Literature DB >> 33551777

Modulation of Functional Connectivity and Low-Frequency Fluctuations After Brain-Computer Interface-Guided Robot Hand Training in Chronic Stroke: A 6-Month Follow-Up Study.

Cathy C Y Lau1, Kai Yuan1, Patrick C M Wong2, Winnie C W Chu3, Thomas W Leung4, Wan-Wa Wong5, Raymond K Y Tong1.   

Abstract

Hand function improvement in stroke survivors in the chronic stage usually plateaus by 6 months. Brain-computer interface (BCI)-guided robot-assisted training has been shown to be effective for facilitating upper-limb motor function recovery in chronic stroke. However, the underlying neuroplasticity change is not well understood. This study aimed to investigate the whole-brain neuroplasticity changes after 20-session BCI-guided robot hand training, and whether the changes could be maintained at the 6-month follow-up. Therefore, the clinical improvement and the neurological changes before, immediately after, and 6 months after training were explored in 14 chronic stroke subjects. The upper-limb motor function was assessed by Action Research Arm Test (ARAT) and Fugl-Meyer Assessment for Upper-Limb (FMA), and the neurological changes were assessed using resting-state functional magnetic resonance imaging. Repeated-measure ANOVAs indicated that long-term motor improvement was found by both FMA (F[2,26] = 6.367, p = 0.006) and ARAT (F[2,26] = 7.230, p = 0.003). Seed-based functional connectivity analysis exhibited that significantly modulated FC was observed between ipsilesional motor regions (primary motor cortex and supplementary motor area) and contralesional areas (supplementary motor area, premotor cortex, and superior parietal lobule), and the effects were sustained after 6 months. The fALFF analysis showed that local neuronal activities significantly increased in central, frontal and parietal regions, and the effects were also sustained after 6 months. Consistent results in FC and fALFF analyses demonstrated the increase of neural activities in sensorimotor and fronto-parietal regions, which were highly involved in the BCI-guided training. Clinical Trial Registration: This study has been registered at ClinicalTrials.gov with clinical trial registration number NCT02323061.
Copyright © 2021 Lau, Yuan, Wong, Chu, Leung, Wong and Tong.

Entities:  

Keywords:  brain-computer interface; fractional amplitude low-frequency fluctuations; functional magnet resonance imaging; rehabilatation robotics; stroke

Year:  2021        PMID: 33551777      PMCID: PMC7855586          DOI: 10.3389/fnhum.2020.611064

Source DB:  PubMed          Journal:  Front Hum Neurosci        ISSN: 1662-5161            Impact factor:   3.169


  53 in total

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5.  Motor Imagery Training After Stroke Increases Slow-5 Oscillations and Functional Connectivity in the Ipsilesional Inferior Parietal Lobule.

Authors:  Xu Wang; Hewei Wang; Xin Xiong; Changhui Sun; Bing Zhu; Yiming Xu; Mingxia Fan; Shanbao Tong; Limin Sun; Xiaoli Guo
Journal:  Neurorehabil Neural Repair       Date:  2020-02-26       Impact factor: 3.919

6.  Brain-Machine Interface in Chronic Stroke: Randomized Trial Long-Term Follow-up.

Authors:  Ander Ramos-Murguialday; Marco R Curado; Doris Broetz; Özge Yilmaz; Fabricio L Brasil; Giulia Liberati; Eliana Garcia-Cossio; Woosang Cho; Andrea Caria; Leonardo G Cohen; Niels Birbaumer
Journal:  Neurorehabil Neural Repair       Date:  2019-02-05       Impact factor: 3.919

7.  Correlation between cerebral reorganization and motor recovery after subcortical infarcts.

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Journal:  Brain Res       Date:  2009-06-03       Impact factor: 3.252

9.  Interhemispheric Functional Reorganization and its Structural Base After BCI-Guided Upper-Limb Training in Chronic Stroke.

Authors:  Kai Yuan; Xin Wang; Cheng Chen; Cathy Choi-Yin Lau; Winnie Chiu-Wing Chu; Raymond Kai-Yu Tong
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2020-11-06       Impact factor: 3.802

Review 10.  Brain-computer interfaces for post-stroke motor rehabilitation: a meta-analysis.

Authors:  María A Cervera; Surjo R Soekadar; Junichi Ushiba; José Del R Millán; Meigen Liu; Niels Birbaumer; Gangadhar Garipelli
Journal:  Ann Clin Transl Neurol       Date:  2018-03-25       Impact factor: 4.511

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  1 in total

1.  Multimodal Human-Exoskeleton Interface for Lower Limb Movement Prediction Through a Dense Co-Attention Symmetric Mechanism.

Authors:  Kecheng Shi; Fengjun Mu; Rui Huang; Ke Huang; Zhinan Peng; Chaobin Zou; Xiao Yang; Hong Cheng
Journal:  Front Neurosci       Date:  2022-04-25       Impact factor: 5.152

  1 in total

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