Suppr超能文献

在步态适应过程中,服务于运动和认知控制的不同β波段振荡网络。

Distinct β Band Oscillatory Networks Subserving Motor and Cognitive Control during Gait Adaptation.

作者信息

Wagner Johanna, Makeig Scott, Gola Mateusz, Neuper Christa, Müller-Putz Gernot

机构信息

Laboratory of Brain-Computer Interfaces, Institute for Knowledge Discovery, Graz University of Technology, 8010 Graz, Austria, Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California-San Diego, La Jolla, California 92093-0559,

Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California-San Diego, La Jolla, California 92093-0559.

出版信息

J Neurosci. 2016 Feb 17;36(7):2212-26. doi: 10.1523/JNEUROSCI.3543-15.2016.

Abstract

UNLABELLED

Everyday locomotion and obstacle avoidance requires effective gait adaptation in response to sensory cues. Many studies have shown that efficient motor actions are associated with μ rhythm (8-13 Hz) and β band (13-35 Hz) local field desynchronizations in sensorimotor and parietal cortex, whereas a number of cognitive task studies have reported higher behavioral accuracy to be associated with increases in β band power in prefrontal and sensory cortex. How these two distinct patterns of β band oscillations interplay during gait adaptation, however, has not been established. Here we recorded 108 channel EEG activity from 18 participants (10 males, 22-35 years old) attempting to walk on a treadmill in synchrony with a series of pacing cue tones, and quickly adapting their step rate and length to sudden shifts in pacing cue tempo. Independent component analysis parsed each participant's EEG data into maximally independent component (IC) source processes, which were then grouped across participants into distinct spatial/spectral clusters. Following cue tempo shifts, mean β band power was suppressed for IC sources in central midline and parietal regions, whereas mean β band power increased in IC sources in or near medial prefrontal and dorsolateral prefrontal cortex. In the right dorsolateral prefrontal cortex IC cluster, the β band power increase was stronger during (more effortful) step shortening than during step lengthening. These results thus show that two distinct patterns of β band activity modulation accompany gait adaptations: one likely serving movement initiation and execution; and the other, motor control and inhibition.

SIGNIFICANCE STATEMENT

Understanding brain dynamics supporting gait adaptation is crucial for understanding motor deficits in walking, such as those associated with aging, stroke, and Parkinson's. Only a few electromagnetic brain imaging studies have examined neural correlates of human upright walking. Here, application of independent component analysis to EEG data recorded during treadmill walking allowed us to uncover two distinct β band oscillatory cortical networks that are active during gait adaptation to shifts in the tempo of an auditory pacing cue: (8-13 Hz) μ rhythm and (13-35 Hz) β band power decreases in central and parietal cortex and (14-20 Hz) β band power increases in frontal brain areas. These results provide a fuller framework for electrophysiological studies of cortical gait control and its disorders.

摘要

未标注

日常行走和避障需要根据感官线索进行有效的步态适应。许多研究表明,高效的运动行为与感觉运动皮层和顶叶皮层中的μ节律(8 - 13赫兹)和β频段(13 - 35赫兹)局部场去同步化有关,而一些认知任务研究报告称,行为准确性提高与前额叶和感觉皮层中β频段功率增加有关。然而,在步态适应过程中,这两种不同模式的β频段振荡如何相互作用尚未明确。在此,我们记录了18名参与者(10名男性,年龄在22 - 35岁之间)的108通道脑电图活动,他们试图在跑步机上与一系列节奏提示音同步行走,并迅速调整步幅和步长以适应节奏提示音速度的突然变化。独立成分分析将每个参与者的脑电图数据解析为最大独立成分(IC)源过程,然后将这些过程在参与者之间分组为不同的空间/频谱簇。在节奏提示音速度改变后,中央中线和顶叶区域的IC源的平均β频段功率受到抑制,而内侧前额叶和背外侧前额叶皮层或其附近的IC源的平均β频段功率增加。在右侧背外侧前额叶皮层IC簇中,在(更费力的)步幅缩短过程中β频段功率增加比步幅延长过程中更强。因此,这些结果表明,两种不同模式的β频段活动调制伴随着步态适应:一种可能用于运动发起和执行;另一种用于运动控制和抑制。

意义声明

理解支持步态适应的脑动力学对于理解行走中的运动缺陷至关重要,例如与衰老、中风和帕金森病相关的缺陷。只有少数电磁脑成像研究考察了人类直立行走的神经关联。在此,将独立成分分析应用于跑步机行走过程中记录的脑电图数据,使我们能够揭示两种不同的β频段振荡皮层网络,它们在步态适应听觉节奏提示音速度变化时活跃:中央和顶叶皮层中(8 - 13赫兹)的μ节律和(13 - 35赫兹)的β频段功率降低,以及额叶脑区中(14 - 20赫兹)的β频段功率增加。这些结果为皮层步态控制及其障碍的电生理研究提供了更完整的框架。

