语言产生中与任务和刺激相关的皮层网络:探索基于脑磁图(MEG)和功能磁共振成像(fMRI)的功能连接的相似性。

Task- and stimulus-related cortical networks in language production: Exploring similarity of MEG- and fMRI-derived functional connectivity.

作者信息

Liljeström Mia, Stevenson Claire, Kujala Jan, Salmelin Riitta

机构信息

Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland; Aalto NeuroImaging, Aalto University, Espoo, Finland; Department of Neurological Sciences, Helsinki University, Helsinki, Finland; Department of Neurology, Helsinki University Central Hospital, Helsinki, Finland.

Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland; Aalto NeuroImaging, Aalto University, Espoo, Finland.

出版信息

Neuroimage. 2015 Oct 15;120:75-87. doi: 10.1016/j.neuroimage.2015.07.017. Epub 2015 Jul 11.

Abstract

Large-scale networks support the dynamic integration of information across multiple functionally specialized brain regions. Network analyses of haemodynamic modulations have revealed such functional brain networks that show high consistency across subjects and different cognitive states. However, the relationship between the slowly fluctuating haemodynamic responses and the underlying neural mechanisms is not well understood. Resting state studies have revealed spatial similarities in the estimated network hub locations derived using haemodynamic and electrophysiological recordings, suggesting a direct neural basis for the widely described functional magnetic resonance imaging (fMRI) resting state networks. To truly understand the nature of the relationship between electrophysiology and haemodynamics it is important to move away from a task absent state and to establish if such networks are differentially modulated by cognitive processing. The present parallel fMRI and magnetoencephalography (MEG) experiment investigated the structural similarities between haemodynamic networks and their electrophysiological counterparts when either the stimulus or the task was varied. Connectivity patterns underlying action vs. object naming (task-driven modulations), and action vs. object images (stimulus-driven modulations) were identified in a data driven all-to-all connectivity analysis, with cross spectral coherence adopted as a metric of functional connectivity in both MEG and fMRI. We observed a striking difference in functional connectivity between conditions. The spectral profiles of the frequency-specific network similarity differed significantly for the task-driven vs. stimulus-driven connectivity modulations. While the greatest similarity between MEG and fMRI derived networks was observed at neural frequencies below 30 Hz, haemodynamic network interactions could not be attributed to a single frequency band. Instead, the entire spectral profile should be taken into account when assessing the correspondence between MEG and fMRI networks. Task-driven network hubs, evident in both MEG and fMRI, were found in cortical regions previously associated with language processing, including the posterior temporal cortex and the inferior frontal cortex. Network hubs related to stimulus-driven modulations, however, were found in regions related to object recognition and visual processing, including the lateral occipital cortex. Overall, the results depict a shift in network structure when moving from a task dependent modulation to a stimulus dependent modulation, revealing a reorganization of large-scale functional connectivity during task performance.

摘要

大规模网络支持跨多个功能专门化脑区的信息动态整合。对血流动力学调制的网络分析揭示了这种功能性脑网络,其在不同个体和不同认知状态下表现出高度一致性。然而,缓慢波动的血流动力学反应与潜在神经机制之间的关系尚未得到充分理解。静息状态研究揭示了使用血流动力学和电生理记录得出的估计网络枢纽位置的空间相似性,这表明广泛描述的功能磁共振成像(fMRI)静息状态网络具有直接的神经基础。为了真正理解电生理学与血流动力学之间关系的本质,重要的是摆脱无任务状态,并确定此类网络是否受到认知加工的差异调制。当前的并行fMRI和脑磁图(MEG)实验研究了在刺激或任务变化时血流动力学网络与其电生理对应物之间的结构相似性。在数据驱动的全对全连接性分析中,识别了动作与物体命名(任务驱动调制)以及动作与物体图像(刺激驱动调制)背后的连接模式,采用交叉谱相干作为MEG和fMRI中功能连接性的度量指标。我们观察到不同条件下功能连接性存在显著差异。任务驱动与刺激驱动连接调制的频率特异性网络相似性的频谱分布存在显著差异。虽然在低于30Hz的神经频率下观察到MEG和fMRI衍生网络之间的最大相似性,但血流动力学网络相互作用不能归因于单个频带。相反,在评估MEG和fMRI网络之间的对应关系时应考虑整个频谱分布。在MEG和fMRI中均明显的任务驱动网络枢纽位于先前与语言加工相关的皮质区域,包括颞后皮质和额下回。然而,与刺激驱动调制相关的网络枢纽位于与物体识别和视觉加工相关的区域,包括枕外侧皮质。总体而言,结果描绘了从任务依赖调制转变为刺激依赖调制时网络结构的变化,揭示了任务执行期间大规模功能连接性的重组。

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