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大脑功能网络的活动和强度与表现相关的变化。我们提出了一种多模式的方法，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的经颅磁刺激(RTMS)来扰乱网络节点的功能，提供更有力的因果证据，证明这些网络影响行为。通过联合使用TMS和脑电(EEG)，对EEG数据进行脑磁图网络分析策略将实现对目标网络受到影响的确认。每年将对20名研究对象的样本进行大约2次多模式实验(每名受试者4-6次神经成像会议)。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/去激活如何与绩效(即证据整合的速度、准确性和评级)相关联。同样的实验设计将用于功能磁共振成像、脑磁图和rTMS/EEG。我们的方法允许研究可用fMRI测量的标准网络，用脑磁共振仪确认这些网络，发现可用脑磁共振仪测量的新网络，使用TMS证明这些网络参与了假设的任务，并与TMS同时进行EEG以确认用fMRI和MEG检测到的网络的中断。这一全面的方法论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订中发挥重要作用。

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.