Structural and functional correlates of motor imagery BCI performance: Insights from the patterns of fronto-parietal attention network
Structural and functional correlates of motor imagery BCI performance: Insights from the patterns of fronto-parietal attention network
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运动想象脑机接口表现的结构和功能相关性：来自额顶叶注意力网络模式的见解
DOI：
10.1016/j.neuroimage.2016.04.030
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发表时间：
2016-07
期刊：
影响因子：
5.7
通讯作者：
Xu Peng
中科院分区：
文献类型：
--
作者：
Zhang Tao;Liu Tiejun;Li Fali;Li Mengchen;Liu Dongbo;Zhang Rui;He Hui;Li Peiyang;Gong Jinnan;Luo Cheng;Yao Dezhong;Xu Peng
基于运动想象（MI）的脑机接口（BCI）已广泛用于运动障碍患者的运动能力康复和假肢控制。然而，MI-BCI 表现在不同受试者之间表现出很大的差异，并且潜在的神经机制仍不清楚。多项研究表明，额顶注意力网络 (FPAN) 和 MI 都参与对 BCI 控制至关重要的高级认知过程。因此，我们假设 FPAN 可能在 MI-BCI 性能中发挥重要作用。在我们的研究中，我们记录了由每位受试者的 MI 脑电图 (EEG) 信号、T1 加权结构和静息态功能 MRI 数据组成的多模态数据集。使用通用空间模式从 EEG 信号中提取 MI 特征来评估 MI-BCI 性能。一种皮质结构特征（皮质厚度（CT））和节点中心性的两种测量（度中心性（DC）和特征向量中心性（EC））分别从结构和功能MRI数据中得出。根据从 EEG 和 MRI 中提取的信息，使用相关性分析来阐明 FPAN 和 MI-BCI 表现之间的关系。我们的结果表明，右侧腹侧顶内沟的 DC、左侧顶下叶的 EC 和 CT 以及右侧背外侧前额叶皮质的 CT 与 MI-BCI 表现显着相关。此外，接收器操作特征分析和机器学习分类表明，左侧IPL的EC和CT可以有效地从高资质BCI用户中预测出低资质BCI用户，准确率达到83.3%。这些研究结果一致表明，拥有高效 FPAN 的个人在 MI-BCI 上的表现会更好。我们的研究结果可能会加深对 MI-BCI 表现个体差异的理解，也可能提供一种新的生物标志物来预测个体 MI-BCI 表现。
Motor imagery (MI)-based brain-computer interfaces (BCIs) have been widely used for rehabilitation of motor abilities and prosthesis control for patients with motor impairments. However, MI-BCI performance exhibits a wide variability across subjects, and the underlying neural mechanism remains unclear. Several studies have demonstrated that both the fronto-parietal attention network (FPAN) and MI are involved in high-level cognitive processes that are crucial for the control of BCIs. Therefore, we hypothesized that the FPAN may play an important role in MI-BCI performance. In our study, we recorded multi-modal datasets consisting of MI electroencephalography (EEG) signals, T1-weighted structural and resting-state functional MRI data for each subject. MI-BCI performance was evaluated using the common spatial pattern to extract the MI features from EEG signals. One cortical structural feature (cortical thickness (CT)) and two measurements (degree centrality (DC) and eigenvector centrality (EC)) of node centrality were derived from the structural and functional MRI data, respectively. Based on the information extracted from the EEG and MRI, a correlation analysis was used to elucidate the relationships between the FPAN and MI-BCI performance. Our results show that the DC of the right ventral intraparietal sulcus, the EC and CT of the left inferior parietal lobe, and the CT of the right dorsolateral prefrontal cortex were significantly associated with MI-BCI performance. Moreover, the receiver operating characteristic analysis and machine learning classification revealed that the EC and CT of the left IPL could effectively predict the low-aptitude BCI users from the high-aptitude BCI users with 83.3% accuracy. Those findings consistently reveal that the individuals who have efficient FPAN would perform better on MI-BCI. Our findings may deepen the understanding of individual variability in MI-BCI performance, and also may provide a new biomarker to predict individual MI-BCI performance.
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影响因子：
5
作者：
Scolari M;Seidl-Rathkopf KN;Kastner S
通讯作者：
Kastner S
影响因子：
2.7
作者：
Vidaurre, Carmen;Blankertz, Benjamin
通讯作者：
Blankertz, Benjamin
影响因子：
3.8
作者：
Sack AT;Schuhmann T
通讯作者：
Schuhmann T
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：
5.7
作者：
Pereira F;Mitchell T;Botvinick M
通讯作者：
Botvinick M