Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, Oulu, Finland; Research unit of Medical Imaging, Physics and Technology, the Faculty of Medicine, University of Oulu, Oulu, Finland.
Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, Oulu, Finland; Research unit of Medical Imaging, Physics and Technology, the Faculty of Medicine, University of Oulu, Oulu, Finland.
Neuroimage. 2017 Mar 1;148:352-363. doi: 10.1016/j.neuroimage.2017.01.024. Epub 2017 Jan 12.
This study investigated lag structure in the resting-state fMRI by applying a novel independent component (ICA) method to magnetic resonance encephalography (MREG) data. Briefly, the spatial ICA (sICA) was used for defining the frontal and back nodes of the default mode network (DMN), and the temporal ICA (tICA), which is enabled by the high temporal resolution of MREG (TR=100ms), was used to separate both neuronal and physiological components of these two spatial map regions. Subsequently, lag structure was investigated between the frontal (DMNvmpf) and posterior (DMNpcc) DMN nodes using both conventional method with all-time points and a sliding-window approach. A rigorous noise exclusion criterion was applied for tICs to remove physiological pulsations, motion and system artefacts. All the de-noised tICs were used to calculate the null-distributions both for expected lag variability over time and over subjects. Lag analysis was done for the three highest correlating denoised tICA pairs. Mean time lag of 0.6s (± 0.5 std) and mean absolute correlation of 0.69 (± 0.08) between the highest correlating tICA pairs of DMN nodes was observed throughout the whole analyzed period. In dynamic 2min window analysis, there was large variability over subjects as ranging between 1-10sec. Directionality varied between these highly correlating sources an average 28.8% of the possible number of direction changes. The null models show highly consistent correlation and lag structure between DMN nodes both in continuous and dynamic analysis. The mean time lag of a null-model over time between all denoised DMN nodes was 0.0s and, thus the probability of having either DMNpcc or DMNvmpf as a preceding component is near equal. All the lag values of highest correlating tICA pairs over subjects lie within the standard deviation range of a null-model in whole time window analysis, supporting the earlier findings that there is a consistent temporal lag structure across groups of individuals. However, in dynamic analysis, there are lag values exceeding the threshold of significance of a null-model meaning that there might be biologically meaningful variation in this measure. Taken together the variability in lag and the presence of high activity peaks during strong connectivity indicate that individual avalanches may play an important role in defining dynamic independence in resting state connectivity within networks.
本研究通过将一种新的独立成分（ICA）方法应用于磁共振脑电（MREG）数据，来研究静息态功能磁共振成像中的滞后结构。简而言之，使用空间独立成分分析（sICA）来定义默认模式网络（DMN）的额区和后区节点，而时间独立成分分析（tICA）则利用 MREG（TR=100ms）的高时间分辨率来分离这两个空间图谱区域的神经元和生理成分。随后，使用传统的全时点方法和滑动窗口方法，研究了额区（DMNvmpf）和后区（DMNpcc）DMN 节点之间的滞后结构。应用严格的噪声排除标准对 tIC 进行处理，以去除生理脉动、运动和系统伪影。所有去噪的 tIC 均用于计算时间和受试者的无偏分布。对三个相关性最高的去噪 tICA 对进行了滞后分析。在整个分析期间，观察到 DMN 节点之间的最高相关 tICA 对的平均滞后时间为 0.6s（±0.5std），平均绝对相关系数为 0.69（±0.08）。在 2 分钟动态窗口分析中，由于受试者之间的差异很大，滞后时间在 1-10 秒之间变化。这些高度相关的源之间的方向变化平均为可能方向变化的 28.8%。零模型显示 DMN 节点之间的相关性和滞后结构在连续和动态分析中都非常一致。在整个时间窗口分析中，所有去噪 DMN 节点之间的零模型平均滞后时间为 0.0s，因此 DMNpcc 或 DMNvmpf 作为前导成分的概率几乎相等。在整个时间窗口分析中，所有受试者的最高相关 tICA 对的滞后值均落在零模型的标准偏差范围内，这支持了先前的研究结果，即在个体之间的组群中存在一致的时间滞后结构。然而，在动态分析中，存在超过零模型显著阈值的滞后值，这意味着在该测量中可能存在具有生物学意义的变化。总的来说，滞后的可变性和强连接期间高活动峰值的存在表明，个体雪崩可能在定义网络中静息状态连接的动态独立性方面发挥重要作用。
