3. 对左西年老师的一篇文章有不解之处：
Title： Decreased interhemispheric coordination in schizophrenia: A resting state fMRI study
In order to determine whether deficits in VMHC could be attributed to abnormalities in RSFC in one hemisphere or the other, we performed separate GLMs on the residualized time series data in
nonsymmetric MNI space, with global VMHC and FD as covariates. Seeds (spheres with 4 mm radius) for these analyses were generated based on each local maximum and its homotopic homolog. These were thresholded at p=.05 (corrected for Gaussian Random Fields). To visualize these results, for each seed pair, we computed the right–left asymmetry in positive FC in the group difference region (see Fig. 2). These analyses showed that both hemispheres were involved in the observed effects.
1. 如您所料，我的体素为90，改为45后就好了；
2. 我的假设是，结构的侧化可能会影响功能的侧化，所以想在对比两组的功能侧化VMHC时，将结构的侧化（Compute as the left-right difference in GM volume）做为一个covariate，所以需要计算每个受试者VMHM图，“Reduced Interhemispheric Resting State Functional Connectivity in Cocaine Addiction”一文是这么算的：VMHM=1-I左侧的GM-右侧的GMI/左侧的GM+右侧的GM。想请问下，VMHM的计算是否可以在REST上实现，或者可以调动一个小程序？
3. 好的，真心感谢！！
Submitted by YAN Chao-Gan on Mon, 10/08/2012 - 23:51 Permalink
Re: Questions about the lateralization analysis
1. 看一下这个代码：Data(1:31,:,:)=0;
2. 这个idea很好！！！只需要把所有被试的灰质体积整理成一个被试的样子就可以。
Submitted by wleewell on Fri, 10/12/2012 - 23:17 Permalink
Re: Questions about the lateralization analysis
谢谢回复，进一步还有一些问题：
1. 利用以下代码，对对称的mask“'35 subjects GM_Sym mask”制作其单侧为mask，输出为“Sym”，但生成的图像包含了全脑约2/3，而非一半，望分析下原因。
[Data Vox Head]=rest_readfile('35 subjects GM_Sym mask.nii');
Data(1:31,:,:)=0;
rest_WriteNiftiImage(Data,Head,'Sym.nii');
2. 我事实上是想在VMHC分析中，把VMHM作为covariate，每个subject制作一个VMHM map，这里不明白您说的所有被试整理成一个样子是什么意思？可否在DPARSFA中，直接输入mwco*的GM map，利用VMHC模块得出VMHM？
3. 对左西年老师的一篇文章有不解之处：
Title： Decreased interhemispheric coordination in schizophrenia: A resting state fMRI study
In order to determine whether deficits in VMHC could be attributed to abnormalities in RSFC in one hemisphere or the other, we performed separate GLMs on the residualized time series data in
nonsymmetric MNI space, with global VMHC and FD as covariates. Seeds (spheres with 4 mm radius) for these analyses were generated based on each local maximum and its homotopic homolog. These were thresholded at p=.05 (corrected for Gaussian Random Fields). To visualize these results, for each seed pair, we computed the right–left asymmetry in positive FC in the group difference region (see Fig. 2). These analyses showed that both hemispheres were involved in the observed effects.
根据这一步的结果图，我猜测可能是以VMHC异常区域为种子点，分别计算左右侧种子点与全脑其他区域的FC，然后对左右侧做一个paired t-test，观察是否有侧化效应，但感觉这样做意义很模糊，或者是否我理解错了，想看看您的看法。
Submitted by YAN Chao-Gan on Sun, 10/21/2012 - 10:18 Permalink
Re: Questions about the lateralization analysis
1. 你看一下你的图像，x方向有多少个体素？31是针对61个体素（61*73*61）的情况。
2. 不太明白你想做什么。用GM是不可以得到每个被试的GM VMHC图，只能在组上做一个分析，得到组的VMHC图。
3. 我再仔细看一下。
Submitted by wleewell on Sun, 10/21/2012 - 18:18 Permalink
Re: Questions about the lateralization analysis
1. 如您所料，我的体素为90，改为45后就好了；
2. 我的假设是，结构的侧化可能会影响功能的侧化，所以想在对比两组的功能侧化VMHC时，将结构的侧化（Compute as the left-right difference in GM volume）做为一个covariate，所以需要计算每个受试者VMHM图，“Reduced Interhemispheric Resting State Functional Connectivity in Cocaine Addiction”一文是这么算的：VMHM=1-I左侧的GM-右侧的GMI/左侧的GM+右侧的GM。想请问下，VMHM的计算是否可以在REST上实现，或者可以调动一个小程序？
3. 好的，真心感谢！！
Submitted by YAN Chao-Gan on Tue, 10/23/2012 - 01:40 Permalink
Re: Questions about the lateralization analysis
VMHC是左右侧的相似性，并不能衡量侧化问题。现有的算法，也并不能基于每个被试的GM图，计算出一个VMHC图。
VMHC的模块在REST中也有，可以调用。