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实现认知过程，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参与实现认知过程的人脑区域网络内的控制和信息处理的时间动态来在这个一般性问题上取得进展。我感兴趣的是具体说明这些网络的一般性质，这是至关重要的，以及在实际上具体说明他们的细节，这将有助于理解特定的过程和应用，如脑损伤的康复。我将通过研究同步和格兰杰因果关系（跨时间序列的可预测性）来实现这一点，以及脑源EEG（64通道）和MEG（151通道）头皮记录信号之间的同步，这些信号用于许多认知过程，无论是经验上的，还是理论上的，都是通过进行实验来调用这些过程，理论上是通过模拟大脑区域之间的通信。脑源的EEG和MEG记录的信号的定位是一个困难的问题，几个互补的方法，将采取，包括波束形成（空间滤波）和独立成分分析（统计盲源分离技术），在一定程度上由功能磁共振成像研究类似的范例。测量同步的各种方法，包括小波和希尔伯特变换/分析信号分析，将被用来获得在各个频带中的神经源的活动的相位和幅度的时频画像。此外，相位、幅度、同步和格兰杰因果关系的交叉频率分析将通过定制软件完成。最后，在与通信工程师的合作下，我将在所讨论的过程的各个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个尽可能完整的图片的时间动态网络的大脑区域，参与实施规范的认知过程。

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.