CRCNS: Targeted Stimulations in Brain Network of Networks

这个项目的目标是研究心理状态的特征，使用状态之间转换的诱导作为一种实验策略。这项工作是由一种新的网络理论推动的--网络网络(Non)--强调薄弱环节的重要性。该项目将确定一种新的网络模式，同时确定网络标记和计算，使不同处理模块之间的信息传输和选通具有特异性和稳健性。前瞻性干预旨在为脑网络结构理论提供强大的经验约束，超越这里测试的特定非假设，包括替代网络模型，例如“无标度”和“富俱乐部”，以获得对前瞻性干预的可证伪预测。这些结果也可以很容易地应用于其他系统，从代谢、蛋白质和遗传网络到社会网络和互联网。本研究将测试网络理论关于体内功能磁共振(FMRI)和局部场电位(LFP)记录的关联结构的特定预测。研究人员将根据啮齿动物的Connectome数据结合功能连接分析结构连通性，确定大脑网络拓扑中关键节点的网络结构和位置。建议的非框架将揭示这些关键节点--“超级传播者”和“超级抑制者”--的位置，并对大脑网络的级联神经活动、健壮性和脆弱性进行预测。

The goal of this project is to investigate the signature of mental states, using the induction of transitions among states as an experimental strategy. The work is driven by a novel network theory -- Network of Networks (NoN) -- that emphasizes the importance of weak links. The project will identify a new network paradigm with the concomitant identification of network markers and computations that confer specificity and robustness to information transmission and gating between different processing modules. The prospective interventions are designed to provide strong empirical constraints on theories of brain network structure beyond the specific NoN hypothesis tested here, including alternate network models, e.g "scale-free" and "rich-club," in order to obtain falsifiable predictions for the prospective interventions. The results can also be readily applied to other systems ranging from metabolic, protein and genetic networks to social networks and the Internet.The present study will test specific predictions of network theory with regards to the correlation structure of in-vivo functional magnetic resonance (fMRI) and local field potential (LFP) recordings. The investigators will determine network structure and location of key nodes in the brain network topology by analyzing the structural connectivity based on Connectome data from rodents combined with functional connectivity. The proposed NoN framework will reveal the location of such key nodes -- "superspreaders" and "superinhibitors" -- and make predictions on cascading neural activity, robustness and vulnerability of the brain network.