CRCNS: Multifocal causal mapping of brain networks supporting human cognition

CRCNS：支持人类认知的大脑网络的多焦点因果图谱

• 批准号: 10612128
• 负责人: Aapo Nummenmaa
• 金额: $ 25.95万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10612128
• 关键词: Address; Algorithms; Anterior; Area; Behavior; Behavioral; Brain; Brain Mapping; Brain region; Cognition; Cognitive; Complement; Complex; Computer Models; Computing Methodologies; Consumption; Custom; Data; Devices; Diagnostic; Dorsal; Electroencephalography; Ensure; FDA approved; Freedom; Functional Magnetic Resonance Imaging; Generations; Goals; Head; Hearing; Human; Inferior frontal gyrus; Judgment; Language; Lateral; Left; Lesion; Location; Magnetic Resonance Imaging; Methods; Modeling; Motor; Motor Cortex; Neuronavigation; Neurosciences; Participant; Patients; Pattern; Population; Procedures; Protocols documentation; Research; Response Latencies; Role; Semantics; Societies; Standardization; Statistical Models; Synaptic Transmission; Synaptic plasticity; System; Techniques; Technology; Testing; Therapeutic; Time; Transcranial magnetic stimulation; Work; base; clinical application; cognitive function; cognitive neuroscience; computerized data processing; computerized tools; cortex mapping; design; electric field; experimental study; in vivo; insight; instrument; language comprehension; language processing; millisecond; network models; neural network; neuroimaging; neuronal circuitry; novel; novel strategies; operation; phonology; relating to nervous system; response; semantic processing; sound; speech processing; support network; tool; treatment optimization; virtual

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.

项目概要：神经影像学方法，如功能性MRI和磁/脑电图（MEG/EEG）不能直接揭示局部大脑活动和行为之间的因果关系。为了允许因果推理，经颅磁刺激（TMS）已被用来扰乱局部皮层活动，以创建临时的“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，其中多个节点以不同的毫秒级延迟激活，而今天的TMS技术主要限于单通道设备，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，我们可以与目前的TMS设备的目标，形成了一个关键的障碍，在探索人类大脑中的网络层次的因果推理。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扰乱多个皮层位置。消除这一障碍将为深入了解复杂的认知功能如何从皮层网络中产生开辟全新的途径。为了识别认知操作背后的神经网络，需要两个步骤。首先，网络节点被识别为TMS感应电场的强度与相关的基于行为或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有效地实现，以快速连续地生成不同的定制电场图案。接下来，网络节点之间的信息流是通过刺激节点在快速的时间连续性。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。该仪器的网络级映射能力将首先在已知网络（电机系统）的测试台实验中进行验证。然后，我们继续研究，确定网络节点的语言理解。最后，我们将使用多焦刺激方法来研究语言理解网络中的信息流，并建立一个神经元回路的神经块模型作为理论基础。从长远来看，更广泛的社会可能会受益于所提出的研究的所有应用，通过网络级TMS疗法，这些疗法被优化用于调节涉及听觉，言语和语言处理等关键功能的大脑区域之间的功能连接。