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Functional segregation in the default mode network:The left and right TPJ in attention, semantic and social processing

默认模式网络中的功能分离：注意力、语义和社交处理中的左右TPJ

基本信息

批准号：
321786689
负责人：
Professor Dr. Danilo Bzdok
金额：
--
依托单位：
Biomedical Engineering Department
依托单位国家：
德国
项目类别：
Research Grants
财政年份：
2017
资助国家：
德国
起止时间：
2016-12-31 至 2020-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/321786689?language=en
关键词：
Functional segregation default mode network

项目摘要

The advent of functional neuroimaging techniques has enabled the serendipity discovery of the default-mode network (DMN) 15 years ago. Despite considerable research progress in healthy and disease, this network, a major source of energy consumption in the human brain, has almost exclusively been studied as a cohesive unit. In particular, it has seldom been appreciated that the right and left temporo-parietal junction (TPJ) within the DMN are diverging from cognitive, anatomical, and clinical perspectives.The three proposed work packages focus on the causal role of the left and right TPJ across diverse cognitive processes. Experimental tasks will prompt attentional reorienting, semantic processing and theory of mind as archetypical processes that exemplify the broader cognitive domains attention, language comprehension and social cognition. Established psychological paradigms will trigger lateralized neural responses within this major brain network by tasks related to attention (known for right-lateralized TPJ activation), semantic processing (known for left-lateralized TPJ activation) and social cognition (known for bilateral TPJ activation). These neural responses in the healthy brain will be causally perturbed by applying repetitive transcranial magnetic stimulation (rTMS) to the either left or right TPJ during task processing. rTMS wil also be combined with functional neuroimaging to probe adaptive short-term reorganization and plasticity in the healthy system. The induced dysfunction in the TPJ node is expected to entail functional activity and connectivity alterations of DMN dynamics that are specific to the ongoing task.The approach exploits prior knowledge by quantitative target region definition using coordinate-based activation likelihood meta-analysis of previously published studies in each cognitive domain. The approach is multimodal by combining neuroimaging of attentional, semantic and social cognitive performance with meta-analytically constrained rTMS perturbations. Finally, the approach is multivariate by analyzing the neuroimaging results using advanced statistical-learning methods. These methods will model i) between-node interaction patterns of the DMN (i.e., group-sparse graph lasso and dynamic causal modeling), ii) local differences in activation patterns of individual DMN nodes (searchlight analysis), and iii) complex brain-behavior relationships (Lasso/Ridge/ElasticNet regression with task performance). In this way, data-driven bottom-up and experimental top-down methods will be closely intertwined to elucidate functional segregation in the DMN. New insight is expected to emerge from the perturbation of the key regions during distinct cognitive operations that are analyzed by multivariate statistical analyses. Induced plasticity effects on the system level will contribute to a better understanding of general properties of brain mechanisms that allow for rapid functional compensation in response to focal dysfunctions.

15年前，功能性神经成像技术的出现使得人们意外地发现了默认模式网络（DMN）。尽管在健康和疾病方面的研究取得了相当大的进展，但这个网络是人类大脑能量消耗的主要来源，几乎完全是作为一个内聚单元来研究的。特别是，很少有人意识到，右和左颞顶骨交界处（TPJ）内的DMN是从认知，解剖学和临床perspectives.The三个拟议的工作包侧重于在不同的认知过程中的因果作用的左和右TPJ分歧。实验任务将促使注意力重新定向、语义加工和心理理论作为典型过程，体现了更广泛的认知领域注意力、语言理解和社会认知。既定的心理范式将通过与注意力（以右侧TPJ激活而闻名）、语义处理（以左侧TPJ激活而闻名）和社会认知（以双侧TPJ激活而闻名）相关的任务来触发这个主要大脑网络内的侧化神经反应。在任务处理过程中，通过对左侧或右侧TPJ施加重复经颅磁刺激（rTMS），健康大脑中的这些神经反应将受到因果干扰。rTMS还将与功能性神经成像相结合，以探测健康系统中的适应性短期重组和可塑性。在TPJ节点诱导的功能障碍，预计将需要功能活动和连接DMN动态的变化，具体到正在进行的task.The方法利用先验知识的定量目标区域的定义，使用基于坐标的激活可能性荟萃分析以前发表的研究在每个认知域。该方法是多模态的注意力，语义和社会认知性能与元分析约束rTMS扰动相结合的神经成像。最后，该方法是多变量的神经影像学结果分析使用先进的学习方法。这些方法将建模i）DMN的节点间交互模式（即，组稀疏图套索和动态因果建模），ii）个体DMN节点激活模式的局部差异（探照灯分析），以及iii）复杂的大脑行为关系（Lasso/Ridge/ElasticNet回归与任务性能）。通过这种方式，数据驱动的自下而上和实验自上而下的方法将紧密交织在一起，以阐明DMN中的功能隔离。新的见解，预计将出现从扰动的关键区域在不同的认知操作，分析多元统计分析。系统水平上的诱导可塑性效应将有助于更好地理解大脑机制的一般特性，这些机制允许对局部功能障碍做出快速功能补偿。