Isolation and minimization of head motion-induced signal variations in fMRI data using independent component analysis
Isolation and minimization of head motion-induced signal variations in fMRI data using independent component analysis
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DOI：
10.1002/mrm.20893
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发表时间：
2006-06-01
影响因子：
3.3
通讯作者：
Krolik, Jeffrey L.
中科院分区：
文献类型：
--
作者：
Liao, Rui;McKeown, Martin J.;Krolik, Jeffrey L.
采集功能磁共振成像数据期间与任务相关的头部运动对于运动校正和任务相关激活的估计来说是一个严重的混乱。大多数传统运动校正算法中实现的成本函数会比较两个体积的相似性，但无法考虑并非由运动（例如大脑激活）引起的信号变化。因此，我们最近提出了一种新的 fMRI 运动校正方法的理论基础，称为运动校正独立分量分析（MCICA），该方法允许对 fMRI 时间序列中存在的大脑激活进行隐式建模，并减轻运动引起的信号变化，而无需直接估计运动参数（Liao 等人，IEEE Transactions on Medical Imaging 2005；25：29-44）。为了探索非运动相关信号变化对配准误差的影响，我们进行了几个先前提出的测试模拟（Freire 等人，IEEE Transactions on Medical Imaging 2002；21：470-484）来评估 MCICA 的性能，并将其与传统的基于差值平方的测量（例如 LS-SPM 和 LS-AIR）进行比较。我们证明，对于模拟数据和真实的 fMRI 图像，所提出的 MCICA 方法表现良好。具体来说，在模拟中，MCICA 对于添加模拟激活更加稳健，并且在对模拟任务相关运动进行校正后不会导致检测到错误激活。根据运动 fMRI 实验的实际数据，与其他方法相比，在使用 MCICA 进行预处理后，派生的持续任务相关 ICA 成分的时间进程与潜在的行为任务更加相关，并且相关的激活图更集中在初级运动皮质和辅助运动皮质中，而在大脑边缘没有虚假激活。我们的结论是，评估运动损坏体积相对于系列中其他体积的统计特性（如 MCICA 所做的那样）是区分运动引起的信号变化和 fMRI 数据中其他变异源的准确方法。
Task-related head movement during acquisition of fMRI data represents a serious confound for both motion correction and estimates of task-related activation. Cost functions implemented in most conventional motion-correction algorithms compare two volumes for similarity but fail to account for signal variability that is not due to motion (e.g., brain activation). We therefore recently proposed the theoretical basis for a novel method for fMRI motion correction, termed motion-corrected independent component analysis (MCICA), that allows for brain activation present in an fMRI time-series to be implicitly modeled and mitigates motion-induced signal changes without having to directly estimate the motion parameters (Liao et al., IEEE Transactions on Medical Imaging 2005;25:29-44). To explore the effects of non-movement-related signal changes on registration error, we performed several previously proposed test simulations (Freire et al., IEEE Transactions on Medical Imaging 2002;21:470-484) to evaluate the performance of MCICA and compare it with the conventional square-of-difference-based measures such as LS-SPM and LS-AIR. We demonstrate that for both simulated data and real fMRI images, the proposed MCICA method performs favorably. Specifically, in simulations MCICA was more robust to the addition of simulated activation, and did not lead to the detection of false activations after correction for simulated task-correlated motion. With actual data from a motor fMRI experiment, the time course of the derived continually task-related ICA component became more correlated with the underlying behavioral task after preprocessing with MCICA compared to other methods, and the associated activation map was more clustered in the primary motor and supplementary motor cortices without spurious activation at the brain edge. We conclude that assessing the statistical properties of a motion-corrupted volume in relation to other volumes in the series, as is done with MCICA, is an accurate means of differentiating between motion-induced signal changes and other sources of variability in fMRI data.