The neural architecture of pragmatic processing

项目总结 功能磁共振成像(FMRI)在阐明大脑功能方面具有无可估量的价值。 建筑。这对认知能力尤其重要，因为动物模型的实用性有限，比如 语言。然而，人类认知神经科学领域一直在苦苦挣扎，因为许多发现是 不可复制，存在统计缺陷，或由于使用分歧而难以跨研究进行比较 分析方法。许多人现在已经认识到需要更强大、更可复制、更有意义的科学。 我们在此建议开发和传播一种强大的工具，该工具可以提高外地的能力 建立一个稳健和积累的研究企业。利用过去几年在我们实验室收集的数据 十年(&gt；800名神经典型参与者在&gt；1200次扫描会议上)，我们建议开发和 公开提供四个大脑网络的概率功能图谱，这些图谱对于高水平 认知：语言选择网络(支持语言处理；Fedorenko等人，2010， 2011)，领域通用多需求(MD)网络(支持认知等执行功能 控制；邓肯，2010)，默认模式网络(DMN)(支持内部定向认知和 情境模型的构建；Buckner&DiNicola，2019)，以及心理理论网络(支持 一般社会推理；Saxe&Kanwisher，2003)。这些地图集将基于大量的 针对以下目标的成熟且经过广泛验证的本地化任务的单独激活图 网络(前三个网络有700多个参与者，TOM网络有大约150个参与者)，可以 用于估计公共大脑空间中的任何给定位置属于特定位置的概率 功能网络。在目标1中，我们将开发这些概率地图集。为此，我们将聚合所有 每个本地化程序任务的相关数据，通过一个跨两个 常用软件包(SPM，Friston，1997；Freesurfer，Dale等人，1999)，并覆盖 相关体积和表面空间中的单独激活图。我们还将提取一组密钥 个体水平的神经标志物，因此它们的分布可以规范地用于与其他 人口。在目标2中，我们将制作地图集(以及组成个体的激活地图和神经标记) 向公众开放。为此，我们将创建一个强大和互动的网络平台，以传播 地图集。拟议中的项目是弥合两个根本不同和目前 脑功能成像中的分离分析传统--组平均方法和功能 个体大脑的定位--通过提供共同的参照系：基于概率功能图谱 关于四个高级大脑网络的成熟和广泛使用的定位器。能够获得更多 直截了当地比较不同研究的结果必然会导致更严格和透明的 科学从而提高了我们对人类交流的理解和相关能力。

PROJECT SUMMARY Functional magnetic resonance imaging (fMRI) has been invaluable for illuminating the brain’s functional architecture. It has been especially important for cognitive abilities where animal models have limited utility, like language. However, the field of human cognitive neuroscience has been struggling in that many findings are not replicable, suffer from statistical flaws, or are difficult to compare across studies due to the use of divergent analytic approaches. Many have now recognized the need for more robust, replicable, and meaningful science. We here propose the development and dissemination of a powerful tool that can improve the field’s ability to establish a robust and cumulative research enterprise. Leveraging the data collected in our lab over the last ten years (>800 neurotypical participants across >1,200 scanning sessions), we propose to develop and make publicly available probabilistic functional atlases for four brain networks critical for high-level cognition: the language-selective network (which supports language processing; Fedorenko et al., 2010, 2011), the domain-general Multiple Demand (MD) network (which supports executive functions like cognitive control; Duncan, 2010), the Default Mode network (DMN) (which supports internally-directed cognition and construction of situation models; Buckner & DiNicola, 2019), and the Theory of Mind network (which supports general social inference; Saxe & Kanwisher, 2003). These atlases will be created based on large numbers of individual activation maps for well-established and extensively validated ‘localizer’ tasks targeting these networks (700+ participants for the first three networks, and ~150 participants for the ToM network) and can be used to estimate the probability that any given location in the common brain space belongs to a particular functional network. In Aim 1, we will develop these probabilistic atlases. To do so, we will aggregate all the relevant data for each of the localizer tasks, preprocess it through a uniform pipeline across two most commonly used software packages (SPM, Friston, 1997; and FreeSurfer, Dale et al., 1999), and overlay the individual activation maps in the relevant volume and surface spaces. We will additionally extract a set of key individual-level neural markers, so that their distributions can be used normatively for comparisons with other populations. In Aim 2, we will make the atlases (and constituent individual activation maps and neural markers) publicly available. To do so, we will create a robust and interactive web-based platform for the dissemination of the atlases. The proposed project is a critical step to bridge two fundamentally different and currently disjoint analytic traditions in functional brain imaging—group-averaging approaches and functional localization in individual brains—by providing common reference frames: probabilistic functional atlases based on well-established and widely used localizers for four high-level brain networks. The ability to more straightforwardly compare findings across diverse studies is bound to lead to more rigorous and transparent science thus improving our understanding of human communication and related abilities.