Chen Zikuan, Calhoun Vince
The Mind Research Network and LBERI, Albuquerque, NM, United States.
Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, United States.
Front Neurosci. 2018 Feb 2;12:15. doi: 10.3389/fnins.2018.00015. eCollection 2018.
Spatial smoothing is a widely used preprocessing step in functional magnetic resonance imaging (fMRI) data analysis. In this work, we report on the spatial smoothing effect on task-evoked fMRI brain functional mapping and functional connectivity. Initially, we decomposed the task fMRI data into a collection of components or networks by independent component analysis (ICA). The designed task paradigm helps identify task-modulated ICA components (highly correlated with the task stimuli). For the ICA-extracted primary task component, we then measured the task activation volume at the task response foci. We used the task timecourse (designed) as a reference to order the ICA components according to the task correlations of the ICA timecourses. With the re-ordered ICA components, we calculated the inter-component function connectivity (FC) matrix (correlations among the ICA timecourses). By repeating the spatial smoothing of fMRI data with a Gaussian smoothing kernel with a full width at half maximum (FWHM) of {1, 3, 6, 9, 12, 15, 20, 25, 30, 35} mm, we measured the spatial smoothing effects. Our results show spatial smoothing reveals the following effects: (1) It decreases the task extraction performance of single-subject ICA more than that of multi-subject ICA; (2) It increases the task volume of multi-subject ICA more than that of single-subject ICA; (3) It strengthens the functional connectivity of single-subject ICA more than that of multi-subject ICA; and (4) It impacts the positive-negative imbalance of single-subject ICA more than that of multi-subject ICA. Our experimental results suggest a 23 voxel FWHM spatial smoothing for single-subject ICA in achieving an optimal balance of functional connectivity, and a wide range (25 voxels) of FWHM for multi-subject ICA.
空间平滑是功能磁共振成像（fMRI）数据分析中广泛使用的预处理步骤。在这项工作中，我们报告了空间平滑对任务诱发的fMRI脑功能映射和功能连接的影响。最初，我们通过独立成分分析（ICA）将任务fMRI数据分解为一组成分或网络。设计的任务范式有助于识别任务调制的ICA成分（与任务刺激高度相关）。对于ICA提取的主要任务成分，我们随后在任务响应焦点处测量任务激活体积。我们使用任务时间历程（设计的）作为参考，根据ICA时间历程的任务相关性对ICA成分进行排序。利用重新排序的ICA成分，我们计算了成分间功能连接（FC）矩阵（ICA时间历程之间的相关性）。通过使用半高全宽（FWHM）为{1、3、6、9、12、15、20、25、30、35}mm的高斯平滑核重复fMRI数据的空间平滑，我们测量了空间平滑效果。我们的结果表明空间平滑揭示了以下影响：（1）它对单受试者ICA任务提取性能的降低比对多受试者ICA的降低更多；（2）它对多受试者ICA任务体积的增加比对单受试者ICA的增加更多；（3）它对单受试者ICA功能连接的增强比对多受试者ICA的增强更多；（4）它对单受试者ICA正负不平衡的影响比对多受试者ICA的影响更大。我们的实验结果表明，对于单受试者ICA，2至3体素FWHM的空间平滑可实现功能连接的最佳平衡，而对于多受试者ICA，则为较宽范围（2至5体素）的FWHM。
