Neurology, Yale University, New Haven, CT, USA; State Key Laboratory for Strength and Vibration of Mechanical Structures, School of Aerospace Engineering, Xi'an Jiaotong University, Xi'an, China.
Neurology, Yale University, New Haven, CT, USA; Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA.
Neuroimage. 2019 Nov 1;201:116003. doi: 10.1016/j.neuroimage.2019.07.016. Epub 2019 Jul 8.
Dynamic attention states are necessary to navigate the ever changing task demands of daily life. Previous investigations commonly utilize a block paradigm to study sustained and transient changes in attention networks. fMRI investigations have shown that sustained attention in visual block design attention tasks corresponds to decreased signal in the default mode and visual processing networks. While task negative networks are anticipated to decrease during active task engagement, it is unexpected that visual networks would also be suppressed during a visual task where event-related fMRI studies have found transient increases to visual stimuli. To resolve these competing results, the current investigations utilized intracranial EEG to directly interrogate visual and default mode network dynamics during a visual continuous performance task. We used the electrophysiological data to model expected fMRI signals and to maximize interpretation of current results with previous investigations. Results show broadband gamma power decreases in the default mode network, corresponding to previous EEG and fMRI findings. Meanwhile, visual processing regions including the primary visual cortex and fusiform gyrus demonstrate both sustained decreases during task engagement and stimuli-driven transient increases in gamma power. Modeled fMRI based on gamma power reproduces signal decreases reported in the fMRI literature, and emphasizes the insensitivity of fMRI to transient, regularly spaced signal changes embedded within sustained network dynamics. The signal processing functions of the dynamic visual and default mode network changes explored in this study are unknown but may be elucidated through further investigation.
动态注意状态对于应对日常生活中不断变化的任务需求是必要的。之前的研究通常采用块范式来研究注意力网络的持续和瞬态变化。fMRI 研究表明,在视觉块设计注意力任务中持续注意力对应于默认模式和视觉处理网络信号的降低。虽然在主动任务参与期间预计任务负网络会减少,但在视觉任务中,视觉网络也会受到抑制,这是出乎意料的,因为事件相关 fMRI 研究发现视觉刺激会出现短暂增加。为了解决这些相互竞争的结果,当前的研究利用颅内 EEG 直接在视觉连续性能任务期间探究视觉和默认模式网络的动态。我们使用电生理数据来模拟预期的 fMRI 信号,并最大限度地利用以前的研究来解释当前的结果。结果显示,默认模式网络中的宽带伽马功率降低,与以前的 EEG 和 fMRI 发现相对应。同时,视觉处理区域包括初级视觉皮层和梭状回,在任务参与期间表现出持续降低,并且在伽马功率驱动的刺激下出现短暂增加。基于伽马功率建模的 fMRI 再现了 fMRI 文献中报道的信号降低,并强调了 fMRI 对嵌入在持续网络动态中的瞬态、规则间隔信号变化的不敏感性。本研究中探索的动态视觉和默认模式网络变化的信号处理功能尚不清楚,但通过进一步研究可能会阐明。