CRCNS: Multifocal causal mapping of brain networks supporting human cognition

项目概述：神经成像方法，如功能性MRI和脑磁/脑电图（MEG/EEG）不能直接揭示脑区域活动和行为之间的因果关系。为了进行因果推断，经颅磁刺激（TMS）被用于扰乱局部皮层活动，以产生暂时的“虚拟病变”。然而，即使是简单的行为任务也需要广泛分布的大脑网络，其中有多个节点以不同的毫秒级延迟被激活，而今天的TMS技术主要局限于单通道设备，每次只针对一个大脑区域。大量的网络节点和少量的皮质区域之间的不匹配，我们可以用现有的经颅磁刺激设备来定位，这在探索人脑网络层面的因果推理中形成了一个关键的障碍。为了消除这一障碍，我们需要TMS技术，它可以在特定的处理阶段干扰多个皮质位置。消除这一障碍将为深入了解复杂的认知功能是如何从皮层网络产生的开辟全新的途径。为了识别认知操作背后的神经网络，需要两个步骤。首先，网络节点被确定为tms诱发的电场强度与相关行为或基于神经成像的变量最大相关的位置。利用多通道TMS阵列前所未有的快速连续产生不同定制电场模式的能力，可以有效地实现这一目标。其次，通过刺激节点的快速时间演替来探索网络节点之间的信息流。为此，多通道阵列在几毫秒内在电场模式之间切换的能力至关重要。该仪器的网络级映射能力将首先在已知网络（电机系统）的试验台实验中得到验证。然后，我们继续研究识别语言理解背后的网络节点。最后，我们将使用多焦点刺激方法来研究语言理解网络中的信息流，并建立潜在神经回路的神经质量模型作为理论基础。从长远来看，更广泛的社会可能会受益于这项研究的所有应用，通过网络级TMS疗法，优化了涉及听力、言语和语言处理等关键功能的大脑区域之间的功能连接。

PROJECT SUMMARY: Neuroimaging methods such as functional MRI and magneto- / electroencephalography (MEG/EEG) cannot directly reveal causal relationships between regional brain activity and behavior. To allow causal inference, transcranial magnetic stimulation (TMS) has been used to perturb local cortical activity to create temporary "virtual lesions”. However, even simple behavioral tasks employ widely distributed brain networks with multiple nodes activated at different millisecond-level latencies, whereas today’s TMS technology is mainly limited to single-channel devices that target only one brain area at a time. The mismatch between the large number of network nodes and the small number of cortical areas we can target with present TMS devices forms a critical barrier in exploring network-level causal inferences in the human brain. To remove this barrier, we need TMS technology that can perturb multiple cortical locations at specific processing stages. Removing this barrier would open entirely new avenues for gaining insight into how complex cognitive functions emerge from cortical networks. For the identification of neural networks underlying cognitive operations, two steps are necessary. First, network nodes are identified as locations where the strength of the TMS-induced electric field and relevant behavioral or neuroimaging-based variables maximally correlate. This can be achieved efficiently using the unprecedented ability of the multichannel TMS array to generate different customized electric field patterns in rapid succession. Next, information flow between the network nodes is explored by stimulating the nodes in rapid temporal succession. For this, the ability of the multichannel array to switch between electric field patterns in milliseconds is crucial. The network-level mapping capabilities of the instrument will be first verified in a testbench experiment in a known network (motor system). We then proceed to studies that identify network nodes underlying language comprehension. Finally, we will use the multifocal stimulation approach to investigate information flow in the language comprehension network and develop a neural mass model of the underlying neuronal circuits for a theoretical basis. In the long term, the wider society may benefit from all applications of the proposed research through network-level TMS therapies that are optimized for modulating functional connectivity between brain regions involved in critical functions such as hearing, speech, and language processing.