Multimodal Neuroimaging of Cognitive Control

在当前的研究中，我们研究了与认知控制有关的脑功能网络的活动和强度的表现相关的变化。我们提出了一种多模态方法，使用功能磁共振成像（fMRI）和脑磁图（MEG）来识别这些网络。我们还建议使用重复经颅磁刺激（rTMS）来破坏网络节点的功能，为这些网络影响行为提供更有力的因果证据。通过获得经颅磁刺激和脑电图（EEG）相结合的数据，通过对EEG数据的MEG网络分析策略来确认目标网络受到了影响。每年将进行大约2次多模式实验（每个受试者4-6次神经成像），每次研究的样本为20名受试者。涉及任务转换和证据整合的认知范式将用于驱动这些大脑网络，并了解它们的激活/失活如何与表现（即速度、准确性和证据整合评级）相关。同样的实验设计将用于fMRI、MEG和rTMS/EEG。我们的方法允许用功能磁共振成像（fMRI）研究可测量的标准网络，用脑磁图（MEG）确认这些网络，用脑磁图（MEG）发现可测量的新网络，用脑磁图（TMS）证明这些网络参与了假设的任务，并同时用脑磁图（TMS）确认功能磁共振成像（fMRI）和脑磁图（MEG）检测到的网络中断。这种综合的方法将增加对任务转换和证据整合背后的神经网络的理解，前者是一般认知表现的一个组成部分，后者在信念的形成和修正中起着重要作用。

In the current study we investigate performance-related changes in activity and intensity of functional brain networks involved in cognitive control. We propose a multimodal approach, using functional magnetic resonance imaging (fMRI) and magnetoencephalograpy (MEG) to identify these networks. We also propose use of repeated transcranial magnetic stimulation (rTMS) to disrupt the function of network nodes, providing stronger causal evidence that these networks affect behaviour. Through access to combined TMS and electroencephalography (EEG), confirmation that the targeted networks were affected will be achieved by using MEG network analysis strategies on EEG data. Approximately 2 multimodal experiments per year will be carried out (4-6 neuroimaging sessions per subject) on samples of 20 subjects per study. Cognitive paradigms involving task switching and integration of evidence will be used to drive these brain networks and understand how their activation/deactivation is associated with performance (i.e., speed, accuracy, and ratings of evidence integration). The same experimental design will be used for fMRI, MEG and rTMS/EEG. Our methodology allows the study of the standard networks measurable with fMRI, confirmation of these with MEG, the discovery of new networks measurable with MEG, demonstration of the involvement of these networks in the hypothesized tasks using TMS, and EEG simultaneous with TMS to confirm the interruption to the networks detected with fMRI and MEG. This comprehensive methodology will increase understanding of the neural networks underlying task switching and evidence integration, the former being an integral aspect of general cognitive performance, and the latter taking an important role in the formation and revision of beliefs.