Literature DB >> 27655931

Resting-state Functional Connectivity is an Age-dependent Predictor of Motor Learning Abilities.

Alison Mary1,2, Vincent Wens2,3, Marc Op de Beeck2,3, Rachel Leproult1,2, Xavier De Tiège2,3, Philippe Peigneux1,2.   

Abstract

This magnetoencephalography study investigates how ageing modulates the relationship between pre-learning resting-state functional connectivity (rsFC) and subsequent learning. Neuromagnetic resting-state activity was recorded 5 min before motor sequence learning in 14 young (19-30 years) and 14 old (66-70 years) participants. We used a seed-based beta-band power envelope correlation approach to estimate rsFC maps, with the seed located in the right primary sensorimotor cortex. In each age group, the relation between individual rsFC and learning performance was investigated using Pearson's correlation analyses. Our results show that rsFC is predictive of subsequent motor sequence learning but involves different cross-network interactions in the two age groups. In young adults, decreased coupling between the sensorimotor network and the cortico-striato-cerebellar network is associated with better motor learning, whereas a similar relation is found in old adults between the sensorimotor, the dorsal-attentional and the DMNs. Additionally, age-related correlational differences were found in the dorsolateral prefrontal cortex, known to subtend attentional and controlled processes. These findings suggest that motor skill learning depends-in an age-dependent manner-on subtle interactions between resting-state networks subtending motor activity on the one hand, and controlled and attentional processes on the other hand.
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Entities:  

Keywords:  ageing; cross-network interaction; magnetoencephalography; motor sequence learning; resting-state functional connectivity

Mesh:

Year:  2017        PMID: 27655931     DOI: 10.1093/cercor/bhw286

Source DB:  PubMed          Journal:  Cereb Cortex        ISSN: 1047-3211            Impact factor:   5.357


  8 in total

1.  Age-Related Declines in Motor Performance are Associated With Decreased Segregation of Large-Scale Resting State Brain Networks.

Authors:  B R King; P van Ruitenbeek; I Leunissen; K Cuypers; K-F Heise; T Santos Monteiro; L Hermans; O Levin; G Albouy; D Mantini; S P Swinnen
Journal:  Cereb Cortex       Date:  2018-12-01       Impact factor: 5.357

Review 2.  Towards understanding neural network signatures of motor skill learning in Parkinson's disease and healthy aging.

Authors:  Evelien Nackaerts; Nicholas D'Cruz; Bauke W Dijkstra; Moran Gilat; Thomas Kramer; Alice Nieuwboer
Journal:  Br J Radiol       Date:  2019-05-14       Impact factor: 3.039

3.  Connectivity in Large-Scale Resting-State Brain Networks Is Related to Motor Learning: A High-Density EEG Study.

Authors:  Simon Titone; Jessica Samogin; Philippe Peigneux; Stephan Swinnen; Dante Mantini; Genevieve Albouy
Journal:  Brain Sci       Date:  2022-04-21

4.  Resting-state brain network features associated with short-term skill learning ability in humans and the influence of N-methyl-d-aspartate receptor antagonism.

Authors:  Zhenxiang Zang; Lena S Geiger; Urs Braun; Hengyi Cao; Maria Zangl; Axel Schäfer; Carolin Moessnang; Matthias Ruf; Janine Reis; Janina I Schweiger; Luanna Dixson; Alexander Moscicki; Emanuel Schwarz; Andreas Meyer-Lindenberg; Heike Tost
Journal:  Netw Neurosci       Date:  2018-10-01

5.  Frequency-Dependent Intrinsic Electrophysiological Functional Architecture of the Human Verbal Language Network.

Authors:  Tim Coolen; Vincent Wens; Marc Vander Ghinst; Alison Mary; Mathieu Bourguignon; Gilles Naeije; Philippe Peigneux; Niloufar Sadeghi; Serge Goldman; Xavier De Tiège
Journal:  Front Integr Neurosci       Date:  2020-05-26

6.  Resting-state fMRI functional connectivity of the left temporal parietal junction is associated with visual temporal order threshold.

Authors:  Monika Lewandowska; Jan Nikadon; Tomasz Wolak; Krzysztof Tołpa; Tomasz Piotrowski; Mateusz Chojnowski; Joanna Dreszer
Journal:  Sci Rep       Date:  2022-09-24       Impact factor: 4.996

7.  The electrophysiological connectome is maintained in healthy elders: a power envelope correlation MEG study.

Authors:  N Coquelet; A Mary; P Peigneux; S Goldman; V Wens; X De Tiège
Journal:  Sci Rep       Date:  2017-10-25       Impact factor: 4.379

8.  Age-related reduction in motor adaptation: brain structural correlates and the role of explicit memory.

Authors:  Noham Wolpe; James N Ingram; Kamen A Tsvetanov; Richard N Henson; Daniel M Wolpert; James B Rowe
Journal:  Neurobiol Aging       Date:  2020-02-20       Impact factor: 4.673

  8 in total

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