التفاصيل البيبلوغرافية
| العنوان: |
EEG Oscillations Are Modulated in Different Behavior-Related Networks during Rhythmic Finger Movements. |
| المؤلفون: |
Seeber, Martin, Scherer, Reinhold, Müller-Putz, Gernot R. |
| المصدر: |
Journal of Neuroscience; 11/16/2016, Vol. 36 Issue 46, p11671-11681, 11p |
| مصطلحات موضوعية: |
MOVEMENT disorders, ELECTROENCEPHALOGRAPHY, BODY movement, SENSORIMOTOR integration, HIGHER nervous activity, PREFRONTAL cortex, CONFERENCES & conventions, PHYSIOLOGY |
| مستخلص: |
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. We separated 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 α (10–12 Hz) and β (18–24 Hz), but increase for high γ (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 β (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. [ABSTRACT FROM AUTHOR] |
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