Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, New Jersey, United States of America.
Behavioral and Neural Sciences Graduate Program, Rutgers University, Newark, New Jersey, United States of America.
PLoS Biol. 2022 Aug 18;20(8):e3001686. doi: 10.1371/journal.pbio.3001686. eCollection 2022 Aug.
How cognitive task behavior is generated by brain network interactions is a central question in neuroscience. Answering this question calls for the development of novel analysis tools that can firstly capture neural signatures of task information with high spatial and temporal precision (the "where and when") and then allow for empirical testing of alternative network models of brain function that link information to behavior (the "how"). We outline a novel network modeling approach suited to this purpose that is applied to noninvasive functional neuroimaging data in humans. We first dynamically decoded the spatiotemporal signatures of task information in the human brain by combining MRI-individualized source electroencephalography (EEG) with multivariate pattern analysis (MVPA). A newly developed network modeling approach-dynamic activity flow modeling-then simulated the flow of task-evoked activity over more causally interpretable (relative to standard functional connectivity [FC] approaches) resting-state functional connections (dynamic, lagged, direct, and directional). We demonstrate the utility of this modeling approach by applying it to elucidate network processes underlying sensory-motor information flow in the brain, revealing accurate predictions of empirical response information dynamics underlying behavior. Extending the model toward simulating network lesions suggested a role for the cognitive control networks (CCNs) as primary drivers of response information flow, transitioning from early dorsal attention network-dominated sensory-to-response transformation to later collaborative CCN engagement during response selection. These results demonstrate the utility of the dynamic activity flow modeling approach in identifying the generative network processes underlying neurocognitive phenomena.
大脑网络相互作用如何产生认知任务行为是神经科学的一个核心问题。回答这个问题需要开发新的分析工具，这些工具首先可以以高时空精度（“在哪里”和“何时”）捕获任务信息的神经特征，然后允许对将信息与行为联系起来的大脑功能的替代网络模型进行经验测试（“如何”）。我们概述了一种适用于此目的的新网络建模方法，并将其应用于人类的非侵入性功能神经影像学数据。我们首先通过将 MRI 个体化源脑电图（EEG）与多元模式分析（MVPA）相结合，动态解码人类大脑中任务信息的时空特征。然后，一种新开发的网络建模方法——动态活动流建模——模拟了在更具因果可解释性（相对于标准功能连接[FC]方法）的静息状态功能连接（动态、滞后、直接和定向）上的任务诱发活动的流动。我们通过将该模型应用于阐明大脑中感觉运动信息流动的网络过程来证明该建模方法的实用性，从而准确预测行为背后的经验反应信息动力学。将模型扩展到模拟网络损伤表明认知控制网络（CCN）作为反应信息流动的主要驱动因素的作用，从早期背侧注意网络主导的感觉转变为反应选择期间后期协作 CCN 的参与。这些结果表明，动态活动流建模方法在识别神经认知现象背后的生成网络过程方面具有实用性。
