Resting state changes in functional connectivity correlate with movement recovery for BCI and robot-Assisted upper-extremity training after stroke

Bálint Várkuti, Cuntai Guan, Yaozhang Pan, Kok Soon Phua, Kai Keng Ang, Christopher Wee Keong Kuah, Karen Chua, Beng Ti Ang, Niels Birbaumer, Ranganathan Sitaram

Research output: Contribution to journalArticle

121 Citations (Scopus)

Abstract

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 imagery (MI EEG-BCI) and compared pretreatment and posttreatment RS-fMRI. Methods. In all, 9 adults with upper-extremity paresis were trained for 4 weeks with a MANUS shoulder-elbow robotic rehabilitation paradigm. In 3 participants, robot-Assisted movement began if no voluntary movement was initiated within 2 s. In 6 participants, MI-BCI-based movement was initiated if motor imagery was detected. RS-fMRI and Fugl-Meyer (FM) upper-extremity motor score were assessed before and after training. Results. The individual gain in FM scores over 12 weeks could be predicted from functional connectivity changes (FCCs) based on the pre-post differences in RS-fMRI measurements. Both the FM gain and FCC were numerically higher in the MI-BCI group. Increases in FC of the supplementary motor area, the contralesional and ipsilesional motor cortex, and parts of the visuospatial system with mostly association cortex regions and the cerebellum correlated with individual upper-extremity function improvement. Conclusion. FCC may predict the steepness of individual motor gains. Future training could therefore focus on directly inducing these beneficial increases in FC. Evaluation of the treatment groups suggests that MI is a potential facilitator of such neuroplasticity.

Original languageEnglish
Pages (from-to)53-62
Number of pages10
JournalNeurorehabilitation and Neural Repair
Volume27
Issue number1
DOIs
Publication statusPublished - Jan 2013

Fingerprint

Upper Extremity
Stroke
Magnetic Resonance Imaging
Rehabilitation
Imagery (Psychotherapy)
Motor Cortex
Electroencephalography
Brain-Computer Interfaces
Neuronal Plasticity
Robotics
Paresis
Elbow
Cerebellum
Therapeutics

Keywords

  • functional brain imaging
  • hemiparesis
  • neuroplasticity
  • outcomes assessment
  • physical therapy
  • stroke rehabilitation

ASJC Scopus subject areas

  • Clinical Neurology
  • Rehabilitation
  • Neurology

Cite this

Resting state changes in functional connectivity correlate with movement recovery for BCI and robot-Assisted upper-extremity training after stroke. / Várkuti, Bálint; Guan, Cuntai; Pan, Yaozhang; Phua, Kok Soon; Ang, Kai Keng; Kuah, Christopher Wee Keong; Chua, Karen; Ang, Beng Ti; Birbaumer, Niels; Sitaram, Ranganathan.

In: Neurorehabilitation and Neural Repair, Vol. 27, No. 1, 01.2013, p. 53-62.

Research output: Contribution to journalArticle

Várkuti, Bálint ; Guan, Cuntai ; Pan, Yaozhang ; Phua, Kok Soon ; Ang, Kai Keng ; Kuah, Christopher Wee Keong ; Chua, Karen ; Ang, Beng Ti ; Birbaumer, Niels ; Sitaram, Ranganathan. / Resting state changes in functional connectivity correlate with movement recovery for BCI and robot-Assisted upper-extremity training after stroke. In: Neurorehabilitation and Neural Repair. 2013 ; Vol. 27, No. 1. pp. 53-62.
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AU - Phua, Kok Soon

AU - Ang, Kai Keng

AU - Kuah, Christopher Wee Keong

AU - Chua, Karen

AU - Ang, Beng Ti

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