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Hello, I have a multi session fMRI, however, the slice number in each sessoin is different, can DparsfA still run such analysis,

 Dear all,

大家好，最近我打算用rest试着跑VMHC，出现以下问题：

Hi Guys,

大家好！向大家求教一个问题：能否采用reho或alff进行纵向研究？ 我对同一批人间隔三年进行了两次resting－fmri的扫描，但是我不知道采用配对样本t检验对前后数据进行处理在方法学上是否行得通，因为fmri的可重复性并不是特别好。另外，如果可以的话，在数据处理时，除了采用配对样本t检验之外，还有没有其他的事情需要引起注意（就是和横断面研究有什么区别）？先谢谢大家了！

Data Processing Assistant for Resting-State fMRI (DPARSF) is a convenient plug-in software based on SPM and REST. You just need to arrange your DICOM files, and click a few buttons to set parameters, DPARSF will then give all the preprocessed (slice timing, realign, normalize, smooth) data, functional connectivity, ReHo, ALFF/fALFF, degree centrality, voxel-mirrored homotopic connectivity (VMHC) results. DPARSF can also create a report for excluding subjects with excessive head motion and generate a set of pictures for easily checking the effect of normalization. You can use DPARSF to extract ROI time courses efficiently if you want to perform small-world analysis. DPARSF basic edition is very easy to use while DPARSF advanced edition (alias: DPARSFA) is much more flexible and powerful. DPARSFA can parallel the computation for each subject, and can be used to reorient your images interactively or define regions of interest interactively. You can skip or combine the processing steps in DPARSF advanced edition freely. Please download a MULTIMEDIA COURSE to know more about how to use this software. Add DPARSF's directory to MATLAB's path and enter "DPARSF" or "DPARSFA" in the command window to enjoy DPARSF basic edition or advanced edition. The latest release is DPARSF_V2.2_130309.  DOWNLOAD  Multimedia Course: Data Processing of Resting-State fMRI

New features of DPARSF_V2.2_130309:
1. Advanced Edition: Fixed a bug in "Smooth by DARTEL" caused in previous revision (DPARSF_V2.2_130214). (Thanks for the report of Maki Koyama).

New features of DPARSF_V2.2_130303:
1. Advanced Edition: Fixed a bug in processing of multiple sessions - only process the last session in normalization and smooth.
2. Basic Edition: Fixed a bug caused in the previous release (V2.2_130214) - cannot load the correct masks (Thanks for the report of Tao Yang).

New features of DPARSF_V2.2_130224:
1.    This release fixed some minor bugs, but will not affect the results of any data analysis. The bugs appear in uncommon parameter settings and stop the processing in the worst cases.
2.    Updated the DPARSFVersion information in the template parameter files.
3.    DPARSF basic edition will also output the results of ReHo/ALFF/fALFF after Z-standardization (subtract the whole brain mean and divide by the whole brain standard deviation).
4.    Fixed a bug in nuisance covariates regression when CovMat is not defined.
5.    Fixed a bug that can not save the ROI signals correctly in text file if the data is not in double format. (Thanks for the report by H. Baetschmann)
6.    Fixed a bug in creating mean functional image for 4D files. (Thanks for the report and revision by S. Orsolini)
7.    Fixed a bug in displaying ROI Templates in linux system. (Thanks for the report by Han Zhang)

