Fast dynamics of brain regional networks underlying cognition

认知过程是通过区域网络在大脑中实现的，这些区域网络基于来自遗传编程和环境导向可塑性(学习)的复杂连接，对传入(感觉)和存储(记忆)信息执行各种计算。显然，在每一次认知“操作”中，信息和控制信号必须在这些大脑区域之间传递，例如，新感觉信息的前馈扫描、错误反馈、注意信号的上升或下降对到来的刺激的准备等。一些区域网络正在变得相当好地理解，至少在它们可能进行的计算类型方面是这样。我们已经开始使用先进的技术来表征这些网络的快速动态，例如阅读网络和注意力定向网络。我所说的“快速动态”是指在几十到几百毫秒的时间尺度上，在网络所涉及的区域之间以不同的振荡频率传输信息和控制信号。我将在几个重要方向展开这项工作。首先，我打算调查各种干扰对我们过去看到的网络的快速动力学的影响，例如阅读、注意力、知觉意识，以及一些对我们来说是新的网络，例如工作记忆、视觉和听觉意象。干扰的类型包括自然干扰，如阅读困难、习惯使用大麻、死亡和经颅刺激，既有阴极直流电刺激(Tdcs)，也有噪声刺激(Trns)。其次，我打算研究大脑刺激如何影响这些网络中的快速动力学，无论是正常的还是挑战的。在正常网络的情况下，可能会改善时间精度和性能，而对于受到挑战的网络，可能会部分恢复正常的时间动力学。第三，我将开始表征皮质-皮质下回路的快速动力学。我将采用直接涉及特定皮质下回路的技术，例如杏仁核、枕叶和MD丘脑，并且可能有可靠的皮质镜像(例如，杏仁核的前额叶，枕叶的PPC)。我将进行联合注册的fMRI-EEG研究，使用fMRI来验证皮质下的参与，并绘制出功能和有效的连接，以识别皮质镜像区域，并使用EEG来研究这个网络的快速动力学。最后，我将继续进行振荡大脑网络的计算建模的理论研究，目的是表征支持认知的振荡大脑区域之间的相互作用。我预计，这项工作不仅将有助于从根本上理解大脑区域神经网络如何实施认知过程，而且最终还将在改善阅读障碍等大脑挑战方面找到应用。老龄化和药物使用。**

Cognitive processes are implemented in the brain by regional networks that perform a variety of computations on incoming (sensory) and stored (memory) information based on intricate connectivity derived from both genetic programming and environmentally-directed plasticity (learning). It is clear that during each cognitive "operation" information and control signals must be passed among these brain regions, e.g., the feedforward sweep of new sensory information, error feedback, attentional signals turning up or down readiness for incoming stimuli, etc. Some of the regional networks are becoming fairly well understood, at least in terms of the sort of computations they might be doing. We have begun to characterize the fast dynamics of these networks using advanced techniques, for example the reading network and the attention orienting network. By "fast dynamics" I mean the transfer of information and control signals, at various oscillatory frequencies, between the regions involved in the network on a time scale of tens to hundreds of ms. I will expand this work in several important directions. First, I intend to investigate the effects of disruptions of various sorts on the fast dynamics both of the networks we have looked at in the past, e.g. reading, attention, perceptual consciousness, and of some that are new to us, e.g., working memory, visual and auditory imagery. The types of disruptions include natural ones, such as dyslexia, habitual cannabis use, dying, and transcranial stimulation, both cathodal direct current stimulation (tDCS) and noise (tRNS). Second I intend to investigate how brain stimulation might affect the fast dynamics in these networks, both normal and challenged. In the case of normal networks there might be improved temporal precision as well as improved performance, and for challenged networks there might be partial restoration of normal temporal dynamics. Third, I will begin to characterize the fast dynamics of cortico-subcortical circuits. I will employ techniques that directly involve particular subcortical circuits, such as amygdala and pulvinar and MD thalamus and that might have reliable cortical mirrors (e.g., prefrontal for amygdala, PPC for pulvinar). I will do co-registered fMRI-EEG studies, using fMRI to verify the subcortical involvement and to map out the functional and effective connectivity so as to identify cortical mirror regions, and EEG to study the fast dynamics of this network. Finally, I will continue theoretical studies of computational modeling of oscillatory brain networks with the goal of characterizing the interactions between oscillating brain regions that support cognition. I expect this work will not only be fruitful for a fundamental understanding of how brain-regional neural networks implement cognitive processes, but also will eventually find application in ameliorating brain challenges such as dyslexia. aging and drug use.**