Courellis Hristos, Mullen Tim, Poizner Howard, Cauwenberghs Gert, Iversen John R
Swartz Center for Computational Neuroscience, University of California, San DiegoSan Diego, CA, United States.
Department of Bioengineering, University of California, San DiegoSan Diego, CA, United States.
Front Neurosci. 2017 May 17;11:180. doi: 10.3389/fnins.2017.00180. eCollection 2017.
Quantification of dynamic causal interactions among brain regions constitutes an important component of conducting research and developing applications in experimental and translational neuroscience. Furthermore, cortical networks with dynamic causal connectivity in brain-computer interface (BCI) applications offer a more comprehensive view of brain states implicated in behavior than do individual brain regions. However, models of cortical network dynamics are difficult to generalize across subjects because current electroencephalography (EEG) signal analysis techniques are limited in their ability to reliably localize sources across subjects. We propose an algorithmic and computational framework for identifying cortical networks across subjects in which dynamic causal connectivity is modeled among user-selected cortical regions of interest (ROIs). We demonstrate the strength of the proposed framework using a "reach/saccade to spatial target" cognitive task performed by 10 right-handed individuals. Modeling of causal cortical interactions was accomplished through measurement of cortical activity using (EEG), application of independent component clustering to identify cortical ROIs as network nodes, estimation of cortical current density using cortically constrained low resolution electromagnetic brain tomography (cLORETA), multivariate autoregressive (MVAR) modeling of representative cortical activity signals from each ROI, and quantification of the dynamic causal interaction among the identified ROIs using the Short-time direct Directed Transfer function (SdDTF). The resulting cortical network and the computed causal dynamics among its nodes exhibited physiologically plausible behavior, consistent with past results reported in the literature. This physiological plausibility of the results strengthens the framework's applicability in reliably capturing complex brain functionality, which is required by applications, such as diagnostics and BCI.
对大脑区域间动态因果交互作用进行量化，是实验性和转化性神经科学开展研究及开发应用的重要组成部分。此外，在脑机接口（BCI）应用中具有动态因果连接性的皮层网络，相较于单个脑区，能更全面地呈现与行为相关的脑状态。然而，由于当前脑电图（EEG）信号分析技术在跨个体可靠定位源的能力方面存在局限，皮层网络动力学模型难以在不同个体间进行推广。我们提出了一种算法和计算框架，用于识别不同个体间的皮层网络，其中在用户选择的感兴趣皮层区域（ROI）之间对动态因果连接性进行建模。我们使用10名右利手个体执行的“向空间目标伸手/扫视”认知任务，展示了所提出框架的优势。通过使用（EEG）测量皮层活动、应用独立成分聚类将皮层ROI识别为网络节点、使用皮层约束低分辨率电磁脑断层扫描（cLORETA）估计皮层电流密度、对每个ROI的代表性皮层活动信号进行多元自回归（MVAR）建模，以及使用短时直接定向传递函数（SdDTF）量化已识别ROI之间的动态因果交互作用，来完成因果皮层交互作用的建模。由此产生的皮层网络及其节点间计算出的因果动力学表现出符合生理的行为，与文献中先前报道的结果一致。结果的这种生理合理性增强了该框架在可靠捕捉复杂脑功能方面的适用性，而这是诊断和BCI等应用所需要的。