New features of DPARSF_V2.2_121225:
1.    Support parallel computing! If you installed the MATLAB parallel computing toolbox, you can set the number of "Parallel Workers", DPARSFA will distribute the subjects into different CPU cores.
2.    In addressing head motion concerns in resting-state fMRI analyses (Power et al., 2012; Satterthwaite et al., 2012; Van Dijk et al., 2012), we provide Friston 24-parameter correction (See Yan et al., 2013 for a comprehensive assessment of head motion on functional connectomics) as well as voxel-specific head motion calculation and correction (Satterthwaite et al., 2013; Yan et al., 2013). DPARSF also calculate the voxel-specific mean framewise displacement (FD) and volume-level mean FD (Power) (Power et al., 2012) or FD (Jenkinson) (i.e., relative RMS; Jenkinson et al., 2002) for accounting head motion at group-level analysis. The data scrubbing approach is also supported with different methods: 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. 
3.    According to (Weissenbacher et al., 2009), the nuisance covariate regression could be performed before filtering and at very early stage. Users can also choose Template Parameters: TRADITIONAL order to have the same order as the previous version.
4.    Support .nii.gz in all the steps. No longer need to convert 4D .nii.gz into 3D .img/.hdr. Simply put .nii.gz under FunImg or any starting directory name, DPARSFA will handle the .nii.gz by itself.
5.    If the Number of Time Points is set to 0, then DPARSFA will not check the number of time points.
6.    If TR is set to 0, then DPARSFA will retrieve the TR information from the NIfTI images. Please ensure the TR information in NIfTI images are correct!
7.    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!!!
8.    Support calculating resting-state fMRI metrics in native space and warping by DARTEL. The mask files and/or region of interest (ROI) files in standard space are warped into native space by using the parameters estimated in segmentation or DARTEL.
9.    Spatial normalization and smooth can be performed on the calculated resting-state fMRI derivatives.
10.    Supporting extracting ROI time course using masks with multiple labels. More template ROI definitions are supported: Dosenbach’s 160 functional ROIs (Dosenbach et al., 2010), Andrews-Hanna’s default mode network ROIs (Andrews-Hanna et al., 2010), Craddock’s clustering ROIs (Craddock et al., 2011), AAL atlas and Harvard-Oxford atlas.
11.    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 (Shehzad et al., 2011).
12.    For VMHC analyses, support normalizing the data further to a symmetric template. 1) Get the T1 images in MNI space (e.g., wco*.img or wco*.nii under T1ImgNewSegment or T1ImgSegment) for each subject, and then create a mean T1 image template (averaged across all the subjects). 2) Create a symmetric T1 template by averaging the mean T1 template (created in Step 1) with it's flipped version (flipped over x axis). 3) Normalize the T1 image in MNI space (e.g., wco*.img or wco*.nii under T1ImgNewSegment or T1ImgSegment) for each subject to the symmetric T1 template (created in Step 2), and apply the transformations to the functional data (which have been normalized to MNI space beforehand). Please see a reference from Zuo et al., 2010.
13.    A group analyses function was added as y_GroupAnalysis_Image by scripting call. T tests or F tests could be performed for a given set of regressors.
14.    Template Parameters:
         Calculate in Original Space (warp by DARTEL)
         Calculate in Original Space (warp by information from unified segmentation)
         Calculate in MNI Space (warp by DARTEL)
         Calculate in MNI Space (warp by information from unified segmentation)
         Calculate in MNI Space: TRADITIONAL order [This is the order used in DPARSF basie edition as well as DPARSFA V2.1]
         Intraoperative Processing
         Task fMRI data preprocessing
         VBM (New Segment and DARTEL)
         VBM (unified segmentation)
         Blank

Many thanks to Dr. Chris Rorden for suggesting features 4-7. Many thanks to Dr. Susan Whitfield-Gabrieli for discussing the head motion scrubbing regressors.

   老师您好:
            对于提取时间序列有个疑问,选择一个ROI,我们能够得到它的一个时间序列,比如210个时间点,就有210个数据,那我们如何做比较呢?比如两组间的比较.

各位老师: 我想咨询一下双样本T检验的mask问题, 我是MRI新手，我的感受是Rest软件最大的困难对我来说是每一个统计都让你选mask,而我不会制作mask，不点mask总觉得分析不规范，为此我为了mask摸索了一个月, 才敢用此软件。而SPM的分析只是点击，选默认mask。我根据严老师讲课视频和论坛中有老师写的"先用两组单样本T检验显著的脑区，在RESTimage Calculator:中根据公式(abs(i1)> threshold)+(abs(i2)> threshold)>0取他们的并集做一个mask供双样本检验用，这样小一点的mask才有可能使有些信息活下来的"这个原则，我进行了mask制作.但我不知道我做得对不对: 1. 我用DPARS算出两组各个人的smRehoMap等, 先对两组分别进行smReho-1 MAP单样本的T检验,因我没有假设,所以这步mask 我选的Rest软件自带的brain61X72X61模板作为mask(因我看文章中阳性区域报的结果太多了,尽管基底节更多一些), 然后, 单样本T检验中得到病人和对照组各自的T图,下一步,取两组T检验显著的脑区,先去看threshold value, 用RestSlice Viewer中将病人T图呈现出来, 我选择P=0.05, 得出Threshold=2.1448,又将对照组T图呈现出来, 选择P=0.05,得出Thresthold=2.093.有了threshold, 可以在Rest Image Calculator中根据公式: (abs(i1)> threshold)+(abs(i2)> threshold)>0, 输入(abs(i1)> 2.1448)+(abs(i2)> 2.093)>0, 出来的mask是个红色图(见图1,光盘映像文件，需用Rest Sliceview打开),我不知道这个过程或公式对否, abs是什么意思,是否不要加? 2. 于是, 我去掉abs, 又用(i1> 2.1448)+(i2> 2.093)>0(我看视频上为(i1>1.96+i2>1.96)>0)公式,出来的图比刚才那个图少些脑组织(见图2),不知道哪个对? 3. 最后我用第一个公式做出来的mask,作双样本T检验, 结果统计出来的下面的T图(见图3),但不知道是什么意思,这是 smReho 最后分析出来的是结果吗,我选P=0.05，cluster size=85, rmm=4,选了个图像裁了下来（见图4）? 4. 另外一个问题是如果不用大脑或灰质模板做mask的话,是否每做一个变量都要做mask,如分析mReho,ALFF, mALFF,fALFF等要按上述方法制作阳性结果并集的mask吗? 请各位能给予回复和指导,谢谢~~~~