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. V{\'a}rkuti and Cuntai Guan and Yaozhang Pan and K. Phua and K. Ang and C. Kuah and K. Chua and B. Ang and N. Birbaumer and R. 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… Expand
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The longitudinal changes in band-limited resting-state (RS) functional connectivity (FC) networks in association with post-stroke UL functional recovery achieved by a multimodal intervention involving motor attempt and robotic hand-exoskeleton are examined to predict novel FC patterns that can be important for higher level cognitive functions. Expand
The Reorganization of Resting-State Brain Networks Associated With Motor Imagery Training in Chronic Stroke Patients
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It is found that the patients in the MIT group showed more improvement in the FM-UL scores compared with the CRT group, and the clustering coefficient was significantly increased in the Massachusetts Institute of Technology (MIT) group but not in the CRt group, which might contribute to the motor recovery in stroke patients through the following network reorganization. Expand
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The results provide first evidence that fNIRS can detect brain activity associated with single-trial lower limb motor preparation in stroke patients, and encourage further investigation of fNirS suitability for BCI applications in rehabilitation of patients with lower limb Motor impairment after stroke. Expand
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Solid support for stroke recovery mechanism in terms of interhemispheric interaction and its structural substrates is provided, which could further enhance the understanding of BCI training in stroke rehabilitation. Expand
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TLDR
The findings demonstrate the therapeutic potential of a BCI-driven neurorehabilitation approach using the unaffected hemisphere in this uncontrolled sample of chronic stroke survivors and demonstrate thatBCI- driven neurore rehabilitation can be effectively delivered in the home environment, thus increasing the probability of future clinical translation. Expand
Differential Amplitude of Low-Frequency Fluctuations in brain networks after BCI Training with and without tDCS in Stroke
  • M. Hu, F. Ji, +13 authors J. Zhou
  • Computer Science, Medicine
  • 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
  • 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. Expand
Resting-State Functional Connectivity in Stroke Patients After Upper Limb Robot-Assisted Therapy: A Pilot Study
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A cortical reorganization following training is observed, consistent with motor improvements, in sub-acute post-stroke survivors treated with robot-assisted therapies. Expand
Changes in electroencephalography complexity and functional magnetic resonance imaging connectivity following robotic hand training in chronic stroke
TLDR
In conclusion, robotic hand training significantly facilitated stroke motor recovery, and FD, along with connectivity analysis can detect neuroplasticity changes. Expand
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