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Can I read Multimodal and Hemispheric Graph-theoretical Brain Network Predictors of Learning Efficacy for Frontal Alpha Asymmetry Neurofeedback on EtoBox?
Multimodal and Hemispheric Graph-theoretical Brain Network Predictors of Learning Efficacy for Frontal Alpha Asymmetry Neurofeedback by Linling Li; Yutong Li; Zhaoxun Li; Gan Huang; Zhen Liang; Li Zhang; Feng Wan; Manjun Shen; Xue Han; Zhiguo Zhang is a Neuroscience article available to read on EtoBox.
What is Multimodal and Hemispheric Graph-theoretical Brain Network Predictors of Learning Efficacy for Frontal Alpha Asymmetry Neurofeedback about?
EEG neurofeedback using frontal alpha asymmetry (FAA) has been widely used for emotion regulation, but its effectiveness is controversial. Studies indicated that individual differences in neurofeedback training can be traced to neuroanatomical and neurofunctional features. However, they only focused on regional brain structure or function and overlooked possible neural correlates of the brain network. Besides, no neuroimaging predictors for FAA neurofeedback protocol have been reported so far. We designed a single-blind pseudo-controlled FAA neurofeedback experiment and collected multimodal neuroimaging data from healthy participants before training. We assessed the learning performance for evoked EEG modulations during training (L1) and at rest (L2), and investigated performance-related predictors based on a combined analysis of multimodal brain networks and graph-theoretical features. The main findings of this study are described below. First, both real and sham groups could increase their FAA during training, but only the real group showed a significant increase in FAA at rest. Second, the predictors during training blocks and at rests were different: L1 was correlated with the
Who reads Multimodal and Hemispheric Graph-theoretical Brain Network Predictors of Learning Efficacy for Frontal Alpha Asymmetry Neurofeedback?
It is typically read by researchers, students, and practitioners in Neuroscience.
- Author
- Linling Li; Yutong Li; Zhaoxun Li; Gan Huang; Zhen Liang; Li Zhang; Feng Wan; Manjun Shen; Xue Han; Zhiguo Zhang
- Publisher
- Springer Science and Business Media LLC
- Published
- 2023
- Language
- EN
- Field
- Neuroscience (Life Sciences)