如何做spm8第二阶段(20210315231544)

1、Group Analysis (Random Effects Analysis) In order to run the group statistical analysis, all images must be the same size, have the same voxel sizes and the same origins. If mat | files are present (note that they often are not for the swr images), then they must all contain identical information. T

2、his means not only that they should begin as images of equivalent size and dimensions, but that they should be normalized and smoothed to the same sizes. Presumably, you should work with a statistically significant number of subjects (perhaps 6?) What you should do: Group analysis is considerably ea

3、sier if all data is preprocessed and analyzed the same way and contrasts were all entered in the same order for each subject. Make a directory for the group analysis, so you don t overwrite files (SPM.mat and all the images resulting from Estimation) in your subject directories. Make sure you are in

4、 your new group directory in Matlab, then start SPM. Select Design Type- One Sample t-test One Sample T-Test: Is the mean signal value different than 0? ? Two Sample T-Test: Is the mean signal value of Group1 different than Group2? ? Paired T-Test: Is the mean signal value of Condition1 different th

5、an Condition2 (for one group)? ? One Way Anova: Is the mean signal value of 3 or more groups/conditions different? ? Simple Regression (Correlation): Does the variable a in a linear regression equal 0? y=ax+b (y is the value of the contrast and x the predictive factor) ? Multiple Regression: Does th

6、e variable a in a linear regression equal 0 for each predictor? y=a1x1+anxn+b (y is the value of the contrast and x1n the predictive factors) ? Ancova: Does the mean signal value of a group/condition 1 differ from one or more other groups/conditions when the effect of a predictive factor x is contro

7、lled? Done” ” Selectmages ”(Navigate through each subject directory and choose the correct con*.img by clicking it. Do NOT hit until you have selected all the individual images that will contribute to that group analysis. GMsca: Grand Mean Scaling: no grand mean scaling explicitly mask images: No Gl

8、obal Calculations: Omit A new SPM.mat is created Estimate: (Select the newly created SPM.mat) All of the files that estimation usually creates are created for the group analysis: beta img and hdr; mask img and hdr, ResMS img and hdr, RPV img and hdr) When it runs (which should be quick) you have ima

9、ges that show you significant activation across the group as a whole. (Although a contrast window appears at this point, it may give you trouble. Close it and then hit results, select the newly created spm.mat and follow the steps below.) Contrasts: You will still need to define a contrast: Click ,

10、navigate to the SPM.mat file generated for the group analysis, click it). Define the contrast in the contrast manager: Probably 1 will do, assuming the contrast was already defined for individuals and at this second order level you just want to see all of the data that survives the second order grou

11、p analysis. Click“ Done”. You can look at these images just as you would look at individual images of activation results: Click , navigate to the SPM.mat file of interest, click it, choose (or define) the contrast from the contrast manager, and click “ Done”). Troubleshooting Images with Different C

12、haracteristics If your subjects have slightly different sized images (different origin and dimensions), this problem can be fixed after the fact by coregistering the images you want to change to some target image. Here s an example: Coregister your “ wrong smoothed images to a good one, like so: # o

13、f subjects: 1 Which option: Coregister and reslice Target Image: swargood_em1.img Source image: swarwrong_em1.img (This one will be registered and resliced to resemble the Target image.) Other images Done Check your image dimensions, origins etc. by displaying the images with SPM . Troubleshooting C

14、ontrasts: If you enter the contrasts into the contrast manager in the same order for each subject, this should assure that con* files are named the same way for each subject. If you have not done this, you can display your contrast names in the contrast manager and get the correspondence of numbered

15、 con* files to the named contrasts (, navigate to and select your SPM mat file, hit done, your contrast names and their corresponding con images will be displayed). 作为一个规范的原 理，贝氏定理对于所有机率的解释是有效的； 然而， 频率主义者和贝叶斯主义者对于在应用 中机率如何被赋值有着不同的看法： 频率主义者根据随机事件发生的频率，或者总体样本 里面的个数来赋值机率；贝叶斯主义者要根据未知的命题来赋值机率。 贝氏定理是关于随机事

16、件 A 和 B 的条件机率和边缘机率的。 其中 L(AB) 是在 B 发生的情况下 A 发生的可能性。 在贝氏定理中，每个名词都有约定俗成的名称： %26#8226; Pr(A) 是 A 的先验机率或边缘机率。之所以称为先验是因为它不考 虑任何 B 方面的因素。 %26#8226; Pr(AB) 是已知 B 发生后 A 的条件机率，也由于得自 B 的取值而被 称作 A 的后验机率。 %26#8226; Pr(BA) 是已知 A 发生后 B 的条件机率，也由于得自 A 的取值而被 称作 B 的后验机率。 %26#8226; Pr(B) 是 B 的先验机率或边缘机率，也作标准化常量( normal

17、ized con sta nt) 按这些术语，Bayes定理可表述为： 后验机率=(相似度*先验机率)/标准化常量 也就是说，后验机率与先验机率和相似度的乘积成正比。 另外，比例Pr(BA)/Pr(B)也有时被称作标准相似度(standardised likelihood), Bayes 定理可表述为： 后验机率=标准相似度*先验机率 有用的就是Ke,激活的像素数目，T及Z表 面有意思区域的强度，最重要的就是最后的 XYZ的坐标，可得到相关激活区的脑皮层定 位。 在volume 表格里面点击右键-print text table，再到 matlab里面复制这些数据到 excel，把最后的 MN

