A systematic investigation of the invariance of resting-state network patterns: is resting-state fMRI ready for pre-surgical planning?

A systematic investigation of the invariance of resting-state network patterns: is resting-state fMRI ready for pre-surgical planning?
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DOI:
10.3389/fnhum.2013.00095
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发表时间:
2013
影响因子:
2.9
通讯作者:
Schöpf V
Schöpf V
中科院分区:
医学3区
文献类型:
--
作者:
Kollndorfer K;Fischmeister FP;Kasprian G;Prayer D;Schöpf V

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目的:静息状态网络(RSN)的测量已被用于研究广泛的疾病,如痴呆或癫痫。这就提出了一个问题,即这种方法是否也可以作为术前规划工具。生成可靠的功能连接模式至关重要,特别是对于术前计划,因为这些模式可能直接影响结果。研究方法:这项研究调查了四种常用的静息状态条件的再现性:固定在白色屏幕上的黑色十字准线;固定在黑色屏幕的中心;闭上眼睛并固定“Entspann dich!”(英语,“放松”)。10名健康的右利手男性受试者(平均年龄,25岁; SD 2)参加了实验。计算了四种条件下不同RSN的空间重叠。结果:在单个受试者和组水平上计算每个种子区域在所有四种条件下的空间重叠。在单个主题和组水平的激活地图是高度稳定的,特别是阅读网络(RNW)。视觉网络(VIN)的一致性最低。在单个受试者水平,空间重叠值范围为0.31(VIN)至0.45(RNW)。结论:这些研究结果表明,RSN测量是一个可靠的工具,以评估语言相关的网络在临床设置。一般来说,静息状态条件显示出相当的激活模式,因此没有特定的条件似乎是优选的。
Objectives: Measurements of resting-state networks (RSNs) have been used to investigate a wide range of diseases, such as dementia or epilepsy. This raises the question whether this method could also serve as a pre-surgical planning tool. Generating reliable functional connectivity patterns is of crucial importance, particularly for pre-surgical planning, as these patterns may directly affect the outcome. Methods: This study investigated the reproducibility of four commonly used resting-state conditions: fixation of a black crosshair on a white screen; fixation of the center of a black screen; eyes-closed and fixation of the words “Entspann dich!” (Engl., “relax”). Ten healthy, right-handed male subjects (mean age, 25 years; SD 2) participated in the experiment. The spatial overlap for different RSNs across the four conditions was calculated. Results: The spatial overlap across all four conditions was calculated for each seed region on a single subject and at the group level. Activation maps at the single-subject and group levels were highly stable, especially for the reading network (RNW). The lowest consistency measures were found for the visual network (VIN). At the single-subject level spatial overlap values ranged from 0.31 (VIN) to 0.45 (RNW). Conclusion: These findings suggest that RSN measurements are a reliable tool to assess language-related networks in clinical settings. Generally, resting-state conditions showed comparable activation patterns, therefore no specific conditions appears to be preferable.
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