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运动学习的神经关联:网络通信与局部振荡

Neural correlates of motor learning: Network communication versus local oscillations.

作者信息

Mottaz Anaïs, Savic Branislav, Allaman Leslie, Guggisberg Adrian G

机构信息

Division of Neurorehabilitation, Department of Clinical Neurosciences, University Hospital of Geneva, University of Geneva, Switzerland.

SIB Text Mining Group, Swiss Institute of Bioinformatics, Carouge, Switzerland.

出版信息

Netw Neurosci. 2024 Oct 1;8(3):714-733. doi: 10.1162/netn_a_00374. eCollection 2024.

Abstract

Learning new motor skills through training, also termed motor learning, is central for everyday life. Current training strategies recommend intensive task-repetitions aimed at inducing local activation of motor areas, associated with changes in oscillation amplitudes ("event-related power") during training. More recently, another neural mechanism was suggested to influence motor learning: modulation of functional connectivity (FC), that is, how much spatially separated brain regions communicate with each other before and during training. The goal of the present study was to compare the impact of these two neural processing types on motor learning. We measured EEG before, during, and after a finger-tapping task (FTT) in 20 healthy subjects. The results showed that training gain, long-term expertise (i.e., average motor performance), and consolidation were all predicted by whole-brain alpha- and beta-band FC at motor areas, striatum, and mediotemporal lobe (MTL). Local power changes during training did not predict any dependent variable. Thus, network dynamics seem more crucial than local activity for motor sequence learning, and training techniques should attempt to facilitate network interactions rather than local cortical activation.

摘要

通过训练学习新的运动技能,也称为运动学习,对日常生活至关重要。当前的训练策略建议进行密集的任务重复,旨在诱导运动区域的局部激活,这与训练期间振荡幅度(“事件相关功率”)的变化有关。最近,另一种神经机制被认为会影响运动学习:功能连接(FC)的调制,即空间上分离的脑区在训练前和训练期间相互交流的程度。本研究的目的是比较这两种神经处理类型对运动学习的影响。我们在20名健康受试者进行手指敲击任务(FTT)之前、期间和之后测量了脑电图。结果表明,运动学习的训练增益、长期专业技能(即平均运动表现)和巩固均由运动区域、纹状体和颞中叶(MTL)的全脑α和β波段功能连接预测。训练期间的局部功率变化不能预测任何因变量。因此,对于运动序列学习,网络动态似乎比局部活动更为关键,训练技术应尝试促进网络交互,而非局部皮质激活。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/88e3/11340994/245d3c4fca57/netn-8-3-714-g001.jpg

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