striatum, prefrontal and secondary motor cortices are greater when executing consolidated sequences than untrained ones. By contrast, sequence representations in the hippocampus and dorsomedial striatum becomes less engaged. Our findings show, for the first time in humans, that complementary sequence-specific motor representations evolve distinctively during critical phases of skill acquisition and consolidation.

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  • Confounding in GWAS can cause biases in downstream applications, including estimation of heritability and genetic correlation, Mendelian Randomization analyses, and inferences of natural selection.

    “Confounding can cause biases in downstream applications, including: estimation of heritability and genetic correlation6,7, Mendelian Randomization analyses13, and inferences of natural selection4,5.”

    From Family-GWAS reveals effects of environment and mating on genetic associations (2024) · cites this paper as [5]

    The citation is attached to the claim about Mendelian Randomization analyses. The Materials and methods passages discuss fMRI multivariate analysis and statistical testing, not GWAS confounding or Mendelian Randomization; searches for those terms found no relevant passage in our copy. (LLM, conf=0.76)

    Experimental — accuracy not yet measured

  • Confounding in GWAS can cause biases in downstream applications, including estimation of heritability and genetic correlation, Mendelian Randomization analyses, and inferences of natural selection.

    “Confounding can cause biases in downstream applications, including: estimation of heritability and genetic correlation6,7, Mendelian Randomization analyses13, and inferences of natural selection4,5.”

    From Family-GWAS reveals effects of environment and mating on genetic associations (2024) · cites this paper as [4]

    The Materials and methods passages on multivariate pattern analysis concern fMRI representations of motor sequences, not genetic association studies. Searches for GWAS confounding, heritability, genetic correlation, Mendelian randomization, and natural selection found no passage addressing the claim in our copy. (LLM, conf=0.78)

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  • Genetic associations (including those from GWAS) can be affected by gene-environment correlations and non-random mating, such as indirect genetic effects, assortative mating, and population stratification, leading to biased inferences.

    “However, genetic associations can be affected by gene-environment correlations and non-random mating, which can lead to biased inferences in downstream analyses.”

    From Family-GWAS reveals effects of environment and mating on genetic associations (2024) · cites this paper as [5]

    The Introduction discusses interpretation of brain activity during motor-sequence learning, and the Materials and methods discusses statistical testing of imaging data (page numbers unavailable). Searches for gene–environment correlation, non-random mating, assortative mating, and population stratification found no passage addressing bias in genetic associations. (LLM, conf=0.78)

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  • Genetic associations (including those from GWAS) can be affected by gene-environment correlations and non-random mating, such as indirect genetic effects, assortative mating, and population stratification, leading to biased inferences.

    “However, genetic associations can be affected by gene-environment correlations and non-random mating, which can lead to biased inferences in downstream analyses.”

    From Family-GWAS reveals effects of environment and mating on genetic associations (2024) · cites this paper as [4]

    The Materials and methods section discusses fMRI multivariate analysis and motor-sequence performance, not genetic associations. Searches for gene–environment correlation, non-random mating, GWAS, assortative mating, and population stratification found no passage in our copy addressing the claim. (LLM, conf=0.78)

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  • Analytical techniques including PCA and linear mixed models reduce population stratification confounding but often leave residual confounding in GWAS results.

    “Analytical techniques including principal component analysis (PCA) and linear mixed models have been developed to reduce population stratification confounding12, but these techniques often leave residual confounding in GWAS results5,8,10.”

    From Family-GWAS reveals effects of environment and mating on genetic associations (2024) · cites this paper as [5]

    The citation is attached to the claim that PCA and linear mixed models reduce population-stratification confounding. The Materials and methods section discusses multivariate analysis of brain-imaging data, not GWAS; searches for PCA, linear mixed models, population stratification, and genetic ancestry found no passage addressing that claim. The later residual-confounding clause carries different citations. (LLM, conf=0.78)

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Authors
Pinsard, B., Boutin, A., Gabitov, E., Lungu, O., Benali, H., & Doyon, J.
Affiliations
  • Centre National de la Recherche Scientifique (FR)
  • Montreal Neurological Institute and Hospital (CA)
  • Institut Universitaire de Gériatrie de Montréal (CA)
Corresponding author
  • Pinsard, Basile
  • Doyon, Julien
ORCID
Venue
eLife
Publisher
eLife Sciences Publications, Ltd
Published
2019-03-18
Volume
8
Pages
e39324
ISSN
2050-084X (ISSN-L)
DOI
10.7554/elife.39324
PMC
PMC6461441
OpenAlex
W2950681580
Cited by
1,066
References
108
Citations by year
2026: 63 · 2025: 124 · 2024: 130 · 2023: 155 · 2022: 210 · 2021: 209 · 2020: 136 · 2019: 37 · 2018: 2
FWCI
85.98
Type
journal article
Language
en
Open access
Licence
Locations
  • eLife (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • HAL (submittedVersion, other-oa)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • Canadian Institutes of Health Research (MOP 97830)
  • Ministère de l'Education Nationale, de l'Enseignement Superieur et de la Recherche (PhD scholarship)
  • Sorbonne Université (PhD study abroad grant)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Motor Control and Adaptation
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Neuroscience and Music Perception
Keywords
motor sequence learning, skill consolidation, dorsolateral striatum, dorsomedial striatum, hippocampus, secondary motor cortex, prefrontal cortex, corticostriatal circuits, multivariate pattern analysis
MeSH terms
Adolescent, Corpus Striatum, Efferent Pathways, Female, Hippocampus, Humans, Learning, Magnetic Resonance Imaging, Male, Motor Cortex, Nerve Net, Neuronal Plasticity, Young Adult

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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