A fundamental challenge in cognitive neuroscience is to develop theoretical frameworks that effectively span the gap between brain and behavior, between neuroscience and psychology. Here, we attempt to bridge this divide by formalizing an integrative cognitive neuroscience approach using dynamic field theory (DFT). We begin by providing an overview of how DFT seeks to understand the neural population dynamics that underlie cognitive processes through previous applications and comparisons to other modeling approaches. We then use previously published behavioral and neural data from a response selection Go/Nogo task as a case study for model simulations. Results from this study served as the 'standard' for comparisons with a model-based fMRI approach using dynamic neural fields (DNF). The tutorial explains the rationale and hypotheses involved in the process of creating the DNF architecture and fitting model parameters. Two DNF models, with similar structure and parameter sets, are then compared. Both models effectively simulated reaction times from the task as we varied the number of stimulus-response mappings and the proportion of Go trials. Next, we directly simulated hemodynamic predictions from the neural activation patterns from each model. These predictions were tested using general linear models (GLMs). Results showed that the DNF model that was created by tuning parameters to capture simultaneously trends in neural activation and behavioral data quantitatively outperformed a Standard GLM analysis of the same dataset. Further, by using the GLM results to assign functional roles to particular clusters in the brain, we illustrate how DNF models shed new light on the neural populations' dynamics within particular brain regions. Thus, the present study illustrates how an interactive cognitive neuroscience model can be used in practice to bridge the gap between brain and behavior.

中文翻译：

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使用动态神经场的基于模型的功能神经成像：一种综合认知神经科学方法


认知神经科学的一个基本挑战是开发有效跨越大脑与行为、神经科学与心理学之间差距的理论框架。在这里，我们试图通过使用动态场理论（DFT）形式化综合认知神经科学方法来弥合这一鸿沟。我们首先概述 DFT 如何通过之前的应用以及与其他建模方法的比较来理解认知过程背后的神经群体动态。然后，我们使用之前发布的响应选择 Go/Nogo 任务的行为和神经数据作为模型模拟的案例研究。这项研究的结果作为与使用动态神经场 (DNF) 的基于模型的功能磁共振成像方法进行比较的“标准”。本教程解释了创建 DNF 架构和拟合模型参数过程中涉及的基本原理和假设。然后比较具有相似结构和参数集的两个 DNF 模型。当我们改变刺激-反应映射的数量和围棋试验的比例时，这两个模型都有效地模拟了任务的反应时间。接下来，我们直接根据每个模型的神经激活模式模拟血流动力学预测。这些预测使用一般线性模型 (GLM) 进行了测试。结果表明，通过调整参数来同时捕获神经激活和行为数据趋势而创建的 DNF 模型在数量上优于同一数据集的标准 GLM 分析。此外，通过使用 GLM 结果将功能角色分配给大脑中的特定簇，我们说明了 DNF 模型如何为特定大脑区域内神经群体的动态提供新的线索。 因此，本研究说明了如何在实践中使用交互式认知神经科学模型来弥合大脑与行为之间的差距。