The neural architecture of pragmatic processing

语用处理的神经结构

• 批准号: 10406543
• 负责人: Evelina Fedorenko
• 金额: $ 23.26万
• 依托单位: MASSACHUSETTS INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-05-21 至 2023-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10406543
• 关键词: Anatomy; Animal Model; Architecture; Area; Atlases; Award; Behavior; Brain; Brain imaging; Brain region; Broca' s area; Cerebellum; Cognition; Communication; Computer software; Data; Development; Functional Magnetic Resonance Imaging; Future; Human; Individual; Inferior frontal gyrus; Investigation; Language; Lead; Lesion; Linguistics; Link; Literature; Location; Maps; Mental Processes; Meta-Analysis; Mind; Modeling; Network-based; Neurosciences; Online Systems; Output; Parents; Participant; Patients; Perception; Population; Probability; Research; Research Personnel; Resort; Scanning; Science; Short-Term Memory; Signal Transduction; Structure of superior temporal sulcus; Surface; association cortex; base; brain surgery; cognitive ability; cognitive control; cognitive neuroscience; cognitive process; executive function; face perception; imaging study; improved; inter-individual variation; interest; language comprehension; language processing; parent grant; relating to nervous system; response; social; theories; tool; Anatomy; Animal Model; Architecture; Area; Atlases; Award; Behavior; Brain; Brain imaging; Brain region; Broca' s area; Cerebellum; Cognition; Communication; Computer software; Data; Development; Functional Magnetic Resonance Imaging; Future; Human; Individual; Inferior frontal gyrus; Investigation; Language; Lead; Lesion; Linguistics; Link; Literature; Location; Maps; Mental Processes; Meta-Analysis; Mind; Modeling; Network-based; Neurosciences; Online Systems; Output; Parents; Participant; Patients; Perception; Population; Probability; Research; Research Personnel; Resort; Scanning; Science; Short-Term Memory; Signal Transduction; Structure of superior temporal sulcus; Surface; association cortex; base; brain surgery; cognitive ability; cognitive control; cognitive neuroscience; cognitive process; executive function; face perception; imaging study; improved; inter-individual variation; interest; language comprehension; language processing; parent grant; relating to nervous system; response; social; theories; tool

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.

项目摘要 功能磁共振成像（fMRI）对于阐明大脑的功能非常宝贵 建筑学。对于动物模型效用有限的认知能力，尤其重要 语言。但是，人类认知神经科学领域一直在挣扎，因为许多发现是 不可复制，患有统计缺陷，或由于使用不同而难以在研究中进行比较 分析方法。现在，许多人已经认识到需要更健壮，可复制和有意义的科学。 我们在这里提出了一个强大工具的开发和传播，该工具可以提高该领域的能力 建立强大而累积的研究企业。利用最后一个实验室收集的数据 十年（> 1200个扫描会议中的800名神经典型参与者），我们建议开发和 使公开可用的四个脑网络的概率功能地图 认知：语言选择网络（支持语言处理； Fedorenko等，2010， 2011年），域总需求（MD）网络（支持执行功能，例如认知功能 控制; Duncan，2010年），默认模式网络（DMN）（支持内部指导的认知和 构建情况模型； Buckner＆Dinicola，2019年）和思维网络理论（支持 一般社会推论； Saxe＆Kanwisher，2003年）。这些地图集将基于大数字创建 个人激活图，以实现这些目标的良好和广泛验证的“本地化”任务 网络（前三个网络的700多个参与者，TOM网络的约150名参与者）可以是 用于估计公共空间中任何给定位置属于特定位置的概率 功能网络。在AIM 1中，我们将开发这些概率地图集。为此，我们将汇总所有 每个本地化任务的相关数据，通过两个统一管道进行预处理 常用的软件包（SPM，Friston，1997； Freesurfer，Dale等，1999），并覆盖 相关体积和表面空间中的个体激活图。我们还将提取一组密钥 个体级的神经标记，以便可以正常使用它们的分布来与其他分布 人群。在AIM 2中，我们将制作地图集（以及组成的个体激活图和神经标记） 公开可用。为此，我们将创建一个强大而互动的基于网络的平台，以传播 地图集。拟议的项目是桥接两个根本不同的关键步骤，目前 功能性脑成像中的分析传统 - 平衡方法和功能 通过提供常见的参考帧：基于概率的功能图像 在四个高级大脑网络的建立良好和广泛使用的本地化上。有更多的能力 直接比较跨潜水员研究的发现必将导致更严格和透明 科学从而提高了我们对人类交流和相关能力的理解。