Resting State Changes in Functional Connectivity Correlate With Movement Recovery for BCI and Robot-Assisted Upper-Extremity Training After Stroke

@article{Vrkuti2013RestingSC,
  title={Resting State Changes in Functional Connectivity Correlate With Movement Recovery for BCI and Robot-Assisted Upper-Extremity Training After Stroke},
  author={B{\'a}lint V{\'a}rkuti and Cuntai Guan and Yaozhang Pan and Kok Soon Phua and Kai Keng Ang and Christopher Wee Keong Kuah and Karen Sui-Geok Chua and Beng Ti Ang and Niels Birbaumer and Ranganatha Sitaram},
  journal={Neurorehabilitation and Neural Repair},
  year={2013},
  volume={27},
  pages={53 - 62}
}
Background. Robot-assisted training may improve motor function in some hemiparetic patients after stroke, but no physiological predictor of rehabilitation progress is reliable. Resting state functional magnetic resonance imaging (RS-fMRI) may serve as a method to assess and predict changes in the motor network. Objective. The authors examined the effects of upper-extremity robot-assisted rehabilitation (MANUS) versus an electroencephalography-based brain computer interface setup with motor… 

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Differential Amplitude of Low-Frequency Fluctuations in brain networks after BCI Training with and without tDCS in Stroke

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  • Psychology, Biology
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  • 2018
TLDR
The results suggested that stroke patients have lower ALFF in the ipsilesional somatomotor network compared to controls at baseline, and alterations in higher-level cognitive networks such as the default mode network (DMN) and salience networks accompany motor recovery after intervention; though the MI-BCI alone group and MI- BCI combined with tDCS group exhibit differential patterns.
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