Neural synchronization, brain networks, and cognitive processes

可以说，认知神经科学的主要任务是了解大脑如何执行认知过程，如感知、注意力、记忆、决策和意识。我在这个提案中的目标是通过解决人类大脑区域网络中控制和信息处理的时间动态来在这个普遍问题上取得进展，这些区域涉及实施认知过程。我感兴趣的是指定这些网络的一般属性，这是至关重要的，以及实际指定它们的细节，这将有助于理解特定的过程和应用，如脑损伤的康复。为此，我将通过研究脑电(通道)和脑磁图(151通道)头皮记录信号的大脑来源之间的同步和格兰杰因果关系(跨时间序列的可预测性)以及大脑内部的同步来实现这一点，这既是通过进行调用这些过程的实验进行经验验证，也是通过对大脑区域之间的通信进行建模从理论上讲。脑电和脑磁图记录信号的脑源定位是一个困难的问题，将采取几种互补的方法，包括波束形成(空间滤波)和独立分量分析(一种统计盲源分离技术)，在一定程度上受到类似范式的功能磁共振研究的指导。将使用各种测量同步的方法，包括小波和希尔伯特变换/解析信号分析，以获得不同频带中神经源活动的相位和幅度的时频图像。此外，相位、幅度、同步和格兰杰因果关系的交叉频率分析将由定制软件完成。最后，在与通信工程师的合作下，我将在相关流程的不同阶段对这些区域之间的通信进行建模。同样，我们的目标是建立一幅尽可能完整的关于实施规范认知过程的大脑区域网络的时间动力学图景。

Arguably the main task of cognitive neuroscience is to understand how the brain implements cognitive processes such as perception, attention, memory, decision making, and consciousness. My goal in this proposal is to make progress on this general problem by addressing the temporal dynamics of control and information processing within networks of human brain areas that are involved in implementing cognitive processes. I am interested both in specifying the general properties of these networks, which is of fundamental importance, as well as in actually specifying their details, which will be useful in understanding specific processes and in applications such as rehabilitation from brain injuries. I will do this by studying synchronization and Granger causality (predictability across time series) between, and also synchronization within, brain sources of EEG (64-channel) and MEG (151-channel) scalp-recorded signals for a number of cognitive processes, both empirically by conducting experiments that invoke those processes and theoretically by modeling communication between brain areas. Localization of brain sources of EEG- and MEG-recorded signals is a difficult problem and several complementary approaches will be taken, including beamforming (spatial filtering) and independent component analysis (a statistical blind source separation technique), guided to some extent by fMRI studies of similar paradigms. Various approaches to measuring synchronization, including wavelet and Hilbert transform/analytic signal analysis, will be used to obtain time-frequency portraits of phase and amplitude of the activity of the neural sources in various frequency bands. In addition, cross-frequency analysis of phase, amplitude, synchronization, and Granger causality will be accomplished by custom software. Finally, in collaboration with a communications engineer I will model the communications between these areas during various phases of the process in question. Again, the goal is to establish as complete a picture as possible of the temporal dynamics of the networks of brain areas that are involved in implementing canonical cognitive processes.