相似文献

1
Distinct β Band Oscillatory Networks Subserving Motor and Cognitive Control during Gait Adaptation.
J Neurosci. 2016 Feb 17;36(7):2212-26. doi: 10.1523/JNEUROSCI.3543-15.2016.
2
Trial-by-trial source-resolved EEG responses to gait task challenges predict subsequent step adaptation.
Neuroimage. 2019 Oct 1;199:691-703. doi: 10.1016/j.neuroimage.2019.06.018. Epub 2019 Jun 7.
3
Electrocortical activity correlated with locomotor adaptation during split-belt treadmill walking.
J Physiol. 2023 Sep;601(17):3921-3944. doi: 10.1113/JP284505. Epub 2023 Jul 31.
4
Spatially Distinct Beta-Band Activities Reflect Implicit Sensorimotor Adaptation and Explicit Re-aiming Strategy.
J Neurosci. 2020 Mar 18;40(12):2498-2509. doi: 10.1523/JNEUROSCI.1862-19.2020. Epub 2020 Feb 7.
6
Independent Causal Contributions of Alpha- and Beta-Band Oscillations during Movement Selection.
J Neurosci. 2016 Aug 17;36(33):8726-33. doi: 10.1523/JNEUROSCI.0868-16.2016.
7
Level of participation in robotic-assisted treadmill walking modulates midline sensorimotor EEG rhythms in able-bodied subjects.
Neuroimage. 2012 Nov 15;63(3):1203-11. doi: 10.1016/j.neuroimage.2012.08.019. Epub 2012 Aug 14.
8
Beta-band oscillations as a biomarker of gait recovery in spinal cord injury patients: A quantitative electroencephalography analysis.
Clin Neurophysiol. 2020 Aug;131(8):1806-1814. doi: 10.1016/j.clinph.2020.04.166. Epub 2020 May 22.
9
Electrocortical activity is coupled to gait cycle phase during treadmill walking.
Neuroimage. 2011 Jan 15;54(2):1289-96. doi: 10.1016/j.neuroimage.2010.08.066. Epub 2010 Sep 9.
10
Different Faces of Medial Beta-Band Activity Reflect Distinct Visuomotor Feedback Signals.
J Neurosci. 2023 Dec 6;43(49):8472-8486. doi: 10.1523/JNEUROSCI.2238-22.2023.

引用本文的文献

1
Ecological Resonance Is Reflected in Human Brain Activity.
Psychophysiology. 2025 Sep;62(9):e70136. doi: 10.1111/psyp.70136.
3
Beta-band desynchronization in the human hippocampus during movement preparation in a delayed reach task.
Exp Brain Res. 2025 Jun 23;243(7):180. doi: 10.1007/s00221-025-07124-6.
9
Perceptual information processing in table tennis players: based on top-down hierarchical predictive coding.
Cogn Neurodyn. 2024 Dec;18(6):3951-3961. doi: 10.1007/s11571-024-10171-4. Epub 2024 Sep 13.

本文引用的文献

1
Independent Component Analysis of Gait-Related Movement Artifact Recorded using EEG Electrodes during Treadmill Walking.
Front Hum Neurosci. 2015 Dec 1;9:639. doi: 10.3389/fnhum.2015.00639. eCollection 2015.
2
Isolating gait-related movement artifacts in electroencephalography during human walking.
J Neural Eng. 2015 Aug;12(4):046022. doi: 10.1088/1741-2560/12/4/046022. Epub 2015 Jun 17.
3
High and low gamma EEG oscillations in central sensorimotor areas are conversely modulated during the human gait cycle.
Neuroimage. 2015 May 15;112:318-326. doi: 10.1016/j.neuroimage.2015.03.045. Epub 2015 Mar 24.
5
Training voluntary motor suppression with real-time feedback of motor evoked potentials.
J Neurophysiol. 2015 May 1;113(9):3446-52. doi: 10.1152/jn.00992.2014. Epub 2015 Mar 4.
6
The posterior parietal cortex (PPC) mediates anticipatory motor control.
Brain Stimul. 2014 Nov-Dec;7(6):800-6. doi: 10.1016/j.brs.2014.08.003. Epub 2014 Aug 12.
8
EEG beta suppression and low gamma modulation are different elements of human upright walking.
Front Hum Neurosci. 2014 Jul 8;8:485. doi: 10.3389/fnhum.2014.00485. eCollection 2014.
9
Recalibration of inhibitory control systems during walking-related dual-task interference: a mobile brain-body imaging (MOBI) study.
Neuroimage. 2014 Jul 1;94:55-64. doi: 10.1016/j.neuroimage.2014.03.016. Epub 2014 Mar 15.
10
Information processing in the primate basal ganglia during sensory-guided and internally driven rhythmic tapping.
J Neurosci. 2014 Mar 12;34(11):3910-23. doi: 10.1523/JNEUROSCI.2679-13.2014.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验