Some questions relative to your New edition sofeware

Submitted by wleewell on Mon, 09/24/2012 - 17:51

论坛老师好！

有几个问题想看看大家的意见。

1、AlphaSim与Gaussian random-field theory校正那个更严格，分别都更适用于那些情况呢？通常我发现GRF校正voxel P值设为z>2.3，这个值相当于p值多少呢，可否再设的松一点？REST新版加入了GRF校正，可否就该校正的参数设置及意义做一讲解呢？

2、前面严老师说REST中的REST smoothest可以估计校正要求的平滑值，对此有些不解：统计学检验图估计smooth值有什么意义？

3、我发现REST slice viewer与xjview好像有点差异，REST中输入voxel水平的p值0.01，对应T值2.70，而xjview中输入0.01对应T值2.32，不知什么原因？

4、新版DPARSFA中，将协变量剔除提前到filter前有什么意义？

麻烦了！

Submitted by YAN Chao-Gan on Mon, 09/24/2012 - 23:44

1. 我正在修改Course的第二部分。但鉴于第二部分本身比较长，定位不一定容易，可能考虑单出一个部分介绍GRF校正吧。关于Z>2.3，你有兴趣的话可以阅读一下之前的一个post: http://www.restfmri.net/forum/node/1126 (In addition, as a Note in my code: FSL's Z>2.3 corresponds to one-tailed VoxelPThreshold = 0.0107. |Z|>2.3 corresponds to two-tailed VoxelPThreshold = 0.0214.) 理论上，单个体素的P值是是可以设定在不同P值，不一定非要对应于2.3。可能用FSL做GRF的比较多，大家就用这个默认参考值已对应了。
2. 请参考http://www.restfmri.net/forum/node/1118
“My initial intention to write this script is want to perform the Gaussian Random Field Theory Correction like easythresh in FSL. Easythresh estimated the smoothness from the Z statistical image, so did this script.

However, the y_Smoothest (soon as rest_Smoothest in the new REST) script also could estimate the smoothness from residual file, and this way maybe better than the estimation based on Z statistical image.

According to Steve Smith, “Hi - you're right that easythresh isn't exactly the same, because it uses the zstat image and not the residuals to estimate smoothness.
However in my experience it isn't ever very different.” (https://www.jiscmail.ac.uk/cgi-bin/webadmin?A2=ind0902&L=fsl&P=R48770&1=fsl&9=A&I=-3&J=on&d=No+Match%3BMatch%3BMatches&z=4), estimation from Z statistical image is acceptable.

Thus, you can put the spmT image into y_GRF_Threhold, and then it will convert the T image into Z image by calling y_TFRtoZ, and later estimate the smoothness from the Z image. This procedure will be put the new REST which will be released by the end of this month.

This script is based on FSL’s tech report [Flitney, D.E., & Jenkinson, M. 2000. Cluster Analysis Revisited. Tech. rept. Oxford Centre for Functional Magnetic Resonance Imaging of the Brain, Department of Clinical Neurology, Oxford University, Oxford, UK. TR00DF1.] and their program. However, discrepancy of smoothness estimation from AFNI or SPM to FSL may exist, as a recent post in FSL: https://www.jiscmail.ac.uk/cgi-bin/webadmin?A2=ind1208&L=fsl&P=R43270&1=fsl&9=A&J=on&d=No+Match%3BMatch%3BMatches&z=4.”

3. 因为REST用双侧检验，而xjview用单侧检验(like SPM)。

4. 可参考Weissenbacher, A., Kasess, C., Gerstl, F., Lanzenberger, R., Moser, E., Windischberger, C., 2009. Correlations and anticorrelations in resting-state functional connectivity MRI: a quantitative comparison of preprocessing strategies. Neuroimage 47, 1408-1416.
另外，也可见：http://www.conventus.de/fileadmin/media/2012/resting/abstracts/1_Abstract.pdf

Submitted by wleewell on Tue, 09/25/2012 - 14:30

我试用了下REST中的Gaussian Random Field Theory Correction，输入了T检验图，设置了voxel-level P值与Cluster-level P值，双侧检验，出现以下报错：
Exception occured. (MATLAB:undefinedVarOrClass)
Undefined variable "Header" or class "Header.mat".
26#line, rest_TFRtoZ, in "C:\Program Files\fMRI software\REST_V1.8PRE_120905\REST_V1.8PRE_120905\rest_TFRtoZ.m"
56#line, rest_GRF_Threshold, in "C:\Program Files\fMRI software\REST_V1.8PRE_120905\REST_V1.8PRE_120905\rest_GRF_Threshold.m"
304#line, run_pushbutton_Callback, in "C:\Program Files\fMRI software\REST_V1.8PRE_120905\REST_V1.8PRE_120905\rest_GRFCorrection_gui.m"
96#line, gui_mainfcn, in "C:\Program Files\MATLAB\R2008b\toolbox\matlab\guide\gui_mainfcn.m"
34#line, rest_GRFCorrection_gui, in "C:\Program Files\fMRI software\REST_V1.8PRE_120905\REST_V1.8PRE_120905\rest_GRFCorrection_gui.m"
0#line, @(hObject,eventdata)rest_GRFCorrection_gui('run_pushbutton_Callback',hObject,eventdata,guidata(hObject)), in ""
不知什么原因？
想再了解下，在rest中做GRF校正，如果设置z值>2.3，two-tailed，是不是填入voxel-level p值0.0214，cluster-level p值0.05就行？利用同样的参数输入voxel-level p值0.0214，校正后0.05，做AlphaSim校正，会不会和GRF校正的结果一样？也就是这两种方法原理上相同，只是参数输入有所不同？