Department of Radiology, University Medical Center Freiburg, Breisacher Str 60a, PH2a, Freiburg 79106, Germany.
Neuroimage. 2013 Jan 15;65:216-22. doi: 10.1016/j.neuroimage.2012.10.015. Epub 2012 Oct 13.
Current resting-state network analysis often looks for coherent spontaneous BOLD signal fluctuations at frequencies below 0.1 Hz in a multiple-minutes scan. However hemodynamic signal variation can occur at a faster rate, causing changes in functional connectivity at a smaller time scale. In this study we proposed to use MREG technique to increase the temporal resolution of resting-state fMRI. A three-dimensional single-shot concentric shells trajectory was used instead of conventional EPI, with a TR of 100 ms and a nominal spatial resolution of 4 × 4 × 4 mm(3). With this high sampling rate we were able to resolve frequency components up to 5 Hz, which prevents major physiological noises from aliasing with the BOLD signal of interest. We used a sliding-window method on signal components at different frequency bands, to look at the non-stationary connectivity maps over the course of each scan session. The aim of the study paradigm was to specifically observe visual and motor resting-state networks. Preliminary results have found corresponding networks at frequencies above 0.1 Hz. These networks at higher frequencies showed better stability in both spatial and temporal dimensions from the sliding-window analysis of the time series, which suggests the potential of using high temporal resolution MREG sequences to track dynamic resting-state networks at sub-minute time scale.
目前的静息态网络分析通常在多分钟扫描中寻找低于 0.1 Hz 的频率下的连贯自发 BOLD 信号波动。然而，血液动力学信号变化可以以更快的速度发生，导致在较小的时间尺度上功能连接发生变化。在这项研究中，我们提出使用 MREG 技术来提高静息态 fMRI 的时间分辨率。使用三维单-shot 同心壳轨迹代替传统的 EPI，重复时间为 100ms，名义空间分辨率为 4×4×4mm（3）。通过这种高采样率，我们能够分辨高达 5Hz 的频率分量，从而防止主要的生理噪声与感兴趣的 BOLD 信号混叠。我们在不同频带的信号分量上使用滑动窗口方法，观察每个扫描会话过程中的非稳态连接图。该研究范式的目的是专门观察视觉和运动静息态网络。初步结果发现，在高于 0.1Hz 的频率下存在相应的网络。这些更高频率的网络在时间序列的滑动窗口分析中，在空间和时间维度上都表现出更好的稳定性，这表明使用高时间分辨率 MREG 序列来跟踪亚分钟时间尺度的动态静息态网络具有潜力。
