4.7 Article

EEG Oscillations Are Modulated in Different Behavior-Related Networks during Rhythmic Finger Movements

期刊

JOURNAL OF NEUROSCIENCE
卷 36, 期 46, 页码 11671-11681

出版社

SOC NEUROSCIENCE
DOI: 10.1523/JNEUROSCI.1739-16.2016

关键词

EEG source imaging; finger movements; large-scale networks; neural oscillations; sensorimotor system; spectral profiles

资金

  1. Land Steiermark project rE(EG)map!
  2. BioTechMed Graz

向作者/读者索取更多资源

Sequencing and timing of body movements are essential to perform motoric tasks. In this study, we investigate the temporal relation between cortical oscillations and human motor behavior (i.e., rhythmic finger movements). High-density EEG recordings were used for source imaging based on individual anatomy. Weseparated sustained and movement phase-related EEG source amplitudes based on the actual finger movements recorded by a data glove. Sustained amplitude modulations in the contralateral hand area show decrease for alpha (10-12 Hz) and beta (18-24 Hz), but increase for high gamma (60-80 Hz) frequencies during the entire movement period. Additionally, we found movement phase-related amplitudes, which resembled the flexion and extension sequence of the fingers. Especially for faster movement cadences, movement phase-related amplitudes included high beta (24-30 Hz) frequencies in prefrontal areas. Interestingly, the spectral profiles and source patterns of movement phase-related amplitudes differed from sustained activities, suggesting that they represent different frequency-specific large-scale networks. First, networks were signified by the sustained element, which statically modulate their synchrony levels during continuous movements. These networks may upregulate neuronal excitability in brain regions specific to the limb, in this study the right hand area. Second, movement phase-related networks, which modulate their synchrony in relation to the movement sequence. We suggest that these frequency-specific networks are associated with distinct functions, including top-down control, sensorimotor prediction, and integration. The separation of different large-scale networks, we applied in this work, improves the interpretation of EEG sources in relation to human motor behavior.

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