Questions for New functions of DARRSF and REST

老师们好！
看了DPARSF与REST的新功能，有以下问题想明确一下，比较多，麻烦大家了！
1. In addressing head motion concerns in resting-state fMRI analyses (Power et al., 2012; Satterthwaite et al., 2012b; Van Dijk et al., 2012), we provide voxel-specific head motion calculation and correction (Fig. 4) (Satterthwaite et al., 2012a; Yan et al., 2012). DPARSF also calculate the voxel-specific mean framewise displacement (FD) and volume-level mean FD (Power et al., 2012) for accounting head motion at group-level analysis.
* voxel-specific head motion calculation and correction：是否预处理除Realign外，做基于Voxel水平的头动校正，加强了纠正头动的效果？

2. The data scrubbing approach is also supported with different methods (Fig. 5): 1) model each bad time point as a separate regressor in nuisance covariates regression, 2) delete bad time points, 3) interpolate bad time points with nearest neighbor, linear or cubic spline interpolation.
是否选择Head motion scrubbing regressor后，可以自动完成上述三步？
voxel-specific mean framewise displacement (FD) and volume-level mean FD：得到这些参数后，在群组分析时，应以此为协变量吗？

3. If Slice Number is set to 0, then retrieve the slice number from the NIfTI images. The slice order is then assumed as interleaved scanning: [1:2:SliceNumber, 2:2:SliceNumber]. The reference slice is set to the slice acquired at the middle time point, i.e., ceil(SliceNumber/2). SHOULD BE EXTREMELY CAUTIOUS!!!
是否可以理解为：层数从图像中自行识别，然后，扫描顺序将被识别为间隔扫描，参考层设置为时间中点的层数SliceNumber/2？那么Slice number与order还需要自己填入吗？

4. Spatial normalization and smooth can be performed on the calculated resting-state fMRI derivatives.
该功能怎么理解？resting-state fMRI derivatives具体指什么？

5. More resting-state fMRI metrics are included, e.g., voxel-mirrored homotopic connectivity (VMHC) (Zuo et al., 2010), Degree Centrality (Buckner et al., 2009) and connectome-wide association studies based on multivariate distance matrix regression.
Degree Centrality是否有默认的r值？因为新版rest中是要自己设置r的。connectome-wide association studies based on multivariate distance matrix regression：该功能包含内容不大明白，严老师可否具体说明一下？

6. Gaussian random field (GRF) theory multiple comparison correction (like easythresh in FSL) was supported. The smoothness could be evaluated for GRF correction or AlphaSim correction. (GUI by Xin-Di Wang, algorithm by YAN Chao-Gan)
REST Smoothest有些不明白，为什么是input T map估计平滑核，FWHM在预处理中已经设定了啊。REST-GRF中，只需输入voxel-level与Cluster-level的P值？无需输入smooth值？