Probing the Functional and Structural Connectivity Underlying EEG Traveling Waves

Qin, Yun; Zhang, Nan; Chen, Yan; Tan, Yue; Yang, Zhenglin; Shi, Yi; Luo, Cheng; Liu, Tiejun; Yao, Dezhong

doi:10.1007/s10548-021-00862-0

Cited by 2 publications

(3 citation statements)

References 62 publications

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“…This study recorded brain signals immediately after the introduction of feedback, indicating that the long-term effects of feedback-induced neuroplasticity were not explored. It is also possible that neurofeedback is more effective at altering the organization of structural connections within the brain than the strength of functional connections during training 50 , 51 .…”

Section: Discussionmentioning

confidence: 99%

Effects of visual-electrotactile stimulation feedback on brain functional connectivity during motor imagery practice

Phunruangsakao,

Achanccaray,

Bhattacharyya

et al. 2023

Sci Rep

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The use of neurofeedback is an important aspect of effective motor rehabilitation as it offers real-time sensory information to promote neuroplasticity. However, there is still limited knowledge about how the brain’s functional networks reorganize in response to such feedback. To address this gap, this study investigates the reorganization of the brain network during motor imagery tasks when subject to visual stimulation or visual-electrotactile stimulation feedback. This study can provide healthcare professionals with a deeper understanding of the changes in the brain network and help develop successful treatment approaches for brain–computer interface-based motor rehabilitation applications. We examine individual edges, nodes, and the entire network, and use the minimum spanning tree algorithm to construct a brain network representation using a functional connectivity matrix. Furthermore, graph analysis is used to detect significant features in the brain network that might arise in response to the feedback. Additionally, we investigate the power distribution of brain activation patterns using power spectral analysis and evaluate the motor imagery performance based on the classification accuracy. The results showed that the visual and visual-electrotactile stimulation feedback induced subject-specific changes in brain activation patterns and network reorganization in the $$\alpha$$ α band. Thus, the visual-electrotactile stimulation feedback significantly improved the integration of information flow between brain regions associated with motor-related commands and higher-level cognitive functions, while reducing cognitive workload in the sensory areas of the brain and promoting positive emotions. Despite these promising results, neither neurofeedback modality resulted in a significant improvement in classification accuracy, compared with the absence of feedback. These findings indicate that multimodal neurofeedback can modulate imagery-mediated rehabilitation by enhancing motor-cognitive communication and reducing cognitive effort. In future interventions, incorporating this technique to ease cognitive demands for participants could be crucial for maintaining their motivation to engage in rehabilitation.

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Section: Discussionmentioning

confidence: 99%

Effects of visual-electrotactile stimulation feedback on brain functional connectivity during motor imagery practice

Phunruangsakao,

Achanccaray,

Bhattacharyya

et al. 2023

Sci Rep

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“…In order to go beyond these limitations, combining M/EEG with other neuroimaging techniques that have higher signal to noise-ratio and higher spatial resolution is an increasingly popular approach. An example can be found in the paper by Qin et al (2021) in this special issue. However, in order to really integrate different neuroimaging methods-and "integration" here is meant in contrast to a mere "parallel processing" and subsequent comparison-we have to understand how the neural signals acquired by different techniques are underpinned by the same electrical or neurophysiological activity of the brain.…”

Section: Models That Bridge Spatial and Temporal Scalesmentioning

confidence: 99%

“…Computational models serve this purpose. Therefore, in this special issue, we called for and accepted papers that covered micro- (Saponati et al 2021;Kohl et al 2021), meso-(Byrne et al 2021) and macroscale modelling of M/EEG dynamics (Allouch et al 2021;Qin et al 2021), as well as articles that show how models in M/EEG are applied today to improve data quality (Billings et al 2021;Bhutada et al 2021) as well as in a clinical context (Hutt and Lefebvre 2021;Sancristóbal et al 2021). Two more articles (Glomb et al 2021;Orczyk and Kajikawa 2021) summarize and provide a perspective on some aspects of the burgeoning field.…”

mentioning

confidence: 99%