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首页> 外文期刊>Cerebral cortex >The Richness of Task-Evoked Hemodynamic Responses Defines a Pseudohierarchy of Functionally Meaningful Brain Networks
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The Richness of Task-Evoked Hemodynamic Responses Defines a Pseudohierarchy of Functionally Meaningful Brain Networks

机译:任务诱发的血流动力学反应的丰富性定义了功能有意义的脑网络的伪层次

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摘要

Functional magnetic resonance imaging can measure distributed and subtle variations in brain responses associated with task performance. However, it is unclear whether the rich variety of responses observed across the brain is functionally meaningful and consistent across individuals. Here, we used a multivariate clustering approach that grouped brain regions into clusters based on the similarity of their task-evoked temporal responses at the individual level, and then established the spatial consistency of these individual clusters at the group level. We observed a stable pseudohierarchy of task-evoked networks in the context of a delayed sequential motor task, where the fractionation of networks was driven by a gradient of involvement in motor sequence preparation versus execution. In line with theories about higher-level cognitive functioning, this gradient evolved in a rostro-caudal manner in the frontal lobe. In addition, parcellations in the cerebellum and basal ganglia matched with known anatomical territories and fiber pathways with the cerebral cortex. These findings demonstrate that subtle variations in brain responses associated with task performance are systematic enough across subjects to define a pseudohierarchy of task-evoked networks. Such networks capture meaningful functional features of brain organization as shaped by a given cognitive context.
机译:功能磁共振成像可以测量与任务绩效相关的大脑反应的分布和细微变化。但是,尚不清楚在整个大脑中观察到的丰富反应在功能上是否有意义并且在各个个体之间是否一致。在这里,我们使用了一种多元聚类方法,根据个体区域上任务执行的时间响应的相似性,将大脑区域分为多个聚类,然后在分组级别上建立了各个聚类的空间一致性。我们观察到在延迟的顺序运动任​​务的情况下任务诱发网络的稳定伪层次结构,其中网络的分数由参与运动序列准备与执行的梯度驱动。与有关高级认知功能的理论相一致,该梯度以额叶尾状的方式在额叶中演化。此外,小脑和基底神经节中的碎片与已知的解剖区域和大脑皮质的纤维通路相匹配。这些发现表明,与任务执行相关的大脑反应的细微变化足以使受测者系统地定义任务诱发网络的伪等级。这样的网络捕获了由给定认知环境塑造的有意义的大脑组织功能特征。

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