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      Negative covariation between task-related responses in alpha/beta-band activity and BOLD in human sensorimotor cortex: An EEG and fMRI study of motor imagery and movements

      , , , , ,
      NeuroImage
      Elsevier BV

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          Abstract

          Similar to the occipital alpha rhythm, electroencephalographic (EEG) signals in the alpha- and beta-frequency bands can be suppressed by movement or motor imagery and have thus been thought to represent the "idling state" of the sensorimotor cortex. A negative correlation between spontaneous alpha EEG and blood-oxygen-level-dependent (BOLD) signals has been reported in combined EEG and fMRI (functional Magnetic Resonance Imaging) experiments when subjects stayed at the resting state or alternated between the resting state and a task. However, the precise nature of the task-induced alpha modulation remains elusive. It was not clear whether alpha/beta rhythm suppressions may co-vary with BOLD when conducting tasks involving varying activations of the cortex. Here, we quantified the task-evoked responses of BOLD and alpha/beta-band power of EEG directly in the cortical source domain, by using source imaging technology, and examined their covariation across task conditions in a mixed block and event-related design. In this study, 13 subjects performed tasks of right-hand, right-foot or left-hand movement and motor imagery when EEG and fMRI data were separately collected. Task-induced increase of BOLD signal and decrease of EEG amplitudes in alpha and beta bands were shown to be co-localized at the somatotopic sensorimotor cortex. At the corresponding regions, the reciprocal changes of the two signals co-varied in the magnitudes across imagination and movement conditions. The spatial correspondence and negative covariation between the two measurements were further shown to exist at somatotopic brain regions associated with different body parts. These results suggest an inverse functional coupling relationship between task-induced changes of BOLD and low-frequency EEG signals. Copyright (c) 2009 Elsevier Inc. All rights reserved.

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          Author and article information

          Journal
          NeuroImage
          NeuroImage
          Elsevier BV
          10538119
          February 2010
          February 2010
          : 49
          : 3
          : 2596-2606
          Article
          10.1016/j.neuroimage.2009.10.028
          2818527
          19850134
          025f263e-469c-4d71-8ebf-1250ea11c9e5
          © 2010

          https://www.elsevier.com/tdm/userlicense/1.0/

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