CRCNS: Multifocal causal mapping of brain networks supporting human cognition

项目摘要：功能核磁共振和脑磁图(MEG/EEG)等神经成像方法不能直接揭示局部大脑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)被用来扰乱局部皮质活动，以创造临时的“虚拟损伤”。然而，即使是简单的行为任务也使用了广泛分布的大脑网络，多个节点在不同的毫秒级延迟激活，而今天的TMS技术主要限于一次只针对一个大脑区域的单通道设备。目前的TMS设备可以瞄准的大量网络节点和少量皮质区域之间的不匹配形成了在人脑中探索网络级因果推理的关键障碍。为了消除这一障碍，我们需要TMS技术，这种技术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大脑皮质网络如何产生复杂的认知功能开辟全新的途径。为了识别认知操作背后的神经网络，有两个步骤是必要的。首先，网络节点被识别为TMS诱发电场的强度与相关的行为或基于神经成像的变量最大相关的位置。这可以使用多通道TMS阵列的前所未有的能力来高效地实现，以快速连续地产生不同的定制电场图案。接下来，通过以快速的时间顺序刺激节点来探索网络节点之间的信息流。为此，多通道阵列在毫秒内在电场模式之间切换的能力至关重要。该仪器的网络级测绘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，确定语言理解的基础网络节点。最后，我们将使用多焦点刺激的方法来研究语言理解网络中的信息流，并建立一个潜在神经元回路的神经质量模型作为理论基础。从长远来看，通过网络级TMS疗法，更广泛的社会可能会从拟议研究的所有应用中受益，这种疗法针对调节涉及听力、语音和语言处理等关键功能的大脑区域之间的功能连接进行了优化。

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.