Multimodal Neuroimaging Predictors of Learning Performance of Sensorimotor Rhythm Up-Regulation Neurofeedback.
Multimodal Neuroimaging Predictors of Learning Performance of Sensorimotor Rhythm Up-Regulation Neurofeedback.
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感觉运动节律上调神经反馈学习表现的多模态神经影像预测因子
DOI：
10.3389/fnins.2021.699999
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发表时间：
2021
影响因子：
4.3
通讯作者：
Zhang Z
中科院分区：
文献类型：
--
作者：
Li L;Wang Y;Zeng Y;Hou S;Huang G;Zhang L;Yan N;Ren L;Zhang Z
脑电(EEG)神经反馈(NFB)是一种常用的神经调节方法，通过对EEG信号的实时视觉或听觉反馈，帮助人们选择性地增强或抑制他/她的大脑活动。感觉运动节奏(SMR)NFB方案已被应用于改善认知表现，但很大一部分参与者未能自我调节自己的大脑活动，无法从NFB训练中受益。因此，确定SMR上调NFB训练成绩的神经预测因子，对于更好地理解SMR NFB个体差异的机制具有重要意义。27名健康受试者(男性12名，年龄：23.1±2.36岁)完成3次SMR上调NFB训练和收集多模式神经影像数据[静息状态脑电、结构磁共振成像(MRI)和静息状态功能磁共振成像(FMRI)]。对NFB学习指数与从多模式神经影像数据中提取的解剖和功能脑特征进行相关分析，以确定NFB学习成绩的神经解剖学和神经生理学预测因子。最后，对机器学习模型进行训练，以使用来自每个通道的特征以及多通道特征来预测NFB的学习性能。根据我们的结果，大多数参与者能够成功地增加SMR功率，并且NFB的学习成绩与一系列神经成像特征显著相关，包括静息状态EEG功率、MRI的灰质/白质体积、静息状态fMRI的区域和功能连接(FC)。重要的是，预测分析的结果表明，与单通道特征相比，多通道特征可以更好地预测NFB学习指数。总之，这项研究强调了多模式神经成像技术作为一种工具来解释会话中NFB学习成绩的个体差异的重要性，并为早期识别无法从NFB训练中受益的个人提供了一个理论框架。
Electroencephalographic (EEG) neurofeedback (NFB) is a popular neuromodulation method to help one selectively enhance or inhibit his/her brain activities by means of real-time visual or auditory feedback of EEG signals. Sensory motor rhythm (SMR) NFB protocol has been applied to improve cognitive performance, but a large proportion of participants failed to self-regulate their brain activities and could not benefit from NFB training. Therefore, it is important to identify the neural predictors of SMR up-regulation NFB training performance for a better understanding the mechanisms of individual difference in SMR NFB. Twenty-seven healthy participants (12 males, age: 23.1 ± 2.36) were enrolled to complete three sessions of SMR up-regulation NFB training and collection of multimodal neuroimaging data [resting-state EEG, structural magnetic resonance imaging (MRI), and resting-state functional MRI (fMRI)]. Correlation analyses were performed between within-session NFB learning index and anatomical and functional brain features extracted from multimodal neuroimaging data, in order to identify the neuroanatomical and neurophysiological predictors for NFB learning performance. Lastly, machine learning models were trained to predict NFB learning performance using features from each modality as well as multimodal features. According to our results, most participants were able to successfully increase the SMR power and the NFB learning performance was significantly correlated with a set of neuroimaging features, including resting-state EEG powers, gray/white matter volumes from MRI, regional and functional connectivity (FC) of resting-state fMRI. Importantly, results of prediction analysis indicate that NFB learning index can be better predicted using multimodal features compared with features of single modality. In conclusion, this study highlights the importance of multimodal neuroimaging technique as a tool to explain the individual difference in within-session NFB learning performance, and could provide a theoretical framework for early identification of individuals who cannot benefit from NFB training.
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DOI：
10.1073/pnas.0504136102
发表时间：
2005-07-05
影响因子：
11.1
作者：
Fox, MD;Snyder, AZ;Raichle, ME
通讯作者：
Raichle, ME
影响因子：
4.7
作者：
Kleih, S. C.;Nijboer, F.;Kuebler, A.
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Kuebler, A.
影响因子：
2.5
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通讯作者：
Joliot, Marc
DOI：
10.1111/j.2517-6161.1995.tb02031.x
发表时间：
1995-01-01
影响因子：
5.8
作者：
BENJAMINI, Y;HOCHBERG, Y
通讯作者：
HOCHBERG, Y
影响因子：
5.1
作者：
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Wood G