Yuan Li-Xia, Zhao Na, Wang Xiu-Qin, Lv Ya-Ting, He Hongjian
Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, China.
Institute of Psychological Sciences, Hangzhou Normal University, Hangzhou, China.
Front Neurosci. 2021 Mar 16;15:619412. doi: 10.3389/fnins.2021.619412. eCollection 2021.
Local activity metrics of resting-state functional MRI (RS-fMRI), such as the amplitude of low-frequency fluctuation (ALFF), fractional ALFF (fALFF), regional homogeneity (ReHo), and degree centrality (DC), are widely used to detect brain abnormalities based on signal fluctuations. Although signal changes with echo time (TE) have been widely studied, the effect of TE on local activity metrics has not been investigated. RS-fMRI datasets from 12 healthy subjects with eyes open (EO) and eyes closed (EC) were obtained with a four-echo gradient-echo-planar imaging pulse sequence with the following parameters: repetition time/TE1/TE2/TE3/TE4 = 2,000/13/30.93/48.86/66.79 ms. Six representative regions were selected for simulating the spatial feature of TE dependency of local activity metrics. Moreover, whole-brain local activity metrics were calculated from each echo dataset and compared between EO and EC conditions. Dice overlap coefficient (DOC) was then employed to calculate the overlap between the maps. We found that all the local activity metrics displayed different TE dependency characteristics, while their overall change patterns were similar: an initial large change followed by a slow variation. The maps for local activity metrics also varied greatly with TE. For ALFF, fALFF, ReHo, and DC, the DOCs for voxels in four TE datasets were 6.87, 0.73, 5.08, and 0.93%, respectively. Collectively, these findings demonstrate that local metrics are greatly dependent on TE. Therefore, TE should be carefully considered for the optimization of data acquisition and multi-center data analysis in RS-fMRI.
静息态功能磁共振成像（RS-fMRI）的局部活动指标，如低频波动幅度（ALFF）、分数ALFF（fALFF）、局部一致性（ReHo）和度中心性（DC），被广泛用于基于信号波动检测脑异常。尽管回波时间（TE）对信号变化的影响已得到广泛研究，但TE对局部活动指标的影响尚未得到研究。使用具有以下参数的四回波梯度回波平面成像脉冲序列，获得了12名健康受试者睁眼（EO）和闭眼（EC）状态下的RS-fMRI数据集：重复时间/TE1/TE2/TE3/TE4 = 2000/13/30.93/48.86/66.79毫秒。选择六个代表性区域来模拟局部活动指标的TE依赖性空间特征。此外，从每个回波数据集中计算全脑局部活动指标，并在EO和EC条件之间进行比较。然后使用骰子重叠系数（DOC）来计算图谱之间的重叠。我们发现，所有局部活动指标都表现出不同的TE依赖性特征，但其总体变化模式相似：最初变化较大，随后变化缓慢。局部活动指标的图谱也随TE有很大变化。对于ALFF、fALFF、ReHo和DC，四个TE数据集中体素的DOC分别为6.87%、0.73%、5.08%和0.93%。总体而言，这些发现表明局部指标在很大程度上依赖于TE。因此，在RS-fMRI的数据采集优化和多中心数据分析中应仔细考虑TE。
