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

基本信息

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
项目类别：
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

项目摘要

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.

项目摘要：神经影像学方法，例如功能性MRI和磁性 /脑电图（MEG / EEG），无法直接揭示区域大脑活动与行为之间的因果关系。为了允许因果推断，经颅磁刺激（TMS）已用于扰动局部皮质活性以创建临时的“虚拟病变”。但是，即使是简单的行为任务，员工在不同毫秒级别的潜伏期中都广泛分布了多个节点，而当今的TMS技术主要仅限于一次仅针对一个大脑区域的单渠道设备。我们可以用当前TMS设备靶向的大量网络节点与少量的皮质区域之间的不匹配是探索人脑中网络级别因果的关键障碍。为了消除此障碍，我们需要TMS技术，这些技术可以在特定的处理阶段扰动多个皮质位置。消除此障碍将开辟全新的途径，以了解对皮质网络中复杂的认知功能如何出现。为了识别认知操作的神经元网络，需要两个步骤。首先，网络节点被识别为TMS诱导的电场强度以及相关行为或基于神经成像的变量的位置。使用多通道TMS阵列的前所未有的能力，可以有效地实现这一目标，从而在快速成功中生成不同的自定义电场模式。接下来，通过快速暂时的成功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的电场模式之间切换的能力至关重要。该仪器的网络级映射功能将首先在已知网络（电机系统）的测试台实验中进行验证。然后，我们继续进行识别语言理解潜在网络节点的研究。最后，我们将使用多灶性刺激方法来研究语言理解网络中的信息流，并开发基础神经元回路的神经元质量模型以理论为基础。从长远来看，广泛的社会可以通过网络级TMS疗法的所有应用中的所有应用中受益，这些疗法是针对调节关键功能（例如听力，语音和语言处理）的大脑区域之间功能连通性进行了优化的。