Multimodal Neuroimaging of Cognitive Control

在目前的研究中，我们调查了与认知控制有关的功能性脑网络的活动和强度的表现相关的变化。我们提出了一个多模态的方法，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。通过结合TMS和脑电图（EEG）的访问，通过对EEG数据使用MEG网络分析策略来确认目标网络受到影响。每年将对每项研究20例受试者的样本进行约2次多模式实验（每例受试者4-6次神经影像学检查）。涉及任务切换和证据整合的认知范式将被用来驱动这些大脑网络，并了解它们的激活/失活如何与表现相关联（即，速度、准确性和证据整合等级）。相同的实验设计将用于fMRI、MEG和rTMS/EEG。我们的方法允许研究的标准网络可测量的功能磁共振成像，确认这些与脑磁图，发现新的网络可测量的MEG，演示这些网络的参与，在假设的任务，使用TMS和EEG同时与TMS确认中断的功能磁共振成像和MEG检测到的网络。这种全面的方法将增加对任务切换和证据整合的神经网络的理解，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发挥重要作用。

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.