Department of Radiology, University of Cincinnati, Cincinnati, Ohio, USA.
Fondazione Bruno Kessler, Center for Information and Communication Technology, Trento, Italy.
Brain Connect. 2021 Feb;11(1):45-55. doi: 10.1089/brain.2020.0776. Epub 2021 Jan 18.
How components of the distributed brain networks that support cognition participate in typical functioning remains a largely unanswered question. An important subgroup of regions in the larger network are , which are areas that are highly connected to several other functionally specialized sets of regions, and are likely important for sensorimotor integration. The present study attempts to characterize involved in typical expressive language functioning using a data-driven, multimodal, full multilayer magnetoencephalography (MEG) connectivity-based pipeline. Twelve adolescents, 16-18 years of age (five males), participated in this study. Participants underwent MEG scanning during a verb generation task. MEG and structural connectivity were calculated at the whole-brain level. Amplitude/amplitude coupling (AAC) was used to compute functional connections both within and between discrete frequency bins. AAC values were then multiplied by a binary structural connectivity matrix, and then entered into full multilayer network analysis. Initially, hubs were defined based on multilayer versatility and subsequently reranked by a novel measure called delta centrality on interconnectedness (DCI). DCI is defined as the percent change in interfrequency interconnectedness after removal of a hub. We resolved regions that are important for between-frequency communication among other areas during expressive language, with several potential theoretical and clinical applications that can be generalized to other cognitive domains. Our multilayer, data-driven framework captures nonlinear connections that span across scales that are often missed in conventional analyses. The present study suggests that crucial hubs may be conduits for interfrequency communication between action and perception systems that are crucial for typical functioning.
支持认知的分布式大脑网络的组成部分如何参与典型的功能仍然是一个尚未得到解答的问题。在更大的网络中，有一个重要的区域亚组，这些区域与几个其他功能专门化的区域高度连接，并且可能对感觉运动整合很重要。本研究试图使用基于数据驱动的、多模态的、全多层脑磁图（MEG）连接的管道来描述参与典型表达性语言功能的 。 12 名青少年（5 名男性）参与了这项研究。参与者在动词生成任务期间接受 MEG 扫描。在全脑水平计算 MEG 和结构连接。幅度/幅度耦合（AAC）用于计算离散频带内和频带之间的功能连接。然后，将 AAC 值乘以二进制结构连接矩阵，然后将其输入全多层网络分析。最初，根据多层多功能性定义了枢纽，然后通过称为互连通性的差中心度（DCI）的新度量重新对枢纽进行排名。DCI 定义为去除枢纽后，频间互连通性的百分比变化。 我们解决了在表达语言期间其他区域之间进行频间通信的重要区域，具有几个可以推广到其他认知领域的潜在理论和临床应用。 我们的多层、数据驱动的框架捕获了跨越尺度的非线性连接，这些连接在传统分析中经常被忽略。本研究表明，关键枢纽可能是动作和感知系统之间的频间通信的通道，这对典型功能至关重要。
