如何做spm8第二階段

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). 作為一個規(guī)范的原 理，貝氏定理對于所有機率的解釋是有效的； 然而， 頻率主義者和貝葉斯主義者對于在應用 中機率如何被賦值有著不同的看法： 頻率主義者根據(jù)隨機事件發(fā)生的頻率，或者總體樣本 里面的個數(shù)來賦值機率；貝葉斯主義者要根據(jù)未知的命題來賦值機率。 貝氏定理是關于隨機事

16、件 A 和 B 的條件機率和邊緣機率的。 其中 L(AB) 是在 B 發(fā)生的情況下 A 發(fā)生的可能性。 在貝氏定理中，每個名詞都有約定俗成的名稱： %26#8226; Pr(A) 是 A 的先驗機率或邊緣機率。之所以稱為先驗是因為它不考 慮任何 B 方面的因素。 %26#8226; Pr(AB) 是已知 B 發(fā)生后 A 的條件機率，也由于得自 B 的取值而被 稱作 A 的后驗機率。 %26#8226; Pr(BA) 是已知 A 發(fā)生后 B 的條件機率，也由于得自 A 的取值而被 稱作 B 的后驗機率。 %26#8226; Pr(B) 是 B 的先驗機率或邊緣機率，也作標準化常量( normal

17、ized con sta nt) 按這些術語，Bayes定理可表述為： 后驗機率=(相似度*先驗機率)/標準化常量 也就是說，后驗機率與先驗機率和相似度的乘積成正比。 另外，比例Pr(BA)/Pr(B)也有時被稱作標準相似度(standardised likelihood), Bayes 定理可表述為： 后驗機率=標準相似度*先驗機率 有用的就是Ke,激活的像素數(shù)目，T及Z表 面有意思區(qū)域的強度，最重要的就是最后的 XYZ的坐標，可得到相關激活區(qū)的腦皮層定 位。 在volume 表格里面點擊右鍵-print text table，再到 matlab里面復制這些數(shù)據(jù)到 excel，把最后的 MN

18、I坐標轉(zhuǎn)化為talairach 坐標(借助軟件 MNI SPACE UTILITY 或 Talairach Daemon Client ( TD Client )得到 其坐標所對應的腦皮層區(qū)域。cluster level :表示 這幾列的數(shù)據(jù)是以cluster為單位來說明的 voxel level :表示是以像素為單位說明的 p-correct表示經(jīng)過P值矯正后的P值 p-uncorrect表示沒有經(jīng)過矯正的 p值 p-fwe和p-fdr是兩種p值矯正方式，fwe稍強烈一些，fdr稍 弱一些 那些0.0000類的數(shù)據(jù)不代表零，是接近于 0,你在表格上雙 擊它，會在 matlab中顯示精確的值

19、最不明白的地方就是 set level那塊的p和c是什么意思， 期待高手回答 以一般的mask作說明（mask的值為1和0）。當一幅 圖作為mask的時候，你可以把它想象成一張不透明 的帶孔的紙，孔為1,其它部分為0,從數(shù)據(jù)來講就是 就是以1和0組成的矩陣。以這個矩陣和要被 mask 的圖做數(shù)乘，可想而知，和 mask中1相乘的保留了 原來的值，而和0相乘的就變?yōu)?了。這樣一來，就 只保留了你想要的區(qū)域。 根據(jù)以上原理，用 A去maskB和用B去maskA就是 不一樣的。前者保留的是被Amask后的B的信號，而 后者反之。 當然，mask也不一定都為1或0,還可以是其它的數(shù) 值，這取決于插值的

20、方法和閾值等。原理還是矩陣間 的點乘。至于交集并集什么的，只是更具需要做圖形 上的選取而已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. 相關數(shù)據(jù)的統(tǒng)計分析 2011-05-20 20:56 首先說明的是SPM統(tǒng)計分析的基本思想。SPM是基于體素值進行圖形處理的，該處理在 零假設下，其分布是已知的概率分布函數(shù)（通常為T分布或F分布）。SPM的成功主要源于簡單 的思想，即用標準的統(tǒng)計檢驗分析每一個體素，利用統(tǒng)計參數(shù)分析的結果重建一個圖像 SPM.mat。SPM.mat被解釋為統(tǒng)計處理的空間擴展，這種分析是參照穩(wěn)定高斯場的概率行為做 出來的。 在做統(tǒng)計分析以前，要先把被試做年齡和性別的匹配，去除不匹配的被試，然后做單樣本

26、 t檢驗和雙樣本t檢驗。做t檢驗的數(shù)據(jù)是 mALFF、smReHo、zFC.（如果是vbm，就直接做雙 樣本t檢驗） 第一步，ImCalc。因為SPM是和0進行比較的，所以第一步要將 mALFF和smReHo都減1， 這一步利用SPM中的ImCalc在來做，zFC不需要減1。這里要注意 SPM的ImCalc只能一個圖 像一個圖像的做，如果要做很多圖像，考慮寫腳本。 第二步，單樣本t檢驗，這一步用來形成雙樣本t檢驗的Mask。用單樣本T檢驗分別對 patients和controls進行檢驗，并保存，。具體步驟是：（1）選擇 Specify 2nd-level，選擇 one-sample t-te

27、st輸入圖像（如 m_ALFF-1、sm_Reho-1、zFC），并設置 directory（這是輸出 結果SPM.mat的存放路徑），如果想去掉年齡和性別的影響（2）點擊Estimate，選擇上一步生 成的 SPM.mat,運行，這一步產(chǎn)生RPV ResMS mask，beta_0001,beat_0002,beta_0003（ 3） 點擊resluts ，輸入上一步產(chǎn)生的SPM.mat定義contrast ，選擇t-contrast，然后輸入名字， 比如patients 或是normal等，輸入值1。在彈出的對話框中， mask with others 選擇no，title for comparison輸入一個名字，p value adjustment to選擇 FDR extend threshold 選擇 10.輸出的結果是 con_0001和spmT_0001，同時也改變了 SPM.mat.接著點擊save，把結果存下 來（如patients