Mutual-information-based approach for neural connectivity during self-paced finger lifting task
Mutual-information-based approach for neural connectivity during self-paced finger lifting task
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DOI：
10.1002/hbm.20386
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发表时间：
2008-03-01
影响因子：
4.8
通讯作者：
Wu, Yu-Te
中科院分区：
文献类型：
--
作者：
Chen, Chun-Chuan;Hsieh, Jen-Chuen;Wu, Yu-Te
神经元组装之间的频率依赖性调制可以在神经连接的背景下提供有见地的功能组织机制。本文提出了一种联合时频交叉互信息（TFCMI）方法，利用脑磁图（MEG）研究了自步速手指抬起任务中大脑神经元的细微连接。表面肌电图（sEMG）取自指总伸肌。进行了模态内（MEG-MEG）和模态间（sEMG-MEG）研究。TFCMI方法测量在预定频带内信号功率的时间动态的线性和非线性依赖性。使用Morlet小波将跨通道的MEG和sEMG信号的每个单次试验转换到时频域，以获得更好的时间谱（功率）信息。与宽带分析中的相干方法（仅线性依赖）相比，TFCMI方法在包括对近中额中央皮层和双侧初级感觉运动区的检测、事件相关区域和非事件相关区域的清晰划分以及sEMG - MEG模态间研究的鲁棒性方面表现出优势，即，皮质肌通讯我们的结论是，这种新的TFCMI方法有望更好地解开复杂的功能组织的背景下，振荡编码的通信。
Frequency-dependent modulation between neuronal assemblies may provide insightful mechanisms of functional organization in the context of neural connectivity. We present a conjoined time-frequency cross mutual information (TFCMI) method to explore the subtle brain neural connectivity by magnetoencephalography (MEG) during a self-paced finger lifting task. Surface electromyogram (sEMG) was obtained from the extensor digitorum communis. Both within-modality (MEG-MEG) and between-modality studies (sEMG-MEG) were conducted. The TFCMI method measures both the linear and nonlinear dependencies of the temporal dynamics of signal power within a pre-specified frequency band. Each single trial of MEG across channels and sEMG signals was transformed into time-frequency domain with use of the Morlet wavelet to obtain better temporal spectral (power) information. As compared to coherence approach (linear dependency only) in broadband analysis, the TFCMI method demonstrated advantages in encompassing detection for the mesial frontocentral cortex and bilateral primary sensorimotor areas, clear demarcation of event- and non-event-related regions, and robustness for sEMG - MEG between-modality study, i.e., corticomuscular communication. We conclude that this novel TFCMI method promises a possibility to better unravel the intricate functional organizations of brain in the context of oscillation-coded communication.