Evidence map›Paper›PMID 30519188›Full record

ArticleFrontiers in physiology2018

Improved Neural Control of Movements Manifests in Expertise-Related Differences in Force Output and Brain Network Dynamics.

Christian Gölz, Claudia Voelcker-Rehage, Karin Mora, Eva-Maria Reuter, Ben Godde, Michael Dellnitz, Claus Reinsberger, Solveig Vieluf

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Article in Frontiers in physiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Christian GölzInstitute of Sports Medicine, Paderborn University, Paderborn, Germany.
Claudia Voelcker-RehageInstitute of Human Movement Science and Health, Chemnitz University of Technology, Chemnitz, Germany.
Karin MoraDepartment of Mathematics, Paderborn University, Paderborn, Germany.
Eva-Maria ReuterCentre for Sensorimotor Performance, School of Human Movement and Nutrition Sciences, The University of Queensland, Brisbane, QLD, Australia.
Ben GoddeDepartment of Psychology & Methods, Jacobs University Bremen, Bremen, Germany.
Michael DellnitzDepartment of Mathematics, Paderborn University, Paderborn, Germany.
Claus ReinsbergerInstitute of Sports Medicine, Paderborn University, Paderborn, Germany.
Solveig VielufInstitute of Sports Medicine, Paderborn University, Paderborn, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

It is well-established that expertise developed through continuous and deliberate practice has the potential to delay age-related decline in fine motor skills. However, less is known about the underlying mechanisms, that is, whether expertise leads to a higher performance level changing the initial status from which age-related decline starts or if expertise-related changes result in qualitatively different motor output and neural processing providing a resource of compensation for age-related changes. Thus, as a first step, this study aims at a better understanding of expertise-related changes in fine motor control with respect to force output and respective electrophysiological correlates. Here, using a multidimensional approach, we investigated fine motor control of experts and novices in precision mechanics during the execution of a dynamic force control task. On the level of force output, we analyzed precision, variability, and complexity. We further used dynamic mode decomposition (DMD) to analyze the electrophysiological correlates of force control to deduce brain network dynamics. Experts' force output was more precise, less variable, and more complex. Task-related DMD mean mode magnitudes within the α-band at electrodes over sensorimotor relevant areas were reduced in experts, and lower DMD mean mode magnitudes related to the force output in novices. Our results provide evidence for expertise dependent central adaptions with distinct and more complex organization and decentralization of sensorimotor subsystems. Results from our multidimensional approach can be seen as a step forward in understanding expertise-related changes and exploiting their potential as resources for healthy aging.

Indexed as

EEGfine motor expertiseforce controlsensorimotor networktask-related brain activity

Identifiers

PMID30519188
PMCPMC6258820

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.