18、I坐标转化为talairach 坐标(借助软件 MNI SPACE UTILITY 或 Talairach Daemon Client ( TD Client )得到 其坐标所对应的脑皮层区域。cluster level :表示 这几列的数据是以cluster为单位来说明的 voxel level :表示是以像素为单位说明的 p-correct表示经过P值矫正后的P值 p-uncorrect表示没有经过矫正的 p值 p-fwe和p-fdr是两种p值矫正方式，fwe稍强烈一些，fdr稍 弱一些 那些0.0000类的数据不代表零，是接近于 0,你在表格上双 击它，会在 matlab中显示精确的值

19、最不明白的地方就是 set level那块的p和c是什么意思， 期待高手回答 以一般的mask作说明（mask的值为1和0）。当一幅 图作为mask的时候，你可以把它想象成一张不透明 的带孔的纸，孔为1,其它部分为0,从数据来讲就是 就是以1和0组成的矩阵。以这个矩阵和要被 mask 的图做数乘，可想而知，和 mask中1相乘的保留了 原来的值，而和0相乘的就变为0了。这样一来，就 只保留了你想要的区域。 根据以上原理，用 A去maskB和用B去maskA就是 不一样的。前者保留的是被Amask后的B的信号，而 后者反之。 当然，mask也不一定都为1或0,还可以是其它的数 值，这取决于插值的

20、方法和阈值等。原理还是矩阵间 的点乘。至于交集并集什么的，只是更具需要做图形 上的选取而已Random Effects An alysis SPM will use con*img or ess*img files to compute the statistical sig nifica nee of each voxel, based on the estimati on of the effect computed for each subject. Let jsmagine an experiment with two groups of subjects A %26 B, each

21、group of subjects have to perform two tasks 1 %26 2. In additi on, a baseli ne condition is measured (condition 3). We have a well-desig ned study with 6 groups of measures A1, A2, A3, B1, B2 %26 B3. For each subject, the model is convolved with the hrf and the matrix is: con diti on 1, 2, 3. Then y

22、ou can assessfor the effect of the conditions 1 and 2. For the condition 1, you can enter a t contrast 1 0 - as well as for the condition 2 0 1- (let say con1 and con2). You can also compare con ditio ns 1 %26 2 using t or F con trasts: F1 or T1 -%26 -1 1 (ess1, con3 and con4) At the group level, yo

23、u can look at the differe nee betwee n con diti ons 1 and 2 for one group. Here, you can perform either a on e-sample o t-test on images con3 or con4 or a paired t-test on images coni versus con2. The result is the same, as you will oppose the same regressors. If you want to compare groups A %26 B i

24、n the condition 1, you can perform a two sample t-test on images con1. A full an alysis could also be performed with an ANOVA. You can use 4 con ditio ns, groupA1%26gt;3, groupA2%26gt;3, groupB1%26gt;3, groupB2%26gt;3, i.e. con1 %26 con2 images. Then, you can assessthe differenee between groups 11-,

25、 between con dition 1 - 1 - or the in teraction 1 - - 1. 相关数据的统计分析 2011-05-20 20:56 首先说明的是SPM统计分析的基本思想。SPM是基于体素值进行图形处理的，该处理在 零假设下，其分布是已知的概率分布函数（通常为T分布或F分布）。SPM的成功主要源于简单 的思想，即用标准的统计检验分析每一个体素，利用统计参数分析的结果重建一个图像 SPM.mat。SPM.mat被解释为统计处理的空间扩展，这种分析是参照稳定高斯场的概率行为做 出来的。 在做统计分析以前，要先把被试做年龄和性别的匹配，去除不匹配的被试，然后做单样本

26、 t检验和双样本t检验。做t检验的数据是 mALFF、smReHo、zFC.（如果是vbm，就直接做双 样本t检验） 第一步，ImCalc。因为SPM是和0进行比较的，所以第一步要将 mALFF和smReHo都减1， 这一步利用SPM中的ImCalc在来做，zFC不需要减1。这里要注意 SPM的ImCalc只能一个图 像一个图像的做，如果要做很多图像，考虑写脚本。 第二步，单样本t检验，这一步用来形成双样本t检验的Mask。用单样本T检验分别对 patients和controls进行检验，并保存，。具体步骤是：（1）选择 Specify 2nd-level，选择 one-sample t-te

27、st输入图像（如 m_ALFF-1、sm_Reho-1、zFC），并设置 directory（这是输出 结果SPM.mat的存放路径），如果想去掉年龄和性别的影响（2）点击Estimate，选择上一步生 成的 SPM.mat,运行，这一步产生RPV ResMS mask，beta_0001,beat_0002,beta_0003（ 3） 点击resluts ，输入上一步产生的SPM.mat定义contrast ，选择t-contrast，然后输入名字， 比如patients 或是normal等，输入值1。在弹出的对话框中， mask with others 选择no，title for comparison输入一个名字，p value adjustment to选择 FDR extend threshold 选择 10.输出的结果是 con_0001和spmT_0001，同时也改变了 SPM.mat.接着点击save，把结果存下 来（如patients